The present invention relates generally to a vehicle vision system for a vehicle and, more particularly, to a vehicle vision system that utilizes one or more cameras at a vehicle.
Use of imaging sensors in vehicle imaging systems is common and known. Examples of such known systems are described in U.S. Pat. Nos. 5,949,331; 5,670,935 and/or 5,550,677, which are hereby incorporated herein by reference in their entireties. Trailer assist systems are known that may determine an angle of a trailer hitched at a vehicle. Examples of such known systems are described in U.S. Pat. Nos. 9,085,261 and/or 6,690,268, which are hereby incorporated herein by reference in their entireties.
The present invention provides a driver assistance system or vision system or imaging system for a vehicle that utilizes a camera disposed at a rear portion of a vehicle and having a field of view exterior of the vehicle. When the vehicle is positioned near a trailer rearward of the vehicle, the field of view of the rearward viewing camera encompasses at least a portion of a trailer coupler of the trailer stationary at a distance from the vehicle. A control includes an image processor operable to process image data captured by the camera. The image data is representative of at least the trailer coupler of the trailer. The control, responsive to image processing at the control of image data captured by the camera, detects a location of the trailer coupler using a detector model. The detector model is based on an ensemble regression tree algorithm.
These and other objects, advantages, purposes and features of the present invention will become apparent upon review of the following specification in conjunction with the drawings.
A vehicle and trailer maneuvering system or maneuver assist system and/or driving assist system operates to capture images exterior of the vehicle and of a trailer being or to be towed by the vehicle and may process the captured image data to determine a path of travel for the vehicle and trailer or the vehicle toward the trailer and 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 and trailer in a rearward direction or the vehicle toward the trailer, or to maneuver the vehicle and trailer in a rearward direction or the vehicle toward the trailer. The vision system includes an image processor or image processing system that is operable to receive image data from one or more imaging sensors (e.g., cameras) and that may provide an output to a display device for displaying images representative of the captured image data. Optionally, the vision system may provide a display, such as a rearview display or a top down or bird's eye or surround view display or the like.
Referring now to the drawings and the illustrative embodiments depicted therein, a vehicle 10 includes a vehicle and trailer maneuvering system or maneuver assist system and/or driving assist system 12 that is operable to assist in backing up or reversing the vehicle with a hitched trailer that is hitched at the rear of the vehicle via a hitch 14 or operable to assist in backing up or reversing the vehicle toward a trailer to be hitched, and the system may maneuver the vehicle 10 (and optionally the trailer 16) toward a desired or selected location. The trailer maneuver assist system 12 includes at least one exterior viewing vehicle-based imaging sensor or camera, such as a rearward viewing imaging sensor or camera 18 (and the system may optionally include multiple exterior viewing imaging sensors or cameras, such as a sideward/rearward viewing camera at respective sides of the vehicle), which captures image data representative of the scene exterior and rearward of the vehicle 10, with the field of view of the camera encompassing the hitch 14 and/or trailer 16 and/or trailer coupler 15, and with the camera 18 having a lens for focusing images at or onto an imaging array or imaging plane or imager of the camera (
With advanced driver-assistance or driving-assistance systems (ADAS), Trailer Hitch Assist (THA) features assist a driver of a vehicle with maneuvering (i.e., reversing) the vehicle towards the trailer coupler 15 of a trailer without the need for manual steering, acceleration, or brake input by the driver. The system may automatically align the vehicle with the trailer so that the trailer coupler 15 of the trailer only needs to be lowered onto the hitch 14 (i.e., a hitch ball of the hitch of the vehicle).
The towing vehicle generally couples with the trailer via a coupler 15 and a hitch ball 14. The hitch ball 14 is the attachment point for the vehicle's hitch and allows the towing vehicle to get to the exact location required for attachment. Therefore, it is advantageous for a trailer hitch assist system to detect the coupler 15 position so that it may accurately assist in maneuvering the vehicle to the detected trailer 16 and coupler 15.
The system of the present invention determines a position of the coupler 15 while the trailer is multiple meters (e.g., approximately 3-4 meters) from the towing vehicle. The system processes image data captured by the rearward viewing imaging sensor or camera disposed at the towing vehicle to detect the coupler ball position of the trailer based on an “ensemble regression tree” algorithm, or any other similar or suitable algorithm. This system detects or determines one or more outline landmarks representative of portions of a coupler region. The system detects or determines the coupler 15 position as one of the landmark points from the coupler region in a variety of different trailer orientations and with a variety of road types and lighting conditions.
Referring now to
In the second step, the system divides the annotated data into a training set at 22 and a testing set at 24. That is, a portion of the annotated data becomes training data and a separate portion of the annotated data becomes testing data. For example, the system, using the training set, may train cascades of regression trees using the ensemble method at 26. In the third step, the system tunes the parameters of the cascading regression trees based on the training and testing data error determined during training to generate a detection model for detecting the coupler point of the trailer. The generated model detects landmark points on the testing set of data at 28 to validate the accuracy of the generated model. In some examples, intensity values are used as features of the generated model.
Referring now to
The system passes any patches that are not negative (i.e., the SVM determined the coupler region may be present) to a second stage of the classifier. In some examples, the second stage includes a nonlinear SVM. The nonlinear SVM accurately filters out the patches that the linear SVM designated as potentially including the coupler region (i.e., false positives). Due to the increased processing time of the nonlinear SVM, limiting processing to only the patches passed by the linear SVM substantially reduces overall processing time and increases the efficiency of the system. The nonlinear SVM (i.e., the trailer detection stage) may output a bounding box that highlights the location of the coupler region in the frame of captured image data. The system, using the partial image or bounding box, may generate an output image with marked landmark points (
Thus, optionally, the system processes the image in two steps, with a first step that is less-computationally intensive to eliminate areas that the system determines with a high degree of confidence do not include the trailer coupler. Then, during a second step, the system may apply different or more thorough processing or more computationally intensive processing to the remaining areas, thus reducing the amount of resources needed to process the entire frame of image data.
In some implementations, the coupler point detection is performed for a number of consecutive frames (e.g., five frames) to increase the accuracy of the coupler point determination. In some implementations, an unsupervised learning method (e.g., mixture models, K-Means, etc.) is used to generate a single point as the coupler's location in the camera image. However, other types of learning may also be used (e.g., reinforcement learning, supervised learning, etc.). Known algorithms may be practiced to determine the three dimensional location (i.e., x-coordinates, y-coordinates, and z-coordinates) of the coupler in camera/vehicle frame (e.g., Structure from Motion). The system may include a variety of sensors to determine the z-coordinate (e.g., a two-dimensional or three-dimensional imaging sensor or a distance sensor or the like).
Thus, aspects of the present invention provide a system that generates a coupler point detector model that is trained on trailer image data annotated with landmarks that indicate the shape of the coupler. The model is trained based on an ensemble regression tree algorithm, or any other appropriate algorithm and/or method, and is capable of accurately and robustly detecting the coupler point of a trailer in a variety of different orientations, road types, and lighting conditions. The system may also be used for trailer angle estimation.
The system may utilize aspects of the trailering assist systems or trailer angle detection systems or trailer hitch assist systems described in U.S. Pat. Nos. 10,638,025; 9,085,261 and/or 6,690,268, and/or U.S. Publication Nos. US-2020-0017143; US-2019-0347825; US-2019-0275941; US-2019-0118860; US-2019-0064831; US-2019-0042864; US-2019-0039649; US-2019-0143895; US-2019-0016264; US-2018-0276839; US-2018-0276838; US-2018-0253608; US-2018-0215382; US-2017-0254873; US-2017-0050672; US-2015-0217693; US-2014-0160276; US-2014-0085472 and/or US-2015-0002670, and/or U.S. patent application Ser. No. 16/947,379, filed on Jul. 30, 2020, Ser. No. 16/946,542, filed on Jun. 26, 2020, Ser. No. 15/929,535, filed on May 8, 2020, and/or Ser. No. 16/850,300, filed on Apr. 16, 2020, and/or U.S. provisional application 62/880,194, filed on Jul. 30, 2019, which are all hereby incorporated herein by reference in their entireties.
The system includes an image processor operable to process image data captured by the camera or cameras, such as for detecting objects or other vehicles or pedestrians or the like in the field of view of one or more of the cameras. For example, the image processor may comprise an image processing chip selected from the EYEQ family of image processing chips available from Mobileye Vision Technologies Ltd. of Jerusalem, Israel, and may include object detection software (such as the types described in U.S. Pat. Nos. 7,855,755; 7,720,580 and/or 7,038,577, which are hereby incorporated herein by reference in their entireties), and may analyze image data to detect vehicles and/or other objects. Responsive to such image processing, and when an object or other vehicle is detected, the system may generate an alert to the driver of the vehicle and/or may generate an overlay at the displayed image to highlight or enhance display of the detected object or vehicle, in order to enhance the driver's awareness of the detected object or vehicle or hazardous condition during a driving maneuver of the equipped vehicle.
The vehicle may include any type of sensor or sensors, such as imaging sensors or radar sensors or lidar sensors or ultrasonic sensors or the like. The imaging sensor or camera may capture image data for image processing and may comprise any suitable camera or sensing device, such as, for example, a two dimensional array of a plurality of photosensor elements arranged in at least 640 columns and 480 rows (at least a 640×480 imaging array, such as a megapixel imaging array or the like), with a respective lens focusing images onto respective portions of the array. The photosensor array may comprise a plurality of photosensor elements arranged in a photosensor array having rows and columns. Preferably, the imaging array has at least 300,000 photosensor elements or pixels, more preferably at least 500,000 photosensor elements or pixels and more preferably at least 1 million photosensor elements or pixels. The imaging array may capture color image data, such as via spectral filtering at the array, such as via an RGB (red, green and blue) filter or via a red/red complement filter or such as via an RCC (red, clear, clear) filter or the like. The logic and control circuit of the imaging sensor may function in any known manner, and the image processing and algorithmic processing may comprise any suitable means for processing the images and/or image data.
For example, the vision system and/or processing and/or camera and/or circuitry may utilize aspects described in U.S. Pat. Nos. 9,233,641; 9,146,898; 9,174,574; 9,090,234; 9,077,098; 8,818,042; 8,886,401; 9,077,962; 9,068,390; 9,140,789; 9,092,986; 9,205,776; 8,917,169; 8,694,224; 7,005,974; 5,760,962; 5,877,897; 5,796,094; 5,949,331; 6,222,447; 6,302,545; 6,396,397; 6,498,620; 6,523,964; 6,611,202; 6,201,642; 6,690,268; 6,717,610; 6,757,109; 6,802,617; 6,806,452; 6,822,563; 6,891,563; 6,946,978; 7,859,565; 5,550,677; 5,670,935; 6,636,258; 7,145,519; 7,161,616; 7,230,640; 7,248,283; 7,295,229; 7,301,466; 7,592,928; 7,881,496; 7,720,580; 7,038,577; 6,882,287; 5,929,786 and/or 5,786,772, and/or U.S. Publication Nos. US-2014-0340510; US-2014-0313339; US-2014-0347486; US-2014-0320658; US-2014-0336876; US-2014-0307095; US-2014-0327774; US-2014-0327772; US-2014-0320636; US-2014-0293057; US-2014-0309884; US-2014-0226012; US-2014-0293042; US-2014-0218535; US-2014-0218535; US-2014-0247354; US-2014-0247355; US-2014-0247352; US-2014-0232869; US-2014-0211009; US-2014-0160276; US-2014-0168437; US-2014-0168415; US-2014-0160291; US-2014-0152825; US-2014-0139676; US-2014-0138140; US-2014-0104426; US-2014-0098229; US-2014-0085472; US-2014-0067206; US-2014-0049646; US-2014-0052340; US-2014-0025240; US-2014-0028852; US-2014-005907; US-2013-0314503; US-2013-0298866; US-2013-0222593; US-2013-0300869; US-2013-0278769; US-2013-0258077; US-2013-0258077; US-2013-0242099; US-2013-0215271; US-2013-0141578 and/or US-2013-0002873, which are all hereby incorporated herein by reference in their entireties. The system may communicate with other communication systems via any suitable means, such as by utilizing aspects of the systems described in International Publication Nos. WO 2010/144900; WO 2013/043661 and/or WO 2013/081985, and/or U.S. Pat. No. 9,126,525, 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.
Changes and modifications in the specifically described embodiments can be carried out without departing from the principles of the invention, which is intended to be limited only by the scope of the appended claims, as interpreted according to the principles of patent law including the doctrine of equivalents.
The present application claims priority of U.S. provisional applications, Ser. No. 62/883,202, filed Aug. 6, 2019, and Ser. No. 62/880,194, filed Jul. 30, 2019, which are hereby incorporated herein by reference in their entireties.
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