確率的な安全保証を備えた反応型動作計画
Reactive motion planning with probabilistic safety guarantees
他エージェントの予測モデルをMPCに組み込み、post-bloating・SVM・conformal分析で予測の一般化保証を与える動作計画手法を提案し、自動運転高速道路シナリオで検証した。
著者: Yuxiao Chen, Ugo Rosolia, Chuchu Fan, Aaron D. Ames, Richard Murray
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots. This paper considers the problem of motion planning, where the controlled agent shares the environment with multiple uncontrolled agents. First, a predictive model of the uncontrolled agents is trained to predict all possible trajectories within a short horizon based on the scenario. The prediction is then fed to a motion planning module based on model predictive control. We proved generalization bound for the predictive model using three different methods, post-bloating, support vector machine (SVM), and conformal analysis, all capable of generating stochastic guarantees of the correctness of the predictor. The proposed approach is demonstrated in simulation in a scenario emulating autonomous highway driving.
