Trailer backup assist system with predictive hitch angle functionality

Information

  • Patent Grant
  • 10710585
  • Patent Number
    10,710,585
  • Date Filed
    Friday, September 1, 2017
    6 years ago
  • Date Issued
    Tuesday, July 14, 2020
    3 years ago
Abstract
A trailer backup assist system is provided herein. The system includes a calibration feature for calibrating an imaging device used for hitch angle detection. The system also employs multiple hitch angle detection methods, a number of which may run in parallel to increase the intervals at which a hitch angle can be measured. The system additionally includes a predictive feature in which a hitch angle can be predicted in instances where a trailer sensing device fails. The system is further configured to estimate a trailer length and generate steering commands that are invariant to the estimated trailer length under certain conditions.
Description
FIELD OF THE INVENTION

The present invention generally relates to systems for assisting an operator in backing a trailer, and more particularly, to systems using imager-based hitch angle detection.


BACKGROUND OF THE INVENTION

Reversing a vehicle while towing a trailer can be challenging for many drivers, particularly for drivers that drive with a trailer on an infrequent basis or with various types of trailers. Some systems used to assist an operator in backing a trailer rely on hitch angle measurements to determine the position of the trailer relative to the vehicle. Thus, the accuracy and reliability of the hitch angle measurements can be critical to the operation of the trailer backup assist system. In systems employing imager-based hitch angle detection, improper calibration of an imaging device can lead to inaccurate hitch angle measurements. Furthermore, in instances where the imaging device becomes obstructed, such systems may be forced offline and rendered unable to determine a hitch angle between the vehicle and the trailer. To function properly, some systems require a user to input measurements such as trailer length. This is not only cumbersome on the user but may lead to erroneous measurements being inputted to the system. Accordingly, there is a need for a trailer backup assist system that overcomes the problems mentioned above. The present disclosure is intended to satisfy this need.


SUMMARY OF THE INVENTION

According to a first aspect of the present invention, a calibration method is provided herein. The method includes the steps of: using an imaging device to capture an image of a rear bumper; and providing a controller configured to process the captured image, identify a boundary separating the rear bumper from a ground; compare the identified boundary to an ideal boundary, and determine an offset between the identified boundary and the ideal boundary.


Embodiments of the first aspect can include any one or a combination of the following features:

    • the ideal boundary includes a continuous line;
    • the ideal boundary includes a line having a break;
    • the ideal boundary is overlaid onto the captured image;
    • the offset is defined by a vector having a horizontal component, a vertical component, and a rotational component;
    • the controller determines the offset by iterating on candidates for each of the horizontal, vertical, and rotational components until the identified boundary overlaps with the ideal boundary; and
    • the controller is further configured to generate a warning if the offset is unable to be determined.


According to a second aspect of the present invention, a trailer backup assist system is provided. The system includes a device configured to sense a trailer and a controller configured to determine a hitch angle between a vehicle and the trailer based on data provided by the device. If the device fails, the controller predicts the hitch angle based on a last known hitch angle, a last known angular velocity of the trailer, and an execution cycle time.


Embodiments of the second aspect can include one or a combination of the following features:

    • the device comprises an imaging device and the controller is configured to predict the hitch angle if the imaging device becomes obstructed or otherwise fails;
    • the controller is further configured to predict the hitch angle up until an error band reaches a threshold;
    • the controller is further configured to determine an upper and lower error band of the predicted hitch angle, and enact a countermeasure if the upper error band reaches a maximum controllable hitch angle or the lower error band reaches a minimum controllable hitch angle, whichever comes first;
    • the upper and lower error bands are each determined based on a predicted hitch angle, an initial degree error at the moment the device fails, an accumulative vehicle travel distance, and a trailer length;
    • the countermeasure comprises at least one of steering the vehicle to keep from exceeding the maximum controllable hitch angle or the minimum controllable hitch angle and reducing a speed of the vehicle; and
    • the last known angular velocity is based on an angular velocity that is adjusted by a percentage error.


According to a third aspect of the present invention, a method of determining hitch angle between a vehicle and a trailer is provided. The method includes the steps of: selecting at least one hitch angle detection method amongst a plurality of hitch angle detection methods; using the selected at least one hitch angle detection method to determine a hitch angle between a vehicle and a trailer; and transitioning to another hitch angle detection method in the event the selected at least one hitch angle detection method becomes unavailable.


Embodiments of the third aspect can include one or a combination of the following features:

    • the plurality of hitch angle detections methods each employ imager-based hitch angle detection;
    • the plurality of hitch angle detection methods are ranked based on a confidence score assigned to each hitch angle detection method, and wherein the at least one selected hitch angle detection includes an available hitch angle detection method having the highest confidence score;
    • the step of limiting the speed of the vehicle based on the confidence score assigned to the at least one selected hitch angle detection method;
    • the step of predicting the hitch angle during the transitioning between the at least one selected hitch angle detection method and another hitch angle detection method or if each of the plurality of hitch angle detection methods become unavailable; and
    • the step of using the determined hitch angle to control at least one of a hitch angle operating range, a speed limit of the vehicle, and the curvature of a backing path of the trailer.


According to a fourth aspect of the present invention, a trailer backup assist system is provided. A steering input device is configured to provide a curvature command based on user input. A controller is configured to estimate a trailer length based on a vehicle and trailer yaw rate. The controller generates a steering command based on the estimated trailer length, the curvature command, a maximum steering angle, and a vehicle speed. The generated steering command is invariant to the estimated trailer length under certain conditions.


Embodiments of the fourth aspect can include one or a combination of the following features:

    • the steering input device includes a rotatable knob configured to allow a user to input a desired direction and curvature of a backing path of a trailer backed by a vehicle;
    • the controller includes a curvature input scaling module configured to scale the curvature command by a maximum effective curvature to generate a curvature input based on the maximum steering angle and the estimated trailer length;
    • the controller further includes a curvature mapping module configured to generate a reference hitch angle based on the curvature input and the estimated trailer length;
    • the controller further includes a subtractor configured to subtract an estimated hitch angle from the reference hitch angle to generate a signal provided to a proportional-integral (PI) controller configured to generate a control variable, wherein the estimated hitch angle is based on the vehicle and trailer yaw rate;
    • the controller further includes a hitch angle controller configured to generate the steering angle based on the control variable, the estimated trailer length, and the vehicle speed;
    • the controller further includes a proportional coefficient of the PI controller based only on the estimated trailer length such that a transient response of the controller is invariant to the estimated trailer length; and
    • the certain conditions include one of a zero curvature command, a maximum curvature command, and a minimum curvature command.


These and other features, advantages, and objects of the present invention will be further understood and appreciated by those skilled in the art by reference to the following specification, claims, and appended drawings.





BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:



FIG. 1 is a top perspective view of a vehicle attached to a trailer with one embodiment of a hitch angle sensor for operating a trailer backup assist system;



FIG. 2 is a block diagram illustrating one embodiment of the trailer backup assist system;



FIG. 3 illustrates a kinematic relationship between the vehicle and the trailer;



FIG. 4 illustrates a steering input device having a rotatable knob for operating the trailer backup assist system;



FIG. 5 illustrates the rotatable knob for selecting a desired curvature of a trailer and a corresponding schematic diagram illustrating the vehicle and the trailer with various trailer curvature paths correlating with desired curvatures that may be selected;



FIG. 6 is a flow diagram of a method for calibrating an imaging device of the vehicle;



FIG. 7 illustrates a captured image showing the edges of a rear bumper of the vehicle;



FIG. 8 illustrates a captured image in which an ideal boundary between the rear bumper and a ground is identified according to one embodiment;



FIG. 9 illustrates a captured image in which the ideal boundary is identified according to an alternative embodiment;



FIG. 10 illustrates a captured image in which an identified boundary is compared to the ideal boundary to determine an offset used in calibrating the imaging device;



FIG. 11 is a graph illustrating the deviation between actual hitch angle and predicted hitch angle over a vehicle travel distance;



FIG. 12 is a flow diagram of a method for determining a hitch angle between the vehicle and the trailer;



FIG. 13 illustrates a curvature input scaling module used to scale a curvature command inputted using the steering input device;



FIG. 14 illustrates a controller of the trailer backup assist system including the curvature input scaling module; and



FIG. 15 is a graph illustrating a family of closed-loop equilibria as a function of the curvature command for a number of estimated trailer lengths.





DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

For purposes of description herein, it is to be understood that the disclosed trailer backup assist system and the related methods may assume various alternative embodiments and orientations, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the inventive concepts defined in the appended claims. While various aspects of the trailer backup assist system and the related methods are described with reference to a particular illustrative embodiment, the disclosed invention is not limited to such embodiments, and additional modifications, applications, and embodiments may be implemented without departing from the disclosed invention. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.


As used herein, the term “and/or,” when used in a list of two or more items, means that any one of the listed items can be employed by itself, or any combination of two or more of the listed items can be employed. For example, if a composition is described as containing components A, B, and/or C, the composition can contain A alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination.


Referring to FIGS. 1 and 2, reference numeral 10 generally designates a trailer backup assist (TBA) system for assisting a vehicle 12 in reversing a trailer 14. The vehicle 12 is embodied as a pickup truck that is pivotally attached to one embodiment of the trailer 14 that has a box frame 16 with an enclosed cargo area 18, a single axle 20 operably coupled to wheels 22 and 24, and a tongue 26 longitudinally extending forward from the enclosed cargo area 18. The illustrated trailer 14 also has a trailer hitch connector in the form of a coupler assembly 28 that is connected to a vehicle hitch connector in the form of a hitch ball 30 and drawbar 31. The coupler assembly 28 latches onto the hitch ball 30 to provide a pivoting hitch 32 that allows for articulation of a hitch angle (e.g., hitch angle γ; FIG. 3) between the vehicle 12 and the trailer 14. As defined herein, the hitch angle corresponds to the angle formed between the center longitudinal axis of the vehicle 12 and of the trailer 14. It should be appreciated that additional embodiments of the trailer 14 may alternatively couple with the vehicle 12 to provide a pivoting connection, such as by connecting with a fifth wheel connector. It is also contemplated that additional embodiments of the trailer 14 may include more than one axle and may have various shapes and sizes configured for different loads and items, such as a boat trailer or a flatbed trailer.


The TBA system 10 also includes an imaging device 34 located at the rear of the vehicle 12 and configured to image a rear-vehicle scene. The imaging device 34 may be centrally located at an upper region of a vehicle tailgate 35 such that the imaging device 34 is elevated relative to the tongue 26 of the trailer 14. The imaging device 34 has a field of view 36 located and oriented to capture one or more images that may include the tongue 26 of the trailer 14 and the hitch ball 30, among other things. Image data is supplied to a controller 38 of the TBA system 10 and is processed by the controller 38 to determine the hitch angle between the vehicle 12 and the trailer 14. Additional information regarding image-based hitch angle detection and associated methodologies can be found in commonly assigned U.S. Pat. No. 9,610,975 to Hu et al., issued Apr. 4, 2017, and entitled “HITCH ANGLE DETECTION FOR TRAILER BACKUP ASSIST SYSTEM,” the entire disclosure of which is incorporated by reference herein.


The controller 38 is configured with a microprocessor 40 and/or other analog and/or digital circuitry for processing one or more logic routines stored in a memory 42. The logic routines may include a hitch angle detection routine 44, an operating routine 46, and a curvature routine 47. Information from the imaging device 34 or other components of the TBA system 10 can be supplied to the controller 38 via a communication network of the vehicle 12, which can include a controller area network (CAN), a local interconnect network (LIN), or other conventional protocols used in the automotive industry. It should be appreciated that the controller 38 may be a stand-alone dedicated controller or may be a shared controller integrated with the imaging device 34 or other component of the TBA system 10 in addition to any other conceivable onboard or off-board vehicle control systems.


With respect to the present embodiment, the controller 38 is configured to communicate with a variety of vehicle equipment. The TBA system 10 may include a vehicle sensor module 48 that monitors certain dynamics of the vehicle 12. The vehicle sensor module 48 may generate a plurality of signals that are communicated to the controller 38 such as a vehicle speed signal generated by a speed sensor 50 and a vehicle yaw rate signal generated by a vehicle yaw rate sensor 52. A trailer sensor module 53 is provided that monitors certain dynamics of the trailer 14. The trailer sensor module 53 includes a trailer yaw rate sensor 54 configured to generate a trailer yaw rate signal that is provided to the controller 38.


A steering input device 55 is provided to enable a driver to control or otherwise modify a desired curvature (e.g., desired curvature 56; FIG. 5) of a backing path of the trailer 14. The steering input device 55 may be communicatively coupled to the controller 38 in a wired or wireless manner and provides the controller 38 with input defining the desired curvature of the backing path of the trailer 14. In response to the input, the controller 38 generates corresponding steering commands that are supplied to a power assist steering system 57 of the vehicle 12. In one embodiment, the steering input device 55 includes a rotatable knob 58 operable between a number of rotated positions that each provide an incremental change to the desired curvature 56 of the backing path of the trailer 14.


The knob 58 may be rotatable about a rotational axis extending through a top surface or face of the knob 58. In other embodiments, the knob 58 may be rotatable about a rotational axis extending substantially parallel to a top surface or face of the knob 58. Furthermore, the steering input device 55, according to additional embodiments, may include alternative devices for providing the desired input, such as a joystick, a keypad, a series of depressible buttons or switches, a sliding input device, various user interfaces on a touch-screen display, a vision-based system for receiving gestures, a control interface on a portable device, and other conceivable input devices as generally understood by one having ordinary skill in the art. It is contemplated that the steering input device 55 may also function as an input device for other features, such as providing inputs for other vehicle features or systems.


According to one embodiment, the controller 38 of the TBA system 10 may control the power assist steering system 57 of the vehicle 12 to operate steered wheels 60 of the vehicle 12 for moving the vehicle 12 in such a manner that the trailer 14 reacts in accordance with the desired curvature 56 of the backing path of the trailer 14. The power assist steering system 57 may be an electric power-assisted steering (EPAS) system that includes an electric steering motor 62 for turning the steered wheels 60 to a steering angle based on a steering command generated by the controller 38, whereby the steering angle may be sensed by a steering angle sensor 64 of the power assist steering system 57 and provided to the controller 38. The steering command may be provided for autonomously steering the vehicle 12 during a backup maneuver and may alternatively be provided manually via a rotational position (e.g., a steering wheel angle) of a steering wheel 66 or the rotatable knob 58. However, in some embodiments, the steering wheel 66 of the vehicle 12 may be mechanically coupled with the steered wheels 60 of the vehicle 12, such that the steering wheel 66 moves in concert with steered wheels 60 via an internal torque, thereby preventing manual intervention with the steering wheel 66 during autonomous steering of the vehicle 12. In such instances, the power assist steering system 57 may include a torque sensor 68 that senses torque (e.g., gripping and/or turning) on the steering wheel 66 that is not expected from autonomous control of the steering wheel 66 and therefore indicative of manual intervention by the driver. In some embodiments, external torque applied to the steering wheel 66 may serve as a signal to the controller 38 that the driver has taken manual control and for the TBA system 10 to discontinue autonomous steering functionality.


The controller 38 of the TBA system 10 may also communicate with a vehicle brake control system 70 of the vehicle 12 to receive vehicle speed information such as individual wheel speeds of the vehicle 12. Additionally or alternatively, vehicle speed information may be provided to the controller 38 by a powertrain control system 72 and/or the speed sensor 50, among other conceivable means. It is conceivable that individual wheel speeds may be used to determine a vehicle yaw rate, which can be provided to the controller 38 in the alternative, or in addition to, the vehicle yaw rate measured by yaw rate sensor 52 of the vehicle sensor module 48. In some embodiments, the controller 38 may provide braking commands to the vehicle brake control system 70, thereby allowing the TBA system 10 to regulate the speed of the vehicle 12 during a backup maneuver of the trailer 14. It should be appreciated that the controller 38 may additionally or alternatively regulate the speed of the vehicle 12 via interaction with the powertrain control system 72.


Through interaction with the power assist steering system 57, the vehicle brake control system 70, and/or the powertrain control system 72 of the vehicle 12, the potential for unacceptable trailer backup conditions can be reduced. Examples of unacceptable trailer backup conditions include, but are not limited to, a vehicle over-speed condition, a high hitch angle rate, hitch angle dynamic instability, a trailer jackknife condition, sensor failure, and the like. In such circumstances, the driver may be unaware of the failure until the unacceptable trailer backup condition is imminent or already happening. Therefore, it is disclosed herein that the controller 38 of the TBA system 10 can generate an alert signal corresponding to a notification of an actual, impending, and/or anticipated unacceptable trailer backup condition, and prior to driver intervention, generate a counter measure to prevent such an unacceptable trailer backup condition.


According to one embodiment, the controller 38 may communicate with one or more devices, including a vehicle alert system 74, which may prompt visual, auditory, and tactile warnings. For instance, vehicle brake lights 76 and vehicle emergency flashers may provide a visual alert and a vehicle horn 78 and/or speaker 80 may provide an audible alert. Additionally, the controller 38 and/or vehicle alert system 74 may communicate with a human machine interface (HMI) 82 of the vehicle 12. The HMI 82 may include a touchscreen vehicle display 84 such as a center-stack mounted navigation or entertainment display capable of displaying images indicating the alert. Such an embodiment may be desirable to notify the driver of the vehicle 12 that an unacceptable trailer backup condition is afoot. Further, it is contemplated that the controller 38 may communicate via wireless communication with one or more electronic portable devices such as portable electronic device 86, which is embodied as a smartphone. The portable electronic device 86 may include a display 88 for displaying one or more images and other information to a user. In response, the portable electronic device 86 may provide feedback information, such as visual, audible, and tactile alerts.


When the imaging device 34 is installed on the vehicle 12, it is important to minimize errors arising during installation or at a later time. Such errors generally result from improper alignment between the imaging device 34 and the vehicle 12 in terms of yaw, pitch, and roll. These errors may be caused by various factors such as manufacturing variability, part-to-part variability over time, damage to the vehicle, or parts replacement, for example, all of which have the potential of changing the alignment between the imaging device 34 and the vehicle 12. Initially, these errors are calibrated before the imaging device 34 can be used to support functions such as imager-based hitch angle detection. If not properly calibrated, the resultant errors may negatively impact the accuracy and robustness of functions instituted by the imaging device 34.


Referring to FIG. 6, a flow diagram is shown describing a method 90 of calibrating an imaging device (e.g., image device 34), which is typically mounted to the rear of a vehicle and configured to image a rear-vehicle scene. However, it will be understood that the method 90 may be similarly instituted to calibrate imaging devices located elsewhere on the vehicle.


At step 95, a benchmark installation is prepared. For example, the benchmark installation includes positioning the imaging device in the ideal orientation relative to the vehicle. In the embodiment of FIG. 1, for instance, the imaging device is ideally mounted to the upper region of a vehicle tailgate 35 and is oriented to capture images of a rear-vehicle scene including the rear bumper. Once the imaging device is ideally positioned, the imaging device is operated to capture an image that includes a rear bumper that is similarly configured as the rear bumper 96 (FIG. 1) of the vehicle 12 at step 100. According to one embodiment, the image is captured against a homogenous background (e.g., an evenly illuminated white wall) or is compiled from a series of images captured while the vehicle is moving at a predefined speed. The speed may be a specific speed or a speed range and is generally selected to prevent excessive vibration of the imaging device and further allow for the ground to be blurred out in the compiled image.


At step 105, edge detection is conducted on the captured image. For purposes of illustration, FIG. 7 is a sample captured image showing detected edges (e.g., edge 106) of rear bumper 96′, which is similarly configured to the rear bumper 96 of the vehicle 12. At step 110, an ideal boundary between the rear bumper and the ground is identified in the captured image based on one or more edges detected at step 105. For purposes of illustration, FIG. 8 shows an ideal boundary 111 identified between the detected edge 106 shown in FIG. 7 and ground 112. As shown, the ideal boundary 111 is a continuous line spanning across the image and separates the rear bumper 96 from the ground 112. In an alternative embodiment, shown in FIG. 9, the ideal boundary 111 contains a central break and is identified from edges only appearing in lower corner portions 113 of the captured image. In other words, a majority portion 114 of the captured image is ignored when identifying the ideal boundary 111. The majority portion generally corresponds to areas of the captured image where a valid boundary is unlikely to be present. Optionally, as shown in FIG. 9, the majority portion 114 includes a lower central area 114′ of the captured image to avoid processing areas where a trailer hitch connector is likely to be detected and may possibly corrupt the shape of the ideal boundary 111. By ignoring certain portions of the captured image, the captured image is effectively reduced in size, thereby allowing the ideal boundary 111 to be more quickly identified. In yet another alternative embodiment, the ideal boundary 111 may be generated from computer drawings or the like. At step 115, the ideal boundary 111 and its position are recorded to be later used to calibrate other imaging devices of the same model and similarly installed on identical vehicle models. Thus, it is to be understood that steps 95-115 need only be conducted once per vehicle model. As such, steps 95-115 may be carried out in a lab setting if desired.


In contrast, steps 120-150 are generally conducted on the assembly line and are repeated for each vehicle of the same model. For purposes of understanding, steps 120-150 will be described with respect to the embodiment of vehicle 12 shown in FIG. 1. At step 120, the ideal boundary 111 and its position, as predetermined at step 110, are provided to controller 38 (e.g., stored on memory 42). Next, at step 125, the controller 38 operates the imaging device 34 to capture an image that includes the rear bumper 96 of the vehicle 12. At step 130, the controller 38 processes the captured image to identify the boundary between the rear bumper 96 of the vehicle 12 and the ground. Once identified, the controller 38 compares the position of the identified boundary to the position of the stored ideal boundary 111. For example, the stored ideal boundary 111 may be overlaid onto the captured image. If the position of the identified boundary overlaps with the position of the stored ideal boundary 111, the installation of the imaging device 34 is complete and no calibration is needed. In other words, the imaging device 34 was installed free of errors and is ready for use. As such, the method 90 ends at step 140.


Alternatively, if the position of the identified boundary fails to overlap with the position of the stored ideal boundary 111, the controller 38 determines an offset between the identified boundary and the stored ideal boundary 111 at step 145. For purposes of illustration, FIG. 10 is a sample image showing an identified boundary 146 that is offset with respect to the stored ideal boundary 111. In the depicted embodiment, the offset is defined as a vector having a horizontal component X, a vertical component Y, and a rotational component θ. It is contemplated that the offset can be determined by iterating on reasonable candidates for each of components X, Y, and θ until the identified boundary 146 overlaps with the stored ideal boundary 111 or vice versa. Once the offset has been determined, the calibration of the imaging device 34 is complete and the imaging device 34 is now ready for use. Accordingly, the method 100 ends at step 150.


It should be appreciated that the imaging device 34 may be calibrated multiple times during the life of the vehicle 12. For example, it is contemplated that the foregoing steps may be executed at regular time intervals, once per ignition cycle, if replacement of the imaging device 34 is detected, and/or if a collision is detected. It is further contemplated that the controller 38 may inhibit calibration of the imaging device 34 in instances where the orientation and/or position of the rear bumper 96 have changed substantially, the shape of the identified boundary 146 is unable to be matched to the stored ideal boundary 111 (typically due to damage or modification of the rear bumper 96), the rear bumper 96 is not securely attached to the vehicle 12, or the imaging device 34 is not securely fixed to the vehicle 12 (e.g., the tailgate 35 is not secure), for example. Additionally or alternatively, the calibration of the imaging device 34 may be inhibited if the values of components X, Y, and 0 exceed a predetermined threshold(s) or if the error between pixels of the boundary and the identified boundary exceed a threshold.


In the event the controller 38 inhibits calibration of the imaging device 34, a warning may be provided to a user of the TBA system 10. The warning may be generated by the controller 38 and carried out by existing vehicle components such as the display 34, speaker 80, for example, as well as portable electronic device 86. It is contemplated that the warning may be visual, auditory, haptic, or a combination thereof. In instances where damage to the vehicle 12 is detected (e.g., via inertial and/or perimeter sensors), the TBA system 10 may store a corresponding Diagnostic Trouble Code (DTC) and/or warn the user that the imaging device 34 and/or rear bumper 96 may require repair.


As described herein, the TBA system 10 features imager-based hitch angle detection, among other things. As a downside, there are instances where the imaging device 34 may be obstructed from tracking the trailer 14 or other objects in the imaged scene useful for hitch angle detection. For example, obstruction may occur when debris or other objects are deposited on the lens of the imaging device 34, the imaging device 34 experiences glare due to direct impingement of sunlight, or is unable to reliably image key features in the scene. In such instances where the imaging device 34 becomes obstructed, it is contemplated that the TBA system 10 may report the condition to the driver and may additionally cease imager-based hitch angle detection along with any other functions that rely on the processing of image data. While such instances are generally infrequent, the driver may become frustrated nonetheless if certain functions of the TBA system 10 become unavailable. Accordingly, a solution is needed that minimizes the downtime of image-based hitch angle detection due to the inability of the imaging device 34 to reliably image the scene.


In such a situation, the TBA system 10 may be configured to predict hitch angles using a “predictive model method,” which may be embodied in the hitch angle detection routine 44 and will be described in greater detail below with reference to FIG. 3, which illustrates a kinematic relationship between the vehicle 12 and the trailer 14. To predict a hitch angle, the controller 38 first determines an angular velocity {dot over (γ)} of the trailer 14, which can be determined by the following equation:











γ
.

=



v
D


sin





γ

+


(

1
+


L
D


cos





γ


)



v
W


tan





δ



,




(
1
)








where:


γ is the hitch angle (β−α) between the vehicle 12 and the trailer 14,


δ is the steering angle of steered wheels 60 of the vehicle 12,


L is the drawbar length between the hitch 32 and a rear axle 155 of the vehicle 12,


D is the trailer length between the hitch 32 and effective axle 20 of the trailer 14,


W is the wheelbase length between a front axle 157 and the rear axle 155 of the vehicle 12, and


ν is the longitudinal speed of the vehicle 12. It is to be noted that the function






v
W





tan δ corresponds to the yaw rate of the vehicle 12 and can be otherwise supplied by vehicle yaw rate sensor 52 (FIG. 2).


In calculating the angular velocity {dot over (γ)} of the trailer 14, it is assumed that the trailer length D, drawbar length L, and wheelbase length W are known. The steering angle δ and the longitudinal speed ν may be readily provided to the controller 38 by steering angle sensor 64 (FIG. 2) and speed sensor 50 (FIG. 2), respectively. Under normal operating conditions, the hitch angle γ can be determined pursuant to any known imager-based hitch angle detection method. Thus, so long as the imaging device 34 is unobstructed, or in other words, able to reliably track the trailer 14, the controller 38 is able to determine the angular velocity {dot over (γ)} of the trailer 14.


However, if the imaging device 34 suddenly becomes obstructed such that imager-based hitch angle detection becomes unavailable, the controller 38 can predict the hitch angle based on predetermined information including a last known hitch angle, a last known angular velocity of the trailer 14, and an execution cycle time of the image processor (e.g., microprocessor 40, FIG. 2) as represented by the following equation:

γplk+{dot over (γ)}lktc  (2)

where:


γp is a predicted hitch angle,


γlk is the last known hitch angle,


{dot over (γ)}lk is the last known angular velocity of the trailer 14, and


tc is the execution cycle time of the image processor. Thus, so long as the controller 38 is able to iterate equation 1 at least once before the imaging device 34 becomes obstructed, the controller 38 will have sufficient information to predict the hitch angle γp by iterating equation 2. The controller 38 may again calculate the angular velocity {dot over (γ)} of the trailer 14 by substituting the predicted hitch angle γp into equation 1, followed in turn by again predicting the hitch angle γp using the recalculated angular velocity {dot over (γ)} as the last known angular velocity {dot over (γ)}lk in equation 2. Thus, through stepwise reiteration of equations 1 and 2, the controller 38 is able predict the hitch angle in instances where imager-based hitch detection is unavailable or otherwise unreliable.


The predictive model method outlined above may be implemented for extended durations. However, as time progresses, the predicted hitch angle may begin to deviate from the true or actual hitch angle. Referring to FIG. 11, a graph is shown illustrating the deviation between actual hitch angle and predicted hitch angle over a vehicle travel distance D. For exemplary purposes, the controller 38 begins predicting the hitch angle when the imaging device 34 becomes obstructed at an arbitrary distance D0. As the vehicle 12 continues to travel, the error band of the predicted hitch angle, shown as upper and lower error bands, start to increase exponentially with respect to the vehicle travel distance D. Thus, hitch angle prediction tends to lose reliability as vehicle travel distance increases. In operation, the controller 38 continues to predict the hitch angle up until the error band reaches a threshold. For example, the controller 38 would stop predicting the hitch angle when the upper error band reaches a maximum controllable hitch angle 160 or the lower error band reaches a minimum controllable hitch angle 162, whichever comes first. In the illustrated embodiment, the upper error band is shown to reach the maximum controllable hitch angle at distance Dmax, thus prompting the controller 38 to stop predicting the hitch angle.


The degree error between the predicted hitch angle and the true hitch angle is determined by the following equation:










e
=

e
0

s
D



,




(
3
)








where:


e is the degree error,


e0 is an initial degree error at the moment the imaging device 34 becomes obstructed (e.g., 0.5 to 1 degree depending on the accuracy of hitch angle detection),


s is an accumulative vehicle travel distance determined by an odometer of the vehicle 12, and


D is the trailer length, which is assumed to be known.


Knowing the degree error e, the error band is determined by the following equations:

γ+p+e  (4)
γp−e  (5)

where:


γ+ is the upper error band,


γ is the lower error band,


γp is the predicted hitch angle determined from equation 2, and


e is the degree error determined from equation 3. Alternatively, the determination of the upper and lower error bands may include an error adjustment incorporated into each iteration of equation 1. That is, the angular velocity {dot over (γ)} determined using equation 1 is adjusted by a percentage error and the adjusted angular velocity is then used as the last known angular velocity {dot over (γ)}lk when predicting the hitch angle γp in equation 2.


Specifically, with respect to the upper error band γ+, the adjustment made to the angular velocity {dot over (γ)} is given by the following equation:

{dot over (γ)}adj={dot over (γ)}+|{dot over (γ)}ε|  (6)

With respect to the lower error band γ, the adjustment made to the angular velocity {dot over (γ)} is given by the following equation:

{dot over (γ)}adj={dot over (γ)}−|{dot over (γ)}ε|  (7)

where:


{dot over (γ)}adj is an adjusted angular velocity,


{dot over (γ)} is the angular velocity determined in equation 1, and


ε is a percentage error and is derived through experimentation. Accordingly, from equations 6 and 7, it can be seen that the adjusted angular velocity {dot over (γ)}adj associated with the upper and lower error bands will differ and therefore produce different predicted hitch angles γp when used as the last known angular velocity {dot over (γ)}lk in equation 2. Thus, equation 2 is iterated twice, once using the adjusted angular velocity {dot over (γ)}adj determined in equation 6, and a second time using the adjusted angular velocity {dot over (γ)}adj determined in equation 7. Each of the resulting predicted hitch angles γp is then used in the corresponding equation 4, 5 to determine the upper error band γ+ and the lower error band γ, respectively.


In the event the upper error band γ+ approaches or reaches the maximum controllable hitch angle or the lower error band γ reaches the minimum controllable hitch angle, the controller 38 may enact a countermeasure. For example the countermeasure may include providing steering commands to the power assist steering system 57 (FIG. 2) for steering the vehicle 12 in an attempt to keep the hitch angle from exceeding the maximum controllable hitch angle or falling below the minimum controllable hitch angle. Additionally or alternatively, the countermeasure may include providing braking commands to the vehicle brake control system 70 (FIG. 2) to reduce the speed of the vehicle 12. Additionally or alternatively still, the countermeasure may include instructing the driver to clean the lens of the imaging device 34, instructing the driver to take control of the steering input device 55 (FIG. 2), ramp out steering control, bringing autonomous steering functionality offline, or a combination thereof. In some embodiments, the countermeasure(s) may be applied at a predetermined vehicle travel distance that occurs prior to the upper or lower error bands γ+, γ reaching the maximum or minimum controllable hitch angles, respectively.


In the present embodiment, the controller 38 implements error band determination and functions both as an image processor and steering controller. In alternative embodiments where the image processor and steering controller are separate, it is contemplated that error band determination may be implemented by the image processor, steering controller, or a combination thereof. Generally, if the image processor and steering controller are together used to implement error band determination, additional traffic on the vehicle communication network (e.g., CAN bus) can be avoided at the expense of requiring additional hardware. If error band determination is only implemented using the steering controller, greater accuracy can be achieved at the expense of increased traffic on the vehicle communication network. Alternatively, if error band determination is only implemented using the image processor, additional traffic on the vehicle communication network can be avoided at the expense of accuracy. In instances where only one of the image processor and the steering controller is used to implement error band determination, a copy of the same may be supplied to the other of the image processor and the steering controller. Typically it is preferable to implement error band determination using both the image processor and the steering controller when there is no network interface (e.g., CAN interface) to accommodate the transmission of error band signals.


It is to be understood that the predictive model method described herein can be used to mitigate failure in other devices configured to sense the trailer 14. Such devices may include yaw rate sensors, Hall effect sensors, rotational potentiometers, and the like. In operation, data from these devices may be used by a controller to predict the hitch angle between a vehicle and a trailer. Accordingly, if one of these devices becomes unavailable, through failure or some other factor, the predictive model method may be used to determine the hitch angle.


Referring to FIG. 12, a method of determining hitch angle between the vehicle 12 and the trailer 14 is illustrated. The method may be executed by the controller 38 of the TBA system 10 and may be embodied in the hitch angle detection routine 44. At step 170, the controller 38 selects at least one hitch angle detection method amongst a plurality of hitch angle detection methods. The hitch angle detection methods may each employ imager-based hitch angle detection and include any of the hitch angle detection methods described in U.S. Pat. No. 9,610,975 to Hu et al. For example, the hitch angle detection methods may include the template matching method, the centerline method, and/or the steady state method, as described in U.S. Pat. No. 9,610,975. It is contemplated herein that the hitch angle detection methods may be ranked based on a confidence score assigned to each hitch angle detection method. The confidence score may be based on the reliability and/or robustness of a given hitch angle detection method. For example, the template matching method is typically the most reliable and most robust, followed by the steady-state method and the centerline method. Accordingly, the at least one selected hitch angle detection method typically includes an available hitch angle detection method having the highest confidence score.


At step 175, the controller 38 uses the selected at least one hitch angle detection method to determine a hitch angle between a vehicle 12 and a trailer 14. In determining the hitch angle, other related data may become available such as, but not limited to, hitch angle error band, hitch angle rate, hitch angle rate error band, hitch angle accuracy, etc. According to one embodiment, it is contemplated that all of the hitch angle detection methods may be used in parallel to determine the hitch angle and the hitch angle determined by the hitch angle detection method having the highest confidence score is used by the TBA system 10 to employ functions related to the backing of the trailer 14. In other embodiments, only the hitch angle detection method having the highest confidence score is used. Alternatively, some, but not all, of the hitch angle detection methods may be used in parallel, if desired. In any event, it is contemplated that the number of selected hitch angle detection methods may be limited by the hardware capabilities of the TBA system 10 or certain components thereof (e.g., the imaging device 34 and controller 38). As such, the number of hitch angle detection methods used in parallel may be selected so as to minimize computational strain on the TBA system 10 and/or related components. Furthermore, it is contemplated that the controller 38 may limit the speed of the vehicle 12 based on the confidence score assigned to the at least one selected hitch angle detection method. That is, the lower the confidence score, the greater the speed restriction imposed on the vehicle 12. To limit the speed of the vehicle 12, the controller 38 may output a brake command to the vehicle brake control system 70 of the vehicle 12.


At step 180, the controller 38 transitions to another hitch angle detection method if the selected at least one hitch angle detection method becomes unavailable. As described herein, imager-based hitch angle detection is reliant on the ability of the imaging device 34 to accurately capture images of a rear-vehicle scene and typically including the trailer 14 or components thereof. As such, when the imaging device 34 is obstructed by debris on the lens, glare from the sun, etc., the image quality of the images captured by the imaging device 34 may suffer. Accordingly, there may be instances where some hitch angle detection methods are available and others become unavailable.


The controller 38 may determine that a particular hitch angle detection method becomes unavailable if the image quality of the captured images falls below a threshold associated with the particular hitch angle detection method. Thus, in embodiments where only the hitch angle detection method having the highest confidence score is used and suddenly becomes unavailable, the controller 38 may transition to another hitch angle detection method that is available and has the next highest confidence score. In embodiments where some, but not all, of the hitch angle detection methods are used in parallel, if one of the selected hitch angle detection methods suddenly becomes unavailable, the controller 38 may replace it with another hitch angle detection method that is available and has the highest confidence score amongst the unselected hitch angle detection methods. In this manner, the total selected hitch angle detection methods in use remains the same. By using more than one hitch angle detection method, the hitch angle may be determined at greater intervals since it is possible that each selected hitch angle detection method may require a certain period of time in which to determine the hitch angle. Thus, by increasing the number of hitch angle detection methods in use, the likelihood that a hitch angle can be determined at any given point in time is increased.


At step 185, the controller 38 predicts the hitch angle during the transitioning between the at least one selected hitch angle detection method and another hitch angle detection method or if each of the plurality of hitch angle detection methods become unavailable. To predict the hitch angle, the controller 38 may use the predictive model method described previously herein. Regardless of which method(s) is used to determine the hitch angle, the controller 38 may apply a digital filter to the determined hitch angle and other trailer related data in some embodiments. At step 190, the controller 38 uses the determined or predicted hitch angle to control at least one of a hitch angle operating range, a speed limit of the vehicle 12, and the desired curvature 56 (FIG. 5) of the backing path of the trailer 14. For example, if the determined or predicted hitch angle has a hitch angle accuracy of ±10% and the TBA system 10 has a maximum controllable hitch angle of 50 degrees, the controller 38 may limit the maximum controllable hitch angle to 40 degrees in light of the hitch angle accuracy of the determined hitch angle. Furthermore, the controller 38 may limit the speed of the vehicle 12 or bring the vehicle 12 to a full stop at some predetermined deceleration of the trailer 14.


Existing TBA systems may employ a curvature routine that requires an operator to measure the trailer length D for input into system memory. Such systems exhibit certain drawbacks, such as the introduction of human error and/or the inability for the TBA system to operate immediately upon connecting, for example, trailer 14 with vehicle 12. Accordingly, the present controller 38 may incorporate a yaw-rate based routine embodied in the curvature routine 47 (FIG. 2) that is operational without knowledge of the trailer length D or a detected hitch angle in order to ensure stability and jackknife avoidance. However, it is contemplated that the controller 38 may also incorporate standard curvature routines that require the trailer length D to be inputted and function by detecting hitch angles directly such as by the use of an imaging device (e.g., imaging device 34). An example of a standard curvature routine is described in U.S. Pat. No. 8,909,426 to Rhode et al., issued Dec. 9, 2014, and entitled “TRAILER PATH CURVATURE CONTROL FOR TRAILER BACKUP ASSIST,” the entire disclosure of which is incorporated herein by reference.


As shown in FIGS. 4 and 5, the TBA system 10 disclosed herein provides the knob 58 for driver input. The driver indicates the desired direction and curvature of the backing path by turning the knob 58. The various positions or knob angles of knob 58a-58e are interpreted by the controller 38 as requests to cause the trailer 14 to follow paths similar to k(a)-k(e), respectively. According to one embodiment, position 58a can correspond to an at-rest position P(AR) of knob 58 (which may be spring-biased to such a position), which corresponds to backing along a substantially straight path k(a), and various other positions 58b, 58c being within a left range R(L) and the other positions 58d, 58e being within a right-side range R(R) of the motion of knob 58.


Referring to FIG. 13, a curvature input scaling module 200 of the controller 38 is shown. The knob angle of the knob 58 may be mapped into the interval [−1, 1] by some (possibly nonlinear) function k. Since the knob angle is a function of time, the value of the mapping function is also a function of time. For convenience, this time-varying quantity is referred to herein as a “curvature command” and is denoted simply k(t). When using the curvature routine 47, the curvature command k(t) is provided by the knob 58 to the curvature input scaling module 200 and is scaled by a maximum effective curvature {circumflex over (k)}2max to generate a curvature input {circumflex over (k)}2(t), where {circumflex over (k)}2(t)={circumflex over (k)}2max·k(t). As shown, the maximum effective curvature {circumflex over (k)}2max is defined by the composition ϕ(δmaxb, {circumflex over (D)})∘Γ({circumflex over (γ)}jk, {circumflex over (D)}).


ϕ(δmaxb, {circumflex over (D)}) corresponds to an effective jackknife angle {circumflex over (γ)}jk and is provided by the following equation:












γ
^

jk

=


Φ


(


δ
max
b

,

D
^


)


=


cos

-
1


(




-

D
^



L







tan
2



(

δ
max
b

)



±

W





L
2




tan
2



(

δ
max
b

)



+

W
2

-



D
^

2




tan
2



(

δ
max
b

)










L
2




tan
2



(

δ
max
b

)



+

W
2



)



,




(
8
)








where:


δmaxb is a constant defined by a maximum steering angle δmax less a configurable buffer Δbuf, where Δbuf≥0,


{circumflex over (D)} is an estimated trailer length,


L is the drawbar length and is assumed to be known, and


W is the vehicle wheelbase and is assumed to be known. The effective jackknife angle {circumflex over (γ)}jk may be less than a theoretical jackknife angle, since in practice, the controller 38 may generate some overshoot in hitch angle, and it is generally desirable to retain additional steering lock to ensure quick transitioning from maximum curvature to zero curvature.


Γ({circumflex over (γ)}jk, {circumflex over (D)}) corresponds to the maximum effective curvature {circumflex over (k)}2max and is provided by the following equation:












k
^

2
max

=


Γ


(



γ
^

jk

,

D
^


)








:=








sin







γ
^

jk



L
+


D
^


cos







γ
^

jk






,




(
9
)








where:


{circumflex over (γ)}jk is the effective jackknife angle determined by equation 8,


L is the drawbar length, and


{circumflex over (D)} is the estimated trailer length. With respect to this disclosure, the curvature input scaling module 200, as defined by the sequential input-output of the composition ϕ(δmaxb, {circumflex over (D)})∘Γ({circumflex over (γ)}jk, {circumflex over (D)})·k(t), is denoted by K(k(t), δmaxb, {circumflex over (D)}) for purposes of simplicity.


With reference to FIG. 14, the controller 38 is shown including the curvature input scaling module 200. The curvature input scaling module 200 is in communication with an estimator 202 configured to generate the estimated trailer length {circumflex over (D)} and an estimated hitch angle {circumflex over (γ)}(t) based on a vehicle yaw rate ω1(t) and a trailer yaw rate ω2(t). As described herein, the vehicle and trailer yaw rates ω1(t), ω2(t) may be provided to the controller 38 via vehicle yaw rate sensor 52 and trailer yaw rate sensor 54, respectively. The estimator 202 provides the estimated trailer length {circumflex over (D)} to the curvature input scaling module 200 to enable the curvature input {circumflex over (k)}2(t) to be computed as described previously herein with reference to FIG. 13. The estimator 202 also provides the estimated trailer length {circumflex over (D)} to a curvature mapping module 204 of the controller 38, which is denoted by p({circumflex over (k)}2(t), {circumflex over (D)}) for the purposes of simplicity. The curvature mapping module 204 is configured to generate a reference hitch angle {circumflex over (γ)}ref(t) based on the estimated trailer length D received from the estimator 202 and the curvature input {circumflex over (k)}2(t) received from the curvature input scaling module 200. The reference hitch angle {circumflex over (γ)}ref(t) corresponds to a steady-state hitch angle needed to achieve the curvature input {circumflex over (k)}2(t) and is provided by the following equation:













γ
^

ref



(
t
)


=


p


(




k
^

2



(
t
)


,

D
^


)


=


sin

-
1


(






k
^

2



(
t
)



L

+




k
^

2



(
t
)




D
^




1
-


(




k
^

2



(
t
)



L

)

2

+


(




k
^

2



(
t
)




D
^


)

2








(




k
^

2



(
t
)




D
^


)

2

+
1


)



,




(
10
)








where:


{circumflex over (k)}2(t) is the curvature input,


L is the drawbar length, and


{circumflex over (D)} is the estimated trailer length.


The reference hitch angle {circumflex over (γ)}ref(t), as provided by the curvature mapping module 204, and the estimated hitch angle {circumflex over (γ)}(t), as provided by the estimator 202, are received by a subtractor 206 configured to generate a signal e(t) defined by the reference hitch angle {circumflex over (γ)}ref(t) less the estimated hitch angle {circumflex over (γ)}(t). The estimated hitch angle {circumflex over (γ)}(t) is provided by the following equation:












γ
^



(
t
)


=



sin

-
1







ω
2



(
t
)




D
^






v
2



(
t
)


+



ω
1
2



(
t
)




L
2






+


tan

-
1







ω
1



(
t
)



L


v


(
t
)






,




(
11
)








where:


ω1(t) is the vehicle yaw rate,


ω2(t) is the trailer yaw rate,


L is the drawbar length,


{circumflex over (D)} is the estimated trailer length, and


ν(t) is vehicle speed. In real-time implementation, a Kalman filter may be used with the estimated hitch angle {circumflex over (γ)}(t) along with an internal state measurement thereof.


The signal e(t) is provided to a proportional-integral (PI) controller 208 to generate a control variable u(t) defined by the following equation:

u(t)=Kpe(t)+Ki0te(τ)dτ,  (12)

where:


e(t) is the signal generated by the subtractor 206,


Kp is a proportional coefficient having a non-negative value, and


Ki is an integral coefficient having a non-negative value.


The control variable u(t) is provided to a hitch angle controller 210 along with the estimated trailer length {circumflex over (D)} and the estimated hitch angle {circumflex over (γ)}(t), as provided by the estimator 202, and a vehicle speed υ(t), as provided by speed sensor 50 (FIG. 2), to generate a steering command in the form of a steering angle δ(t) supplied to the power assist steering system 57 (FIG. 2). The hitch angle controller 210 is denoted by g(u(t), {circumflex over (γ)}(t), υ(t), {circumflex over (D)}) for purposes of simplicity and is provided by the following equation:














(


u


(
t
)


,


γ
^



(
t
)


,

v


(
t
)


,

D
^


)


=


tan

-
1


(


W


v


(
t
)




(

1
+


L

D
^




cos


(


γ
^



(
t
)


)




)





(




v


(
t
)



D
^




sin


(


γ
^



(
t
)


)



-

u


(
t
)



)


)


,




(
13
)








where:


u(t) is the control variable generated by the PI controller 208,


{circumflex over (D)} is the estimated trailer length,


{circumflex over (γ)}(t) is the estimated hitch angle provided by the estimator 202,


W is the vehicle wheelbase,


{circumflex over (D)} is the estimated trailer length, and


υ(t) is the vehicle speed as provided by speed sensor 50 (FIG. 2).


When the controller 38 is configured according to the embodiment shown in FIG. 14, the trailer 14 arrives at the same equilibrium under steady state conditions (e.g., under a zero, maximum, or minimum curvature command k(t)) regardless of what estimated trailer length {circumflex over (D)} is used. For purposes of illustration and understanding, FIG. 15 illustrates a family of closed-loop equilibria γeq(in degrees) as a function of the curvature command k(t) for estimated trailer lengths {circumflex over (D)}∈{1.5, 2, 2.5, . . . , 8}. For exemplary purposes, the equilibria γeq correspond to the case where W=3.98 m, L=1.35 m, δmaxb=21°, υ(t)=5, Kp=0.75. For purposes of simplicity, it is assumed that the integral control is turned off, that is, Ki=0. As shown in FIG. 15, dashed line 212 corresponds to {circumflex over (D)}=1.5 m and dashed line 214 corresponds to {circumflex over (D)}=8 m. Notably, the estimated trailer lengths {circumflex over (D)}∈{1.5, 2, 2.5, . . . , 8} converge to the same equilibria γeq at a zero curvature command k(t)=0, a maximum curvature command k(t)=1, and a minimum curvature command k(t)=−1. Thus, under certain conditions (e.g., steady-state conditions), the generated steering angle δ(t) is invariant to the estimated trailer length {circumflex over (D)}. In some embodiments, the transient response of the controller 38 may also be invariant to the estimated trailer length {circumflex over (D)}. This is accomplished by gain scheduling the proportional coefficient Kp based only on the estimated trailer length {circumflex over (D)}. For example, the proportional coefficient Kp may be defined by the function







1


.14


D
^


+
.57


.





In this manner, the closed-loop dynamics are shaped in a uniform manner.


It is to be understood that variations and modifications can be made on the aforementioned structures and methods without departing from the concepts of the present invention, and further it is to be understood that such concepts are intended to be covered by the following claims unless these claims by their language expressly state otherwise.

Claims
  • 1. A method of determining an offset for calibrating an imaging device comprising the steps of: using the imaging device to capture an image of a rear bumper; andproviding a controller configured to: process the captured image;identify a boundary separating the rear bumper from a ground based on the captured image;compare the identified boundary to an ideal boundary;determine an offset between the identified boundary and the ideal boundary, wherein the offset is defined by a vector having a horizontal component, a vertical component, and a rotational component, and the controller determines the offset by iterating on candidates for each of the horizontal, vertical, and rotational components until the identified boundary overlaps with the ideal boundary; andcalibrating the imaging device based on the determined offset.
  • 2. The method of claim 1, wherein the ideal boundary comprises a continuous line.
  • 3. The method of claim 1, wherein the ideal boundary comprises a line having a break.
  • 4. The method of claim 1, wherein the ideal boundary is overlaid onto the captured image.
  • 5. The method of claim 1, wherein the controller is further configured to generate a warning if the offset is unable to be determined.
  • 6. A calibration method, comprising the steps of: using an imaging device to capture an image of a rear bumper; andproviding a controller configured to: process the captured image;identify a boundary separating the rear bumper from a ground based on the captured image;compare the identified boundary to an ideal boundary; andcalibrate the imaging device by determining an offset between the identified boundary and the ideal boundary, wherein the offset is defined by a vector having a horizontal component, a vertical component, and a rotational component, and the controller determines the offset by iterating on candidates for each of the horizontal, vertical, and rotational components until the identified boundary overlaps with the ideal boundary.
US Referenced Citations (528)
Number Name Date Kind
3542390 Fikes et al. Nov 1970 A
3605088 Savelli Sep 1971 A
3756624 Taylor Sep 1973 A
3787077 Sanders Jan 1974 A
3833928 Gavit et al. Sep 1974 A
3860257 Mesley Jan 1975 A
3944972 Chandler Mar 1976 A
4040006 Kimmel Aug 1977 A
4042132 Bohman et al. Aug 1977 A
4122390 Kollitz et al. Oct 1978 A
4212483 Howard Jul 1980 A
4320267 Greve et al. Mar 1982 A
4366966 Ratsko et al. Jan 1983 A
4430637 Koch-Ducker et al. Feb 1984 A
4518044 Wiegardt et al. May 1985 A
4735432 Brown Apr 1988 A
4752080 Rogers Jun 1988 A
4848449 Martinet et al. Jul 1989 A
4848499 Martinet et al. Jul 1989 A
4852901 Beasley et al. Aug 1989 A
4897642 DiLullo et al. Jan 1990 A
4943080 Reimer Jul 1990 A
4947097 Tao Aug 1990 A
5001639 Breen Mar 1991 A
5056905 Jensen Oct 1991 A
5097250 Hernandez Mar 1992 A
5108123 Rubenzik Apr 1992 A
5108158 Breen Apr 1992 A
5132851 Bomar et al. Jul 1992 A
5142278 Moallemi et al. Aug 1992 A
5152544 Dierker, Jr. et al. Oct 1992 A
5191328 Nelson Mar 1993 A
5244226 Bergh Sep 1993 A
5246242 Penzotti Sep 1993 A
5247442 Kendall Sep 1993 A
5261495 Szymczak Nov 1993 A
5270689 Hermann Dec 1993 A
5282641 McLaughlin Feb 1994 A
5289892 Notsu Mar 1994 A
5290057 Pellerito Mar 1994 A
5313389 Yasui May 1994 A
5359165 Leveque et al. Oct 1994 A
5430261 Malone Jul 1995 A
5436413 Katakami Jul 1995 A
5442810 Jenquin Aug 1995 A
5455557 Noll et al. Oct 1995 A
5521633 Nakajima et al. May 1996 A
5523947 Breen Jun 1996 A
5541778 DeFlorio Jul 1996 A
5558350 Kimbrough et al. Sep 1996 A
5559696 Borenstein Sep 1996 A
5579228 Kimbrough et al. Nov 1996 A
5586814 Steiner Dec 1996 A
5631656 Hartman et al. May 1997 A
5650764 McCullough Jul 1997 A
5690347 Juergens et al. Nov 1997 A
5719713 Brown Feb 1998 A
5747683 Gerum et al. May 1998 A
5821852 Fairchild Oct 1998 A
5905433 Wortham May 1999 A
5951035 Phillips, Jr. et al. Sep 1999 A
5957232 Shimizu et al. Sep 1999 A
5969325 Hecht et al. Oct 1999 A
5970619 Wells Oct 1999 A
5980048 Rannells, Jr. et al. Nov 1999 A
5999091 Wortham Dec 1999 A
6041582 Tiede et al. Mar 2000 A
6042196 Nakamura et al. Mar 2000 A
6056371 Lin et al. May 2000 A
6100795 Otterbacher et al. Aug 2000 A
6111524 Lesesky et al. Aug 2000 A
6124709 Allwine Sep 2000 A
6142372 Wright Nov 2000 A
6151175 Osha Nov 2000 A
6178650 Thibodeaux Jan 2001 B1
6198992 Winslow Mar 2001 B1
6217177 Rost Apr 2001 B1
6218828 Bates et al. Apr 2001 B1
6223104 Kamen et al. Apr 2001 B1
6223114 Boros et al. Apr 2001 B1
6268800 Howard Jul 2001 B1
6292094 Deng et al. Sep 2001 B1
6318747 Ratican Nov 2001 B1
6351698 Kubota et al. Feb 2002 B1
6389342 Kanda May 2002 B1
6409288 Yoshida et al. Jun 2002 B2
6472865 Tola et al. Oct 2002 B1
6480104 Wall et al. Nov 2002 B1
6483429 Yasui et al. Nov 2002 B1
6494476 Masters et al. Dec 2002 B2
6498977 Wetzel et al. Dec 2002 B2
6501376 Dieckmann et al. Dec 2002 B2
6539288 Ishida et al. Mar 2003 B2
6567731 Chandy May 2003 B2
6568093 Kogiso et al. May 2003 B2
6577952 Geier et al. Jun 2003 B2
6601386 Hori et al. Aug 2003 B1
6636197 Goldenberg et al. Oct 2003 B1
6668225 Oh et al. Dec 2003 B2
6687609 Hsiao et al. Feb 2004 B2
6704653 Kuriya et al. Mar 2004 B2
6712378 Austin Mar 2004 B1
6750406 Komatsu et al. Jun 2004 B2
6801125 McGregor et al. Oct 2004 B1
6806809 Lee et al. Oct 2004 B2
6820888 Griffin Nov 2004 B1
6837432 Tsikos et al. Jan 2005 B2
6838979 Deng et al. Jan 2005 B2
6847916 Ying Jan 2005 B1
6854557 Deng et al. Feb 2005 B1
6857494 Kobayashi et al. Feb 2005 B2
6879240 Kruse Apr 2005 B2
6956468 Lee et al. Oct 2005 B2
6959970 Tseng Nov 2005 B2
6999856 Lee et al. Feb 2006 B2
7005974 McMahon et al. Feb 2006 B2
7006127 Mizusawa et al. Feb 2006 B2
7008088 Pisciotti Mar 2006 B2
7028804 Eki et al. Apr 2006 B2
7032705 Zheng et al. Apr 2006 B2
7036840 Kwilinski May 2006 B2
7038667 Vassallo et al. May 2006 B1
7039504 Tanaka et al. May 2006 B2
7046127 Boddy May 2006 B2
7058493 Inagaki Jun 2006 B2
7085634 Endo et al. Aug 2006 B2
7089101 Fischer et al. Aug 2006 B2
7117077 Michi et al. Oct 2006 B2
7136754 Hahn et al. Nov 2006 B2
7139650 Lubischer Nov 2006 B2
7154385 Lee et al. Dec 2006 B2
7159890 Craig et al. Jan 2007 B2
7165820 Rudd, III Jan 2007 B2
7167785 Lohberg et al. Jan 2007 B2
7170285 Spratte Jan 2007 B2
7171330 Kruse et al. Jan 2007 B2
7175194 Ball Feb 2007 B2
7191865 Spark Mar 2007 B2
7204504 Gehring et al. Apr 2007 B2
7219913 Atley May 2007 B2
7225891 Gehring et al. Jun 2007 B2
7229139 Lu et al. Jun 2007 B2
7237790 Gehring et al. Jul 2007 B2
7239958 Grougan et al. Jul 2007 B2
7255061 Denton Aug 2007 B2
7269489 Deng et al. Sep 2007 B2
7272481 Einig et al. Sep 2007 B2
7295907 Lu et al. Nov 2007 B2
7309075 Ramsey et al. Dec 2007 B2
7310084 Shitanaka et al. Dec 2007 B2
7315299 Sunda et al. Jan 2008 B2
7319927 Sun et al. Jan 2008 B1
7401871 Lu et al. Jul 2008 B2
7405557 Spratte et al. Jul 2008 B2
7413266 Lenz et al. Aug 2008 B2
7425889 Widmann et al. Sep 2008 B2
7436298 Yuasa et al. Oct 2008 B2
7447585 Tandy, Jr. et al. Nov 2008 B2
7451020 Goetting et al. Nov 2008 B2
7463137 Wishart et al. Dec 2008 B2
7504995 Lawrence et al. Mar 2009 B2
7532109 Takahama et al. May 2009 B2
7540523 Russell et al. Jun 2009 B2
7546191 Lin et al. Jun 2009 B2
7548155 Schutt et al. Jun 2009 B2
7550686 Girke et al. Jun 2009 B2
7568716 Dietz Aug 2009 B2
7623952 Unruh et al. Nov 2009 B2
7648153 Metternich et al. Jan 2010 B2
7690737 Lu Apr 2010 B2
7696862 Herschell et al. Apr 2010 B2
7706944 Tanaka et al. Apr 2010 B2
7715953 Shephard May 2010 B2
7731302 Tandy, Jr. et al. Jun 2010 B2
7744109 Groh Jun 2010 B2
7760077 Day Jul 2010 B2
7777615 Okuda et al. Aug 2010 B2
7793965 Padula Sep 2010 B2
7798263 Tandy, Jr. et al. Sep 2010 B2
7825782 Hermann Nov 2010 B2
7827917 Henderson Nov 2010 B1
7837004 Yasuda Nov 2010 B2
7878545 Rhymer et al. Feb 2011 B2
7904222 Lee et al. Mar 2011 B2
7905507 Perri Mar 2011 B2
7932815 Martinez et al. Apr 2011 B2
7950751 Offerle et al. May 2011 B2
7953536 Katrak May 2011 B2
7969326 Sakakibara Jun 2011 B2
7974444 Hongo Jul 2011 B2
8010252 Getman et al. Aug 2011 B2
8010253 Lundquist Aug 2011 B2
8033955 Farnsworth Oct 2011 B2
8036792 Dechamp Oct 2011 B2
8038166 Piesinger Oct 2011 B1
8044776 Schofield et al. Oct 2011 B2
8044779 Hahn et al. Oct 2011 B2
8068019 Bennie et al. Nov 2011 B2
8073594 Lee et al. Dec 2011 B2
8108116 Mori et al. Jan 2012 B2
8138865 North et al. Mar 2012 B2
8138899 Ghneim Mar 2012 B2
8139109 Schmiedel et al. Mar 2012 B2
8157284 McGhie et al. Apr 2012 B1
8165770 Getman et al. Apr 2012 B2
8167444 Lee et al. May 2012 B2
8170726 Chen et al. May 2012 B2
8174576 Akatsuka et al. May 2012 B2
8179238 Roberts, Sr. et al. May 2012 B2
8180543 Futamura et al. May 2012 B2
8190364 Rekow May 2012 B2
8191915 Freese et al. Jun 2012 B2
8192036 Lee et al. Jun 2012 B2
8215436 DeGrave et al. Jul 2012 B2
8223204 Hahn Jul 2012 B2
8224078 Boncyk et al. Jul 2012 B2
8244442 Craig et al. Aug 2012 B2
8260518 Englert Sep 2012 B2
8267485 Barlsen et al. Sep 2012 B2
8279067 Berger et al. Oct 2012 B2
8280607 Gatti et al. Oct 2012 B2
8308182 Ortmann et al. Nov 2012 B2
8326504 Wu et al. Dec 2012 B2
8332097 Chiba et al. Dec 2012 B2
8342560 Albers et al. Jan 2013 B2
8362888 Roberts, Sr. et al. Jan 2013 B2
8374749 Tanaka Feb 2013 B2
8380390 Sy et al. Feb 2013 B2
8380416 Offerle et al. Feb 2013 B2
8390696 Komoto et al. Mar 2013 B2
8393632 Vortmeyer et al. Mar 2013 B2
8401744 Chiocco Mar 2013 B2
8427288 Schofield et al. Apr 2013 B2
8430792 Noll Apr 2013 B2
8451107 Lu et al. May 2013 B2
8469125 Yu et al. Jun 2013 B2
8504243 Kageyama Aug 2013 B2
8519948 Cruz-Hernandez et al. Aug 2013 B2
8548680 Ryerson et al. Oct 2013 B2
8548683 Cebon et al. Oct 2013 B2
8571758 Klier et al. Oct 2013 B2
8576115 Basten Nov 2013 B2
8626382 Obradovich Jan 2014 B2
8675953 Elwell et al. Mar 2014 B1
8755982 Heckel et al. Jun 2014 B2
8755984 Rupp et al. Jun 2014 B2
8768535 Kossira et al. Jul 2014 B2
8786417 Holmen et al. Jul 2014 B2
8798860 Dechamp Aug 2014 B2
8807261 Subrt et al. Aug 2014 B2
8825328 Rupp et al. Sep 2014 B2
8833789 Anderson Sep 2014 B2
8886400 Kossira et al. Nov 2014 B2
8888120 Trevino Nov 2014 B2
8888121 Trevino et al. Nov 2014 B2
8909426 Rhode et al. Dec 2014 B2
8930140 Trombley et al. Jan 2015 B2
8939462 Adamczyk et al. Jan 2015 B2
8955865 Fortin et al. Feb 2015 B2
8972109 Lavoie et al. Mar 2015 B2
9008913 Sears et al. Apr 2015 B1
9026311 Pieronek et al. May 2015 B1
9033284 Van Staagen May 2015 B2
9042603 Elwart et al. May 2015 B2
9082315 Lin et al. Jul 2015 B2
9102271 Trombley et al. Aug 2015 B2
9108598 Headley Aug 2015 B2
9114832 Wang et al. Aug 2015 B2
9120358 Motts et al. Sep 2015 B2
9120359 Chiu et al. Sep 2015 B2
9132856 Shephard Sep 2015 B2
9156496 Greenwood et al. Oct 2015 B2
9164955 Lavoie et al. Oct 2015 B2
9180890 Lu et al. Nov 2015 B2
9187124 Trombley et al. Nov 2015 B2
9227474 Liu Jan 2016 B2
9229453 Lee Jan 2016 B1
9238483 Hafner et al. Jan 2016 B2
9248858 Lavoie et al. Feb 2016 B2
9296422 Lavoie Mar 2016 B2
9315151 Taylor et al. Apr 2016 B2
9315212 Kyrtsos et al. Apr 2016 B1
9321483 Headley Apr 2016 B2
9335162 Kyrtsos et al. May 2016 B2
9340228 Xu et al. May 2016 B2
9352777 Lavoie et al. May 2016 B2
9393996 Goswami et al. Jul 2016 B2
9428188 Schwindt et al. Aug 2016 B2
9434414 Lavoie Sep 2016 B2
9499018 Gehrke et al. Nov 2016 B2
9500497 Lavoie et al. Nov 2016 B2
9610974 Herzog et al. Apr 2017 B2
9616923 Lavoie et al. Apr 2017 B2
9623904 Lavoie et al. Apr 2017 B2
9676377 Hafner et al. Jun 2017 B2
9714051 Lavoie Jul 2017 B2
9798953 Hu Oct 2017 B2
9827818 Hu et al. Nov 2017 B2
9836060 Ghneim et al. Dec 2017 B2
9840278 Lavoie et al. Dec 2017 B2
9983404 Asada May 2018 B2
10046800 Hu et al. Aug 2018 B2
20010024333 Rost Sep 2001 A1
20010037164 Hecker Nov 2001 A1
20010052434 Ehrlich et al. Dec 2001 A1
20020128764 Hecker et al. Sep 2002 A1
20020149673 Hirama et al. Oct 2002 A1
20030052969 Satoh Mar 2003 A1
20030234512 Holub Dec 2003 A1
20040017285 Zielinski et al. Jan 2004 A1
20040021291 Haug et al. Feb 2004 A1
20040093139 Wildey et al. May 2004 A1
20040130441 Lee et al. Jul 2004 A1
20040189595 Yuasa et al. Sep 2004 A1
20040207525 Wholey et al. Oct 2004 A1
20040222881 Deng et al. Nov 2004 A1
20050000738 Gehring et al. Jan 2005 A1
20050071373 Long Mar 2005 A1
20050074143 Kawai Apr 2005 A1
20050128059 Vause Jun 2005 A1
20050206224 Lu Sep 2005 A1
20050206225 Offerle et al. Sep 2005 A1
20050206229 Lu et al. Sep 2005 A1
20050206231 Lu et al. Sep 2005 A1
20050236201 Spannheimer et al. Oct 2005 A1
20050236896 Offerle et al. Oct 2005 A1
20060041358 Hara Feb 2006 A1
20060071447 Gehring et al. Apr 2006 A1
20060076828 Lu et al. Apr 2006 A1
20060092129 Choquet et al. May 2006 A1
20060103511 Lee et al. May 2006 A1
20060111820 Goetting et al. May 2006 A1
20060142936 Dix Jun 2006 A1
20060155455 Lucas et al. Jul 2006 A1
20060171704 Bingle et al. Aug 2006 A1
20060244579 Raab Nov 2006 A1
20060250501 Widmann et al. Nov 2006 A1
20070027581 Bauer et al. Feb 2007 A1
20070058273 Ito et al. Mar 2007 A1
20070090688 Haemmerling et al. Apr 2007 A1
20070132560 Nystrom et al. Jun 2007 A1
20070152424 Deng et al. Jul 2007 A1
20070198190 Bauer et al. Aug 2007 A1
20070263902 Higuchi Nov 2007 A1
20070271267 Lim et al. Nov 2007 A1
20070285808 Beale Dec 2007 A1
20080030361 Peissner et al. Feb 2008 A1
20080143593 Graziano et al. Jun 2008 A1
20080147277 Lu et al. Jun 2008 A1
20080177443 Lee et al. Jul 2008 A1
20080180526 Trevino Jul 2008 A1
20080231701 Greenwood et al. Sep 2008 A1
20080231707 Fontana Sep 2008 A1
20080312792 Dechamp Dec 2008 A1
20090005932 Lee et al. Jan 2009 A1
20090045924 Roberts, Sr. et al. Feb 2009 A1
20090079828 Lee et al. Mar 2009 A1
20090082935 Leschuk et al. Mar 2009 A1
20090085775 Otsuka et al. Apr 2009 A1
20090093928 Getman et al. Apr 2009 A1
20090101429 Williams Apr 2009 A1
20090102922 Ito Apr 2009 A1
20090157260 Lee Jun 2009 A1
20090198425 Englert Aug 2009 A1
20090219147 Bradley et al. Sep 2009 A1
20090228182 Waldbauer et al. Sep 2009 A1
20090231441 Walker et al. Sep 2009 A1
20090248346 Fennel et al. Oct 2009 A1
20090271078 Dickinson Oct 2009 A1
20090300701 Karaoguz et al. Dec 2009 A1
20090306854 Dechamp Dec 2009 A1
20090306861 Schumann et al. Dec 2009 A1
20090326775 Nishida Dec 2009 A1
20100063670 Brzezinski et al. Mar 2010 A1
20100063702 Sabelstrom et al. Mar 2010 A1
20100152989 Smith et al. Jun 2010 A1
20100156667 Bennie et al. Jun 2010 A1
20100171828 Ishii Jul 2010 A1
20100222964 Dechamp Sep 2010 A1
20100324770 Ramsey et al. Dec 2010 A1
20100332049 Sy et al. Dec 2010 A1
20110001825 Hahn Jan 2011 A1
20110018231 Collenberg Jan 2011 A1
20110022282 Wu et al. Jan 2011 A1
20110025482 Algueera et al. Feb 2011 A1
20110050903 Vorobiev Mar 2011 A1
20110087398 Lu et al. Apr 2011 A1
20110112721 Wang et al. May 2011 A1
20110125457 Lee et al. May 2011 A1
20110149077 Robert Jun 2011 A1
20110160956 Chung et al. Jun 2011 A1
20110216199 Trevino et al. Sep 2011 A1
20110257860 Getman et al. Oct 2011 A1
20110267366 Ichinose Nov 2011 A1
20110281522 Suda Nov 2011 A1
20110290882 Gu et al. Dec 2011 A1
20120030626 Hopkins et al. Feb 2012 A1
20120041658 Turner Feb 2012 A1
20120086808 Lynam et al. Apr 2012 A1
20120087480 Yang et al. Apr 2012 A1
20120095649 Klier et al. Apr 2012 A1
20120109471 Wu May 2012 A1
20120112434 Albers et al. May 2012 A1
20120185131 Headley Jul 2012 A1
20120191285 Woolf et al. Jul 2012 A1
20120200706 Greenwood et al. Aug 2012 A1
20120265416 Lu et al. Oct 2012 A1
20120271512 Rupp et al. Oct 2012 A1
20120271514 Lavoie et al. Oct 2012 A1
20120271515 Rhode et al. Oct 2012 A1
20120271522 Rupp et al. Oct 2012 A1
20120283909 Dix Nov 2012 A1
20120283910 Lee et al. Nov 2012 A1
20120288156 Kido Nov 2012 A1
20120310594 Watanabe Dec 2012 A1
20120316732 Auer Dec 2012 A1
20130006472 McClain et al. Jan 2013 A1
20130024064 Shepard Jan 2013 A1
20130027195 Van Wiemeersch et al. Jan 2013 A1
20130041524 Brey Feb 2013 A1
20130082453 Padula Apr 2013 A1
20130141578 Chundrlik, Jr. et al. Jun 2013 A1
20130148748 Suda Jun 2013 A1
20130158803 Headley Jun 2013 A1
20130158863 Skvarce et al. Jun 2013 A1
20130179038 Goswami et al. Jul 2013 A1
20130207834 Mizutani et al. Aug 2013 A1
20130226390 Luo et al. Aug 2013 A1
20130250114 Lu Sep 2013 A1
20130253814 Wirthlin Sep 2013 A1
20130261843 Kossira et al. Oct 2013 A1
20130268160 Trombley et al. Oct 2013 A1
20140005918 Qiang Jan 2014 A1
20140025260 McClure Jan 2014 A1
20140052337 Lavoie et al. Feb 2014 A1
20140058614 Trombley et al. Feb 2014 A1
20140058622 Trombley et al. Feb 2014 A1
20140058655 Trombley et al. Feb 2014 A1
20140058668 Trombley et al. Feb 2014 A1
20140067154 Yu et al. Mar 2014 A1
20140067155 Yu et al. Mar 2014 A1
20140085472 Lu et al. Mar 2014 A1
20140088797 McClain et al. Mar 2014 A1
20140088824 Ishimoto Mar 2014 A1
20140121930 Allexi et al. May 2014 A1
20140125795 Yerke May 2014 A1
20140156148 Kikuchi Jun 2014 A1
20140160276 Pliefke et al. Jun 2014 A1
20140172232 Rupp et al. Jun 2014 A1
20140183841 Jones Jul 2014 A1
20140188344 Lavoie Jul 2014 A1
20140188346 Lavoie Jul 2014 A1
20140200759 Lu et al. Jul 2014 A1
20140210456 Crossman Jul 2014 A1
20140218506 Trombley et al. Aug 2014 A1
20140218522 Lavoie et al. Aug 2014 A1
20140222288 Lavoie et al. Aug 2014 A1
20140236532 Trombley et al. Aug 2014 A1
20140249691 Hafner et al. Sep 2014 A1
20140267688 Aich et al. Sep 2014 A1
20140267689 Lavoie Sep 2014 A1
20140277941 Chiu et al. Sep 2014 A1
20140277942 Kyrtsos et al. Sep 2014 A1
20140297128 Lavoie et al. Oct 2014 A1
20140297129 Lavoie et al. Oct 2014 A1
20140303847 Lavoie Oct 2014 A1
20140307095 Wierich Oct 2014 A1
20140309888 Smit et al. Oct 2014 A1
20140324295 Lavoie Oct 2014 A1
20140343795 Lavoie Nov 2014 A1
20140354811 Weber Dec 2014 A1
20140358429 Shutko et al. Dec 2014 A1
20140379217 Rupp et al. Dec 2014 A1
20150002669 Reed et al. Jan 2015 A1
20150002670 Bajpai Jan 2015 A1
20150025732 Min et al. Jan 2015 A1
20150035256 Klank et al. Feb 2015 A1
20150057903 Rhode et al. Feb 2015 A1
20150066296 Trombley et al. Mar 2015 A1
20150066298 Sharma et al. Mar 2015 A1
20150070161 Mizuno et al. Mar 2015 A1
20150077557 Han et al. Mar 2015 A1
20150105975 Dunn Apr 2015 A1
20150115571 Zhang et al. Apr 2015 A1
20150120141 Lavoie et al. Apr 2015 A1
20150120143 Schlichting Apr 2015 A1
20150134183 Lavoie et al. May 2015 A1
20150138340 Lavoie May 2015 A1
20150149040 Hueger et al. May 2015 A1
20150158527 Hafner et al. Jun 2015 A1
20150165850 Chiu et al. Jun 2015 A1
20150197278 Boos et al. Jul 2015 A1
20150203156 Hafner et al. Jul 2015 A1
20150210254 Pieronek et al. Jul 2015 A1
20150210317 Hafner et al. Jul 2015 A1
20150217693 Pliefke et al. Aug 2015 A1
20150232092 Fairgrieve et al. Aug 2015 A1
20150269444 Lameyre et al. Sep 2015 A1
20160001705 Greenwood et al. Jan 2016 A1
20160009288 Yu Jan 2016 A1
20160023603 Vico et al. Jan 2016 A1
20160039456 Lavoie et al. Feb 2016 A1
20160052548 Singh et al. Feb 2016 A1
20160059780 Lavoie Mar 2016 A1
20160059888 Bradley et al. Mar 2016 A1
20160059889 Herzog et al. Mar 2016 A1
20160096549 Herzog et al. Apr 2016 A1
20160129939 Singh et al. May 2016 A1
20160152263 Singh et al. Jun 2016 A1
20160153778 Singh et al. Jun 2016 A1
20160229452 Lavoie et al. Aug 2016 A1
20160272024 Bochenek et al. Sep 2016 A1
20160280267 Lavoie et al. Sep 2016 A1
20160304122 Herzog et al. Oct 2016 A1
20160364620 Akiyama Dec 2016 A1
20160375831 Wang et al. Dec 2016 A1
20170073005 Ghneim et al. Mar 2017 A1
20170098131 Shashua Apr 2017 A1
20170101130 Lavoie Apr 2017 A1
20170106796 Lavoie Apr 2017 A1
20170174130 Hu et al. Jun 2017 A1
20170177949 Hu et al. Jun 2017 A1
20170259850 Yamashita et al. Sep 2017 A1
20170297491 Tanaka Oct 2017 A1
20170297619 Lavoie et al. Oct 2017 A1
20170297620 Lavoie et al. Oct 2017 A1
20170313351 Lavoie Nov 2017 A1
20180025499 Strano et al. Jan 2018 A1
Foreign Referenced Citations (115)
Number Date Country
101610420 Dec 2009 CN
202159367 Mar 2012 CN
102582686 Sep 2013 CN
3923676 Jan 1991 DE
3931518 Apr 1991 DE
9208595 Aug 1992 DE
19526702 Feb 1997 DE
10030738 Aug 2001 DE
10031244 Jan 2002 DE
10065230 Jul 2002 DE
10122562 Jul 2002 DE
10154612 May 2003 DE
10312548 May 2004 DE
10333998 Feb 2005 DE
102004050149 Apr 2006 DE
102005042957 Mar 2007 DE
102005043466 Mar 2007 DE
102005043467 Mar 2007 DE
102005043468 Mar 2007 DE
102006002294 Jul 2007 DE
102006048947 Apr 2008 DE
102006056408 Jun 2008 DE
102008020838 Nov 2008 DE
102007029413 Jan 2009 DE
102008004160 Aug 2009 DE
102008045436 Mar 2010 DE
102006035021 Apr 2010 DE
102008043675 May 2010 DE
102009007990 Aug 2010 DE
102009012253 Sep 2010 DE
102009027041 Dec 2010 DE
102009038552 Feb 2011 DE
102010006323 Aug 2011 DE
102008004158 Oct 2011 DE
102008004159 Oct 2011 DE
102008004160 Oct 2011 DE
102010021052 Nov 2011 DE
102010029184 Nov 2011 DE
102010045519 Mar 2012 DE
102011104256 Jul 2012 DE
102011101990 Oct 2012 DE
102012005707 Oct 2012 DE
202012010517 Dec 2012 DE
102011108440 Jan 2013 DE
102011120814 Jun 2013 DE
102012006206 Oct 2013 DE
102012206133 Oct 2013 DE
102012019234 Apr 2014 DE
102013000198 Jul 2014 DE
0418653 Mar 1991 EP
0433858 Jun 1991 EP
1312492 May 2003 EP
1361543 Nov 2003 EP
1653490 May 2006 EP
1655191 May 2006 EP
1593552 Mar 2007 EP
1810913 Jul 2007 EP
2388180 Nov 2011 EP
2452549 May 2012 EP
2487454 Aug 2012 EP
2551132 Jan 2013 EP
2644477 Oct 2013 EP
2682329 Jan 2014 EP
1569073 Sep 2014 EP
2803944 Nov 2014 EP
2515379 Apr 1983 FR
2265587 Oct 1993 GB
2342630 Apr 2000 GB
2398048 Aug 2004 GB
2398049 Aug 2004 GB
2398050 Aug 2004 GB
61006458 Jan 1986 JP
6159491 Mar 1986 JP
6385568 Jun 1988 JP
01095980 Apr 1989 JP
01095981 Apr 1989 JP
09267762 Oct 1997 JP
09328078 Dec 1997 JP
10001063 Jan 1998 JP
10119739 May 1998 JP
11124051 May 1999 JP
11278319 Oct 1999 JP
2002012172 Jan 2002 JP
2002068032 Mar 2002 JP
2003034261 Feb 2003 JP
2003045269 Feb 2003 JP
2003148938 May 2003 JP
2003175852 Jun 2003 JP
3716722 Nov 2005 JP
2007186118 Jul 2007 JP
2008027138 Feb 2008 JP
2009171122 Jul 2009 JP
2012105158 May 2012 JP
2012166580 Sep 2012 JP
2012166647 Sep 2012 JP
2014002056 Jan 2014 JP
20140105199 Sep 2014 KR
8503263 Aug 1985 WO
0044605 Aug 2000 WO
2005005200 Jan 2005 WO
2005116688 Dec 2005 WO
2006042665 Apr 2006 WO
2012059207 May 2012 WO
2012103193 Aug 2012 WO
2013048994 Apr 2013 WO
2013070539 May 2013 WO
2013186208 Dec 2013 WO
2014019730 Feb 2014 WO
2014037500 Mar 2014 WO
2014070047 May 2014 WO
2014092611 Jun 2014 WO
2014123575 Aug 2014 WO
2014174027 Oct 2014 WO
2015074027 May 2015 WO
2015187467 Dec 2015 WO
Non-Patent Literature Citations (70)
Entry
Jae Il Roh, Hyunsuk Lee, Woojin Chung, “Control of a Car with a Trailer Using the Driver Assistance System”, IEEE, International Conference on Robotics and Biomimetics, Dec. 7-11, 2011; Phuket, Thailand, pp. 2890-2895.
M. Khatib, H. Jaouni, R. Chatila, and J.P. Laumond; “Dynamic Path Modification for Car-Like Nonholonomic Mobile Robots,” IEEE, International Conference on Robotics and Automation, Albuquerque, New Mexico, Apr. 1997, 6 pages.
SH. Azadi, H.R. Rezaei Nedamani, and R. Kazemi, “Automatic Parking of an Articulated Vehicle Using ANFIS”, Global Journal of Science, Engineering and Technology (ISSN: 2322-2441), 2013, pp. 93-104, Issue No. 14.
F. Cuesta and A. Ollero, “Intelligent System for Parallel Parking of Cars and Tractor-Trailers”, Intelligent Mobile Robot Navigation, STAR, 2005, pp. 159-188, Springer-Verlag Berlin Heidelberg.
“Ford Super Duty: Truck Technologies”, Brochure, Sep. 2011, 2 pages.
Kristopher Bunker, “2012 Guide to Towing”, Trailer Life, 2012, 38 pages.
A. Gonzalez-Cantos, “Backing-Up Maneuvers of Autonomous Tractor-Trailer Vehicles using the Qualitative Theory of Nonlinear Dynamical Systems,” International Journal of Robotics Research, Jan. 2009, vol. 28, 1 page.
L. Chu, Y. Fang, M. Shang, J. Guo, F. Zhou, “Estimation of Articulation Angle for Tractor Semi-Trailer Based on State Observer”, ACM Digital Library, ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Automation, vol. 2, Mar. 2010, 1 page.
M. Wagner, D. Zoebel, and A. Meroth, “Adaptive Software and Systems Architecture for Driver Assistance Systems” International Journal of Machine Learning and Computing, Oct. 2011, vol. 1, No. 4, 7 pages.
F.W. Kienhöfer; D. Cebon, “An Investigation of ABS Strategies for Articulated Vehicles”, Cambridge University, Engineering Department, United Kingdom, date unknown, 13 pages.
C. Lundquist; W. Reinelt; O. Enqvist, “Back Driving Assistant for Passenger Cars with Trailer”, ZF Lenksysteme GmbH, Schwäbisch Gmünd, Germany, 2006 (SAE Int'l) Jan. 2006, 8 pages.
Zhe Leng; Minor, M., “A Simple Tractor-Trailer Backing Control Law for Path Following”, IEEE, Intelligent Robots and Systems (IROS) IEEE/RSJ International Conference, Oct. 2010, 2 pages.
Kinjo, H.; Maeshiro, M.; Uezato, E.; Yamamoto, T., “Adaptive Genetic Algorithm Observer and its Application to Trailer Truck Control System”, IEEE, SICE-ICASE International Joint Conference, Oct. 2006, 2 pgs.
J. Roh; H. Lee; W. Chung, “Control of a Car with a Trailer Using the Driver Assistance System”, IEEE, International Conference on Robotics and Biomimetics; Phuket, Thailand, Dec. 2011, 6 pages.
A. Gonzalez-Cantos; J.I. Maza; A. Ollero, “Design of a Stable Backing Up Fuzzy Control of Autonomous Articulated Vehicles for Factory Automation”, Dept. of Systems Engineering and Automatic Control, University of Seville, Spain, 2001, 5 pages.
Altafini, C.; Speranzon, A.; Wahlberg, B., “A Feedback Control Scheme for Reversing a Truck and Trailer Vehicle”, IEEE, Robotics and Automation, IEEE Transactions, Dec. 2001, vol. 17, No. 6, 2 pages.
Zare, A. Sharafi; M. Kamyad, A.V., “A New Approach in Intelligent Trailer Parking”, IEEE, 2010 2nd International Mechanical and Electrical Technology (ICMET), Sep. 2010, 1 page.
Tanaka, K.; Sano, M., “A Robust Stabilization Problem of Fuzzy Control Systems and its Application to Backing up Control of a Truck-trailer”, IEEE Transactions on Fuzzy Systems, May 1994, vol. 2, No. 2, 1 page.
Sharafi, M. Zare; A. Kamyad; A.V. Nikpoor, S., “Intelligent Parking Method for Truck in Presence of Fixed and Moving Obstacles and Trailer in Presence of Fixed Obstacles: Advanced Fuzzy Logic Technologies in Industrial Applications”, IEEE, 2010 International Electronics and Information Engineering (ICEIE), Aug. 2010, vol. 2, 1 page.
Hodo, D. W.; Hung, J.Y.; Bevly, D. M.; Millhouse, S., “Effects of Sensor Placement and Errors on Path Following Control of a Mobile Robot-Trailer System”, IEEE, American Control Conference, Jul. 2007, 1 page.
Sharafi, M. Zare; A. Kamyad; A.V. Nikpoor, S., “Intelligent Parking Method for Trailers in Presence of Fixed and Moving Obstacles”, IEEE, 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Aug. 2010, vol. 6, 1 page.
Chieh Chen; Tomizuka, M., “Steering and Independent Braking Control for Tractor-Semitrailer Vehicles in Automated Highway Systems”, IEEE, Proceedings of the 34th IEEE Conference on Decision and Control, Dec. 1995, vol. 2, 1 page.
P. Bolzern, R.M. Desantis, A. Locatelli, “An Input-Output Linearization Approach to the Control of an n-Body Articulated Vehicle”, J. Dyn. Sys., Meas., Control, Sep. 2001, vol. 123, No. 3, 3 pages.
Dieter Zöbel, David Polock, Philipp Wojke, “Steering Assistance for Backing Up Articulated Vehicles”, Systemics, Cybernetics and Informatics; vol. 1, No. 5, date unknown, 6 pages.
J.R. Billing; J.D. Patten; R.B. Madill, “Development of Configurations for Infrastructure-Friendly Five- and Six-Axle SemiTrailers”, National Research Council of Canada and Ontario Ministry of Transportation, date unknown, 11 pages.
Jesus Morales, Anthony Mandow, Jorge L. Martinez, and Alfonso Garcia-Cerezo, “Driver Assistance System for Backward Maneuvers in Passive Multi-Trailer Vehicles”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2012, 7 pages.
Cedric Pradalier and Kane Usher, “Experiments in Autonomous Reversing of a Tractor-Trailer System”, 6th International Conference on Field and Service Robotics, inria-00195700, Version 1, Dec. 2007, 10 pages.
Andri Riid, Alar Leibak, Ennu Rüstern, “Fuzzy Backing Control of Truck and Two Trailers”, Tallinn University of Technology; Tallinn, Estonia, date unknown, 6 pages.
Jane McGrath, “How to Avoid Jackknifing”, A Discovery Company, date unknown, 3 pages.
Claudio Altafini, Alberto Speranzon, and Karl Henrik Johansson, “Hybrid Control of a Truck and Trailer Vehicle”, Springer-Verlag Berlin Heidelberg, HSCC 2002, LNCS 2289; 2002, 14 pages.
Jujnovich, B.; Roebuck, R.; Odhams, A.; David, C., “Implementation of Active Rear Steering of a Tractor Semitrailer”, Cambridge University, Engineering Department; Cambridge, United Kingdom, date unknown, 10 pages.
A.M.C. Odhams; R.L. Roebuck; C. Cebon, “Implementation of Active Steering on a Multiple Trailer Long Combination Vehicle”, Cambridge University, Engineering Department; Cambridge, United Kingdom, date unknown, 13 pages.
Cedric Pradalier and Kane Usher, “Robust Trajectory Tracking for a Reversing Tractor-Trailer System”, (Draft), Field and Service Robotics Conference, CSIRO ICT Centre, Jul. 2007, 16 pages.
Stahn, R.; Heiserich, G.; Stopp, A., “Laser Scanner-Based Navigation for Commercial Vehicles”, IEEE, 2007 IEEE Intelligent Vehicles Symposium, Jun. 2007, 1 page.
Lee Yong H.; Weiwen Deng; Chin Yuen-Kwok Steve; Mckay Neil, “Feasibility Study for a Vehicle-Trailer Backing Up Control”, Refdoc.fr, SAE Transactions, vol. 113, No. 6, 2004, 1 page.
A.M.C. Odhams; R.L. Roebuck; B.A. Jujnovich; D. Cebon, “Active Steering of a Tractor-Semi-Trailer” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Sage Journals, vol. 225, No. 7, Jul. 2011, 1 page.
Haviland, G S, “Automatic Brake Control for Trucks—What Good Is It?”, TRID, Society of Automotive Engineers, Sep. 1968, 1 page.
William E. Travis; David W. Hodo; David M. Bevly; John Y. Hung, “UGV Trailer Position Estimation Using a Dynamic Base RTK System”, American Institute of Aeronautics and Astronautics, date unknown, 12 pages.
“VSE Electronic Trailer Steering”, ETS for Trailers, version 2009, VSE Trailer Systems B.V., 2009, 28 pages.
“Telematics Past, Present, and Future,” Automotive Service Association, www.ASAshop.org, May 2008, 20 pages.
“Fully Automatic Trailer Tow Hitch With LIN Bus,” https://webista.bmw.com/webista/show?id=1860575499&lang=engb&print=1, date unknown, 5 pages.
“VBOX Yaw Rate Sensor With Integral Accelerometers,” Racelogic, www.racelogic.co.uk, date unknown, 2 pages.
P.D.C.R Jayarathna; J.V Wijayakulasooriya; S.R. Kodituwakku, “Fuzzy Logic and Neural Network Control Systems for Backing up a Truck and a Trailer”, International Journal of Latest Trends in Computing, vol. 2, No. 3, Sep. 2011, 8 pages.
Olof Enqvist, “AFS-Assisted Trailer Reversing,” Institutionen för systemteknik Deartment of Electrical Engineering, Jan. 27, 2006, 57 pages.
Gouet-Brunet, V.; Lameyre, B., “Object recognition and segmentation in videos by connecting heterogeneous visual features”, Computer Vision and Image Understanding, Jul. 2008, 2 pgs., vol. 111, Issue 1.
Alpine Electronics of America, Inc., “Alpine Electronics Introduces Two New Driver Assist Solutions”, press release, Jan. 7, 2010, 2 pgs., Torrance, California.
Wagner, M.; Zobel, D.; Meroth, A., “An Adaptive Software and Systems Architecture for Drivers Assistance Systems based on Service Orientation”, International Journal of Machine Learning and Computing, Oct. 2011, pp. 359-366, vol. 1, No. 4, Germany.
“Rearview Parking Assist Systems”, Donmar Sunroofs & Accessories, Brochure, Aug. 2013, 13 pgs.
“Trailer Vision”, Trailer Vision Ltd., Brochure, www.trailervision.co.uk, Date Unknown, 4 pgs.
Novak, Domen; Dovzan, Dejan; Grebensek, Rok; Oblak, Simon, “Automated Parking System for a Truck and Trailer”, International Conference on Advances in the Internet, Processing, Systems and Interdisciplinary Research, Florence, 2007, WorldCat.org, 13 pgs.
Haviland, G S, “Automatic Brake Control for Trucks—What Good Is It?”, TRID, Society of Automotive Engineers, Sep. 1968, 1 pg.
Altafini, C.; Speranzon, A.; Wahlberg, B., “A Feedback Control Scheme for Reversing a Truck and Trailer Vehicle”, IEEE, Robotics and Automation, IEEE Transactions, Dec. 2001, vol. 17, No. 6, 2 pgs.
Claudio Altafini, Alberto Speranzon, and Karl Henrik Johansson, “Hybrid Control of a Truck and Trailer Vehicle”, Springer-Verlag Berlin Heidelberg, HSCC 2002, LNCS 2289; 2002, pp. 21-34.
Divelbiss, A.W.; Wen, J.T.; “Trajectory Tracking Control of a Car-Trailer System”, IEEE, Control Systems Technology, Aug. 6, 2002, vol. 5, No. 3, 1 pg.
Guanrong, Chen; Delin, Zhang; “Backing up a Truck-Trailer with Suboptimal Distance Trajectories”, IEEE, Proceedings of the Fifth IEEE International Conference, vol. 2, Aug. 6, 2002, New Orleans, LA, ISBN:0-7803-3645-3, 1 pg.
“Understanding Tractor-Trailer Performance”, Caterpillar, 2006, pp. 1-28.
C. Lundquist; W. Reinelt; O. Enqvist, “Back Driving Assistant for Passenger Cars with Trailer”, ZF Lenksysteme GmbH, Schwäbisch Gmünd, Germany, 2006 (SAE Int'l) Jan. 2006, pp. 1-8.
Olof Enqvist, “AFS—Assisted Trailer Reversing,” Institutionen för systemteknik Deartment of Electrical Engineering, Jan. 27, 2006, 57 pgs.
Cedric Pradalier, Kane Usher, “Robust Trajectory Tracking for a Reversing Tractor-Trailer System”, (Draft), Field and Service Robotics Conference, CSIRO ICT Centre, Jul. 2007, 16 pages.
Hodo, D. W.; Hung, J.Y.; Bevly, D. M.; Millhouse, S., “Effects of Sensor Placement and Errors on Path Following Control of a Mobile Robot-Trailer System”, IEEE, American Control Conference, Jul. 30, 2007, 1 pg.
Cedric Pradalier, Kane Usher, “Experiments in Autonomous Reversing of a Tractor-Trailer System”, 6th International Conference on Field and Service Robotics, inria-00195700, Version 1, Dec. 2007, 10 pgs.
Zhe Leng; Minor, M., “A Simple Tractor-Trailer Backing Control Law for Path Following”, IEEE, Intelligent Robots and Systems (IROS) IEEE/RSJ International Conference, Oct. 2010, 2 pgs.
“2012 Edge—Trailer Towing Selector”, Brochure, Preliminary 2012 RV & Trailer Towing Guide Information, 2011, 3 pgs.
“Ford Super Duty: Truck Technologies”, Brochure, Sep. 2011, 2 pgs.
J. Roh; H. Lee; W. Chung, “Control of a Car with a Trailer Using the Driver Assistance System”, IEEE, International Conference on Robotics and Biomimetics; Phuket, Thailand, Dec. 2011, 1 pg.
Payne, M.L.;Hung, J.Y, and Bevy, D.M; “Control of a Robot-Trailer System Using a Single Non-Collacted Sensor”, IEEE, 38th Annual Conference on IEEE Industrial Electronics Society, Oct. 25-28, 2012, 2 pgs.
“Optionally Unmanned Ground Systems for any Steering-Wheel Based Vehicle” Universal. Unmanned., Kairos Autonomi, website: http://www.kairosautonomi.com/pronto4_system.html, retrieved Sep. 26, 2014, 2 pgs.
Micah Steele, R. Brent Gillespie, “Shared Control Between Human and Machine: Using a Haptic Steering Wheel to Aid in Land Vehicle Guidance”, University of Michigan, Date Unknown, 5 pgs.
Skybitz, website, 2012, pp. 1-3, http://www.skybitz.com/products-services/hardware/bat-xtndr/.
Verma, V.S.; Guntur, R.R.; Womg, J.Y.; “Directional Behavior During Braking of a Tractor/Semitrailer”, TRID, International Journal of Vehicle Design, May 1980, pp. 195-220, vol. 1, No. 3, Inderscience Enterprises Limited, ISSN: 1477-5360.
Related Publications (1)
Number Date Country
20190071088 A1 Mar 2019 US