用‘意外潜力’量化驾驶交互程度，提升自动驾驶评估精度。
Surprise Potential as a Measure of Interactivity in Driving Scenarios
- 通过分析车辆对他人行为的意外预期，构建交互性度量指标。
- 在nuScenes数据集上相关性超0.82，优于现有方法。
- 适合自动驾驶交互场景评估与运动规划验证。
验证自动驾驶汽车（AV）的安全性与性能需基于真实道路日志。然而，典型驾驶日志多为无事件场景，路权参与者间互动极少。识别真实日志中的交互场景，有助于筛选关键信号，更准确评估AV表现。本文提出一种新度量——‘意外潜力’，用于识别交互场景。首先，定义描述该度量家族的设计空间三个维度；其次，在nuScenes数据集上系统评估并比较该设计空间内不同实例。通过人类偏好学习的奖励模型衡量与人类直觉的一致性，评估度量有效性。所提意外潜力与人类对齐奖励函数相关性超过0.82，优于现有方法。最后，基于筛选出的交互场景验证运动规划器，展示下游应用价值。
原文摘要 · Abstract (English)
Validating the safety and performance of an autonomous vehicle (AV) requires benchmarking on real-world driving logs. However, typical driving logs contain mostly uneventful scenarios with minimal interactions between road users. Identifying interactive scenarios in real-world driving logs enables the curation of datasets that amplify critical signals and provide a more accurate assessment of an AV's performance. In this paper, we present a novel metric that identifies interactive scenarios by measuring an AV's surprise potential on others. First, we identify three dimensions of the design space to describe a family of surprise potential measures. Second, we exhaustively evaluate and compare different instantiations of the surprise potential measure within this design space on the nuScenes dataset. To determine how well a surprise potential measure correctly identifies an interactive scenario, we use a reward model learned from human preferences to assess alignment with human intuition. Our proposed surprise potential, arising from this exhaustive comparative study, achieves a correlation of more than 0.82 with the human-aligned reward function, outperforming existing approaches. Lastly, we validate motion planners on curated interactive scenarios to demonstrate downstream applications.
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