CN116321003A - Deployment method, system and medium for intersection vehicle-road cooperation road side sensor - Google Patents

Deployment method, system and medium for intersection vehicle-road cooperation road side sensor Download PDF

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Publication number
CN116321003A
CN116321003A CN202310273909.3A CN202310273909A CN116321003A CN 116321003 A CN116321003 A CN 116321003A CN 202310273909 A CN202310273909 A CN 202310273909A CN 116321003 A CN116321003 A CN 116321003A
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vehicle
collision
intersection
sensor
collision point
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陈稷栋
蔡营
戈小中
陆鑫
谢昊
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Dongfeng Yuexiang Technology Co Ltd
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Dongfeng Yuexiang Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention relates to a deployment method, a system and a medium of a sensor at the intersection and a road cooperation road side, wherein the deployment method comprises the following steps that S1, a vehicle runs at the intersection, positioning data information of the vehicle and a running track of the vehicle are obtained in real time based on a vehicle-mounted GPS and a high-precision map, the running track of the vehicle is input into a vehicle collision point simulation test algorithm, and the vehicle collision point and the running track with the collision point are output; s2, inputting the running track with the collision points and the collision points of the vehicle into a collision point limit algorithm, and outputting a collision limit range; s3, in the collision limit range, completing sensor deployment according to the coverage range of the sensor. The invention not only improves the traffic efficiency of the vehicles at the intersection, but also further improves the driving safety of the vehicles.

Description

Deployment method, system and medium for intersection vehicle-road cooperation road side sensor
Technical Field
The invention relates to the technical field of sensor deployment, in particular to a method, a system and a medium for deploying sensors at a road side of intersection vehicle-road cooperation.
Background
The vehicle-road cooperation is a road traffic system which adopts advanced wireless communication, new generation internet and other technologies, carries out vehicle-road dynamic real-time information interaction in all directions, develops vehicle active safety control and road cooperation management on the basis of full-time empty dynamic traffic information acquisition and fusion, fully realizes effective cooperation of human-vehicle roads, ensures traffic safety and improves traffic efficiency, thereby forming safety, high efficiency and environmental protection, the crossroad is used as a scene with highest traffic complexity, the safety of vehicle driving cannot be ensured, and the efficiency of vehicle traffic is low, so that the deployment of sensors on the vehicle-road cooperation road side of the crossroad is solved, and the vehicle-road cooperation system becomes a problem to be solved urgently.
Disclosure of Invention
In view of the above problems, the invention provides a deployment method, a deployment system and a deployment medium for a sensor on the side of an intersection and a vehicle road, which not only improve the passing efficiency of vehicles at the intersection, but also further improve the driving safety of the vehicles.
In order to achieve the above object and other related objects, the present invention provides the following technical solutions:
an intersection vehicle-road cooperation roadside sensor deployment method, the method comprising:
s1, a vehicle runs at an intersection, positioning data information of the vehicle and a running track of the vehicle are obtained in real time based on a vehicle-mounted GPS and a high-precision map, the running track of the vehicle is input into a vehicle collision point simulation test algorithm, and a vehicle collision point and a running track with the collision point are output;
s2, inputting the running track with the collision points and the collision points of the vehicle into a collision point limit algorithm, and outputting a collision limit range;
s3, in the collision limit range, completing sensor deployment according to the coverage range of the sensor.
Further, in step S2, the collision point limit algorithm includes:
s21, determining the turning starting point coordinates M (x 1 ，y 1 ) And the circle center coordinate point (x) of the circular arc track made during turning r ，y r ) Thereby obtaining the turning radius R,
Figure BDA0004135557890000011
s22, based on the turning radius R and the vehicle collision point, obtaining the time spent by the vehicle passing through the collision point as T,
Figure BDA0004135557890000012
wherein v is the real-time speed of the vehicle, (x) p ，y p ) For the coordinates of the collision point of the vehicle, T mbk For the deceleration time in moderate braking, T brt For vehicle braking reaction time, T plt Is the preliminary prediction time;
s23, obtaining the collision limit range G based on the time T spent by the collision point of the vehicle,
L max ＝v max T
Figure BDA0004135557890000021
wherein v is max Maximum speed for vehicle, (x) p ，y p ) For the coordinates of the collision point of the vehicle, L max Is the distance the vehicle passes the collision point.
Further, in step S22, the preliminary predicted time T plt The method comprises the following steps:
T plt ＝T com +T cpt wherein T is com T is the transmission delay cpt Is a constant parameter between 150-220 ms.
Further, in step S3, the determining of the coverage of the sensor includes:
s31, calculating the angle of view: confirming the horizontal field angle of the sensor according to the lane width, and calculating the vertical field angle of the sensor according to the mode of blind facing of sensor deployment;
s32, detecting distance calculation: maximum distance from the coverage boundary according to the location where the device can be installed;
s33, identifying objects according to sensor clustering and a deep learning algorithm, and confirming pedestrians identifying the road zebra crossings and identifying the small vehicles in the farthest range.
Further, in step S33, the sensor clustering and deep learning algorithm identifies an object, and fuses the object and transmits the object to the RSU device in real time.
Further, in step S1, the vehicle collision point simulation test algorithm includes:
s11, acquiring a running track of a vehicle, dividing the running track of the vehicle at equal intervals, and outputting track data information divided at equal intervals;
s12, carrying out partial derivative operation on each section of track based on the track data information divided at equal intervals, and outputting slope data information of each section of track;
s13, setting a first preset threshold value and a second preset threshold value based on the slope data information of each track, and outputting a vehicle collision point if the slope data information of each track is between the first preset threshold value and the second preset threshold value.
Further, the values of the first preset threshold and the second preset threshold are any real number between 0 and 1, wherein the real number comprises 0 and 1.
To achieve the above and other related objects, the present invention also provides an intersection vehicle-road-side sensor deployment system, including a computer device programmed or configured to execute the steps of any one of the intersection vehicle-road-side sensor deployment methods.
To achieve the above and other related objects, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program programmed or configured to execute any one of the intersection vehicle road cooperation road side sensor deployment methods.
The invention has the following positive effects:
1. the invention cooperates with the multiple sensors and transmits the data to the RSU equipment in real time, thus not only providing safety data information for the vehicle in time, but also further improving the safety of the vehicle.
2. The invention increases the effectiveness of sensor deployment and can improve the running efficiency and the running safety of the vehicle.
3. The invention provides the limit range of vehicle collision and the range covered by the sensors, reasonably deploys the sensors, selects proper quantity and performance of the sensors, and reduces the cost of the sensors.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic view of a vehicle collision point according to the present invention;
fig. 3 is a schematic diagram of data transmission between the sensor and RSU device according to the present invention.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example 1: as shown in fig. 1 or fig. 2, a method for deploying sensors at an intersection and a vehicle road, the method comprising:
s1, a vehicle runs at an intersection, positioning data information of the vehicle and a running track of the vehicle are obtained in real time based on a vehicle-mounted GPS and a high-precision map, the running track of the vehicle is input into a vehicle collision point simulation test algorithm, and a vehicle collision point and a running track with the collision point are output;
s2, inputting the running track with the collision points and the collision points of the vehicle into a collision point limit algorithm, and outputting a collision limit range;
s3, in the collision limit range, completing sensor deployment according to the coverage range of the sensor.
In this embodiment, in step S2, the collision point limit algorithm includes:
s21, determining the turning starting point coordinates M (x 1 ，y 1 ) And the circle center coordinate point (x) of the circular arc track made during turning r ，y r ) Thereby obtaining the turning radius R,
Figure BDA0004135557890000031
s22, based on the turning radius R and the vehicle collision point, obtaining the time spent by the vehicle passing through the collision point as T,
Figure BDA0004135557890000032
wherein v is the real-time speed of the vehicle, (x) p ，y p ) For the coordinates of the collision point of the vehicle, T mbk For the deceleration time in moderate braking, T brt For vehicle braking reaction time, T plt Is the preliminary prediction time;
s23, obtaining the collision limit range G based on the time T spent by the collision point of the vehicle,
L max ＝v max T
Figure BDA0004135557890000033
wherein v is max Maximum speed for vehicle, (x) p ，y p ) For the coordinates of the collision point of the vehicle, L max Is the distance the vehicle passes the collision point.
In the present embodiment, in step S22, the preliminary predicted time T plt The method comprises the following steps:
T plt ＝T com +T cpt wherein T is com T is the transmission delay cpt Is a constant parameter between 150-220 ms.
Example 2: the present invention is further described and illustrated below on the basis of an intersection vehicle-road cooperation road side sensor deployment method of embodiment 1.
As shown in fig. 3, road image data information is obtained in real time based on a camera sensor, yolov3 is adopted for real-time detection, the category and the position of a target are output, road point cloud data information is obtained in real time based on a laser radar sensor, the road point cloud data information is received and analyzed in a multithreading manner, point cloud cluster data information and tracking information are output, radar data information is obtained in real time based on the radar sensor, the radar data information, the category and the position of the target, the point cloud cluster data information and the tracking information are fused, and the fused data information is transmitted to an RSU device in real time.
The determining of the coverage of the sensor comprises:
s31, calculating the angle of view: confirming the horizontal field angle of the sensor according to the lane width, and calculating the vertical field angle of the sensor according to the mode of blind facing of sensor deployment;
s32, detecting distance calculation: maximum distance from the coverage boundary according to the location where the device can be installed;
s33, identifying objects according to sensor clustering and a deep learning algorithm, and confirming pedestrians identifying the road zebra crossings and identifying the small vehicles in the farthest range.
In this embodiment, in step S33, the sensor clustering and deep learning algorithm identifies an object, and fuses the object and transmits the object to the RSU device in real time.
In this embodiment, in step S1, the vehicle collision point simulation test algorithm includes:
s11, acquiring a running track of a vehicle, dividing the running track of the vehicle at equal intervals, and outputting track data information divided at equal intervals;
s12, carrying out partial derivative operation on each section of track based on the track data information divided at equal intervals, and outputting slope data information of each section of track;
s13, setting a first preset threshold value and a second preset threshold value based on the slope data information of each track, and outputting a vehicle collision point if the slope data information of each track is between the first preset threshold value and the second preset threshold value.
In this embodiment, the values of the first preset threshold and the second preset threshold are any real number between 0 and 1, including 0 and 1.
To achieve the above and other related objects, the present invention also provides an intersection vehicle-road-side sensor deployment system, including a computer device programmed or configured to execute the steps of any one of the intersection vehicle-road-side sensor deployment methods.
To achieve the above and other related objects, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program programmed or configured to execute any one of the intersection vehicle road cooperation road side sensor deployment methods.
Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In summary, the invention not only improves the traffic efficiency of the intersection, but also further improves the driving safety of the vehicles.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims (9)

1. An intersection vehicle-road cooperation road side sensor deployment method, which is characterized by comprising the following steps:
s1, a vehicle runs at an intersection, positioning data information of the vehicle and a running track of the vehicle are obtained in real time based on a vehicle-mounted GPS and a high-precision map, the running track of the vehicle is input into a vehicle collision point simulation test algorithm, and a vehicle collision point and a running track with the collision point are output;
s2, inputting the running track with the collision points and the collision points of the vehicle into a collision point limit algorithm, and outputting a collision limit range;
s3, in the collision limit range, completing sensor deployment according to the coverage range of the sensor.
2. The intersection vehicle road cooperation roadside sensor deployment method according to claim 1, wherein in step S2, the collision point limit algorithm comprises:
s21, determining the turning starting point coordinates M (x 1 ，y 1 ) And the circle center coordinate point (x) of the circular arc track made during turning r ，y r ) Thereby obtaining the turning radius R,
Figure FDA0004135557880000011
s22, based on the turning radius R and the vehicle collision point, obtaining the time spent by the vehicle passing through the collision point as T,
Figure FDA0004135557880000012
wherein v is the real-time speed of the vehicle, (x) p ，y p ) For the coordinates of the collision point of the vehicle, T mbk For the deceleration time in moderate braking, T brt For vehicle braking reaction time, T plt Is the preliminary prediction time;
s23, obtaining the collision limit range G based on the time T spent by the collision point of the vehicle,
L max ＝v max T
Figure FDA0004135557880000013
wherein v is max Maximum speed for vehicle, (x) p ，y p ) For the coordinates of the collision point of the vehicle, L max Is the distance the vehicle passes the collision point.
3. The intersection vehicle road cooperation roadside sensor deployment method according to claim 2, wherein in step S22, the preliminary prediction time T plt The method comprises the following steps:
T plt ＝T com +T cpt wherein T is com T is the transmission delay cpt Is a constant parameter between 150-220 ms.
4. The intersection vehicle road cooperation roadside sensor deployment method according to claim 1, wherein in step S3, the determination of the coverage of the sensor comprises:
s31, calculating the angle of view: confirming the horizontal field angle of the sensor according to the lane width, and calculating the vertical field angle of the sensor according to the mode of blind facing of sensor deployment;
s32, detecting distance calculation: maximum distance from the coverage boundary according to the location where the device can be installed;
s33, identifying objects according to sensor clustering and a deep learning algorithm, and confirming pedestrians identifying the road zebra crossings and identifying the small vehicles in the farthest range.
5. The method for deploying sensor at the intersection and the road side according to claim 4, wherein in step S33, the sensor clustering and deep learning algorithm identifies objects and fuses the objects and transmits the objects to the RSU device in real time.
6. The intersection vehicle road cooperation roadside sensor deployment method according to claim 1, wherein in step S1, the vehicle collision point simulation test algorithm comprises:
s11, acquiring a running track of a vehicle, dividing the running track of the vehicle at equal intervals, and outputting track data information divided at equal intervals;
s12, carrying out partial derivative operation on each section of track based on the track data information divided at equal intervals, and outputting slope data information of each section of track;
s13, setting a first preset threshold value and a second preset threshold value based on the slope data information of each track, and outputting a vehicle collision point if the slope data information of each track is between the first preset threshold value and the second preset threshold value.
7. The intersection vehicle road cooperation roadside sensor deployment method of claim 6, wherein: the values of the first preset threshold and the second preset threshold are any real number between 0 and 1, wherein the real number comprises 0 and 1.
8. An intersection co-vehicle roadside sensor deployment system comprising computer equipment programmed or configured to perform the steps of the intersection co-vehicle roadside sensor deployment method of any one of claims 1 to 7.
9. A computer readable storage medium having stored thereon a computer program programmed or configured to perform the intersection vehicle road co-road side sensor deployment method of any one of claims 1 to 7.
CN202310273909.3A 2023-03-20 2023-03-20 Deployment method, system and medium for intersection vehicle-road cooperation road side sensor Pending CN116321003A (en)

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