自动从轨迹数据中提取交通场景描述，提升自动驾驶行为对比效率
Trajectory-to-Action Pipeline (TAP): Automated Scenario Description Extraction for Autonomous Vehicle Behavior Comparison
- 基于规则与交叉熵优化，自动从轨迹中学习场景标签
- 在Waymo数据集上比ADE高30%精度，比DTW高24%精度
- 可发现独特驾驶行为，适合自动驾驶安全评估场景
场景描述语言（SDL）为自动驾驶车辆遇到的交通场景提供结构化、可解释的表征，支持场景相似性搜索和边缘案例检测等关键任务。本文提出轨迹到行为管道（TAP），一种可扩展且自动化的从大规模轨迹数据集中提取SDL标签的方法。TAP采用基于规则的交叉熵优化方法，直接从数据中学习参数，增强了在多样化驾驶情境下的泛化能力。在Waymo开放运动数据集（WOMD）上，TAP在识别行为相似轨迹方面，相比平均位移误差（ADE）提升30%，相比动态时间规整（DTW）提升24%。此外，TAP实现了独特驾驶行为的自动化检测，简化了自动驾驶测试中的安全评估流程。该工作为可扩展的基于场景的自动驾驶行为分析奠定了基础，未来可拓展至多智能体情境。
原文摘要 · Abstract (English)
Scenario Description Languages (SDLs) provide structured, interpretable embeddings that represent traffic scenarios encountered by autonomous vehicles (AVs), supporting key tasks such as scenario similarity searches and edge case detection for safety analysis. This paper introduces the Trajectory-to-Action Pipeline (TAP), a scalable and automated method for extracting SDL labels from large trajectory datasets. TAP applies a rules-based cross-entropy optimization approach to learn parameters directly from data, enhancing generalization across diverse driving contexts. Using the Waymo Open Motion Dataset (WOMD), TAP achieves 30% greater precision than Average Displacement Error (ADE) and 24% over Dynamic Time Warping (DTW) in identifying behaviorally similar trajectories. Additionally, TAP enables automated detection of unique driving behaviors, streamlining safety evaluation processes for AV testing. This work provides a foundation for scalable scenario-based AV behavior analysis, with potential extensions for integrating multi-agent contexts.
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