TAU-R1: Visual Language Model for Traffic Anomaly Understanding

TL;DR

This paper introduces TAU-R1, a novel vision-language framework for traffic anomaly understanding, supported by a new real-world dataset, achieving effective classification and reasoning in traffic safety applications.

Contribution

The paper presents a new dataset, Roundabout-TAU, and a two-layer TAU-R1 model with a specialized training strategy for improved traffic anomaly understanding.

Findings

TAU-R1 achieves high accuracy in anomaly classification.

The framework effectively generates detailed event summaries.

The dataset facilitates comprehensive traffic anomaly analysis.

Abstract

Traffic Anomaly Understanding (TAU) is important for traffic safety in Intelligent Transportation Systems. Recent vision-language models (VLMs) have shown strong capabilities in video understanding. However, progress on TAU remains limited due to the lack of benchmarks and task-specific methodologies. To address this limitation, we introduce Roundabout-TAU, a dataset constructed from real-world roundabout videos collected in collaboration with the City of Carmel, Indiana. The dataset contains 342 clips and is annotated with more than 2,000 question-answer pairs covering multiple aspects of traffic anomaly understanding. Building on this benchmark, we propose TAU-R1, a two-layer vision-language framework for TAU. The first layer is a lightweight anomaly classifier that performs coarse anomaly categorisation, while the second layer is a larger anomaly reasoner that generates detailed event summaries. To improve task-specific reasoning, we introduce a two-stage training strategy consisting of decomposed-QA-enhanced supervised fine-tuning followed by TAU-GRPO, a GRPO-based post-training method with TAU-specific reward functions. Experimental results show that TAU-R1 achieves strong performance on both anomaly classification and reasoning tasks while maintaining deployment efficiency. The dataset and code are available at: https://github.com/siri-rouser/TAU-R1