Automatic Obstacle Avoidance Path Planning Optimisation for Intelligent Vehicles Based on Optimal Control Theory

Self-driving intelligent vehicles are an important part of the future intelligent transport system, and the study of path planning and automatic obstacle avoidance is also a key technology for the realization of self-driving vehicles. In this paper, we first constructed a dynamics model of an intelligent car, and then, using the optimal control theory, we designed the performance index function of automated obstacle avoidance path planning. This function took into account factors such as driving efficiency, energy consumption, and ride comfort. Consequently, we constructed an optimization algorithm model for automated obstacle avoidance path planning for intelligent cars and experimentally verified its effectiveness. The obstacle avoidance experiments conducted on straight and curved roads demonstrate that the path planning performance index function, which is based on optimal control theory and the optimal path planning method in this paper, can meet the obstacle avoidance requirements of local path planning while ensuring ride comfort. In the static obstacle experiment, the vehicle speed is 63 km/h and 84 km/h in two cases. This paper proposes a path planning function for obstacle avoidance that uses a smaller swing angle and maximum distance away from the obstacle than the traditional approach. The performance of the obstacle avoidance function based on optimal control theory in this paper is 47% higher than that of the traditional obstacle avoidance function. This verifies the effectiveness of optimal control theory in automatic obstacle avoidance path planning optimization for intelligent vehicles.