Multi-Obstacle Avoidance Algorithm for Autonomous Vehicles

This paper presents a local path planning approach that avoids both static and dynamic multi-obstacles for autonomous car. The proposed algorithm is able to alter the predefined waypoints from global path planning when obstacles are detected. A set of paths are generated based on the sigmoid function for overtaking obstacles. Every single path is assessed according to a cost function which includes path safety, comfortability, and length. Genetic algorithm is used to optimize parameters of the selected sigmoid function for smoothness and minimum length. Non-linear Model Predictive Controller is designed for trajectory tracking considering vehicle's dynamics. Simulation results reveal that the algorithm carries out obstacle avoidance and can handle multi-obstacles furthermore, the lateral acceleration is reduced by 44.27% and 50% in both static and dynamic scenarios effectively.