分析2500起自动驾驶车祸，发现不同自动化等级的事故模式差异。
Data-Driven Analysis of Crash Patterns in SAE Level 2 and Level 4 Automated Vehicles Using K-means Clustering and Association Rule Mining
- 用聚类与关联规则挖掘事故数据，识别4类典型碰撞行为。
- 发现光照、路面状况等多因素在不同场景下影响事故概率。
- 为车企和监管机构提供针对性安全优化建议，适合政策制定者参考。
自动驾驶车辆（AV）有望减少人为驾驶失误，提升交通安全性并支持可持续出行。然而，近期事故数据显示，自动驾驶行为可能偏离预期安全效果，引发对其在混合交通环境中安全性和可靠性的担忧。以往研究多依赖小规模加州数据，且聚焦有限的SAE自动化等级。本研究基于美国国家公路交通安全管理局（NHTSA）超过2,500起事故记录，涵盖SAE Level 2与Level 4，揭示潜在的事故动态。构建两阶段数据挖掘框架：首先使用K-means聚类，根据时间、空间与环境因素将事故划分为4个显著的行为簇；随后采用关联规则挖掘（ARM），提取各簇中事故模式与光照条件、路面状况、车辆动态及环境因素之间的可解释多变量关系。研究成果为自动驾驶开发者、安全监管机构及政策制定者提供了制定部署策略与降低事故风险的行动指南。
原文摘要 · Abstract (English)
Automated Vehicles (AV) hold potential to reduce or eliminate human driving errors, enhance traffic safety, and support sustainable mobility. Recently, crash data has increasingly revealed that AV behavior can deviate from expected safety outcomes, raising concerns about the technology's safety and operational reliability in mixed traffic environments. While past research has investigated AV crash, most studies rely on small-size California-centered datasets, with a limited focus on understanding crash trends across various SAE Levels of automation. This study analyzes over 2,500 AV crash records from the United States National Highway Traffic Safety Administration (NHTSA), covering SAE Levels 2 and 4, to uncover underlying crash dynamics. A two-stage data mining framework is developed. K-means clustering is first applied to segment crash records into 4 distinct behavioral clusters based on temporal, spatial, and environmental factors. Then, Association Rule Mining (ARM) is used to extract interpretable multivariate relationships between crash patterns and crash contributors including lighting conditions, surface condition, vehicle dynamics, and environmental conditions within each cluster. These insights provide actionable guidance for AV developers, safety regulators, and policymakers in formulating AV deployment strategies and minimizing crash risks.
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