未知環境における自動運転のための安全な軌道生成
Safe Trajectory Synthesis for Autonomous Driving in Unforeseen Environments
低忠実度モデルで軌道を設計し、高忠実度モデルの保守的な前方可到達集合を用いて障害物との衝突を排除することで、未知環境でもリアルタイムに安全を保証する軌道計画手法を提案した。
著者: Shreyas Kousik, Sean Vaskov, Matthew Johnson-Roberson, Ramanarayan Vasudevan
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Path planning for autonomous vehicles in arbitrary environments requires a guarantee of safety, but this can be impractical to ensure in real-time when the vehicle is described with a high-fidelity model. To address this problem, this paper develops a method to perform trajectory design by considering a low-fidelity model that accounts for model mismatch. The presented method begins by computing a conservative Forward Reachable Set (FRS) of a high-fidelity model's trajectories produced when tracking trajectories of a low-fidelity model over a finite time horizon. At runtime, the vehicle intersects this FRS with obstacles in the environment to eliminate trajectories that can lead to a collision, then selects an optimal plan from the remaining safe set. By bounding the time for this set intersection and subsequent path selection, this paper proves a lower bound for the FRS time horizon and sensing horizon to guarantee safety. This method is demonstrated in simulation using a kinematic Dubin's car as the low-fidelity model and a dynamic unicycle as the high-fidelity model.
