Trailer angle detection using rear backup camera

A method for determining trailer angle includes determining angles of a trailer hitch relative to a data recording device via an angle measuring device while the trailer hitch pivots about a hitch ball of the device. While the trailer hitch pivots about the hitch ball of the device, trailer hitch information is determined responsive to processing of captured image data and responsive to determined angles of the trailer hitch. After determining trailer hitch information, data representative of the determined information is provided to an electronic device, which is disposed at a vehicle having a rear backup camera that has a field of view that encompasses a hitch ball of the vehicle and at least a portion of a hitch of a trailer hitched to the vehicle. Using the provided data, and responsive to processing of captured image data, an angle of the trailer relative to the vehicle is determined.

FIELD OF THE INVENTION

BACKGROUND OF THE INVENTION

At present, the trailer angle (the angle of a towed trailer relative to the towing vehicle) is determined utilizing a target reference installed on the trailer tongue, where a rear camera of the towing vehicle locates this target pattern and the system estimates the trailer angle. But this method requires the user to install the target pattern sticker on the trailer tongue for each trailer and enter the distance measurements related to the location of the target pattern sticker. There are several issues associated with such an approach, including that the user may make measurement errors, the target pattern sticker may get dirty, and/or the user may forget to install the sticker.

SUMMARY OF THE INVENTION

The present invention provides a driver assistance system or vision system or imaging system for a vehicle that utilizes one or more cameras (preferably one or more CMOS cameras) to capture image data representative of images exterior of the vehicle, and provides a system and method for determining the angle of the trailer without the use of a target pattern. The system and method uses the trailer images (as captured by the vehicle's rear backup camera) and the corresponding ground truth angle information to train a neural network, such as, for example, a Convolution Neural Network (or other suitable machine learning algorithm). The trained model is then deployed at a vehicle. This model, when subjected to a test image, will then predict the correct angle by looking at or processing the image data. The test image is preprocessed before feeding into the Convolution Neural Network (CNN). Preprocessing may include, but is not limited to, operations such as scaling the image, cropping the image, and/or transforming the image. With the use of communication network bus (such as a CAN bus) data, trailer tracking will be used to obtain a more robust estimate of the angle.

LEGEND

100Subject vehicle equipped with backup camera and camera processing ECU100aData recording device or vehicle102Camera processing ECU104Backup camera at subject vehicle104acamera at data recording device or vehicle105Data recorder106Preprocessing module108Convolution Neural Network110Trailer angle estimation and tracking module111Vehicle communication bus (CAN/FlexRay etc.)112Angle measurement ground truth sensor200Trailer at vehicle200aTrailer at data collecting device or vehicle202Trailer tongue or hitch of trailer200202aTrailer tongue or hitch of trailer200a300Trailer angle estimation module302Image data captured by and communicated from the backup camera304Output estimated trailer angle306CNN selection & view localization308a-narray of convolution neural network with classification output309a-narray of convolution neural network with trailer edge detection output310Decision merge312Trailer edge detection output314Trailer length estimation

DESCRIPTION OF THE PREFERRED EMBODIMENTS

A vehicle vision system and/or driving assist system and/or object detection system and/or alert system operates to capture images exterior of the vehicle and may process the captured image data to display images and/or to detect objects at or near the vehicle and in the predicted path of the vehicle, such as to assist a driver of the vehicle in maneuvering the vehicle in a rearward direction. For example, image data captured by a rear backup camera of the vehicle may be processed for object detection during a reversing maneuver of the vehicle and/or images derived from the captured image data may be displayed at a display screen for viewing by the driver during a reversing maneuver of the vehicle. The vision system includes an image processor or image processing system that is operable to receive image data from one or more cameras and provide an output to a display device for displaying images representative of the captured image data. Optionally, the vision system may provide display, such as a rearview display or a top down or bird's eye or surround view display or the like. For example, the rear camera may comprise part of a multi-camera vision system of the vehicle, and image data captured by the camera and other exterior viewing cameras of the multi-camera vision system are processed for display of images surrounding the vehicle during a parking maneuver of the vehicle.

Referring now to the drawings and the illustrative embodiments depicted therein, a vehicle100includes an imaging system or vision system that includes at least one exterior viewing imaging sensor or camera, such as a rearward viewing imaging sensor or camera104and captures image data representative of a rearward scene, and that is towing a trailer200. Optionally, the system may optionally include multiple exterior viewing imaging sensors or cameras, such as a forward viewing camera at the front (or at the windshield) of the vehicle, and a sideward/rearward viewing camera at respective sides of the vehicle), which captures images exterior of the vehicle, with the camera having a lens for focusing images at or onto an imaging array or imaging plane or imager of the camera. The vision system includes a control or electronic control unit (ECU) or camera processor102that is operable to process image data captured by the camera (such as the rear backup camera) or cameras and may detect objects or the like and/or provide displayed images at a display device for viewing by the driver of the vehicle. The data transfer or signal communication from the camera to the ECU may comprise any suitable data or communication link, such as a vehicle network bus111or the like of the equipped vehicle.

FIG. 1illustrates a view of the subject vehicle100attached to the trailer200, with the subject vehicle being equipped with the backup camera104and the camera processing unit102.

FIG. 2illustrates the system architecture of the major components of the targetless trailer angle detection system, comprising the backup camera104connected to the camera processing ECU102. The ECU102is interfaced with the vehicle communication network or bus111that receives the vehicle information (such as speed, steering angle, etc.) and transmits or communicates the estimated trailer angle and angle rate information.

FIG. 3illustrates the data recording setup that comprises a data recording device or vehicle100aequipped with a data recorder105connected to a camera104aat the data recording vehicle and a ground truth sensor112of the data recording vehicle. The ground truth sensor112is capable of highly accurately measuring the trailer angle (of the trailer hitch202aof the trailer200aattached at the data recording device or vehicle) and transmitting the measured trailer angle over the communication interface to the data recorder105. The ground truth sensor may comprise a digital sensor that outputs an actual measured trailer angle. For example, the sensor may comprise an electromechanical sensor that is disposed at or attached at the trailer hitch joint at the data recording vehicle, whereby the sensor senses the physical pivoting movement of the trailer relative to the data recording or training vehicle, and generates an output (such as a digital or analog output) indicative of the measured or sensed trailer angle. The data recorder105records the synchronized information of the camera data and the ground truth data in real time. This synchronized data will be used during the training phase of the machine learning algorithm.

The data collecting device or vehicle may comprise a movable vehicle or platform that moves with the training trailer200aattached thereto, such that image data captured by the camera104ais processed and correlated with or compared with or synchronized with the true or actual or determined angle measurements provided by the sensor112. Optionally, the data collecting device may comprise a stationary platform or device and the trailer hitch202amay comprise only a hitch portion of a selected trailer type, whereby the hitch portion may be pivoted through a range of angles relative to a hitch ball of the data collecting device in order to synchronize or correlate or compare the image data captured by the camera104aduring the pivotal movement of the trailer hitch with the angle measurements provided by the sensor112. The training phase or process may synchronize or correlate or compare image data captured by the camera104awhile pivoting various trailer hitches with the angle measurements provided by the sensor112. For example, the system may first train the control (such as via collecting data while pivoting the trailer hitch and capturing image data and while measuring the trailer angle) to recognize a Y bar trailer hitch and determine the angles of the trailer hitch, and then may train the control to recognize a V bar trailer hitch and determine the angles of the trailer hitch, and so on, so a trained control (with the trained software as trained at the data collecting device), when implemented in a vehicle, can recognize any typical trailer hitch configuration of a trailer being towed by the vehicle and can determine the trailer angle of the trailer relative to the vehicle as the vehicle and trailer are moved or driven along a road (in either the forward or reverse direction).

The initial setup or training phase or process functions to determine trailer hitch information responsive to collecting trailer hitch data while processing by the processor of image data captured by the camera and responsive to determined angles of the trailer hitch relative to the data recording device as determined by the angle measuring device. Data derived from or representative of the determined trailer hitch information is provided to an electronic device (such as a controller or processor of the vehicle or of the rear backup camera of the vehicle or the like). A subject vehicle, having a hitch ball at a rear portion of the vehicle, and having a rear backup camera, is equipped with the electronic device. A hitch of a trailer is hitched or connected to the hitch ball at the rear portion of the equipped or subject vehicle, whereby the rear backup camera has a field of view that encompasses the hitch ball at the rear portion of the equipped vehicle and at least a portion of the hitch of the trailer hitched to the hitch ball at the rear portion of the equipped vehicle. Using the data representative of the determined trailer hitch information that was provided to the electronic device, and responsive to processing by the processor of image data captured by the rear backup camera, an angle of the trailer relative to the equipped vehicle is determined as the equipped vehicle and trailer move along the road.

FIG. 4illustrates a block diagram of the trailer angle estimation algorithm comprising a preprocessing module106that receive the images from the backup camera104. The preprocessed image or image data is input to the trained convolution neural network (CNN)108that outputs the trailer angle.

The CNN may extract features out of any type of the hitch bar (e.g., a Y bar, a V bar or a straight bar or the like) to estimate the angle of the trailer relative to the vehicle, and not the exact trailer type. Thus, during the training phase, the system first trains the network or model with different trailer bar types, so the system can better estimate the trailer angle for a particular type of trailer when that particular type of trailer is attached at the subject vehicle. Thus, the system learns trailer types and a range of angles for the different trailer types when the controller is at a data recording vehicle (during the training phase). After the system learns the trailer types and angles for different trailer types during the training phase, the system (or just a trained controller or trained model or software) is disposed at or implemented at a subject vehicle (e.g., a production vehicle that will be driven along a road) and connected to (such as via a communication network of the vehicle) an already existing rear backup camera of the vehicle (no special camera is required for the trailer angle detection system). When the trained controller is disposed at a vehicle equipped with the trailer angle determining system, the controller (via processing of image data captured by the rear backup camera) determines or estimates the angle of the trailer relative to the vehicle as the vehicle tows a trailer during normal operating conditions.

The CNN108is trained using an end to end learning technique, where the network is trained utilizing preprocessed camera data and the synchronized ground truth data collected from the ground truth sensor112. When the trained CNN108is subjected to the preprocessed image or image data in real time it predicts the corresponding trailer angle, and this instantaneous trailer angle is processed via the trailer angle estimation and tracking module110, which provides a smooth trailer angle output. The trailer angle estimation and tracking module110may utilize the vehicle data and a kinematic model of the vehicle and trailer assembly to improve the accuracy of prediction.

FIG. 5illustrates an example of the end to end convolution neural network (CNN). The CNN108comprises multiple convolution layers and activation layers and fully connected layers to process the input image or image data so as to arrive at the determined trailer angle. For example, the CNN includes a first convolution layer to identify or detect features and that generates a map of values that is input into the next layer (an activation layer), which further processes or filters the input to detect higher level features. The fully connected layer receives an input from the last of the other layers and generates a value or vector or plurality of values that are used to identify or classify the type of trailer. Based on the training of the system, the values output from the CNN can be processed to determine or identify or classify the trailer type and angle of the trailer.

In one embodiment, the preprocessing module106transforms the image such that it appears as if it is viewed from above the tow ball, such that the region of interest is selected (such as by focusing on the trailer tongue102and hitch assembly as shown inFIGS. 6A and 6B). Also, the pre-processing transformation allows for better control of the mapping between the change of angle of the trailer and the number of pixels, and thus the system can obtain a greater number of pixels per degree for angles close to zero as compared to angles further from zero.

In another embodiment, the preprocessing module106provides multiple transformed views such that different views of the trailer tongue102are visible, so that the CNN108may extract the features out of the trailer tongue102via different viewing angles to estimate the trailer tongue position and hence the trailer angle relative to the towing vehicle.

In another embodiment, the preprocessing module106process the image or image data to generate the depth information such as an inverse transform. Since the trailer tongue102will always be above the ground, the CNN108may utilize the depth information to eliminate the background and extract more information from the trailer tongue102.

In another embodiment, the CNN108performs a multi-step classification. In the first step it predicts with a certain confidence whether a trailer (or trailer tongue) is present and attached, present and not attached, or not present behind the vehicle. In the second step, if presence of the trailer (or trailer tongue) is true, then it classifies the trailer (or trailer tongue) to be of a particular category. Examples of trailers are utility trailer, box trailer, etc. Examples of trailer tongues are V-shaped, Y-shaped, etc.

In another embodiment, the CNN may classify the type of trailer, invariant of the payload on it, such as shown inFIG. 7. For example, a trailer with a boat or a bike as payload. As the appearance of the trailer from the backup camera is highly sensitive to the trailer angle it is important to utilize an array of ‘n’ micro CNNs308a-nto detect the type of the trailer (otherwise, a very large deep CNN may be needed, which may not be practical to implement in the ECU). The Trailer Angle Estimation module300provides the trailer angle304. The CNN Selection and View Localization module306takes the angle estimate as an input and selects an appropriate micro CNN (or a combination of micro CNNs) which is tuned for that range of angle. The decisions from the micro-CNN(s) are then combined in the Decision Merge module310to recognize the trailer type. The CNN Selection and View Localization module306reduces the search space by localizing the position of the trailer in the entire image.

Optionally (and such as shown inFIG. 8), an array of CNN1-n309aprovides three dimensional bounding boxes312[right rectangular prism] encapsulating the end of the trailer when the back edge of the trailer starts appearing in the view. The trailer length estimation module314utilizes the trailer angle estimation information and the corresponding length of the 3D bounding box to estimate the trailer length.

Therefore, the present invention provides a system and method that uses image data captured by the vehicle's rear backup camera and data input from a training process (such as ground truth angle information from an angle measurement ground truth sensor), and that processes the data to train a Convolution Neural Network (or other type of machine learning algorithm). The trained model is deployed at a vehicle and, responsive to a test image data input, will process the test image data input and predict the correct trailer angle. The test image is preprocessed before feeding into the Convolution Neural Network (CNN). Preprocessing may include scaling, cropping, and/or transforming the image. With the use of communication network bus (such as a CAN bus) data, trailer tracking will be used to obtain a more robust estimate of the angle.

The camera or sensor may comprise any suitable camera or sensor. Optionally, the camera may comprise a “smart camera” that includes the imaging sensor array and associated circuitry and image processing circuitry and electrical connectors and the like as part of a camera module, such as by utilizing aspects of the vision systems described in International Publication Nos. WO 2013/081984 and/or WO 2013/081985, which are hereby incorporated herein by reference in their entireties.

Optionally, the vision system may include a display for displaying images captured by one or more of the imaging sensors for viewing by the driver of the vehicle while the driver is normally operating the vehicle. Optionally, for example, the vision system may include a video display device, such as by utilizing aspects of the video display systems described in U.S. Pat. Nos. 5,530,240; 6,329,925; 7,855,755; 7,626,749; 7,581,859; 7,446,650; 7,338,177; 7,274,501; 7,255,451; 7,195,381; 7,184,190; 5,668,663; 5,724,187; 6,690,268; 7,370,983; 7,329,013; 7,308,341; 7,289,037; 7,249,860; 7,004,593; 4,546,551; 5,699,044; 4,953,305; 5,576,687; 5,632,092; 5,708,410; 5,737,226; 5,802,727; 5,878,370; 6,087,953; 6,173,501; 6,222,460; 6,513,252 and/or 6,642,851, and/or U.S. Publication Nos. US-2014-0022390; US-2012-0162427; US-2006-0050018 and/or US-2006-0061008, which are all hereby incorporated herein by reference in their entireties. Optionally, the vision system (utilizing the forward viewing camera and a rearward viewing camera and other cameras disposed at the vehicle with exterior fields of view) may be part of or may provide a display of a top-down view or bird's-eye view system of the vehicle or a surround view at the vehicle, such as by utilizing aspects of the vision systems described in International Publication Nos. WO 2010/099416; WO 2011/028686; WO 2012/075250; WO 2013/019795; WO 2012/075250; WO 2012/145822; WO 2013/081985; WO 2013/086249 and/or WO 2013/109869, and/or U.S. Publication No. US-2012-0162427, which are hereby incorporated herein by reference in their entireties.