Intelligent driving systems aim to achieve a zero-collision mobility experience, requiring interdisciplinary efforts to enhance safety performance. This work focuses on risk identification, the process of identifying and analyzing risks stemming from dynamic traffic participants and unexpected events. While significant advances have been made in the community, the current evaluation of different risk identification algorithms uses independent datasets, leading to difficulty in direct comparison and hindering collective progress toward safety performance enhancement. To address this limitation, we introduce \textbf{RiskBench}, a large-scale scenario-based benchmark for risk identification. We design a scenario taxonomy and augmentation pipeline to enable a systematic collection of ground truth risks under diverse scenarios. We assess the ability of ten algorithms to (1) detect and locate risks, (2) anticipate risks, and (3) facilitate decision-making. We conduct extensive experiments and summarize future research on risk identification. Our aim is to encourage collaborative endeavors in achieving a society with zero collisions. We have made our dataset and benchmark toolkit publicly on the project page: https://hcis-lab.github.io/RiskBench/

翻译：摘要：智能驾驶系统旨在实现零碰撞的出行体验，这需要跨学科的合作来提升安全性能。本研究聚焦于风险识别——即识别并分析由动态交通参与者及突发事件引发风险的过程。尽管学界已取得显著进展，但目前不同风险识别算法的评估仍依赖独立数据集，导致难以进行直接比较，并阻碍了系统性提升安全性能的集体进步。为解决此局限，我们提出了**RiskBench**，一个大规模的情景式风险识别基准测试平台。我们设计了一种情景分类体系与数据增强流程，以系统化地收集多样化情景下的真实风险标注数据。我们评估了十种算法在以下三方面的能力：（1）检测与定位风险；（2）预判风险；（3）辅助决策制定。通过大量实验，我们总结了风险识别领域的未来研究方向。本研究旨在推动协作努力，共同迈向零碰撞社会。我们已将数据集与基准测试工具包公开在项目页面：https://hcis-lab.github.io/RiskBench/