Motorcade optimal trajectory control method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of trajectory planning, in particular to a motorcade optimal trajectory control method and device, electronic equipment and a storage medium, and the method comprises the steps: determining an initial state and a target state of a motorcade; based on the relative motion between the vehicles, dispersing the road space where the motorcade is located into uniform grid spaces according to a preset rule, generating discrete grid spaces, representing the discrete grid spaces with a state matrix, and generating a graph space for a motorcade state space; based on a vehicle team state space graph space, a step-by-step expansion strategy that only one intelligent network connection vehicle is allowed to move within any step number is adopted, a distributed random A * algorithm is used for solving a vehicle team recombination optimization moving track, and optimal track control is carried out on a vehicle team. Therefore, the vehicle team can be efficiently converted from any initial layout state to any target layout, efficient cooperative driving of the intelligent network connection vehicle team is realized, the road traffic capacity can be greatly improved in a traffic bottleneck road section, and meanwhile, the traffic safety of the vehicle team is guaranteed.

Description

technical field [0001] The present application relates to the technical field of trajectory planning, in particular to a method, device, electronic equipment and storage medium for optimal trajectory control of a fleet. Background technique [0002] Intelligent network technology has been rapidly developed and applied in the field of traffic engineering, which has played a very positive role in effectively alleviating urban traffic congestion and improving urban road traffic capacity. Under the environment of intelligent network connection, how to regroup and arrange the fleet by means of communication between vehicles to optimize the traffic efficiency at any time is one of the hotspots in the field of intelligent transportation research, that is, the fleet is required to dynamically adjust the arrangement of vehicles according to different road conditions Combination forms to suit different traffic demands. [0003] Many algorithms for finding the shortest path have been ...

AI Technical Summary

Problems solved by technology

[0004] However, the graph space of the intelligent connected vehicle reorganization optimization problem has the characteristics of combinatorial explosion. When the number of vehicles increases, the size of the graph space will increase exponentially, and the existing algorithms cannot meet its requirements for algorithm efficiency.

Although the traditional A* algorithm can find the possible range of the target point through the distance estimation function, thereby reducing the range of the graph space that needs to be searched, and improving the efficiency of the algorithm, the traditional A* algorithm is still an NP-hard problem in essence. Exponential complexity, when the scale of the problem increases, the solution time will also increase sharply, and for multiple shortest paths of the same length, the traditional A* algorithm will return the same shortest path each time, and cannot realize multi-vehicle movement Global optimum of optimization results

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