Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning

Abstract-- Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution can only be ensured with the use of problemspecific samplingdistributions. Due to the large variety of driving situations within the context of automated driving, it is very challenging to manually design such distributions. This paper introduces therefore a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently towards the optimal solution. A benchmark highlights that the CNN predicts future vehicle poses with a higher accuracy compared to uniform sampling and a state-of-the-art A*-based approach. Combining this CNNguided samplingwith the motion planner Bidirectional RRT* reduces the computation time by up to an order of magnitude and yields a faster convergence to a lower cost as well as a success rate of 100 % in the tested scenarios. I. INTRODUCTION Motion planning is one of the major pillars in the software architecture of automated vehicles. Its task is to compute a safe trajectory from start goal taking into account the vehicle's constraints, the non-convex surrounding as well as the comfort requirements of passengers. In structured environments, such as highway driving, it is sufficient to solve the motion planning problem locally close to the lane centerline.