aceToward explainable traffic crash liability analysis via neural-symbolic multimodal reasoning: Integrating visual perception with legal knowledge graphs
Determining traffic crash liability relies on rigorous on-site evidence and legal provisions and involves substantial manual intervention and potential human error. However, intelligent traffic monitoring systems focus on detection and tracking and cannot provide structured, rule-grounded decision support for formal crash-liability analysis. Moreover, emerging multimodal large language models (MLLMs) suffer from object hallucination that distorts or omits physical evidence, and their opaque reasoning cannot prod…
On the DeepAccident benchmark, grounding crash analysis in predefined legal rules cut object hallucination to 2.8% and reached 94.0% legal accuracy, giving reviewers traceable evidence-to-rule chains.
The reported results come from a held-out benchmark under a fixed legal-output evaluation protocol, and the system yields only preliminary assessments intended for human review.
Evidence
- Peer-reviewedAccident Analysis & Prevention2026-10-14
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Truvace Impact Record TRV-2026-1404, v1: “Toward explainable traffic crash liability analysis via neural-symbolic multimodal reasoning: Integrating visual perception with legal knowledge graphs.” Truvace, 2026-10-15. /record/TRV-2026-1404 (accessed at citation time). sha256 ede8330683de8aef…
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