敵対的道路モデルを用いた自動運転の安全な動作計画
Safe Motion Planning for Autonomous Driving using an Adversarial Road Model
敵対的道路モデルとのゲーム理論的定式化により可視性理論で安全集合を計算し、最適化ベースの動作計画の終端制約として用いることで、短い予測ホライズンでも市街地・田舎道を安全に走行できることを示した論文。
著者: Alexander Liniger, Luc van Gool
分類: cs.RO, cs.SY, eess.SY, math.OC
原文アブストラクト
This paper presents a game-theoretic path-following formulation where the opponent is an adversary road model. This formulation allows us to compute safe sets using tools from viability theory, that can be used as terminal constraints in an optimization-based motion planner. Based on the adversary road model, we first derive an analytical discriminating domain, which even allows guaranteeing safety in the case when steering rate constraints are considered. Second, we compute the discriminating kernel and show that the output of the gridding based algorithm can be accurately approximated by a fully connected neural network, which can again be used as a terminal constraint. Finally, we show that by using our proposed safe sets, an optimization-based motion planner can successfully drive on city and country roads with prediction horizons too short for other baselines to complete the task.
