デモンストレーションから自動運転車の安全概念を学習
Learning Autonomous Vehicle Safety Concepts from Demonstrations
周囲の車両の合理的な挙動をデータから学習し、制御理論を用いて自動運転車の安全概念を合成する手法を提案した。
著者: Karen Leung, Sushant Veer, Edward Schmerling, Marco Pavone
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Evaluating the safety of an autonomous vehicle (AV) depends on the behavior of surrounding agents which can be heavily influenced by factors such as environmental context and informally-defined driving etiquette. A key challenge is in determining a minimum set of assumptions on what constitutes reasonable foreseeable behaviors of other road users for the development of AV safety models and techniques. In this paper, we propose a data-driven AV safety design methodology that first learns ``reasonable'' behavioral assumptions from data, and then synthesizes an AV safety concept using these learned behavioral assumptions. We borrow techniques from control theory, namely high order control barrier functions and Hamilton-Jacobi reachability, to provide inductive bias to aid interpretability, verifiability, and tractability of our approach. In our experiments, we learn an AV safety concept using demonstrations collected from a highway traffic-weaving scenario, compare our learned concept to existing baselines, and showcase its efficacy in evaluating real-world driving logs.
