条件付き行動予測による運転者インタラクションの特定
Identifying Driver Interactions via Conditional Behavior Prediction
自車の将来軌道を条件として他エージェントの軌道分布を予測するモデルを構築し、確率論に基づくインタラクティブ度スコアを提案した論文。
著者: Ekaterina Tolstaya, Reza Mahjourian, Carlton Downey, Balakrishnan Varadarajan, Benjamin Sapp, Dragomir Anguelov
分類: cs.RO
原文アブストラクト
Interactive driving scenarios, such as lane changes, merges and unprotected turns, are some of the most challenging situations for autonomous driving. Planning in interactive scenarios requires accurately modeling the reactions of other agents to different future actions of the ego agent. We develop end-to-end models for conditional behavior prediction (CBP) that take as an input a query future trajectory for an ego-agent, and predict distributions over future trajectories for other agents conditioned on the query. Leveraging such a model, we develop a general-purpose agent interactivity score derived from probabilistic first principles. The interactivity score allows us to find interesting interactive scenarios for training and evaluating behavior prediction models. We further demonstrate that the proposed score is effective for agent prioritization under computational budget constraints.
