Traffic Anomaly Detection

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11 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding
Aug 26, 2026Yuqiang Lin, Yan Shi, Sam Lockyer +5Traffic Anomaly Detection Anomaly Detection
University of Bath, Bath BA2 7AY, UK · University of Washington, Seattle, WA 98195, USA
The 10th AI City Challenge
Aug 17, 2026Zheng Tang, Shuo Wang, David C. Anastasiu +34Traffic Anomaly Detection Intelligent Transportation Systems
NVIDIA · Santa Clara University · University at Albany, SUNY +9
UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations
Aug 13, 2026Peng Li, Qianqian Xu, Shilong Bao +2Pedestrian Intention Prediction Intelligent Transportation Systems
School of Computer Science and Technology, University of Chinese Academy of Sciences (UCAS), Beijing, China · State Key Laboratory of AI Safety, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China · Beijing Academy of Artificial Intelligence (BAAI), Beijing, China +1
From Detection to Understanding: TAR and TAR-Bench for Multi-Task Traffic Anomaly Reasoning
Aug 10, 2026Han Zhang, Yilin Zhao, Zaid Pervaiz Bhat +5Video-Language Model Evaluation Video Anomaly Detection
NVIDIA · Santa Clara University
Drift-Aware RL-based Wavelet Denoising for Network-Traffic Anomaly Detection
Jul 22, 2026Priyalakshmi Sheela, Indrakshi DeyReinforcement Learning Concept Drift
Walton Institute, Department of Computing, South East Technological University, Waterford, Ireland
From Mobile Data to Business Insights: An End-to-End Analytics Framework for Large-Scale Urban Mobility Analysis and Decision Support
Jul 3, 2026Thiago Andrade, Shazia Tabassum, Miguel E. P. Silva +2Intelligent Transportation Systems Traffic Anomaly Detection
LIAAD, INESC-TEC, Porto, Portugal. · Mobile Network Analytics, NOS SGPS, Lisboa, Portugal.
A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks
Jun 9, 2026Carlos Natalino, Flávia P. Monteiro, Paolo MontiFew-Shot Learning Siamese Neural Networks
Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden · Federal University of Western Par´a (UFOPA), 68040-255 Santar´em, Par´a, Brazil
Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twins
May 3, 2026Rei Tamaru, Pei Li, Bin RanRepresentation Learning Digital Twins
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA · Department of Civil and Architecture Engineering and Construction Management, University of Wyoming, Laramie, WY, USA
Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference
Apr 26, 2026Xiaowei Mao, Bowen Sui, Weijie Zhang +7Efficient VLM Inference Anomaly Localization
School of Computer Science and Technology, Beijing Jiaotong University · 2Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education · 3Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence
Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic
Apr 17, 2026Yasmin Souza Lima, Rodrigo Moreira, Larissa F. Rodrigues Moreira +2Network Intrusion Detection Unsupervised Anomaly Detection
Institute of Exact and Technological Sciences Federal University of Viçosa (UFV) – MG – Brazil · University of São Paulo (USP) 05.508-010 – São Paulo – SP – Brazil · Department of Informatics – School of Engineering University of Minho (UMinho) – Braga – Portugal
TAU-R1: Visual Language Model for Traffic Anomaly Understanding
Mar 19, 2026Yuqiang Lin, Kehua Chen, Sam Lockyer +12Reasoning for VLMs Traffic Anomaly Detection
University of Bath, BA2 7AY, United Kingdom · University of Washington, WA 98105, USA · City of Carmel, Indiana, USA +1

Aug 26, 2026J/K move · Enter open · S save

TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding

Yuqiang Lin, Yan Shi, Sam Lockyer, Harish Tayyar Madabushi+4

University of Bath, Bath BA2 7AY, UK · University of Washington, Seattle, WA 98195, USA

Traffic Anomaly Detection Anomaly Detection Video Reasoning Agentic RAG

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Traffic Anomaly Understanding (TAU) requires models and systems to detect, reason about, and explain anomalous events in transportation videos. To address this challenge, we propose TAU-Agent, an agentic retrieval-augmented framework for traffic anomaly understanding. Given a task query, a central retrieval agent orchestrates two visual perception tools, namely a Video Captioning Tool and an Open-Vocabulary Tracking Tool, to retrieve and select query-relevant evidence, including captions, temporal intervals, and object trajectories. The selected evidence, together with sampled video frames and the input query, is provided to a supervised fine-tuned vision-language model for final reasoning and answer generation. We evaluate TAU-Agent on both the in-domain and the out-of-domain benchmarks from the AI City Challenge 2026. TAU-Agent achieves scores of 0.6779 on Track 3, 0.3998 on Track 7, and 67.9275 on Track 8, ranking second, twelfth, and fifth, respectively. Code is available at: https://github.com/siri-rouser/TAU-Agent.