提出新指标MEI，精准量化自动驾驶横向冲突的紧急程度。
Modified-Emergency Index (MEI): A Criticality Metric for Autonomous Driving in Lateral Conflict
- 改进时间估算方法，更准确评估避让时机
- 在1500+冲突数据上表现优于现有指标
- 适合城市自动驾驶安全评估与测试
可靠的自动驾驶安全评估对建立信任至关重要。临界性指标为安全评估提供了客观手段，但现有指标多聚焦纵向冲突，难以精确量化城市环境中常见的横向冲突风险。本文提出改进型紧急指数（MEI），用于量化横向冲突中的避让努力程度。相比原版紧急指数（EI），MEI优化了避让可用时间的估计，实现更精确的风险量化。我们在基于Argoverse-2的公开横向冲突数据集上验证了MEI，从中提取了超过1,500个高质量自动驾驶冲突案例，包含500多个关键事件。将MEI与成熟的ACT指标及广泛使用的PET指标对比，结果表明其在准确量化临界性和捕捉风险演变方面持续领先。研究证实MEI是评估城市冲突的有力工具，有助于完善自动驾驶安全评估体系。开源代码已发布于https://github.com/AutoChengh/MEI。
原文摘要 · Abstract (English)
Effective, reliable, and efficient evaluation of autonomous driving safety is essential to demonstrate its trustworthiness. Criticality metrics provide an objective means of assessing safety. However, as existing metrics primarily target longitudinal conflicts, accurately quantifying the risks of lateral conflicts - prevalent in urban settings - remains challenging. This paper proposes the Modified-Emergency Index (MEI), a metric designed to quantify evasive effort in lateral conflicts. Compared to the original Emergency Index (EI), MEI refines the estimation of the time available for evasive maneuvers, enabling more precise risk quantification. We validate MEI on a public lateral conflict dataset based on Argoverse-2, from which we extract over 1,500 high-quality AV conflict cases, including more than 500 critical events. MEI is then compared with the well-established ACT and the widely used PET metrics. Results show that MEI consistently outperforms them in accurately quantifying criticality and capturing risk evolution. Overall, these findings highlight MEI as a promising metric for evaluating urban conflicts and enhancing the safety assessment framework for autonomous driving. The open-source implementation is available at https://github.com/AutoChengh/MEI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。