Vehicular forward viewing image capture system

Information

  • Patent Grant
  • 11148583
  • Patent Number
    11,148,583
  • Date Filed
    Monday, September 28, 2020
    3 years ago
  • Date Issued
    Tuesday, October 19, 2021
    2 years ago
Abstract
A vehicular forward viewing image capture system includes an accessory module configured for attachment at an in-cabin side of a windshield of a vehicle, whereby the imaging sensor views through the windshield forward of the equipped vehicle. With the accessory module attached at the in-cabin side of the windshield, and while the vehicle is traveling along a road, a control processes captured image data to determine (i) presence of an object of interest viewed by the imaging sensor and (ii) location and/or movement of the object of interest viewed by the imaging sensor. The control, responsive to the processing of captured image data and responsive to vehicle data received via a vehicle communication bus, at least in part provides at least one selected from the group consisting of (a) traffic sign recognition, (b) traffic light status detection, (c) adaptive cruise control and (d) pedestrian detection.
Description
FIELD OF THE INVENTION

The present invention relates to automatic headlamp control systems for vehicles and, more particularly, to automatic headlamp control systems that automatically adjust the high and low beam states of a vehicle headlamp.


BACKGROUND OF THE INVENTION

Automotive forward lighting systems are evolving in several areas including the use of image-based sensors, typically referred to as Automatic High Beam (AHB) control systems, to maximize the use of high beam road illumination when appropriate, the use of steerable beam systems, typically referred to as Adaptive Front Lighting (AFL) systems, to provide a greater range of beam pattern options particularly for driving on curved roads or during turn maneuvers wherein the beam pattern may be biased or supplemented in the direction of the curve or turn, and the combination of such AHB and AFL systems.


U.S. Pat. No. 6,097,023 (which is hereby incorporated herein by reference in its entirety) describes an automatic high beam control system which utilizes an optical system, an image sensor, and signal processing including spectral, spatial and temporal techniques to determine ambient lighting conditions, the road environment, and the presence of other road users in order to automatically control the selection of the appropriate forward lighting state such that user forward vision is optimized while minimizing the impact of headlamp caused glare on other road users in all lighting conditions.


While AHB systems that utilize the features and concepts described within the above identified U.S. patent have achieved performance levels that have resulted in considerable commercial success, it is desired to provide additional features and techniques, which may increase the utility, improve the performance, facilitate the manufacture, and simplify the installation of such systems.


SUMMARY OF THE INVENTION

The present invention provides an automatic headlamp control system that is operable to automatically control or adjust the high beam state of a vehicle's headlamps. The headlamp control system is operable to spread out or de-focus a captured image to spread out the imaging of a light source so that an image of a distant light source is captured by at least two pixels of an image array sensor. The image array sensor thus may receive at least a portion of the light source (which may be a red tail light of a leading vehicle) on a red sensing pixel to enhance early detection of the distance tail light. The headlamp control system may provide enhanced control of the headlamps when the vehicle is driven around curves or bends in the road and may be operable in response to a steering wheel angle of the vehicle. The headlamp control system may be adjustable to align the optical axis of the imaging system with a vehicle axis in response to detection of light sources and tracking of movement of the light sources as the vehicle travels along the road.


These and other objects, advantages, purposes and features of the present invention will become apparent upon review of the following specification in conjunction with the drawings.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a sectional view of an accessory module and image sensing device and processor in accordance with the present invention;



FIG. 2 is a perspective view of the accessory module as seen through the windshield of a vehicle;



FIG. 3 is a side elevation and partial sectional view of an image sensing device useful with the present invention;



FIG. 4 is a plan view of an array of photo sensors of an image sensing device of the present invention;



FIG. 5 is a side elevation of a lens and image sensing device of the present invention;



FIG. 6 is another side elevation of a lens and image sensing device similar to FIG. 5;



FIG. 7 is a schematic of a focused light source and de-focused light source as captured by pixels of an imaging sensor;



FIG. 8 is a side elevation of a lens and image sensing device of the present invention;



FIG. 9 is a chart showing the region of interest and average time gained in low beam for several vehicles as the vehicles travel around different degrees of curvatures in the road;



FIG. 10 is a perspective view of a lens and image sensing device of the present invention;



FIGS. 11A-D are schematics of a quad of pixels of an image sensor in accordance with the present invention;



FIG. 12 is a graph of a plurality of line profiles generated in accordance with the present invention;



FIG. 13 is a graph of the slope of the line profiles of FIG. 12;



FIG. 14 is a graph of the edge slope vs. focal distance for the focusing algorithm of the present invention;



FIG. 15A is a perspective view of a lens holder in accordance with the present invention; and



FIG. 15B is a plan view of the lens holder of FIG. 15A.





DESCRIPTION OF THE PREFERRED EMBODIMENTS

In order to facilitate the description of features and enhancements, some specific configurations, to be reviewed in conjunction with the drawings, is described below. It will be understood that the components and values described are for illustrative purposes and do not limit the scope of the disclosed invention.


With reference to FIG. 1, a vehicle 10 includes an automatic high beam control system 11 made up of an optical system 12, an image sensor 13, a digital processor 14, a printed circuit board assembly 15, which contains all the necessary electronic components and interconnections to support operation of the image sensor 13 and digital processor 14, a connection 16 to the vehicle wiring system, and a housing assembly or accessory module or windshield electronics module 18.


The optical system 12 is held by features of the housing assembly 18 in a constrained spatial relationship with the image sensor 13, such that an optical system axis 17 is perpendicular to the active plane of the image sensor 13 and passes generally through its center point, and such that the distance between the optical system 12 and the image sensor 13 may be adjusted to bring the optical system focal plane into a predetermined relationship with the active plane of the image sensor 13 during the manufacturing process and thereafter locked in position. The housing assembly may utilize aspects of the modules or assemblies described in U.S. Pat. Nos. 6,968,736; 6,877,888; 6,824,281; 6,690,268; 6,672,744; 6,593,565; 6,516,664; 6,501,387; 6,428,172; 6,386,742; 6,341,523; 6,329,925; 6,326,613; 6,250,148 and 6,124,886, and/or U.S. patent applications, Ser. No. 10/456,599, filed Jun. 6, 2003, now U.S. Pat. No. 7,004,593; Ser. No. 10/538,724, filed Jun. 13, 2005 and published Mar. 9, 2006 as U.S. Publication No. US-2006-0050018, and/or Ser. No. 11/201,661, filed Aug. 11, 2005, now U.S. Pat. No. 7,480,149, and/or PCT Application No. PCT/US03/40611, filed Dec. 19, 2003; PCT Application No. PCT/US03/03012, filed Jan. 31, 2003, and/or PCT Application No. PCT/US04/15424, filed May 18, 2004, and/or Ireland patent applications, Ser. No. S2004/0614, filed Sep. 15, 2004; Ser. No. S2004/0838, filed Dec. 14, 2004; and Ser. No. S2004/0840, filed Dec. 15, 2004, which are all hereby incorporated herein by reference in their entireties.


The accessory module or housing assembly 18 includes an outer housing 18a that is removably attached to an attachment plate 19, which is fixedly attached to the upper central region of the inside surface of the vehicle's windshield 20 such that the optical system axis 17 is substantially horizontal and substantially parallel to the vehicle's principal central axis. Preferably, the housing assembly 18 is positioned at the windshield such that the light rays that pass through the optical system 12 to the image sensor 13 also pass through a portion of the vehicle's windshield swept by the vehicle's windshield wiper system.


As shown in FIG. 3, optical system 12 includes a lens 31, or combination of lenses or optical elements, with a focal length f (such as a focal length of, for example, about 4.5 mm), an optical stop, N (such as an optical stop of, for example, about 1.8) and a spherical field of view (FOV) 34 (such as about 60 degrees). Optical system 12 also includes a cylindrical lens holder 32, an infrared filter 33, and an image sensor 13 positioned with its active surface perpendicular to optical axis 17. The infrared filter 33 facilitates the determination of color attributes within the visible light spectrum of objects within the monitored scene by preventing near infrared energy, with a wavelength above about 750 nm, from reaching the image sensor 13, and may be optionally placed at the front (object side) or back (image side) of the optical system 12, between the optical system 12 and image sensor 13, or on the surface of the image sensor 13. The optical system and lens holder and sensor may be located at an imaging sensor module, such as by utilizing aspects of the modules described in U.S. patent applications, Ser. No. 10/534,632, filed May 11, 2005, now U.S. Pat. No. 7,965,336, and/or U.S. provisional application, Ser. No. 60/731,183, filed Oct. 28, 2005, which are hereby incorporated herein by reference in their entireties.


The image sensor 13 is preferably, but not limited to, a CMOS photosensor array, such as part number MT9V125 produced by Micron Corporation, with 640 rows and 480 columns of 5.6 micron square photosensor elements and circuitry to measure the quantity of photons that impinge each photosensor element during a controllable period of time. In the described configuration, the image sensor is oriented such that the 640 photosensor rows are horizontal and the 480 columns are vertical. Thus, in combination with the optical system 12, the image sensor 13 has about a 48 degree horizontal field of view and about a 36 degree vertical field of view.


In order to extract color information from the image or image data, one of a number of filter types, each able to transmit a particular band of wavelength, covers the active surface of each of the photosensor elements of the array. The most commonly used color filter pattern, and therefore the most economically available as a standard feature of a commercially available image sensor, is the Bayer pattern in which either a blue, green or red pass filter is applied to the surface of each of the image sensor photosensor elements, such as shown in FIG. 4. Alternate rows of the photosensor elements may be covered by alternating red and green filters and alternating blue and green filters such that every 2 by 2 block of photosensors within the array contains one red filter, one blue filter and two diagonally opposite green filters. Since each photosensor is filtered to measure only the blue, green or red light energy content of the portion of the image formed by the optical system at its active surface, it is necessary to combine the measured values of filtered light from each of the photosensors of a 2 by 2 block such that an interpolated red, green and blue color value (RGB) may be assigned to the center point of the block. The color value for each of the 2 by 2 blocks of the array is calculated, thus creating a 639 by 479 array of color values with the same spacing as the photosensor array. This array of color picture elements is commonly termed a pixel array. A variety of algorithms may be used to perform this interpolation, commonly termed demosaicing, and the particular algorithm may be selected depending on the particular the application, the desired final image quality and available computing power. For the AHB control system described herein, each pixel RGB value is derived by combining the red value, the average of the two green values, and the blue value of its associated four photosensor elements.


Optionally, the imaging device or module may comprise an imaging or intelligent headlamp control (IHC) module. For example, an intelligent headlamp control module may have a dimensionally small cavity around a lens holder 132 (FIGS. 15A and 15B), thus making threaded lens insertion very difficult in an automated lens insertion situation. In order to manufacture such an IHC module or lens holder 132, crush ribs 132a may be disposed or established at an interior surface of the barrel 132b of the lens holder. By introducing vertical crush ribs to the barrel, the axes of motion are decreased to one. In a threaded lens situation, the automated lens insertion procedure could have as many as four axis of motion, thus increasing the cost of the module or system.


In order to accommodate the crush ribs, several lens modifications may be needed, since a typical existing lens often has threads which may bind in the crush ribs. Thus, in order to accommodate the crush ribs, the lens may be manufactured or modified to have a smooth bore in order to engage the control surfaces of the ribs evenly along the barrel of the lens holder.


The crush rib feature of the lens holder secures the lens during manufacture. However, it is desirable to apply an adhesive at the lens holder and lens to substantially permanently attach the lens to the plastic barrel of the lens holder. Lateral grooves around the barrel of the lens may be provided to allow the flow of adhesive around the lens for a more uniform lens attachment or securement. Such grooves do not interfere with the crush ribs during insertion as much as threads would and allow the flow of adhesive greater than a substantially smooth bore.


In order to control alignment and precision of the insertion of the lens down the barrel (toward and away from the imager), a gripper may be provided to isolate the head of the lens between two overlapping control surfaces that would hold the lens and control the toward and away (from the imager) motion during the focusing process.


The control surface of the printed circuit board (PCB) during the lens insertion process preferably facilitates several criteria, both mechanical and electrical, during such an automated process. The nest may secure the imager board and backplate simultaneously in a repeatable orientation for lens insertion. The nest may also allow for electrical attachment to the ECU by way of pogo pins or the like during the focusing process. It may also articulate in order to secure the ECU board to the backplate after the focusing process.


In order to achieve enhanced AHB control system performance, it is desirable to detect the tail lamp of a leading vehicle at about 200 meters. The minimum tail light emitting surface area to meet legal requirements is about 50 cm2, which can be achieved using about an 80 mm diameter circular or about a 71 mm square emitting surface. However, in most cases, tail lamp designs are driven by vehicle styling considerations and a desire on the part of vehicle manufacturers to exceed minimum requirements by a safe margin. Such design and styling constraints often result in tail lamp emitting surfaces with either a horizontal or vertical dimension of at least about 125 mm.


Using the above described optical system 12 and imaging array 13, each photosensor element of the array has an effective horizontal field of view FOVph and an effective vertical field of view FOVpv, where:

FOVph=FOVpv=48 degrees/640 pixels=0.075 degrees/pixel.


Thus, each photosensor element of the array subtends a region of a vertical plane at distance R meters from the imager, which measures ph by pv where:

ph=pv=FOVph×R×1000×PI/180 mm=1.309×R mm.


Thus, at a distance of about 200 m, the portion of the forward scene subtended by each photosensor element of the array measures about 262 mm by about 262 mm, which is approximately twice the dimension of a typical tail lamp of a vehicle. Since the dimension of the image of a tail lamp at about 200 meters is in the order of half that of a photosensor element, it is possible that the tail lamp image will lie entirely on the blue and green detecting photosensor elements of a 2 by 2 block of photosensor elements, and thus may result in a pixel value with no red content. In order to assure that the red content of the image of the tail lamp at about 200 meters is measured and results in a red component of the calculated pixel color value, it is desirable that the tail lamp image dimension be in the order of about one and a half times that of a photosensor element, such that no matter where it lies or is received on the photosensor array it will cover at least a portion of at least one red detecting photosensor.


This could be achieved by reducing the field of view of the optical system by a factor of about three, resulting in a horizontal field of view of about 16 degrees and a significant reduction in other aspects of system performance. It could also be achieved by increasing the number of photosensor elements in each row of the image sensor by a factor of about three, with a corresponding reduction of photosensor element dimension, or by increasing the number of photosensor elements in each row of the image sensor by a factor of about three, maintaining the photosensor element dimension, and changing the optical system specification in order to maintain about a 48 degree horizontal field of view. However, this is typically not a practical solution due to the cost or availability of such image sensors and the cost of the additional processing capacity required to handle the significantly increased amount of image data.


It is thus an aspect of the present invention to increase the effective size of the image at the image sensor of a tail lamp at 200 meters, so that its presence and color may be detected, without changing the optical system or image sensor and while maintaining the ability to perform other object detection tasks to support additional features and functions, such as described below.


The following analysis of the optical system contained within the AHB control system will serve to explain the principles used to establish an optimal configuration.



FIG. 5 shows an imaging device having a thin lens 31 with focal length f mm, effective diameter a mm, and optical stop N, where N=f/a. An object plane OP, which is perpendicular to the optical axis of lens 31 and at a distance s mm from its optical center, and an image plane 13a parallel to the object plane at a distanced mm from, and on the opposite side of, the optical center of lens 31. The focal length f is the distance from the optical center of lens 31 to the point at which light rays from a distant point on the optical axis 17 converge. Light rays that pass through lens 31 behave according to the thin lens equation 1/s+1/d=1/f. Thus, an image of the point P on the object plane OP is in focus at point I on the image plane 13a.



FIG. 6 shows the same configuration as FIG. 5 with the addition of a point P′ at distance s′ from the optical center of the lens 31. According to the thin lens equation (1), a focused image of point P′ would be formed at point I′ which is at distance d′ from the optical center of the lens. However, since the image plane 13a has remained at the distance d from the lens, the light rays from point P′ continue through point I′ and diverge until they reach the image plane 13a resulting in an unfocused image of point P′ with diameter C, commonly termed the circle of confusion.

For a thin lens: 1/s+1/d=1/f  (1)
or: d=f×s/(s−f)
similarly: d′=f×s′/(s′−f)
by similar triangles: C=a×(d−d′)/d′
by definition: a=f/N
by substitution: C=f2×(s′−s)/N×s′×(s−f)  (2)


Consider now the object point to be replaced by a circular uniform light source of diameter D. When placed at point P, the image of the disc at the image plane would appear as a focused disc of diameter D′ with uniform intensity (such as shown at A in FIG. 7). A plot of image intensity across an extended diametral line would appear as line A′ with a step change from background image intensity to disc image intensity. When placed at point P′, the previously described circle of confusion, with diameter C, would be created at the image plane 13a for each point on the disc, resulting in an unfocused image of the disc with diameter D″, where D″=D′+C (such as shown at B in FIG. 7). A plot of image intensity across an extended diametral line would appear as line B′ with gradual change from background intensity to full disc image intensity over a distance equal to the circle of confusion, C.


Rearranging equation (1) to solve for s:

s=f×s′×(C×N+f)/(C×N×s′+f2)  (3)


For the optical system described above, f=4.5 mm and N=1.8. Thus, C=0.0056 mm, s′=200 m, and s=1.933 m. Thus, the same amount of light energy has been distributed over a greater image area. By focusing the lens to focus an image of an object P that is about 1.993 m from the lens, the defocused object P′ at about 200 m from the lens will be defocused to have a diameter that will allow the image to be received by more than one pixel of the imaging array sensor or camera. Such an expanded image of the light source (by de-focusing the image of an object at about 200 meters) will be received by more than one pixel and will be at least partially received by a red sensing pixel so that the system can determine whether or not a distant light source is a red light source and thus whether or not the distant light source may be a taillight of a leading vehicle.


Focusing or adjustment of the lens may be performed via any suitable focusing means, such as a linear slide mechanism that has a high resolution motor and controller, such as an MX-80 miniature slide with a Vix 250 IM motion controller commercially available from Parker-Hannifin. Such as slide mechanism and motor may provide a resolution of about 1 μm and a repeatability of about 2 μm or thereabouts. Such characteristics make good resolution possible across the focus range so that the captured image data will illustrate or approximate an enhanced or optimum focus value.


Once an image was loaded by the software, the image may be analyzed by many different means including line profiles and color analysis. For the processing of the image data, a processor system or software, such as Data Translation's DT Vision Foundry©, may be used to analyze the images using software masks or regions of interest (ROI's). The exemplary DT Vision Foundry© software provides an imaging software designed to assist machine vision systems in operation, programming, and data management.


The imaging software was used to process image ROI's using a line profile tool. The line profile tool examines the pixel gain values which the line mask lays. The line profile tool graphically illustrates the gain values across the mask. It also may provide a data export tool which enables the user to view and manipulate the intensity values gathered from the imager.


Imager color values are independent to a color pixel. Four pixels in a grid pattern, as shown in FIG. 11A, are often referred to as a “quad”. The values taken from the imager are typically intensity or gain values (0 to 255) from the pixels in the quad. There are many different ways in which displays and compilers interpret the intensity values from the imager. The most straight forward way is to collect each pixel's intensity value and compile it into a 64-bit RGGB image. This method of compilation yields enhanced resolution because all of the pixel data is transferred; however, the file sizes may be very large. Many software designers use compression techniques to keep the file sizes lower.


The first method of compression is to get the maximum pixel value from the green pixels in the quad. The second green pixel in the quad is set to the maximum green pixel's value. This method works well in most applications. However, since focus is directly related to the sharpness of the edges of the target in an image, the green pixel values should not be extended to allow the edge to appear sharper than it is. As shown by the edges B and C in FIGS. 11C and 11D, if the edge of the target image lies across the imager in a vertical orientation bisecting the two green pixels of the quad, the second green pixel is then set to the maximum green pixel. This allows for a false edge to be seen by the image processing software. One way that the quad detects the edge correctly is if the edge of a target bisects the green pixels of the quad evenly, as shown by edge A in FIG. 11B. To counter this effect, software designers often average the green pixels together to allow the pixel to take into account the pixel effected by the edge of a target image.


While some visual resolution may be lost by averaging the green pixels, for matters of image processing the data is typically more telling. If the target image edge falls on the imager, such as edge B or C of FIGS. 11C and 11D, the value will be less than the maximum value because the green pixels in the quad are averaged. By examining the difference in intensity value of the quads either side of the averaged edge quad, the edge location within the quad can be approximated.


Focus is directly related to the sharpness of the edges of the target image. Optionally, two image processing tools may be used: a histogram and a line profile. After image data are captured and stored, the automatic focus software processes the image data with a histogram tool. A histogram of a region of interest (ROI) in an image will illustrate, using a bar graph, the number of pixels at their respective intensity value. The theory behind using a histogram is that as a black and white target image is processed, a more focused image would have more pixels in extremes and fewer pixels in the medium intensity range. However, in order to create an algorithm to focus the imager, a more perceivable pattern to predict the focal distance was needed.


Optionally, and desirably, the intelligent headlamp control (IHC) may focus by capturing individual picture frames and measuring a line profile through a target image consisting of several dark bars separated by different spacing. After finding the line profiles across the target, the slope of the line profiles is calculated to find the sharpness of the edges of the target image. If the target image is sharp (in focus), the peak of the slope will be abrupt and large. If the edges of the target image are blurry, the peak of the slope will be smaller and less abrupt (as can be seen with reference to FIG. 13).


Thus, a line profile tool may be implemented to examine the pixels in a one dimensional mask or line. Such a line profile tool charts the intensity values detected by the imager along the line mask, such as shown in FIG. 12. By using the line profile tool, the edges of the target image are able to be examined. The theory used for the line profile is that as the line mask crossed from a black to white or vice versa target area, the detected intensity value curve would have less of a slope in an unfocused image than a more focused image. The imaging software may include a derivative function to find the slope of the line profile as shown in FIG. 13.


To find the slope, the intensity value of the pixels in the line profile can be calculated in several ways. However, in order to simplify the calculation, the following method to find slope was derived:










Y
n

-

(

Y

n
-
1


)




X
n

-

(

X

n
-
1


)



=

S
n


;




where Y is the intensity value of the pixels 1, 2, 3, 4 . . . n, and X is the position of that pixel along the line profile.


Once the slope had been determined by the above equation, the maximum and minimum slopes from that image were recorded along with the reference distance away from the starting back focal length as shown in FIG. 14. As the images are processed, a pattern exists that references the maximum and minimum slopes to focal length. This focal length curve is the basis of the software algorithm.


After the slope of the line profile has been plotted, the slope can be plotted against the back focal distance by finding the maximum and minimum slope. As shown in FIG. 14, the correlation between rising edge slopes of several line profiles and back focal distances may be determined. Once near-focus has been achieved, the curve of FIG. 14 provides a reference to the nominal back focal length and creates a quick focus procedure.


After creating several “Slope vs. Distance” curves, the tolerance range for the back focal distance may be approximately ±100 microns from nominal. The tolerance specification given for the lens may be ±210 microns. These tolerance stack-ups require an individual focus for each lens to imager combination.


Using the same line profile slope plot as in FIG. 13, the pixel length of the target image can be found to verify magnification. Since the size of the target is critical in the IHC algorithm, the magnification needs to be verified during focus calculations. To verify the magnification of the lens, the distance, in pixels, is measured between the peak rising edge slope and the peak falling edge slope taken from a target near the center of the field of view. That pixel length is verified against a predetermined value for that target. The production target has not been designed making that pixel length variable to the target.


The software design of the automated focus system is a closed loop software controlled system that utilizes the focus curve as shown in FIG. 14 as a virtual feedback. The design proposal for the focus algorithm created by the author is as follows:


move slide to preset near focus distance (greater than nominal focal distance);


capture image and process line profiles;


increment slide toward imager by 25 steps (microns);


capture image and process line profiles;


repeat steps 3 and 4 one or more times;


calculate present focal distance according to pre-calculated focal curve;


move to nominal focal distance according to pre-calculated focal curve; and


capture image and verify correct focal distance with line profile and histogram.


Since a single line profile may allow for failure in the above process, many line profiles are preferably examined and averaged in order to effectively focus a lens to an imager in a production setting. However, the single line profile used in experimentation exhibits the predictability and repeatability of the focal length curve.


Thus, the software needed to control the auto-focus system should accomplish three things: (1) control the imager; (2) calculate image data; and (3) control the slide table for adjustment. In order to control the imager, the digital video signal from the imager may be coming in through USB, and the software may capture individual frames for calculation upon command. The software also may be capable of reading and writing exposure and gain values for image capture.


The GUI must be able to interactively place and store multiple line ROI's in order to take measurements across the image. Line profiles that correlate with the line ROI's are calculated to find the measure of focus at that specific position. The derivative of each line profile is taken in order to find the slope of the line profile. After these calculations are complete for one image, the peak slopes from each line profile will be averaged together. This number will be compared to a stored table that the program will be able to access in order to find the approximate distance (number of steps) away from nominal focus. After the nominal focus has been reached by means of line profile, the GUI is able to calculate histograms from multiple rectangular ROI's. These histograms may reveal the percentage of data between the two peaks of the histograms, and the control may average that percentage to give a validation to the line profile. The software and control are further able to either virtually or physically interface to the slide controller package in order to input distances and command operations to the slide controller.


After completing several iterations of the focus experiment, several conclusions were made. The target design should have a white object on a black background with angled edges by which line profiles and histograms may discern a sharp focused edge. The histogram method of detecting focus was adequate for detecting focus. However, this method required the motion control to step through many increments of focal length until the histogram criteria reached acceptable limits. Although this method would be adequate and relatively simple to implement, the line profile method revealed a more calculable and immediate pattern of focus.


The line profile method of measuring focus is readily chartable and predictable across the focal distance range. However, although the boundary region between a white and black target may be readily examined, whereby the sharpness of the target is only measured at that point in the target image, more line profiles may be needed to be performed across the image for real-world or non black and white target applications.


Thus, the mathematical algorithm and the curve data methodology discussed above may be implemented as a focus algorithm for present and future forward facing imager systems (and optionally rearward facing imager systems and/or sideward facing imager systems and the like). Although each imager and lens combination will have its own focus curve data, the initial data collection will be a minimal component to the setup procedure. Preferably, the focus can and should be attained by going directly to a calculated focal distance processed by initial out-of-focus images captured by the imager. By using this procedure, attaining focus in a production scenario can be greatly hastened.


Real lenses do not typically focus all rays perfectly due to spherical or other aberrations, diffraction effects from wave optics and the finite aperture of the lens, resulting in an unfocused image with a circle of confusion CL, which is an essentially constant value dependent on lens quality and independent of the distance of the object or the image from the optical center of the lens. The combined effects result in a total circle of confusion CT for images received by the image sensor. Further, the magnification M of the lens is defined as the ratio of the image dimension LI to the object dimension LO. Thus, M=LI/LO.


High volume component manufacturing and assembly processes used in the production of optical systems at low cost typically result in part to part variations in their optical characteristics. Additionally, dimensional tolerances are associated with the manufacture of the housing assembly components, with the placement of the imaging array die in its package, and with the placement of the imaging array on the printed circuit board assembly.


Forward facing image capture and signal processing components, such as incorporated in the described AHB control system, may be used to obtain information regarding the location, movement and other characteristics of many objects of relevance to the driving task or operation of the vehicle within the image sensor's field of view, in order to provide additional features and functions, such as, but not limited to, traffic lane detection and lane departure warning, traffic sign recognition, traffic light status detection, fog detection, rain sensing, and/or to supplement or complement the use of other sensors or sensing technologies in the provision of other features and functions such as, but not limited to, adaptive cruise control, pre-crash sensing, pedestrian detection, etc., thus increasing the utility and value of the AHB control system and reducing the cost and space requirements associated with providing each of the features and functions as independent stand-alone systems.


In order to provide optimum detection and characterization of the wide range of objects of relevance or interest in support of the above listed features and functions, amongst others, under a wide range of lighting conditions, it is necessary to consider the requirements of each detection task or group of similar detection tasks.


Typical fixed beam forward lighting systems offer two beam patterns, low and high, with low beam preferably providing the maximum forward and lateral road illumination without causing direct glare to other oncoming or leading vehicle operators such that it may be used in all driving conditions, and high beam preferably providing the maximum forward illumination range with reduced lateral coverage to provide improved forward visibility during night driving when other road users are not present within the extended illuminated region, that is, the region in which they would be subject to glare from the vehicle's high beam headlamps.


Optionally, AHB control systems may incorporate a low speed threshold below which high beam selection is inhibited, in order to avoid activation of high beams when unnecessary, such as when driving at a speed which is safe given the forward visibility provided by the low beam lighting, or when undesirable, such as when driving on city roads.


There are, however, circumstances above this low speed threshold in which it is desirable to maintain, or switch to, a low beam state, even in the absence of other road users in the high beam illumination region. One such circumstance occurs when the vehicle is driven round a bend with a short radius of curvature. In this situation the value of the long range illumination resulting from the use of high beams is reduced since it is not aligned with the vehicle trajectory and typically illuminates off road regions. However, the wider short range beam pattern resulting from the use of low beams can provide increased illumination along the left or right hand curving vehicle trajectory.


Thus, an aspect of the present invention is to improve existing AHB control systems by inhibiting high beam selection when improved relevant road illumination may be provided by the use of low beam headlights, and in particular when the radius of curvature of the current, or instantaneous, vehicle trajectory falls below a predetermined threshold value.


The threshold value is ideally determined based on the specific high and low beam patterns generated by the lighting equipment installed on the vehicle. Additionally, since headlight beam patterns are typically asymmetric, and since the distance to the edge of the road is different when driving round a left or right hand curve, the left and right hand vehicle trajectory radius of curvature threshold values may be different.


The current or instantaneous vehicle trajectory radius of curvature may be obtained or derived from several sources, including, but not limited to steering wheel angular position. The correlation between the vehicle trajectory radius of curvature and steering wheel angular position may be readily established with knowledge of the vehicle mechanical configuration. Thus, steering wheel angular position thresholds which are substantially equivalent to vehicle trajectory radius of curvature thresholds may be derived. Typically, the current, or instantaneous, angular position of the vehicle steering wheel is measured by a rotary encoder, or equivalent sensing device, and is made available to other vehicle control systems either directly or via a vehicle communication bus such as a CAN or the like. By accessing this signal or vehicle bus message, and comparing it to a predetermined threshold, high beam activation may be inhibited to achieve the previously described benefits.


If the vehicle is driven round a long steady curve with a radius of curvature which corresponds to the steering wheel angular threshold, it is possible that the instantaneous steering wheel angular value will oscillate about the angular threshold, resulting in a potentially annoying or inappropriate oscillation between high and low beam states. Thus, the AHB control system may incorporate a time based filter, which may be adaptive, and which may be non-symmetrical, to regulate what might otherwise be frequent annoying or inappropriate switching between the low and high beam states. Depending on the characteristics of the time based filtering system, it may be beneficial to incorporate hysteresis in the angular threshold values, such that the values for left and right increasing steering wheel angles are greater than the values for left and right decreasing steering wheel angles.


Automatic image based high beam control systems (such as described in U.S. Pat. No. 6,097,023, which is hereby incorporated herein by reference in its entirety), in which an image of the scene forward of the vehicle is focused by an optical system, may have a horizontal field of view equal to, but not limited to, approximately +/−22.5 degrees about the imaging system centerline. The image may be focused or imaged onto a rectangular array image capture device such as, but not limited to, a 640×480 CMOS color imager, which captures image data and provides sequential frames of data indicative of the light energy reflected or emitted by objects in the region subtended by each element of the array. The image capture rate may be at a rate in the range of about 5 to 120 times per second or more, with processing being performed on the data to determine the presence, location and characteristics of objects within the monitored scene and to determine characteristics of the monitored scene, such as general illumination level, and to utilize several defined regions of the monitored scene for several different purposes. For example, the region of the scene which generally corresponds to the region of influence of the vehicle high beam pattern, may be used to determine the need to inhibit high beam activation if other road users are detected within that region. The regions to the left and right of the first region may be used to anticipate the upcoming entry of other road users into the first region in order to facilitate a rapid and appropriate response upon entry or just prior to entry of the first region. The upper central region of the monitored scene may be used to determine ambient lighting conditions such that a first threshold may be established below which low beam headlights are activated, and a second threshold may be established above which high beam activation may be inhibited, while the lower horizontal portion of the ambient lighting condition detection region may be used to detect urban lighting conditions or the like. Other processing of the captured image data may be implemented depending on the particular application of the image sensor and processor, while remaining within the spirit and scope of the present invention.


While the segmentation of the monitored scene into fixed regions, such as described above, provides many benefits and efficiencies to the image processing routines used to characterize vehicular light sources, ambient lighting conditions, and non-vehicular light sources etc., they only provide optimal performance when they are appropriately aligned with the monitored scene, such as when driving on a flat straight road. Much of the typical driving experience, however, is on curved and undulating roads. It is, therefore, desirable to have dynamic segmentation of the monitored scene such that the various regions employed align appropriately according to the upcoming road topology and geometry.


An additional aspect of the present invention is to improve the performance of AHB control systems by providing a dynamic segmentation of the monitored scene.


When driving on a curved road, it is beneficial to extend the region used to detect and monitor vehicular light sources in the direction of road curvature in order to provide a sufficiently early detection of other road users and thus the inhibition of high beams. This region may be extended in accordance with the vehicle trajectory radius of curvature as determined by the steering wheel angle or other means as previously described. Additionally, the upcoming road condition may be anticipated by other means, such as vehicle pitch as may be monitored by an accelerometer, combination of accelerometers or other means, such as by detection of vehicle roll, visible horizon tilt and/or yaw detection and/or in response to a GPS output or the like.


An additional aspect of the present invention is to improve the performance of AHB control systems, when used in conjunction with adaptive forward lighting (AFL) systems, by actively changing the region of high beam inhibit in response to the vehicle trajectory radius of curvature, in order that it corresponds to the current region of influence of the high beam pattern.


Typical AFL systems are responsive to the vehicle trajectory radius of curvature and provide improved road surface illumination when driving on curved roads by mechanically, or otherwise, adjusting the beam direction or providing supplementary illumination in the direction of the curve. While this provides a solution to the problem addressed above for fixed beam systems, it introduces a shortcoming for a typical fixed beam high beam control system when used to control high beam selection in an AFL system.


While the detection of leading and on-coming/approaching vehicles occurs across a wide field of view, the inhibition of high beam selection occurs in a narrower fixed region which corresponds to the region of influence of the fixed high beam pattern. When driving around a curve with an AFL system, the region of influence of the adaptive high beam pattern is extended in the direction of the curve, thus reducing the effective response time to a vehicle entering the region of high beam inhibit from the direction of the road curvature. It is, therefore advantageous to modify the region of high beam inhibit in correspondence with the modified high beam pattern.


While this may be accomplished through image processing and scene analysis, it is preferable, in order to minimize the complexity, and therefore to minimize the cost of implementation, of the image processing algorithms employed, to use a signal indicative of the vehicle trajectory radius of curvature or the AFL system beam direction control signal. As previously described, the steering wheel angle may be most conveniently used since it correlates to the vehicle trajectory radius of curvature and is commonly available from the vehicle data bus. The region of high beam inhibit may be adjusted in a continuous fashion in correspondence with the instantaneous high beam direction, or it may be adjusted in one or more steps according to one or more steering wheel angle threshold values.


An additional aspect of the present invention is to improve the performance of existing AHB control systems by improving the characterization of non-vehicular light sources when driving on curved roads.


In order to enhance AHB control system performance, it is desirable to provide accurate detection and characterization of other vehicular road users in order to assure the appropriate inhibition of high beam use. Additionally, all other light sources within the monitored scene may be characterized as non-vehicular in order to enhance or maximize forward vision by enabling high beams whenever appropriate and also to avoid the annoyance caused to the user by inappropriate returns to low beams due to the false characterization of non-vehicular light sources as vehicular sources.


Spatial, spectral and temporal techniques are typically used to aid in the appropriate characterization of light sources. It is, however, particularly challenging to correctly characterize light sources when driving round road curves which are provided with reflective signs to indicate road curvature. To achieve the greatest visibility of these signs or reflectors, they are typically located and oriented to provide the maximum possible reflection of light from the host vehicle headlights, that is, at a height above ground level which is similar to that of typical vehicle lights and oriented such that they reflect light from the headlight beams directly back towards the host vehicle as it progresses around the bend. Thus, the spectral and geometric characteristics and locations of these signs or reflectors may be similar to that of other vehicles traveling along the curve or bend in the road.


An additional aspect of the present invention is to improve the performance of existing AHB control systems by providing an automatic repeating alignment of the sensor to the vehicle centerline and the horizontal plane such that the various regions of interest within the scene monitored by the sensor are optimally maintained regardless of vehicle and high beam control system module geometric manufacturing and assembly tolerances, and other sources of misalignment such as vehicle attitude variations due to a wide range of possible vehicle loading conditions.


In order to take advantage of the environmental protection offered by the vehicle cabin, the frequently cleaned optically clear path offered by the vehicle windshield (which is cleaned or wiped by the windshield wipers when the wipers are activated), and the relatively high vantage point offered at the upper region or top of the windshield, AHB control systems are preferably mounted centrally on the upper inside surface of the front windshield of a vehicle and with a forward field of view through the region cleaned or wiped by the windshield wipers.


Typical vehicle body structures, windshields and assembly systems each contribute to the geometric tolerance associated with the surface to which the AHB control system module is attached. The module also has some degree of geometric tolerance associated with its components and assembly methods. It is not unusual to encounter a total stack up of tolerances which result in a potential vertical and horizontal misalignment of +/−2 degrees from the theoretically ideal condition. This is a significant value and may result in errors in determining lane widths and object sizes and distances and the like.


It is known to provide a mechanical adjustment means to allow for the correction of this misalignment at the installation of the AHB control system to the vehicle. This is, however, often undesirable since it often is expensive to apply manual labor to the alignment of components on each vehicle equipped with an AHB control system on the vehicle assembly line. It is additionally undesirable since the alignment procedure is subject to operator error.


Also, during the lifetime of the vehicle the windshield may be damaged and require replacement. In such an event it would be necessary, and likely at an unacceptable cost, to provide the alignment techniques, tools and equipment to every service operation that may be required to replace a windshield and remount the AHB control system module in order to return the vehicle to its original performance level.


Additionally, in normal use, a typical vehicle experiences many different loading conditions which cause it to adopt a wide range of pitch and roll attitudes, causing an AHB control system which is attached to the vehicle to view the forward scene from perspectives different from the ideal, or initially considered design condition, potentially resulting in different headlight actuation decisions than contemplated by the original system specification.


Thus, it is beneficial for an AHB control system to include a feature which automatically compensates for an initial misalignment condition and additionally is capable of correcting for temporary vehicle conditions and re-installation misalignments which may occur during the use of the vehicle.


In order to achieve optimum performance of the AHB control system, it is desirable to determine which of the array elements of the image capture device fall into each of the defined regions. Since the regions are defined relative to the forward scene, it is desirable to determine a particular point within the forward scene and to relate that point to a particular array element of the image capture device.


The particular point in the forward scene may be defined as a particular distant point which lies on the forward extended vehicle centerline on the horizontal plane which passes through the center of the optical system associated with the image capture device. When driving on a substantially flat and substantially straight road, the distant point may be the point within the forward scene at which the headlights of an oncoming vehicle or the tail lamps of a slower leading vehicle are first detected. As the distance between the host and target vehicles decreases, the image of the target vehicle expands within the imaged scene, towards the left if traveling in a leftward lane, centrally if in the same lane, and towards the right if traveling in a rightward lane. Thus the described distant point may be called the focus of expansion or FOE.


In order to determine the imaging array element or pixel which subtends the FOE in the as assembled and as loaded vehicle, it is necessary to identify the array element which first detects a new light source, which has the potential to be a vehicular source, within that region of the monitored scene which could potentially contain the FOE, to continue to track the light source as it expands in the image as the distance between the detected source and the host vehicle decreases until it is confirmed that the source is vehicular, and to monitor the host vehicle trajectory until it reaches the point in the road where the new light source would have been initially detected in order to confirm that the road traveled for the duration of the monitoring period was substantially flat and substantially straight. If it is determined that the point or light source is a vehicle and the host vehicle and approaching vehicle are traveling along a substantially flat and substantially straight road, the location of the initial distant point or FOE may be compared to an expected location and the axis of the imaging system may be adjusted accordingly so that the imaging system is directed at the desired or appropriate or optimal angle relative to the vehicle. Optionally, the imaging system may be adjusted in response to a detection of lane markers along a straight and/or flat road, and/or pitch information from a bus or accelerometer and/or roll information from an accelerometer or bus information. Optionally, the system may only monitor for new light sources when the steering wheel angle or steering angle (SA) is approximately 0 degrees, such as when the steering angle is about 0 degrees+\−0.1 degrees or other threshold angle. Thus, adjustment and/or alignment of the image sensor may occur by tracking movement of light sources through the images when the vehicle is traveling substantially straight, so that the control may compare the tracked light sources to expected locations and paths through the captured images as the vehicle moves along the substantially straight path and may adjust the field of view or viewing angle of the image sensor accordingly.


An additional aspect of the present invention is to improve AHB control systems by providing a detection of left and right hand road systems and an automatic configuration to assure appropriate left or right hand operation. AHB control systems are often installed on both left and right hand drive vehicles and may be configured differently for use on left and right hand drive road systems, preferably having an asymmetric response to activity within the monitored scene. It is possible to configure the system to operate on either a left or right hand drive vehicle and to supply the system to the vehicle assembly plant for installation on a corresponding either left or right hand drive vehicle. In such cases it is desirable to identify the AHB control system module with a particular part number in order to assure the installation of the correct configuration, which results in part number proliferation and the potential for operator error, either at the module manufacturing location where the wrong label may be attached to the product, or at the vehicle assembly plant, particularly if left and right hand drive vehicles are built on the same assembly line, where the wrong part may be selected for installation.


In order to reduce part number proliferation, it is possible to provide a switch on the AHB control system module in order to configure it for operation on a left or right hand drive vehicle. This solution, however, is also subject to operator error and may result in incorrect and inappropriate control of the vehicle high beams during nighttime driving. Additionally, during the normal use of a right hand drive vehicle, it may be driven on a left hand drive road system for a period of time before returning to a right hand drive road system and vice versa, such as when taking a vehicle by ferry or Channel tunnel from the United Kingdom to mainland Europe for vacation or in the course of business. Again it is possible to provide a switch to allow the vehicle operator to configure the system appropriately for the country of use, however, this is inconvenient and may result in inappropriate high beam activation in the event that the operator forgets to, or is unaware of the need to switch to the alternate configuration, or is unaware of the availability of a reconfiguration switch. Thus, there is a need to provide an automatic configuration of AHB control systems such that they analyze the monitored scene, recognize a left or right hand drive road system and self-configure accordingly.


The system may track the light sources and adjust the image sensor only when the steering angle is within a threshold value of straight or substantially straight, in order to avoid misinterpretation of source locations. Also, the system may take into account the environment at which the vehicle is traveling, and may not track and adjust when the vehicle is traveling in high lighting environments, such as cities and the like, and may function when there are limited other light sources in the forward field of view. The system may also take into account the speed of the vehicle and thus the speed of the light moving through the captured images.


Optionally, when the vehicle is being passed on tight curves or the like, the control may determine when to adjust the headlamps of the vehicle in response to the location and departure of the passing vehicle's taillights within the field of view. For example, the control may take into account situations when the forward vehicle departs the field of view while the steering angle of the subject vehicle remains relatively constant, and may adjust the headlamps accordingly. Such tracking may also provide an indication that the road continues to curve ahead of the subject vehicle, and may provide information useful in conjunction with an adaptive cruise control (ACC) system and/or a lane departure warning (LDW) or the like.


Adaptive Front Lighting vehicles can illuminate curved road areas and vehicles that previously were not illuminated very well. The AHBC algorithm could, at significant expense and speed penalties, decide when the vehicle was on a curve and change its operation. If the AHBC system could use steering wheel angle (SWA) from the automobile bus, it could react significantly faster, with fewer mistakes, without the calculation penalties of internal SWA calculation.


We have done several studies on how the steering wheel angle will improve AHBC functionality when used with AFLS. For example, 14 vehicles in 7 video clips which had both AFLS cars and standard cars were studied. These clips showed vehicles traveling on a curve and were examined to determine how to better detect these vehicles. The region of interest was extended manually in the direction of the curved travel. The SWA was not used during such examination and evaluation, but it is envisioned that the use of steering wheel angle would allow these results to be done automatically. Based on such evaluations, the optimum size and location of the region of interest (ROI) may be found. The ROI is the vehicle processing region. Surprisingly (and as shown in FIG. 9), the optimum ROI change was not a linear function of the SWA size. It was just a simple rule to extend the ROI one amount in the curve direction. During a sharp turn of the vehicle, the vehicle is seen for a short time and for gentle curves the vehicle is seen for a longer time. Results vary with the orientation of the target vehicle with respect to the host vehicle. This is related to the beam intensity pattern.


The smaller amount of gained low beam time on a sharp curve is not to be belittled. The total time that the vehicle was visible in the imaging system on sharp curves averaged around 3-4 seconds. The addition of 0.6 seconds of low beam time is significant since these vehicle detections usually only had a couple seconds of low beam, so the added time is a significant benefit. For the gentle curves the vehicles were visible in the scene for longer, and were detected in low beam for about 1.6 seconds more, thus the added time was significant.


In the future when more target vehicles have adaptive front lighting, this effect may not be so static. Then the ROI may need to be changed as a function of the steering wheel angle. The desired approach was to extend the ROI 5 or 6 degrees in the direction of the curve denoted by the steering wheel angle. With no or small SWA, the ROI is unchanged. This approach is enough to provide significant benefit for the AHBC vehicle with AFLS. The AFLS system does point the headlights in the direction of the curve and this use of SWA will allow the quicker detection of the target vehicle so that the effect of the AFLS is minimized for other drivers. The other vehicles will not get as much unexpected glare at unexpected angles on a curve, because they will be detected sooner by the host AHBC vehicle.


Optionally, the SWA may be used to filter out the false alarm targets from reflectors and house and yard lights on curves. When the vehicle is driven along the road, the system does not know how the road curves ahead and if it did, such as through SWA, the system could better filter out such false targets. This is particularly evident when the false target is straight ahead, while the road curves. In such cases, the false target is in the same position as a real vehicle. With the use of the steering wheel angle, this false alarm can be minimized, and it is estimated that at least about a third of these kinds of false alarms could be eliminated. These also are the most resistant false alarms since they look the most like real car lights.


Finally, the use of SWA could allow us to better filter out the non-straight roads that should be ignored to adapt the focus of expansion. The system can accumulate the focus of expansion data when it detects the lane markers, and may only accumulate when the FOE is close enough to the manufacturing value. The system could better filter out these curved road times and should allow the system to get a better, quicker, measure of the real FOE for a given key cycle.


Therefore, the use of SWA will allow quicker use of low beams on curves where the AFLS system will otherwise glare opposing vehicles more than the non-AFLS vehicle. The detection time, when low beam is on, may preferably increase by about 30 percent or more. The use of SWA will also allow better filtering out of noise sources which falsely trigger low beam, and it will more quickly provide adaptive focus of expansion in general driving.


The imaging sensor for the headlamp control of the present invention may comprise any suitable sensor, and may utilize various imaging sensors or imaging array sensors or cameras or the like, such as a CMOS imaging array sensor, a CCD sensor or other sensors or the like, such as the types described in U.S. Pat. Nos. 6,946,978; 7,004,606; 5,550,677; 5,760,962; 6,097,023; 5,796,094 and/or 5,715,093; and/or U.S. patent applications, Ser. No. 09/441,341, filed Nov. 16, 1999, now U.S. Pat. No. 7,339,149; and/or Ser. No. 11/105,757, filed Apr. 14, 2005, now U.S. Pat. No. 7,526,103, and/or PCT Application No. PCT/US2003/036177 filed Nov. 14, 2003, published Jun. 3, 2004 as PCT Publication No. WO 2004/047421, which are all hereby incorporated herein by reference in their entireties.


Optionally, the imaging sensor may be suitable for use in connection with other vehicle imaging systems, such as, for example, a blind spot detection system, where a blind spot indicator may be operable to provide an indication to the driver of the host vehicle that an object or other vehicle has been detected in the lane or area adjacent to the side of the host vehicle. In such a blind spot detector/indicator system, the blind spot detection system may include an imaging sensor or sensors, or ultrasonic sensor or sensors, or sonar sensor or sensors or the like. For example, the blind spot detection system may utilize aspects of the blind spot detection and/or imaging systems described in U.S. Pat. Nos. 7,038,577; 6,882,287; 6,198,409; 5,929,786 and/or 5,786,772, and/or U.S. patent applications, Ser. No. 11/315,675, filed Dec. 22, 2005, now U.S. Pat. No. 7,720,580; and/or Ser. No. 11/239,980, filed Sep. 30, 2005, now U.S. Pat. No. 7,881,496, and/or U.S. provisional applications, Ser. No. 60/696,953, filed Jul. 6, 2006; Ser. No. 60/628,709, filed Nov. 17, 2004; Ser. No. 60/614,644, filed Sep. 30, 2004; and/or Ser. No. 60/618,686, filed Oct. 14, 2004, and/or of the reverse or backup aid systems, such as the rearwardly directed vehicle vision systems described in U.S. Pat. Nos. 5,550,677; 5,760,962; 5,670,935; 6,201,642; 6,396,397; 6,498,620; 6,717,610; 6,757,109 and/or 7,005,974, and/or of the rain sensors described in U.S. Pat. Nos. 6,250,148 and 6,341,523, and/or of other imaging systems, such as the types described in U.S. Pat. Nos. 6,353,392 and 6,313,454, and U.S. patent application, Ser. No. 10/422,512, filed Apr. 24, 2003, now U.S. Pat. No. 7,123,168, with all of the above referenced U.S. patents, patent applications and provisional applications and PCT applications being commonly assigned and being hereby incorporated herein by reference.


Optionally, the mirror assembly and/or accessory module or windshield electronics module may include one or more displays, such as for displaying the captured images or video images captured by the imaging sensor or sensors of the vehicle, such as the displays of the types disclosed in U.S. Pat. Nos. 7,004,593; 5,530,240 and/or 6,329,925, which are hereby incorporated herein by reference, and/or display-on-demand or transflective type displays, such as the types disclosed in U.S. Pat. Nos. 6,690,268; 5,668,663 and/or 5,724,187, and/or in U.S. patent applications, Ser. No. 10/054,633, filed Jan. 22, 2002, now U.S. Pat. No. 7,195,381; Ser. No. 11/021,065, filed Dec. 23, 2004, now U.S. Pat. No. 7,255,451; Ser. No. 10/528,269, filed Mar. 17, 2005, now U.S. Pat. No. 7,274,501; Ser. No. 10/533,762, filed May 4, 2005, now U.S. Pat. No. 7,184,190; Ser. No. 10/538,724, filed Jun. 13, 2005 and published Mar. 9, 2006 as U.S. Publication No. US-2006-0050018; Ser. No. 11/226,628, filed Sep. 14, 2005 and published Mar. 9, 2006 as U.S. Publication No. US-2006-0061008; Ser. No. 10/993,302, filed Nov. 19, 2004, now U.S. Pat. No. 7,338,177; and/or Ser. No. 11/284,543, filed Nov. 22, 2005, now U.S. Pat. No. 7,370,983, and/or PCT Application No. PCT/US03/29776, filed Sep. 9, 2003; and/or PCT Application No. PCT/US03/35381, filed Nov. 5, 2003, and/or PCT Application No. PCT/US03/40611, filed Dec. 19, 2003, which are all hereby incorporated herein by reference, or may include or incorporate video displays or the like, such as the types described in PCT Application No. PCT/US03/40611, filed Dec. 19, 2003, and/or U.S. patent applications, Ser. No. 10/538,724, filed Jun. 13, 2005 and published Mar. 9, 2006 as U.S. Publication No. US-2006-0050018; and/or Ser. No. 11/284,543, filed Nov. 22, 2005, now U.S. Pat. No. 7,370,983, and/or U.S. provisional applications, Ser. No. 60/732,245, filed Nov. 1, 2005; Ser. No. 60/759,992, filed Jan. 18, 2006; and/or Ser. No. 60/836,219, filed Aug. 8, 2006, which are hereby incorporated herein by reference.


The imaging sensor may be incorporated at or in an accessory module or windshield electronics module (such as described above), or may be incorporated at or in an interior rearview mirror assembly of the vehicle, while remaining within the spirit and scope of the present invention. Optionally, the mirror assembly and/or module may support one or more other accessories or features, such as one or more electrical or electronic devices or accessories. For example, illumination sources or lights, such as map reading lights or one or more other lights or illumination sources, such as illumination sources of the types disclosed in U.S. Pat. Nos. 6,690,268; 5,938,321; 5,813,745; 5,820,245; 5,673,994; 5,649,756; 5,178,448; 5,671,996; 4,646,210; 4,733,336; 4,807,096; 6,042,253; 6,971,775 and/or 5,669,698, and/or U.S. patent applications, Ser. No. 10/054,633, filed Jan. 22, 2002, now U.S. Pat. No. 7,195,381; and/or Ser. No. 10/933,842, filed Sep. 3, 2004, now U.S. Pat. No. 7,249,860, which are hereby incorporated herein by reference, may be included in the mirror assembly or module. The illumination sources and/or the circuit board may be connected to one or more buttons or inputs for activating and deactivating the illumination sources. Optionally, the mirror assembly or module may also or otherwise include other accessories, such as microphones, such as analog microphones or digital microphones or the like, such as microphones of the types disclosed in U.S. Pat. Nos. 6,243,003; 6,278,377 and/or 6,420,975, and/or in PCT Application No. PCT/US03/308877, filed Oct. 1, 2003. Optionally, the mirror assembly may also or otherwise include other accessories, such as a telematics system, speakers, antennas, including global positioning system (GPS) or cellular phone antennas, such as disclosed in U.S. Pat. No. 5,971,552, a communication module, such as disclosed in U.S. Pat. No. 5,798,688, a voice recorder, transmitters and/or receivers, such as for a garage door opener or a vehicle door unlocking system or the like (such as a remote keyless entry system), a digital network, such as described in U.S. Pat. No. 5,798,575, a memory mirror system, such as disclosed in U.S. Pat. No. 5,796,176, a hands-free phone attachment, a video device for internal cabin surveillance (such as for sleep detection or driver drowsiness detection or the like) and/or video telephone function, such as disclosed in U.S. Pat. Nos. 5,760,962 and/or 5,877,897, a remote keyless entry receiver, a seat occupancy detector, a remote starter control, a yaw sensor, a clock, a carbon monoxide detector, status displays, such as displays that display a status of a door of the vehicle, a transmission selection (4wd/2wd or traction control (TCS) or the like), an antilock braking system, a road condition (that may warn the driver of icy road conditions) and/or the like, a trip computer, a tire pressure monitoring system (TPMS) receiver (such as described in U.S. Pat. Nos. 6,124,647; 6,294,989; 6,445,287; 6,472,979 and/or 6,731,205; and/or U.S. patent application Ser. No. 11/232,324, filed Sep. 21, 2005, now U.S. Pat. No. 7,423,522, and/or an ONSTAR® system and/or any other accessory or circuitry or the like (with all of the above-referenced patents and PCT and U.S. patent applications being commonly assigned, and with the disclosures of the referenced patents and patent applications being hereby incorporated herein by reference in their entireties).


Optionally, the mirror assembly or module may include one or more user inputs for controlling or activating/deactivating one or more electrical accessories or devices of or associated with the mirror assembly or module or vehicle. The mirror assembly or module may comprise any type of switches or buttons, such as touch or proximity sensing switches, such as touch or proximity switches of the types described in PCT Application No. PCT/US03/40611, filed Dec. 19, 2003; and/or U.S. Pat. Nos. 6,001,486; 6,310,611; 6,320,282 and 6,627,918; and/or U.S. patent applications, Ser. No. 09/817,874, filed Mar. 26, 2001, now U.S. Pat. No. 7,224,324; Ser. No. 10/956,749, filed Oct. 1, 2004, now U.S. Pat. No. 7,446,924; Ser. No. 10/933,842, filed Sep. 3, 2004, now U.S. Pat. No. 7,249,860; Ser. No. 11/021,065, filed Dec. 23, 2004, now U.S. Pat. No. 7,255,451; and/or Ser. No. 11/140,396, filed May 27, 2005, now U.S. Pat. No. 7,360,932, which are hereby incorporated herein by reference, or the inputs may comprise other types of buttons or switches, such as those described in U.S. patent applications, Ser. No. 11/029,695, filed Jan. 5, 2005, now U.S. Pat. No. 7,253,723; and/or Ser. No. 11/451,639, filed Jun. 13, 2006, now U.S. Pat. No. 7,527,403, which are hereby incorporated herein by reference, or such as fabric-made position detectors, such as those described in U.S. Pat. Nos. 6,504,531; 6,501,465; 6,492,980; 6,452,479; 6,437,258 and 6,369,804, which are hereby incorporated herein by reference. Other types of switches or buttons or inputs or sensors may be incorporated to provide the desired function, without affecting the scope of the present invention.


Optionally, the user inputs or buttons may comprise user inputs for a garage door opening system, such as a vehicle based garage door opening system of the types described in U.S. Pat. Nos. 6,396,408; 6,362,771 and 5,798,688, and/or U.S. patent application Ser. No. 10/770,736, filed Feb. 3, 2004, now U.S. Pat. No. 7,023,322; and/or U.S. provisional applications, Ser. No. 60/502,806, filed Sep. 12, 2003; and Ser. No. 60/444,726, filed Feb. 4, 2003, which are hereby incorporated herein by reference. The user inputs may also or otherwise function to activate and deactivate a display or function or accessory, and/or may activate/deactivate and/or commence a calibration of a compass system of the mirror assembly and/or vehicle. The compass system may include compass sensors and circuitry within the mirror assembly or within a compass pod or module at or near or associated with the mirror assembly. Optionally, the user inputs may also or otherwise comprise user inputs for a telematics system of the vehicle, such as, for example, an ONSTAR® system as found in General Motors vehicles and/or such as described in U.S. Pat. Nos. 4,862,594; 4,937,945; 5,131,154; 5,255,442; 5,632,092; 5,798,688; 5,971,552; 5,924,212; 6,243,003; 6,278,377; 6,420,975; 6,946,978; 6,477,464; 6,678,614 and/or 7,004,593, and/or U.S. patent applications, Ser. No. 10/645,762, filed Aug. 20, 2003, now U.S. Pat. No. 7,167,796; and Ser. No. 10/964,512, filed Oct. 13, 2004, now U.S. Pat. No. 7,308,341; and/or PCT Application No. PCT/US03/40611, filed Dec. 19, 2003, and/or PCT Application No. PCT/US03/308877, filed Oct. 1, 2003, which are all hereby incorporated herein by reference.


Optionally, the accessory module may utilize aspects of other accessory modules or windshield electronics modules or the like, such as the types described in U.S. patent applications, Ser. No. 10/958,087, filed Oct. 4, 2004, now U.S. Pat. No. 7,188,963; and/or Ser. No. 11/201,661, filed Aug. 11, 2005, now U.S. Pat. No. 7,480,149, and/or U.S. Pat. Nos. 7,004,593; 6,824,281; 6,690,268; 6,250,148; 6,341,523; 6,593,565; 6,428,172; 6,501,387; 6,329,925 and 6,326,613, and/or in PCT Application No. PCT/US03/40611, filed Dec. 19, 2003, and/or Ireland patent applications, Ser. No. S2004/0614, filed Sep. 15, 2004; Ser. No. S2004/0838, filed Dec. 14, 2004; and Ser. No. S2004/0840, filed Dec. 15, 2004, which are all hereby incorporated herein by reference.


The reflective element of the rearview mirror assembly of the vehicle may comprise an electro-optic or electrochromic reflective element or cell, such as an electrochromic mirror assembly and electrochromic reflective element utilizing principles disclosed in commonly assigned U.S. Pat. Nos. 6,690,268; 5,140,455; 5,151,816; 6,178,034; 6,154,306; 6,002,544; 5,567,360; 5,525,264; 5,610,756; 5,406,414; 5,253,109; 5,076,673; 5,073,012; 5,117,346; 5,724,187; 5,668,663; 5,910,854; 5,142,407 and/or 4,712,879, and/or U.S. patent applications, Ser. No. 10/054,633, filed Jan. 22, 2002, now U.S. Pat. No. 7,195,381; Ser. No. 11/021,065, filed Dec. 23, 2004, now U.S. Pat. No. 7,255,451; Ser. No. 11/226,628, filed Sep. 14, 2005 and published Mar. 9, 2006 as U.S. Publication No. US-2006-0061008, and/or PCT Application No. PCT/US2006/018567, filed May 15, 2006, which are all hereby incorporated herein by reference, and/or as disclosed in the following publications: N. R. Lynam, “Electrochromic Automotive Day/Night Mirrors”, SAE Technical Paper Series 870636 (1987); N. R. Lynam, “Smart Windows for Automobiles”, SAE Technical Paper Series 900419 (1990); N. R. Lynam and A. Agrawal, “Automotive Applications of Chromogenic Materials”, Large Area Chromogenics: Materials and Devices for Transmittance Control, C. M. Lampert and C. G. Granquist, EDS., Optical Engineering Press, Wash. (1990), which are hereby incorporated by reference herein. The thicknesses and materials of the coatings on the substrates of the electrochromic reflective element, such as on the third surface of the reflective element assembly, may be selected to provide a desired color or tint to the mirror reflective element, such as a blue colored reflector, such as is known in the art and/or such as described in U.S. Pat. Nos. 5,910,854 and 6,420,036, and in PCT Application No. PCT/US03/29776, filed Sep. 9, 2003, which are all hereby incorporated herein by reference.


Optionally, use of an elemental semiconductor mirror, such as a silicon metal mirror, such as disclosed in U.S. Pat. Nos. 6,286,965; 6,196,688; 5,535,056; 5,751,489 and 6,065,840, and/or in U.S. patent application, Ser. No. 10/993,302, filed Nov. 19, 2004, now U.S. Pat. No. 7,338,177, which are all hereby incorporated herein by reference, can be advantageous because such elemental semiconductor mirrors (such as can be formed by depositing a thin film of silicon) can be greater than 50 percent reflecting in the photopic (SAE J964a measured), while being also substantially transmitting of light (up to 20 percent or even more). Such silicon mirrors also have the advantage of being able to be deposited onto a flat glass substrate and to be bent into a curved (such as a convex or aspheric) curvature, which is also advantageous since many passenger-side exterior rearview mirrors are bent or curved.


Optionally, the reflective element may include a perimeter metallic band, such as the types described in PCT Application No. PCT/US03/29776, filed Sep. 19, 2003; and/or PCT Application No. PCT/US03/35381, filed Nov. 5, 2003; and/or U.S. patent applications, Ser. No. 11/021,065, filed Dec. 23, 2004, now U.S. Pat. No. 7,255,451; and/or Ser. No. 11/226,628, filed Sep. 14, 2005 and published Mar. 9, 2006 as U.S. Publication No. US-2006-0061008, which are hereby incorporated herein by reference. Optionally, the reflective element may include indicia formed at and viewable at the reflective element, such as by utilizing aspects of the reflective elements described in PCT Application No. PCT/US2006/018567, filed May 15, 2006, which are hereby incorporated herein by reference.


Optionally, the reflective element of the mirror assembly may comprise a single substrate with a reflective coating at its rear surface, without affecting the scope of the present invention. The mirror assembly thus may comprise a prismatic mirror assembly or other mirror having a single substrate reflective element, such as a mirror assembly utilizing aspects described in U.S. Pat. Nos. 6,318,870; 6,598,980; 5,327,288; 4,948,242; 4,826,289; 4,436,371 and 4,435,042; and PCT Application No. PCT/US04/015424, filed May 18, 2004; and U.S. patent application, Ser. No. 10/933,842, filed Sep. 3, 2004, now U.S. Pat. No. 7,249,860, which are hereby incorporated herein by reference. Optionally, the reflective element may comprise a conventional prismatic or flat reflective element or prism, or may comprise a prismatic or flat reflective element of the types described in PCT Application No. PCT/US03/29776, filed Sep. 19, 2003; U.S. patent applications, Ser. No. 10/709,434, filed May 5, 2004, now U.S. Pat. No. 7,420,756; Ser. No. 10/933,842, filed Sep. 3, 2004, now U.S. Pat. No. 7,249,860; Ser. No. 11/021,065, filed Dec. 23, 2004, now U.S. Pat. No. 7,255,451; and/or Ser. No. 10/993,302, filed Nov. 19, 2004, now U.S. Pat. No. 7,338,177, and/or PCT Application No. PCT/US2004/015424, filed May 18, 2004, which are all hereby incorporated herein by reference, without affecting the scope of the present invention.


Changes and modifications to the specifically described embodiments may be carried out without departing from the principles of the present invention, which is intended to be limited by the scope of the appended claims, as interpreted in accordance with the principles of patent law.

Claims
  • 1. A vehicular forward viewing image capture system, said vehicular forward viewing image capture system comprising: an accessory module configured for attachment at an in-cabin side of a windshield of a vehicle equipped with said vehicular forward viewing image capture system;wherein said accessory module comprises a camera, said camera comprising a lens and an imaging sensor, said imaging sensor comprising a two-dimensional array of photosensor elements;wherein an optical axis of said lens is perpendicular to an image plane of said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, said imaging sensor has a field of view through the windshield forward of the equipped vehicle;a control, said control comprising a digital processor for processing image data captured by said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and while the equipped vehicle is traveling along a road, said control processes captured image data to determine (i) presence of an object of interest in the forward field of view of said imaging sensor and (ii) location of the object of interest in the forward field of view of said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and responsive to the processing of captured image data, said control determines presence of a taillight of another vehicle ahead of the equipped vehicle, and wherein said control is operable to determine presence of a taillight that is 200 meters ahead of the equipped vehicle;wherein said control receives vehicle data via a vehicle communication bus; andwherein the received vehicle data comprises vehicle speed data; andwherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and responsive to the processing of captured image data and responsive to the received vehicle data, said control at least in part provides at least one selected from the group consisting of (a) traffic sign recognition, (b) traffic light status detection, (c) adaptive cruise control and (d) pedestrian detection.
  • 2. The vehicular forward viewing image capture system of claim 1, wherein the received vehicle speed data at least comprises a speed signal indicative of speed of the equipped vehicle.
  • 3. The vehicular forward viewing image capture system of claim 2, wherein an output of said control when the speed signal is indicative of the speed of the equipped vehicle being above a threshold speed level is different than the output of said control when the speed signal is indicative of the speed of the equipped vehicle being below a threshold speed level.
  • 4. The vehicular forward viewing image capture system of claim 1, wherein said vehicle communication bus comprises a CAN vehicle communication bus.
  • 5. The vehicular forward viewing image capture system of claim 1, wherein captured image data is processed at said control to determine a characteristic of the object of interest present in the forward field of view of said imaging sensor.
  • 6. The vehicular forward viewing image capture system of claim 1, wherein multiple frames of captured image data are processed at said control in determining the location of the object of interest present in the forward field of view of said imaging sensor.
  • 7. The vehicular forward viewing image capture system of claim 1, wherein multiple frames of captured image data are processed at said control to determine movement of the object of interest present in the forward field of view of said imaging sensor.
  • 8. The vehicular forward viewing image capture system of claim 1, wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, captured image data is processed at said control to determine a general illumination level at the equipped vehicle in the forward field of view of said imaging sensor.
  • 9. The vehicular forward viewing image capture system of claim 1, wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, captured image data is processed at said control to determine a road condition of the road ahead of the equipped vehicle.
  • 10. The vehicular forward viewing image capture system of claim 1, wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, captured image data is processed at said control to determine an incline of the road ahead of the equipped vehicle.
  • 11. The vehicular forward viewing image capture system of claim 1, wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and responsive to processing of captured image data, said control determines presence of other road users in a region of influence of a vehicle headlamp of the equipped vehicle, and wherein said control is operable to inhibit adjustment of a beam pattern of the vehicle headlamp of the equipped vehicle if other road users are detected within the region of influence of the vehicle headlamp of the equipped vehicle.
  • 12. The vehicular forward viewing image capture system of claim 1, wherein said vehicular forward viewing image capture system automatically compensates for misalignment of said imaging sensor.
  • 13. The vehicular forward viewing image capture system of claim 12, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive at least in part to a focus of expansion ahead of the vehicle.
  • 14. The vehicular forward viewing image capture system of claim 13, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive to determination of location and movement of another vehicle relative to the focus of expansion as the equipped vehicle travels along a straight section of the road.
  • 15. The vehicular forward viewing image capture system of claim 13, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive to tracking movement of light sources through multiple frames of captured image data when the equipped vehicle travels along a straight section of the road.
  • 16. The vehicular forward viewing image capture system of claim 1, wherein the received vehicle data comprises vehicle trajectory data.
  • 17. The vehicular forward viewing image capture system of claim 1, wherein the received vehicle data comprises vehicle GPS data.
  • 18. The vehicular forward viewing image capture system of claim 1, wherein the received vehicle data comprises vehicle pitch data.
  • 19. The vehicular forward viewing image capture system of claim 1, wherein the received vehicle data comprises vehicle yaw data.
  • 20. The vehicular forward viewing image capture system of claim 1, wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, said imaging sensor views through a portion of the windshield that is swept by a windshield wiper of the equipped vehicle.
  • 21. The vehicular forward viewing image capture system of claim 1, wherein said accessory module comprises said control and an electrical connector for electrically connecting to a vehicle wiring system when said accessory module is attached at the in-cabin side of the windshield of the equipped vehicle.
  • 22. The vehicular forward viewing image capture system of claim 1, wherein said accessory module is configured to removably attach at an attachment plate that is fixedly attached at the in-cabin side of the windshield of the equipped vehicle.
  • 23. The vehicular forward viewing image capture system of claim 22, wherein, with said accessory module removably attached at the attachment plate that is fixedly attached at the in-cabin side of the windshield of the equipped vehicle, the principal viewing axis of said imaging sensor is parallel to the principal central axis of the equipped vehicle.
  • 24. The vehicular forward viewing image capture system of claim 1, wherein said control at least in part provides at least one selected from the group consisting of (a) traffic sign recognition, (b) traffic light status detection, (c) adaptive cruise control and (d) pedestrian detection responsive to processing of multiple frames of captured image data.
  • 25. The vehicular forward viewing image capture system of claim 24, wherein the imaging sensor captures multiple frames of image data at an image capture rate of greater than or equal to five frames per second and less than or equal to 120 frames per second.
  • 26. A vehicular forward viewing image capture system, said vehicular forward viewing image capture system comprising: an accessory module configured for attachment at an in-cabin side of a windshield of a vehicle equipped with said vehicular forward viewing image capture system;wherein said accessory module comprises a camera, said camera comprising a lens and an imaging sensor, said imaging sensor comprising a two-dimensional array of photosensor elements;wherein an optical axis of said lens is perpendicular to an image plane of said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, said imaging sensor has a field of view through the windshield forward of the equipped vehicle;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, said imaging sensor views through a portion of the windshield that is swept by a windshield wiper of the equipped vehicle;a control, said control comprising a digital processor for processing multiple frames of image data captured by said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and while the equipped vehicle is traveling along a road, said control processes multiple frames of captured image data to determine (i) presence of an object of interest in the forward field of view of said imaging sensor, (ii) location of the object of interest in the forward field of view of said imaging sensor and (iii) movement of the object of interest present in the forward field of view of said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and responsive to the processing of captured image data, said control determines presence of a taillight of another vehicle ahead of the equipped vehicle, and wherein said control is operable to determine presence of a taillight that is 200 meters ahead of the equipped vehicle;wherein said control receives vehicle data via a vehicle communication bus;wherein the received vehicle data comprises vehicle speed data; andwherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and responsive to the processing of multiple frames of captured image data and responsive to the received vehicle data, said control at least in part provides at least one selected from the group consisting of (a) traffic sign recognition, (b) traffic light status detection, (c) adaptive cruise control and (d) pedestrian detection.
  • 27. The vehicular forward viewing image capture system of claim 26, wherein the received vehicle speed data at least comprises a speed signal indicative of speed of the equipped vehicle.
  • 28. The vehicular forward viewing image capture system of claim 27, wherein an output of said control when the speed signal is indicative of the speed of the equipped vehicle being above a threshold speed level is different than the output of said control when the speed signal is indicative of the speed of the equipped vehicle being below a threshold speed level.
  • 29. The vehicular forward viewing image capture system of claim 26, wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, captured image data is processed at said control to determine a road condition of the road ahead of the equipped vehicle.
  • 30. The vehicular forward viewing image capture system of claim 26, wherein said vehicular forward viewing image capture system automatically compensates for misalignment of said imaging sensor.
  • 31. The vehicular forward viewing image capture system of claim 30, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive at least in part to a focus of expansion ahead of the vehicle.
  • 32. The vehicular forward viewing image capture system of claim 31, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive to determination of location and movement of another vehicle relative to the focus of expansion as the equipped vehicle travels along a straight section of the road.
  • 33. The vehicular forward viewing image capture system of claim 31, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive to tracking movement of light sources through multiple frames of captured image data when the equipped vehicle travels along a straight section of the road.
  • 34. The vehicular forward viewing image capture system of claim 26, wherein the received vehicle data comprises vehicle trajectory data.
  • 35. The vehicular forward viewing image capture system of claim 26, wherein the received vehicle data comprises vehicle GPS data.
  • 36. The vehicular forward viewing image capture system of claim 26, wherein the received vehicle data comprises vehicle pitch data.
  • 37. The vehicular forward viewing image capture system of claim 26, wherein the received vehicle data comprises vehicle yaw data.
  • 38. The vehicular forward viewing image capture system of claim 26, wherein said accessory module comprises said control and an electrical connector for electrically connecting to a vehicle wiring system when said accessory module is attached at the in-cabin side of the windshield of the equipped vehicle.
  • 39. The vehicular forward viewing image capture system of claim 26, wherein said accessory module is configured to removably attach at an attachment plate that is fixedly attached at the in-cabin side of the windshield of the equipped vehicle.
  • 40. A vehicular forward viewing image capture system, said vehicular forward viewing image capture system comprising: an accessory module configured for attachment at an in-cabin side of a windshield of a vehicle equipped with said vehicular forward viewing image capture system;wherein said accessory module comprises a camera, said camera comprising a lens and an imaging sensor, said imaging sensor comprising a two-dimensional array of photosensor elements;wherein an optical axis of said lens is perpendicular to an image plane of said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, said imaging sensor has a field of view through the windshield forward of the equipped vehicle;a control, said control comprising a digital processor for processing multiple frames of image data captured by said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and while the equipped vehicle is traveling along a road, said control processes multiple frames of captured image data to determine (i) presence of an object of interest in the forward field of view of said imaging sensor and (ii) movement of the object of interest present in the forward field of view of said imaging sensor;wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and responsive to the processing of captured image data, said control determines presence of a taillight of another vehicle ahead of the equipped vehicle, and wherein said control is operable to determine presence of a taillight that is 200 meters ahead of the equipped vehicle;wherein said control receives vehicle data via a vehicle communication bus;wherein the received vehicle data comprises (i) vehicle speed data and (ii) vehicle trajectory data; andwherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, and responsive to the processing of multiple frames of captured image data and responsive to the received vehicle data, said control at least in part provides at least one selected from the group consisting of (a) traffic sign recognition, (b) traffic light status detection, (c) adaptive cruise control and (d) pedestrian detection.
  • 41. The vehicular forward viewing image capture system of claim 40, wherein, with said accessory module attached at the in-cabin side of the windshield of the equipped vehicle, captured image data is processed at said control to determine a road condition of the road ahead of the equipped vehicle.
  • 42. The vehicular forward viewing image capture system of claim 40, wherein said vehicular forward viewing image capture system automatically compensates for misalignment of said imaging sensor.
  • 43. The vehicular forward viewing image capture system of claim 42, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive at least in part to a focus of expansion ahead of the vehicle.
  • 44. The vehicular forward viewing image capture system of claim 43, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive to determination of location and movement of another vehicle relative to the focus of expansion as the equipped vehicle travels along a straight section of the road.
  • 45. The vehicular forward viewing image capture system of claim 43, wherein said vehicular forward viewing image capture system determines misalignment of said imaging sensor responsive to tracking movement of light sources through multiple frames of captured image data when the equipped vehicle travels along a straight section of the road.
  • 46. The vehicular forward viewing image capture system of claim 40, wherein the received vehicle data comprises vehicle GPS data.
  • 47. The vehicular forward viewing image capture system of claim 40, wherein the received vehicle data comprises vehicle pitch data.
  • 48. The vehicular forward viewing image capture system of claim 40, wherein the received vehicle data comprises vehicle yaw data.
  • 49. The vehicular forward viewing image capture system of claim 40, wherein said accessory module comprises said control and an electrical connector for electrically connecting to a vehicle wiring system when said accessory module is attached at the in-cabin side of the windshield of the equipped vehicle.
  • 50. The vehicular forward viewing image capture system of claim 40, wherein said accessory module is configured to removably attach at an attachment plate that is fixedly attached at the in-cabin side of the windshield of the equipped vehicle.
CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent application Ser. No. 16/125,891, filed Sep. 10, 2018, now U.S. Pat. No. 10,787,116, which is a continuation of U.S. patent application Ser. No. 15/262,479, filed Sep. 12, 2016, now U.S. Pat. No. 10,071,676, which is a continuation of U.S. patent application Ser. No. 14/164,682, filed Jan. 27, 2014, now U.S. Pat. No. 9,440,535, which is a continuation of U.S. patent application Ser. No. 13/887,727, filed May 6, 2013, now U.S. Pat. No. 8,636,393, which is a continuation of U.S. patent application Ser. No. 13/452,130, filed Apr. 20, 2012, now U.S. Pat. No. 8,434,919, which is a continuation of U.S. patent application Ser. No. 13/173,039, filed Jun. 30, 2011, now U.S. Pat. No. 8,162,518, which is a continuation of U.S. patent application Ser. No. 12/377,054, filed Feb. 10, 2009, now U.S. Pat. No. 7,972,045, which is a 371 of PCT Application No. PCT/US2007/075702, filed Aug. 10, 2007, which claims the benefit of U.S. provisional applications, Ser. No. 60/845,381, filed Sep. 18, 2006; and Ser. No. 60/837,408, filed Aug. 11, 2006, which are hereby incorporated herein by reference in their entireties.

US Referenced Citations (1145)
Number Name Date Kind
1472509 Bitter Oct 1923 A
2074251 Braun Mar 1937 A
2148119 Grist Feb 1939 A
2240843 Gillespie May 1941 A
2317400 Paulus et al. Apr 1943 A
2331144 Sitter Oct 1943 A
2339291 Paulus et al. Jan 1944 A
2424288 Severy Jul 1947 A
2598420 Onksen, Jr. May 1952 A
2632040 Rabinow Mar 1953 A
2750583 McCullough Jun 1956 A
2762932 Falge et al. Sep 1956 A
2827594 Rabinow Mar 1958 A
2855523 Berger Oct 1958 A
2856146 Lehder Oct 1958 A
2863064 Rabinow Dec 1958 A
2892094 Lehovec Jun 1959 A
2907920 Mcllvaine Oct 1959 A
2912593 Deuth Nov 1959 A
2934676 Miller et al. Apr 1960 A
2959709 Vanaman Nov 1960 A
3008532 Reed Nov 1961 A
3011580 Reid Dec 1961 A
3069654 Hough Dec 1962 A
3085646 Paifve Apr 1963 A
3158835 Hipkins Nov 1964 A
3172496 Rabinow Mar 1965 A
3179845 Kulwiec Apr 1965 A
3201750 Morin Aug 1965 A
3208070 Boicey Sep 1965 A
3249761 Baumanns May 1966 A
3271577 Miller et al. Sep 1966 A
3325680 Amacher Jun 1967 A
3367616 Bausch Feb 1968 A
3411843 Moller Nov 1968 A
3486066 Jones et al. Dec 1969 A
3515472 Schwitzgebel Jun 1970 A
3572428 Monaco Mar 1971 A
3623671 Hargroves Nov 1971 A
3673560 Barsh et al. Jun 1972 A
3680951 Jordan et al. Aug 1972 A
3689695 Rosenfield et al. Sep 1972 A
3708668 Tilley Jan 1973 A
3751711 Schick Aug 1973 A
3845572 McCanney Nov 1974 A
3876940 Wickord et al. Apr 1975 A
3971065 Bayer Jul 1976 A
3985424 Steinacher Oct 1976 A
3986022 Hyatt Oct 1976 A
4003445 De Bruine Jan 1977 A
4037134 Loper Jul 1977 A
4044853 Melke Aug 1977 A
4049961 Marcy Sep 1977 A
4058796 Oishi et al. Nov 1977 A
4093364 Miller Jun 1978 A
4127778 Leitz Nov 1978 A
4139801 Linares Feb 1979 A
4143264 Gilbert et al. Mar 1979 A
4176728 Otteblad et al. Dec 1979 A
4200361 Malvano et al. Apr 1980 A
4209853 Hyatt Jun 1980 A
4214266 Myers Jul 1980 A
4218698 Bart et al. Aug 1980 A
4236099 Rosenblum Nov 1980 A
4238778 Ohsumi Dec 1980 A
4243196 Toda et al. Jan 1981 A
4247870 Gabel et al. Jan 1981 A
4249160 Chilvers Feb 1981 A
4254931 Aikens et al. Mar 1981 A
4257703 Goodrich Mar 1981 A
4266856 Wainwright May 1981 A
4277804 Robison Jul 1981 A
4278142 Kono Jul 1981 A
4281898 Ochiai et al. Aug 1981 A
4288814 Talley et al. Sep 1981 A
RE30835 Giglia Dec 1981 E
4348652 Barnes et al. Sep 1982 A
4348653 Tsuzuki et al. Sep 1982 A
4355271 Noack Oct 1982 A
4357558 Massoni et al. Nov 1982 A
4357594 Ehrlich et al. Nov 1982 A
4381888 Momiyama May 1983 A
4389537 Tsunoda et al. Jun 1983 A
4389639 Torii et al. Jun 1983 A
4390742 Wideman Jun 1983 A
4390895 Sato et al. Jun 1983 A
4401181 Schwarz Aug 1983 A
4403208 Hodgson et al. Sep 1983 A
4420238 Felix Dec 1983 A
4431896 Lodetti Feb 1984 A
4441125 Parkinson Apr 1984 A
4443057 Bauer et al. Apr 1984 A
4460831 Oettinger et al. Jul 1984 A
4464789 Sternberg Aug 1984 A
4471228 Nishizawa et al. Sep 1984 A
4481450 Watanabe et al. Nov 1984 A
4483011 Brown Nov 1984 A
4485402 Searby Nov 1984 A
4491390 Tong-Shen Jan 1985 A
4495589 Hirzel Jan 1985 A
4512637 Ballmer Apr 1985 A
4521804 Bendell Jun 1985 A
4529275 Ballmer Jul 1985 A
4529873 Ballmer et al. Jul 1985 A
4532550 Bendell et al. Jul 1985 A
4538181 Taylor Aug 1985 A
4546551 Franks Oct 1985 A
4549208 Kamejima et al. Oct 1985 A
4564833 Seko et al. Jan 1986 A
4566032 Hirooka et al. Jan 1986 A
4571082 Downs Feb 1986 A
4572619 Reininger et al. Feb 1986 A
4580875 Bechtel et al. Apr 1986 A
4587522 Warren May 1986 A
4588041 Tsuchihashi May 1986 A
4599544 Martin Jul 1986 A
4600913 Caine Jul 1986 A
4601053 Grumet Jul 1986 A
4603946 Kato et al. Aug 1986 A
4614415 Hyatt Sep 1986 A
4620141 McCumber et al. Oct 1986 A
4623222 Itoh et al. Nov 1986 A
4625329 Ishikawa et al. Nov 1986 A
4626850 Chey Dec 1986 A
4629941 Ellis et al. Dec 1986 A
4630109 Barton Dec 1986 A
4632509 Ohmi et al. Dec 1986 A
4638287 Umebayashi et al. Jan 1987 A
4645320 Muelling et al. Feb 1987 A
4645975 Meitzler et al. Feb 1987 A
4647161 Muller Mar 1987 A
4647975 Alston et al. Mar 1987 A
4653316 Fukuhara Mar 1987 A
4665321 Chang et al. May 1987 A
4669825 Itoh et al. Jun 1987 A
4671614 Catalano Jun 1987 A
4671615 Fukada et al. Jun 1987 A
4672457 Hyatt Jun 1987 A
4676601 Itoh et al. Jun 1987 A
4679077 Yuasa et al. Jul 1987 A
4681431 Sims et al. Jul 1987 A
4688085 Imaide Aug 1987 A
4690508 Jacob Sep 1987 A
4692798 Seko et al. Sep 1987 A
4693788 Berg et al. Sep 1987 A
4697883 Suzuki et al. Oct 1987 A
4699484 Howell et al. Oct 1987 A
4701022 Jacob Oct 1987 A
4701613 Watanabe et al. Oct 1987 A
4713685 Nishimura et al. Dec 1987 A
4717830 Botts Jan 1988 A
4727290 Smith et al. Feb 1988 A
4728804 Norsworthy Mar 1988 A
4731669 Hayashi et al. Mar 1988 A
4731769 Schaefer et al. Mar 1988 A
4741603 Miyagi et al. May 1988 A
4755664 Holmes et al. Jul 1988 A
4758883 Kawahara et al. Jul 1988 A
4768135 Kretschmer et al. Aug 1988 A
4772942 Tuck Sep 1988 A
4779095 Guerreri Oct 1988 A
4785280 Fubini et al. Nov 1988 A
4789904 Peterson Dec 1988 A
4793690 Gahan et al. Dec 1988 A
4799267 Kamejima et al. Jan 1989 A
4805015 Copeland Feb 1989 A
4816828 Feher Mar 1989 A
4817948 Simonelli Apr 1989 A
4820933 Hong et al. Apr 1989 A
4825232 Howdle Apr 1989 A
4833469 David May 1989 A
4833534 Paff et al. May 1989 A
4838650 Stewart et al. Jun 1989 A
4839749 Franklin Jun 1989 A
4841348 Shizukuishi et al. Jun 1989 A
4843463 Michetti Jun 1989 A
4847489 Dietrich Jul 1989 A
4847772 Michalopoulos et al. Jul 1989 A
4849731 Melocik Jul 1989 A
4855822 Narendra et al. Aug 1989 A
4859031 Berman et al. Aug 1989 A
4862037 Farber et al. Aug 1989 A
4863130 Marks, Jr. Sep 1989 A
4867561 Fujii et al. Sep 1989 A
4870264 Beha Sep 1989 A
4871917 O'Farrell et al. Oct 1989 A
4872051 Dye Oct 1989 A
4881019 Shiraishi et al. Nov 1989 A
4882466 Friel Nov 1989 A
4882565 Gallmeyer Nov 1989 A
4883349 Mittelhauser Nov 1989 A
4884055 Memmola Nov 1989 A
4886960 Molyneux et al. Dec 1989 A
4891559 Matsumoto et al. Jan 1990 A
4892345 Rachael, III Jan 1990 A
4895790 Swanson et al. Jan 1990 A
4896030 Miyaji Jan 1990 A
4899296 Khattak Feb 1990 A
4900133 Berman Feb 1990 A
4905151 Weiman et al. Feb 1990 A
4906940 Greene et al. Mar 1990 A
4907870 Brucker Mar 1990 A
4910591 Petrossian et al. Mar 1990 A
4916374 Schierbeek et al. Apr 1990 A
4917477 Bechtel et al. Apr 1990 A
4926346 Yokoyama May 1990 A
4930742 Schofield et al. Jun 1990 A
4931937 Kakinami et al. Jun 1990 A
4936533 Adams et al. Jun 1990 A
4937796 Tendler Jun 1990 A
4948246 Shigematsu Aug 1990 A
4949186 Peterson Aug 1990 A
4953305 Van Lente et al. Sep 1990 A
4954962 Evans, Jr. et al. Sep 1990 A
4956591 Schierbeek et al. Sep 1990 A
4961625 Wood et al. Oct 1990 A
4963788 King et al. Oct 1990 A
4966441 Conner Oct 1990 A
4967319 Seko Oct 1990 A
4970509 Kissinger, Sr. Nov 1990 A
4970589 Hanson et al. Nov 1990 A
4970653 Kenue Nov 1990 A
4971405 Hwang Nov 1990 A
4971430 Lynas Nov 1990 A
4974078 Tsai Nov 1990 A
4975703 Delisle et al. Dec 1990 A
4985847 Shioya et al. Jan 1991 A
4987357 Masaki Jan 1991 A
4987410 Berman et al. Jan 1991 A
4991054 Walters Feb 1991 A
5001558 Burley et al. Mar 1991 A
5003288 Wilhelm Mar 1991 A
5003339 Kikuchi et al. Mar 1991 A
5008739 D'Luna et al. Apr 1991 A
5008946 Ando Apr 1991 A
5012082 Watanabe Apr 1991 A
5012092 Kobayashi et al. Apr 1991 A
5012335 Cohodar Apr 1991 A
5016977 Baude et al. May 1991 A
5020114 Fujioka et al. May 1991 A
5027001 Torbert Jun 1991 A
5027104 Reid Jun 1991 A
5027200 Petrossian et al. Jun 1991 A
5031101 Kamimura et al. Jul 1991 A
5036437 Macks Jul 1991 A
5044706 Chen Sep 1991 A
5044956 Behensky et al. Sep 1991 A
5050966 Berman Sep 1991 A
5051906 Evans, Jr. et al. Sep 1991 A
5055668 French Oct 1991 A
5059877 Teder Oct 1991 A
5059947 Chen Oct 1991 A
5063603 Burt Nov 1991 A
5064274 Alten Nov 1991 A
5072154 Chen Dec 1991 A
5075768 Wirtz et al. Dec 1991 A
5080207 Horneffer Jan 1992 A
5080309 Ivins Jan 1992 A
5081585 Kurami et al. Jan 1992 A
5086253 Lawler Feb 1992 A
5086510 Guenther et al. Feb 1992 A
5087969 Kamada et al. Feb 1992 A
5096287 Kakinami et al. Mar 1992 A
5097362 Lynas Mar 1992 A
5100093 Rawlinson Mar 1992 A
5101351 Hattori Mar 1992 A
5111289 Lucas et al. May 1992 A
5113721 Polly May 1992 A
5115398 De Jong May 1992 A
5121200 Choi Jun 1992 A
5122957 Hattori Jun 1992 A
5124549 Michaels et al. Jun 1992 A
5128769 Arai et al. Jul 1992 A
5130709 Toyama et al. Jul 1992 A
5133605 Nakamura Jul 1992 A
5137238 Hutten Aug 1992 A
5139327 Tanaka Aug 1992 A
5144685 Nasar et al. Sep 1992 A
5146340 Dickerson et al. Sep 1992 A
5148014 Lynam et al. Sep 1992 A
5153760 Ahmed Oct 1992 A
5155426 Kurami Oct 1992 A
5155775 Brown Oct 1992 A
5159557 Ogawa Oct 1992 A
5160780 Ono et al. Nov 1992 A
5160971 Koshizawa Nov 1992 A
5161632 Asayama Nov 1992 A
5162841 Terashita Nov 1992 A
5162861 Tamburino et al. Nov 1992 A
5163002 Kurami Nov 1992 A
5165108 Asayama Nov 1992 A
5166681 Bottesch et al. Nov 1992 A
5168355 Asayama Dec 1992 A
5168378 Black Dec 1992 A
5170374 Shimohigashi et al. Dec 1992 A
5172235 Wilm et al. Dec 1992 A
5172317 Asanuma et al. Dec 1992 A
5173881 Sindle Dec 1992 A
5177462 Kajiwara Jan 1993 A
5177606 Koshizawa Jan 1993 A
5177685 Davis et al. Jan 1993 A
5182502 Slotkowski et al. Jan 1993 A
5184956 Langlais et al. Feb 1993 A
5185812 Yamashita et al. Feb 1993 A
5187383 Taccetta et al. Feb 1993 A
5189561 Hong Feb 1993 A
5193000 Lipton et al. Mar 1993 A
5193029 Schofield et al. Mar 1993 A
5193894 Lietar et al. Mar 1993 A
5204536 Vardi Apr 1993 A
5204778 Bechtel Apr 1993 A
5208701 Maeda May 1993 A
5208750 Kurami et al. May 1993 A
5212468 Adell May 1993 A
5214408 Asayama May 1993 A
5216408 Shirakawa Jun 1993 A
5218414 Kajiwara Jun 1993 A
5220508 Ninomiya et al. Jun 1993 A
5223814 Suman Jun 1993 A
5223907 Asayama Jun 1993 A
5225827 Persson Jul 1993 A
5229941 Hattori Jul 1993 A
5230400 Kakinami et al. Jul 1993 A
5231379 Wood et al. Jul 1993 A
5233527 Shinnosuke Aug 1993 A
5234070 Noah et al. Aug 1993 A
5235178 Hegyi Aug 1993 A
5237249 Levers Aug 1993 A
5243524 Ishida et al. Sep 1993 A
5245422 Borcherts et al. Sep 1993 A
5246193 Faidley Sep 1993 A
5249126 Hattori Sep 1993 A
5249128 Markandey et al. Sep 1993 A
5249157 Taylor Sep 1993 A
5251680 Minezawa et al. Oct 1993 A
5253050 Karasudani Oct 1993 A
5253109 O'Farrell et al. Oct 1993 A
5265172 Markandey et al. Nov 1993 A
5266873 Arditi et al. Nov 1993 A
5267160 Ito et al. Nov 1993 A
5276389 Levers Jan 1994 A
5285060 Larson et al. Feb 1994 A
5289182 Brillard et al. Feb 1994 A
5289321 Secor Feb 1994 A
5291424 Asayama et al. Mar 1994 A
5293162 Bachalo Mar 1994 A
5298732 Chen Mar 1994 A
5301115 Nouso Apr 1994 A
5302956 Asbury et al. Apr 1994 A
5304980 Maekawa Apr 1994 A
5305012 Faris Apr 1994 A
5307136 Saneyoshi Apr 1994 A
5307419 Tsujino et al. Apr 1994 A
5309137 Kajiwara May 1994 A
5313072 Vachss May 1994 A
5318143 Parker et al. Jun 1994 A
5321556 Joe Jun 1994 A
5325096 Pakett Jun 1994 A
5325386 Jewell et al. Jun 1994 A
5327288 Wellington et al. Jul 1994 A
5329206 Slotkowski et al. Jul 1994 A
5331312 Kudoh Jul 1994 A
5336980 Levers Aug 1994 A
5341437 Nakayama Aug 1994 A
5343206 Ansaldi et al. Aug 1994 A
5345266 Denyer Sep 1994 A
5347456 Zhang et al. Sep 1994 A
5351044 Mathur et al. Sep 1994 A
D351370 Lawlor et al. Oct 1994 S
5355118 Fukuhara Oct 1994 A
5359666 Nakayama et al. Oct 1994 A
5367457 Ishida Nov 1994 A
5369590 Karasudani Nov 1994 A
5371535 Takizawa Dec 1994 A
5373911 Yasui Dec 1994 A
5374852 Parkes Dec 1994 A
5379196 Kobayashi et al. Jan 1995 A
5379353 Hasegawa et al. Jan 1995 A
5381338 Wysocki et al. Jan 1995 A
5386285 Asayama Jan 1995 A
5388048 Yavnayi et al. Feb 1995 A
5394333 Kao Feb 1995 A
5398041 Hyatt Mar 1995 A
5406395 Wilson et al. Apr 1995 A
5406414 O'Farrell et al. Apr 1995 A
5408330 Squicciarini et al. Apr 1995 A
5408346 Trissel et al. Apr 1995 A
5410346 Saneyoshi et al. Apr 1995 A
5414257 Stanton May 1995 A
5414439 Groves et al. May 1995 A
5414461 Kishi et al. May 1995 A
5414625 Hattori May 1995 A
5416313 Larson et al. May 1995 A
5416318 Hegyi May 1995 A
5416478 Morinaga May 1995 A
5416711 Gran et al. May 1995 A
5424952 Asayama Jun 1995 A
5426294 Kobayashi et al. Jun 1995 A
5430431 Nelson Jul 1995 A
5430450 Holmes Jul 1995 A
5434407 Bauer et al. Jul 1995 A
5434927 Brady et al. Jul 1995 A
5436839 Dausch et al. Jul 1995 A
5440428 Hegg et al. Aug 1995 A
5444478 Lelong et al. Aug 1995 A
5448180 Kienzler et al. Sep 1995 A
5450057 Watanabe Sep 1995 A
5451822 Bechtel et al. Sep 1995 A
5457493 Leddy et al. Oct 1995 A
5459660 Berra Oct 1995 A
5461357 Yoshioka et al. Oct 1995 A
5461361 Moore Oct 1995 A
5465079 Bouchard et al. Nov 1995 A
5467284 Yoshioka et al. Nov 1995 A
5469298 Suman et al. Nov 1995 A
5471515 Fossum et al. Nov 1995 A
5473515 Liu Dec 1995 A
5475366 Van Lente et al. Dec 1995 A
5475494 Nishida et al. Dec 1995 A
5481257 Brubaker et al. Jan 1996 A
5482133 Iwata et al. Jan 1996 A
5483060 Sugiura et al. Jan 1996 A
5483168 Reid Jan 1996 A
5483453 Uemura et al. Jan 1996 A
5487116 Nakano et al. Jan 1996 A
5488496 Pine Jan 1996 A
5493269 Durley et al. Feb 1996 A
5493392 Blackmon et al. Feb 1996 A
5498866 Bendicks et al. Mar 1996 A
5500766 Stonecypher Mar 1996 A
5508592 Lapatovich et al. Apr 1996 A
5510983 Lino Apr 1996 A
5515448 Nishitani May 1996 A
5521633 Nakajima et al. May 1996 A
5528698 Kamei et al. Jun 1996 A
5529138 Shaw et al. Jun 1996 A
5530240 Larson et al. Jun 1996 A
5530330 Baiden et al. Jun 1996 A
5530420 Tsuchiya et al. Jun 1996 A
5530771 Maekawa Jun 1996 A
5535144 Kise Jul 1996 A
5535314 Alves et al. Jul 1996 A
5537003 Bechtel et al. Jul 1996 A
5539397 Asanuma et al. Jul 1996 A
5541590 Nishio Jul 1996 A
5545960 Ishikawa Aug 1996 A
5550677 Schofield et al. Aug 1996 A
5555136 Waldmann et al. Sep 1996 A
5555312 Shima et al. Sep 1996 A
5555503 Kyrtsos et al. Sep 1996 A
5555555 Sato et al. Sep 1996 A
5558123 Castel et al. Sep 1996 A
5559695 Daily Sep 1996 A
5562336 Gotou Oct 1996 A
5566224 ul Azam et al. Oct 1996 A
5568027 Teder Oct 1996 A
5568316 Schrenk et al. Oct 1996 A
5572315 Krell Nov 1996 A
5574443 Hsieh Nov 1996 A
5576687 Blank et al. Nov 1996 A
5581464 Woll et al. Dec 1996 A
5582383 Mertens et al. Dec 1996 A
5588123 Loibl Dec 1996 A
5594222 Caldwell Jan 1997 A
5596319 Spry Jan 1997 A
5596382 Bamford Jan 1997 A
5598164 Reppas et al. Jan 1997 A
5602457 Anderson et al. Feb 1997 A
5612686 Takano et al. Mar 1997 A
5612883 Shaffer et al. Mar 1997 A
5614788 Mullins Mar 1997 A
5614885 Van Lente et al. Mar 1997 A
5615857 Hook Apr 1997 A
5619370 Guinosso Apr 1997 A
5627586 Yamasaki May 1997 A
5633944 Guibert et al. May 1997 A
5634709 Iwama Jun 1997 A
5638116 Shimoura et al. Jun 1997 A
5642299 Hardin et al. Jun 1997 A
5646612 Byon Jul 1997 A
5648835 Uzawa Jul 1997 A
5650944 Kise Jul 1997 A
5660454 Mori et al. Aug 1997 A
5661303 Teder Aug 1997 A
5666028 Bechtel et al. Sep 1997 A
5667896 Carter et al. Sep 1997 A
5668663 Varaprasad et al. Sep 1997 A
5670935 Schofield et al. Sep 1997 A
5673019 Dantoni Sep 1997 A
5675489 Pomerleau Oct 1997 A
5676484 Chamberlin et al. Oct 1997 A
5677851 Kingdon et al. Oct 1997 A
5677979 Squicciarini et al. Oct 1997 A
5680263 Zimmermann et al. Oct 1997 A
D388107 Huckins Dec 1997 S
5699044 Van Lente et al. Dec 1997 A
5699057 Ikeda et al. Dec 1997 A
5699149 Kuroda et al. Dec 1997 A
5706355 Raboisson et al. Jan 1998 A
5707129 Kobayashi Jan 1998 A
5708410 Blank et al. Jan 1998 A
5710633 Klappenbach et al. Jan 1998 A
5715093 Schierbeek et al. Feb 1998 A
5719551 Flick Feb 1998 A
5724187 Varaprasad et al. Mar 1998 A
5724316 Brunts Mar 1998 A
5737226 Olson et al. Apr 1998 A
5757949 Kinoshita et al. May 1998 A
5760826 Nayar Jun 1998 A
5760828 Cortes Jun 1998 A
5760931 Saburi et al. Jun 1998 A
5760962 Schofield et al. Jun 1998 A
5761094 Olson et al. Jun 1998 A
5764139 Nojima et al. Jun 1998 A
5765116 Wilson-Jones et al. Jun 1998 A
5765940 Levy et al. Jun 1998 A
5781105 Bitar et al. Jul 1998 A
5781437 Wiemer et al. Jul 1998 A
5786772 Schofield et al. Jul 1998 A
5790403 Nakayama Aug 1998 A
5790973 Blaker et al. Aug 1998 A
5793308 Rosinski et al. Aug 1998 A
5793420 Schmidt Aug 1998 A
5796094 Schofield et al. Aug 1998 A
5798575 O'Farrell et al. Aug 1998 A
5804719 Didelot et al. Sep 1998 A
5808589 Fergason Sep 1998 A
5811888 Hsieh Sep 1998 A
5820097 Spooner Oct 1998 A
5835255 Miles Nov 1998 A
5835613 Breed et al. Nov 1998 A
5835614 Aoyama et al. Nov 1998 A
5837994 Stam et al. Nov 1998 A
5841126 Fossum et al. Nov 1998 A
5844505 Van Ryzin Dec 1998 A
5844682 Kiyomoto et al. Dec 1998 A
5845000 Breed et al. Dec 1998 A
5847755 Wixson et al. Dec 1998 A
5848802 Breed et al. Dec 1998 A
5850176 Kinoshita et al. Dec 1998 A
5850254 Takano et al. Dec 1998 A
5867591 Onda Feb 1999 A
5877707 Kowalick Mar 1999 A
5877897 Schofield et al. Mar 1999 A
5878370 Olson Mar 1999 A
5883193 Karim Mar 1999 A
5883684 Millikan et al. Mar 1999 A
5883739 Ashihara et al. Mar 1999 A
5884212 Lion Mar 1999 A
5890021 Onoda Mar 1999 A
5890083 Franke et al. Mar 1999 A
5896085 Mori et al. Apr 1999 A
5899956 Chan May 1999 A
5904725 Iisaka et al. May 1999 A
5905457 Rashid May 1999 A
5912534 Benedict Jun 1999 A
5914815 Bos Jun 1999 A
5920367 Kajimoto et al. Jul 1999 A
5922036 Yasui et al. Jul 1999 A
5923027 Stam et al. Jul 1999 A
5929784 Kawaziri et al. Jul 1999 A
5929786 Schofield et al. Jul 1999 A
5938320 Crandall Aug 1999 A
5938810 De Vries, Jr. et al. Aug 1999 A
5940120 Frankhouse et al. Aug 1999 A
5942853 Piscart Aug 1999 A
5949331 Schofield et al. Sep 1999 A
5955941 Pruksch et al. Sep 1999 A
5956181 Lin Sep 1999 A
5959367 O'Farrell et al. Sep 1999 A
5959555 Furuta Sep 1999 A
5961571 Gorr et al. Oct 1999 A
5963247 Banitt Oct 1999 A
5964822 Alland et al. Oct 1999 A
5971552 O'Farrell et al. Oct 1999 A
5982288 Sawatari et al. Nov 1999 A
5986796 Miles Nov 1999 A
5990469 Bechtel et al. Nov 1999 A
5990649 Nagao et al. Nov 1999 A
5991427 Kakinami et al. Nov 1999 A
6001486 Varaprasad et al. Dec 1999 A
6009336 Harris et al. Dec 1999 A
6020704 Buschur Feb 2000 A
6028537 Suman et al. Feb 2000 A
6031484 Bullinger et al. Feb 2000 A
6037860 Zander et al. Mar 2000 A
6037975 Aoyama Mar 2000 A
6049171 Stam et al. Apr 2000 A
6052124 Stein et al. Apr 2000 A
6057754 Kinoshita et al. May 2000 A
6066933 Ponziana May 2000 A
6084519 Coulling et al. Jul 2000 A
6087953 DeLine et al. Jul 2000 A
6091833 Yasui et al. Jul 2000 A
6094198 Shashua Jul 2000 A
6097023 Schofield et al. Aug 2000 A
6097024 Stam et al. Aug 2000 A
6100811 Hsu et al. Aug 2000 A
6107939 Sorden Aug 2000 A
6116743 Hoek Sep 2000 A
6122597 Saneyoshi et al. Sep 2000 A
6124647 Marcus et al. Sep 2000 A
6124886 DeLine et al. Sep 2000 A
6139172 Bos et al. Oct 2000 A
6140980 Spitzer et al. Oct 2000 A
6144022 Tenenbaum et al. Nov 2000 A
6144158 Beam Nov 2000 A
6150014 Chu et al. Nov 2000 A
6150930 Cooper Nov 2000 A
6151065 Steed et al. Nov 2000 A
6151539 Bergholz et al. Nov 2000 A
6158655 DeVries, Jr. et al. Dec 2000 A
6166628 Andreas Dec 2000 A
6170955 Campbell et al. Jan 2001 B1
6172613 DeLine et al. Jan 2001 B1
6175164 O'Farrell et al. Jan 2001 B1
6175300 Kendrick Jan 2001 B1
6176590 Prevost et al. Jan 2001 B1
6188939 Morgan et al. Feb 2001 B1
6198409 Schofield et al. Mar 2001 B1
6201642 Bos Mar 2001 B1
6211907 Scaman et al. Apr 2001 B1
6218934 Regan Apr 2001 B1
6219444 Shashua et al. Apr 2001 B1
6222447 Schofield et al. Apr 2001 B1
6222460 DeLine et al. Apr 2001 B1
6226061 Tagusa May 2001 B1
6229319 Johnson May 2001 B1
6243003 DeLine et al. Jun 2001 B1
6247819 Turnbull et al. Jun 2001 B1
6250148 Lynam Jun 2001 B1
6259412 Duroux Jul 2001 B1
6259423 Tokito et al. Jul 2001 B1
6266082 Yonezawa et al. Jul 2001 B1
6266442 Laumeyer et al. Jul 2001 B1
6278377 DeLine et al. Aug 2001 B1
6281804 Haller et al. Aug 2001 B1
6285393 Shimoura et al. Sep 2001 B1
6285778 Nakajima et al. Sep 2001 B1
6291905 Drummond et al. Sep 2001 B1
6291906 Marcus et al. Sep 2001 B1
6292752 Franke et al. Sep 2001 B1
6294989 Schofield et al. Sep 2001 B1
6297781 Turnbull et al. Oct 2001 B1
6302545 Schofield et al. Oct 2001 B1
6310611 Caldwell Oct 2001 B1
6311119 Sawamoto et al. Oct 2001 B2
6313454 Bos et al. Nov 2001 B1
6315421 Apfelbeck et al. Nov 2001 B1
6317057 Lee Nov 2001 B1
6318870 Spooner et al. Nov 2001 B1
6320176 Schofield et al. Nov 2001 B1
6320282 Caldwell Nov 2001 B1
6324450 Iwama Nov 2001 B1
6326613 Heslin et al. Dec 2001 B1
6329925 Skiver et al. Dec 2001 B1
6333759 Mazzilli Dec 2001 B1
6341523 Lynam Jan 2002 B2
6353392 Schofield et al. Mar 2002 B1
6359392 He Mar 2002 B1
6362729 Hellmann et al. Mar 2002 B1
6366213 DeLine et al. Apr 2002 B2
6366236 Farmer et al. Apr 2002 B1
6370329 Teuchert Apr 2002 B1
6388565 Bernhard et al. May 2002 B1
6388580 Graham May 2002 B1
6389340 Rayner May 2002 B1
6392218 Kuehnle May 2002 B1
6396397 Bos et al. May 2002 B1
6396408 Drummond et al. May 2002 B2
6411204 Bloomfield et al. Jun 2002 B1
6411328 Franke et al. Jun 2002 B1
6420975 DeLine et al. Jul 2002 B1
6424273 Gulla et al. Jul 2002 B1
6428172 Hutzel et al. Aug 2002 B1
6429594 Stam et al. Aug 2002 B1
6430303 Naoi et al. Aug 2002 B1
6433676 DeLine et al. Aug 2002 B2
6433817 Guerra Aug 2002 B1
6441748 Takagi et al. Aug 2002 B1
6442465 Breed et al. Aug 2002 B2
6445287 Schofield et al. Sep 2002 B1
6445809 Sasaki et al. Sep 2002 B1
6449540 Rayner Sep 2002 B1
6452148 Bendicks et al. Sep 2002 B1
6466136 DeLine et al. Oct 2002 B2
6466684 Sasaki et al. Oct 2002 B1
6469739 Bechtel et al. Oct 2002 B1
6472977 Pochmuller Oct 2002 B1
6472979 Schofield et al. Oct 2002 B2
6477260 Shimomura Nov 2002 B1
6477464 McCarthy et al. Nov 2002 B2
6483438 DeLine et al. Nov 2002 B2
6485155 Duroux et al. Nov 2002 B1
6497503 Dassanayake et al. Dec 2002 B1
6498620 Schofield et al. Dec 2002 B2
6509832 Bauer et al. Jan 2003 B1
6513252 Schierbeek Feb 2003 B1
6515378 Drummond et al. Feb 2003 B2
6516272 Lin Feb 2003 B2
6516664 Lynam Feb 2003 B2
6523964 Schofield et al. Feb 2003 B2
6534884 Marcus et al. Mar 2003 B2
6535242 Strumolo et al. Mar 2003 B1
6539306 Turnbull Mar 2003 B2
6540193 DeLine Apr 2003 B1
6547133 Devries, Jr. et al. Apr 2003 B1
6553130 Lemelson et al. Apr 2003 B1
6559435 Schofield et al. May 2003 B2
6570998 Ohtsuka et al. May 2003 B1
6574033 Chui et al. Jun 2003 B1
6577334 Kawai et al. Jun 2003 B1
6578017 Ebersole et al. Jun 2003 B1
6587573 Stam et al. Jul 2003 B1
6587968 Leyva Jul 2003 B1
6589625 Kothari et al. Jul 2003 B1
6593011 Liu et al. Jul 2003 B2
6593565 Heslin et al. Jul 2003 B2
6593698 Stam et al. Jul 2003 B2
6593960 Sugimoto et al. Jul 2003 B1
6594583 Ogura et al. Jul 2003 B2
6611202 Schofield et al. Aug 2003 B2
6611610 Stam et al. Aug 2003 B1
6614579 Roberts et al. Sep 2003 B2
6617564 Ockerse et al. Sep 2003 B2
6627918 Getz et al. Sep 2003 B2
6631316 Stam et al. Oct 2003 B2
6631994 Suzuki et al. Oct 2003 B2
6636258 Strumolo Oct 2003 B2
6648477 Hutzel et al. Nov 2003 B2
6650233 DeLine et al. Nov 2003 B2
6650455 Miles Nov 2003 B2
6653614 Stam et al. Nov 2003 B2
6672731 Schnell et al. Jan 2004 B2
6674562 Miles Jan 2004 B1
6674878 Retterath et al. Jan 2004 B2
6678056 Downs Jan 2004 B2
6678590 Burchfiel Jan 2004 B1
6678614 McCarthy et al. Jan 2004 B2
6680792 Miles Jan 2004 B2
6681163 Stam et al. Jan 2004 B2
6690268 Schofield et al. Feb 2004 B2
6700605 Toyoda et al. Mar 2004 B1
6703925 Steffel Mar 2004 B2
6704621 Stein et al. Mar 2004 B1
6710908 Miles et al. Mar 2004 B2
6711474 Treyz et al. Mar 2004 B1
6714331 Lewis et al. Mar 2004 B2
6717524 DeLine et al. Apr 2004 B2
6717610 Bos et al. Apr 2004 B1
6728393 Stam et al. Apr 2004 B2
6728623 Takenaga et al. Apr 2004 B2
6735506 Breed et al. May 2004 B2
6738088 Uskolovsky et al. May 2004 B1
6741186 Ross May 2004 B2
6741377 Miles May 2004 B2
6744353 Sjonell Jun 2004 B2
6754367 Ito et al. Jun 2004 B1
6757109 Bos Jun 2004 B2
6762867 Lippert et al. Jul 2004 B2
6764210 Akiyama Jul 2004 B2
6765480 Tseng Jul 2004 B2
6774988 Stam et al. Aug 2004 B2
6784828 Delcheccolo et al. Aug 2004 B2
6794119 Miles Sep 2004 B2
6795221 Urey Sep 2004 B1
6801127 Mizusawa et al. Oct 2004 B2
6801244 Takeda et al. Oct 2004 B2
6802617 Schofield et al. Oct 2004 B2
6806452 Bos et al. Oct 2004 B2
6807287 Hermans Oct 2004 B1
6811330 Tozawa Nov 2004 B1
6812463 Okada Nov 2004 B2
6813545 Stromme Nov 2004 B2
6819231 Berberich et al. Nov 2004 B2
6819779 Nichani Nov 2004 B1
6822563 Bos et al. Nov 2004 B2
6823241 Shirato et al. Nov 2004 B2
6823261 Sekiguchi Nov 2004 B2
6824281 Schofield et al. Nov 2004 B2
6831261 Schofield et al. Dec 2004 B2
6838980 Gloger et al. Jan 2005 B2
6842189 Park Jan 2005 B2
6847487 Burgner Jan 2005 B2
6850629 Jeon Feb 2005 B2
6853738 Nishigaki et al. Feb 2005 B1
6859148 Miller et al. Feb 2005 B2
6861809 Stam Mar 2005 B2
6864930 Matsushita et al. Mar 2005 B2
6873253 Veziris Mar 2005 B2
6882287 Schofield Apr 2005 B2
6888447 Hori et al. May 2005 B2
6889161 Winner et al. May 2005 B2
6891563 Schofield et al. May 2005 B2
6898518 Padmanabhan May 2005 B2
6906620 Nakai et al. Jun 2005 B2
6906639 Lemelson et al. Jun 2005 B2
6909753 Meehan et al. Jun 2005 B2
6914521 Rothkop Jul 2005 B2
6928180 Stam et al. Aug 2005 B2
6932669 Lee et al. Aug 2005 B2
6933837 Gunderson et al. Aug 2005 B2
6940423 Takagi et al. Sep 2005 B2
6946978 Schofield Sep 2005 B2
6950035 Tanaka et al. Sep 2005 B2
6953253 Schofield et al. Oct 2005 B2
6956469 Hirvonen et al. Oct 2005 B2
6959994 Fujikawa et al. Nov 2005 B2
6961178 Sugino et al. Nov 2005 B2
6961661 Sekiguchi Nov 2005 B2
6963661 Hattori et al. Nov 2005 B1
6967569 Weber et al. Nov 2005 B2
6968736 Lynam Nov 2005 B2
6975775 Rykowski et al. Dec 2005 B2
6980092 Turnbull et al. Dec 2005 B2
6989736 Berberich et al. Jan 2006 B2
6990397 Albou et al. Jan 2006 B2
6995687 Lang et al. Feb 2006 B2
7004593 Weller et al. Feb 2006 B2
7004606 Schofield Feb 2006 B2
7005974 McMahon et al. Feb 2006 B2
7012507 DeLine et al. Mar 2006 B2
7012727 Hutzel et al. Mar 2006 B2
7023331 Kodama Apr 2006 B2
7027387 Reinold et al. Apr 2006 B2
7027615 Chen Apr 2006 B2
7030738 Ishii Apr 2006 B2
7030775 Sekiguchi Apr 2006 B2
7030778 Ra Apr 2006 B2
7038577 Pawlicki et al. May 2006 B2
7046448 Burgner May 2006 B2
7057505 Iwamoto Jun 2006 B2
7057681 Hinata et al. Jun 2006 B2
7062300 Kim Jun 2006 B1
7065432 Moisel et al. Jun 2006 B2
7068289 Satoh et al. Jun 2006 B2
7068844 Javidi et al. Jun 2006 B1
7085633 Nishira et al. Aug 2006 B2
7085637 Breed et al. Aug 2006 B2
7091837 Nakai et al. Aug 2006 B2
7092548 Laumeyer et al. Aug 2006 B2
7095432 Nakayama et al. Aug 2006 B2
7106213 White Sep 2006 B2
7110021 Nobori et al. Sep 2006 B2
7110156 Lawlor et al. Sep 2006 B2
7113867 Stein Sep 2006 B1
7116246 Winter et al. Oct 2006 B2
7121028 Shoen et al. Oct 2006 B2
7123168 Schofield Oct 2006 B2
7133661 Hatae et al. Nov 2006 B2
7149613 Stam et al. Dec 2006 B2
7151996 Stein Dec 2006 B2
7167796 Taylor et al. Jan 2007 B2
7171027 Satoh Jan 2007 B2
7184585 Hamza et al. Feb 2007 B2
7187498 Bengoechea et al. Mar 2007 B2
7188963 Schofield et al. Mar 2007 B2
7195381 Lynam et al. Mar 2007 B2
7202776 Breed Apr 2007 B2
7202987 Varaprasad et al. Apr 2007 B2
7205904 Schofield Apr 2007 B2
7221363 Roberts et al. May 2007 B2
7224324 Quist et al. May 2007 B2
7227459 Bos et al. Jun 2007 B2
7227611 Hull et al. Jun 2007 B2
7235918 McCullough et al. Jun 2007 B2
7248283 Takagi et al. Jul 2007 B2
7248344 Morcom Jul 2007 B2
7249860 Kulas et al. Jul 2007 B2
7253723 Lindahl et al. Aug 2007 B2
7255451 McCabe et al. Aug 2007 B2
7271951 Weber et al. Sep 2007 B2
7304661 Ishikura Dec 2007 B2
7311406 Schofield et al. Dec 2007 B2
7325934 Schofield et al. Feb 2008 B2
7325935 Schofield et al. Feb 2008 B2
7337055 Matsumoto et al. Feb 2008 B2
7338177 Lynam Mar 2008 B2
7339149 Schofield et al. Mar 2008 B1
7344261 Schofield et al. Mar 2008 B2
7355524 Schofield Apr 2008 B2
7360932 Uken et al. Apr 2008 B2
7362883 Otsuka et al. Apr 2008 B2
7370983 DeWind et al. May 2008 B2
7375803 Bamji May 2008 B1
7380948 Schofield et al. Jun 2008 B2
7388182 Schofield et al. Jun 2008 B2
7402786 Schofield et al. Jul 2008 B2
7403659 Das et al. Jul 2008 B2
7420756 Lynam Sep 2008 B2
7423248 Schofield et al. Sep 2008 B2
7423821 Bechtel et al. Sep 2008 B2
7425076 Schofield et al. Sep 2008 B2
7429998 Kawauchi et al. Sep 2008 B2
7432248 Roberts et al. Oct 2008 B2
7432967 Bechtel et al. Oct 2008 B2
7446924 Schofield et al. Nov 2008 B2
7459664 Schofield et al. Dec 2008 B2
7460007 Schofield et al. Dec 2008 B2
7463138 Pawlicki et al. Dec 2008 B2
7468652 DeLine et al. Dec 2008 B2
7474963 Taylor et al. Jan 2009 B2
7480149 DeWard et al. Jan 2009 B2
7489374 Utsumi et al. Feb 2009 B2
7495719 Adachi et al. Feb 2009 B2
7525604 Xue Apr 2009 B2
7526103 Schofield et al. Apr 2009 B2
7533998 Schofield et al. May 2009 B2
7541743 Salmeen et al. Jun 2009 B2
7543946 Ockerse et al. Jun 2009 B2
7545429 Travis Jun 2009 B2
7548291 Lee et al. Jun 2009 B2
7551103 Schofield Jun 2009 B2
7561181 Schofield et al. Jul 2009 B2
7565006 Stam et al. Jul 2009 B2
7566639 Kohda Jul 2009 B2
7566851 Stein et al. Jul 2009 B2
7567291 Bechtel et al. Jul 2009 B2
7605856 Imoto Oct 2009 B2
7613327 Stam et al. Nov 2009 B2
7616781 Schofield et al. Nov 2009 B2
7619508 Lynam et al. Nov 2009 B2
7629996 Rademacher et al. Dec 2009 B2
7633383 Dunsmoir et al. Dec 2009 B2
7639149 Katoh Dec 2009 B2
7650030 Shan et al. Jan 2010 B2
7653215 Stam Jan 2010 B2
7655894 Schofield et al. Feb 2010 B2
7663798 Tonar et al. Feb 2010 B2
7676087 Dhua et al. Mar 2010 B2
7679498 Pawlicki et al. Mar 2010 B2
7683326 Stam et al. Mar 2010 B2
7702133 Muramatsu et al. Apr 2010 B2
7719408 DeWard et al. May 2010 B2
7720580 Higgins-Luthman May 2010 B2
7724434 Cross et al. May 2010 B2
7731403 Lynam et al. Jun 2010 B2
7742864 Sekiguchi Jun 2010 B2
7786898 Stein et al. Aug 2010 B2
7791694 Molsen et al. Sep 2010 B2
7792329 Schofield et al. Sep 2010 B2
7825600 Stam et al. Nov 2010 B2
7842154 Lynam Nov 2010 B2
7843451 Lafon Nov 2010 B2
7854514 Conner et al. Dec 2010 B2
7855755 Weller et al. Dec 2010 B2
7855778 Yung et al. Dec 2010 B2
7859565 Schofield et al. Dec 2010 B2
7873187 Schofield et al. Jan 2011 B2
7877175 Higgins-Luthman Jan 2011 B2
7881496 Camilleri et al. Feb 2011 B2
7903324 Kobayashi et al. Mar 2011 B2
7903335 Nieuwkerk et al. Mar 2011 B2
7914187 Higgins-Luthman et al. Mar 2011 B2
7914188 DeLine et al. Mar 2011 B2
7930160 Hosagrahara et al. Apr 2011 B1
7949152 Schofield et al. May 2011 B2
7965357 Van De Witte et al. Jun 2011 B2
7972045 Schofield Jul 2011 B2
7991522 Higgins-Luthman Aug 2011 B2
7994462 Schofield et al. Aug 2011 B2
7995067 Navon Aug 2011 B2
8004392 DeLine et al. Aug 2011 B2
8017898 Lu et al. Sep 2011 B2
8027691 Bernas et al. Sep 2011 B2
8045760 Stam et al. Oct 2011 B2
8063759 Bos et al. Nov 2011 B2
8064643 Stein et al. Nov 2011 B2
8082101 Stein et al. Dec 2011 B2
8090153 Schofield et al. Jan 2012 B2
8094002 Schofield et al. Jan 2012 B2
8095310 Taylor et al. Jan 2012 B2
8098142 Schofield et al. Jan 2012 B2
8100568 DeLine et al. Jan 2012 B2
8116929 Higgins-Luthman Feb 2012 B2
8120652 Bechtel et al. Feb 2012 B2
8162518 Schofield Apr 2012 B2
8164628 Stein et al. Apr 2012 B2
8179437 Schofield et al. May 2012 B2
8184159 Luo May 2012 B2
8203440 Schofield et al. Jun 2012 B2
8203443 Bos et al. Jun 2012 B2
8222588 Schofield et al. Jul 2012 B2
8224031 Saito Jul 2012 B2
8233045 Luo et al. Jul 2012 B2
8254635 Stein et al. Aug 2012 B2
8288711 Heslin et al. Oct 2012 B2
8289142 Pawlicki et al. Oct 2012 B2
8289430 Bechtel et al. Oct 2012 B2
8300058 Navon et al. Oct 2012 B2
8305471 Bechtel et al. Nov 2012 B2
8308325 Takayanagi et al. Nov 2012 B2
8314689 Schofield et al. Nov 2012 B2
8324552 Schofield et al. Dec 2012 B2
8325028 Schofield et al. Dec 2012 B2
8325986 Schofield et al. Dec 2012 B2
8339526 Minikey, Jr. et al. Dec 2012 B2
8350683 DeLine et al. Jan 2013 B2
8362883 Hale et al. Jan 2013 B2
8378851 Stein et al. Feb 2013 B2
8386114 Higgins-Luthman Feb 2013 B2
8405726 Schofield et al. Mar 2013 B2
8414137 Quinn et al. Apr 2013 B2
8434919 Schofield May 2013 B2
8452055 Stein et al. May 2013 B2
8481910 Schofield et al. Jul 2013 B2
8481916 Heslin et al. Jul 2013 B2
8492698 Schofield et al. Jul 2013 B2
8508593 Schofield et al. Aug 2013 B1
8513590 Heslin et al. Aug 2013 B2
8531278 DeWard et al. Sep 2013 B2
8531279 DeLine et al. Sep 2013 B2
8534887 DeLine et al. Sep 2013 B2
8538205 Sixsou et al. Sep 2013 B2
8543330 Taylor et al. Sep 2013 B2
8553088 Stein et al. Oct 2013 B2
8593521 Schofield et al. Nov 2013 B2
8599001 Schofield et al. Dec 2013 B2
8629768 Bos et al. Jan 2014 B2
8636393 Schofield Jan 2014 B2
8637801 Schofield et al. Jan 2014 B2
8643724 Schofield et al. Feb 2014 B2
8656221 Sixsou et al. Feb 2014 B2
8665079 Pawlicki et al. Mar 2014 B2
8676491 Taylor et al. Mar 2014 B2
8686840 Drummond et al. Apr 2014 B2
8692659 Schofield et al. Apr 2014 B2
8818042 Schofield et al. Aug 2014 B2
9008369 Schofield et al. Apr 2015 B2
9018577 Lu et al. Apr 2015 B2
9171217 Pawlicki et al. Oct 2015 B2
9191634 Schofield et al. Nov 2015 B2
9428192 Schofield et al. Aug 2016 B2
9440535 Schofield Sep 2016 B2
10071676 Schofield Sep 2018 B2
10787116 Schofield Sep 2020 B2
20010002451 Breed May 2001 A1
20020003571 Schofield et al. Jan 2002 A1
20020005778 Breed et al. Jan 2002 A1
20020011611 Huang et al. Jan 2002 A1
20020029103 Breed et al. Mar 2002 A1
20020060522 Stam et al. May 2002 A1
20020080235 Jeon Jun 2002 A1
20020113873 Williams Aug 2002 A1
20020116106 Breed et al. Aug 2002 A1
20020126002 Patchell Sep 2002 A1
20020126875 Naoi et al. Sep 2002 A1
20020135468 Bos et al. Sep 2002 A1
20030040864 Stein Feb 2003 A1
20030070741 Rosenberg et al. Apr 2003 A1
20030103142 Hitomi et al. Jun 2003 A1
20030122930 Schofield et al. Jul 2003 A1
20030125855 Breed et al. Jul 2003 A1
20030128106 Ross Jul 2003 A1
20030137586 Lewellen Jul 2003 A1
20030191568 Breed Oct 2003 A1
20030202683 Ma et al. Oct 2003 A1
20030209893 Breed et al. Nov 2003 A1
20030222982 Hamdan et al. Dec 2003 A1
20040016870 Pawlicki et al. Jan 2004 A1
20040021947 Schofield et al. Feb 2004 A1
20040022416 Lemelson et al. Feb 2004 A1
20040086153 Tsai et al. May 2004 A1
20040096082 Nakai et al. May 2004 A1
20040146184 Hamza et al. Jul 2004 A1
20040148063 Patchell Jul 2004 A1
20040164228 Fogg et al. Aug 2004 A1
20040200948 Bos et al. Oct 2004 A1
20050036325 Furusawa et al. Feb 2005 A1
20050073853 Stam Apr 2005 A1
20050131607 Breed Jun 2005 A1
20050219852 Stam et al. Oct 2005 A1
20050226490 Phillips et al. Oct 2005 A1
20050237385 Kosaka et al. Oct 2005 A1
20060018511 Stam et al. Jan 2006 A1
20060018512 Stam et al. Jan 2006 A1
20060050018 Hutzel et al. Mar 2006 A1
20060091813 Stam et al. May 2006 A1
20060095175 deWaal et al. May 2006 A1
20060103727 Tseng May 2006 A1
20060250224 Steffel et al. Nov 2006 A1
20060250501 Wildmann et al. Nov 2006 A1
20070024724 Stein et al. Feb 2007 A1
20070104476 Yasutomi et al. May 2007 A1
20070109406 Schofield et al. May 2007 A1
20070115357 Stein et al. May 2007 A1
20070120657 Schofield et al. May 2007 A1
20070154063 Breed Jul 2007 A1
20070154068 Stein et al. Jul 2007 A1
20070193811 Breed et al. Aug 2007 A1
20070221822 Stein et al. Sep 2007 A1
20070229238 Boyles et al. Oct 2007 A1
20070230792 Shashua et al. Oct 2007 A1
20070242339 Bradley Oct 2007 A1
20080036576 Stein et al. Feb 2008 A1
20080043099 Stein et al. Feb 2008 A1
20080137908 Stein et al. Jun 2008 A1
20080147321 Howard et al. Jun 2008 A1
20080231710 Asari et al. Sep 2008 A1
20080234899 Breed et al. Sep 2008 A1
20080239393 Navon Oct 2008 A1
20080266396 Stein Oct 2008 A1
20090052003 Schofield et al. Feb 2009 A1
20090066065 Breed et al. Mar 2009 A1
20090113509 Tseng et al. Apr 2009 A1
20090143986 Stein et al. Jun 2009 A1
20090182690 Stein Jul 2009 A1
20090190015 Bechtel et al. Jul 2009 A1
20090201137 Weller et al. Aug 2009 A1
20090243824 Peterson et al. Oct 2009 A1
20090256938 Bechtel et al. Oct 2009 A1
20090300629 Navon et al. Dec 2009 A1
20100125717 Navon May 2010 A1
20100172547 Akutsu Jul 2010 A1
20110018700 Stein et al. Jan 2011 A1
20110219217 Sixsou et al. Sep 2011 A1
20110280495 Sixsou et al. Nov 2011 A1
20110307684 Kreinin et al. Dec 2011 A1
20120002053 Stein et al. Jan 2012 A1
20120045112 Lundblad et al. Feb 2012 A1
20120056735 Stein et al. Mar 2012 A1
20120069185 Stein Mar 2012 A1
20120105639 Stein et al. May 2012 A1
20120140076 Rosenbaum et al. Jun 2012 A1
20120200707 Stein et al. Aug 2012 A1
20120212593 Na'aman et al. Aug 2012 A1
20120233841 Stein Sep 2012 A1
20120314071 Rosenbaum et al. Dec 2012 A1
20130135444 Stein et al. May 2013 A1
20130141580 Stein et al. Jun 2013 A1
20130147957 Stein Jun 2013 A1
20130169536 Wexler et al. Jul 2013 A1
20130271584 Wexler et al. Oct 2013 A1
20130308828 Stein et al. Nov 2013 A1
20140015976 DeLine et al. Jan 2014 A1
20140033203 Dogon et al. Jan 2014 A1
20140049648 Stein et al. Feb 2014 A1
20140082307 Kreinin et al. Mar 2014 A1
20140093132 Stein et al. Apr 2014 A1
20140122551 Dogon et al. May 2014 A1
20140125799 Bos et al. May 2014 A1
20140156140 Stein et al. Jun 2014 A1
20140160244 Berberian et al. Jun 2014 A1
20140161323 Livyatan et al. Jun 2014 A1
20140198184 Stein et al. Jul 2014 A1
Foreign Referenced Citations (512)
Number Date Country
519193 Aug 2011 AT
1008142 Jan 1996 BE
1101522 May 1981 CA
2392578 May 2001 CA
2392652 May 2001 CA
644315 Jul 1984 CH
2074262 Apr 1991 CN
2185701 Dec 1994 CN
1104741 Jul 1995 CN
2204254 Aug 1995 CN
1194056 Sep 1998 CN
1235913 Nov 1999 CN
1383032 Dec 2002 CN
102193852 Sep 2011 CN
102542256 Jul 2012 CN
1152627 Aug 1963 DE
1182971 Dec 1964 DE
1190413 Apr 1965 DE
1196598 Jul 1965 DE
1214174 Apr 1966 DE
2064839 Jul 1972 DE
3004247 Aug 1981 DE
3040555 May 1982 DE
3101855 Aug 1982 DE
3240498 May 1984 DE
3248511 Jul 1984 DE
3433671 Mar 1985 DE
3515116 Oct 1986 DE
3528220 Feb 1987 DE
3535588 Apr 1987 DE
3601388 Jul 1987 DE
3637165 May 1988 DE
3636946 Jun 1988 DE
3642196 Jun 1988 DE
3734066 Apr 1989 DE
3737395 May 1989 DE
3838365 Jun 1989 DE
3833022 Apr 1990 DE
3839512 May 1990 DE
3839513 May 1990 DE
3937576 May 1990 DE
3840425 Jun 1990 DE
3844364 Jul 1990 DE
9010196 Sep 1990 DE
4015927 Nov 1990 DE
3932216 Apr 1991 DE
4007646 Sep 1991 DE
4107965 Sep 1991 DE
4111993 Oct 1991 DE
4015959 Nov 1991 DE
4116255 Dec 1991 DE
4023952 Feb 1992 DE
4130010 Mar 1992 DE
4032927 Apr 1992 DE
4133882 Apr 1992 DE
4035956 May 1992 DE
4122531 Jan 1993 DE
4124654 Jan 1993 DE
4137551 Mar 1993 DE
4136427 May 1993 DE
4300941 Jul 1993 DE
4206142 Sep 1993 DE
4214223 Nov 1993 DE
4231137 Feb 1994 DE
4328304 Mar 1994 DE
4328902 Mar 1994 DE
4332612 Apr 1994 DE
4238599 Jun 1994 DE
4337756 Jun 1994 DE
4344485 Jun 1994 DE
4304005 Aug 1994 DE
4332836 Sep 1994 DE
4407082 Sep 1994 DE
4407757 Sep 1994 DE
4411179 Oct 1994 DE
4412669 Oct 1994 DE
4418122 Dec 1994 DE
4423966 Jan 1995 DE
4336288 Mar 1995 DE
4428069 Mar 1995 DE
4434698 Mar 1995 DE
4341409 Jun 1995 DE
4446452 Jun 1995 DE
69107283 Jul 1995 DE
4403937 Aug 1995 DE
19505487 Sep 1995 DE
19518978 Nov 1995 DE
069302975 Dec 1996 DE
29703084 Apr 1997 DE
29805142 May 1998 DE
19755008 Jul 1999 DE
19829162 Jan 2000 DE
10237554 Mar 2004 DE
000010251949 May 2004 DE
4480341 May 2005 DE
19530617 Feb 2009 DE
0048492 Mar 1982 EP
0049722 Apr 1982 EP
0072406 Feb 1983 EP
0169734 Jan 1986 EP
0176615 Apr 1986 EP
0202460 Nov 1986 EP
0340735 Nov 1989 EP
0341985 Nov 1989 EP
0348691 Jan 1990 EP
0353200 Jan 1990 EP
0354561 Feb 1990 EP
0360880 Apr 1990 EP
0361914 Apr 1990 EP
0387817 Sep 1990 EP
0426503 May 1991 EP
0433538 Jun 1991 EP
0450553 Oct 1991 EP
0454516 Oct 1991 EP
0455524 Nov 1991 EP
0459433 Dec 1991 EP
473866 Mar 1992 EP
0477986 Apr 1992 EP
0479271 Apr 1992 EP
0487100 May 1992 EP
0487332 May 1992 EP
0487465 May 1992 EP
0492591 Jul 1992 EP
0495508 Jul 1992 EP
0496411 Jul 1992 EP
0501345 Sep 1992 EP
0505237 Sep 1992 EP
0513476 Nov 1992 EP
0514343 Nov 1992 EP
0527665 Feb 1993 EP
529346 Mar 1993 EP
0532379 Mar 1993 EP
0533508 Mar 1993 EP
0550397 Jul 1993 EP
0558027 Sep 1993 EP
0564858 Oct 1993 EP
0567059 Oct 1993 EP
0582236 Feb 1994 EP
0586857 Mar 1994 EP
0588815 Mar 1994 EP
0590588 Apr 1994 EP
0591743 Apr 1994 EP
0602962 Jun 1994 EP
0605045 Jul 1994 EP
0606586 Jul 1994 EP
0617296 Sep 1994 EP
0626654 Nov 1994 EP
0640903 Mar 1995 EP
0642950 Mar 1995 EP
0654392 May 1995 EP
0667708 Aug 1995 EP
0677428 Oct 1995 EP
0686865 Dec 1995 EP
0687594 Dec 1995 EP
0697641 Feb 1996 EP
733252 Sep 1996 EP
0756968 Feb 1997 EP
0788947 Aug 1997 EP
0830267 Mar 1998 EP
0860325 Aug 1998 EP
0874331 Oct 1998 EP
0889801 Jan 1999 EP
0893308 Jan 1999 EP
0899157 Mar 1999 EP
0913751 May 1999 EP
0949818 Oct 1999 EP
1022903 Jul 2000 EP
1058220 Dec 2000 EP
1065642 Jan 2001 EP
1074430 Feb 2001 EP
1115250 Jul 2001 EP
1170173 Jan 2002 EP
1236126 Sep 2002 EP
1257971 Nov 2002 EP
1359557 Nov 2003 EP
1727089 Nov 2006 EP
1741079 Jan 2007 EP
1748644 Jan 2007 EP
1754179 Feb 2007 EP
1790541 May 2007 EP
1806595 Jul 2007 EP
1837803 Sep 2007 EP
1887492 Feb 2008 EP
1930863 Jun 2008 EP
1978484 Oct 2008 EP
2068269 Jun 2009 EP
2101258 Sep 2009 EP
2131278 Dec 2009 EP
2150437 Feb 2010 EP
2172873 Apr 2010 EP
2187316 May 2010 EP
2365441 Sep 2011 EP
2377094 Oct 2011 EP
2383679 Nov 2011 EP
2383713 Nov 2011 EP
2395472 Dec 2011 EP
2431917 Mar 2012 EP
2448251 May 2012 EP
2463843 Jun 2012 EP
2602741 Jun 2013 EP
2605185 Jun 2013 EP
2629242 Aug 2013 EP
2674323 Dec 2013 EP
2690548 Jan 2014 EP
2709020 Mar 2014 EP
2728462 May 2014 EP
2250218 Apr 2006 ES
2610401 Aug 1988 FR
2641237 Jul 1990 FR
2646383 Nov 1990 FR
2674201 Sep 1992 FR
2674354 Sep 1992 FR
2687000 Aug 1993 FR
2706211 Dec 1994 FR
2721872 Jan 1996 FR
914827 Jan 1963 GB
1000265 Aug 1965 GB
1008411 Oct 1965 GB
1054064 Jan 1967 GB
1098608 Jan 1968 GB
1098610 Jan 1968 GB
1106339 Mar 1968 GB
1178416 Jan 1970 GB
1197710 Jul 1970 GB
2210835 Jun 1989 GB
2233530 Jan 1991 GB
2255649 Nov 1992 GB
2261339 May 1993 GB
2262829 Jun 1993 GB
9310728 Jul 1993 GB
2267341 Dec 1993 GB
2271139 Apr 1994 GB
2275452 Aug 1994 GB
2280810 Feb 1995 GB
2289332 Nov 1995 GB
970014 Jul 1998 IE
S5539843 Mar 1980 JP
55156901 Dec 1980 JP
S5685110 Jul 1981 JP
S5871230 Apr 1983 JP
58110334 Jun 1983 JP
58122421 Jul 1983 JP
59114139 Jul 1984 JP
59127200 Jul 1984 JP
S6047737 Mar 1985 JP
6080953 May 1985 JP
S6078312 May 1985 JP
S60206746 Oct 1985 JP
60240545 Nov 1985 JP
S60219133 Nov 1985 JP
S60255537 Dec 1985 JP
S6141929 Feb 1986 JP
S6185238 Apr 1986 JP
S61105245 May 1986 JP
S61191937 Aug 1986 JP
6079889 Oct 1986 JP
61-260217 Nov 1986 JP
S61285151 Dec 1986 JP
S61285152 Dec 1986 JP
62001652 Jan 1987 JP
S6221010 Jan 1987 JP
S6226141 Feb 1987 JP
62080143 Apr 1987 JP
S6216073 Apr 1987 JP
6272245 May 1987 JP
S62115600 May 1987 JP
62131837 Jun 1987 JP
S62253543 Nov 1987 JP
S62253546 Nov 1987 JP
S62287164 Dec 1987 JP
63811446 Jan 1988 JP
63258236 Oct 1988 JP
63258237 Oct 1988 JP
63192788 Dec 1988 JP
6414700 Jan 1989 JP
01123587 May 1989 JP
H1168538 Jul 1989 JP
01242917 Sep 1989 JP
H01233129 Sep 1989 JP
H01265400 Oct 1989 JP
H01275237 Nov 1989 JP
H0268237 Mar 1990 JP
02190978 Jul 1990 JP
H236417 Aug 1990 JP
H02212232 Aug 1990 JP
H2117935 Sep 1990 JP
H0314739 Jan 1991 JP
H0374231 Mar 1991 JP
03099952 Apr 1991 JP
03266739 May 1991 JP
H03246413 Nov 1991 JP
03282707 Dec 1991 JP
03282709 Dec 1991 JP
03286399 Dec 1991 JP
H03273953 Dec 1991 JP
H042909 Jan 1992 JP
H0410200 Jan 1992 JP
04114587 Apr 1992 JP
04127280 Apr 1992 JP
04137014 May 1992 JP
H04137112 May 1992 JP
H04194827 Jul 1992 JP
04239400 Aug 1992 JP
04242391 Aug 1992 JP
H04238219 Aug 1992 JP
04250786 Sep 1992 JP
04291405 Oct 1992 JP
H04303047 Oct 1992 JP
H0516722 Jan 1993 JP
H0538977 Feb 1993 JP
0577657 Mar 1993 JP
05050883 Mar 1993 JP
H05137144 Jun 1993 JP
H05155287 Jun 1993 JP
05189694 Jul 1993 JP
H05172638 Jul 1993 JP
05-213113 Aug 1993 JP
H05201298 Aug 1993 JP
05244596 Sep 1993 JP
H05229383 Sep 1993 JP
05298594 Nov 1993 JP
05313736 Nov 1993 JP
H05297141 Nov 1993 JP
06048247 Feb 1994 JP
H0640286 Feb 1994 JP
06076200 Mar 1994 JP
H0672234 Mar 1994 JP
06107035 Apr 1994 JP
06113215 Apr 1994 JP
06117924 Apr 1994 JP
06150198 May 1994 JP
H06162398 Jun 1994 JP
H06174845 Jun 1994 JP
H06191344 Jul 1994 JP
06215291 Aug 1994 JP
6227318 Aug 1994 JP
06230115 Aug 1994 JP
H06229739 Aug 1994 JP
H06229759 Aug 1994 JP
06247246 Sep 1994 JP
6266825 Sep 1994 JP
06267304 Sep 1994 JP
06270733 Sep 1994 JP
06274626 Sep 1994 JP
06276524 Sep 1994 JP
H06262963 Sep 1994 JP
H06267303 Sep 1994 JP
H06275104 Sep 1994 JP
06295601 Oct 1994 JP
H06289138 Oct 1994 JP
H06293236 Oct 1994 JP
05093981 Nov 1994 JP
06310740 Nov 1994 JP
06321007 Nov 1994 JP
H06321010 Nov 1994 JP
H06324144 Nov 1994 JP
06337938 Dec 1994 JP
06341821 Dec 1994 JP
H06332370 Dec 1994 JP
07002021 Jan 1995 JP
07004170 Jan 1995 JP
07025286 Jan 1995 JP
H072022 Jan 1995 JP
732936 Feb 1995 JP
07032935 Feb 1995 JP
07047878 Feb 1995 JP
07052706 Feb 1995 JP
H0737180 Feb 1995 JP
H0740782 Feb 1995 JP
H0746460 Feb 1995 JP
07069125 Mar 1995 JP
07078240 Mar 1995 JP
H0764632 Mar 1995 JP
H0771916 Mar 1995 JP
H07057200 Mar 1995 JP
H07078258 Mar 1995 JP
07105496 Apr 1995 JP
H07101291 Apr 1995 JP
H07105487 Apr 1995 JP
H07108873 Apr 1995 JP
H07108874 Apr 1995 JP
07125571 May 1995 JP
07137574 May 1995 JP
H07125570 May 1995 JP
H730149 Jun 1995 JP
H07141588 Jun 1995 JP
H07144577 Jun 1995 JP
07186818 Jul 1995 JP
07192192 Jul 1995 JP
06000927 Aug 1995 JP
07242147 Sep 1995 JP
H07239714 Sep 1995 JP
H07249128 Sep 1995 JP
H07280563 Oct 1995 JP
H07315122 Dec 1995 JP
H0840138 Feb 1996 JP
H0840140 Feb 1996 JP
H0843082 Feb 1996 JP
H0844999 Feb 1996 JP
H0850697 Feb 1996 JP
H08138036 May 1996 JP
08166221 Jun 1996 JP
08235484 Sep 1996 JP
H08320997 Dec 1996 JP
02630604 Apr 1997 JP
H0991596 Apr 1997 JP
09330415 Dec 1997 JP
10038562 Feb 1998 JP
10063985 Mar 1998 JP
H1090188 Apr 1998 JP
10134183 May 1998 JP
10171966 Jun 1998 JP
H10222792 Aug 1998 JP
10261189 Sep 1998 JP
H1123305 Jan 1999 JP
11069211 Mar 1999 JP
11078737 Mar 1999 JP
H1178693 Mar 1999 JP
H1178717 Mar 1999 JP
11250228 Sep 1999 JP
H11259634 Sep 1999 JP
11345392 Dec 1999 JP
2000016352 Jan 2000 JP
2000085474 Mar 2000 JP
2000113374 Apr 2000 JP
2000127849 May 2000 JP
2000207575 Jul 2000 JP
2000215299 Aug 2000 JP
2000305136 Nov 2000 JP
2000311289 Nov 2000 JP
2001001832 Jan 2001 JP
2001092970 Apr 2001 JP
2001180401 Jul 2001 JP
2001188988 Jul 2001 JP
2001297397 Oct 2001 JP
2001351107 Dec 2001 JP
2002022439 Jan 2002 JP
2002046506 Feb 2002 JP
200274339 Mar 2002 JP
2002079895 Mar 2002 JP
2002084533 Mar 2002 JP
2002099908 Apr 2002 JP
2002109699 Apr 2002 JP
2002175534 Jun 2002 JP
2002211428 Jul 2002 JP
2002341432 Nov 2002 JP
2003030665 Jan 2003 JP
200376987 Mar 2003 JP
2003083742 Mar 2003 JP
3395289 Apr 2003 JP
2003123058 Apr 2003 JP
2003150938 May 2003 JP
2003168197 Jun 2003 JP
2003178397 Jun 2003 JP
2003217099 Jul 2003 JP
2003248895 Sep 2003 JP
2003259361 Sep 2003 JP
2003281700 Oct 2003 JP
20041658 Jan 2004 JP
2004032460 Jan 2004 JP
2004146904 May 2004 JP
2004336613 Nov 2004 JP
2004355139 Dec 2004 JP
2005182158 Jul 2005 JP
200088351000 Mar 1995 KR
1020010018981 Oct 2002 KR
1004124340000 Mar 2004 KR
336535 Jul 1971 SE
1988009023 Nov 1988 WO
1990004528 May 1990 WO
1993000647 Jan 1993 WO
1993004556 Mar 1993 WO
1993010550 May 1993 WO
1993011631 Jun 1993 WO
1993021596 Oct 1993 WO
1994019212 Sep 1994 WO
1995018979 Jul 1995 WO
1995023082 Aug 1995 WO
1996002817 Feb 1996 WO
1996015921 May 1996 WO
1996018275 Jun 1996 WO
199621581 Jul 1996 WO
1986005147 Sep 1996 WO
1996034365 Oct 1996 WO
1996038319 Dec 1996 WO
1997001246 Jan 1997 WO
1997029926 Aug 1997 WO
1997035743 Oct 1997 WO
1997048134 Dec 1997 WO
1998010246 Mar 1998 WO
1998014974 Apr 1998 WO
1999023828 May 1999 WO
1999043242 Sep 1999 WO
1999059100 Nov 1999 WO
2000015462 Mar 2000 WO
2001026332 Apr 2001 WO
2001039018 May 2001 WO
2001039120 May 2001 WO
2001064481 Sep 2001 WO
2001070538 Sep 2001 WO
2001077763 Oct 2001 WO
2001080068 Oct 2001 WO
2001080353 Oct 2001 WO
2002071487 Sep 2002 WO
2003065084 Aug 2003 WO
2003093857 Nov 2003 WO
2004004320 Jan 2004 WO
2004005073 Jan 2004 WO
2005098751 Oct 2005 WO
2005098782 Oct 2005 WO
2008134715 Nov 2008 WO
2013121357 Aug 2013 WO
Non-Patent Literature Citations (520)
Entry
Xie et al., “Active and Intelligent Sensing of Road Obstacles: Application to The European Eureka-PROMETHEUS Project”, Fourth International Conference on Computer Vision, IEEE, 1993, Abstract.
Xu et al., “3 DOF modular eye for smart car” School of Mechanical & Production Engineering Nanyang Technologies University, Intelligent Transportation Systems, 1999. Proc, Oct. 5-8, 1999, pp. 501-505.
Xu et al., “Cast shadow detection in video segmentation”, Pattern Recognition Letters, vol. 26, Nov. 4, 2003.
Yadid-Pecht et al., “Wide Intrascene Dynamic Range CMOS APS Using Dual Sampling,” IEEE Transactions on Electron Devices, vol. 44, No. 10, Oct. 1997.
Yamada et al., “Wide Dynamic Range Vision Sensor for Vehicles,” 1994 Vehicle Navigation & Information Systems Conference Proceedings, pp. 405-408, 1994.
Yazigi, “Technology: Promethean Plans for Next Generation of Cars”, The New York Times, Sep. 13, 1992.
Yee, “Portable Camera Mount”, Feb. 1986, Abstract.
Yeh et al., “Image-Based Dynamic Measurement for Vehicle Steering Control”, Proceedings of the Intelligent Vehicles 94 Symposium, 1994, Abstract.
Yerazunis et al., “An inexpensive, all solid-state video and data recorder for accident reconstruction” Mitsubishi Technical Report TR-99-29 (presented at the 1999 SAE International Congress and Exposition, Detroit, MI, Mar. 3, 1999.), Apr. 24, 1999.
Yoneyama et al., “Moving cast shadow elimination for robust vehicle extraction based on 2D joint vehicle/shadow models”, Proceeding of IEEE International Conference on Advanced Video and Signal Based Surveillance, 2003.
Yoneyama et al., “Robust vehicle and traffic information extraction for highway surveillance”, EURASIP Journal on Applied Signal Processing, pp. 2305-2321, 2005.
Young et al., “Cantata: Visual Programming Environment for the Khoros System, ACM SIGGRAPH Computer Graphics-Special focus: modular visualization environments (MVEs)”, vol. 29, issue 2, Mar. 16, 1995.
Young et al., “Improved Obstacle Detection by Sensor Fusion”, IEEE Colloquium on “Prometheus and Drive”, Oct. 15, 1992, Abstract.
Yu et al., “Vehicles Recognition By Video Camera” 1995.
Yu, “Road tracking, lane segmentation and obstacle recognition by mathematical morphology,” Intelligent Vehicles '92 Symposium, Proceedings of the IEEE 1992 Conference, p. 166-172.
Yuji et al., “Accidents and Near-Misses Analysis by Using Video Drive-Recorders in a Fleet Test”, Proceedings of the 17th International Technical Conference on the Enhanced Safety of Vehicles (ESV) Conference, Jun. 4-7, 2001 Amsterdam, TheNetherlands. National Highway Traffic Safety Administration, Washington, DC. HS 809 20, Jun. 2001.
Zheng et al., “An Adaptive System for Traffic Sign Recognition,” IEEE Proceedings of the Intelligent Vehicles '94 Symposium, pp. 165-170 (Oct. 1994).
Zidek, “Lane Position Tracking”, Aerospace and Electronics Conference, National Proceedings of the IEEE 1994, Abstract.
Zigman, “Light Filters to Improve Vision”, Optometry and Vision Science, vol. 69, No. 4, pp. 325-328, Apr. 15, 1992.
IPR Proceeding IPR2015-00950 filed Mar. 27, 2015 on U.S. Pat. No. 8,636,393.
Najm, “Comparison of alternative crash-avoidance sensor technologies”, Jan. 6, 1995, Abstract.
Nashman et al., “Real-time Visual Processing for Autonomous Driving,” in Proceedings of the IEEE Intelligent Vehicles, vol. 93, Jun. 1993, pp. 14-16.
Nathan, Digital Video Data Handling, NASA JPL Tech Report 32-877, Pasadena, CA, Jan. 5, 1966.
National Museum of Scotland archives regarding VVL's imputer photos.
Navon, “SoC IP Qualification & Emulation Environment”, Dec. 8-9, 2004.
Nguyen et al., “Obstacle detection using bi-spectrum CCD camera and image processing”, Proceedings of the Intelligent Vehicles '92 Symposium, Jun. 29-Jul. 1, 1992, p. 42-50.
Nixon et al., “128.times.128 CMOS Photodiode-Type Active Pixel Sensor With On-Chip Timing, Control and Signal Chain Electronics” 1995.
Nixon et al., “256.times.256 CMOS Active Pixel Sensor Camera-on-a-Chip,” IEEE Journal of Solid-State Circuits, vol. 31, No. 12, Paper FA 11.1, 1996.
No Hands Across America Journal, web page at http://www.cs.cmu.edu/.about.tjochem/nhaa/Journal.html.
No Hands Across American Official Press Release web page at http://www.cs.cmu.edu/.about.tjochem/nhaa/official_press_release.html.
Nolan, “Survey of Electronic Displays”, SAE Paper No. 750364, published Feb. 1, 1975.
Oldenburg, “Comments on the Autronic Eye”, 2002.
Ortega et al., “An Interactive, Reconfigurable Display System for Automotive Instrumentation”, SAE Paper No. 860173, published Mar. 1, 1986.
Otsuka, “Flat Dot Matrix Display Module for Vehicle Instrumentation”, SAE Paper No. 871288, published Nov. 8, 1987.
Pacaud et al., “Ground Speed Sensing,” Lucas International Symposium, Paris, France 1989.
Paetzold, “Interpretation of visually sensed urban environment for a self-driving car” Ruhr-Universitat Bochum, Dissertation, Sep. 2000.
Page et al., “Advanced technologies for collision avoidance,” Eureka on Campus (Summer 1992).
Paradiso et al., “Wide-Range Precision Alignment for the Gem Muon System,” Oct. 1993.
Paradiso, “Application of miniature cameras in video straightness monitor systems”, Draper Laboratory, Jun. 1994.
Paradiso, “Electronics for precision alignment of the Gem Muon System”, Proceedings of the 1994 LeCroy Electronics For Future Colliders Conference, May 1994.
Parent, “Automatic Driving for Small Public Urban Vehicles,” Intelligent Vehicles Symposium, Tokyo, Jul. 14-16, 1993.
Parker (ed.), McGraw-Hill Dictionary of Scientific and Technical Terms Fifth Edition (1993).
Parnell, “Reconfigurable Vehicle”. No. 2002-01-0144. SAE Technical Paper, 2002. Xilinx WPI 53, Nov. 19, 2001.
Pelco Fixed Focal Length Lenses Product Specification, Apr. 1996.
Peng et al., “Experimental Automatic Lateral Control System for an Automobile,” California Partners for Advanced Transit and Highways (PATH), Jan. 1, 1992.
Peng, “Vehicle Lateral Control for Highway Automation,” Ph.D. Thesis—University of California Berkeley, 1992.
Philips Components, PCA82C200, Stand-alone CAN-controller, Jan. 22, 1991.
Philomin et al., “Pedestrain Tracking from a Moving Vehicle”, Proceedings of the IEEE, Intelligent Vehicles Symposium, IV, 2000.
Photographs evidencing a Watec WAT-660D camera and photographs evidencing the mounting bracket used for attaching the WatecWAT-660D, the model of camera which was used as the forward facing camera on Navlab 6.
Piccioli et al., “Robust road sign detection and recognition from image sequences”, 1994.
Pollard, “Evaluation of the Vehicle Radar Safety Systems' Rashid Radar Safety Brake Collision Warning System”, U.S. Dept. of Transportation, National Highway Traffic Safety Administration, Feb. 29, 1988.
Pomerleau, “Alvinn: An Autonomous Land Vehicle in a Neural Network”, Technical Report AIP-77 Department of Psychology, Carnegie Mellon University, Mar. 13, 1990.
Pomerleau, “RALPH: Rapidly Adapting Lateral Position Handler”, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, pp. 506-511., 1995.
Pomerleau et al., “Run-Off-Road Collision Avoidance Countermeasures Using IVHS Countermeasures TASK 3-vol. 1”, U.S. Dept. of Transportation, National Highway Traffic Safety Administration, Final Report, Aug. 23, 1995.
Pomerleau et al., “Rapidly Adapting Machine Vision for Automated Vehicle Steering”, pp. 19-27, Apr. 30, 1996.
Pomerleau, “Run-Off-Road Collision Avoidance Using Ivhs Countermeasures”, Robotics Institute, Task 6 Interim Report, Sep. 10, 1996.
Porter et al., “Compositing Digital Images,” Computer Graphics (Proc. Siggraph), vol. 18, No. 3, pp. 253-259, Jul. 1984.
Prasad, “Performance of Selected Event Data Recorders”, National Highway Traffic Safety Administration. Washington, DC, Sep. 2001.
Prati et al., “Detecting moving shadows: algorithms and evaluation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, Jul. 1, 2003.
Pratt, “Digital Image Processing, Passage-ED.3”, John Wiley & Sons, US, Jan. 1, 2001, pp. 657-659, XP002529771.
Priese et al., “New Results on Traffic Sign Recognition”, IEEE Proceedings of the Intelligent Vehicles 1994 Symposium.
Priese et al., “Traffic Sign Recognition Based on Color Image”, Universitat Koblenz-Landau, 1993, pp. 95-100.
Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation, 1992. Power Electronics and Motion Control, Date of Conference Nov. 9-13, 1992.
Proceedings of the Intelligent Vehicles Symposium, 1992-present.
Proceedings of the Intelligent Vehicles Symposium, Tokyo, Jul. 14-16, 1993.
Pynn et al., “Automatic identification of cracks in road surfaces” 7th International Conference on Image Processing and Its Application, CP465, Jan. 1999, pp. 671-675, Abstract.
Raboisson et al., “Obstacle Detection in Highway Environment by Colour CCD Camera and Image Processing Prototype Installed in a Vehicle”, Proceedings of the IEEE Intelligent Symposium 1994.
Radatz, “The IEEE Standard Dictionary of Electrical and Electronics Terms,” Sixth Edition, Standards Coordinating Committee 10, Terms and Definitions, 1996.
Raglan Tribe Video-1994; 1994; Raglan Tribe; “Robot Car Raglan Tribe” http://www.youtube.com/watch?v=AlLZhcnpXYI.
Ramesh et al., “Real-Time Video Surveillance and Monitoring for Automotive Applications”, SAE Technical Paper 2000-01-0347, Mar. 6, 2000, Abstract.
“Generation of Vision Technology,” published by VLSI Vision Limited, pub. date unknown.
“All-seeing screens for tomorrow's cars”, Southend Evening Echo, Oct. 4, 1991.
“CCD vs. CMOS,” Teledyne DALSA Inc., accessed at https://www.teledynedalsa.com/imaging/knowledgecenter/appnotes/ccd-vs-cmos/.
“Final Report of the Working Group on Advanced Vehicle Control Systems (AVCS)” Mobility 2000, Mar. 1990.
“How an Image Intensifier Tube Works,” PHOTONIS Group, accessed at http://www.nightvision.nl/faq-reader/how-does-an-image-intensifier-work.html.
“How does an image intensifier work?” accessed at; http://www.nightvision.nl/faq-reader/how-does-an-imageintensifier-work.html.
“Image intensified CCD high speed cameras,” Stanford Computer Optics, Inc., accessed at http://www.stanfordcomputeroptics.com/technology/iccd-systemoverview.html.
“Magic Eye on safety”, Western Daily Press, Oct. 10, 1991.
“On-screen technology aims at safer driving”, Kent Evening Post Oct. 4, 1991.
“The Electromagnetic and Visible Spectra,” Light Waves and Color—Lesson 2, accessed at http://www.physicsclassroom.com/class/light/Lesson-2/The-Electromagnetic-and-Visible-Spectra.
“Versatile LEDs Drive Machine vision in Automated Manufacture,” http://www.digikey.ca/en/articles/techzone/2012/jan/versatileleds-drive-machine-vision-in-automated-manufacture.
“Vision Systems 101: An Introduction,” Teledyne DALSA Inc., accessed at; https://www.teledynedalsa.com/imaging/products/visionsystems/vs101/.
3M, “Automotive Rear View Mirror Button Repair System”, Automotive Engineered Systems Division, Jun. 1996.
Abshire et al., “Confession Session: Learning from Others Mistakes,” 2011 IEEE International Symposium on Circuits and Systems (ISCAS), 2011.
Achler et al., “Vehicle Wheel Detector using 2D Filter Banks,” IEEE Intelligent Vehicles Symposium of Jun. 2004.
Ackland et al., “Camera on a chip”, Digest of Technical Papers of the 42nd Solid-State Circuits Conference (ISSCC), Paper TA 1.2, 1996.
Alley, “Algorithms for automatic guided vehicle navigation and guidance based on Linear Image Array sensor data”, Masters or PhD. Thesis, Dec. 31, 1988.
Altan, “LaneTrak: a vision-based automatic vehicle steering system”, Applications in Optical Science and Engineering. International Society for Optics and Photonics, 1993, Abstract.
Amidi, “Integrated Mobile Robot Control”, M.S. Thesis, Carnegie Mellon University, May 1990.
An et al., “Aspects of Neural Networks in Intelligent Collision Avoidance Systems for Prometheus”, JFIT 93, pp. 129-135, Mar. 1993.
Arain et al., “Action planning for the collision avoidance system using neural networks”, Intelligent Vehicle Symposium, Tokyo, Japan, Jul. 1993.
Arain et al., “Application of Neural Networks for Traffic Scenario Identification”, 4th Prometheus Workshop, University of Compiegne, Paris, France, pp. 102-111, Sep. 1990.
Ashley, “Smart Cars and Automated Highways”, Mechanical Engineering 120.5 (1998): 58, Abstract.
Aufrere et al., “A model-driven approach for real-time road recognition”, Machine Vision and Applications 13, 2001, pp. 95-107.
Auty et al., “Image acquisition system for traffic monitoring applications” IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology. International Society for Optics and Photonics, Mar. 14, 1995.
Aw et al., “A 128.times.128 Pixel Standard-CMOS Image Sensor with Electronic Shutter,” IEEE Journal of Solid-State Circuits, vol. 31, No. 12, Dec. 1996.
Ballard et al., “Computer Vision”, 1982, p. 88-89, sect. 3.4.1.
Barron et al., “The role of electronic controls for future automotive mechatronic systems”, IEEE/ASME Transactions on mechatronics 1.1, Mar. 1996, pp. 80-88.
Batavia et al., “Overtaking vehicle detection using implicit optical flow”, Proceedings of the IEEE Transportation Systems Conference, Nov. 1997, pp. 729-734.
Batavia, “Driver-Adaptive Lane Departure Warning Systems”, The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania, 15213, Sep. 20, 1999.
Bederson, “A miniature Space-Variant Active Vision System: Cortex-I”, Masters or Ph.D. Thesis, Jun. 10, 1992.
Begault, “Head-Up Auditory Displays for Traffic Collision Avoidance System Advisories: A Preliminary Investigation”, Human Factors, 35(4), Dec. 1993, pp. 707-717.
Behringer et al., “Simultaneous Estimation of Pitch Angle and Lane Width from the Video Image of a Marked Road,” pp. 966-973, Sep. 12-16, 1994.
Behringer, “Road recognition from multifocal vision”, Intelligent Vehicles' 94 Symposium, Proceedings of the. IEEE, 1994, Abstract.
Belt et al., “See-Through Turret Visualization Program”, No. NATICK/TR-02/005. Honeywell Inc., Minn, MN Sensors and Guidance Products, 2002.
Bensrhair et al., “A cooperative approach to vision-based vehicle detection” Intelligent Transportation Systems, IEEE, 2001.
Bertozzi et al., “Obstacle and lane detection on ARGO”, IEEE Transactions on Image Processing, 7(1):62-81, Jan. 1998, pp. 62-81.
Bertozzi et al., “Performance analysis of a low-cost solution to vision-based obstacle detection”, Intelligent Transportation Systems, 1999. Proc., Oct. 5-8, 1999, pp. 350-355.
Bertozzi et al., “Vision-based intelligent vehicles: State of the art and perspectives” Robotics and Autonomous Systems, 32, 2000 pp. 1-16.
Bertozzi et al., “GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection”, IEEE transactions on image processing 7.1 (1998): 62-81.
Betke et al., “Real-time multiple vehicle detection and tracking from a moving vehicle”, Machine Vision and Applications, 2000.
Beucher et al., “Road Segmentation and Obstacle Detection by a Fast Watershed Transformation”, Intelligent Vehicles' 94 Symposium, Proceedings of the. IEEE, 1994.
Blomberg et al., “NightRider Thermal Imaging Camera and HUD Development Program for Collision Avoidance Applications”, Raytheon Commercial Infrared and ELCAN-Texas Optical Technologies, 2000, Abstract.
Borenstein et al., “Where am I? Sensors and Method for Mobile Robot Positioning”, University of Michigan, Apr. 1996, pp. 2, 125-128.
Bosch, “CAN Specification”, Version 2.0, Sep. 1991.
Bow, “Pattern Recognition and Image Preprocessing (Signal Processing and Communications)”, CRC Press, Jan. 15, 2002, pp. 557-559.
Brackstone et al., “Dynamic Behavioral Data Collection Using an Instrumented Vehicle”, Transportation Research Record: Journal of the Transportation Research Board, vol. 1689, Paper 99-2535, 1999.
Brandt, “A CRT Display System for a Concept Vehicle”, SAE Paper No. 890283, published Feb. 1, 1989.
Brauckmann et al., “Towards all around automatic visual obstacle sensing for cars”, Intelligent Vehicles' 94 Symposium, Proceedings of the. IEEE, 1994.
Britell et al., “Collision avoidance through improved communication between tractor and trailer” Proceedings: International Technical Conference on the Enhanced Safety of Vehicles. vol. 1998. National Highway Traffic Safety Administration, 1998.
Broggi et al., “ARGO and the MilleMiglia in Automatico Tour”, IEEE Intelligent Systems, Jan.-Feb. 1999, pp. 55-64.
Broggi et al., “Architectural Issues on Vision-based automatic vehicle guidance: The experience of the ARGO Project”, Academic Press, 2000.
Broggi et al., “Automatic Vehicle Guidance: The Experience of the ARGO Vehicle”, World Scientific Publishing Co., 1999.
Broggi et al., “Multi-Resolution Vehicle Detection using Artificial Vision,” IEEE Intelligent Vehicles Symposium of Jun. 14-17, 2004.
Broggi et al., “Vision-based Road Detection in Automotive Systems: A real-time expectation-driven approach”, Journal of Artificial Intelligence Research, 1995.
Broggi, “Robust Real-time Lane and Road Detection in Critical Shadow Conditions”, International Symposium on Computer Vision, IEEE, 1995, pp. 21-23.
Brown, “A Survey of Image Registration Techniques”, vol. 24, ACM Computing Surveys, pp. 325-376, Dec. 4, 1992.
Brown, “Scene Segmentation and Definition for Autonomous Robotic Navigation Using Structured Light Processing”, Doctoral Dissertation, University of Delaware, Army Science Conference Proceedings, Jun. 22-25, 1992, vol. 1, Dec. 31, 1988, pp. 189-203,Abstract.
Brunelli et al., “Template Matching: Matched Spatial Filters and Beyond,” Pattern Recognition, vol. 30, No. 5, 1997.
Bucher et al., “Image processing and behavior planning for intelligent vehicles”, IEEE Transactions on Industrial electronics 50.1 (2003): 62-75.
Burger et al., “Estimating 3-D Egomotion from Perspective Image Sequences”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, No. 11, pp. 1040-1058, Nov. 1990.
Burt et al., “A Multiresolution Spline with Application to Image Mosaics”, ACM Transactions on Graphics, vol. 2. No. 4, pp. 217-236, Oct. 1983.
Cardiles, “Implementation de la commande d'un vehicule electrique autonome grace a un capteur de distance et d'angle base sur une camera lineaire” IUP de Mathematiques Appliquees et Industrielles, May 8, 1998.
Carley et al., “Synthesis Tools for Mixed-Signal ICs: Progress on Frontend and Backend Strategies,” Proceedings of the 33rd Design Automation Conference, 1996.
Cartledge, “Jaguar gives cat more lives”, Birmingham Post, Oct. 10, 1991.
Cassiano et al., “Review of filtering methods in mobile vision from ground vehicles in low light conditions”, Proc. SPIE 1613, Mobile Robots VI, 322, Feb. 14, 1992.
Chapuis et al., “Road Detection and Vehicles Tracking by Vision for an On-Board ACC System in the VELAC Vehicle”, 2000.
Charkari et al., “A new approach for real time moving vehicle detection”, Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, JP, Jul. 26-30, 1993.
Chern et al., “The lane recognition and vehicle detection at night for a camera-assisted car on highway”, Robotics and Automation, 2003. Proceedings. ICRA'03. IEEE International Conference on. vol. 2. IEEE, 2003, Abstract.
Chien et al., “Efficient moving object segmentation algorithm using background registration technique”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 12., No. 7, Jul. 2002.
Clune et al., “Implementation and performance of a complex vision system on a systolic array machine”, Carnegie Mellon University, Jun. 15, 1987.
CMOS sensor page of University of Edinburgh, 2015.
Coghill, “Digital Imaging Technology 101”, Albert Theuwissen, Dalsa Corp, 2003.
Coifman et al., “A real-time computer vision system for vehicle tracking and traffic surveillance”, Transportation Research Part C 6, pp. 271-288, 1998.
Corsi, “Reconfigurable Displays Used as Primary Automotive Instrumentation”, SAE Paper No. 890282, published Feb. 1, 1989.
Crisman et al., “Color Vision for Road Following”, Robotics Institute at Carnegie Mellon University, Proceedings of SPIE Conference on Mobile Robots Nov. 11, 1988, pp. 1-10, Oct. 12, 1988.
Crisman et al., “UNSCARF, A Color Vision System for the Detection of Unstructured Roads” IEEE Paper 1991.
Crisman et al., “Vision and Navigation—The Carnegie Mellon Navlab” Carnegie Mellon University, edited by Charles E. Thorpe, 1990.
Crisman, “SCARF: Color vision system that tracks roads and intersections”, IEEE, 1993.
Crossland, “Beyond Enforcement: In-Car Video Keeps Officers On The Streets”, Traffic technology international. Annual review, 1998, Abstract.
Cucchiara et al., “Vehicle Detection under Day and Night Illumination”, Proceedings of 3rd International ICSC Symposium on Intelligent Industrial Automation (IIA 99), 1999.
Cucchiara et al., “Detecting moving objects, ghosts, and shadows in video streams”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 10, 2003.
Cucchiara et al., “Improving Shadow Suppression in Moving Object Detection with HSV Color Information”, Proceeding of IEEE International Conference on Intelligent Transportation Systems, 2001.
Curry et al., “The Lancashire telemedicine ambulance”, Journal of Telemedicine and telecare 4.4 (1998): 231-238, Dec. 1, 1998, Abstract.
Dagan et al., “Forward collision warning with a single camera”, IEEE Intelligent Vehicles Symposium, 2004.
Dally et al., “Digital Systems Engineering”, The University of Cambridge, United Kingdom, 1998.
Davis et al., “Road Boundary Detection for Autonomous Vehicle Navigation”, Optical Engineering, vol. 25, No. 3, Mar. 1986, pp. 409-414.
Davis, “Vision-Based Navigation for Autonomous Ground Vehicles” Defense Advanced Research Projects Agency, Jul. 18, 1988.
De la Escalera et al., “Neural traffic sign recognition for autonomous vehicles” IEEE, 1994.
De la Escalera et al., “Traffic sign recognition and analysis for intelligent vehicles”, Division of Systems Engineering and Automation, Madrid, Spain, 2003.
Decision-Motions-Bd. R. 125(a), issued Aug. 29, 2006 in connection with Interference No. 105,325, which involved U.S. Appl. No. 09/441,341, filed Nov. 16, 1999 by Schofield et al. and U.S. Pat. No. 5,837,994, issued to Stam et al.
DeFauw, “A System for Small Target Detection, Tracking, and Classification, Intelligent Transportation System”, Intelligent Transportation Systems, 1999. Proceedings. 1999 IEEE/IEEJ/JSAI International Conference on. IEEE, 1999, Abstract.
Denes et al., “Assessment of driver vision enhancement technologies,” Proceedings of SPIE: Collusion Avoidance and Automated Traffic Management Sensors, vol. 2592, Oct. 1995.
DeNuto et al., “LIN Bus and its Potential for use in Distributed Multiplex Applications”, SAE Technical Paper 2001-01-0072, Mar. 5-8, 2001.
Denyer et al., “On-Chip CMOS Sensors for VLSI Imaging Systems”, Dept. of Elect. Engineering, University of Edinburgh, pp. 4b1.1-4b1.5, 1991.
Derutin et al., “Real-time collision avoidance at road-crossings on board the Prometheus-ProLab 2 vehicle”, Intelligent Vehicles' 94 Symposium, Proceedings of the. IEEE, 1994, Abstract.
Devlin, “The Eyellipse and Considerations in the Driver's Forward Field of View,” Society of Automotive Engineers, Inc., Detroit, MI, Jan. 8-12, 1968.
Dickinson et al., “CMOS Digital Camera with Parallel Analog-to-Digital Conversion Architecture”, Apr. 1995.
Dickmanns et al., “A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision,” University of Bundeswehr Munchen, 1986.
Dickmanns et al., “Recursive 3-D road and relative ego-state recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, No. 2, Feb. 1992.
Dickmanns et al.; “An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, No. 6, Nov./Dec. 1990.
Dickmanns, “Vehicles Capable of Dynamic Vision”, Aug. 23, 1997.
Dickmanns, “4-D dynamic vision for intelligent motion control”, Universitat der Bundeswehr Munich, 1991.
Dickmanns et al., “The seeing passenger car ‘VaMoRs-P’”, Oct. 24, 1994.
Dingus et al., “TRAVTEK Evaluation Task C3-Camera Car Study” Final Report/ 9-92 to 5-94. Jun. 1995.
Donnelly Panoramic Vision.TM. on Renault Talisman Concept Car at Frankfort Motor Show, PR Newswire, Frankfort, Germany Sep. 10, 2001.
Doudoumopoulos et al., “CMOS Active Pixel Sensor Technology for High Performance Machine Vision Applications,” SME Applied Machine Vision '96—Emerging Smart Vision Sensors, Jun. 1996.
Draves, “A Video Graphics Controller for Reconfigurable Automotive Displays”, No. 970193. SAE Technical Paper Feb. 24, 1997, Abstract.
Dubrovin et al., “Application of real-time lighting simulation for intelligent front-lighting studies”, 2000 pp. 333-343.
Dubuisson-Jolly, “Vehicle segmentation and classification using deformable templates”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Mar. 1996.
Easton, “Jaguar Adapts Pilot's Night Sights for safer driving”, The Times, Sep. 28, 1991.
Eaton, “Video Incident Capture System”, Technical Memorandum, OIC General Enforcement Branch, Sep. 1991.
Eaton, “An RS-170 Camera for the Military Environment”, Proc. SPIE 0979, Airborne Reconnaissance XII, Feb. 23, 1989, Abstract.
Eid et al., “A 256.times.256 CMOS Active Pixel Image Sensor,” Proceedings of SPIE: Charge-Coupled Devices and Solid State Optical Sensors V, vol. 2415, 1995.
Elwell et al., “Near Infrared Spectroscopy,” accessed at http://www.ucl.ac.uk/medphys/research/borl/intro/nirs, Jan. 6, 1999.
Ernst et al., “Camera calibration for lane and obstacle detection” Intelligent Transportation Systems, 1999 pp. 356-361.
Fancher et al. “Intelligent Cruise Control Field Operational Test (Final Report)”, Final Report, vol. I: Technical Report, May 1998.
Fancher et al., “Fostering Development, Evaluation, and Deployment of Forward Crash Avoidance Systems (FOCAS)” Annual Research Report DOT HS 808 437, May 1995.
Ferryman et al., “Visual Surveillance for Moving Vehicles”, SECURE Project, 2000.
Fletcher, “CMOS light-sensor process makes possible low-cost smart machine-vision systems” Penton Media, Inc. et al., 1993.
Forsyth, “A System for Finding Changes in Colour”, Oxford University, Jul. 23, 1987.
Fossum, “Active Pixel Sensors: Are CCD's dinosaurs?” Proceedings of SPIE, Charge-Coupled Devices and Solid-State Optical Sensors III, vol. 1900, 1993.
Fossum, “CMOS Active Pixel Sensor (APS) Technology for Multimedia Image Capture,” 1997 Multimedia Technology & Applications Conference (MTAC97), 1997.
Fossum, “Low power camera-on-a-chip using CMOS active pixel sensor technology”, 1995 Symposium on Low Power Electronics, San Jose, CA, Oct. 9-10, 1995.
Fowler et al., “A CMOS Area Image Sensor With Pixel-Level A/D Conversion,” Digest of Technical Papers of the 41st Solid-State Circuits Conference (ISSCC), 2001.
Franke et al., “Autonomous driving approaches downtown”, IEEE Intelligent Systems, vol. 13, Nr. 6, 1999.
French et al., “A comparison of IVHS progress in the United States, Europe, and Japan”, IVHA America, Dec. 31, 1993.
Fujimori, “CMOS Passive Pixel Imager Design Techniques”, Massachusetts Institute of Technology, Ph.D. Dissertation for Electrical Engineering and Computer Science, Feb. 2002.
Fung et al., “Effective moving cast shadow detection for monocular color image sequences”, The 11th International Conference on Image Analysis and Processing Proceedings, Palermo, Italy, Sep. 26-28, 2001,p. 404-409.
Gat et al., “A Monocular Vision Advance Warning System for the Automotive Aftemarket”, Aftermarket SAE World Congress & Exhibition, No. 2005-01-1470. SAE Technical Paper, Jan. 1, 2005.
Gavrila et al., “Real-Time Vision for Intelligent Vehicles” IEEE Instrumentation & Measurement Magazine, Jun. 2001, pp. 22-27.
Gavrila, et al., “Real-time object detection for “smart” vehicles”, 1999.
Geary et al., “Passive Optical Lane Position Monitor” Idea Project Final Report Contract ITS-24, Jan. 15, 1996.
Gehrig, “Design, simulation, and implementation of a vision-based vehicle-following system” Doctoral Dissertation, Jul. 31, 2000.
GEM Muon Review Meeting--SSCL Abstract; GEM TN-03-433, Jun. 30, 1993.
Goesch et al., “The First Head Up Display Introduced by General Motors”, SAE Paper No. 890288, published Feb. 1, 1989.
Goldbeck et al., “Lane detection and tracking by video sensors” Intelligent Transportation Systems, 1999. Proc., Oct. 5-8, 1999.
Graefe et al., “Dynamic Vision for Precise Depth Measurement and Robot Control”, Computer Vision for Industry, Jun. 1993.
Graefe, “Vision for Intelligent Road Vehicles”, Universitat de Bundeswehr Muchen, 1993, pp. 135-140.
Greene et al., “Creating Raster Omnimax Images from Multiple Perspective Views Using the Elliptical Weighted Average Filter”, IEEE Computer Graphics and Applications, vol. 6, No. 6, pp. 21-27, Jun. 1986.
Gruss et al., “Integrated sensor and range-finding analog signal processor”, IEEE Journal of Solid-State Circuits, vol. 26, No. 3, Mar. 1991.
Gumkowski et al., “Reconfigurable Automotive Display System”, SAE Paper No. 930456 to Gumkowski, published Mar. 1, 1993.
Hall, “Why I Dislike auto-Dimming Rearview Mirrors,” accessed at http://blog.consumerguide.com/why-i-dislike-autodimming-rearview-mirrors/-, Dec. 21, 2012.
Hamit, “360-Degree Interactivity: New Video and Still Cameras Provide a Global Roaming Viewpoint”, Advanced Imaging, Mar. 1997, p. 50.
Haritaoglu et al., “W4: Real-Time Surveillance of People and Their Activities”, IEEE Transactions Patter Analysis and Machine Intelligence, vol. 22, No. 8, Aug. 2000.
Hebert et al., “3-D Vision Techniques for Autonomous Vehicles”, Defense Advanced Research Projects Agency, Carnegie Mellon University, Feb. 1, 1988.
Hebert et al., “Local Perception for Mobile Robot Navigation in Natural Terrain: Two Approaches”, The Robotics Institute, Carnegie Mellon University, Abstract; Workshop on Computer Vision for Space Applications, Antibes, Sep. 22, 24, 1993, pp. 24-31.
Hebert, “Intelligent unmanned ground vehicles: autonomous navigation research”, Carnegie Mellon (Kluwer Academic Publishers), Boston, 1997, Excerpt.
Herbert et al., “3-D Vision Techniques for Autonomous Vehicles”, Technical Report, Carnegie Mellon University, Aug. 1988.
Hess et al., “A Control Theoretic Model of Driver Steering Behavior,” IEEE Control Systems Magazine, vol. 10, No. 5, Aug. 1990, pp. 3-8.
Shashua et al., “Join Tensors: on 3D-to-3D Alignment of Dynamic Sets”, International Conference on Pattern Recognition (ICPR), Jan. 2000, Barcelona, Spain, pp. 99-102.
Shashua et al., “Kernel Feature Selection with Side Data using a Spectral Approach”, Proc. of the European Conference on Computer Vision (ECCV), May 2004, Prague, Czech Republic.
Shashua et al., “Kernel Principal Angles for Classification Machines with Applications to Image Sequence Interpretation”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2003, Madison.
Shashua et al., “Latent Model Clustering and Applications to Visual Recognition”, International Conference on Computer Vision (ICCV), Rio, Brazil, Oct. 2007.
Shashua et al., “Learning over Sets using Kernel Principal Angles”, Journal of Machine Learning Research, 2003, pp. 913-931.
Shashua et al., “Linear Image Coding for Regression and Classification using the Tensor-rank Principle”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Dec. 2001, Hawaii, pp. 42-49, Abstract.
Shashua et al., “Manifold Pursuit: A New Approach to Appearance Based Recognition”, International Conference on Pattern Recognition (ICPR), Aug. 2002, Quebec, Canada.
Shashua et al., “Multi-frame Infinitesimal Motion Model for the Reconstruction of (Dynamic) Scenes with Multiple Linearly Moving Objects”, International Conference on Computer Vision (ICCV), Jul. 2001,, Vancouver, Canada, pp. 592-599.
Shashua et al., “Multiple View Geometry of Non-planar Algebraic Curves”, International Conference on Computer Vision (ICCV), Vancouver, Canada, Jul. 2001, pp. 181-189.
Shashua et al., “Structural Saliency: the Detection of Globally Salient Structures Using a Locally Connected Network”, International Conference on Computer Vision (ICCV), Tarpon Springs, Florida, pp. 321-327, Jul. 1988.
Shashua et al., “The Study of 3D-from-2D using Elimination”, International Conference on Computer Vision (ICCV), Jun. 1995, Boston, MA, pp. 473-479.
Shashua et al., “Multiple-view Geometry and Photometry, In Recent Progress in Computer Vision”, Springer-Verlag, LNCS series, Invited papers of ACCV'95, Singapore Dec. 1995, 225-240, Abstract.
Shashua et al., “Multiple-view geometry of general algebraic curves”, International Journal of Computer Vision (IJCV), 2004.
Shashua et al., “Multi-way Clustering Using Super-symmetric Non-negative Tensor Factorization”, Proc. of the European Conference on Computer Vision (ECCV), Graz, Austria, May 2006.
Shashua et al., “Nonnegative Sparse PCA”, Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec. 2006.
Shashua et al., “Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision”, International Conference on Machine Learning (ICML), Bonn, Germany, Aug. 2005.
Shashua et al., “Norm-Product Belief Propagation: Primal-Dual Message-Passing for Approximate Inference”, IEEE Trans. on Information Theory, Jun. 28, 2010.
Shashua et al., “Novel View Synthesis in Tensor Space”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 1997, pp. 1034-1040.
Shashua et al., “Off-road Path Following using Region Classification and Geometric Projection Constraints”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2006, NY.
Shashua et al., “Omni-Rig Sensors: What Can be Done With a Non-Rigid Vision Platform?”, Workshop on Applications of Computer Vision (W ACV), pp. 174-179, Princeton, Oct. 1998, pp. 174-179.
Shashua et al., “Omni-rig: Linear Self-recalibration of a Rig with Varying Internal and External Parameters,” International Conference on Computer Vision (ICCV), Jul. 2001, Vancouver, Canada, pp. 135-141.
Shashua et al., “On calibration and reconstruction from planar curves”, European Conference on Computer Vision (ECCV), pp. 256-270, Jun. 2000, Dublin, Ireland, pp. 256-270.
Shashua et al., “On Geometric and Algebraic Aspects of 3D Affine and Projective Structures from Perspective 2D Views”, In Applications of Invariance in Computer Vision, Springer-Verlag LNCS No. 825, 1994, 127-143.
Shashua et al., “On Photometric Issues in 3D Visual Recognition from a Single 2D Image”, International Journal of Computer Vision (IJCV), 21(1/2), 1997 pp. 99-122.
Shashua et al., “On Projection Matrices P.sup.k -P.sup.2, k=3, 6, and their Applications in Computer Vision”, International Journal of Computer Vision (IJCV), 2002, pp. 53-67.
Shashua et al., “On the Reprojection of 3D and 2D Scenes Without Explicit Model Selection”, European Conference on Computer Vision (ECCV), Jun. 2000, Dublin, Ireland, pp. 468-482.
Shashua et al., “On the Structure and Properties of the Quadrifocal Tensor”, European Conference on Computer Vision (ECCV), Jun. 2000, Dublin, Ireland, pp. 354-368.
Shashua et al., “On the Synthesis of Dynamic Scenes from Reference Views”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2000, pp. 133-139.
Shashua et al., “pLSA for Sparse Arrays With Tsallis Pseudo-Additive, Divergence: Noise Robustness and Algorithm”, International Conference on Computer Vision (ICCV), Rio, Brazil, Oct. 2007.
Shashua et al., “Principal Component Analysis Over Continuous Subspaces and Intersection of Half-spaces”, European Conference on Computer Vision (ECCV), May 2002, Copenhagen, Denmark, pp. 133-147.
Shashua et al., “Probabilistic Graph and Hypergraph Matching”, Conf. on Computer Vision and Pattern Recognition (CVPR), Jun. 2008, Anchorage, Alaska.
Shashua et al., “Projective Structure from Uncalibrated Images: Structure from Motion and Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence (P AMI), (vol. 16(8), 1994, pp. 778-790.
Shashua et al., “Q-warping: Direct Computation of Quadratic Reference Surfaces”, IEEE Transactions on Pattern Analysis and Machine Intelligence (P AMI), vol. 23(8), 2001, pp. 920-925.
Shashua et al., “Relative Affine Structure: Canonical Model for 3D from 2D Geometry and Applications,” IEEE, Transactions on Pattern Analysis and Machine Intelligence (P AMI) vol. 18(9), pp. 873-883, Jun. 1994.
Shashua et al., “Relative Affine Structure: Theory and Application for 3D Reconstruction From Perspective Views,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, pp. 483-489, Jun. 1994.
Shashua et al., “Revisiting Single-view Shape Tensors: Theory and Applications,” EP Conference on Computer Vision (ECCV), Copenhagen, DK, pp. 256-270, May 2002.
Shashua et al., “Robust Recovery of Camera Rotation from Three Frames,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, pp. 796-802, Jun. 1996.
Shashua et al., “Shape Tensors for Efficient and Learnable Indexing”, Proceedings of the workshop on Scene Representations, Jun. 1995, Cambridge, MA, pp. 58-65.
Shashua et al., “ShareBoost: Efficient Multiclass Learning with Feature Sharing, Neural Information and Processing Systems (NIPS)”, Dec. 2011.
Shashua et al., “Sparse Image Coding using a 3D Non-negative Tensor Factorization”, International Conference on Computer Vision (ICCV), Beijing, China, Oct. 2005.
Shashua et al., “Taxonomy of Large Margin Principle Algorithms for Ordinal Regression Problems”, Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec. 2002.
Shashua et al., “Tensor Embedding of the Fundamental Matrix”, Kluwer Academic Publishers, Boston, MA, 1998.
Shashua et al., “The Quadric Reference Surface: Applications in Registering Views of Complex 3D Objects”, European Conference on Computer Vision (ECCV), May 1994, Stockholm, Sweden, pp. 407-416.
Shashua et al., “The Quadric Reference Surface: Theory and Applications”, 1994.
Shashua et al., “The Rank 4 Constraint in Multiple (.gtoreq.3) View Geometry”, European Conference on Computer Vision (ECCV), Apr. 1996, Cambridge, United Kingdom, pp. 196-206.
Shashua et al., “The Semi-Explicit Shape Model for Multi-object Detection and Classification”, Proc. of the European Conference on Computer Vision (ECCV), Crete, Greece, pp. 336-349, Sep. 2010.
Shashua et al., “Threading Fundamental Matrices”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 23(1), Jan. 2001, pp. 73-77.
Shashua et al., “Threading Kernel functions: on Bridging the Gap between Representations and Algorithms”, Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec. 2004.
Shashua et al., “Time-varying Shape Tensors for Scenes with Multiply Moving Points”, IEEE Conference on Computer Vision and Pattern, pp. 623-630, Dec. 2001, Hawaii.
Shashua et al., “Trajectory Triangulation over Conic Sections”, International Conference on Computer Vision (ICCV), Greece, 1999, pp. 330-337.
Lasky et al., “Automated Highway Systems (AHS) Classification by Vehicle and Infrastructure”, AHMT Research Report, Jan. 25, 1994.
Leachtenauer, “Resolution requirements and the Johnson criteria revisited,” Proceedings of SPIE, Infrared Imaging Systems: Design, Analysis, Modeling and Testing XIV, vol. 5076, 2003.
LeBlanc et al., “CAPC: A Road-Departure Prevention System”, IEEE, Dec. 1996, pp. 61-71.
Lee et al., “Automatic recognition of a car license plate using color image processing”, IEEE, Nov. 16, 1994.
Lee, “How to Select a Heat Sink”, Electronics Cooling Magazine, Jun. 1, 1995.
Leen et al., “Digital networks in the automotive vehicle”, Dec. 1999.
Lezin, “Video Gear In Police Cruisers Gets Mixed Reviews Critics Say It Violates Privacy Rights and Inhibits Officers From Doing Their Jobs Well”, Mar. 17, 1997.
Linkwitz, “High Precision Navigation: Integration of Navigational and Geodetic Methods,” Springer-Verlag, Jul. 5, 1989, Excerpt.
Lisowski et al., “Specification of a small electric vehicle: modular and distributed approach,” IEEE 1997, pp. 919-924.
Litkouhi et al., “Estimator and Controller Design for LaneTrak, a Vision-Based Automatic Vehicle Steering System,” Proceedings of the 32nd Conference on Decision and Control, San Antonio, Texas, Dec. 1993, pp. 1868-1873.
Litwiller, “CCD vs. CMOS: Facts and Fiction,” Photonics Spectra, Jan. 2001.
Liu Xianghong, “Development of a vision-based object detection and recognition system for intelligent vehicle”, 2000.
Lockwood, “Design of an obstacle avoidance system for automated guided vehicles”, Doctoral thesis, University of Huddersfield, Oct. 1991.
Lowenau et al., “Adaptive light control a new light concept controlled by vehicle dynamics and navigation”, SAE Technical Paper Series, Feb. 23-26, 1998.
Lu et al., “On-chip Automatic Exposure Control Technique, Solid-State Circuits Conference”, ESSCIRC '91. Proceedings-17th European (vol. 1) Abst. Sep. 11-13, 1991.
Lucas Demonstrates Intelligent Cruise Control, Detroit Feb. 27, 1995 available at; http://www.thefreelibrary.com/LUCAS+DEMONSTRATES+INTELLIGENT+CUISE+CONTR OL=a016602459.
Luebbers et al., “Video-image-based neural network guidance system with adaptive view-angles for autonomous vehicles”, Applications of Artificial Neural Networks II. International Society for Optics and Photonics, 1991, Abstract.
Lumia, “Mobile system for measuring retroreflectance of traffic signs”, Optics, Illumination, and Image Sensing for Machine Vision, Mar. 1, 1991, Abstract.
Mackey et al., “Digital Eye-Witness Systems”, Transportation Recording: 2000 and Beyond, May 3-5, 1999, 271-284.
Malik et al., “A Machine Vision Based System for Guiding Lane-change Maneuvers”, California Path Program, Institute of Transportation Studies, University of California, Berkeley, Sep. 1995.
Manigel et al., “Computer control of an autonomous road vehicle by computer vision” —Industrial Electronics, Control and Instrumentation, Proceedings. IECON '91, 1991 International Conference on, p. 19-24 vol. 1, 1991.
Manigel et al., “Vehicle control by computer vision,” Industrial Electronics, IEEE Transactions on, vol. 39, Issue 3, 181-188, Jun. 1992.
Martel-Brisson et al., “Moving cast shadow detection from a Gaussian mixture shadow model”, Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2005.
Masaki, “Vision-based vehicle guidance”, Industrial Electronics, Control, Instrumentation, and Automation, 1992. Power Electronics and Motion Control, Proceedings of the 1992 International Conference on. IEEE, 1992.
Mason et al., “The Golem Group I UCLA Autonomous Ground Vehicle in the DARPA Grand Challenge”, Jun. 12, 2006.
Matthews, “Visual Collision Avoidance,” Oct. 1994, University of Southampton, PhD submission.
Maurer, et al., “VaMoRs-P: an advanced platform for visual autonomous road vehicle guidance”, 1995.
Maurer, “Flexible Automatisierung von StraBenfahrzeugen mit Rechnersehen” Universitat der Buneswehr Milnchen Dissertation, Jul. 27, 2000.
MC68331 User's Manual, Freescale Semiconductor, Inc., 1994.
McKenna et al., “Tracking Groups of People”, Computer Vision and Image Understanding, vol. 80, p. 42-56, 2000.
McTamaney, “Mobile Robots Real-Time Intelligent Control”, FMC Corporation, Winter 1987.
Mei Chen et al., “AURORA: A Vision-Based Roadway Departure Warning System, The Robotics Institute”, Carnegie Mellon University, published, Aug. 5-9, 1995.
Mendis et al., “A 128.times.128 CMOS active pixel image sensor for highly integrated imaging systems”, Dec. 8, 1993.
Mendis et al., “CMOS Active Pixel Image Sensor,” IEEE Transactions on Electron Devices, vol. 41, No. 3, Mar. 1994.
Metzler, “Computer Vision Applied to Vehicle Operation”, Paper from Society of Automotive Engineers, Inc., 1988.
Mikic et al., “Moving shadow and object detection in traffic scenes”, Proceeding of IEEE International Conference on Pattern Recognition, vol. 1, 2000.
Miller, “Evaluation of vision systems for teleoperated land vehicles,” IEEE Control Systems Magazine, Jun. 28, 1988.
Mimuro et al., “Functions and Devices of Mitsubishi Active Safety ASV” Proceedings of the 1996 IEEE Intelligent Vehicles Symposium, Sep. 19-20, 1996, Abstract.
Mironer et al., “Examination of Single Vehicle Roadway Departure Crashes and Potential IVHS Countermeasures,” U.S. Department of Transportation, Aug. 1994.
Miura et al., “Towards Vision-Based Intelligent Navigator: Its Concept and Prototype”, IEEE Transactions on Intelligent Transportation Systems, Jun. 2002.
Miura et al., “Towards intelligent navigator that can provide timely advice on safe and efficient driving” Intelligent Transportation Systems Proceedings, Oct. 5-8, 1999, pp. 981-986.
Mobileye N.V. Introduces EyeQ.TM. Vision System-On-A-Chip High Performance, Low Cost Breakthrough for Driver Assistance Systems, Detroit, Michigan, Mar. 8, 2004.
Moini, “Vision Chips or Seeing Silicon,” Third Revision, Mar. 1997.
Moravec, “Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover”, Computer Science Department, Stanford University, Ph.D. Thesis, Sep. 1980.
Morgan et al., “Road edge tracking for robot road following: a real-time implementation,” vol. 8, No. 3, Aug. 1990.
Mori et al., “Shadow and Rhythm as Sign patterns of Obstacle Detection”, Industrial Electronics, 1993. Conference Proceedings, ISIE'93-Budapest, IEEE International Symposium on. IEEE, 1993, Abstract.
Morris, “E-Z-Pass and transmit using electronic toll tags for traffic monitoring” National Traffic Data Acquisition Conference, PDF pp. 54-63, 1996, 289-298, Abstract.
Motorola Installation Guide, MVE162, Embedded Controller.
Muirhead, “Developments in CMOS Camera Technology,” The Institution of Electrical Engineers, Dec. 5, 1994.
Nadimi et al., “Physical models for moving shadow and object detection in video”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, No. 8, Aug. 2004.
Thomanek et al., “Multiple object recognition and scene interpretation for autonomous road vehicle guidance” Oct. 1994.
Thomas, “Real-time vision guided navigation”, Engineering Applications of Artificial Intelligence, Jan. 31, 1991, Abstract.
Thongkamwitoon et al., “An adaptive real-time background subtraction and moving shadows detection”, Proceeding of IEEE International Conference on Multimedia and Expo. vol. 2, 2004.
Thorpe et al., “Perception for Outdoor Navigation First Year Report”, Defense Advanced Research Projects Agency, Carnegie Mellong University, Dec. 31, 1990.
Thorpe, “Vision and Navigation for the Carnegie-Mellon Navlab”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, No. 3, May 1998.
Thorpe, “1988 Year End Report for Road Following at Carnegie Mellon”, Carnegie Mellon University, May 31, 1989.
Thorpe et al., “Toward autonomous driving: the CMU Navlab. I. Perception”, IEEE Paper, Aug. 1991.
Thorpe et al., “The 1997 Automated Highway Free Agent Demonstration”, 1997 pp. 496-501, 1997.
Tokimaru et al., “CMOS Rear-View TV System with CCD Camera”, National Technical Report vol. 34, No. 3, pp. 329-336, Jun. 1988 (Japan).
Toth et al., “Detection of moving shadows using mean shift clustering and a significance test”, Proceeding of IEEE International Conference on Pattern Recognition, vol. 4, 2004.
Toyota Motor Corporation, “Present and future of safety technology development at Toyota.” 2004.
Trainor et al., “Architectural Synthesis of Digital Signal Processing Algorithms Using ‘IRIS’”, Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, vol. 16, No. 1, 1997.
Tremblay et al., “High resolution smart image sensor with integrated parallel analog processing for multiresolution edge extraction”, Robotics and Autonomous Systems 11, pp. 231-242, with abstract, 1993.
Tribe et al., “Collision Avoidance,” Advances, Issue No. 4, May 1990.
Tribe et al., “Collision Avoidance,” Lucas International Symposium, Paris, France, 1989.
Tribe et al., “Collision Warning,” Autotech '93, Seminar 9, NEC Birmingham, UK, Nov. 1993.
Tribe, “Intelligent Autonomous Systems for Cars, Advanced Robotics and; Intelligent Machines,” Peter Peregrinus, Nov. 1994.
Trivdei et al., “Distributed Video Networks for Incident Detection and Management”, Computer Vision and Robotics Research Laboratory, 2000.
Tsugawa et al., “An automobile with artificial intelligence,” in Proc. Sixth IJCAI, 1979.
Tsugawa et al., “Vision-based vehicle in japan; machine vision systems and driving control systems”, IEEE Transactions on Industrial Electronics, vol. 41, No. 4, Aug. 1994.
Tsutsumi et al., “Vehicle Distance Interval Control Technology” Mitsubishi Electric Advance, Technical Reports, vol. 78, pp. 10-12, Mar. 1997.
Turk et al., “VITS—A Vision System for Autonomous Land Vehicle Navigation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, No. 3, May 3, 1988.
Tzomakas and von Seelen, “Vehicle Detection in Traffic Scenes Using Shadows,” Internal report, Institut Fur Neuroinformatik Bochum, Internal Report 98-06.
Ulmer, “VITA II—active collision avoidance in real traffic” Proceedings of the Intelligent Vehicles '94 Symposium, Oct. 24-26, 1994, Abstract.
Valeo Infos News, “Valeo's revolutionary Lane Departure Warning System makes debut on Nissan Infiniti vehicles”, 04.08 found at http://www.valeo.com/cwscontent/www.valeo.com/medias/fichiers/journaliste- s/en/CP/ldws_uk.pdf, Mar. 31, 2004.
Van Leeuwen et al., “Motion Estimation with a Mobile Camera for Traffic Applications”, IEEE, US, vol. 1, pp. 58-63, Oct. 3, 2000.
Van Leeuwen et al., “Motion Interpretation for In-Car Vision Systems”, IEEE, US, vol. 1, , p. 135-140, Sep. 30, 2002.
Van Leeuwen et al., “Real-Time Vehicle Trackin in Image Sequences”, IEEE, US, vol. 3, pp. 2049-2054, XP010547308, May 21, 2001.
Van Leeuwen et al., “Requirements for Motion Estimation in Image Sequences for Traffic Applications”, IEEE, pp. 354-359, XP002529773, 2000.
Van Leeuwen et al., “Requirements for Motion Estimation in Image Sequences for Traffic Applications”, IEEE, US, vol. 1, 145-150, XP010340272, May 24, 1999.
Vellacott, “CMOS in Camera,” IEE Review, pp. 111-114, May 1994.
Vlacic et al., “Intelligent Vehicle Technologies, Theory and Applications”, Society of Automotive Engineers Inc., edited by SAE International, 2001.
Vosselman et al., “Road traceing by profile matching and Kalman filtering”, Faculty of Geodetic Engineering, 1995.
Wallace et al., “Progress in Robot Road-Following,” Proceedings of the 1986 IEEE International Conference on Robotics and Automation, vol. 3, pp. 1615-1621, 1986.
Wan et al., “A New Edge Detector for Obstacle Detection with a Linear Stereo Vision System”, Proceedings of the Intelligent Vehicles '95 Symposium, Abstract.
Wang et al., “CMOS Video Cameras”, article, 4 pages, University of Edinburgh, UK, 1991.
Wang et al., “A probabilistic method for foreground and shadow segmentation”, Proceeding of IEEE International Conference on Image Processing, Pattern Recognition, vol. 3, Oct. 2, 2003.
Wang, “Camera Calibration by Vanishing Lines for 3-D Computer Vision”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, No. 4, Apr. 15, 1991.
Watec WAT-660D data sheet, found at http://www.wateccameras.com/products.php?prod_id=214.
Web page at http://www.glassrack.net/potrsp1919192.html?utm_source=googlepepla&utm_me-dium=adwords&id=116297830341.
Webpage: http://parts.royaloakschevy.com/showAssembly.aspx?makeName=pontiac&modelYear=1990&modelName=trans-sport&ukey_assembly=5888560&ukey_category=53643&assembly=921201mu10-009mu10-009.
Weisser et al., “Autonomous driving on vehicle test tracks: Overview, implementation and vehicle diagnosis” Intelligent Transportation Systems, pp. 62-67, Oct. 5-8, 1999, Abstract.
Wierwille et al., “Research on Vehicle-Based Driver Status/Performance Monitoring, Part III” Final Report, Sep. 1996.
Wilson, “Technology: A little camera with big ideas—The latest smart vision system,” Financial Times, Jun. 17, 1993.
Wolberg, Digital Image Warping, IEEE Computer Society Press, 1990.
Wolberg, “A Two-Pass Mesh Warping Implementation of Morphing,” Dr. Dobb's Journal, No. 202, Jul. 1993.
Wordenweber, “Driver assistance through lighting.” ESV: 17th International Technical Conference on the Enhanced Safety of Vehicles. Report. No. 476. 2001.
Wright, “Take your hands off that carl”, Edn. vol. 42, No. 26, Dec. 18, 1997, Abstract.
Wuller et al., “The usage of digital cameras as luminance meters”, Proc. SPIE 6502, Digital Photography III, 65020U, Feb. 20, 2007; doi:1031117/12.703205.
Wyatt et al., “Analog VLSI systems for Image Acquisition and Fast Early Vision Processing”, International Journal of Computer Vision, 8:3, pp. 217-223, 1992.
Shashua et al., “Trajectory Triangulation: 3D Reconstruction of Moving Points from a Monocular Image Sequence”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 22(4), 2000, pp. 348-357.
Shashua et al., “Trilinear Tensor: The Fundamental Construct of Multiple-view Geometry and its Applications”, International Workshop on Algebraic Frames for the Perception Action Cycle (AFPAC97), Kiel Germany, Sep. 8-9, 1997. Proceedings appeared inSpringer-Verlag, LNCS series, 1997, 190-206.
Shashua et al., “Trilinearity in Visual Recognition by Alignment”, European Conference on Computer Vision (ECCV), May 1994, Stockholm, Sweden, pp. 479-484.
Shashua et al., “Projective Depth: A Geometric Invariant for 3D Reconstruction From Two Perspective/Orthographic Views and for Visual Recognition,” International Conference on Computer Vision (ICCV), May 1993, Berlin, Germany, pp. 583-590.
Shashua et al., “The Quotient Image: Class Based Recognition and Synthesis Under Varying Illumination Conditions”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 1999, pp. 566-573.
Shashua et al., “The Quotient Image: Class Based Re-rendering and Recognition With Varying Illuminations”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 23(2), 2001, pp. 129-139.
Shashua et al., “Pedestrian Detection for Driving Assistance, Systems: Single-Frame Classification and System Level, Performance”, IEEE Intelligent Vehicles Symposium, Jan. 1, 2004.
Shashua, “On the Relationship Between the Support Vector Machine for classification and Sparsified Fisher's Linear Discriminant,” Neural Processing Letters, 1999, 9(2): 129-139.
Shimizu et al., “A moving image processing system for personal vehicle system”, Nov. 9, 1992, Abstract.
Shirai, “Robot Vision”, Future Generation Computer Systems, 1985.
Shladover et al., “Automatic Vehicle Control Developments in the PATH Program,” IEEE Transaction on Vehicular Technology, vol. 40, No. 1, Feb. 1991, pp. 114-130.
Shladover, “Research and Development Needs for Advanced Vehicle Control Systems,” Micro, IEEE, vol. 13, No. 1, Feb. 1993, pp. 11-19.
Shladover, “Highway Electrification and Automation,” California Partners for Advanced Transit and Highways (PATH), Jan. 1, 1992.
Siala et al., “Moving shadow detection with support vector domain description in the color ratios space”, Proceeding of IEEE International Conference on Pattern Recognition, vol. 4, 2004.
Siegle, “Autonomous Driving on a Road Network,” Proceedings of the Intelligent Vehicles '92 Symposium Detroit, Michigan, ISBN 0-7803-0747-X; Jun. 29-Jul. 1, 1992.
Smith et al., “An Automotive Instrument Panel Employing Liquid Crystal Displays”, SAE Paper No. 770274, published Feb. 1, 1977.
Smith et al., “Optical sensors for automotive applications”, May 11, 1992.
Smith et al., “Vision sensing for intelligent vehicle and highway systems”, Proceedings of the 1994 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Las Vegas, NV, Oct. 5, 1994.
Soatto et al., “The Golem Group/University of California at Los Angeles Autonomous Ground Vehicle in the DARPA Grand Challenge”, Journal of Field Robotics 23(8), 2006, pp. 527-553.
Solder et al., “Visual Detection of Distant Objects”, Intelligent Robots and Systems' 93, IROS'93. Proceedings of the 1993 IEEE/RSJ International Conference on vol. 2. IEEE, 1993, Abstract.
Sole et al., “Solid or not solid: vision for radar target validation”, IEEE Intelligent Vehicles Symposium, 2004.
Sony Operating Manual CCD Color Video Camera Model: DXC-151A, 1993.
Sony Specifications Single Chip CCD Color Video Camera DXC-151A.
Sparks et al., “Multi-Sensor Modules with Data Bus Communication Capability” SAE Technical Paper 1999-01-1277, Mar. 1, 1999, doi: 10.4271/1999-01-1277, http://papers.sae.org/1999-01-1277/, Abstract.
Sridhar, “Multirate and event-driven Kalman filters for helicopter flight”, IEEE Control Systems, Aug. 15, 1993.
Standard J2284/3, “High-Speed CAN (HSC) for Vehicle Applications at 500 Kbps,” issued May 30, 2001.
Stauder et al., “Detection of moving cast shadows for object segmentation”, IEEE Transactions on Multimedia, vol. 1, No. 1, Mar. 1999.
Stein et al., “A Computer Vision System on a Chip: a case study from the automotive domain”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
Stein et al., “Challenges and solutions for Bundling Multiple DAS Applications on a Single Hardware Platform”, Procs. VISION 2008.
Stein et al., “Direct Methods for Estimation of Structure and Motion from three views”, A.I. Memo No. 1594, MA Inst. of Tech., Nov. 1996.
Stein et al., “Internal Camera Calibration using Rotation and Geometric Shapes”, Submitted to the Dept. of Electrical Engineering and Computer Science at MA Inst. of Tech., Masters Thesis, M.I.T., Feb. 1993.
Stein et al., “Model-based brightness constraints: on direct estimation of structure and motion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, Issue 9, Sep. 2000.
Stein et al., “Stereo-assist: Top-down stereo for driver assistance systems”, IEEE Intelligent Vehicles Symposium, 2010.
Stein et al., “Vision-based ACC with a single camera: bounds on range and range rate accuracy”, IEEE Intelligent Vehicles Symposium, 2003.
Stein et al., “A robust method for computing vehicle ego-motion”, Proceedings of the IEEE Intelligent Vehicles Symposium, 2000.
Stein, “Accurate Internal Camera Calibration using Rotation, with Analysis of Sources of Error”, Computer Vision, Proceedings Fifth International Conference on. IEEE, 1995.
Stein, “Geometric and photometric constraints: motion and structure from three views”, Mass. Inst. of Tech., Doctoral Dissertation, 1998.
Stein, “Lens Distortion Calibration Using Point Correspondences”, A.I. Memo No. 1595, M.I.T. Artificial Intelligence Laboratory, Nov. 1996.
Stein, “Tracking from multiple view points: Self-calibration of space and time”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 1999.
Stein et al., “Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame,” A.I. Memo No. 1655, M.I.T. Artificial Intelligence Laboratory, Apr. 1999.
Steiner et al., “Future applications or microsystem technologies in automotive safety systems” Advanced Microsystems for Automotive Applications '98, 1998, pp. 21-42.
Stengel et al., “Intelligent Guidance for Headway and Lane Control”, Princeton University, Department of Mechanical and Aerospace Engineering, New Jersey, 1989.
Stickford, “Candid cameras come to Park”, Grosse Pointe News, Mar. 7, 1996.
Stiller et al., “Multisensor obstacle detection and tracking”, Image and Vision Computing 18, Elsevier, 2000, pp. 389-396.
Sukthankar, “RACCOON: a Real-time Autonomous Car Chaser Operating Optimally at Night”, Oct. 1992.
Sun et al., “On-road vehicle detection using optical sensors: a review”, 2004.
Sun et al., “A Real-time Precrash Vehicle Detection System”, 2002.
Szeliski, Image Mosaicing for Tele-Reality Applications, DEC Cambridge Research Laboratory, CRL 94/2, May 1994.
Taktak et al., “Vehicle detection at night using image processing and pattern recognition”, Centre de Recherche en Automatique de Nancy, 1994.
Taylor, “CCD and CMOS Imaging Array Technologies: Technology Review,” Xerox Research Centre Europe, Technical Report EPC-1998-106, 1998.
Ran et al., “Development of Vision-based Vehicle Detection and Recognition System for Intelligent Vehicles”, Department of Civil and Environmental Engineering, University of Wisconsin at Madison, 1999 TRB Annual Meeting, Nov. 16, 1998.
Raphael et al., “Development of a Camera-Based Forward Collision Alert System”, SAE International, Apr. 12, 2011.
Rayner et al., “I-Witness Black Box Recorder” Intelligent Transportation Systems Program, Final Report for ITS-IDEA Project 84, Nov. 2001.
Redmill, “The OSU Autonomous Vehicle”, 1997.
Regensburger et al., “Visual Recognition of Obstacles on Roads”, Intelligent Robots and Systems, Elsevier, 1994.
Reichardt, “Kontinuierliche Verhaltenssteuerung eines autonomen Fahrzeugs in dynamischer Umgebung” Universitat Kaisserslautern Dissertation, Transation: Continuous behavior control of an autonomous vehicle in a dynamic environment, Jan. 1996.
Reid, “Vision-based guidance of an agriculture tractor”, IEEE Control Systems Magazine, Apr. 30, 1987, Abstract.
Reisman et al., “Crowd Detection in Video Sequences”, IEEE, Intelligent Vehicles Symposium, Jan. 1, 2004.
Reexamination Control No. 90/007,519, dated Jun. 9, 2005, Reexamination of U.S. Pat. No. 6,222,447, issued to Schofield et al.
Reexamination Control No. 90/011,478, dated Mar. 28, 2011, Reexamination of U.S. Pat. No. 6,222,447, issued to Schofield et al.
Reexamination Control No. 90/007,520, dated Jun. 9, 2005, Reexamination of U.S. Pat. No. 5,949,331, issued to Schofield et al.
Reexamination Control No. 90/011,477, dated Mar. 14, 2011, Reexamination of U.S. Pat. No. 5,949,331, issued to Schofield et al.
Ritter et al., “Traffic sign recognition using colour information”, Math, Computing, Modelling, vol. 22, No. 4-7, pp. 149-161, Oct. 1995.
Ritter, “Traffic Sign Recognition in Color Image Sequences”, Institute for Information Technology, 1992, pp. 12-17.
Roberts, “Attentive Visual Tracking and Trajectory Estimation for Dynamic Scene Segmentation”, University of Southampton, PhD submission, Dec. 1994.
Rombaut et al., “Dynamic data temporal multisensory fusion in the Prometheus ProLab2 demonstrator”, IEEE Paper, 1994.
Ross, “A Practical Stereo Vision System”, The Robotics Institute, Carnegie Mellon University, Aug. 25, 1993.
Rowell, “Applying Map Databases to Advanced Navigation and Driver Assistance Systems”, The Journal of Navigation 54.03 (2001): 355-363.
Sahli et al., “A Kalman Filter-Based Update Scheme for Road Following,” IAPR Workshop on Machine Vision Applications, pp. 5-9, Nov. 12-14, 1996.
Salvador et al., “Cast shadow segmentation using invariant color features”, Computer Vision and Image Understanding, vol. 95, 2004.
Sanders, “Speed Racers: Study to monitor driver behavior to determine the role of speed in crashes”, Georgia Research Tech News, Aug. 2002.
Sayer et al., “The Effect of Lead-Vehicle Size on Driver Following Behavior”, University of Michigan Transportation Research Institute, 2000-15, Jun. 2000.
Schneiderman et al., “Visual Processing for Autonomous Driving,” IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, Nov. 30-Dec. 2, 1992.
Schonfeld et al., Compact Hardware Realization for Hough Based Extraction of Line Segments in Image Sequences for Vehicle Guidance, IEEE Paper, 1993, Abstract.
Schumann et al., “An Exploratory Simulator Study on the Use of Active Control Devices in Car Driving,” No. IZF-1992-B-2. Institute for Perception RVO-TNO Soesterber (Netherlands), May 1992.
Schwarzinger et al., “Vision-based car-following: detection, tracking, and identification”, Jul. 1, 1992.
Scott, “Video Image on a Chip”, Popular Science, vol. 237, No. 3, Sep. 1991, pp. 50.
Seelen et al., “Image Processing for Driver Assistance”, 1998.
Seger et al., “Vision Assistance in Scenes with Extreme Contrast,” IEEE Micro, vol. 13, No. 1, Feb. 1993.
Shafer, “Automation and Calibration for Robot Vision Systems”, National Science Foundation, Carnegie Mellon University Research Showcase, May 12, 1988.
Shashua et al., “Two-body Segmentation from Two Perspective Views”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, pp. 263-270, Dec. 2001, Abstract.
Shashua et al., “Direct Estimation of Motion and Extended Scene Structure from a Moving Stereo Rig”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 1998, pp. 211-218.
Shashua et al., “Illumination and View Position in 3D Visual Recognition”, Advances in Neural Information Processing Systems, Morgan Kauffman Publishers, CA 1992 (Proc. Of NIPS '91), pp. 404-411.
Shashua et al., “Image-Based View Synthesis by Combining Trilinear Tensors and Learning Techniques”, ACM Conference on Virtual Reality and Systems (VRST), Sep. 1997, pp. 140-145.
Shashua et al., “Novel View Synthesis by Cascading Trilinear Tensors”, IEEE Transactions on Visualization and Computer Graphics, vol. 4, No. 4, Oct.-Dec. 1998.
Shashua et al., “On Degeneracy of Linear Reconstruction from Three Views: Linear Line Complex and Applications”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 21 (3), 1999, pp. 244-251.
Shashua et al., “3D Reconstruction from Tangent-of-Sight”, European Conference on Computer Vision (ECCV), Jun. 2000, Dublin, Ireland, pp. 220-234.
Shashua et al., “A Geometric Invariant for Visual Recognition and 3D Reconstruction From Two Perspective/Orthographic Views”, Proceedings of the IEEE 2nd Qualitative Vision Workshop, Jun. 1993, New York, NY, pp. 107-117.
Shashua et al., “A Parallel Decomposition Solver for SVM: Distributed Dual Ascend using Fenchel Duality”, Conf. on Computer Vision and Pattern Recognition (CVPR), Jun. 2008, Anchorage, Alaska.
Shashua et al., “A Unifying Approach to Hard and Probabilistic Clustering”, International Conference on Computer Vision (ICCV), Beijing, China, Oct. 2005.
Shashua et al., “Affine 3-D Reconstruction from Two Projective Images of Independently Translating Planes”, International Conference on Computer Vision (ICCV), Jul. 2001, Vancouver, Canada, pp. 238-244.
Shashua et al., “Algebraic Functions for Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) vol. 17(8), Jan. 1994 pp. 779-789.
Shashua et al., “Ambiguity from Reconstruction From Images of Six Points”, International Conference on Computer Vision (ICCV), Jan. 1998, Bombay India, pp. 703-708.
Shashua et al., “Convergent Message-Passing Algorithms for reference over General Graphs with Convex Free Energies”, Conf. on Uncertainty in Al (UAI), Helsinki, Jul. 2008.
Shashua et al., “Doubly Stochastic Normalization for Spectral Clustering”, Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec. 2006.
Shashua et al., “Duality of multi-point and multi-frame geometry: Fundamental shape matrices and tensors”, European Conference on Computer Vision (ECCV), Apr. 1996, Cambridge United Kingdom, pp. 217-227.
Shashua et al., “Dynamic P.sup.n to P.sup.n Alignment”, In Handbook of Computational Geometry for Pattern Recognition, Computer Vision. Neuro computing and Robotics. Eduardo Bayro-Corrochano (eds.), Springer-Verlag, 2004.
Shashua et al., “Feature Selection for Unsupervised and Supervised Inference: the Emergence of Sparsity in a Weight-based Approach”, Journal of Machine Learning Research (JMLR), 6(11): 1885-1887, 2005, pp. 1885-1887.
Shashua et al., “Grouping Contours by Iterated Pairing Network”, Advances in Neural Information Processing Systems 3, (Proc. of NIPS '90), Morgan Kaufmann Publishers, CA, 1991, pp. 335-341.
Shashua et al., “Homography Tensors: On Algebraic Entities That Represent Three Views of Static or Moving Planar Points”, European Conference on Computer Vision (ECCV), Jun. 2000, Dublin, Ireland, pp. 163-177.
Hessburg et al., “An Experimental Study on Lateral Control of a Vehicle,” California Partners for Advanced Transit and Highways (PATH), Jan. 1, 1991.
Hillebrand et al., “High speed camera system using a CMOS image sensor”, IEEE Intelligent Vehicles Symposium., Oct. 3-5, 1999, pp. 656-661, Abstract.
Ho et al., “Automatic spacecraft docking using computer vision-based guidance and control techniques”, Journal of Guidance, Control, and Dynamics, vol. 16, No. 2 Mar.-Apr. 1993.
Hock et al., “Intelligent Navigation for Autonomous Robots Using Dynamic Vision”, XVIIth ISPRS Congress, pp. 900-915, Aug. 14, 1992.
Holst, “CCD Arrays, Cameras, and Displays”, Second Edition, Bellingham, WA: SPIE Optical Engineering Press, 1998 pp. v-xxiii, 7-12, 45-101, and 176-179, excerpts.
Honda Worldwide, “Honda Announces a Full Model Change for the Inspire.” Jun. 18, 2003.
Horprasert et al., “A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection”, Proceeding of IEEE International Conference on Computer vision FRAME- RATE Workshop, 1999.
Hsieh et al., “Shadow elimination for effective moving object detection by Gaussian shadow modeling”, Image and Vision Computing, vol. 21, No. 6, 505-516, 2003.
Hsieh et al., “A shadow elimination method for vehicle analysis”, Proceeding of IEEE International Conference on Pattern Recognition, vol. 4, 2004.
Hu et al., “Action-based Road Horizontal Shape Recognition”, SBA Controle & Automacao, vol. 10, No. 2, May 1999.
Huertgen et al., “Vehicle Environment Sensing by Video Sensors”, No. 1999-01- 0932. SAE Technical Paper, 1999, Abstract.
Huijsing, “Integrated smart sensors”, Sensors and Actuators A, vol. 30, Issues 1-2, pp. 167-174, Jan. 1992.
Hutber et al., “Multi-sensor multi-target tracking strategies for events that become invisible” BMVC '95 Proc. of the 6th British conference on Machine vision, V2, 1995, pp. 463-472.
IEEE 100—The Authoritative Dictionary of IEEE Standards Terms, 7.sup.th Ed. (2000).
Ientilucci, “Synthetic Simulation and Modeling of Image Intensified CCDs (IICCD)”, Master Thesis for Rochester Inst. of Tech., Mar. 31, 2000.
Ishida et al., “Development of a Driver Assistance System”, No. 2003-01-0279. SAE Technical Paper, 2002, Abstract.
Ishihara et al., “Interline CCD Image Sensor with an Anti Blooming Structure,” IEEE International Solid-State Circuits Conference, Session XIII: Optoelectronic Circuits, THPM 13.6, Feb. 11, 1982.
Ishikawa et al., “Visual Navigation of an Autonomous Vehicle Using White Line Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, Abst.
Jaguar Press Releases Autumn 1991 “Jaguar Displays 21st Century Car Technologies”, Jaguar Communications & Public Affairs Dept.
Janssen et al., “Hybrid Approach for Traffic Sign Recognition”, Program for a European Traffic with Highest Efficiency and Unprecendented Safety, Nov. 28, 1993.
Japanese Article “Television Image Engineering Handbook, The Institute of Television Engineers of Japan”, Jan. 17, 1981.
Jochem et al., “PANS: a portable navigation platform”, 1995 IEEE Symposium on Intelligent Vehicles, Detroit, MI, Sep. 25-26, 1995.
Jochem et al., “Life in the Fast Lane”, AI Magazine, vol. 17, No. 2, pp. 11-50, Summer 1996.
Johannes, “A New Microchip Ushers in Cheaper Digital Cameras”, The Wall Street Journal, Aug. 21, 1998, p. B1.
Johnson, “Georgia State Patrol's In-Car Video System”, Council of State Governments, 1992, Abstract.
Juberts et al., “Development and Test Results for a Vision-Based Approach to AVCS.” in Proceedings of the 26th International Symposium on Automotive Technology and Automation, Aachen, Germany, Sep. 1993, pp. 1-9.
Kakinami et al., “Autonomous Vehicle Control System Using an Image Processing Sensor”, No. 950470. SAE Technical Paper, Feb. 1, 1995, Abstract.
Kan et al., “Model-based vehicle tracking from image sequences with an application to road surveillance,” Purdue University, XP000630885, vol. 35, No. 6, Jun. 1996.
Kang et al., “High Dynamic Range Video”, ACM Transactions on Graphics, vol. 22, No. 3, 2003.
Kassel, “Lunokhod-1 Soviet Lunar Surface Vehicle”, Advanced Research Projects Agency, ARPA Order No. 189-1, Dec. 9, 1971.
Kastrinaki et al., “A survey of video processing techniques for traffic applications”, Image and Computing 21, 2003.
Kehtarnavaz et al., “Traffic sign recognition in noisy outdoor scenes”, 1995.
Kehtarnavaz, “Visual control of an autonomous vehicle (BART)—the vehicle-following problem”, IEEE Transactions on Vehicular Technology, Aug. 31, 1991, Abstract.
Kemeny et al., “Multiresolution Image Sensor,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, No. 4, Aug. 1997.
Kenue et al., “LaneLok: Robust Line and Curve Fitting of Lane Boundaries”, Applications in Optical Science and Engineering, International Society for Optics and Photonics, 1993, Abstract.
Kenue, “Lanelok: Detection of Lane Boundaries and Vehicle Tracking Using Image-Processing Techniques,” SPIE Conference on Mobile Robots IV, 1989.
Kidd et al., “Speed Over Ground Measurement”, SAE Technical Paper Series, No. 910272, pp. 29-36, Feb.-Mar. 1991.
Kiencke et al., “Automotive Serial controller Area Network,” SAE Technical Paper 860391, 1986, retrieved from http://papers.sae.org/860391/, accessed Mar. 20, 2015.
Klassen et al., “Sensor Development for Agricultural Vehicle Guidance”, No. 932427. SAE Technical Paper, 1993, Abstract.
Kluge et al., “Representation and Recovery of Road Geometry in YARF,” Carnegie Mellon University, Proceedings of the IEEE, pp. 114-119, 1992.
Knipling, “IVHS Technologies Applied to Collision Avoidance: Perspectives on Six Target Crash Types and Countermeasures,” Technical Paper presented at Safety & Human Factors session of 1993 IVHS America Annual Meeting, Apr. 14-17, 1993, pp. 1-22.
Knipling et al., “Vehicle-Based Drowsy Driver Detection: Current Status and Future Prospects,” IVHS America Fourth Annual Meeting, Atlanta, GA, Apr. 17-20, 1994, pp. 1-24.
Koller et al., “Binocular Stereopsis and Lane Marker Flow for Vehicle Navigation: Lateral and Longitudinal Control,” University of California, Mar. 24, 1994.
Kowalick, “Proactive use of highway recorded data via an event data recorder (EDR) to achieve nationwide seat belt usage in the 90th percentile by 2002” “Seat belt event data recorder (SB-EDR)” Transportation Recording: 2000 and Beyond., May 3-5, 1999, pp. 173-198, 369.
Kozlowski et al., “Comparison of Passive and Active Pixel Schemes for CMOS Visible Imagers,” Proceedings of SPIE Conference on Infrared Readout Electronics IV, vol. 3360, Apr. 1998.
Krotkov, “An agile stereo camera system for flexible image acquisition”, IEEE Journal on Robotics and Automation, Feb. 18, 1988.
Kuan et al., “Autonomous Robotic Vehicle Road Following”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, No. 5, Sep. 1988, pp. 648-658, Abstract.
Kuehnle, “Symmetry-based recognition of vehicle rears”, Pattern Recognition Letters 12, North-Holland, 1991.
Kuhnert, “A vision system for real time road and object recognition for vehicle guidance,” in Proc. SPIE Mobile Robot Conf, Cambridge, MA, Oct. 1986, pp. 267-272.
Kweon et al., “Behavior-Based Intelligent Robot in Dynamic Indoor Environments”, Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, Jul. 7-10, 1992.
Related Publications (1)
Number Date Country
20210009029 A1 Jan 2021 US
Provisional Applications (2)
Number Date Country
60845381 Sep 2006 US
60837408 Aug 2006 US
Continuations (7)
Number Date Country
Parent 16125891 Sep 2018 US
Child 16948656 US
Parent 15262479 Sep 2016 US
Child 16125891 US
Parent 14164682 Jan 2014 US
Child 15262479 US
Parent 13887727 May 2013 US
Child 14164682 US
Parent 13452130 Apr 2012 US
Child 13887727 US
Parent 13173039 Jun 2011 US
Child 13452130 US
Parent 12377054 US
Child 13173039 US