CN110276973B - Automatic intersection traffic rule identification method - Google Patents
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Abstract
An automatic intersection traffic rule identification method belongs to the technical field of intelligent traffic. The method includes step S01, determining a track point where the vehicle driving direction changes according to a driving track formed by vehicle track data points, each vehicle ID of which is sorted by time; step S02, calculating the difference value between the track point and the vehicle track data point before the driving direction is changed, and determining and reserving the track point as the vehicle track data point of the intersection turning; step S03, clustering vehicle track data points turning at the intersection, and identifying a traffic intersection and an intersection type; and step S04, counting the number of vehicle track data points of left turn, right turn and turn around for each traffic intersection, and determining whether the traffic rules of the traffic intersections are that the left turn, the right turn and the turn around are forbidden. The invention can automatically identify all traffic intersections and control information of the intersections, provides application requirements of real-time accurate and safe navigation for automatic unmanned driving, avoids traffic jam, reduces pollution and protects the environment.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to an automatic intersection traffic rule identification method.
Background
The research of the automatic driving technique has attracted extensive attention and has become a hot spot of research of related experts and scholars. The safety of vehicle driving is one of the housekeeping problems in automatic driving, and if the automatic driving vehicle is safely and automatically guided in a road network environment, the automatic driving vehicle is also the key point of research of experts, so that the real-time path planning is a key technology of the automatic vehicle. The automatic navigation system can guide the vehicle to select the optimal path to avoid the congested road section, thereby increasing the traffic capacity of the existing road network. In real-time navigation systems, traffic regulations, especially intersections, play an important role in traffic control systems in urban environments. However, in the urban road network, there are not only one-way roads, two-way roads, three-dimensional traffic, and the like. As the number of vehicles increases, congestion is inevitable. In order to prevent an excessive congestion of a certain road section, the traffic management department often sets some traffic control measures to relieve the traffic pressure of a certain road section, such as forbidding left turn, forbidding right turn and forbidding turning around. For example, setting at a certain intersection: the left turn is prohibited from 6:00 to 18: 00. Such traffic control measures may be active for a temporary period of time or suddenly be added at an intersection. The map is updated generally once in 3-6 months, so that the navigation system cannot acquire the information in time, so that the information is not taken into account when the optimal route is selected, and the no-pass route is possibly included. If the automobile is automatically driven, illegal driving can be caused, and for the automobile driven by people, other roads have to be changed temporarily, so that the driving time is increased, and the problems of congestion, environmental pollution and the like can be caused.
The invention patent application CN201610458509.X discloses a K-means initial clustering center selection method for taxi track data, which is characterized in that vehicles are matched on a road network, and then a newly added road is detected according to the condition that whether a large number of vehicle tracks are not matched on the road network of a map. The method is only used for updating the information of the map about the newly added road, and different control of the traffic intersection cannot be known.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an automatic intersection traffic rule identification method, which can automatically identify all traffic intersections and intersection control information of a city road network, thereby providing the application requirement of real-time accurate and safe navigation for automatic unmanned driving, effectively avoiding traffic jam, reducing pollution and protecting the environment.
The invention is realized by the following technical scheme:
the invention provides an automatic identification method of intersection traffic rules, which comprises the following steps:
step S01, determining track points of vehicle driving direction change according to the driving track formed by the vehicle track data points of each vehicle ID sorted according to time;
step S02, calculating the difference value between the track point and the vehicle track data point before the driving direction is changed, and determining and reserving the track point as the vehicle track data point of the intersection turning;
step S03, clustering vehicle track data points turning at the intersection, and identifying a traffic intersection and an intersection type;
and step S04, counting the number of vehicle track data points of left turn, right turn and turn around for each traffic intersection, and determining whether the traffic rules of the traffic intersections are that the left turn, the right turn and the turn around are forbidden.
The invention collects a large amount of GPS track information by utilizing the running process of the vehicle, thereby providing wide prospect for the application based on the track data. The method can automatically identify all traffic intersections of the city road network and control information of the intersections by mining and analyzing vehicle track data of one day.
Preferably, the method further comprises step S05, wherein the traffic rules and the intersection information are sent to a navigation system.
Preferably, the step S01 includes:
step S11, collecting vehicle track data points, and forming the driving track of each vehicle according to each vehicle ID and time sequence;
in step S12, a vehicle trajectory data point where the vehicle traveling direction changes in each vehicle traveling trajectory is searched for and determined as a trajectory point.
Preferably, the vehicle trajectory data points include GPS coordinates, travel speed, travel angle, time.
Preferably, the step S02 includes:
step S21, calculating a travel distance difference, a travel angle difference, and a time difference between the trajectory point and the vehicle trajectory data point before the change of the travel direction;
step S22, when the driving distance difference, the driving angle difference and the time difference respectively meet the intersection turning conditions, determining that the track point is the vehicle track data point of the intersection turning, otherwise, determining that the track point does not belong to the vehicle track data point of the intersection turning;
and step S23, filtering the vehicle track data points which do not belong to the turn at the intersection and keeping the vehicle track data points of the turn at the intersection.
Preferably, the intersection turning conditions are as follows: the running speed difference is 20-60 seconds; the difference of the driving angles is larger than 90 degrees; the driving distance exceeds 100 meters.
Preferably, the step S03 includes:
step S31, clustering vehicle track data points turning at the intersection based on a density clustering algorithm;
and step S32, automatically identifying urban intersections and intersection types according to a clustering algorithm, wherein the intersection types comprise crossroads, X-shaped intersections, Y-shaped intersections, annular intersections and T-shaped intersections.
Preferably, the step S04 includes:
step S41, according to different directions of a traffic intersection, counting the number of vehicle track data points with a left-turn angle difference of-90 degrees, a right-turn angle difference of 90 degrees and a U-turn angle difference of 180 degrees;
step S42, if the number of the vehicle track data points with the left-turn angle difference of-90 degrees is equal to 0, the traffic rule of the traffic intersection is to forbid the left turn; if the number of the vehicle track data points with the right turn angle difference of 90 degrees is equal to 0, the traffic rule of the traffic intersection is to prohibit right turn; if the number of the vehicle track data points with the turning angle difference of 180 degrees is equal to 0, the traffic rule of the traffic intersection is to prohibit turning.
Preferably, the vehicle trajectory data points are obtained by real-time detection of a vehicle equipped with a GPS.
The invention has the following beneficial effects:
the invention relates to an automatic identification method of intersection traffic rules,
1. the method has real-time performance, and can detect the intersection traffic control information only according to the track data of the floating cars in one day. Therefore, accurate navigation information is provided for navigation of automatic driving, and violation is avoided. Conventional navigation systems must rely on map updates to update the navigation information.
2. The traffic intersection of the urban road can be automatically found. And detecting the urban traffic intersection and the type of the intersection in real time through a density-based clustering algorithm.
3. The method has the advantages that the method does not need to carry out vehicle path matching calculation, the traditional method needs to match the track points of the vehicle to the road at first, and has two defects that firstly, the calculation cost is high, but the precision is low, the vehicle path matching is limited by the precision of urban canyons or GPS points, and the matching accuracy is not high.
4. The method can provide real-time traffic control information for unmanned automatic navigation so as to avoid traffic violation, and can also provide accurate real-time navigation for ordinary drivers so as to avoid no-passing road sections, reduce traffic jam, reduce carbon emission and save energy.
Drawings
FIG. 1 is a general flow chart of an automatic intersection traffic rule recognition method according to the present invention;
fig. 2 is a schematic diagram of a turning track point of the intersection.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
Referring to fig. 1, the invention relates to an automatic intersection traffic rule identification method, which comprises the following steps:
step S01, determining track points of vehicle driving direction change according to the driving track formed by the vehicle track data points of each vehicle ID sorted according to time;
step S02, calculating the difference value between the track point and the vehicle track data point before the driving direction is changed, and determining and reserving the track point as the vehicle track data point of the intersection turning;
step S03, clustering vehicle track data points turning at the intersection, and identifying a traffic intersection and an intersection type;
and step S04, counting the number of vehicle track data points of left turn, right turn and turn around for each traffic intersection, and determining whether the traffic rules of the traffic intersections are that the left turn, the right turn and the turn around are forbidden.
The vehicle track data points of the method are acquired in real time by using a vehicle provided with a GPS acquisition terminal. The vehicle collects a large amount of GPS track information during the driving process.
Specifically, the step S01 includes:
step S11, collecting vehicle track data points, and forming the driving track of each vehicle according to each vehicle ID and time sequence;
in step S12, a vehicle trajectory data point where the vehicle traveling direction changes in each vehicle traveling trajectory is searched for and determined as a trajectory point.
The vehicle track data points comprise GPS coordinates, driving speed, driving angle and time. Each vehicle has a corresponding vehicle ID, and the vehicle trajectory data points under the corresponding vehicle ID are indexed by the vehicle ID. For this reason, in chronological order, it can be seen that the vehicle trajectory data points under the corresponding vehicle IDs are arranged in chronological order to form a travel trajectory. The driving track has non-linear motion tracks due to road limitation, such as intersection turning, turning deviation caused by lane change, driving turning deviation caused by road non-smoothness, driving turning deviation caused by position of driving destination and the like. In this case, if a change in the traveling direction of the vehicle occurs with respect to the vehicle trajectory data points at adjacent times, the vehicle trajectory data point at which the change in the traveling direction occurs is determined as a trajectory point.
Due to the deviation of the above situations, if only the difference of the driving angles is detected to determine whether the track point is the vehicle track data point of the turn at the intersection, a detection error may occur, for example, if the vehicle track data point of the lane change during normal driving, the vehicle track data point of the parking lot or the vehicle track data point of the gas station, or other vehicle track data points not belonging to the turn at the intersection are considered, a statistical error may occur, and the subsequent intersection traffic rule identification cannot be effectively performed. For this reason, not only the difference in the travel angle but also other factors are taken into consideration.
Specifically, the step S02 includes:
step S21, calculating a travel distance difference, a travel angle difference, and a time difference between the trajectory point and the vehicle trajectory data point before the change of the travel direction;
step S22, when the driving distance difference, the driving angle difference and the time difference respectively meet the intersection turning conditions, determining that the track point is the vehicle track data point of the intersection turning, otherwise, determining that the track point does not belong to the vehicle track data point of the intersection turning;
and step S23, filtering the vehicle track data points which do not belong to the turn at the intersection and keeping the vehicle track data points of the turn at the intersection.
The intersection turning conditions are as follows: the running speed difference is 20-60 seconds; the difference of the driving angles is larger than 90 degrees; the driving distance exceeds 100 meters. The special case can be eliminated only under the condition that the three conditions are met, and the track points meeting the conditions are ensured to be vehicle track data points of the turn at the intersection.
The running distance difference can be obtained by calculating through the Pythagorean theorem by using the GPS coordinates of two vehicle track data points, and can also be calculated by using the product of the running speed difference and the running time difference.
Specifically, the step S03 includes:
step S31, clustering vehicle track data points turning at the intersection based on a density clustering algorithm;
and step S32, automatically identifying urban intersections and intersection types according to a clustering algorithm, wherein the intersection types comprise crossroads, X-shaped intersections, Y-shaped intersections, annular intersections and T-shaped intersections.
The density-based clustering algorithm may employ a DBSCAN clustering algorithm. First, a circle is drawn with the center of each data point and the radius of eps. This circle is called the eps neighborhood of xi. Next, the points contained within this circle are counted. If the number of points inside a circle exceeds the density threshold MinPts, the center of the circle is marked as a core point, also called a core object. A point is said to be a boundary point if the number of points in the eps neighborhood of the point is less than the density threshold but falls within the neighborhood of the core point. Points that are neither core points nor boundary points are noise points. Third, all points in the eps neighborhood of core point xi are direct density through xi. Finally, if for xk, both xi and xj are made reachable by xk density, then xi and xj are said to be connected in density. Connecting together the density connected points forms our cluster. And forming all traffic intersections and intersection types of the urban road network after clustering. The traffic intersection and the intersection type form intersection information.
In one embodiment, the step S04 includes:
step S41, according to different directions of a traffic intersection, counting the number of vehicle track data points with a left-turn angle difference of-90 degrees, a right-turn angle difference of 90 degrees and a U-turn angle difference of 180 degrees;
step S42, if the number of the vehicle track data points with the left-turn angle difference of-90 degrees is equal to 0, the traffic rule of the traffic intersection is to forbid the left turn; if the number of the vehicle track data points with the right turn angle difference of 90 degrees is equal to 0, the traffic rule of the traffic intersection is to prohibit right turn; if the number of the vehicle track data points with the turning angle difference of 180 degrees is equal to 0, the traffic rule of the traffic intersection is to prohibit turning.
And when the conditions of left turning, right turning and turning around of the vehicle track data point do not exist, the intersection is considered to be forbidden to turn left, right turning and turning around.
However, in this case, there may be a T-junction, and when only a right turn or a straight run starts from one junction, there is inevitably no possibility of a left turn, and when the number of vehicle trajectory data points whose left turn angle difference is-90 degrees is equal to 0, the traffic rule of the best junction is determined as a left turn prohibition, and there is a determination error, that is, there is no traffic rule of a left turn prohibition.
Therefore, the type and the driving direction of the intersection can be judged before the quantity is judged. Judging whether the type of the intersection is a T-shaped intersection, if so, judging the driving direction of the vehicle intersection, if the driving direction only has straight running and left turning, then no right turning is forbidden, and automatically reducing the quantity of the situations when subsequent right turning angle difference quantity statistics is carried out, or only having straight running and right turning in the driving direction, and automatically reducing the quantity of the situations when subsequent left turning angle difference quantity statistics is carried out. And for other intersection types, normally counting the quantity according to the steps.
The method of the invention also comprises the following steps: in step S05, the traffic rules and intersection information are sent to a navigation system. And at the intersection with the traffic rule, the updated information is sent to the navigation system, so that the control information about all traffic intersections on the map is updated in real time, and the automatic unmanned driving can accurately and safely navigate.
It will be appreciated by persons skilled in the art that the embodiments of the invention described above and shown in the drawings are given by way of example only and are not limiting of the invention. The objects of the present invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the examples, and any variations or modifications of the embodiments of the present invention may be made without departing from the principles.
Claims (4)
1. An automatic intersection traffic rule identification method is characterized by comprising the following steps:
step S01, determining vehicle track data points with changed vehicle driving direction according to the driving track formed by the vehicle track data points with each vehicle ID sorted according to time; the vehicle track data points refer to GPS coordinates, driving speed, driving angle and time;
step S02, calculating the difference value between the vehicle track data point of the vehicle driving direction change and the vehicle track data point before the driving direction change, determining the vehicle track data point of the vehicle driving direction change as the vehicle track data point of the intersection turning and keeping the same; the method specifically comprises the following steps:
step S21, calculating a difference in travel distance, a difference in travel angle, and a difference in time between the vehicle trajectory data point at which the vehicle travel direction is changed and the vehicle trajectory data point before the change in travel direction;
step S22, when the driving distance difference, the driving angle difference and the time difference respectively meet the intersection turning condition, determining that the vehicle track data point of the vehicle driving direction change is the vehicle track data point of the intersection turning, otherwise, the vehicle track data point does not belong to the intersection turning; the intersection turning conditions are as follows: the running speed difference is 20-60 m/s; the difference of the driving angles is larger than 90 degrees; the difference of the travel distance exceeds 100 meters;
step S23, filtering vehicle track data points which do not belong to the turn at the intersection and reserving the vehicle track data points of the turn at the intersection;
step S03, clustering vehicle track data points turning at the intersection, and identifying a traffic intersection and an intersection type; the method specifically comprises the following steps:
step S31, clustering vehicle track data points turning at the intersection based on a clustering algorithm of DBSCAN density;
step S32, automatically identifying traffic intersections and intersection types according to a clustering algorithm, wherein the intersection types include intersections, X-shaped intersections, Y-shaped intersections, annular intersections and T-shaped intersections;
step S04, counting the number of vehicle track data points of left turn, right turn and turn around for each traffic intersection, and determining whether the traffic rules of the traffic intersections are that the left turn, the right turn and the turn around are forbidden; the method specifically comprises the following steps:
step S41, counting the number of vehicle track data points with left-turn angle difference of-90 degrees, right-turn angle difference of 90 degrees and turning angle difference of-180 degrees;
step S42, if the number of the vehicle track data points with the left-turn angle difference of-90 degrees is equal to 0, the traffic rule of the traffic intersection is to forbid the left turn; if the number of the vehicle track data points with the right turn angle difference of 90 degrees is equal to 0, the traffic rule of the traffic intersection is to prohibit right turn; and if the number of the vehicle track data points with the turning angle difference of-180 degrees is equal to 0, the traffic rule of the traffic intersection is to prohibit turning.
2. The method for automatically identifying intersection traffic rules according to claim 1, further comprising a step S05, wherein the traffic rules and the intersection information are transmitted to a navigation system.
3. The method for automatically identifying intersection traffic rules according to claim 1, wherein the step S01 includes:
step S11, collecting vehicle track data points, and forming the driving track of each vehicle according to each vehicle ID and time sequence;
in step S12, vehicle trajectory data points where the direction of travel of the vehicle changes in each vehicle trajectory are found and determined.
4. The method of claim 1, wherein the vehicle trajectory data points are obtained by real-time detection of a vehicle equipped with a GPS.
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| EP4127605A1 (en) * | 2020-03-28 | 2023-02-08 | DEKA Products Limited Partnership | System and method for intersection management by an autonomous vehicle |
| CN112562315B (en) * | 2020-11-02 | 2022-04-01 | 鹏城实验室 | Method, terminal and storage medium for acquiring traffic flow information |
| SG10202010875VA (en) * | 2020-11-02 | 2021-07-29 | Grabtaxi Holdings Pte Ltd | Processing apparatus and method for traffic management of a network of roads |
| CN112489450B (en) * | 2020-12-21 | 2022-07-08 | 阿波罗智联(北京)科技有限公司 | Traffic intersection vehicle flow control method, road side equipment and cloud control platform |
| CN112712696A (en) * | 2020-12-30 | 2021-04-27 | 北京嘀嘀无限科技发展有限公司 | Method and device for determining road section with illegal parking |
| CN113375685B (en) * | 2021-03-31 | 2022-09-30 | 福建工程学院 | Urban intersection center recognition and intersection turning rule extraction method based on sub-track intersection |
| CN113823082B (en) * | 2021-06-07 | 2023-08-18 | 腾讯科技（深圳）有限公司 | Navigation data processing method, device, equipment and storage medium |
| CN113370997A (en) * | 2021-08-11 | 2021-09-10 | 北京赛目科技有限公司 | Control method and device for automatic driving of vehicle, electronic equipment and storage medium |
| CN116052453B (en) * | 2022-12-30 | 2024-10-11 | 广州小鹏自动驾驶科技有限公司 | Road junction determining method, device, electronic equipment and storage medium |
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