A hybrid obstacle avoidance algorithm for autonomous driving based on artificial potential energy field and fuzzy control

A single control algorithm cannot meet the requirements of autonomous driving in various road conditions. Aiming at the difference in the number of road obstacles, a hybrid control algorithm is proposed that uses artificial potential field algorithm to control less obstacle road conditions and fuzzy algorithm to control multi obstacle road conditions. Introducing speed factors into the artificial potential field algorithm to achieve dynamic obstacle avoidance under low obstacle road conditions. We conducted overtaking and obstacle avoidance experiments on target vehicles and obstacle vehicles at different speeds, and the results showed that the artificial potential energy field algorithm can accurately avoid obstacles in low obstacle road conditions. Fuzzy control is used for multi obstacle road conditions. The input variables include the angle between the current position and the target position, the distance between the current position and the obstacle, and the radius of the obstacle. The output variables include the direction angle and distance of the autonomous vehicle. The experimental results show that the algorithm can accurately avoid multiple obstacles. Research has shown that the hybrid obstacle avoidance algorithm combining artificial potential field algorithm and fuzzy control algorithm can be applied to various obstacle situations. The research results have certain practical and reference value for the study of autonomous driving control.