自動運転レースにおける戦略的運動計画の階層的アプローチ
A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing
強化学習で上位方策を学習し、非線形モデル予測制御のコスト関数に反映させることで、安全な軌道を生成しつつ追い抜きや防御の戦略を獲得する手法を提案。
著者: Rudolf Reiter, Jasper Hoffmann, Joschka Boedecker, Moritz Diehl
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample-efficient within a simulation environment. A high-level policy, represented as a neural network, outputs a reward specification that is used within the cost function of a parametric nonlinear model predictive controller (NMPC). By including constraints and vehicle kinematics in the NLP, we are able to guarantee safe and feasible trajectories related to the used model. Compared to classical reinforcement learning (RL), our approach restricts the exploration to safe trajectories, starts with a good prior performance and yields full 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 to avoid getting overtaken by blocking faster vehicles.
