Optimization analysis of autonomous obstacle avoidance path for self-driving vehicles based on improved ant colony algorithm

Abstract

In order to further improve the accurate response and fast response ability of autonomous obstacle avoidance in real dynamic environment, an improved AC (ant colony algorithm) model based on PSO (Particle Swarm Optimization) is proposed to realize the global and fast optimization analysis of autonomous obstacle avoidance path planning for self-driving vehicles. Firstly, an autonomous obstacle avoidance path planning model for self-driving vehicles is established; secondly, the global pheromone is searched by using the cooperation and information sharing mode between particles in PSO; thirdly, the global pheromone updating strategy is used to optimize the path searching ability of AC; finally, an innovative way of integrating PSO and AC is used to obtain the optimal path of autonomous obstacle avoidance for self-driving vehicles. The simulation results show that the AC model is improved by PSO to realize the fast and accurate planning of autonomous obstacle avoidance path for self-driving vehicles.