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.

翻译：在复杂动态驾驶环境中识别重要目标的能力，对于自动驾驶智能体做出安全高效的驾驶决策至关重要，同时也有助于辅助驾驶系统判断何时向驾驶员发出警报。我们以数据驱动的方式研究目标重要性估计问题，并提出了HOIST（模拟交通中人工标注的目标重要性数据集）。HOIST包含带有车辆与行人重要性人工标注标签的驾驶场景。我们进一步提出了一种基于反事实推理来估计目标重要性的新方法：通过修改目标运动轨迹生成反事实场景，并根据这些修改对自车驾驶行为的影响程度来赋予重要性权重。该方法在HOIST数据集上的目标重要性估计任务中显著优于强基线模型。我们还通过消融实验验证了设计选择的合理性，并揭示了所提方法各组成部分的关键作用。