Path planning method and system for unmanned vehicle

Abstract

The invention discloses a path planning method and system for an unmanned vehicle. The method comprises the following steps: randomly generating a plurality of paths from a starting point to a targetpoint, wherein the paths are obtained by connecting a plurality of path points; encoding the path by using the serial numbers of the path points through which the path sequentially passes from the starting point to the target point; dividing paths with the same number of path points into the same sub-population; performing parallel genetic operation on each sub-population; selecting the sub-population with the optimal moderate value from the sub-populations after genetic manipulation, and recording the sub-population as the optimal sub-population; and performing genetic manipulation on each individual in the optimal sub-population, determining an optimal path in combination with the fitness value of each individual in the optimal sub-population, wherein the fitness function, the crossoverprobability function and the mutation probability function in the method are all adaptive functions. According to the method, the algorithm can be prevented from falling into local optimum, the algorithm performance is further improved, the adaptability is higher, and the use experience of vehicle passengers is effectively improved.

Description

technical field [0001] The present invention relates to the technical field of unmanned driving, in particular to a path planning method and system for unmanned vehicles. Background technique [0002] As an important part of the future intelligent transportation system, the unmanned vehicle is a large-scale comprehensive system integrating multiple subsystems such as environmental perception, vehicle state measurement, path planning and underlying control. Efficient, stable and safe autonomous driving of human-driven vehicles. [0003] As the intelligent core of autonomous vehicles, the path planning system determines how the vehicle will reach the target location under various constraints. These constraints include environmental constraints (reflected as safety), system kinematics constraints (represented as feasibility), system dynamics constraints (represented as smoothness and stability), and specific optimization index constraints (such as the shortest time or the shor...

AI Technical Summary

Problems solved by technology

Among them, the path planning method of the traditional genetic algorithm has low search efficiency, is easy to fall into a local opt

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