TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction

2026-06-23

AI Summary

This work addresses the challenge of reconstructing physically consistent vehicle trajectories from sparse and heterogeneous evidence in traffic accident analysis, a task where existing approaches often prioritize semantic or visual plausibility at the expense of quantitative geometric and dynamic fidelity. The paper introduces the first training-free, closed-loop structured inference framework that formulates reconstruction as an iterative process of motion hypothesis generation and refinement anchored to event-specific cues. By integrating geometric, kinematic, and interaction constraints—and augmented with a structured case memory and a consistency diagnostic mechanism—the method enables interpretable corrections even under evidentiary scarcity. Evaluated on real-world accident data, the approach significantly outperforms both data-driven and purely physics-based baselines in terms of trajectory geometric fidelity, velocity consistency, and collision accuracy.

Abstract

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.

Problem

• traffic accident reconstruction
• forensic inverse problem
• physically consistent motion
• geometric fidelity
• kinematic constraints

Innovation

• training-free
• closed-loop inference
• structured reasoning
• traffic accident reconstruction
• physics-consistent motion