反事実推論を用いた知的運転のための物体重要度推定
Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving
自動運転の判断に重要な物体を特定するため、物体の動きを変えた反事実シナリオを生成し、その影響から重要度を推定する手法を提案。人間注釈付きデータセットHOISTで高精度を達成。
著者: Pranay Gupta, Abhijat Biswas, Henny Admoni, David Held
分類: cs.RO
原文アブストラクト
The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It also helps assistive driving systems decide when to alert drivers. We tackle object importance estimation in a data-driven fashion and introduce HOIST - Human-annotated Object Importance in Simulated Traffic. HOIST contains driving scenarios with human-annotated importance labels for vehicles and pedestrians. We additionally propose a novel approach that relies on counterfactual reasoning to estimate an object's importance. We generate counterfactual scenarios by modifying the motion of objects and ascribe importance based on how the modifications affect the ego vehicle's driving. Our approach outperforms strong baselines for the task of object importance estimation on HOIST. We also perform ablation studies to justify our design choices and show the significance of the different components of our proposed approach.
