CN-121981357-A - Vehicle driving path optimization method and system based on vehicle road cloud integration

CN121981357ACN 121981357 ACN121981357 ACN 121981357ACN-121981357-A

Abstract

The invention relates to the field of data processing, in particular to a vehicle driving path optimization method and system based on vehicle road cloud integration, comprising the following steps: taking any speed-limiting road section as a target road section, calculating running abnormality evaluation of the position of each vehicle in the target road section, taking a vehicle corresponding to the maximum value of the running abnormality evaluation as a starting vehicle, taking any vehicle after the starting vehicle as a marking vehicle, calculating the concentration degree from the starting vehicle to the marking vehicle according to a mapping function, taking the latter vehicle adjacent to the marking vehicle as a reference vehicle, calculating the passing efficiency of each abnormal concentration section in response to the difference value between the concentration degree corresponding to the marking vehicle and the concentration degree corresponding to the reference vehicle being greater than a preset threshold, taking the minimum value of the passing efficiency of each running path as corresponding short-board efficiency, and taking the running path with the maximum short-board efficiency as the optimal path. The invention can improve the real-time performance and accuracy of path optimization.

Inventors

QI LIN
AN RAN
ZHOU JIWEI
CHEN KAI
LIU GUIYING
LUO WEIMIN
XU JIANMING

Assignees

山东高速千方国际科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260326

Claims (9)

1. The vehicle driving path optimization method based on the vehicle road cloud integration is characterized by comprising the following steps of: Acquiring a plurality of drivable paths, taking any drivable path as a target path, and segmenting the target path according to a speed limit rule to acquire a plurality of speed limit road sections; Taking any speed-limiting road section as a target road section, calculating running abnormality evaluation of the position of each vehicle in the target road section, taking a vehicle corresponding to the maximum value of the running abnormality evaluation as a starting vehicle, taking any vehicle behind the starting vehicle as a marking vehicle, constructing an abnormal distribution coordinate system from the starting vehicle to the marking vehicle, carrying out nonlinear fitting on points in the abnormal distribution coordinate system to obtain a mapping function, calculating the concentration of the starting vehicle to the marking vehicle according to the mapping function, taking the latter vehicle adjacent to the marking vehicle as a reference vehicle, calculating the concentration of the starting vehicle to the reference vehicle in a similar way, responding to the fact that the difference value between the concentration corresponding to the marking vehicle and the concentration corresponding to the reference vehicle is larger than a preset threshold, taking the physical distance between the starting vehicle and the marking vehicle as an abnormal concentration interval of the target road section, and traversing to obtain the abnormal concentration interval of each speed-limiting road section; and calculating the traffic efficiency of each abnormal concentrated interval, taking the minimum value of the traffic efficiency of each drivable path as the corresponding short-board efficiency, and taking the drivable path with the maximum short-board efficiency as the optimal path.
2. The vehicle travel path optimization method based on vehicle road cloud integration according to claim 1, wherein the calculating the travel abnormality evaluation of the position of each vehicle in the target road section includes: Acquiring the speed of each vehicle in a target road section and the distance between each vehicle and the adjacent rear vehicle; Taking any one vehicle in the target road section as a target vehicle, calculating a first ratio between the speed of the target vehicle and the highest speed limit of the target road section, taking the distance between the target vehicle and the adjacent rear vehicle as a target distance, taking the ratio of the target distance to the maximum value of all the distances in the target road section as a second ratio, and calculating the running abnormality evaluation of the position of the target vehicle by combining the first ratio and the second ratio; And traversing to obtain the driving abnormality evaluation of the position of each vehicle in the target road section.
3. The vehicle travel path optimization method based on vehicle road cloud integration according to claim 1, wherein constructing an abnormal distribution coordinate system from a starting vehicle to a marking vehicle comprises: Marking the starting vehicles, the marking vehicles and each vehicle from the starting vehicles to the marking vehicles, constructing an original coordinate system by taking the marking sequence as a horizontal axis and taking the driving abnormality evaluation as a vertical axis, and mapping the starting vehicles, the marking vehicles and each vehicle from the starting vehicles to the marking vehicles in the original coordinate system to obtain an abnormal distribution coordinate system.
4. The vehicle travel path optimization method based on vehicle road cloud integration according to claim 1, wherein calculating the concentration of the starting vehicle to the marking vehicle according to the mapping function comprises: Calculating a limiting coefficient from the starting vehicle to the marking vehicle; calculating the point-line distance from the starting point vehicle to the point corresponding to each vehicle in the marking vehicle to a mapping function, and carrying out negative correlation mapping on the average value of all the point-line distances by adopting an exponential function to obtain a mapping result; the product of the limiting coefficient and the mapping result is used as the concentration degree from the starting vehicle to the marking vehicle.
5. The vehicle travel path optimization method based on vehicle road cloud integration according to claim 4, wherein calculating the limit coefficient of the starting vehicle to the marking vehicle comprises: And taking the ratio of the total number of marked vehicles corresponding to the marking vehicles to the total number of vehicles of the target road section as a limiting coefficient.
6. The vehicle travel path optimization method based on vehicle road cloud integration according to claim 1, wherein calculating the traffic efficiency of each anomaly concentration zone comprises: taking any abnormal concentrated section as a target section, calculating the abnormal degree of the target section, counting the number of vehicles driven in and the number of vehicles driven out of the target section, and taking the product of the ratio of the number of vehicles driven out to the number of vehicles driven in and the abnormal degree as the passing efficiency of the target section; and traversing to obtain the passing efficiency of each abnormal concentrated interval.
7. The vehicle travel path optimization method based on vehicle road cloud integration according to claim 6, wherein the calculating the degree of abnormality of the target section includes: and carrying out linear fitting on points in an abnormal distribution coordinate system corresponding to the target interval, and taking the absolute value of a linear slope obtained after fitting as the degree of abnormality.
8. The vehicle travel path optimization method based on vehicle road cloud integration according to claim 6, wherein counting the number of vehicles entering and exiting the target section includes: Acquiring a unique identifier of a vehicle driving into a target interval at a preset time and a unique identifier of a vehicle driving out of the target interval at the preset time; Taking an abnormal concentrated interval at any time within a preset time period before a preset time as a history interval, taking a first history interval with an intersection with a target interval as an initial interval corresponding to the target interval, taking the time which is spaced from the preset time by the preset time period as a reference time, and acquiring a unique identifier of a vehicle which drives into the initial interval at the reference time and a unique identifier of a vehicle which drives out of the initial interval at the reference time; And calculating the number of vehicles entering the target section and the number of vehicles exiting the target section according to the four unique identifiers.
9. The vehicle driving path optimization system based on the vehicle-road cloud integration is characterized by comprising a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions realize the vehicle driving path optimization method based on the vehicle-road cloud integration according to any one of claims 1-8 when the computer program instructions are executed by the processor.

Description

Vehicle driving path optimization method and system based on vehicle road cloud integration Technical Field The present invention relates to the field of data processing. More particularly, the invention relates to a vehicle driving path optimization method and system based on vehicle road cloud integration. Background With the continuous expansion of the expressway network scale and the continuous increase of the vehicle conservation amount in China, the traffic jam problem is increasingly prominent, and particularly, the local road section jam phenomenon is serious in holidays and peak hours. The traditional traffic management system mainly relies on fixed-position monitoring equipment and manual inspection to acquire road condition information, and the mode has obvious hysteresis and limitation, so that real-time and accurate path planning service is difficult to provide for vehicles. Disclosure of Invention The embodiment of the application mainly aims to provide a vehicle driving path optimization method and system based on vehicle-road cloud integration, aiming at improving the real-time performance and accuracy of path optimization. In order to achieve the above purpose, an embodiment of the first aspect of the present application provides a vehicle driving path optimization method based on vehicle-road cloud integration, the method comprising obtaining a plurality of drivable paths, taking any drivable path as a target path, segmenting the target path according to speed limit rules to obtain a plurality of speed limit road sections, taking any speed limit road section as a target road section, calculating driving abnormality evaluation of a position of each vehicle in the target road section, taking a vehicle corresponding to a maximum value of the driving abnormality evaluation as a starting vehicle, taking any vehicle behind the starting vehicle as a marking vehicle, constructing an abnormal distribution coordinate system of the starting vehicle to the marking vehicle, performing nonlinear fitting on points in the abnormal distribution coordinate system to obtain a mapping function, calculating a concentration degree of the starting vehicle to the marking vehicle according to the mapping function, taking a next vehicle adjacent to the marking vehicle as a reference vehicle, and then uniformly calculating the concentration degree of the starting vehicle to the reference vehicle, taking a physical distance between the starting vehicle and the marking vehicle as an abnormal concentration section of the target road section in response to a difference value of the concentration degree corresponding to the marking vehicle being greater than a preset threshold, taking a physical distance between the starting vehicle and the marking vehicle as an abnormal concentration section of the target road section, obtaining a shortest path corresponding to the maximum passing efficiency of each calculated passing efficiency as a shortest passing path, and taking the maximum passing efficiency as a traffic board. In some embodiments, the calculation of the driving abnormality evaluation of the position of each vehicle in the target road section comprises the steps of obtaining the speed of each vehicle in the target road section and the distance between each vehicle and the adjacent rear vehicle, taking any vehicle in the target road section as the target vehicle, calculating a first ratio between the speed of the target vehicle and the highest speed limit of the target road section, taking the distance between the target vehicle and the adjacent rear vehicle as the target vehicle distance, taking the ratio of the maximum value of the target vehicle distance and all the vehicle distances in the target road section as a second ratio, calculating the driving abnormality evaluation of the position of each vehicle in the target road section by combining the first ratio and the second ratio, and traversing to obtain the driving abnormality evaluation of the position of each vehicle in the target road section. In some embodiments, constructing the starting vehicle-to-marking vehicle abnormal distribution coordinate system comprises marking the starting vehicle, the marking vehicle and each vehicle from the starting vehicle to the marking vehicle, constructing an original coordinate system by taking the marking sequence as a horizontal axis and taking the driving abnormality evaluation as a vertical axis, and mapping each vehicle from the starting vehicle, the marking vehicle and the starting vehicle to the marking vehicle in the original coordinate system to obtain the abnormal distribution coordinate system. In some embodiments, calculating the concentration of the starting point vehicle to the marking vehicle according to the mapping function comprises calculating a limiting coefficient of the starting point vehicle to the marking vehicle, calculating the point-line distance of the point-to-mapping function correspondin