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AccidentBench: Benchmarking Multimodal Understanding and Reasoning in Vehicle Accidents and Beyond

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Multimodal Understanding and ReasoningLarge-Scale DatasetTraffic AccidentLand SpaceAirplane NavigationShip Motion
Abstract:

Rapid advances in multimodal models demand benchmarks that rigorously evaluate understanding and reasoning in safety-critical, dynamic real-world settings. We present AccidentBench, a large-scale benchmark that combines vehicle accident scenarios with Beyond domains, safety-critical settings in air and water that emphasize spatial and temporal reasoning (e.g., navigation, orientation, multi-vehicle motion). The benchmark contains approximately 2000 videos and over 19000 human-annotated question--answer pairs spanning multiple video lengths (short/medium/long) and difficulty levels (easy/medium/hard). Tasks systematically probe core capabilities: temporal, spatial, and intent understanding and reasoning. By unifying accident-centric traffic scenes with broader safety-critical scenarios in air and water, AccidentBench offers a comprehensive, physically grounded testbed for evaluating models under real-world variability. Evaluations of state-of-the-art models (e.g., Gemini-2.5 Pro and GPT-5) show that even the strongest models achieve only about 18% accuracy on the hardest tasks and longest videos, revealing substantial gaps in real-world temporal, spatial, and intent reasoning. AccidentBench is designed to expose these critical gaps and drive the development of multimodal models that are safer, more robust, and better aligned with real-world safety-critical challenges. The code and dataset are available at: http://accident-bench.site

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

AccidentBench contributes a large-scale benchmark combining vehicle accident scenarios with safety-critical settings in air and water, featuring approximately 2000 videos and over 19000 human-annotated question-answer pairs. The paper resides in the 'Driving Scenario Understanding and Reasoning Benchmarks' leaf, which contains four papers total. This leaf sits within the broader 'Autonomous Driving and Traffic Safety Applications' branch, indicating a moderately populated research direction focused on evaluating vision-language models in driving contexts. The taxonomy shows this is an active but not overcrowded area, with sibling papers addressing related but distinct aspects of driving scenario evaluation.

The taxonomy reveals several neighboring research directions that contextualize AccidentBench's position. Adjacent leaves include 'Safety-Critical Event Detection and Analysis' (six papers on accident detection methods) and 'Trajectory Prediction and Risk Assessment' (two papers on predictive modeling). The broader branch encompasses scenario generation, decision-making, and perception integration, totaling approximately 24 papers across seven leaves. AccidentBench bridges scenario understanding with event analysis by providing a structured evaluation framework, while explicitly excluding trajectory prediction and synthetic scenario generation—domains covered by neighboring leaves. This positioning suggests the work addresses a gap between detection-focused methods and pure scenario generation approaches.

Among 30 candidates examined, the contribution-level analysis shows mixed novelty signals. The core AccidentBench benchmark examined 10 candidates with zero refutable prior work, suggesting this specific combination of accident-centric and beyond-domain scenarios may be relatively novel within the limited search scope. However, the fine-grained reasoning evaluation framework and unified annotation suite each found one refutable candidate among 10 examined, indicating some overlap with existing benchmarking efforts. The statistics reflect a focused literature search rather than exhaustive coverage, so these findings characterize novelty relative to the most semantically similar recent work, not the entire field.

Based on the limited search scope of 30 semantically similar papers, AccidentBench appears to occupy a distinct position by unifying accident scenarios with air and water domains, though its evaluation methodology shows partial overlap with prior benchmarking frameworks. The taxonomy structure confirms this sits in an active research area with established neighboring work on event detection and scenario understanding. The analysis captures top-K semantic matches and does not claim exhaustive coverage of all potentially relevant prior work in multimodal safety evaluation or driving benchmarks.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: multimodal understanding and reasoning in safety-critical scenarios. The field organizes around four main branches that reflect distinct emphases and application domains. Multimodal Model Safety Alignment and Robustness focuses on ensuring that vision-language models behave safely under adversarial or edge-case inputs, addressing vulnerabilities such as jailbreaks and cross-modal misalignments (e.g., Cross-Modality Safety Alignment[18], SafeMLRM[19]). Autonomous Driving and Traffic Safety Applications concentrates on scenario understanding, risk assessment, and reasoning benchmarks tailored to road environments, often leveraging multimodal large language models (MLLMs) for traffic event detection and trajectory prediction (MLLM Traffic Safety Events[3], Risk-Aware Trajectory Prediction[1]). Domain-Specific Safety-Critical Applications Beyond Driving extends multimodal reasoning to areas like construction site inspection and UAV swarm coordination (Construction Safety Inspection[29], MLLM UAV Swarm[21]). General Multimodal Reasoning and Content Understanding encompasses broader capabilities in vision-language tasks, providing foundational techniques that inform safety-critical work but are not exclusively safety-focused (GPT4Video[17], ReasonRec[31]). Within the driving-focused branch, a particularly active line of work develops benchmarks and datasets to evaluate MLLMs on accident scenarios and vulnerable road user interactions, contrasting methods that emphasize real-world crash data versus synthetic scenario generation (VRU-Accident[28], SynSHRP2[11]). AccidentBench[0] sits squarely in this cluster of Driving Scenario Understanding and Reasoning Benchmarks, providing a structured evaluation framework for accident analysis that complements nearby efforts like Vision LLMs Road-Ready[8] and Drive-CLIP[34]. While Vision LLMs Road-Ready[8] explores broader readiness of vision models for driving tasks and Drive-CLIP[34] targets contrastive learning for driving scenes, AccidentBench[0] emphasizes fine-grained reasoning about accident causality and safety-critical event sequences. This positioning highlights ongoing questions about how to balance synthetic versus naturalistic data, the granularity of reasoning required, and the trade-offs between general-purpose MLLM capabilities and domain-specific safety alignment.

Claimed Contributions

AccidentBench benchmark for safety-critical multimodal understanding and reasoning

The authors introduce AccidentBench, a comprehensive benchmark containing approximately 2,000 videos and over 19,000 human-annotated question-answer pairs. It systematically evaluates multimodal models on temporal, spatial, and intent understanding and reasoning across vehicle accidents (83%), airplane navigation (10.2%), and ship motion (6.8%) scenarios, spanning multiple video lengths and difficulty levels.

10 retrieved papers
Fine-grained reasoning evaluation framework across multiple dimensions

The benchmark provides a structured evaluation framework that probes three core capabilities (temporal, spatial, and intent understanding and reasoning) across three difficulty levels using interval-based and accuracy-based formats, allowing systematic assessment of model strengths and weaknesses in safety-critical reasoning tasks.

10 retrieved papers
Can Refute
Unified evaluation suite with high-quality human-annotated datasets

The authors develop a unified evaluation platform that combines diverse safety-critical scenarios across three domains (land, air, water) with rigorous human annotation. This framework enables comprehensive assessment of multimodal models in physically grounded, real-world settings that demand robust understanding and reasoning capabilities.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

AccidentBench benchmark for safety-critical multimodal understanding and reasoning

The authors introduce AccidentBench, a comprehensive benchmark containing approximately 2,000 videos and over 19,000 human-annotated question-answer pairs. It systematically evaluates multimodal models on temporal, spatial, and intent understanding and reasoning across vehicle accidents (83%), airplane navigation (10.2%), and ship motion (6.8%) scenarios, spanning multiple video lengths and difficulty levels.

Contribution

Fine-grained reasoning evaluation framework across multiple dimensions

The benchmark provides a structured evaluation framework that probes three core capabilities (temporal, spatial, and intent understanding and reasoning) across three difficulty levels using interval-based and accuracy-based formats, allowing systematic assessment of model strengths and weaknesses in safety-critical reasoning tasks.

Contribution

Unified evaluation suite with high-quality human-annotated datasets

The authors develop a unified evaluation platform that combines diverse safety-critical scenarios across three domains (land, air, water) with rigorous human annotation. This framework enables comprehensive assessment of multimodal models in physically grounded, real-world settings that demand robust understanding and reasoning capabilities.

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