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 to assist a driver of the vehicle in maneuvering the vehicle with a trailer.
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.
In some systems, when attaching a trailer to the vehicle, the driver has to enter its properties to put the trailer driving aid system into a position to properly calculate the driving aids overlays, when backing up with a trailer attached. Some more advanced systems are able to detect the trailer length and the distance of the trailer's axle to the hatch by watching the trailer's turning behavior when the vehicle and trailer are in motion using visual data processing such as described in U.S. Publication No. US-2014-0160276, which is hereby incorporated herein by reference in its entirety.
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, with the vehicle towing a trailer, and with the system determining the angle of the trailer relative to the vehicle without need for a target disposed at the trailer and in the field of view of the vehicle camera. The system uses captured image data to generate a cylinder view, such that objects located in the camera's field of view that are at the same distance from the camera are constant size. With the cylinder view generated, trailer detection and angle calculation are performed to determine the trailer angle relative to the vehicle.
These and other objects, advantages, purposes and features of the present invention will become apparent upon review of the following specification in conjunction with the drawings.
Referring now to the drawings and the illustrative embodiments depicted therein, a rear vision system 10 for a vehicle 12 is operable to detect the angle of a trailer 14 that is pulled behind the vehicle 12 by using a rear view camera or multi-camera surround view system (
The system is operable to measure the angle (see
The algorithm for the target-less trailer angle detection system can be divided into the blocks shown in
The algorithm input image generates image views for processing. In machine vision, it is preferred that the feature or object to be detected appears in the input image of the algorithm, with the feature or object being undistorted (with the appearance of the object being the same regardless of its location in the image), rotation invariant (with the orientation of the feature or object being the same regardless of its location in the image) and size invariant (with the size of the feature or object being the same regardless of its location, if the distance between the camera and the object is the same). In cases where the feature or object is undistorted and rotation and size invariant, the detection and/or classification of the object is less complex and more repeatable, resulting in a better detection performance (detection rate, detection accuracy, classification rate). A view transform may be developed to achieve an undistorted, rotation and size invariant object or trailer. A cylinder or panoramic view meets the algorithm input image requirements (undistorted, size and rotation invariance). Such a cylinder or panoramic view is similar to the pano-view photo functionality of the iPhone.
The rotation is difficult to handle in object detection since it is computationally expensive. Traditional object detection in the image is limited to pixel row and column, and is therefore two dimensional. In case rotation is not eliminated by the input image transform, a third dimension is added regarding the detection algorithm. Therefore, the number of iterations (or computations) is increased. The output of the detection algorithm would be pixel row, column and object angle.
With reference to
As shown in
The cylinder view is configured such that the upper edge of the image is below the horizon and the bottom edge is beyond the tow ball showing the trailer tongue. In this case, the trailer moves without being rotated or resized from left to right or vice versa where the column corresponds to the trailer angle directly.
The intent of the trailer angle detection block is to locate the trailer position in terms of pixels and rows in the cylinder view image. It is preferred to choose an approach where the most amount of pixels of the trailer are used in the trailer detection. For example, utilizing edges results in more pixels being utilized than corners (1 pixel per corner). The cylinder view is configured so that the trailer hitch is always touching the bottom edge of the cylinder view image. This can be used in detecting the trailer. The Trailer Detection shall output the center line (column in cylinder view) of the trailer.
The trailer detection process includes detecting the trailer in the cylinder view using methods such as motion approach, where the vehicle is moving during a short calibration step (<10 m travel), and optical flow is utilized to perform a foreground/background segmentation and to initially locate or detect the trailer in the image. Edge detection is used to detect the outer edges of the trailer, and the outer edges are utilized to determine the center line of the trailer, with the points (such as all of the points) of the center line of the trailer being averaged together in order to detect the column of the trailer in the cylinder view image. The system may include or may be responsive to a level sensor of the vehicle and/or trailer so that the system determines when the trailer is vertically angled relative to the vehicle, so the system can adjust processing and determination of the location of the trailer portion accordingly.
Optionally, the trailer detection process may include neural network/deep learning, where the trailer is detected in the image using neural networks/deep learning algorithms, such that no initial calibration and/or vehicle motion is required to detect the trailer. A detailed trailer detection (for example using edges) is the same in the learning approach as in the motion approach.
The angle calculation/post processing includes using image processing to detect the outer edges of the trailer. The outer edges of the trailer are utilized to determine the vertical center line of the trailer in the cylinder view image. The vertical center line is used to calculate the trailer angle utilizing the dependency specified in
The Angle calculation may be done using the center line of the trailer. The trailer angle is filtered and conditioned in a post processing step. Vehicle+Trailer kinematic models may be used to supplement the trailer angle calculation in order to overcome temporary dropouts. Kalman filtering or any other tracker may be used to stabilize the output. Average Filtering may not be used since it introduces a delay in the output.
The system may utilize aspects of the trailering or trailer angle detection systems described in U.S. Pat. Nos. 9,085,261 and/or 6,690,268, and/or U.S. Publication Nos. US-2017-0254873; US-2017-0217372; US-2017-0050672; US-2015-0217693; US-2014-0160276; US-2014-0085472 and/or US-2015-0002670, which are hereby incorporated herein by reference in their entireties.
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.
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 ladar 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. 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.
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 the filing benefits of U.S. provisional application Ser. No. 62/474,644, filed Mar. 22, 2017, and Ser. No. 62/466,449, filed Mar. 3, 2017, which are hereby incorporated herein by reference in their entireties.
Number | Name | Date | Kind |
---|---|---|---|
5550677 | Schofield et al. | Aug 1996 | A |
5670935 | Schofield et al. | Sep 1997 | A |
5949331 | Schofield et al. | Sep 1999 | A |
6690268 | Schofield et al. | Feb 2004 | B2 |
7038577 | Pawlicki | May 2006 | B2 |
8930140 | Trombley et al. | Jan 2015 | B2 |
9085261 | Lu et al. | Jul 2015 | B2 |
9296422 | Lavoie | Mar 2016 | B2 |
9446713 | Lu et al. | Sep 2016 | B2 |
9558409 | Pliefke et al. | Jan 2017 | B2 |
20080044061 | Hongo | Feb 2008 | A1 |
20090306474 | Wilson | Dec 2009 | A1 |
20120287232 | Natroshvili | Nov 2012 | A1 |
20130177237 | Schamp | Jul 2013 | A1 |
20140085472 | Lu | Mar 2014 | A1 |
20140160276 | Pliefke et al. | Jun 2014 | A1 |
20140267688 | Aich | Sep 2014 | A1 |
20150002670 | Bajpai | Jan 2015 | A1 |
20150217693 | Pliefke et al. | Aug 2015 | A1 |
20150253428 | Holz | Sep 2015 | A1 |
20150363936 | Hallett | Dec 2015 | A1 |
20160309135 | Ovsiannikov | Oct 2016 | A1 |
20170032210 | Deppieri | Feb 2017 | A1 |
20170050672 | Gieseke et al. | Feb 2017 | A1 |
20170217372 | Lu et al. | Aug 2017 | A1 |
20170254873 | Koravadi | Sep 2017 | A1 |
20180215382 | Gupta et al. | Aug 2018 | A1 |
20180276838 | Gupta et al. | Sep 2018 | A1 |
20180276839 | Diessner et al. | Sep 2018 | A1 |
20190016264 | Potnis et al. | Jan 2019 | A1 |
20190039649 | Gieseke et al. | Feb 2019 | A1 |
20190042864 | Pliefke et al. | Feb 2019 | A1 |
20190064831 | Gali et al. | Feb 2019 | A1 |
20190118860 | Gali et al. | Apr 2019 | A1 |
20190143895 | Pliefke et al. | May 2019 | A1 |
Number | Date | Country | |
---|---|---|---|
20180253608 A1 | Sep 2018 | US |
Number | Date | Country | |
---|---|---|---|
62474644 | Mar 2017 | US | |
62466449 | Mar 2017 | US |