Multi-vehicle collaborative planning method based on distributed crowd-sourcing learning

Abstract

The invention discloses a multi-vehicle collaborative planning method based on crowd-sourcing learning, and belongs to the technical field of multi-vehicle-road collaborative decision making. According to the invention, the edge server is utilized to reduce the requirements of the computing capability and the communication capability of the vehicle; the evolutionary game is used for modeling the process of continuous game between vehicles in routing planning, and when the game state forms a stable situation, each vehicle obtains a routing decision with maximum own benefit; an intersection passing driving decision-making module is deployed on each vehicle, the vehicle is regarded as an independent decision-making individual, and a cooperative driving behavior of multiple vehicles at the intersection is modeled by using the powerful strategy learning capability of deep reinforcement learning; a traffic situation prediction module is calculated and deployed at the roadside edge, and the traffic situation perception under the limited visual field of vehicles is expanded by using the communication capability of multiple vehicles and roads. According to the invention, different aspects of road resources are optimized, space-time utilization of the intersection is optimized, space-time utilization of road resources around the intersection is optimized, and throughput of the intersection is increased.

Description

technical field [0001] The invention relates to the technical fields of road traffic network and multi-vehicle coordination, in particular to a method for multi-vehicle coordination planning based on distributed group intelligence learning. Background technique [0002] The limited urban traffic space resources and the sharp increase of motor vehicles have broken the fragile balance between supply and demand of roads, leading to traffic congestion. Therefore, how to coordinate the driving trajectory of vehicles, make full use of limited road resources, improve traffic efficiency, and alleviate the current situation of road traffic congestion is a current research direction. [0003] Vehicle routing planning has been proven to be an effective way to alleviate urban traffic congestion. Benefiting from intelligent transportation systems and intelligent networked vehicles, routing planning technology has evolved from static routing to dynamic routing based on real-time traffic ...

AI Technical Summary

Problems solved by technology

[0009] The present invention aims at the current multi-vehicle cooperative driving that requires strong computing power and communication capabilities for the vehicles, and the poor communication environment will lead to mutual influence between multiple vehicles, resulting in vehicles getting together and low t

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