构建多行人过街场景库，助力自动驾驶安全分析
PCICF: A Pedestrian Crossing Identification and Classification Framework
- 基于SMIRK扩展出结构化多行人过街数据集MoreSMIRK
- 用空间填充曲线匹配复杂过街场景，准确识别群体行为变化
- 适合自动驾驶系统安全评估与实车部署的场景分析
随着机器人出租车在多国商用落地，其运行需在特定操作设计域（ODD）内执行，并持续监控以应对紧急情况。由于ODD通常覆盖城市区域，机器人出租车必须可靠检测行人等脆弱道路使用者（VRUs）。为应对复杂交通情境，端到端人工智能正逐渐兴起，直接从多模态传感器数据生成车辆控制指令。为此，本文提出PCICF框架，系统性识别与分类VRU场景，支持ODD事故分析。基于现有合成数据集SMIRK，我们扩展出MoreSMIRK——一个系统构建的多行人过街场景结构化词典。通过空间填充曲线（SFCs）将场景多维特征转化为特征模式，与MoreSMIRK中的条目进行匹配。我们在包含超过150段人工标注行人过街视频的真实数据集PIE上评估了PCICF，结果表明其可成功识别并分类复杂过街行为，即使在行人组合并或分裂时亦然。借助高效计算组件如SFCs，PCICF具备在机器人出租车上实现ODD检测的潜力。相关代码、完整MoreSMIRK数据集及实验结果已开源：https://github.com/Claud1234/PCICF
原文摘要 · Abstract (English)
We have recently observed the commercial roll-out of robotaxis in various countries. They are deployed within an operational design domain (ODD) on specific routes and environmental conditions, and are subject to continuous monitoring to regain control in safety-critical situations. Since ODDs typically cover urban areas, robotaxis must reliably detect vulnerable road users (VRUs) such as pedestrians, bicyclists, or e-scooter riders. To better handle such varied traffic situations, end-to-end AI, which directly compute vehicle control actions from multi-modal sensor data instead of only for perception, is on the rise. High quality data is needed for systematically training and evaluating such systems within their OOD. In this work, we propose PCICF, a framework to systematically identify and classify VRU situations to support ODD's incident analysis. We base our work on the existing synthetic dataset SMIRK, and enhance it by extending its single-pedestrian-only design into the MoreSMIRK dataset, a structured dictionary of multi-pedestrian crossing situations constructed systematically. We then use space-filling curves (SFCs) to transform multi-dimensional features of scenarios into characteristic patterns, which we match with corresponding entries in MoreSMIRK. We evaluate PCICF with the large real-world dataset PIE, which contains more than 150 manually annotated pedestrian crossing videos. We show that PCICF can successfully identify and classify complex pedestrian crossings, even when groups of pedestrians merge or split. By leveraging computationally efficient components like SFCs, PCICF has even potential to be used onboard of robotaxis for OOD detection for example. We share an open-source replication package for PCICF containing its algorithms, the complete MoreSMIRK dataset and dictionary, as well as our experiment results presented in: https://github.com/Claud1234/PCICF
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