CN111950483A - Vision-based vehicle front collision prediction method - Google Patents
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Abstract
The invention provides a vision-based vehicle front collision prediction method, which comprises the steps of positioning a front vehicle through a vision target detection algorithm and generating an interested area; preprocessing the image, and detecting key points in the region of interest at the moment; associating the key points with the key points detected in the previous frame of image, and matching the same key points to generate matched sub-images; obtaining a group of optimal matching gametes through screening; and calculating and predicting the collision time TTC, comparing and judging the TTC with a set threshold value, and responding to different working conditions respectively. According to the invention, the vehicle front collision early warning problem is solved by a pure vision scheme, the TTC is obtained by calculation through a computer vision technology, the obtained calculation result is more accurate, and the scheme has no limitation on the road condition.
Description
Technical Field
The invention relates to the technical field of vehicle safety early warning, in particular to a vehicle front collision prediction method based on vision.
Background
With the rapid increase of vehicle keeping quantity, vehicle traffic accidents are also rising year by year, and the requirements of vehicle consumers on safety are also increasing day by day. If the driver can be warned by early warning before the road traffic danger occurs or auxiliary driving is provided, most traffic accidents can be avoided. The front collision early warning system (FCWS) is a subsystem in an Advanced Driving Assistance System (ADAS), and the FCWS can monitor a front vehicle at any time and warn a driver or even actively control a brake pedal to realize braking when a potential collision danger exists. FCWS mainly involves several key technologies: sensing and processing the information of the self vehicle and the obstacle, evaluating the driving safety state and controlling active braking. The patent emphasizes solving the first two key technologies.
The visual technology obtains the positional information of the front vehicle and can be realized through stereoscopic vision or monocular vision, the stereoscopic camera senses the distance by simulating human beings and utilizing binocular parallax, but the problems of large volume, high price and high calculation load exist, and the monocular camera is adopted in the patent, so that the size is small, the cost is low and the calculation load is small.
Patent numbers: CN105574552A provides a vehicle range finding and collision early warning method based on monocular vision, and this technique has the defect when calculating TTC, and d in the computational formula and S can not accurate measurement, therefore there is great error in the TTC that calculates, especially when the road has certain slope. The accuracy requirement on the calculation result of the TTC is high when the vehicle front collision early warning is carried out, so that other methods for calculating the TTC need to be improved.
Disclosure of Invention
The invention provides a vision-based vehicle front collision prediction method, which is applied to a perception and decision system of an intelligent electric drive vehicle, realizes a front collision early warning function by continuously estimating collision time (TTC) by a vision system, and comprehensively considers the speed of the vehicle and the front vehicle, so that the calculated TTC is more accurate, and the accuracy and the effectiveness of the front collision early warning are improved.
In order to achieve the purpose, the technical scheme adopted by the application is as follows:
a vision-based vehicle front impact prediction method, comprising the steps of:
s1, positioning a front vehicle through a visual target detection algorithm and generating an interested area; the target detection algorithm can adopt a target detection algorithm based on a convolutional neural network, such as SSD, YOLO and the like, for rapid detection.
S2, preprocessing the image, including gray processing, Gaussian smoothing and gradient processing, so as to facilitate the detection of key points later;
s3, detecting key points in the region of interest at the moment;
s4, associating the key points with the key points detected in the previous frame of image, and matching the same key points to generate matched pieces; in the matching process, the matching accuracy is improved through cross detection and non-maximum suppression technology;
s5, screening the obtained matchmaker; in the matching process, the matching accuracy is improved through cross detection and non-maximum suppression technology;
s6, calculating and predicting the Time To Collision (TTC); a constant-speed model is adopted:
h0＝f*LCD/d0
h1＝f*LAB/d1
LAB＝LCD
d1＝d0-V0*Δt＝d1*h1/h0-V0*Δt
TTC＝d1/V0＝Δt/(h1/h0-1)
wherein f is the focal length of the camera, h0The distance, h, of the two key points detected and screened out in the previous frame image projected on the imaging plane1For the distance, L, projected on the imaging plane for the two key points corresponding to this momentAB，LCDThe distance between two key points at the same moment; d0Represents the distance from the camera at the previous moment, d1Represents the distance, V, from the camera at that moment0Representing the speed of the vehicle in front of the previous moment, and delta t representing the time required for acquiring two adjacent frames of images; TTC is the predicted time to collision.
S7, setting TTC threshold values A and B, wherein A represents that collision danger exists, and B represents that the situation is urgent;
if TTC > ═ A, it is safe temporarily;
if A > TTC > -B, it shows that there is collision danger, and gives warning to the driver through voice, vibration and other modes;
if TTC < B, the situation is urgent, the voice reminding is carried out, and meanwhile, the brake pedal is controlled to brake actively, and the safety belt is tightened.
Further, the steps are cycled and continuously detected.
The invention has the technical effects that:
the problem of front collision early warning is solved through vision, a matching sub is obtained by using a key point detector and a descriptor matching method, the TTC obtained through further calculation is more accurate, and the road using condition is not limited.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic illustration of the TTC calculation of the present invention;
FIG. 3 is a schematic illustration of an intelligent electrically driven vehicle of an embodiment;
FIG. 4 is a schematic illustration of object detection of an embodiment;
FIG. 5 is a schematic diagram of a Gaussian smoothing image processing technique of an embodiment;
FIG. 6 is a schematic diagram of a gradient processing image processing technique of an embodiment;
FIG. 7 is a schematic diagram of two previous and subsequent image associations according to an embodiment.
Detailed Description
The specific technical scheme of the invention is described by combining the embodiment.
Referring to fig. 3, in the embodiment, an intelligent electrically driven vehicle 1 is used as a carrier, a monocular camera 2 is mounted on a front window of the vehicle for image acquisition, an industrial personal computer 3 is mounted at the rear part of the vehicle, a front collision early warning system is mounted on the vehicle for processing the acquired image, and the vehicle runs on an urban road at a speed of 40 km/h. As shown in fig. 4, when a vehicle appears in the front, the vehicle is first located by a target detection algorithm, and the accuracy and speed of detection are comprehensively considered, and a YOLOv3 target detection algorithm is adopted in the present embodiment. As in fig. 5, the image is gaussian smoothed; as shown in fig. 6, the image is subjected to gradient processing to facilitate the detection of image key points, and a FAST key point detector is adopted in this embodiment. As shown in fig. 7, the key points of the two frames of images before and after are associated, and a break descriptor is used in this embodiment to generate a matching sub. And screening the matched sub-sets to obtain a group of optimal matched sub-sets for calculating and predicting the Time To Collision (TTC). In this embodiment, the time for acquiring two frames of images is 0.1s, and a constant speed model may be adopted assuming that a vehicle ahead moves at a constant speed within 0.1 s. In this embodiment, a is set to 2s, B is set to 0.5s, and the calculated predicted time to collision TTC is set to 1.5s, at which time the vehicle reminds the driver by voice. The vehicle front collision early warning system continuously detects and releases the alarm when the TTC is 2 s.
In another embodiment, the vehicle front collision prediction method is loaded in an industrial personal computer of an experimental vehicle, and the vehicle runs on an expressway at the speed of 80km/h, and an SSD target detection algorithm, a FAST key point detector and a BRIEF descriptor are adopted. In this embodiment, a constant speed model is still adopted, the time taken for acquiring two frames of images is 0.1s, a is set to be 3s, B is set to be 1.5s, the time for collision with a front vehicle is predicted to be 6s through calculation, the vehicle is in a safe state at this time, and the vehicle front collision early warning system continuously detects the time.
Patent CN105574552A provides a vehicle distance measurement and collision early warning method based on monocular vision, which needs to obtain the distance between the vehicle and the vehicle ahead at each time when calculating TTC, the distance acquisition method is the same as the distance measurement method in the document [ xu yang, yao man, high profit, peak ]. The TTC calculation method skillfully omits the calculation of the distance between the vehicle and the front vehicle, and is converted into the calculation of the distance of the key point on the imaging plane, and the positioning of the key point is more accurate than the positioning of the middle point of the edge line at the bottom of the vehicle adopted in the patent CN105574552A, so the calculation accuracy of the TTC is improved.
As shown in fig. 1, the patent application adopts the following technical solutions:
s1, positioning a front vehicle through a visual target detection algorithm, generating an interested area, and representing the interested area as a bounded closed graph or frame on an image.
And S2, preprocessing the image, including gray processing, Gaussian smoothing and gradient processing, so as to facilitate the detection of the subsequent key points.
And S3, detecting key points in the region of interest at the moment.
Keypoints (keypoints) are concepts in computer vision that are used to locate the position of feature points in an image, which can be precisely located in two coordinate directions in the image because of some features. Common key point detection means (Keypoint Detectors) include: SHITOMASI, HARRISS, FAST, BRISK, ORB, SIFT, etc. As shown, C and D are two of the key points detected at the current time. Further, a plurality of feature points can be detected at any one time, and the number of feature points is related to the features of the image and the detection method, and is generally several tens to several hundreds.
And S4, associating the key points with the key points detected in the previous frame of image, and matching the same key points to generate a matched sub. Referring to FIG. 2, C and D correspond to the key points A and B at the previous time, respectively, and A-C and B-D are two sets of matched pairs. Also here, using computer vision techniques, associating two keypoints requires the use of a Descriptor (Descriptor), which is a set of vectors that describe the keypoints, and currently popular descriptors include: BRISK, BRIEF, ORB, FREAK, SIFT, etc. After the key point detector and the descriptor are selected, key point matching can be carried out, and the accuracy is improved through cross detection and non-maximum suppression means in the matching process.
And S5, screening the obtained matchmaker. The matches (containing two keypoints) have coordinates in the image, so the distance between the two keypoints contained in each match can be calculated. And calculating the distances of all the matching sub-sets, then calculating the average value, and deleting the matching sub-sets which are far from the average value to obtain a group of optimal matching sub-sets. Two of the spots were statistically selected.
And S6, calculating TTC. Using a constant speed model (assuming that the vehicle in front is travelling at constant speed, since the detection time is usually less than 0.1s, corresponding assumptions can be made and a constant acceleration model can be used instead), still illustrated by fig. 2:
h0＝f*LCD/d0
h1＝f*LAB/d1
LAB＝LCD
d1＝d0-V0*Δt＝d1*h1/h0-V0*Δt
TTC＝d1/V0＝Δt/(h1/h0-1)
wherein f is the focal length of the camera, h0The distance h projected on the imaging plane for the two key points (A, B) detected and screened out in the last frame of image1For the distance, L, projected on the imaging plane for the two key points (C, D) corresponding to this momentAB，LCDIs the distance between two key points at the same time. d0Represents the distance from the camera at the previous moment, d1Represents the distance, V, from the camera at that moment0Representing the speed of the vehicle ahead at the previous moment, at representing the time required for acquiring two adjacent frames of imagesTime. TTC is the predicted time to collision.
S7, setting TTC threshold values A and B, wherein A represents that collision danger exists, B represents that the situation is urgent, if TTC > is equal to A, temporary safety is indicated, if A > TTC > is equal to B, collision danger exists, a driver is warned through voice, vibration and the like, if TTC < B, the situation is urgent, and the brake pedal is controlled to brake actively and tighten the safety belt while voice reminding is performed.
The FCWS system is periodically circulated and continuously detected, and a flow chart of one period is shown in figure 1.
Claims (6)
1. A vision-based vehicle front collision prediction method is characterized by comprising the following steps:
s1, positioning a front vehicle through a visual target detection algorithm and generating an interested area;
s2, preprocessing the image;
s3, detecting key points in the region of interest at the moment;
s4, associating the key points with the key points detected in the previous frame of image, and matching the same key points to generate matched pieces;
s5, screening the obtained matchmaker;
s6, calculating and predicting the Time To Collision (TTC);
s7, setting TTC threshold values A and B, wherein A represents that collision danger exists, and B represents that the situation is urgent;
if TTC > ═ A, it is safe temporarily;
if A > TTC > -B, it shows that there is collision danger, and gives warning to the driver through voice, vibration and other modes;
if TTC < B, the situation is urgent, the voice reminding is carried out, and meanwhile, the brake pedal is controlled to brake actively, and the safety belt is tightened.
2. The vision-based vehicle front collision prediction method according to claim 1, characterized in that the target detection algorithm of S1 adopts a convolutional neural network-based target detection algorithm for fast detection.
3. The vision-based vehicle front collision prediction method according to claim 1, wherein the preprocessing flow of S2 includes performing gray scale processing, gaussian smoothing, and gradient processing to facilitate the detection of the key points.
4. The vision-based vehicle front collision prediction method according to claim 1, wherein the matching process of S4 is implemented by cross detection and non-maximum suppression technology to improve the matching accuracy.
5. The vision-based vehicle front collision prediction method according to claim 1, wherein S5 selects the best matching sub-set by calculating the distance of the matching sub-sets and averaging them, and deleting the matching sub-sets that are too far from the average value, by using statistical means in the screening process.
6. A vision-based vehicle front collision prediction method according to claim 1, characterized in that in S6 the predicted time to collision TTC is calculated, using a constant velocity model:
h0＝f*LCD/d0
h1＝f*LAB/d1
LAB＝LCD
d1＝d0-V0*Δt＝d1*h1/h0-V0*Δt
TTC＝d1/V0＝Δt/(h1/h0-1)
wherein f is the focal length of the camera, h0The distance, h, of the two key points detected and screened out in the previous frame image projected on the imaging plane1For the distance, L, projected on the imaging plane for the two key points corresponding to this momentAB，LCDThe distance between two key points at the same moment; d0Represents the distance from the camera at the previous moment, d1Represents the distance, V, from the camera at that moment0Representing the speed of the vehicle in front of the previous moment, and delta t representing the time required for acquiring two adjacent frames of images; TTC is the predicted time to collision.
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| CN113306566A (en) * | 2021-06-16 | 2021-08-27 | 上海大学 | Vehicle and pedestrian collision early warning method and device based on sniffing technology |
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| CN113306566B (en) * | 2021-06-16 | 2023-12-12 | 上海大学 | Vehicle pedestrian collision early warning method and device based on sniffing technology |
| CN113124819A (en) * | 2021-06-17 | 2021-07-16 | 中国空气动力研究与发展中心低速空气动力研究所 | Monocular distance measuring method based on plane mirror |
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