A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing

We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample efficiently within a simulation environment. A high-level policy, represented as a neural network, outputs a reward specification that is used within the function of a parametric nonlinear model predictive controller. By including constraints and vehicle kinematics in the nonlinear program, we can guarantee safe and feasible trajectories related to the used model. Compared to classical reinforcement learning, our approach restricts the exploration to safe trajectories, starts with an excellent prior performance and yields complete trajectories that can be passed to a tracking lowest-level controller. We do not address the lowest-level controller in this work and assume perfect tracking of feasible trajectories. We show the superior performance of our algorithm on simulated racing tasks that include high-level decision-making. The vehicle learns to efficiently overtake slower vehicles and avoids getting overtaken by blocking faster ones.