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.

翻译：场景描述语言（SDL）提供了结构化、可解释的嵌入表示，用于描述自动驾驶车辆（AV）遇到的交通场景，支持场景相似性搜索和安全分析中的边缘案例检测等关键任务。本文介绍了轨迹到行动管道（TAP），这是一种从大规模轨迹数据集中自动提取SDL标签的可扩展方法。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，从而增强了在不同驾驶场景中的泛化能力。基于Waymo开放运动数据集（WOMD）的实验表明，TAP在识别行为相似轨迹方面的精度比平均位移误差（ADE）高出30%，比动态时间规整（DTW）高出24%。此外，TAP能够自动检测独特的驾驶行为，从而简化了自动驾驶测试的安全评估流程。这项工作为基于场景的可扩展自动驾驶行为分析奠定了基础，并具有扩展到多智能体场景集成的潜力。