合成驾驶事故数据集SynSHRP2，破解隐私难题，助力自动驾驶安全研究。
SynSHRP2: A Synthetic Multimodal Benchmark for Driving Safety-critical Events Derived from Real-world Driving Data
- 基于真实数据用AI生成匿名化多模态驾驶画面
- 包含1874起车祸与6924次近事故的合成数据
- 适合自动驾驶安全评估与场景理解研究
驾驶安全事故事件（SCEs）如碰撞和险情，对自动驾驶系统开发与安全性评估至关重要。但其稀有性及原始数据中的敏感隐私信息限制了公开可用性。第二代公路战略研究计划（SHRP 2）自然驾驶研究（NDS）是迄今最大规模的自然驾驶研究，从数千名参与者处采集了数百万小时的高分辨率、高频多模态驾驶数据，记录了数千起SCEs。尽管该数据集极具价值，但隐私顾虑与使用限制严重阻碍了原始数据的公开。为此，我们提出SynSHRP2——一个公开可获取的合成多模态驾驶数据集，包含超过1874起车祸和6924次近事故，均源自SHRP 2 NDS。该数据集通过Stable Diffusion与ControlNet生成去标识化关键帧，保留关键安全信息的同时消除个人身份数据。此外，还包含每起事件前后5秒的时间序列运动学数据、事故类型、环境与交通条件等详细标注，以及同步的关键帧与叙事描述，显著提升可用性。本文构建了两个基准任务：事件属性分类与场景理解，验证了SynSHRP2在推动安全研究与自动驾驶系统发展中的潜力。
原文摘要 · Abstract (English)
Driving-related safety-critical events (SCEs), including crashes and near-crashes, provide essential insights for the development and safety evaluation of automated driving systems. However, two major challenges limit their accessibility: the rarity of SCEs and the presence of sensitive privacy information in the data. The Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS), the largest NDS to date, collected millions of hours of multimodal, high-resolution, high-frequency driving data from thousands of participants, capturing thousands of SCEs. While this dataset is invaluable for safety research, privacy concerns and data use restrictions significantly limit public access to the raw data. To address these challenges, we introduce SynSHRP2, a publicly available, synthetic, multimodal driving dataset containing over 1874 crashes and 6924 near-crashes derived from the SHRP 2 NDS. The dataset features de-identified keyframes generated using Stable Diffusion and ControlNet, ensuring the preservation of critical safety-related information while eliminating personally identifiable data. Additionally, SynSHRP2 includes detailed annotations on SCE type, environmental and traffic conditions, and time-series kinematic data spanning 5 seconds before and during each event. Synchronized keyframes and narrative descriptions further enhance its usability. This paper presents two benchmarks for event attribute classification and scene understanding, demonstrating the potential applications of SynSHRP2 in advancing safety research and automated driving system development.
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