CN102303563A - Front vehicle collision warning system and method - Google Patents
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Abstract
The invention provides a front vehicle collision early warning system which comprises a front vehicle identification unit, a distance measurement unit and an alarm unit, wherein the front vehicle identification unit, the distance measurement unit and the alarm unit are electrically connected; the front vehicle identification unit comprises at least one camera for receiving and transmitting a front vehicle signal; the distance measuring unit is connected with the front vehicle identification unit, calculates the distance between the vehicle body and the front vehicle and the collision time, and sends a front vehicle collision alarm signal through the alarm unit when the collision time is smaller than a set threshold value. The implementation of the front vehicle collision early warning method comprises the following steps: step 1, calibrating a camera unit to receive a front vehicle signal; step 2, detecting the effective area; step 3, acquiring horizontal and vertical edges by a connected component analysis method, and integrating; step 4, verifying whether vehicles exist in the effective area; step 5, calculating the distance between the vehicle body and the front vehicle, and judging whether to send an alarm instruction; and 6, sending an alarm signal according to the alarm instruction.
Description
[technical field]
The present invention relates to a kind of front truck collision warning systems and method.
[technical background]
Present deviation accident that causes under the situation that chaufeur is not concentrated because of carelessness, tired, drowsiness or state and the rear-end collision accident 60%-70% that accounts for whole road traffic accident, death toll accounts for 70%.Estimation according to Bureau of Public Road; System of leading vehicle distance with can detect vehicle operating time of the system of the horizontal position when utilization can detect vehicle operating can reduce the generation of these traffic accidents greatly; Therefore, the research of anti-front truck collision system has caused the great attention that each is national.
The front truck checking system mainly contains following several method: based on the Feature Recognition method, based on the recognition methods of model, based on recognition methods of pattern-recognition etc.Based on the Feature Recognition method is directly to use some attribute property of vehicle, like color, shape, symmetry, in image, mates through the characteristic of seeking vehicle and accomplishes vehicle detection; Recognition methods based on model is through setting up two dimension or three-dimensional model for vehicle, in image, carry out search matched then, accomplishing vehicle detection; Based on the recognition methods of pattern-recognition is through gathering great amount of samples, carry out feature extraction and machine learning then, in image, extracts the characteristic driving detection of going forward side by side.At present, some country successfully develops the front truck collision avoidance system that some differ from one another in the world, as: Mobileye_AWS system, GOLD system etc.These systems remind chaufeur to take correct operation measure through giving chaufeur with warning message in advance, reach the purpose that prevents this type accident or reduce the extent of injury of this type accident.
Yet because road environment is complicated, vehicle class is various, and the front truck anti-collision early warning system not only will have good real time performance, but also stronger robustness will be arranged.And existing systems can't well satisfy the requirement of these aspects, the present invention proposes a kind of system that better real-time and robustness are arranged.
[summary of the invention]
To the problems referred to above, the present invention proposes a kind of front truck collision warning systems, is mainly used in to prevent that moving traffic and front truck from bumping, and said system comprises electrical connection:
The front truck diagnosis unit comprise at least one camera, are used for receiving and transmitting the front vehicles signal;
Distance measuring unit; Be connected with said front truck diagnosis unit; Receive said front vehicles signal; Distortion correction parameter and installation site through said camera; And front vehicles location information; Calculate distance and collision time between car body and the front vehicles,, send the instruction of front truck collision warning when collision time during less than preset threshold;
Alarm unit is connected with said distance measuring unit, receives said alarm command, and sends corresponding alerting signal.
The present invention further comprises a kind of method that realizes the front truck collision warning systems, and this method may further comprise the steps:
Step 1 is demarcated image unit, to receive and to transmit the front vehicles signal;
Step 2 is provided with the service area that front vehicles detects according to vehicle running state;
Step 3 is obtained level and vertical edge through the connected component analytic method, and carries out the edge and integrate;
Whether step 4 checking has vehicle in the service area;
Step 5 is calculated the distance between car body and the front vehicles, and judges whether to send alarm command;
Step 6 is sent alerting signal according to alarm command.
Wherein said step 3 can further may further comprise the steps:
Step 31 is calculated the level of vehicle and the binary image of vertical edge according to the grey scale change characteristic in the service area;
Step 32 is used the connected component analysis respectively in horizontal binary image and vertical binary image, search all qualified level and vertical edges;
The level and the vertical edge of step 33 pair acquisition are integrated respectively, and adjacent horizontal edge is combined, and adjacent vertical edge combines;
Adjacent level and vertical edge after the step 34 pair integration are integrated once more, form a plurality of candidate regions that vehicle possibly occur.
Wherein said step 4 can further may further comprise the steps:
Step 41 is removed part according to the entropy of candidate region and is disturbed the candidate region;
Step 42 is further confirmed the candidate region according to the shape facility and the gray scale symmetrical feature of vehicle afterbody;
Whether step 43 affirmation candidate region is vehicle.
Wherein step 42 can adopt the likelihood score function further to confirm the candidate region, and said likelihood score function representation is:
SimilarRate＝A*SymmetryRate+B*ModelRate
Said SimilarRate is final similarity numerical value; Said SymmetryRate is that vehicle afterbody gray scale symmetry is estimated; Said A is that vehicle afterbody gray scale symmetry is estimated weights set in similarity function; Said Mode l Rate is that vehicle afterbody model is estimated, and said B is that vehicle afterbody model is estimated the set weights that account in similarity function.
When a certain candidate region in the said step 43 was vehicle, its parameter need satisfy following formula: EntropyRate＞EntropyTh && SimilarRate＞SimilarTh && RealWidth＞WidthTh && RealHeight＞HeightTh && RealWidth＜γ * RealHeight &&RealHeight＜γ * RealWidth
Wherein EntropyRate is the entropy of candidate region, and EntropyTh is the threshold value of said entropy, and SimilarRate is the measuring similarity of candidate region, the threshold value that SimilarTh must satisfy for the vehicle similarity; RealWidth and RealHeight are respectively the true altitude and the width of vehicle, and WidthTh and HeightTh are respectively the threshold value of height of car and width, and γ is vehicle width and ratio threshold value highly.
The method of calculating car body and front vehicles in the said step 5 is:
D＝h/tan(α+β)，β＝arctan[(y0-y)/f]
Wherein D is the actual distance that car body arrives front vehicles; H is the ground-surface height of said camera distance; α is the luffing angle of camera; Y0 is the intersection point ordinate on optical axis and picture plane; Y is some ordinate of subpoint on the picture plane of front vehicles bottom; F is the camera focal length.
The present invention not only has good real time performance, and stronger robustness is arranged.These systems remind chaufeur to take correct operation measure through giving chaufeur with warning message in advance, reach the purpose that prevents this type accident or reduce the extent of injury of this type accident.
[description of drawings]
Fig. 1 is that front truck collision warning systems of the present invention connect scheme drawing;
Fig. 2 is the schematic flow sheet of front truck anti-collision warning method of the present invention;
Fig. 3 is the shooting model scheme drawing of calculating car body of the present invention and front vehicles distance;
Fig. 4 is a hardware connecting circuit scheme drawing of the present invention.
[specific embodiment]
Referring to Fig. 1, the present invention proposes a kind of front truck collision warning systems, is mainly used in to prevent that moving traffic and front truck from bumping, and said system comprises the front truck diagnosis unit 1 of electrical connection, distance measuring unit 2 and alarm unit 3; Front truck diagnosis unit 1 comprise at least one camera 11, are used for receiving and transmitting front vehicles signal and road conditions image, carry out the detection and Identification of front truck; Distance measuring unit 2 is connected with said front truck diagnosis unit 1; Receive said front vehicles signal; Distortion correction parameter and installation site through said camera 11; And front vehicles location information; Calculate distance and collision time between car body and the front vehicles; When collision time during, send the instruction of front truck collision warning less than preset threshold; Alarm unit 3 is connected with said distance measuring unit 2, receives said alarm command, and sends corresponding alerting signal.Alerting signal can be a sound and light signal.
The present invention has announced a kind of method that realizes the front truck collision warning systems simultaneously, and this method comprises 6 steps, and details are as follows:
Step 1 is demarcated camera 11, to receive and to transmit the front vehicles signal.
The purpose that camera 11 is demarcated mainly is the inside and outside parameter for the camera of asking 11, calculates the distance between car body and the front vehicles with these parameters, and the true altitude of calculating front vehicles and width etc.The method comparative maturity that camera 11 is demarcated, and have a large amount of documents that it has been carried out deep explanation, here just not in detailed introduction.
Step 2 is provided with the service area that front vehicles detects according to vehicle running state.
Because image the first half that camera 11 is taken is a sky; About regional major part be railing or trees etc.; Little to detecting the front vehicles effect; The vehicle dead ahead of just travelling at this moment that needs to detect is extended a part respectively to the left and right with interior vehicle; So the service area scope that can detect is set to: the upper limit is a world intersection line, is limited to a trapezoid area within car body and the road surface intersection line down.
Step 3 is obtained level and vertical edge through the connected component analytic method, and carries out the edge and integrate.
The grey scale change characteristic of some images that in the service area, collect according to said camera 11; Than the low characteristic of road surface gray scale and the grey scale change characteristic on left and right vehicle wheel border, obtain the level of vehicle and the binary image of vertical edge such as the vehicle bottom shadow; Then, in horizontal binary image and vertical binary image, use the connected component analysis respectively, search all qualified level and vertical edges; Moreover, the level and the vertical edge that obtain are integrated respectively, adjacent horizontal edge is combined, adjacent vertical edge combines; At last, adjacent level and vertical edge after integrating are integrated once more, is formed a plurality of candidate regions that vehicle possibly occur, available rectangle is described these zones, these rectangles be exactly the position of vehicle might appear, we claim these regional be the candidate region
Whether step 4 checking has vehicle in the service area.
At first utilize the entropy of candidate region to remove some interference candidate regions, accomplish the first step that vehicle is confirmed; According to the characteristic of vehicle afterbody, the shape facility and the gray scale symmetrical feature that merge the vehicle afterbody are simultaneously further confirmed the candidate region of vehicle then.When using shape facility and gray scale symmetry characteristic to carry out the candidate region affirmation, can design a likelihood score function, its value is confirmed by the Model Matching degree and the gray scale symmetry of candidate region, is expressed as:
SimilarRate＝A*SymmetryRate+B*ModelRate
Wherein SimilarRate is final similarity numerical value; SymmetryRate is that vehicle afterbody gray scale symmetry is estimated, and A is that vehicle afterbody gray scale symmetry is estimated shared weights in similarity function, can be taken as 2; Mode l Rate is that vehicle afterbody model is estimated, and B is that vehicle afterbody model is estimated shared weights in similarity function, can get 1.5.
At last, if a candidate region is considered to vehicle, must satisfy:
EntropyRate＞EntropyTh?&&?SimilarRate＞SimilarTh?&&?RealWidth＞WidthTh?&&?RealHeight＞HeightTh?&&?RealWidth＜γ*RealHeight?&&RealHeight＜γ*RealWidth
Wherein EntropyRate is the entropy of candidate region, and EntropyTh is the threshold value of said entropy, can get the real number between the 3.5-4.5; SimilarRate is the measuring similarity of candidate region, and the threshold value that SimilarTh must satisfy for the vehicle similarity can be got real number between the 1.2-2.0; RealWidth and RealHeight are vehicle true altitude and width; WidthTh and HeightTh are vehicle width and height threshold, and their size is different and different with the position; γ is vehicle width and ratio threshold value highly, can get the real number about 2.0.
Step 5 is calculated the distance between car body and the front vehicles, and judges whether to send alarm command.
After detecting the vehicle in the place ahead; If in certain scope area of the left and right sides, car body the place ahead, vehicle is arranged; So according to the imaging model of the described camera 11 of Fig. 3; Point P is position in world coordinate system, rear vehicle end position; (x is that some P is at plane of delineation mapping point, under the prerequisite of ground level level y); Can get according to trigonometric function, camera apart from the distance D of front vehicles is:
D＝h/tan(α+β)，
β=arctan[(Cy-y)/Fy wherein]
In the formula, h is the ground-surface height of said camera distance; α is the vertically arranged pitch angle of shooting, can obtain through measuring, and with the degree unit; Cy is the ordinate at the center of camera imaging, obtains by camera manufacturer or through camera calibration; Fy is that camera lens is the focal length of unit with the pixel on the vertical direction, Fy=f/ (PiexlH), and wherein f is the camera focal length, and mm is a unit, and PiexlH is the height of a pixel, is unit with mm, and manufacturer provides by camera.
So be apart from leading vehicle distance D ':
D’＝D-d1
Wherein d1 is the distance of camera 11 to vehicle bumper.
After the distance that calculates between car body and the front vehicles, can further calculate the collision time interval of car body and front truck through this distance, when collision time interval during, send alarm command less than a certain setting threshold.
Step 6 is sent alerting signal according to alarm command.
Alerting signal can sound and light signal etc. causes the cue that the driver notes.
Hardware connecting circuit figure as shown in Figure 4, the core cell of this system are control unit 5, and each unit of itself and other is electrically connected, and its inside is provided with distance measuring unit 2 and tests the speed unit 51, and control unit 51 can be made up of digital media processor DM6437; Front truck diagnosis unit 1 are made up of camera 11 and video conversion chip 12; The image pickup signal that receives is passed to control unit 5; After control unit 5 calculates the distance of car body and front vehicles through its in-to-in distance measuring unit 2; Collision time through test the speed unit 51 calculating and front vehicles; When collision time during at interval less than a certain setting threshold; Send alarm command to alarm unit 3; Alarm unit 3 sends alerting signal according to alarm command, and alarm unit 3 can be made up of audio frequency processing circuit 31 and loud speaker 32.
This system also can comprise display unit 4, is used to show that car body and leading vehicle distance, the speed of a motor vehicle or collision time reach information such as synthetic back image at interval, can be made up of LCD display.
This system also can comprise memory cell 7, can adopt modes such as DDR2 or Flash to store.
Each module provides power by system power supply 6, and work under the coordination of control unit 5.
The program of system can leave in the memory cell 7, also can store the information such as correction parameter of camera 11 in the memory cell 7.Test pavement simulating vision signal by camera 11 is taken is input in the control unit 5 through converting digital signal into behind the video conversion chip 12; Control unit 5 detects front vehicles in image digital signal; If detect car body the place ahead vehicle is arranged, the time gap that the distance of calculating car body this moment and front truck, and then calculating so bumps; If this time gap, produces a front truck collision warning instruction less than setting threshold; When system had detected any alarm command and produces, through alarm unit 3, the prompting driver that sounds took care; Simultaneously corresponding information shows through display unit 4.
The present invention not only has good real time performance, and stronger robustness is arranged.These systems remind chaufeur to take correct operation measure through giving chaufeur with warning message in advance, reach the purpose that prevents this type accident or reduce the extent of injury of this type accident.
Claims (10)
1. front truck collision warning systems is characterized in that: said system comprises electrical connection:
The front truck diagnosis unit comprise at least one camera, are used for receiving and transmitting the front vehicles signal;
Distance measuring unit; Be connected with said front truck diagnosis unit; Receive said signals of vehicles; Distortion correction parameter and installation site through said camera; And front vehicles location information; Calculate distance and collision time between car body and the front vehicles,, send the instruction of front truck collision warning when collision time during less than preset threshold;
Alarm unit is connected with said distance measuring unit, receives said alarm command, and sends corresponding alerting signal.
2. method that realizes the front truck anti-collision warning is characterized in that: said method comprising the steps of:
Step 1 is demarcated image unit, to receive and to transmit the front vehicles signal;
Step 2 is provided with the service area that front vehicles detects according to vehicle running state;
Step 3 is obtained level and vertical edge through the connected component analytic method, and carries out the edge and integrate;
Whether step 4 checking has vehicle in the service area;
Step 5 is calculated the distance between car body and the front vehicles, and judges whether to send alarm command;
Step 6 is sent alerting signal according to alarm command.
3. the method for realization front truck anti-collision warning as claimed in claim 2 is characterized in that: said step 3 further may further comprise the steps:
The grey scale change characteristic of the image that step 31 arrives according to said camera collection is calculated the level of vehicle and the binary image of vertical edge in the service area;
Step 32 is used the connected component analysis respectively in horizontal binary image and vertical binary image, search all qualified level and vertical edges;
The level and the vertical edge of step 33 pair acquisition are integrated respectively, and adjacent horizontal edge is combined, and adjacent vertical edge combines;
Adjacent level and vertical edge after the step 34 pair integration are integrated once more, form a plurality of candidate regions that vehicle possibly occur.
4. the method for realization front truck anti-collision warning as claimed in claim 3 is characterized in that: said step 4 further may further comprise the steps:
Step 41 is removed part according to the entropy of candidate region and is disturbed the candidate region;
Step 42 is further confirmed the candidate region according to the shape facility and the gray scale symmetrical feature of vehicle afterbody;
Whether step 43 affirmation candidate region is vehicle.
5. the method for realization front truck anti-collision warning as claimed in claim 4 is characterized in that: said step 42 adopts the likelihood score function further to confirm the candidate region, and said likelihood score function representation is:
SimilarRate＝A*SymmetryRate+B*Mode?l?Rate
Wherein SimilarRate is final similarity numerical value; SymmetryRate is that vehicle afterbody gray scale symmetry is estimated; A is that vehicle afterbody gray scale symmetry is estimated weights set in similarity function; Mode l Rate is that vehicle afterbody model is estimated, and B is that vehicle afterbody model is estimated the set weights that account in similarity function.
6. the method for realization front truck anti-collision warning as claimed in claim 5 is characterized in that: said A is set to 2, and said B is set to 1.5.
7. the method for realization front truck anti-collision warning as claimed in claim 4 is characterized in that: when the candidate region in the said step 43 was vehicle, its parameter satisfied following formula:
EntropyRate＞EntropyTh?&&?SimilarRate＞SimilarTh?&&?RealWidth＞WidthTh?&&?RealHeight＞HeightTh?&&?RealWidth＜γ*RealHeight?&&RealHeight＜γ*RealWidth
Wherein EntropyRate is the entropy of candidate region, and EntropyTh is the threshold value of said entropy, and SimilarRate is the measuring similarity of candidate region, the threshold value that SimilarTh satisfies for the vehicle similarity; RealWidth and RealHeight are respectively the height and the width of vehicle, and WidthTh and HeightTh are respectively the threshold value of height of car and width, and γ is the threshold value of vehicle width and height ratio.
8. the method for realization front truck anti-collision warning as claimed in claim 7 is characterized in that: said EntropyTh is the real number between 3.5～4.5, and said SimilarTh is a real number between 1.2～2.0.
9. the method for realization front truck anti-collision warning as claimed in claim 7 is characterized in that: said γ is 1.0～3.0.
10. the method for realization front truck anti-collision warning as claimed in claim 2 is characterized in that: the method for calculating distance between car body and the front vehicles in the said step 5 is:
Step 51, calculating parameter β: β=arctan[(Cy-y)/Fy], wherein Cy is the ordinate of said camera imaging center; Fy is that camera lens is the focal length of unit with the pixel on the vertical direction;
Step 52 is calculated the distance of said camera to front vehicles: D=h/tan (alpha+beta), and wherein h is the ground-surface height of said camera distance, and α is the vertically arranged pitch angle of said camera;
Step 53, calculate the distance of car body to front vehicles: D '=D-d1,
Wherein d1 is the distance of said camera to the car body bumper/spoiler.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103569110A (en) * | 2012-07-30 | 2014-02-12 | 深圳华一汽车科技有限公司 | Machine vision based forward vehicle collision warning system and method for realizing front vehicle collision warning by system |
| CN105346388A (en) * | 2015-11-27 | 2016-02-24 | 江苏大学 | Auxiliary system for automobile uphill and control method of auxiliary system |
| CN106570487A (en) * | 2016-11-10 | 2017-04-19 | 维森软件技术（上海）有限公司 | Method and device for predicting collision between objects |
| CN107657237A (en) * | 2017-09-28 | 2018-02-02 | 东南大学 | Car crass detection method and system based on deep learning |
| CN107709122A (en) * | 2015-07-15 | 2018-02-16 | 康蒂-特米克微电子有限公司 | The perspective control system of motor vehicles |
| CN109606260A (en) * | 2018-12-26 | 2019-04-12 | 北京蓦然认知科技有限公司 | A kind of method and device of the outer auditory tone cues of vehicle |
| CN111583713A (en) * | 2020-04-16 | 2020-08-25 | 腾讯科技（深圳）有限公司 | Vehicle driving early warning method and device |
| CN112349141A (en) * | 2019-08-09 | 2021-02-09 | 广州汽车集团股份有限公司 | Front collision control method, front collision early warning device and automobile |
| CN115071562A (en) * | 2021-03-16 | 2022-09-20 | 上海博泰悦臻网络技术服务有限公司 | Vehicle scratch and rub early warning method and system, storage medium and vehicle-mounted terminal |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1657569A1 (en) * | 2004-11-12 | 2006-05-17 | Takata Corporation | Vehicle safety control system by image processing |
| CN101016053A (en) * | 2007-01-25 | 2007-08-15 | 吉林大学 | Warning method and system for preventing collision for vehicle on high standard highway |
| US20080285799A1 (en) * | 2007-05-16 | 2008-11-20 | Institute Of Technology, National Defense University | Apparatus and method for detecting obstacle through stereovision |
| CN101391589A (en) * | 2008-10-30 | 2009-03-25 | 上海大学 | Vehicle intelligent alarm method and device |
| CN201380816Y (en) * | 2008-12-29 | 2010-01-13 | 广东铁将军防盗设备有限公司 | Automatic navigation automobile and vehicle obstacle avoidance warning system and corresponding circuit thereof |
-
2011
- 2011-06-16 CN CN201110162152.8A patent/CN102303563B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1657569A1 (en) * | 2004-11-12 | 2006-05-17 | Takata Corporation | Vehicle safety control system by image processing |
| CN101016053A (en) * | 2007-01-25 | 2007-08-15 | 吉林大学 | Warning method and system for preventing collision for vehicle on high standard highway |
| US20080285799A1 (en) * | 2007-05-16 | 2008-11-20 | Institute Of Technology, National Defense University | Apparatus and method for detecting obstacle through stereovision |
| CN101391589A (en) * | 2008-10-30 | 2009-03-25 | 上海大学 | Vehicle intelligent alarm method and device |
| CN201380816Y (en) * | 2008-12-29 | 2010-01-13 | 广东铁将军防盗设备有限公司 | Automatic navigation automobile and vehicle obstacle avoidance warning system and corresponding circuit thereof |
Non-Patent Citations (3)
| Title |
|---|
| 《计算机应用》 20050131 皮燕妮，史中科，黄金 "智能车中基于单目视觉的前车检测和跟踪" 第25卷, 第1期 * |
| 张司兴: "车牌识别算法研究及系统设计", 《中国优秀硕士学位论文全文数据库》 * |
| 皮燕妮，史中科，黄金: ""智能车中基于单目视觉的前车检测和跟踪"", 《计算机应用》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103569110A (en) * | 2012-07-30 | 2014-02-12 | 深圳华一汽车科技有限公司 | Machine vision based forward vehicle collision warning system and method for realizing front vehicle collision warning by system |
| CN107709122B (en) * | 2015-07-15 | 2020-09-25 | 康蒂-特米克微电子有限公司 | Prospective control system for motor vehicle |
| CN107709122A (en) * | 2015-07-15 | 2018-02-16 | 康蒂-特米克微电子有限公司 | The perspective control system of motor vehicles |
| CN105346388A (en) * | 2015-11-27 | 2016-02-24 | 江苏大学 | Auxiliary system for automobile uphill and control method of auxiliary system |
| CN106570487A (en) * | 2016-11-10 | 2017-04-19 | 维森软件技术（上海）有限公司 | Method and device for predicting collision between objects |
| CN107657237B (en) * | 2017-09-28 | 2020-03-31 | 东南大学 | Automobile collision detection method and system based on deep learning |
| CN107657237A (en) * | 2017-09-28 | 2018-02-02 | 东南大学 | Car crass detection method and system based on deep learning |
| CN109606260A (en) * | 2018-12-26 | 2019-04-12 | 北京蓦然认知科技有限公司 | A kind of method and device of the outer auditory tone cues of vehicle |
| CN109606260B (en) * | 2018-12-26 | 2020-12-11 | 北京蓦然认知科技有限公司 | Method and device for prompting sound outside vehicle |
| CN112349141A (en) * | 2019-08-09 | 2021-02-09 | 广州汽车集团股份有限公司 | Front collision control method, front collision early warning device and automobile |
| CN111583713A (en) * | 2020-04-16 | 2020-08-25 | 腾讯科技（深圳）有限公司 | Vehicle driving early warning method and device |
| CN111583713B (en) * | 2020-04-16 | 2024-09-13 | 腾讯科技（深圳）有限公司 | Vehicle driving early warning method and device |
| CN115071562A (en) * | 2021-03-16 | 2022-09-20 | 上海博泰悦臻网络技术服务有限公司 | Vehicle scratch and rub early warning method and system, storage medium and vehicle-mounted terminal |
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