mlsanityData to 14 Oct 2026

RiskBench: A Scenario-based Benchmark for Risk Identification

arXiv 2312.01659 ↗First posted 4 Dec 2023cs.CV cs.LG cs.ROcited by 12

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Abstract (arXiv)

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 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/

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 54 of its 84 references that are in the map. First posted on arXiv 4 Dec 2023.

Builds on

54 of 84 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 5.

12 papers.

  1. 2025What Changed and What Could Have Changed? State-Change Counterfactuals for Procedure-Aware Video Representation Learningcited by 6
  2. 2025Controllable Collision Scenario Generation via Collision Pattern Predictioncited by 3
  3. 2024Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identificationcited by 2
  4. 2025Towards Vision Zero: The TUM Traffic Accid3nD Datasetcited by 2
  5. 2025ATARS: An Aerial Traffic Atomic Activity Recognition and Temporal Segmentation Datasetcited by 2
  6. 2025What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studiescited by 1
  7. 2024On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidancecited by 0
  8. 2026RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Modelscited by 0
  9. 2026Uncertainty-Aware Vision-based Risk Object Identification via Conformal Risk Tube Predictioncited by 0
  10. 2026SceneMiner: Identity-Preserving Multi-Task Fine-Tuning for Unified BEV Scene Miningcited by 0
  11. 2026Action-Slot: Structured Action-Centric Representation Learning for Multi-Agent Atomic Activity Understandingcited by 0
  12. 2026Combining Hierarchical Cognitive Process with Process Supervision for Interpretable Scene Safety Understandingcited by 0