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，这需要跨学科的努力来提升安全性能。本工作聚焦于风险识别——即识别并分析由动态交通参与者及突发事件所引发风险的过程。尽管学术界已取得显著进展，但目前不同风险识别算法的评估采用独立数据集，导致难以进行直接比较，并阻碍了安全性能提升方面的集体进步。为解决这一局限，我们提出了大规模场景化基准测试\textbf{RiskBench}。我们设计了场景分类法与增强流水线，以系统性地收集多样化场景下的真值风险。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）预测风险，以及（3）辅助决策制定。我们开展了大量实验，并总结了风险识别的未来研究方向。我们的目标是激励协同努力，以迈向零碰撞的社会。我们已将数据集与基准测试工具包在项目页面公开：https://hcis-lab.github.io/RiskBench/