  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/
