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 an image sensor such as a camera disposed at a rear portion of the vehicle and having a field of view exterior of and at least rearward of the vehicle and encompassing at least a portion of a trailer coupler of a trailer stationary a distance from the vehicle. The control comprises circuitry that includes an image processor operable to process image data captured by the camera that is representative of at least the front face or front profile of the trailer. The control, responsive to image processing by the image processor at the control of image data captured by the camera, determines a location of the front profile of the trailer and determines a plurality of landmarks corresponding to the location of the front profile. Responsive to determining the plurality of landmarks, the control determines a location of the trailer coupler.
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. The system includes an image processor or image processing system that is operable to receive image data from one or more 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 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 or trailer hitching 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 (
When connecting or hitching a trailer to a vehicle, the driver is typically required to manually reverse the vehicle toward a trailer coupler of a trailer. However, even with the assistance of rearward facing or rearward viewing cameras (e.g., “backup” cameras), the task may still be arduous and largely dependent on the experience and skill of the driver. While some backup cameras include a guideline overlay, there is no guarantee that the hitch will be aligned with the trailer coupler. Thus, it is advantageous to automate the process of hitch and coupler alignment. However, the automation requires accurate localization of the trailer's coupler 15 using the rear view camera. When the vehicle is distant from the trailer (e.g., multiple meters), the coupler 15 may not be clearly visible and/or detectable from the image data of the rear view camera. That is, the when the trailer is further than a threshold distance from the vehicle, the resolution of the camera may be insufficient to accurately identify the trailer coupler 15. A trailer localization system and method of the present invention determines the location of the coupler 15 using a geometry or posture of the trailer (
The trailer or coupler localization system is broken into three steps, each of which is discussed in more detail below. In the first step, the system determines a location of the trailer from image data captured by the rear facing camera. This includes identifying or determining the parameters of a bounding box in which the trailer exists. During the second step, the system extracts and saves landmarks of the front side of the trailer as the system assumes that the front side of the trailer includes the location of the trailer coupler 15. In the third step, the first two steps are repeated for consecutive frames of captured image data to further increase accuracy.
To determine or detect the location of the trailer, the system may use one or more classifiers. Image data captured by the camera(s) may be split into one or more sections or patches and the control may process or evaluate the patches one at a time and determine if a trailer is present in each patch. The control may sweep or process each patch at multiple different scales (i.e., upscaled and downscaled image data). In some implementations, the system uses a two-step classifier. For example, the first stage of the classifier may include a linear Support Vector Machine (SVM) that filters out the majority (e.g., 99 percent) of negative patches (i.e., patches that do not include a trailer). The SVM is efficient and quickly processes the patches, which allows the control to quickly sweep the entire frame of captured image data.
Any patches that are not negative (i.e., the SVM determined a trailer may be present) are passed 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 a trailer (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) outputs a bounding box that highlights the location of the trailer in the frame of captured image data. Thus, the system may process the image data 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. 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.
Referring now to
The trailer detection and landmark determination or estimation, in some implementations, is performed for a number of consecutive frames (e.g., five frames) to increase the accuracy of the coupler point determination. The number of frames may be limited, as the location of the coupler point moves (in the captured image data) as the vehicle moves (i.e., reverses towards the trailer) and accuracy gains diminish. 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.) The determined trailer coupler 15 may be provided in image coordinates (i.e., an x coordinate and a y coordinate pixel position) and known algorithms may be practiced to determine the three dimensional location of the coupler 15 from frames of image data captured by the camera/(e.g., Structure from Motion).
Referring now to
Thus, the system of the present invention provides the ability to automatically align the vehicle's hitch with the trailer coupler from a distance of several meters (e.g., eight meters) and at various orientations with respect to the vehicle (e.g., when the trailer is at an angle, for example, up to 60 degrees, with respect to the vehicle). The system accurately determines the location of the coupler point by determining the geometry of the trail from only images captured by a rear facing camera. This allows the system to determine the coupler at far distances when otherwise the coupler is not detectable via traditional methods. The system may also be used for trailer angle estimation and may use motion of the vehicle to redetect the coupler point in consecutive images making it robust to image noise.
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 applications, Ser. No. 16/946,542, filed on Jun. 26, 2020 and published Dec. 31, 2020 as U.S. Publication No. US-2020-0406967, Ser. No. 15/929,535, filed on May 8, 2020 and published May 8, 2020 as U.S. Publication No. US-2020-0356788, and/or Ser. No. 16/850,300, filed on Apr. 16, 2020, now U.S. Pat. No. 11,417,116, and/or U.S. provisional application Ser. No. 62/883,202, filed Aug. 6, 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 is a continuation of U.S. patent application Ser. No. 16/947,379, filed Jul. 30, 2020, now U.S. Pat. No. 11,613,208, which claims priority of U.S. provisional application Ser. No. 62/880,194, filed Jul. 30, 2019, which is hereby incorporated herein by reference in its entirety.
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 |
6498620 | Schofield et al. | Dec 2002 | B2 |
6690268 | Schofield et al. | Feb 2004 | B2 |
7038577 | Pawlicki et al. | May 2006 | B2 |
7720580 | Higgins-Luthman | May 2010 | B2 |
7855755 | Weller et al. | Dec 2010 | B2 |
9085261 | Lu et al. | Jul 2015 | B2 |
9264672 | Lynam | Feb 2016 | B2 |
9446713 | Lu et al. | Sep 2016 | B2 |
9558409 | Pliefke et al. | Jan 2017 | B2 |
10071687 | Ihlenburg et al. | Sep 2018 | B2 |
10086870 | Gieseke et al. | Oct 2018 | B2 |
10099614 | Diessner | Oct 2018 | B2 |
10160382 | Pliefke et al. | Dec 2018 | B2 |
10532698 | Potnis et al. | Jan 2020 | B2 |
10552976 | Diessner et al. | Feb 2020 | B2 |
10586119 | Pliefke et al. | Mar 2020 | B2 |
10638025 | Gali et al. | Apr 2020 | B2 |
10706291 | Diessner et al. | Jul 2020 | B2 |
10733757 | Gupta et al. | Aug 2020 | B2 |
10755110 | Bajpai | Aug 2020 | B2 |
11417116 | Joseph et al. | Aug 2022 | B2 |
11613208 | Assa | Mar 2023 | B2 |
20140063197 | Yamamoto et al. | Mar 2014 | A1 |
20140085472 | Lu et al. | Mar 2014 | A1 |
20140160276 | Pliefke et al. | Jun 2014 | A1 |
20140267688 | Aich et al. | Sep 2014 | A1 |
20150002670 | Bajpai | Jan 2015 | A1 |
20150217693 | Pliefke et al. | Aug 2015 | A1 |
20160049020 | Kuehnle et al. | Feb 2016 | A1 |
20170050672 | Gieseke et al. | Feb 2017 | A1 |
20170174128 | Hu | Jun 2017 | A1 |
20170217372 | Lu et al. | Aug 2017 | A1 |
20170254873 | Koravadi | Sep 2017 | A1 |
20170280091 | Greenwood et al. | Sep 2017 | A1 |
20170341583 | Zhang et al. | Nov 2017 | A1 |
20180211528 | Seifert | Jul 2018 | A1 |
20180215382 | Gupta et al. | Aug 2018 | A1 |
20180253608 | Diessner et al. | Sep 2018 | A1 |
20180276838 | Gupta et al. | Sep 2018 | A1 |
20180276839 | Diessner et al. | Sep 2018 | A1 |
20180361929 | Zhang | Dec 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 |
20190241126 | Murad et al. | Aug 2019 | A1 |
20190275941 | Lu et al. | Sep 2019 | A1 |
20190297233 | Gali et al. | Sep 2019 | A1 |
20190329821 | Ziebart et al. | Oct 2019 | A1 |
20190347498 | Herman et al. | Nov 2019 | A1 |
20190347825 | Gupta et al. | Nov 2019 | A1 |
20200017143 | Gali | Jan 2020 | A1 |
20200334475 | Joseph et al. | Oct 2020 | A1 |
20200356788 | Joseph et al. | Nov 2020 | A1 |
20200361397 | Joseph et al. | Nov 2020 | A1 |
20200406967 | Yunus et al. | Dec 2020 | A1 |
20210023997 | Vasoya | Jan 2021 | A1 |
20210034902 | Assa et al. | Feb 2021 | A1 |
20210078634 | Jalalmaab et al. | Mar 2021 | A1 |
20210094473 | Gali et al. | Apr 2021 | A1 |
20210170820 | Zhang | Jun 2021 | A1 |
20210170947 | Yunus et al. | Jun 2021 | A1 |
Number | Date | Country | |
---|---|---|---|
20230234502 A1 | Jul 2023 | US |
Number | Date | Country | |
---|---|---|---|
62880194 | Jul 2019 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 16947379 | Jul 2020 | US |
Child | 18190380 | US |