双方向到達可能性を用いた自動運転のリスク評価と計画
Risk Assessment and Planning with Bidirectional Reachability for Autonomous Driving
遮蔽環境下でも前方・後方到達可能性を組み合わせて危険因子を特定し、スロットル操作まで含めた経路計画を行う自動運転手法を提案し、見通しの悪い交差点で衝突率を大幅に低減した。
著者: Ming-Yuan Yu, Ram Vasudevan, Matthew Johnson-Roberson
分類: cs.RO
原文アブストラクト
Knowing and predicting dangerous factors within a scene are two key components during autonomous driving, especially in a crowded urban environment. To navigate safely in environments, risk assessment is needed to quantify and associate the risk of taking a certain action. Risk assessment and planning is usually done by first tracking and predicting trajectories of other agents, such as vehicles and pedestrians, and then choosing an action to avoid collision in the future. However, few existing risk assessment algorithms handle occlusion and other sensory limitations effectively. This paper explores the possibility of efficient risk assessment under occlusion via both forward and backward reachability. The proposed algorithm can not only identify where the risk-induced factors are, but also be used for motion planning by executing low-level commands, such as throttle. The proposed method is evaluated on various four-way highly occluded intersections with up to five other vehicles in the scene. Compared with other risk assessment algorithms, the proposed method shows better efficiency, meaning that the ego vehicle reaches the goal at a higher speed. In addition, it also lowers the median collision rate by 7.5x.
