The present invention relates generally to vision or imaging systems for vehicles and is related to object detection systems and, more particularly, to imaging systems which are operable to determine if a vehicle or object of interest is adjacent to, forward of or rearward of the subject vehicle to assist the driver in changing lanes or parking the vehicle. The present invention also relates generally to a lane departure warning system for a vehicle.
Many lane change aid/side object detection/lane departure warning devices or systems and the like have been proposed which are operable to detect a vehicle or other object that is present next to, ahead of or rearward of the equipped vehicle or in an adjacent lane with respect to the equipped vehicle. Such systems typically utilize statistical methodologies to statistically analyze the images captured by a camera or sensor at the vehicle to estimate whether a vehicle or other object is adjacent to the equipped vehicle. Because such systems typically use statistical methodologies to determine a likelihood or probability that a detected object is a vehicle, and for other reasons, the systems may generate false positive detections, where the system indicates that a vehicle is adjacent to, forward of or rearward of the subject vehicle when there is no vehicle adjacent to, forward of or rearward of the subject vehicle, or false negative detections, where the system, for example, indicates that there is no vehicle adjacent to the subject vehicle when there actually is a vehicle in the adjacent lane.
Such known and proposed systems are operable to statistically analyze substantially all of the pixels in a pixelated image as captured by a pixelated image capture device or camera. Also, such systems may utilize algorithmic means, such as flow algorithms or the like, to track substantially each pixel or most portions of the image to determine how substantially each pixel or most portions of the image has changed from one frame to the next. Such frame by frame flow algorithms and systems may not be able to track a vehicle which is moving at generally the same speed as the equipped vehicle, because there may be little or no relative movement between the vehicles and, consequently, little or no change from one frame to the next. Because the systems may thus substantially continuously analyze substantially every pixel for substantially every frame captured and track such pixels and frames from one frame to the next, such systems may require expensive processing controls and computationally expensive software to continuously handle and process substantially all of the data from substantially all of the pixels in substantially each captured image or frame.
Many automotive lane departure warning (LDW) systems (also known as run off road warning systems) are being developed and implemented on vehicles today. These systems warn a driver of a vehicle when their vehicle crosses the road's land markings or when there is a clear trajectory indicating they will imminently do so. The warnings are typically not activated if the corresponding turn signal is on, as this implies the driver intends to make a lane change maneuver. Additionally, the warning systems may be deactivated below a certain vehicle speed. The driver interface for these systems may be in the form of a visual warning (such as an indicator light) and/or an audible warning (typically a rumble strip sound). One application warns a driver with an indicator light if the vehicle tire is crossing the lane marker and no other vehicle is detected in the driver's corresponding blind spot; and/or further warns the driver with an audible warning if the vehicle is crossing into the adjacent lane and there is a vehicle detected in the driver's blind spot.
There is concern that the current systems will be more of a driver annoyance or distraction than will be acceptable by the consumer market. Using the turn signal as the principle means of establishing to the warning system that the maneuver is intentional does not reflect typical driving patterns and, thus, many intended maneuvers will cause a warning. As a driver gets annoyed by warnings during intended maneuvers, the driver will likely begin to ignore the warnings, which may result in an accident when the warning is appropriate.
Therefore, there is a need in the art for an object detection system, such as a blind spot detection system or lane change assist system or lane departure warning system or the like, which overcomes the short comings of the prior art.
The present invention is intended to provide an object detection system, such as a blind spot detection system, a lane change assist or aid system or device, a lane departure warning system, a side object detection system, a reverse park aid system, a forward park aid system, a forward, sideward or rearward collision avoidance system, an adaptive cruise control system, a passive steering system or the like, which is operable to detect and/or identify a vehicle or other object of interest at the side, front or rear of the vehicle equipped with the object detection system. The object detection system of the present invention, such as a lane change assist system, utilizes an edge detection algorithm to detect edges of objects in the captured images and determines if a vehicle is present in a lane adjacent to the equipped or subject vehicle in response to various characteristics of the detected edges, such as the size, location, distance, intensity, relative speed and/or the like. The system processes a subset of the image data captured which is representative of a target zone or area of interest of the scene within the field of view of the imaging system where a vehicle or object of interest is likely to be present. The system processes the detected edges within the image data subset to determine if they correspond with physical characteristics of vehicles and other objects to determine whether the detected edge or edges is/are part of a vehicle or a significant edge or object at or toward the subject vehicle. The system utilizes various filtering mechanisms, such as algorithms executed in software by a system microprocessor, to substantially eliminate or substantially ignore edges or pixels that are not or cannot be indicative of a vehicle or significant object to reduce the processing requirements and to reduce the possibility of false positive signals.
In accordance with the present invention, portions or subsets of the image data of the captured image which are representative of areas of interest of the exterior scene where a vehicle or object of interest is likely to be present are weighted and utilized more than other portions or other subsets of the image data of the captured image representative of other areas of the exterior scene where such a vehicle or object of interest is unlikely to be present. Thus, in accordance with the present invention, a reduced set or subset of captured image data is processed based on where geographically vehicles of interest are realistically expected to be in the field of view of the image capture device. More specifically, for example, the control may process and weight the portion of the captured image data set that is associated with a lower portion of the image capture device field of view that is typically directed generally toward the road surface. Preferably, less than approximately 75% of the image data captured by the multi-pixel camera arrangement is utilized for object detection, more preferably, less than approximately 66% of the image data captured by the multi-pixel camera arrangement is utilized for object detection, and most preferably, less than approximately 50% of the image data captured by the multi-pixel camera arrangement is utilized for object detection.
It is further envisioned that the control may process or weight image data within the reduced set or subset which is indicative of physical characteristics of a vehicle or object of interest more than other image data within the reduced set which is not likely indicative of or cannot be indicative of such a vehicle or object of interest. The control thus may further reduce the processing requirements within the reduced set or sets of image data of the captured image.
Preferably, a multi-pixel array is utilized, such as a CMOS sensor or a CCD sensor or the like, such as disclosed in commonly assigned U.S. Pat. Nos. 5,550,677; 5,670,935; 5,796,094 and 6,097,023, and U.S. patent application Ser. No. 09/441,341, filed Nov. 16, 1999, now U.S. Pat. No. 7,339,149, which are hereby incorporated herein by reference, or such as an extended dynamic range camera, such as the types disclosed in U.S. provisional application Ser. No. 60/426,239, filed Nov. 14, 2002, which is hereby incorporated herein by reference. Because a multi-pixel array is utilized, the image or portion of the image captured by a particular pixel or set of pixels may be associated with a particular area of the exterior scene and the image data captured by the particular pixel or set of pixels may be processed accordingly.
According to an aspect of the present invention, an object detection system for a vehicle comprises a pixelated imaging array sensor and a control. The imaging array sensor is directed generally exteriorly from the vehicle to capture an image of a scene occurring exteriorly, such as toward the side, front or rear, of the vehicle. The control comprises an edge detection algorithm and is responsive to an output of the imaging array sensor in order to detect edges of objects present exteriorly of the vehicle. The control is operable to process and weight and utilize a reduced image data set or subset representative of a target area of the exterior scene more than other image data representative of other areas of the exterior scene. The target area or zone comprises a subset or portion of the image captured by the imaging array sensor and is representative of a subset or portion of the exterior scene within the field of view of the imaging array sensor. The control thus processes a reduced amount of image data and reduces processing of image data that is unlikely to indicate a vehicle or other object of interest. The imaging array sensor may be directed partially downwardly such that an upper portion of the captured image is generally at or along the horizon.
The control may be operable to process portions of the captured image representative of a target area of the scene and may reduce processing or reduce utilization of other portions of the captured image representative of areas outside of the target area and, thus, reduce the processing of edges or pixels which detect areas where detected edges are likely indicative of insignificant objects or which are not or cannot be indicative of a vehicle or significant object. The control is thus operable to process and weight and utilize image data from certain targeted portions of the captured image more than image data from other portions which are outside of the targeted portions or the target zone or area of interest.
The control may determine whether the detected edges within the target area are part of a vehicle in an adjacent lane in response to various characteristics of the detected edges which may be indicative of a vehicle or a significant object. For example, the control may be operable to process certain areas or portions or subsets of the captured image data or may be operable to process horizontal detected edges and filter out or substantially ignore vertical detected edges. The control may also or otherwise be operable to process detected edges which have a concentration of the edge or edges in a particular area or zone within the captured image. The control thus may determine that one or more detected edges are part of a vehicle in the adjacent lane in response to the edges meeting one or more threshold levels. Also, the control may adjust the minimum or maximum threshold levels in response to various characteristics or driving conditions or road conditions. For example, the control may be operable to process or substantially ignore detected edges in response to at least one of a size, location, intensity, distance, and/or speed of the detected edges relative to the vehicle, and may adjust the minimum or maximum threshold levels or criteria in response to a distance between the detected edges and the subject vehicle, a road curvature, lighting conditions and/or the like.
According to another aspect of the present invention, an imaging system for a vehicle comprises an imaging array sensor having a plurality of photo-sensing or accumulating or light sensing pixels, and a control responsive to the imaging array sensor. The imaging array sensor is positioned at the vehicle and operable to capture an image of a scene occurring exteriorly of the vehicle. The control is operable to process the captured image, which comprises an image data set representative of the exterior scene. The control is operable to apply an edge detection algorithm to the image captured by the imaging array sensor to detect edges or objects present exteriorly of the vehicle. The control may be operable to determine whether the detected edges or objects are indicative of a significant object or object of interest. The control is operable to process a reduced data set or subset of the image data set, which is representative of a target zone or area of the exterior scene, more than other image data representative of areas of the exterior scene which are outside of the target zone. The control thus may process image data of the reduced data set or subset, such as by applying an edge detection algorithm to the reduced data set, and substantially discount or limit processing of the other image data which is outside of the reduced data set or subset of the image or of the target zone of the exterior scene.
The control may be operable to adjust the reduced data set or subset and the corresponding target zone in response to various threshold criterion. The control may be operable to adjust the reduced data set or target zone in response to a distance to a detected edge or object. The control may approximate a distance to a portion of a detected edge or object in response to a location of the pixel or pixels capturing the portion in the captured image. The pixel location may be determined relative to a target pixel which may be directed generally at the horizon and along the direction of travel of the vehicle. For example, the control may be operable to approximate the distance using spherical trigonometry in response to a pixel size, pixel resolution and field of view of the imaging array sensor. The control may access an information array which provides a calculated distance for each pixel within the reduced data set or target zone to approximate the distance to the portion of the detected edge or object.
In order to determine if a detected edge or detected edges is/are part of or indicative of a vehicle, the control may be operable to determine if the detected edge or edges is/are associated with an ellipse or partial ellipse, since the ellipse or partial ellipse may be indicative of a tire of a vehicle near the equipped vehicle, such as a vehicle in a lane adjacent to the equipped vehicle. The control may also be operable to track one or more of the detected edges between subsequent frames captured by the imaging array sensor to classify and/or identify the object or objects associated with the detected edge or edges.
The object detection system or imaging system may comprise a lane change assist system operable to detect vehicles or objects of interest sidewardly of the vehicle. Optionally, the control may be in communication with a forward facing imaging system. The forward facing imaging system may communicate at least one of oncoming traffic information, leading traffic information and lane marking information to the control of the lane change assist system to assist the lane change assist system in readily identifying vehicles at the side of the subject vehicle or adjusting a reduced data set or an area or zone of interest within the captured image. The control may be operable to adjust the reduced data set or target zone in response to the forward facing imaging system.
Optionally, the object detection system or imaging system may comprise a forward facing imaging system, such as a lane departure warning system. The lane departure warning system may provide a warning or alert signal to the driver of the vehicle in response to a detection of the vehicle drifting or leaving its occupied lane.
Optionally, the forward facing imaging system may include or may be in communication with a passive steering system which is operable to adjust a steering direction of the vehicle in response to a detection by the imaging system of the vehicle drifting or leaving its occupied lane. Optionally, the forward facing imaging system may include or may be in communication with an adaptive speed control which is operable to adjust a cruise control or speed setting of the vehicle in response to road conditions or traffic conditions detected by the imaging system. Optionally, the imaging system may be in communication with a remote receiving device to provide image data to a display system remote from the vehicle such that a person remote from the vehicle may receive and view the image data with the remote receiving device to determine the location and/or condition of the vehicle or its occupants.
According to another aspect of the present invention, a lane change assist system for a vehicle comprises an imaging sensor and a control. The imaging sensor is positioned at the vehicle and directed generally sidewardly from the vehicle to capture an image of a scene occurring toward the side of the vehicle. The control is operable to process the image captured by the imaging array sensor to detect objects sidewardly of the vehicle. The captured image comprises an image data set representative of the exterior scene. The control is operable to process a reduced image data set of the image data set more than other image data of the image data set. The reduced image data set is representative of a target zone of the captured image.
The control may be operable to adjust the reduced data set or subset or target zone in response to an adjustment input. In one form, the adjustment input comprises an output from an ambient light sensor, a headlamp control and/or a manual control. The control may be operable to adjust the reduced data set or subset or target zone between a daytime zone and a nighttime zone in response to the output. The control may be operable to adjust a height input for the imaging array sensor such that the daytime zone is generally along the road surface and the nighttime zone is generally at a height of headlamps of vehicles.
In another form, the control may be operable to adjust the reduced data set or subset or target zone in response to a detection of the vehicle traveling through or along a curved section of road. The adjustment input may comprise an output from a forward facing imaging system or a detection by the imaging sensor and control that the vehicle is traveling through a curved section of road, such as by the imaging sensor and control detecting and identifying curved lane markers or the like along the road surface.
It is further envisioned that many aspects of the present invention are suitable for use in other vehicle vision or imaging systems, such as other side object detection systems, forward facing vision systems, such as lane departure warning systems, forward park aid systems or the like, rearward facing vision systems, such as back up aid systems or rearward park aid systems or the like, or panoramic vision systems and/or the like.
The present invention may also or otherwise provide a lane departure warning system that reduces and may substantially eliminate the provision of an unwarranted and/or unwanted visual or audible warning signals to a driver of a vehicle when the driver intends to perform the driving maneuver.
According to another aspect of the present invention, a lane departure warning system includes an imaging sensor mounted at a forward portion of a vehicle and operable to capture an image of a scene generally forwardly of the vehicle, and a control for providing a warning signal to a driver of the vehicle in response to an image captured by the imaging sensor. The control is operable to process the image captured to detect at least one of a lane marking, a road edge, a shoulder edge and another vehicle or object. The lane departure warning system provides the warning signal in response to a detected object or marking and further in response to the vehicle speed or other parameters which provide additional information concerning the likelihood that a warning signal is necessary.
Therefore, the present invention provides an object detection system or imaging system, such as a lane change assist system or other type of object detection or imaging system, which is operable to detect and identify vehicles or other objects of interest exteriorly, such as sidewardly, rearwardly, and/or forwardly of the subject vehicle. The imaging system may primarily process image data within a reduced data set or subset of the captured image data, where the reduced data set is representative of a target zone or area of interest within the field of view of the imaging system, and may adjust the reduced data set or zone or area in response to various inputs or characteristics, such as road conditions, lighting or driving conditions and/or characteristics of the detected edges or objects. The imaging system of the present invention is operable to detect edges of objects, and particularly horizontal edges of objects, to provide improved recognition or identification of the detected objects. The imaging system of the present invention may be operable to limit processing of or to filter or substantially eliminate or reduce the effect of edges or characteristics which are indicative of insignificant objects, thereby reducing the level of processing required on the captured images.
The edge detection process or algorithm of the lane change assist system of the present invention thus may provide for a low cost processing system or algorithm, which does not require the statistical methodologies and computationally expensive flow algorithms of the prior art systems. Also, the edge detection process may detect edges and objects even when there is little or no relative movement between the subject vehicle and the detected edge or object. The present invention thus may provide a faster processing of the captured images, which may be performed by a processor having lower processing capabilities then processors required for the prior art systems. The lane change assist system may also provide a low cost and fast approximation of a longitudinal and/or lateral and/or total distance between the subject vehicle and a detected edge or object exteriorly of the vehicle and may adjust a threshold detection level in response to the approximated distance.
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.
Referring now to the drawings and the illustrative embodiments depicted therein, an object detection system or imaging system, such as a lane change assist or aid system 10, is positioned at a vehicle 12 and is operable to capture an image of a scene occurring sidewardly and rearwardly at or along one or both sides of vehicle 12 (
Camera or imaging sensor 14 of object detection system or lane change assist system 10 is operable to capture an image of the exterior scene within the field of view of the camera. The captured image comprises an image data set, which is representative of the exterior scene, and which is received by control 16. Control 16 is operable to process image data within a reduced data set or subset of the image data set more than other image data of the image data set to reduce the processing requirements of the control. The reduced data set or subset or subsets is/are representative of a target zone or area or areas in the exterior scene where a vehicle or other object of interest may realistically be expected to be present within the exterior scene. The control is thus operable to primarily process the significant or relevant area or areas of the scene more than less relevant areas, and may limit or reduce processing of or substantially ignore the image data representative of some areas of the exterior scene where it is not likely that a vehicle or other object of interest would be present or where a vehicle cannot be present.
Camera or imaging sensor 14 may comprise an imaging array sensor, such as a CMOS sensor or a CCD sensor or the like, such as disclosed in commonly assigned U.S. Pat. Nos. 5,550,677; 5,670,935; 5,796,094 and 6,097,023, and U.S. patent application Ser. No. 09/441,341, filed Nov. 16, 1999, now U.S. Pat. No. 7,339,149, which are hereby incorporated herein by reference, or an extended dynamic range camera, such as the types disclosed in U.S. provisional application Ser. No. 60/426,239, filed Nov. 14, 2002, which is hereby incorporated herein by reference. The imaging sensor 14 may be implemented and operated in connection with other vehicular systems as well, or may be operable utilizing the principles of such other vehicular systems, such as a vehicle headlamp control system, such as the type disclosed in U.S. Pat. No. 5,796,094, which is hereby incorporated herein by reference, a rain sensor, such as the types disclosed in commonly assigned U.S. Pat. Nos. 6,353,392; 6,313,454 and/or 6,320,176, which are hereby incorporated herein by reference, a vehicle vision system, such as a forwardly or sidewardly or rearwardly directed vehicle vision system utilizing the principles disclosed in U.S. Pat. Nos. 5,550,677; 5,670,935 and 6,201,642, and/or in U.S. patent application Ser. No. 09/199,907, filed Nov. 25, 1998, now U.S. Pat. No. 6,717,610, which are hereby incorporated herein by reference, a traffic sign recognition system, a system for determining a distance to a leading vehicle or object, such as utilizing the principles disclosed in U.S. Pat. No. 6,396,397, which is hereby incorporated herein by reference, and/or the like.
Camera 14 preferably comprises a pixelated imaging array sensor which has a plurality of photon accumulating light sensors or pixels 14a. The camera includes circuitry which is operable to individually access each photosensor pixel or element of the array of photosensor pixels and to provide an output or image data set associated with the individual signals to the control 16, such as via an analog to digital converter (not shown). As camera 14 receives light from objects and/or light sources in the target scene, the control 16 may then be operable to process the signal from at least some of the pixels to analyze the image data of the captured image, as discussed below.
Camera 14 may be positioned along one or both sides of vehicle 12, such as at or within the exterior rearview mirror 12a at either or both sides of vehicle 12. However, camera 14 may be positioned elsewhere along either or both sides and/or at the rear of the vehicle and directed sidewardly and rearwardly from the vehicle to capture an image at either side of the vehicle, without affecting the scope of the present invention. Camera 14 may be positioned at vehicle 12 and oriented or angled downwardly so as to capture an image which has an upper edge or region generally at the horizon 15, as can be seen with reference to
Control 16 is responsive to camera 14 and processes the signals received from at least some of the pixels of camera 14 to determine what is in the captured image. The present invention utilizes physical characteristics of vehicles and roads to reduce or filter out or substantially eliminate the signals from some of the pixels and to reduce or eliminate signals or detected images indicative of certain insignificant or unimportant objects detected within the captured image, as discussed below. For example, control 16 may primarily process the image data from pixels of camera 14 that are within a reduced data set or subset of the image data of the captured image. The reduced data set of the captured image may be representative of a targeted area or zone of interest of the exterior scene being captured by the camera. The targeted zone may be selected because it encompasses a geographic area of the exterior scene where a vehicle or other object of interest is likely to be present, while the other image data or areas or portions of the captured image may be representative of areas in the exterior scene where a vehicle or other object of interest is unlikely to be present or cannot be present, as discussed below. The present invention thus may provide for a quicker response time by control 16, since the control 16 does not continually process the signals from substantially all of the pixels 14a of camera 14. Preferably, less than approximately 75% of the image data captured by the camera is utilized for object detection, more preferably, less than approximately 66% of the captured image data is utilized for object detection, and most preferably, less than approximately 50% of the captured image data is utilized for object detection.
Control 16 may include a microprocessor having an edge detection algorithm or function 16a (
Control 16 may be operable to determine which edges detected are horizontal or generally horizontal edges and to limit processing of or to partially filter out or substantially ignore vertical edges. This may be preferred, since many edges in a vehicle in an adjacent lane will be horizontal or parallel to the road surface, such as edges associated with bumper lines, grills, fenders, and/or the like. Control 16 may thus reject or substantially ignore edges which are non-horizontal, thereby reducing the data to be processed. The edge detection algorithm 16a may also provide digital polarization of the captured images to determine horizontal gradients and to substantially ignore the effects of vertical gradients of the detected edges. For example, the edge detection algorithm may use a convolution matrix (such as a one by three matrix or other small matrix or array) which may be processed or applied to the image data in a single pass across the data received from the pixels 14a of the camera 14 to provide horizontally polarized edge detection through the captured image or a portion thereof. Such horizontal polarization greatly reduces the possibility that road signs and/or guardrails and/or the like will be processed and analyzed by the control of the lane change assist system of the present invention, thereby reducing the processing requirements and reducing the possibility of a false positive signal by the control.
Additionally, the edge detection algorithm 16a of control 16 may function to detect and determine if there is more than one vehicle present at the side of the subject vehicle 12. For example, control 16 may distinguish between edges constituting the fronts of different vehicles and edges constituting the front and side of the same vehicle, since the vehicle fronts typically will have more horizontal edges than the vehicle sides.
In order to further reduce the processing requirements and the possibility of a false positive indication, and thus enhance the response time and system performance, control 16 may process signals or image data from pixels that are oriented or targeted or arranged or selected to capture images of objects or items that are at least partially positioned within a predetermined or targeted area or zone of interest. The zone of interest may be defined by an area or region at the side of the subject vehicle where another vehicle or significant object may be positioned, such as in the blind spot region of that side of the vehicle, which would be significant or important to lane change assist system 10. For example, the zone of interest or “polygon of interest” may be directed rearward from the camera and toward or around the center of the adjacent lane. By substantially isolating the zone of interest, or substantially filtering out or substantially ignoring or reducing utilization of edges or signals or image data of the captured image which are representative of areas outside of the zone or area of interest, the system of the present invention may reduce the image data or information to be processed by control 16 and may substantially reduce the possibility that a false positive signal will occur. For example, if an object is detected substantially to one side or the other or substantially at the bottom of the captured image, such an object is not likely to be a vehicle positioned within the blind spot area of the subject vehicle 12, whereby control 16 may reduce processing of or may not process image data from the pixels capturing that area of the scene or may substantially ignore such a detected edge or object in subsequent processing of the image data captured by the pixels 14a of camera 14.
It is further envisioned that control 16 may process the image data of pixels capturing images representative of an area within the zone of interest and may not indicate a positive signal of a vehicle or other significant object in the adjacent lane unless a detected edge within the reduced image data set or subset or zone of interest is greater than a minimum size threshold, or spans a threshold number of pixels. Optionally, control 16 may require that a detected edge span or include a threshold number of pixels that are within a predetermined “hot zone” or specific targeted area within the zone of interest before the edge will be considered significant for further processing. The targeted zone or hot zone may be defined as a reduced zone or area near the center of the zone of interest or targeted road space or adjacent lane. The control 16 thus may require a substantial portion of the detected edge or edges to be within the smaller hot zone before the control may consider the edges to constitute a portion of a vehicle in the adjacent lane or other significant object. This also may substantially reduce the processing requirements and may substantially reduce the possibility of a false positive signal being generated by control 16.
The reduced image data set of the captured image which is representative of the zone of interest of the exterior scene may be adjusted by control 16 in response to various road or driving conditions, lighting conditions, and/or characteristics of the detected edges or objects. The reduced data set or zone of interest thus may be adaptable to various conditions encountered by the vehicle, such that the control may further reduce the processing requirements and enhance the efficiency of the system by primarily processing image data from some pixels and ignoring image data from other pixels depending on the present conditions surrounding the vehicle.
For example, as shown in
As shown in
It is further envisioned that the reduced data set or area or zone of interest may be changed or adapted to accommodate sharp curves in the road that the subject vehicle 12 is traveling through or has traveled through. In situations where a vehicle travels along a sharp curve in the road, a lane change assist system may consider a guardrail or vehicle 18′ in another lane to be a vehicle or object of interest in the adjacent lane, since the other vehicle or object may be positioned generally at or near the zone of interest of the lane change assist system, as can be seen in
Optionally, control 16 may be further operable to substantially eliminate or substantially ignore image data representative of objects or edges which are too large or too small to be considered part of a vehicle in the adjacent lane. If a detected edge is too small, such as if the horizontal pixel span or vertical pixel span is very small, the control may reduce processing of the edge or the edge may be removed from further processing, since it does not represent a significant edge to the lane change assist system 10. Likewise, if an edge is too large, the control may reduce processing of the edge or it may also be removed from further processing since it does not represent a vehicle in the adjacent lane. The threshold size of the detected edge or object may also vary in response to the distance to the edge or object, as discussed below.
Additionally, lane change assist system 10 may be operable to determine whether a detected edge or object is a vehicle in an adjacent lane in response to one or more other detection thresholds or criteria. Further, control 16 may be operable to vary one or more detection thresholds or criteria at which a detected edge or object is considered a vehicle or significant object. The threshold values may thus be variable and may be adjusted in response to driving conditions, road curvature, location of the detected edges and/or the distance between the camera and the detected object and/or the like. For example, the threshold value or values may be adjusted in response to the distance so that control 16 more readily accepts and processes detected edges as the object they are representative of gets closer to or approaches the subject vehicle.
For example, control 16 may have a minimum gradient threshold at which control 16 determines whether or not a detected edge is to be included in further processing of the captured image. Control 16 thus may be operable to determine the vertical and/or horizontal gradient of the detected edges and may substantially eliminate or filter out edges with a gradient below a threshold gradient level, since such edges cannot be representative of a vehicle or object which is significant to the lane change assist system. The control thus may further substantially preclude false positive signals and reduce further processing of the pixel signals.
However, as an object or other vehicle approaches the subject vehicle 12, the detected edge or edges representative of the object tends to resolve or reduces and spreads out the gradient across multiple pixels, thereby reducing the gradient at a particular pixel. Control 16 thus may be further operable to adjust the minimum gradient threshold in response to the distance to the detected object. By using a calculated or estimated or approximated distance to the detected object or a table of perspective calculations or distance approximations, discussed below, the minimum gradient threshold may be reduced proportionally in response to the estimated or tabulated distance data to provide enhanced edge detection at closer ranges.
By detecting edges of objects within the reduced data set or zone or area of interest (and adjusting the zone of interest for particular driving conditions or situations), and by focusing on or concentrating on or primarily processing the horizontal edges detected or other edges which may be indicative of a vehicle or significant object, while substantially filtering out or substantially ignoring other image data or edges or information, the present invention substantially reduces the possibility of false positive signals. In order to further reduce the possibility of such false positive signals, control 16 may be operable to determine a distance between a detected object and the subject vehicle to further filter out or substantially eliminate objects that are not within a predetermined range or threshold distance from the subject vehicle and which, thus, may be insignificant to the lane change assist system of the present invention.
In a preferred embodiment, camera 14 and control 16 may be operable to approximate the distance to an object or vehicle in response to a pixel count of the number of pixels between the pixels capturing the object (or an edge of the object) and the pixels along an edge of the camera or directed toward and along the horizon of the captured image. More particularly, with the camera 14 oriented with the video frame horizontal scan lines or pixels being generally parallel to the horizon, perspective calculations may be made to provide a table of entries of particular distances which correspond to particular horizontal lines or pixels in the video frame which may detect or sense a forward edge of an adjacent vehicle at the ground level, such as an edge corresponding to a shadow of the front of the vehicle 18 or an edge corresponding to the intersection of the tire 18d of the vehicle 18 on the road surface or the like. The distance to an object captured by an edge detected in the captured image may then be approximated by determining a vertical pixel count and retrieving the appropriate distance entry corresponding to that particular horizontal line or pixel count or position. The present invention thus provides for a quick and inexpensive means for determining or estimating or approximating the distance between the subject vehicle and an object or edge detected in the area or zone of interest by determining a horizontal line count from the horizon down to the pixels capturing the detected edge.
As can be seen with reference to
More particularly, in order to determine the total distance between camera 14 and the closest point of a detected edge or object, the lateral distance ψ and longitudinal distance δ may be calculated and used to obtain the total distance τ. Because the lateral distance ψ should be approximately constant for an edge or vehicle detected in the zone or area corresponding to the adjacent lane, the lane change assist system 10 may only calculate or tabulate and access the longitudinal distance δ for the detected edges, whereby the distances may be calculated and tabulated for each horizontal line count down from the horizon or target point. More particularly, the longitudinal distance δ may be calculated or approximated by determining a pixel count (Pixelsβ) downward from the horizon 15 to the detected edge or point φ. The pixel count may be used to obtain a value for the downward angle β (
β=Pixelsβ*v; (1)
where v is the vertical view angle per pixel of the camera and is obtained via the following equation (2):
v=(Optical Field Height Degrees)/(Vertical Pixel Resolution); (2)
where the Optical Field Height Degrees is the vertical angle of view of the camera and the Vertical Pixel Resolution is the number of horizontal rows of pixels of the camera. The downward angle β is then calculated to determine the angle between the horizon and the forward edge of the detected object at the ground. The longitudinal distance δ between the vehicles may then be determined or approximated by the following equation (3):
δ=γ*tan(90°−β); (3)
where γ is the height of the camera 14 above the ground as input to the control 16, and as best shown with reference to
Likewise, if desired, the lateral or sideward location or distance ψ to the closest point φ on the detected edge or object may be calculated by obtaining a horizontal pixel count Pixelβmin, such as by counting or determining the pixels or pixel columns from the alignment point 14b horizontally across the captured image to the pixel column corresponding to the closest point φ. This pixel count value may be used to calculate the lateral distance to the detected edge or object, which may in turn be used to calculate or estimate the total distance to the detected object. More particularly, the lateral angle ω (
ω=Pixelβmin*λ; (4)
where λ is the horizontal view angle per pixel of the camera and is obtained via the following equation (5):
λ=Optical Field Width Degrees/Horizontal Pixel Resolution; (5)
where the Optical Field Width Degrees of camera 14 is the angle of view of the camera and the Horizontal Pixel Resolution is the number of columns of pixels of camera 14.
Optionally, the lateral angle ω (
In determining the perspective geometry, the parameters of a virtual camera 14′ are determined or assigned and implemented (see
The virtual camera geometry may be calculated and used to determine the relationship between each pixel of the captured image and the location on the road surface that the pixel corresponds to. These calculations may be based on an assumption that lines perpendicular to the direction of travel of the subject vehicle may be on a plane which is generally parallel to the horizon and, thus, parallel to the image or pixel lines or rows, since the camera is positioned or oriented such that the horizontal rows of pixels are generally parallel to the horizon. This allows the control to determine the distance along the vehicle forward direction in response to the row of pixels on which the object has been detected, assuming that the camera is detecting an edge of the detected object or other vehicle (such as the front shadow edges, tires or the like) along the pavement or road surface.
An array of pixels 14a′ and a focal length (in pixels) vfl of the virtual camera 14′ is shown in
vfl=(Pixel Resolution/2)/(tan(Frame Angular Size/2)); (6)
where the Frame Angular Size is the angular field of view of the camera 14. This equation may be used to calculate the virtual focal length of an imaginary pinhole camera with an infinitely small pinhole lens in vertical pixels vvfl and the virtual focal length in horizontal pixels hvfl using the pixel resolutions and frame angular sizes in the vertical and horizontal directions, respectively. The virtual focal length is calculated in both vertical pixel units and horizontal pixel units because the vertical and horizontal sizes of the pixels may be different and the camera may have a different pixel resolution and frame angular size or field of view between the vertical and horizontal directions.
The vertical or downward view angle β to the object may be determined by the following equation (7):
β=arctan(Vertical Pixels)/(vvfl); (7)
where Vertical Pixels is the number of pixels or rows of pixels down from the target row or horizon. The view angle thus may be calculated for any line of pixels according to equation (7). An array for each of the view angle values may be calculated and stored for rapid distance calculations. The downward angle β may then be used to calculate the longitudinal distance δ in a similar manner as discussed above. As discussed above, the longitudinal distance calculations assume that for a detected edge or object along a row of pixels, the longitudinal distance to the edge or object is the same for any pixel along the row, since the camera is oriented with the rows of pixels being generally parallel to the horizon and generally perpendicular to the direction of travel of the vehicle.
In order to determine the location and angle and distance to a detected object or edge (which may be represented by a point along an object, such as at coordinate x, y of the pixel array (
vefl=(vvfl2+(y−height/2)2)1/2; (8)
where height/2 is one-half of the vertical image height (in pixels) of the camera. The effective focal length in horizontal pixels (hefl) may then be calculated by converting the effective focal length in vertical pixel units to horizontal pixel units via the following equation (9):
hefl=hvfl*vefl/vvfl. (9)
The horizontal angle ω to the detected point in the image may be calculated via the following equation (10):
ω=arctan(Horizontal Pixels/hefl); (10)
where Horizontal Pixels is the number of columns of pixels (or horizontal distance in pixels) that the point x, y is from the target or alignment or aft point or pixel. The Horizontal Pixels value may be counted or calculated by the control. The calculations for the Horizontal Pixels value may be different for the opposite sides of the vehicle in applications where the zero coordinate of the pixel array may be on the vehicle side of the array for a camera on one side of the vehicle, such as on the passenger side of the vehicle, and may be on the outside of the array for a camera on the other side of the vehicle, such as on the driver side of the vehicle. In the illustrated embodiment of
Such calculations may provide a more precise and true value for the lateral angle ω between the camera 14 and the detected object. The lateral distance ψ to the detected object may thus be calculated by the following equation (11):
ψ=δ*tan(ω). (11)
Accordingly, the actual distance T between camera 14 and the closest point on the detected object may be obtained by the following equation (12):
τ=(δ2+ψ2)1/2. (12)
Because the lateral, longitudinal and total distances are calculated using certain known or obtainable characteristics and geometrical relationships, such as the input height of camera 14 above the ground, the pixel resolution of camera 14, the field of view of the camera, and a pixel count in the horizontal and vertical direction with respect to a target point or alignment target and/or the horizon, the calculated distance and/or angle values for each pixel count or location may be entered into a table to provide a rapid response time for determining the distance to the detected edge or object once the pixel count or location of the detected edge is known.
As discussed above, the lane change assist system may only be concerned with the longitudinal distance δ to the detected edge. Control 16 may thus determine a vertical pixel count and approximate the longitudinal distance to the detected object or edge via equations (1), (2) and (3) or via the data table, thereby significantly reducing the processing requirements of control 16 to estimate or calculate the distance to the detected edges or objects.
Additionally, control 16 may be operable to substantially eliminate or substantially ignore other forms or types of detected edges which are not likely or cannot be part of a vehicle in the adjacent lane. For example, as can be seen in
In order to further reduce the possibility of control 16 generating a false positive signal, control 16 of lane change assist system 10 may be operable to determine an intensity or brightness level associated with the detected edges and to substantially eliminate edges which do not significantly change in brightness level or intensity level from one side of the detected edge to the other. This is preferred, since lines in the road, thin branches on the road and/or many other small articles or objects may not resolve, and thus may result in single edges that do not significantly change in brightness or intensity (or color if a color system is used) across their detected edges. However, a significant change in brightness or intensity would be expected along a detected edge of an automotive body panel or bumper or other component or structure of a vehicle or the like. Accordingly, control 16 may substantially eliminate or substantially ignore edges or objects which do not have a significant brightness or intensity change thereacross, since an edge with an insignificant change in brightness or color signifies an insignificant edge which can be substantially eliminated. By substantially eliminating such insignificant edges, control 16 may further significantly reduce the computational requirements or processing requirements, while also significantly reducing the possibility of a false positive indication.
Control 16 may also be operable to compare image data from consecutive frames or images captured by camera 14 to confirm that a detected edge or object is representative of a vehicle in an adjacent lane and/or to determine the relative speed between the detected object or vehicle and the equipped or subject vehicle 12. By extracting collections of edges or points of interest, such as ellipses, bend maximums in edges and/or the like, from consecutive frames, and correlating such points of interest from one frame to the next, the lane change assist system of the present invention can more effectively verify the pairing of such characteristics or objects. The control may track or correlate the points of interest based on the placement or location of the edges within the captured images, the general direction of travel of the detected edges or groups of edges between consecutive frames, the dimensions, size and/or aspect ratio of the detected edges or objects and/or the like. Confirming such characteristics of edges and groups of edges and objects allows the lane change assist system to track the objects from one captured frame or image to the next. If the relative speed or movement of the detected edge or object is not indicative of the relative speed or movement of a vehicle in the adjacent lane, control 16 may filter out or substantially ignore such detected edges in further processing so as to reduce subsequent processing requirements and to avoid generation of a false positive signal. Lane change assist system 10 may also be operable to connect collections of such objects or edges based on relative motion between the subject vehicle and the detected object or edges. Such connected collections may provide information about the size and shape of the detected object for object classification and identification by control 16.
It is further envisioned that lane change assist system 10 may be operable in conjunction with a lane departure warning system or other forward facing imaging system 22 of vehicle 12, such as a lane departure warning system of the type discussed below or as disclosed in U.S. provisional application Ser. No. 60/377,524, filed May 3, 2002, which is hereby incorporated herein by reference, or any other lane departure warning system or the like, or a headlamp control system, such as disclosed in U.S. Pat. No. 5,796,094, which is hereby incorporated herein by reference, or any forwardly directed vehicle vision system, such as a vision system utilizing principles disclosed in U.S. Pat. Nos. 5,550,677; 5,670,935; 6,201,642 and 6,396,397, and/or in U.S. patent application Ser. No. 09/199,907, filed Nov. 25, 1998, now U.S. Pat. No. 6,717,610, which are hereby incorporated herein by reference. The forward facing imaging system may provide an input to lane change assist system 10 to further reduce any likelihood of a false positive signal from the lane change assist system.
For example, the forward facing imaging system may detect lane markers at the road surface to detect a curvature in the road that the subject vehicle is approaching and/or traveling along. Such information may be communicated to lane change assist system 10, so that control 16 may adapt or shape the reduced image data set or zone of interest as the subject vehicle 12 enters and proceeds along the detected road curvature, as discussed above. Also, the forward facing imaging system may detect headlamps of oncoming or approaching vehicles. If the lane forward and to the left of vehicle 12 has oncoming traffic, control 16 may substantially ignore the left side of the vehicle, since the lane change assist system would not be concerned with a lane change into oncoming traffic. Also, the forward facing imaging system may detect the tail lights or rear portion of a leading vehicle in another lane, and may track the leading vehicle relative to the subject vehicle. As the subject vehicle overtakes the leading vehicle, the lane change assist system may then be alerted as to the presence of the overtaken vehicle, such that edges detected in that lane a short period of time after the overtaken vehicle leaves the range of the forward facing imaging system (the period of time may be calculated based on the relative velocity between the subject vehicle and the overtaken vehicle) may be readily identified as the now overtaken and passed vehicle. By utilizing the vehicle information of a vehicle detected by a forward facing imaging system, the lane change assist system of the present invention (or other side object detection systems or the like) may reduce the amount of processing of the captured images or detected edges, since such a vehicle may be readily identified as the vehicle that was previously detected by the forward facing imaging system. This avoids a duplication of efforts by the forward facing imaging system and lane change assist system of the vehicle.
By primarily processing image data and detected edges in certain areas and/or processing image data and detected edges that meet certain thresholds or criteria, and substantially rejecting or substantially ignoring other information or image data or edges, such as detected edges that are substantially non-horizontal, or other edges that cannot be part of a vehicle, or image data that are not representative of a zone of interest of the exterior scene, the lane change assist system of the present invention substantially reduces the image data to be processed by control 16. It is envisioned that such a reduction in the amount of image data to be processed may allow the lane change assist system to have a control which comprises a micro-processor positioned at the camera. Accordingly, the lane change assist system may be provided as a module which may be positioned at either or both sides of the vehicle, and which may be connected to an appropriate power source or control or accessory of the vehicle.
Therefore, the present invention provides a lane change assist system which is operable to detect and identify vehicles or other objects of interest sidewardly and/or rearwardly of the subject vehicle. The lane change assist system of the present invention is operable to detect edges of objects, and particularly horizontal edges of objects, to provide improved recognition or identification of the detected objects and reduced false positive signals from the lane change assist system. The lane change assist system may primarily process information or image data from a reduced set or subset of image data which is representative of a target zone or area of interest within the exterior scene and may adjust the reduced data set or target zone in response to driving or road conditions or the like. The edge detection process or algorithm of the lane change assist system of the present invention provides for a low cost processing system or algorithm, which does not require the statistical methodologies and computationally expensive flow algorithms of the prior art systems. Also, the edge detection process may detect edges and objects even when there is little or no relative movement between the subject vehicle and the detected edge or object.
The lane change assist system of the present invention thus may be operable to substantially ignore or substantially eliminate or reduce the effect of edges or characteristics which are indicative of insignificant objects, thereby reducing the level of processing required on the captured images and reducing the possibility of false positive detections. The lane change assist system may also provide a low cost and fast approximation of a longitudinal and/or lateral and/or total distance between the subject vehicle and a detected edge or object at a side of the vehicle and may adjust a threshold detection level in response to the approximated distance. The lane change assist system of the present invention may be operable to substantially ignore certain detected edges or provide a positive identification signal depending on the characteristics of the detected object or edge or edges, the driving or road conditions, and/or the distance from the subject vehicle. The present invention thus may provide a faster processing of the captured images, which may be performed by a processor having lower processing capabilities then processors required for the prior art systems.
Although the present invention is described above as a lane change assist or aid system or side object detection system, it is envisioned that many aspects of the imaging system of the present invention are suitable for use in other vehicle vision or imaging systems, such as other side object detection systems, forward facing vision systems, such as lane departure warning systems, forward park aids, passive steering systems, adaptive cruise control systems or the like, rearward facing vision systems, such as back up aids or park aids or the like, panoramic vision systems and/or the like.
For example, an object detection system or imaging system of the present invention may comprise a forward facing lane departure warning system 110 (
Similar to camera 14 of lane change assist system 10, discussed above, camera 114 may be positioned at vehicle 12 and oriented generally downwardly toward the ground to increase the horizontal pixel count across the captured image at the important areas in front of vehicle 12, since any significant lane marking or road edge or the like, or other vehicle approaching or being approached by the subject vehicle, positioned in front of or toward a side of the subject vehicle will be substantially below the horizon and thus substantially within the captured image. The lane departure warning system of the present invention thus may provide an increased portion of the captured image or increased pixel count at important areas of the exterior scene, since the area well above the road or horizon is not as significant to the detection of lane markers and the like and/or other vehicles. Additionally, positioning the camera to be angled generally downwardly also reduces the adverse effects that the sun and/or headlamps of other vehicles may have on the captured images.
Control 116 of lane departure warning system 110 may include an edge detection algorithm or function, such as described above, which is operable to process or may be applied to the individual pixels to determine whether the image captured by the pixels defines an edge or edges of a lane marker or the like. The edge detection function or algorithm of control 116 allows lane departure warning system 110 to interrogate complex patterns in the captured image and separate out particular patterns or edges which may be indicative of a lane marker or the like, and to substantially ignore other edges or patterns which are not or cannot be indicative of a lane marker or the like and thus are insignificant to lane departure warning system 110. Other information in the captured image or frame, which is not associated with significant edges, may then be substantially ignored or filtered out by control 116 via various filtering mechanisms or processing limitations to reduce the information being processed by control 116.
Control 116 may be operable to determine which detected edges are angled or diagonal across and along the captured image and to partially filter out or substantially ignore or limit processing of vertical and/or horizontal edges. This may be preferred, since edges indicative of a lane marker may be angled within the captured image, as can be seen with reference to
In order to further reduce the processing requirements and the possibility of a false detection or indication of a lane marker, and to enhance the response time and system performance, control 116 may primarily process signals or image data from pixels that are oriented or targeted or arranged or selected to capture images of objects or markers that are at least partially positioned within a predetermined or targeted area or zone of interest of the exterior scene. The zone of interest may be defined by an area or region forwardly and toward one or both sides of the subject vehicle where a lane marker or road side or edge may be positioned, which would be significant or important to lane departure warning system 110. By substantially isolating the reduced data set representative of the zone of interest, or substantially filtering out or substantially ignoring edges or signals or image data which are representative of areas outside of the zone or area of interest, the present invention may reduce the image data or information to be processed by control 116 and may substantially reduce the possibility that a false detection of a lane marker or the like will occur. Lane departure warning system 110 may also process edges or image data within a further reduced image data set representative of a targeted portion or hot zone of the zone of interest to further identify and confirm that the detected edge or edges are indicative of a lane marker or the like or a vehicle or object that is significant to the lane departure warning system, such as discussed above with respect to lane change assist system 10.
By detecting edges of objects (such as lane markers, road edges, vehicles and the like) within the zone or area of interest (and optionally adjusting the zone of interest for particular driving conditions or situations), and by focusing on or concentrating on or primarily processing the detected edges or image data which may be indicative of a lane marker or vehicle or significant object, while substantially filtering out or substantially ignoring other edges or information or image data, the present invention substantially reduces the possibility of falsely detecting lane markers or other significant vehicles or objects. Control 116 may be further operable to determine a distance between a detected object and the subject vehicle to further filter out or substantially eliminate objects that are not within a predetermined range or threshold distance from the subject vehicle and which, thus, may be insignificant to the lane departure warning system of the present invention, such as described above with respect to lane change assist system 10.
Control 116 may also be operable to determine or estimate the distance to the detected edge or object in response to the location of the pixel or pixels on the pixelated array which capture the detected edge or object, such as in the manner also discussed above. The distance may thus be determined by determining the pixel location and accessing a table or data list or array to determine the distance associated with the particular pixel.
Control 116 of lane departure warning system 110 may also be operable to determine an intensity or brightness level associated with the detected edges and to substantially eliminate edges which do not significantly change in brightness level or intensity level from one side of the detected edge to the other. This is preferred, since thin branches on the road and/or many other small articles or objects may not resolve, and thus may result in single edges that do not significantly change in brightness or intensity (or color if a color system is used) across their detected edges. However, a sharp or significant change in brightness or intensity would be expected at a detected edge of a lane marker (since a lane marker is typically a white or yellow line segment along a dark or black or gray road surface) or an automotive body panel or bumper or other component or structure of a vehicle or the like. Accordingly, control 16 may substantially eliminate or substantially ignore edges or objects which do not have a significant brightness or intensity change thereacross. By substantially eliminating such insignificant edges, control 16 may further significantly reduce the computational requirements or processing requirements, while also significantly reducing the possibility of a false detection of a lane marker or vehicle. It is further envisioned that lane departure warning system 110 may be capable of detecting lane markings and road edges and other vehicles and modifying the alert signal or process in response to the type of marking, surrounding vehicles or the like and/or the vehicle movement, such as disclosed in U.S. provisional application Ser. No. 60/377,524, filed May 3, 2002, which is hereby incorporated herein by reference.
With reference to
The imaging sensor useful with the present invention is preferably an imaging array sensor, such as a CMOS sensor or a CCD sensor or the like, such as disclosed in commonly assigned U.S. Pat. Nos. 5,550,677; 5,670,935; 5,796,094 and 6,097,023, and U.S. patent application Ser. No. 09/441,341, filed Nov. 16, 1999, now U.S. Pat. No. 7,339,149, which are hereby incorporated herein by reference. The imaging sensor may be implemented and operated in connection with other vehicular systems as well, or may be operable utilizing the principles of such other vehicular systems, such as a vehicle headlamp control system, such as the type disclosed in U.S. Pat. No. 5,796,094, which is hereby incorporated herein by reference, a rain sensor, such as the types disclosed in commonly assigned U.S. Pat. Nos. 6,353,392; 6,313,454 and/or 6,320,176, which are hereby incorporated herein by reference, a vehicle vision system, such as a forwardly directed vehicle vision system utilizing the principles disclosed in U.S. Pat. Nos. 5,550,677; 5,670,935 and 6,201,642, and/or in U.S. patent application Ser. No. 09/199,907, filed Nov. 25, 1998, now U.S. Pat. No. 6,717,610, which are hereby incorporated herein by reference, a traffic sign recognition system, a system for determining a distance to a leading vehicle or object, such as using the principles disclosed in U.S. patent application Ser. No. 09/372,915, filed Aug. 12, 1999, now U.S. Pat. No. 6,396,397, which is hereby incorporated herein by reference, and/or the like.
The lane departure warning system of the present invention is operable to provide a warning signal to a driver of the vehicle under at least one of at least the following three conditions:
1) the vehicle is moving toward the edge of the road at a rapid speed indicating that the vehicle will actually depart from the pavement or shoulder;
2) the vehicle is moving into a lane with oncoming traffic present in that lane; and/or
3) the vehicle is moving into a lane with traffic flowing in the same direction and there is an adjacent vehicle in that lane (regardless of turn signal use).
The lane departure warning system may be operable in response to one or more of the detected conditions and may be further operable in response to various vehicle characteristics or parameters, such as vehicle speed, a distance to the lane marker, shoulder, other vehicle, or any other relevant distance, road conditions, driving conditions, and/or the like.
With respect to the first condition (shown in
1) threshold 1: the edge 113a of the road or pavement 113 (the intersection of the pavement 113 and the shoulder 113b); and/or
2) threshold 2: the edge 113c of the shoulder 113b (the intersection of the shoulder 113b and the grass 113d).
The lane departure warning system of the present invention may then be operable to provide an audible warning, such as a rumble strip sound, when the vehicle is approaching threshold 1 and the vehicle is moving above an established speed. The lane departure warning system may then be operable to provide a more urgent audible warning, such as an alarm, when the vehicle is approaching threshold 2 and is moving above the established speed. If the road does not have a shoulder, such as on some rural roads, there is only one threshold and this may correspond to a threshold 2 warning. The lane departure warning system may be operable to provide the warning signal or signals in response to the vehicle being a particular distance from the detected lane or road or shoulder. The distances to the threshold markings at which the lane departure warning system initiates the warning signal or signals may vary depending on the speed of the vehicle, or other conditions surrounding the vehicle, such as road conditions, driving conditions, or the like.
With respect to the second condition, the lane departure warning system may be operable in response to a single forward facing camera to monitor the lane markings 113e along the road surface and monitor the potential presence of oncoming traffic in an adjacent lane or lanes. Once the presence of oncoming traffic has been established, the lane departure warning system may issue an urgent audible warning if the vehicle begins to cross the lane marking 113e. Furthermore, if the vehicle has already begun to cross into the oncoming traffic lane before oncoming traffic is detected, the lane departure warning system may issue the urgent warning when oncoming traffic is detected.
Similar to the first condition, the lane departure warning system may be operable in response to the second condition to initiate the warning signal in response to different distances between the subject vehicle and the approaching vehicle, depending on the speed of one or both vehicles, the driving conditions, the road conditions and/or the like.
With respect to the third condition (shown in
Again, the lane departure warning system may be operable to initiate the warning signal or signals in response to varying threshold parameters, which may vary depending on the speed of the subject vehicle, the speed of the other detected vehicle, the relative speed of the vehicles, the driving conditions, the road conditions and/or the like. The lane departure warning system of the present invention may be operable to differentiate between the different types of lane markings along roads, such as between solid and dashed lines and double lines.
Optionally, the lane departure warning system may be further operable to detect and recognize stop lights and/or stop signs and/or other road or street signs or markings, and to provide a warning signal to the driver of the vehicle in response to such detection. It is further envisioned that the lane departure warning system of the present invention may be operable to provide an alarm or broadcast an alarm or warning signal on a safety warning band when the forward facing camera detects a stop light or stop sign and the system determines that the vehicle is not going to stop based on the vehicle's current speed and deceleration. This provides a signal or alarm to crossing drivers to warn them of an unsafe condition.
Optionally, the lane departure warning system of the present invention may be operable to determine the road conditions of the road on which the vehicle is traveling and/or the driving conditions surrounding the vehicle. The system may then provide the warning signal or signals in response to variable threshold values, such as different vehicle speeds or distances or the like. For example, wet or snowy roads would change the distance and/or speed thresholds at which the lane departure warning system would provide the warning signal or signals. Also, because darkened or raining conditions may affect visibility of lane markers, road edges and other vehicles, the lane departure warning system of the present invention may be operable to provide a warning signal sooner or at a greater distance from the marker, edge or vehicle in such low visibility conditions. This provides the driver of the subject vehicle a greater amount of time to respond to the warning in such conditions.
The lane departure warning system of the present invention may be integrated with a side object detection system (SOD). For example, the vehicle may be equipped with a camera or image-based side object detection system or a Doppler radar-based side object detection system or other such systems (such as mounted on the side rearview mirrors or at the side of the vehicle) for detecting objects and/or vehicles at one or both sides of the subject vehicle. The lane departure warning threshold level or sensitivity at which the lane departure warning system generates a warning signal may then be adjustable in response to detection of a vehicle or object at a side of the subject vehicle and determination of the location and speed of the detected vehicle. Optionally, the signal generated may increase or decrease in intensity or volume in response to the position or speed of an object or vehicle detected by the side object detection system. For example, the threshold level may take into account the approach speed of the other vehicle to the subject vehicle, and may provide a louder or brighter warning to the driver of the subject vehicle if the approach speed is above a particular threshold level or threshold levels.
The lane departure warning system may be provided with a multi-feature or multi-function forward facing imaging system. The imaging system may combine two or more functions, such as an intelligent headlamp controller (such as the type disclosed in U.S. Pat. Nos. 5,796,094 and 6,097,023, and U.S. patent application Ser. No. 09/441,341, filed Nov. 16, 1999, now U.S. Pat. No. 7,339,149, which are hereby incorporated herein by reference), an image-based smart wiper controller, a rain sensor (such as the types disclosed in commonly assigned U.S. Pat. Nos. 6,353,392; 6,313,454 and/or 6,320,176, which are hereby incorporated herein by reference), an image-based climate control blower controller, an image-based or image-derived or partially derived adaptive cruise-control system (where the imaging may be primary or secondary to a forward facing Doppler radar), and/or other vision systems (such as a forwardly directed vehicle vision system utilizing the principles disclosed in U.S. Pat. Nos. 5,550,677; 5,670,935 and 6,201,642, and/or in U.S. patent application Ser. No. 09/199,907, filed Nov. 25, 1998, now U.S. Pat. No. 6,717,610, which are all hereby incorporated herein by reference), a traffic sign recognition system, a system for determining a distance to a leading vehicle or object (such as using the principles disclosed in U.S. patent application Ser. No. 09/372,915, filed Aug. 12, 1999, now U.S. Pat. No. 6,396,397, which is hereby incorporated herein by reference), and/or the like.
For example, an embodiment of the lane departure warning system of the present invention may be incorporated with or integrated with an intelligent headlamp control system (such as described in U.S. Pat. Nos. 5,796,094 and 6,097,023, and U.S. patent application Ser. No. 09/441,341, filed Nov. 16, 1999, now U.S. Pat. No. 7,339,149, which are hereby incorporated herein by reference) having an imaging array sensor feeding a signal or image to a microcontroller (which may comprise a microprocessor or microcomputer), which is operable to adjust a state of the headlamps in response to a captured image of the scene forwardly of the vehicle. The image captured by the imaging sensor may be analyzed for light sources of interest for the headlamp control, and also for lane markings, road edges, and other objects of interest (such as road signs, stop signs, stop lights and/or the like) for the lane departure warning system. Optionally, the lane departure warning system may be integrated with or tied to an existing headlamp control of the vehicle.
The lane departure warning system of the present invention thus may be implemented as part of one or more other imaging-based systems, and thus may share components, hardware and/or software with the other systems to reduce the incremental costs associated with the lane departure warning system and with the other systems as well. Accordingly, multiple systems may be provided by an automotive supplier as part of a common platform or module for each vehicle of a particular vehicle line or model. The vehicle manufacturer may then choose to activate or enable one or more of the systems of the module, depending on which options are selected on a particular vehicle. Therefore, the addition or selection of the lane departure warning system, or of one or more other imaging-based systems, is associated with an incremental addition of hardware and/or software, and thus of associated costs, in order to install and enable the system on a particular vehicle. The imaging array sensor or sensors of the module may then be interrogated by an appropriate processor or software to extract the light sources or objects of interest or pixels of interest for each respective system of the common or unitary module. For example, an image captured by the imaging array sensor or camera may be processed or analyzed one way for a headlamp control system, and then processed or analyzed another way for the lane departure warning system or for any other enabled functions or systems of the common module. The software may further include common blocks or functions or macros to further enhance the sharing of software between the systems.
Accordingly, a unitary module may be provided to a vehicle assembly plant and may include multiple features, systems or functions, such that the desired features, systems or functions may be enabled for a particular vehicle, with minimal additional software or components or hardware being associated with the features, systems or functions that are enabled. The anchor system of the common or unitary module or platform may be an intelligent headlamp controller, with the additional systems, such as the lane departure warning system of the present invention, being added to or integrated with the anchor system.
The lane departure warning system and any other associated imaging-based systems may be included as part of an interior rearview mirror assembly or as part of an electronic windshield module and/or accessory module assembly, such as disclosed in commonly assigned U.S. Pat. Nos. 6,243,003; 6,278,377 and 6,433,676; U.S. application Ser. No. 10/054,633, filed Jan. 22, 2002, now U.S. Pat. No. 7,195,381; and Ser. No. 09/792,002, filed Feb. 26, 2001, now U.S. Pat. No. 6,690,268; Ser. No. 09/585,379, filed Jun. 1, 2000; Ser. No. 09/466,010, filed Dec. 17, 1999, now U.S. Pat. No. 6,420,975; and Ser. No. 10/355,454, filed Jan. 31, 2003, now U.S. Pat. No. 6,824,281, which are all hereby incorporated herein by reference.
Therefore, the lane departure warning system of the present invention provides a warning signal or signals to a driver of a vehicle based on the detection of various objects, vehicles and conditions surrounding the vehicle. The lane departure warning system of the present invention is thus less likely to provide a warning signal to a driver of the vehicle when the driver intends to maneuver the vehicle in that manner, and thus where such a warning signal is not needed or wanted. The lane departure warning system of the present invention thus avoids annoying, unnecessary warnings, and thus provides improved responses by the driver of the vehicle, since the driver is less likely to ignore the signal provided by the lane departure warning system. The lane departure warning system of the present invention may be implemented with or integrated with one or more other imaging-based systems to reduce the incremental components, hardware, software and costs associated with the implementation of the lane departure warning system.
Optionally, the object detection system or imaging system of the present invention may be operable in conjunction with a passive steering system 210 (
The passive steering assist system of the present invention thus may reduce driver fatigue from driving a vehicle under conditions which require constant driver steering input or adjustment, such as in windy conditions and the like. The passive steering assist system thus may reduce lane drift from side to side within a lane. Also, overall safety may be improved by the reduction in undesired lane maneuvers. Although described as being responsive to the imaging system of the present invention, the passive steering system of the present invention may be responsive to other types of lane departure warning systems or other types of vision or imaging systems, without affecting the scope of the present invention.
Optionally, the object detection system or imaging system of the present invention may be operable in connection with an adaptive speed control system 310 (
Additionally, because the imaging system, such as a forward facing lane departure warning system, may track the lane curvature, the system may also be able to determine if a vehicle which appears in front of the subject vehicle is actually in the same lane as the subject vehicle or if it is in an adjacent lane which is curving with the section of road. The imaging system and adaptive speed control system may then establish if the vehicle speed should be reduced in response to the road curvature and the presence of another vehicle at the curve. Although described as being responsive to the imaging system or lane departure warning system of the present invention, the adaptive speed control system of the present invention may be responsive to other types of lane departure warning systems or other types of vision or imaging systems, particularly other types of forward facing imaging systems, without affecting the scope of the present invention.
It is further envisioned that the imaging system, which may comprise an object detection system, a lane change assist system, a side object detection system, a lane departure warning system or other forward facing vision system, a rear vision system or park aid or panoramic view system, a passive steering system, an adaptive cruise control system or the like, may be in communication with a security monitoring system. The vision or image data from the imaging system may be transmitted to a remote device, such as the vehicle owner's computer monitor or other personal display system remote from the vehicle, so that the owner of the vehicle or other person may view the status of the area surrounding the vehicle when the owner or other person is not in the vehicle. Also, the vision or image data may be provided to or made available to the local police authorities or the like in the event of a theft of the vehicle or of an accident involving the vehicle or of the vehicle being otherwise inoperable (such as when the motorist is stranded). The police or emergency services or the like may use the vision or image data to determine the vehicle location and possibly the condition of the vehicle and/or the driver and/or the passengers. It is further envisioned that the vision or image data may be used in conjunction with the global positioning system (GPS) of the vehicle to precisely locate or pinpoint the vehicle location. The vision or image data may be transmitted to the remote device or to the emergency services or the like via various transmission devices or systems, such as utilizing Bluetooth technology or the like, without affecting the scope of the present invention.
Therefore, the present invention provides a vision or imaging system or object detection system which is operable to detect and process edges within a captured image or images to determine if the edges are indicative of a significant vehicle or object or the like at or near or approaching the subject vehicle. The imaging system may primarily process a reduced image data set representative of a zone of interest of the exterior scene and may process edges that are detected which are representative of an object within the zone of interest of the captured image. The imaging system may adjust the reduced data set or zone of interest in response to various conditions or characteristics or criteria. The imaging system may comprise an object detection system or a lane change assist system operable to detect objects or other vehicles at one or both sides of the subject vehicle. The object detection system may determine the distance to the detected object or edge and may adjust threshold criterion in response to the determined or estimated or calculated distance.
Optionally, the imaging system may comprise a forward facing lane departure warning system which may be operable to detect lane markers or the like and/or vehicles in front of the subject vehicle and to provide an alert signal to the driver of the vehicle that the vehicle is leaving its lane. The lane departure warning system may primarily process edges detected within a zone of interest within the captured image. The lane departure warning system may determine a distance to the detected edge or object and may vary or adjust threshold criterion in response to the determined or estimated or calculated distance.
The forward facing imaging system may be in communication with the lane change assist system of the vehicle and/or may be in communication with other systems of the vehicle, such as a side object detection system, a passive steering system or an adaptive speed control system or the like. The imaging system may communicate to the lane change assist system that the vehicle is approaching a curve in the road or that another vehicle is being approached and passed by the subject vehicle to assist in processing the image data captured by the sensor or camera of the lane change assist system. Optionally, a passive steering system may adjust a steering direction of the vehicle in response to the imaging system, or an adaptive speed control system may adjust a cruise control setting of the vehicle in response to the imaging system. Optionally, an output of the imaging system may be provided to or communicated to a remote receiving and display system to provide image data for viewing at a location remote from the subject vehicle.
Changes and modifications in the specifically described embodiments may be carried our without departing from the principles of the present invention, which is intended to limited only by the scope of the appended claims, as interpreted according to the principles of patent law.
The present application is a continuation of U.S. patent application Ser. No. 15/830,114, filed Dec. 4, 2017, now U.S. Pat. No. 10,118,618, which is a continuation of U.S. patent application Ser. No. 15/413,462, filed Jan. 24, 2017, now U.S. Pat. No. 9,834,216, which is a continuation of U.S. patent application Ser. No. 15/155,350, filed May 16, 2016, now U.S. Pat. No. 9,555,803, which is a continuation of U.S. patent application Ser. No. 14/922,640, filed Oct. 26, 2015, which is a continuation of U.S. patent application Ser. No. 14/195,137, filed Mar. 3, 2014, now U.S. Pat. No. 9,171,217, which is a continuation of U.S. patent application Ser. No. 13/651,659, filed Oct. 15, 2012, now U.S. Pat. No. 8,665,079, which is a continuation of U.S. patent application Ser. No. 12/559,856, filed Sep. 15, 2009, now U.S. Pat. No. 8,289,142, which is a divisional application of U.S. patent application Ser. No. 12/329,029, filed Dec. 5, 2008, now U.S. Pat. No. 7,679,498, which is a divisional application of U.S. patent application Ser. No. 11/408,776, filed Apr. 21, 2006, now U.S. Pat. No. 7,463,138, which is a divisional application of U.S. patent application Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577, which claims priority of U.S. provisional applications, Ser. No. 60/433,700, filed Dec. 16, 2002, and Ser. No. 60/377,524, filed May 3, 2002, which are all hereby incorporated herein by reference in their entireties.
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. et al. | May 1952 | A |
2632040 | Rabinow | Mar 1953 | A |
2827594 | Rabinow | Mar 1953 | A |
2750583 | McCullough | Jun 1956 | A |
2762932 | Falge | Sep 1956 | A |
2907920 | McIlvane | Oct 1956 | A |
2855523 | Berger | Oct 1958 | A |
2856146 | Lehder | Oct 1958 | A |
2863064 | Rabinow | Dec 1958 | A |
2892094 | Lehovec | Jun 1959 | A |
2912593 | Deuth | Nov 1959 | A |
2934676 | Miller | Apr 1960 | A |
2959709 | Vanaman et al. | Nov 1960 | A |
3008532 | Reed | Nov 1961 | A |
3011580 | Reid | Dec 1961 | A |
3069654 | Hough | Dec 1962 | A |
3085646 | Paufve | Apr 1963 | A |
3158835 | Hipkins | Nov 1964 | A |
3172496 | Rabinow et al. | 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 et al. | 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 | 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 | 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 | Tsuchuhashi | 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 | Oct 1986 | A |
4623222 | Ito 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 | Müller | 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 | Watanbe 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 | 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 | Makino 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 | 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 | Green 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 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 | Nov 1990 | A |
4970589 | Hanson | 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 | 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 | Ari | 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 | Sep 1992 | A |
5148014 | Lynam | 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 et al. | Nov 1992 | A |
5161632 | Asayama et al. | Nov 1992 | A |
5162841 | Terashita | Nov 1992 | A |
5162861 | Tamburino | 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 et al. | 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 et al. | 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 | Miezawa 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 et al. | 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 et al. | 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 | 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 | Iino | 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 et al. | Oct 1996 | A |
5566224 | ul Azam et al. | Oct 1996 | A |
5568027 | Teder | Oct 1996 | A |
5568316 | Schrenck 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 et al. | 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 | Nayer | 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 | 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 | DeVries, 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 | 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 | 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 | Gutta 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 | Raynar | 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 | Poechmueller | 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 et al. | 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 | Sjönell | 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 | 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 | Takayanazi 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 |
9171217 | Pawlicki et al. | Oct 2015 | B2 |
9191634 | Schofield et al. | Nov 2015 | B2 |
9428192 | Schofield et al. | Aug 2016 | B2 |
9555803 | Pawlicki et al. | Jan 2017 | B2 |
9834216 | Pawlicki et al. | Dec 2017 | B2 |
1011861 | Pawlicki et al. | Nov 2018 | A1 |
20010002451 | Breed | May 2001 | A1 |
20020003571 | Schofield et al. | Jan 2002 | A1 |
20020005778 | Breed | 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 | 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 | Feb 2004 | A1 |
20040022416 | Lemelson | 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 | 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 | Krenin 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 |
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 | Oct 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 |
4480341 | Mar 1996 | DE |
069302975 | Dec 1996 | DE |
29703084 | Jun 1997 | DE |
29805142 | Jun 1998 | DE |
19755008 | Jul 1999 | DE |
19829162 | Jan 2000 | DE |
10237554 | Mar 2004 | DE |
000010251949 | May 2004 | DE |
19530617 | Feb 2009 | DE |
0048492 | Mar 1982 | EP |
0049722 | Apr 1982 | EP |
0072406 | Feb 1983 | EP |
0176615 | Apr 1986 | EP |
0202460 | Nov 1986 | EP |
0169734 | Oct 1989 | 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 |
0527665 | Feb 1991 | 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 |
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 |
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 |
0487332 | Oct 1997 | 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 |
1257971 | Nov 2000 | EP |
1058220 | Dec 2000 | EP |
1065642 | Jan 2001 | EP |
1074430 | Feb 2001 | EP |
1115250 | Jul 2001 | EP |
0830267 | Dec 2001 | EP |
1170173 | Jan 2002 | EP |
1236126 | Sep 2002 | EP |
0860325 | Nov 2002 | EP |
1359557 | Nov 2003 | EP |
1727089 | Nov 2006 | 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 |
1741079 | May 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 |
6079889 | May 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 |
61260217 | 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 |
63011446 | 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 |
H05137144 | 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 |
H06229759 | Feb 1993 | JP |
0577657 | Mar 1993 | JP |
05050883 | Mar 1993 | JP |
H06332370 | May 1993 | JP |
H05155287 | Jun 1993 | JP |
05189694 | Jul 1993 | JP |
H05172638 | Jul 1993 | JP |
05213113 | 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 |
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 |
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 |
11069211 | Mar 1999 | JP |
11078737 | Mar 1999 | JP |
H1178693 | Mar 1999 | JP |
H1178717 | Mar 1999 | JP |
H1123305 | Jul 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 |
2002074339 | 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 |
2003076987 | 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 |
2000883510000 | Mar 1995 | KR |
1020010018981 | Oct 2002 | KR |
1004124340000 | Mar 2004 | KR |
336535 | Jul 1971 | SE |
WO1986005147 | Sep 1986 | WO |
WO1988009023 | Nov 1988 | WO |
WO1990004528 | May 1990 | WO |
WO1993000647 | Jan 1993 | WO |
WO1993004556 | Mar 1993 | WO |
WO1993010550 | May 1993 | WO |
WO1993011631 | Jun 1993 | WO |
WO1993021596 | Oct 1993 | WO |
WO1994019212 | Sep 1994 | WO |
WO1995018979 | Jul 1995 | WO |
WO1995023082 | Aug 1995 | WO |
WO1996002817 | Feb 1996 | WO |
WO1996015921 | May 1996 | WO |
WO1996018275 | Jun 1996 | WO |
WO1996021581 | Jul 1996 | WO |
WO1996034365 | Oct 1996 | WO |
WO1996038319 | Dec 1996 | WO |
WO1997001246 | Jan 1997 | WO |
WO1997029926 | Aug 1997 | WO |
WO1997035743 | Oct 1997 | WO |
WO1997048134 | Dec 1997 | WO |
WO1998010246 | Mar 1998 | WO |
WO1998014974 | Apr 1998 | WO |
WO1999043242 | Feb 1999 | WO |
WO1999023828 | May 1999 | WO |
WO1999059100 | Nov 1999 | WO |
WO2000015462 | Mar 2000 | WO |
WO2001026332 | Apr 2001 | WO |
WO2001039018 | May 2001 | WO |
WO2001039120 | May 2001 | WO |
WO2001064481 | Sep 2001 | WO |
WO2001070538 | Sep 2001 | WO |
WO2001077763 | Oct 2001 | WO |
WO2001080068 | Oct 2001 | WO |
WO2001080353 | Oct 2001 | WO |
WO2002071487 | Sep 2002 | WO |
WO2003065084 | Aug 2003 | WO |
WO2003093857 | Nov 2003 | WO |
WO2004004320 | Jan 2004 | WO |
WO2004005073 | Jan 2004 | WO |
WO2005098751 | Oct 2005 | WO |
WO2005098782 | Oct 2005 | WO |
WO2008134715 | Nov 2008 | WO |
WO2013121357 | Aug 2013 | WO |
Entry |
---|
“All-seeing screens for tomorrow's cars”, Southend Evening Echo, Oct. 4, 1991. |
“Final Report of the Working Group on Advanced Vehicle Control Systems (AVCS)” Mobility 2000, Mar. 1990. |
“Magic Eye on safety”, Western Daily Press, Oct. 10, 1991. |
“On-screen technology aims at safer driving”, Kent Evening Post Oct. 4, 1991. |
“Versatile LEDs Drive Machine vision in Automated Manufacture,” http://www.digikey.ca/en/articles/techzone/2012/jan/versatileleds-drive-machine-vision-in-automated-manufacture. |
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 128×128 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. |
Dérutin 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 München, 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/ Sep. 1992 to May 1994. Jun. 1995. |
Donnelly Panoramic Vision™ 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 256×256 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 Müchen, 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. |
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, 7th 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. |
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 128×128 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™ 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. |
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. |
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. |
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., “128×128 CMOS Photodiode-Type Active Pixel Sensor With On-Chip Timing, Control and Signal Chain Electronics” 1995. |
Nixon et al., “256×256 CMOS Active Pixel Sensor Camera-on-a-Chip,” IEEE Journal of Solid-State Circuits, vol. 31, No. 12, Paper FA 11.1, 1996. |
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. |
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”, Universität 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=AILZhcnpXYI. |
Ramesh et al., “Real-Time Video Surveillance and Monitoring for Automotive Applications”, SAE Technical Paper 2000-01-0347, Mar. 6, 2000, Abstract. |
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. |
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. |
Schönfeld 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 AI (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 Pn to Pn 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., “Nomography 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. |
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. 181-186. |
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 Pk-P2, 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 (≥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. |
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 in Springer-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 Fishers 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. |
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. |
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 vehicles 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. |
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/journalistes/en/CP/Idws_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 Tracking 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, pp. 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. |
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. |
Wördenweber, “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 car!”, Edn. vol. 42, No. 26, Dec. 18, 1997, Abstract. |
Wüller et al., “The usage of digital cameras as luminance meters”, Proc. SPIE 6502, Digital Photography III, 65020U, Feb. 20, 2007; doi:10.1117/12.703205. |
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. |
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, The Netherlands, 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. |
Number | Date | Country | |
---|---|---|---|
20190061760 A1 | Feb 2019 | US |
Number | Date | Country | |
---|---|---|---|
60433700 | Dec 2002 | US | |
60377524 | May 2002 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 12329029 | Dec 2008 | US |
Child | 12559856 | US | |
Parent | 11408776 | Apr 2006 | US |
Child | 12329029 | US | |
Parent | 10427051 | Apr 2003 | US |
Child | 11408776 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 15830114 | Dec 2017 | US |
Child | 16177734 | US | |
Parent | 15413462 | Jan 2017 | US |
Child | 15830114 | US | |
Parent | 15155350 | May 2016 | US |
Child | 15413462 | US | |
Parent | 14922640 | Oct 2015 | US |
Child | 15155350 | US | |
Parent | 14195137 | Mar 2014 | US |
Child | 14922640 | US | |
Parent | 13651659 | Oct 2012 | US |
Child | 14195137 | US | |
Parent | 12559856 | Sep 2009 | US |
Child | 13651659 | US |