无需训练，通过闭环推理重构交通事故，更贴近专家分析流程。
TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction
- 构建事件锚定的运动假设，迭代优化满足几何与动力学约束
- 在真实事故数据上提升几何保真度、速度一致性与碰撞精度
- 适合需要可解释性与人工干预的司法取证场景
交通事故重建是一项需要从稀疏异构证据中恢复物理一致运动的法医逆问题。现有基于学习的方法主要优化语义合理性或视觉真实性，而非与可测量的几何和动力学量一致。本文提出TRACER，一种无需训练的闭合回路结构化推理框架。该框架不直接生成稠密轨迹，而是基于结构化案例记忆和一致性驱动诊断，在几何、运动学及交互约束下构建并迭代修正事件锚定的运动假设。此设计支持证据不足时的渐进式、可解释修正，使重建过程更贴近人类专家的工作流程。在真实事故数据上的实验表明，TRACER在几何保真度、速度一致性与碰撞准确率上均优于数据驱动和物理基基线方法。
原文摘要 · Abstract (English)
Traffic accident reconstruction is a forensic inverse problem that requires recovering physically consistent motion from sparse and heterogeneous evidence. Existing learning-based approaches predominantly optimize for semantic plausibility or visual realism, rather than quantitative agreement with measurable geometry and dynamics. Here, we present TRACER, a training-free framework that formulates reconstruction as a closed-loop structured inference process. Instead of directly generating dense trajectories, our framework constructs and iteratively refines event-anchored motion hypotheses under geometric, kinematic, and interaction constraints, guided by structured case memory and consistency-driven diagnosis. This design enables incremental, interpretable corrections when evidence is insufficient, making the accident reconstruction process more aligned with the workflow of human experts. Experiments on real-world accident data show that TRACER achieves improved geometric fidelity, velocity consistency, and collision accuracy over both data-driven and physics-based baselines.
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