CN110299027B - Vehicle lane change monitoring and safety early warning method based on track data and map data - Google Patents
Vehicle lane change monitoring and safety early warning method based on track data and map data Download PDFInfo
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- CN110299027B CN110299027B CN201910633065.2A CN201910633065A CN110299027B CN 110299027 B CN110299027 B CN 110299027B CN 201910633065 A CN201910633065 A CN 201910633065A CN 110299027 B CN110299027 B CN 110299027B
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Abstract
The invention is suitable for the field of automobile monitoring in traffic safety, and provides a vehicle lane change monitoring and safety early warning method based on track data and map data.
Description
Technical Field
The invention relates to the field of traffic safety, in particular to a method for monitoring vehicle lane change behaviors (single lane change, continuous lane change and frequent lane change) and early warning of traffic safety by combining vehicle track data with map data.
Background
According to the statistical data issued by the traffic administration of the ministry of public security, 3172 thousands of motor vehicles are newly registered in the country in 2018, the number of the motor vehicles is up to 3.27 hundred million, and the number of the motor vehicle drivers is up to 4.09 million. However, the automobile civilization, especially the driving civilization, progresses slowly, traffic congestion is aggravated to a certain extent, and a large number of road traffic accidents are induced, especially bad driving habits such as random lane changing and the like. The lane change behavior affects the traffic volume, speed and density of roads, and many traffic phenomena occur along with the lane change, such as: road congestion, vehicle queuing and overflow, a crowded lane, overtaking, meeting, etc. The main causes of frequent traffic accidents include the random lane change, overtaking, and the non-specified yielding of vehicles on urban roads. According to the statistics of traffic police departments, more than 50% of light traffic accidents are related to illegal lane change, and more than 30% of rear-end accidents are caused by illegal lane change. Therefore, how to effectively monitor illegal lane changing behaviors and carry out safety early warning on drivers who change lanes randomly and frequently is a problem which needs to be solved urgently for reducing and preventing traffic accidents and ensuring traffic safety.
At present, the monitoring of vehicle lane changing is mainly based on a video snapshot system and mainly distributed in key areas such as road intersections, high-speed entrances and exits, the lane changing monitoring accuracy based on videos is high, but the monitoring of lane changing behaviors cannot be realized in places without video monitoring. Along with the rapid development of the internet technology, the popularization of vehicle-mounted Beidou, GPS and mobile phone positioning and the continuous improvement of basic map precision (Baidu and God), the related research on the aspect of analyzing traffic safety based on the track data is more and more, for example, the dangerous and abnormal driving behaviors such as vehicle speed, rapid acceleration, rapid deceleration and rapid turning are analyzed based on the Beidou or the GPS data, but the monitoring on vehicle lane change is less. In the analysis based on big dipper, GPS data at present, to the not enough utilization of orbit, especially combine the analysis of map to be few.
In the current research of track data, except for the matching of GPS point matching and a map, the electronic map is less utilized, and the advantages of simple distance calculation of the electronic map, rich basic information such as a road network and the like, large POI (point of interest) point information amount and the like are not fully explored; the combined use of trajectory data and map data is a trend of future development.
Disclosure of Invention
The invention aims to provide a vehicle lane change monitoring and safety early warning method based on track data and map data, which is used for monitoring a road section without a monitoring snapshot system in real time, detecting abnormal driving behaviors of a vehicle, particularly solving the problems of rapid lane change, frequent lane change and the like, reducing traffic blind areas and maintaining road safety more comprehensively. In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention discloses a vehicle lane change monitoring and safety early warning method based on track data and map data, which comprises the following steps:
A. matching of trajectory data to a digital map: obtaining the Beidou or GPS track data of a certain vehicle, quickly matching the Beidou or GPS track data to a digital map, associating the track data with the digital map, and determining a road section when the vehicle runs;
B. track segmentation: the length of the road section is segmented, namely, the straight line distance of the vehicle running road section along the vehicle running direction is automatically and equally segmented from the starting point of the track, the distance of the segmented section is b, the length range of b is 50-200 m, and when the road is bent, the road length is calculated by using the center line of the road;
C. calculating a trace path ratio alpha: the trace-to-trace ratio is:
wherein a is the length of a vehicle running track and the unit is meter;
D. safety early warning: and when alpha is greater than 1.1, carrying out safety early warning.
The invention further adopts the technical scheme that the vehicle lane change monitoring and safety early warning method based on the track data and the map data further comprises the following steps:
E. calculating the maximum longitudinal displacement β of the vehicle: every time the vehicle changes lanes, the vehicle generates the maximum single longitudinal displacement c in the vertical direction of the road, namely the vertical maximum distance between a track point and the initial track line direction of the vehicle, and the maximum longitudinal displacement beta is obtained in two conditions:
changing lanes in a single direction or continuously changing lanes in a single direction for more than 2 times, and taking the sum of c;
two-way back and forth lane changing, wherein beta is the sum of absolute values of c;
F. safety early warning: and when the beta is greater than 7.5, carrying out safety early warning.
The invention further adopts the technical scheme that the vehicle lane change monitoring and safety early warning method based on the track data and the map data is characterized by further comprising the following steps of:
G. and (3) judging lane changing behavior: when the track-road ratio alpha is greater than 1.05 and the maximum longitudinal displacement beta is greater than 3.5, judging that the vehicle has lane change behavior;
H. and (3) judging the frequent lane changing behavior: when the lane change behavior is judged to be the lane change behavior for more than n times within the distance range of 10 continuous segmentation intervals, judging the lane change behavior to be the frequent lane change behavior, wherein n is more than or equal to 2;
I. safety early warning: and when the lane change is determined to be frequent, carrying out safety early warning.
The invention further adopts the technical scheme that the vehicle lane change monitoring and safety early warning method based on the track data and the map data is characterized by further comprising the following steps of:
J. calculating a safety risk coefficient R: r ═ 2.2 α + γ, where γIs the coefficient of longitudinal displacement, and,
K. and (4) safety risk assessment: the safety risk of vehicle lane change is classified into 5 grades:
according to a further technical scheme, the vehicle lane change monitoring and safety early warning method based on the track data and the map data further comprises the step A of correcting the track data and matching by utilizing the air accessibility and the road network among the track points.
The invention has the beneficial effects that:
the method monitors illegal behaviors such as vehicle rolling line driving, repeated frequent lane changing and the like in real time by monitoring the random lane changing of the vehicle, and avoids and warns road congestion and traffic accidents in time. Map data track data are fully utilized, the phenomenon that most video snapshot systems only exist in other sections of blind areas caused by road intersections and sections with multiple accidents at present is overcome, and road safety is maintained more comprehensively.
Drawings
FIG. 1 is a technical flow chart of the present invention
FIG. 2 is a schematic diagram of a lane change of a vehicle according to embodiment 1 of the present invention
FIG. 3 is a schematic diagram of a lane change of a vehicle according to embodiment 2 of the present invention
FIG. 4 is a schematic diagram of a lane change of a vehicle according to embodiment 3 of the present invention
Detailed Description
The technical solution of the present invention will be further explained with reference to the drawings in the embodiments of the present invention.
Example 1
As shown in fig. 2, the implementation process of the present invention is a vehicle lane change monitoring and safety pre-warning method based on track data and map data, comprising the following steps:
step S1, matching of trajectory data with digital map: obtaining the Beidou or GPS track data of a certain vehicle, quickly matching the Beidou or GPS track data to a digital map, associating the track data with the digital map, determining a road section when the vehicle runs, and correcting the track data and matching by utilizing the air accessibility between track points and a road network;
step S2, track segmentation: segmenting the length of a road section to which a certain vehicle belongs when running on an urban road, namely automatically and equally segmenting the straight line distance of the road section on which the vehicle runs along the running direction of the vehicle from the starting point of a track, wherein the segmented interval distance is 150 meters;
step S3, calculating a "track-to-road ratio" α: the lane-changing running distance of the vehicle in a certain distance between all the sections is 180 meters, and the track-to-road ratio
Namely 1.2;
and step S4, carrying out safety early warning.
Example 2
As shown in fig. 3, the implementation process of the present invention is a vehicle lane change monitoring and safety pre-warning method based on track data and map data, comprising the following steps:
step S1, matching of trajectory data with digital map: obtaining the Beidou or GPS track data of a certain vehicle, quickly matching the Beidou or GPS track data to a digital map, associating the track data with the digital map, determining a road section when the vehicle runs, and correcting the track data and matching by utilizing the air accessibility between track points and a road network;
step S2, track segmentation: segmenting the length of the road section to which the vehicle belongs when the vehicle runs, namely automatically and equally segmenting the straight line distance of the road section on which the vehicle runs along the running direction of the vehicle from the starting point of the track, wherein the segmentation interval distance is 100 meters;
step S3, calculating the maximum longitudinal displacement β of the vehicle: the vehicle changes the lane twice in the same direction within a certain distance between all the sections, the maximum single longitudinal displacement c is 3.5 meters and 3 meters respectively, and the sum of the maximum single longitudinal displacement c and the maximum single longitudinal displacement beta is 6.5 meters;
step S4, safety early warning: since β <7.5, no safety precaution will be made.
Example 3
As shown in fig. 3, the implementation process of the present invention is a vehicle lane change monitoring and safety pre-warning method based on track data and map data, comprising the following steps:
step S1, matching of trajectory data with digital map: obtaining the Beidou or GPS track data of a certain vehicle, quickly matching the Beidou or GPS track data to a digital map, associating the track data with the digital map, determining a road section when the vehicle runs, and correcting the track data and matching by utilizing the air accessibility between track points and a road network;
step S2, track segmentation: segmenting the length of the road section to which the vehicle belongs when the vehicle runs, namely automatically and equally segmenting the straight line distance of the road section on which the vehicle runs along the running direction of the vehicle from the starting point of the track, wherein the segmentation interval distance is 100 meters;
step S3, calculating the maximum longitudinal displacement β of the vehicle: the vehicle changes the lane twice in different directions within a certain distance between all the sections, the maximum single longitudinal displacement c is 4.5 meters and-3.5 meters respectively, and the sum of the absolute values of the two is taken as beta, namely 8 meters;
and step S4, carrying out safety early warning.
Claims (5)
1. A vehicle lane change monitoring and safety early warning method based on track data and map data is characterized by comprising the following steps:
A. matching of trajectory data to a digital map: obtaining the Beidou or GPS track data of a certain vehicle, quickly matching the Beidou or GPS track data to a digital map, associating the track data with the digital map, and determining a road section when the vehicle runs;
B. track segmentation: the length of the road section is segmented, namely, the straight line distance of the vehicle running road section along the vehicle running direction is automatically and equally segmented from the starting point of the track, the distance of the segmented section is b, the length range of b is 50-200 m, and when the road is bent, the road length is calculated by using the center line of the road;
C. calculating a trace path ratio alpha: the trace-to-trace ratio is:
wherein a is the length of a vehicle running track and the unit is meter;
D. safety early warning: and when alpha is greater than 1.1, carrying out safety early warning.
2. The vehicle lane-change monitoring and safety pre-warning method based on the track data and the map data as claimed in claim 1, further comprising the steps of:
E. calculating the maximum longitudinal displacement β of the vehicle: every time the vehicle changes lanes, the vehicle generates the maximum single longitudinal displacement c in the vertical direction of the road, namely the vertical maximum distance between a track point and the initial track line direction of the vehicle, and the maximum longitudinal displacement beta is obtained in two conditions:
changing lanes in a single direction or continuously changing lanes in a single direction for more than 2 times, and taking the sum of c;
two-way back and forth lane changing, wherein beta is the sum of absolute values of c;
F. safety early warning: and when the beta is greater than 7.5, carrying out safety early warning.
3. The vehicle lane-change monitoring and safety pre-warning method based on the track data and the map data as claimed in claim 2, further comprising the steps of:
G. and (3) judging lane changing behavior: when the track-road ratio alpha is greater than 1.05 and the maximum longitudinal displacement beta is greater than 3.5, judging that the vehicle has lane change behavior;
H. and (3) judging the frequent lane changing behavior: when the lane change behavior is judged to be the lane change behavior for more than n times within the distance range of 10 continuous segmentation intervals, judging the lane change behavior to be the frequent lane change behavior, wherein n is more than or equal to 2;
I. safety early warning: and when the lane change is determined to be frequent, carrying out safety early warning.
4. The vehicle lane-change monitoring and safety pre-warning method based on the trajectory data and the map data as claimed in any one of claims 2 to 3, further comprising the steps of:
J. calculating a safety risk coefficient R: r2.2 α + γ, where γ is the longitudinal displacement coefficient,
K. and (4) safety risk assessment: the safety risk of vehicle lane change is classified into 5 grades:
5. the vehicle lane-change monitoring and safety early warning method based on the track data and the map data as claimed in claim 1, wherein the step a further comprises the steps of:
A1. and correcting the track data and the matching by utilizing the null accessibility between the track points and the road network.
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| CN110705484B (en) * | 2019-10-08 | 2023-05-02 | 弈人（上海）科技有限公司 | Method for recognizing continuous lane change illegal behaviors by utilizing driving track |
| CN110728842B (en) * | 2019-10-23 | 2021-10-08 | 江苏智通交通科技有限公司 | Abnormal driving early warning method based on reasonable driving range of vehicles at intersection |
| CN113160546B (en) * | 2020-01-22 | 2023-03-10 | 阿里巴巴集团控股有限公司 | Dangerous road section identification method and device |
| CN112885144B (en) * | 2021-01-20 | 2022-05-31 | 同济大学 | Early warning method and system for vehicle crash event in construction operation area |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1775601A (en) * | 2005-11-18 | 2006-05-24 | 吉林大学 | Vehicle driving trace predicating and lane deviation evaluating method |
| CN105374212A (en) * | 2015-12-14 | 2016-03-02 | 上海交通大学 | Intelligent terminal sensing-based highway vehicle lane identification method and system |
| CN105405306A (en) * | 2015-12-24 | 2016-03-16 | 小米科技有限责任公司 | Vehicle alarm method and device |
| CN106952473A (en) * | 2017-04-01 | 2017-07-14 | 深圳市元征科技股份有限公司 | Road service system detection method and device |
| CN108242145A (en) * | 2016-12-26 | 2018-07-03 | 高德软件有限公司 | Abnormal track point detecting method and device |
| CN108263306A (en) * | 2017-12-26 | 2018-07-10 | 福建工程学院 | A kind of recognition methods of new hand's vehicle and terminal |
| CN108764111A (en) * | 2018-05-23 | 2018-11-06 | 长安大学 | A kind of detection method of vehicle abnormality driving behavior |
| CN109360445A (en) * | 2018-07-09 | 2019-02-19 | 重庆大学 | A kind of high speed lane-change risk checking method based on the distribution of laterally and longitudinally kinematics character |
| CN109544909A (en) * | 2018-10-29 | 2019-03-29 | 华蓝设计（集团）有限公司 | Driver's lane-change behavior analysis method based on video frequency vehicle track of taking photo by plane |
| CN109559532A (en) * | 2018-12-10 | 2019-04-02 | 北京工业大学 | Expressway exit shunting zone bus or train route Cooperative Security pre-warning and control method |
| CN109887303A (en) * | 2019-04-18 | 2019-06-14 | 齐鲁工业大学 | Random change lane behavior early warning system and method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102004028404A1 (en) * | 2004-06-14 | 2006-01-19 | Daimlerchrysler Ag | Method for estimating the course of a lane of a motor vehicle |
| DE102012215562B4 (en) * | 2012-09-03 | 2024-03-07 | Robert Bosch Gmbh | Method for determining an avoidance trajectory for a motor vehicle and safety device or safety system |
-
2019
- 2019-07-12 CN CN201910633065.2A patent/CN110299027B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1775601A (en) * | 2005-11-18 | 2006-05-24 | 吉林大学 | Vehicle driving trace predicating and lane deviation evaluating method |
| CN105374212A (en) * | 2015-12-14 | 2016-03-02 | 上海交通大学 | Intelligent terminal sensing-based highway vehicle lane identification method and system |
| CN105405306A (en) * | 2015-12-24 | 2016-03-16 | 小米科技有限责任公司 | Vehicle alarm method and device |
| CN108242145A (en) * | 2016-12-26 | 2018-07-03 | 高德软件有限公司 | Abnormal track point detecting method and device |
| CN106952473A (en) * | 2017-04-01 | 2017-07-14 | 深圳市元征科技股份有限公司 | Road service system detection method and device |
| CN108263306A (en) * | 2017-12-26 | 2018-07-10 | 福建工程学院 | A kind of recognition methods of new hand's vehicle and terminal |
| CN108764111A (en) * | 2018-05-23 | 2018-11-06 | 长安大学 | A kind of detection method of vehicle abnormality driving behavior |
| CN109360445A (en) * | 2018-07-09 | 2019-02-19 | 重庆大学 | A kind of high speed lane-change risk checking method based on the distribution of laterally and longitudinally kinematics character |
| CN109544909A (en) * | 2018-10-29 | 2019-03-29 | 华蓝设计（集团）有限公司 | Driver's lane-change behavior analysis method based on video frequency vehicle track of taking photo by plane |
| CN109559532A (en) * | 2018-12-10 | 2019-04-02 | 北京工业大学 | Expressway exit shunting zone bus or train route Cooperative Security pre-warning and control method |
| CN109887303A (en) * | 2019-04-18 | 2019-06-14 | 齐鲁工业大学 | Random change lane behavior early warning system and method |
Non-Patent Citations (2)
| Title |
|---|
| Trajectory Data Based Clustering and Feature Analysis of Vehicle Lane-Changing Behavior;Meng-Yuan Pang;《IEEE》;20190704;第229-233页 * |
| 基于VAT的信号交叉口危险变道行为风险特性分析;郝艳萍;《武汉理工大学学报(交通科学与工程版)》;20190228;第43卷(第1期);第92-96页 * |
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