Autonomous Vehicle Motion Planning using Kernelized Movement Primitives

Understanding and modeling human driver behavior and subsequently applying these patterns in various scenarios is crucial for autonomous vehicle motion planning. However, solution that naturally encodes human driving skills remains challenging due to the difficulty of balancing the variability of human behavior and the robustness in various driving environments. To tackle this issue, a novel motion planning approach is proposed based on Kernelized Movement Primitive (KMP) in this paper to adaptively learn human driving behavior in a stochastic way by employing Gaussian mixture model(GMM) and Gaussian mixture regression(GMR). The Kullback-Leibler(KL) divergence is utilized to minimize the information loss between imitating reference behavior and adapting new tasks and therefore generates robust motion trajectories in a variety of driving situations. The proposed approach is evaluated by a mature urban driving simulator CARLA. The experimental results shows its capability of generating robust driving trajectories by naturally adapting human driving skill into various driving situations.