A convolution-based grid map reconfiguration method for autonomous driving in highly constrained environments

This paper proposes a convolution-based method for reconfiguring highly constrained environments, which considers the contour and heading of an autonomous vehicle. The vehicle with possible different heading angles is taken as the kernels. The multiple convolutions between the kernels and the environment are performed to generate a three-dimensional grid map, which significantly improves the computational efficiency of the collision detection algorithm. Moreover, a hierarchical and multistage trajectory planning method based on the reconfigured grid map is proposed. The superiority of the proposed method is verified by comparative simulations and real-time experiments.