Traffic Accident Anticipation

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Traffic accident anticipation studies how to estimate danger before a collision occurs, either from video over the next seconds or from spatial and temporal patterns in road-crash data. Video methods use temporal transformers, object or graph reasoning, vision-language models, and future-feature prediction; systems may also model risk as a changing curve to improve warning lead time. Geographic forecasting commonly uses spatiotemporal networks such as ConvLSTMs and CNN–Transformers to identify risky road segments or emerging crash hotspots, while video methods are tested on benchmarks such as DAD, DoTA, DADA-2000, CAP, and Nexar. The central challenge is giving early, reliable warnings without excessive false alarms across rare events and unfamiliar settings, alongside concerns about label quality and whether reported gains beat strong historical baselines.

Latest papers

22 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Replication Failure and Trivial Baselines in Road-Level Crash Prediction
Sep 28, 2026Maurya PatelTraffic Accident Anticipation Graph Neural Networks
School of Computer Science and Engineering University of Westminster London, United Kingdom
Progressive Risk Estimation for Accident Anticipation
Sep 26, 2026Samet Hicsonmez, Eray Çakar, Nermin Samet +1Traffic Accident Anticipation Autonomous Driving
Independent Researcher, Paris, France · Department of Computer Engineering, KUIS AI, Koç University, Istanbul, Turkey · Valeo.ai, Paris, France
Observe Before You Alert: Adaptive Driver Alerting with Vision-Language Models
Sep 8, 2026Yuhang Wang, Lingyao Li, Hao ZhouActive Perception VLMs for Autonomous Driving
Department of Civil Engineering University of South Florida United States · School of Information University of South Florida United States
SynCrash: A Multi-Stage Pipeline for Zero-Shot Accident Detection and Localization in Traffic Surveillance Video
Aug 30, 2026Arkya Jyoti Bagchi, Ritul Jangir, Varun RaskarTraffic Accident Anticipation Anomaly Localization
Indian Institute of Technology Jodhpur, India
Forecasting the Emergence and Evolution of Crash Hotspots: A Unified Deep Learning Framework for Proactive Traffic Safety
Jul 27, 2026Jingwen Zhu, Keshu Wu, Pei Li +3Intelligent Transportation Systems Traffic Accident Anticipation
Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, United States · Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, United States · Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, United States
CARA: Concept-Aware Risk Attention for Interpretable Collision Anticipation
Jul 24, 2026Zhishan Tao, Ruoyu Wang, Yucheng Wu +6Autonomous Driving Traffic Accident Anticipation
Shanghai Jiao Tong University · The University of Hong Kong · Fudan University +3
MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction
Jul 20, 2026Zhen Yu, Yachao Yuan, Zixiang Peng +2Traffic Accident Anticipation Long Short-Term Memory Networks
School of Future Science and Engineering, Soochow University, Suzhou, Jiangsu, China · Key Laboratory of General Artificial Intelligence and Large Models in Provincial Universities, Soochow University, Suzhou, Jiangsu, China · College of Computing and Intelligent Systems, University of Khorfakkan, Sharjah, UAE
FLaRA: Predicting Future Latent Representations for Accident Anticipation
Jun 12, 2026Lorenzo Caselli, Tomaso Trinci, Tommaso Bianconcini +4Autonomous Driving Traffic Accident Anticipation
Department of Information Engineering, University of Florence, Italy. · 2Verizon Connect, Florence, Italy.
A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation
Jun 8, 2026Siyuan Li, Xiaoyang Bi, Mengshi QiZero-Shot Learning Autonomous Driving
State Key Laboratory of Networking and Switching Technology · Beijing University of Posts and Telecommunications
PaSBench-Video: A Streaming Video Benchmark for Proactive Safety Warning
Jun 1, 2026Yusong Zhao, Yuejin Xie, Youliang Yuan +4Traffic Accident Anticipation Video-Language Models
The Chinese University of Hong Kong, Shenzhen · Tsinghua University
SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
May 27, 2026Kangyu Wu, Peng Cui, Guoxi Chen +1Autonomous Driving Traffic Accident Anticipation
School of Automation, Southeast University, Nanjing, China · Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China
Road Risk Monitor: A Deployable U.S. Road Incident Forecasting System with Live Weather and Road-Level Tiles
May 5, 2026Anton IvchenkoIntelligent Transportation Systems Traffic Accident Anticipation
Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation
Apr 29, 2026Yanchen Guan, Haicheng Liao, Chengyue Wang +4Autonomous Driving Datasets Synthetic Data Augmentation
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau SAR, 999078, China. · Department of Civil Engineering, University of Macau, Macau SAR, 999078, China. · Department of Computer and Information Science, University of Macau, Macau SAR, 999078, China. +1
VAGNet: Vision-based Accident Anticipation with Global Features
Apr 10, 2026Vipooshan Vipulananthan, Charith D. ChitraranjanTraffic Accident Anticipation Autonomous Driving
Department of Computer Science and Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka
Beyond the Beep: Scalable Collision Anticipation and Real-Time Explainability with BADAS-2.0
Apr 7, 2026Roni Goldshmidt, Hamish Scott, Lorenzo Niccolini +1Traffic Accident Anticipation Long-Tail Autonomous Driving
Nexar AI
SECURE: Stable Early Collision Understanding via Robust Embeddings in Autonomous Driving
Apr 1, 2026Wenjing Wang, Wenxuan Wang, Songning LaiTraffic Accident Anticipation Adversarial Robustness
Xiamen University Malaysia · Xinjiang University · HKUST(GZ)
RiskProp: Collision-Anchored Self-Supervised Risk Propagation for Early Accident Anticipation
Mar 28, 2026Yiyang Zou, Tianhao Zhao, Peilun Xiao +9Monotonicity Constraints Traffic Accident Anticipation
School of Computer Science, Wuhan University · Zhongguancun Academy, Beijing, China · Didi Chuxing +1
Collision-Aware Vision-Language Learning for End-to-End Driving with Multimodal Infraction Datasets
Mar 26, 2026Alex Koran, Dimitrios Sinodinos, Hadi Hojjati +3End-to-End Autonomous Driving Traffic Accident Anticipation
McGill University, Montréal, Canada · Mila - Québec AI Institute, Montréal, Canada · Nissan Motor Corporation, Yokohama, Japan
VLM-AutoDrive: Post-Training Vision-Language Models for Safety-Critical Autonomous Driving Events
Mar 18, 2026Mohammad Qazim Bhat, Yufan Huang, Niket Agarwal +7VLM Post-Training VLMs for Autonomous Driving
NVIDIA, California, USA
A Lightweight Digital-Twin-Based Framework for Edge-Assisted Vehicle Tracking and Collision Prediction
Mar 7, 2026Murat Arda Onsu, Poonam Lohan, Burak Kantarci +3Traffic Accident Anticipation Intelligent Transportation Systems
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada. · Nokia Bell Labs, 600 March Road, Kanata, ON K2K 2E6, Canada.
Weather-Related Crash Risk Forecasting: A Deep Learning Approach for Heterogenous Spatiotemporal Data
Mar 4, 2026Abimbola Ogungbire, Srinivas PulugurthaTraffic Accident Anticipation Spatiotemporal Forecasting
Traffic Engineering, Gresham Smith, Alpharetta, 30009, GA - USA. · Civil Engineering, University of Mississippi, Oxford, 38677, MS - USA.
Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features
Feb 17, 2026Lei Han, Mohamed Abdel-Aty, Zubayer Islam +1Traffic Accident Anticipation Intelligent Transportation Systems
Smart and Safe Transportation lab at the Department of Civil, Environmental and Construction Engineering, University of Central Florida, USA

Sep 28, 2026J/K move · Enter open · S save

Replication Failure and Trivial Baselines in Road-Level Crash Prediction

Maurya Patel

School of Computer Science and Engineering University of Westminster London, United Kingdom

Traffic Accident Anticipation Graph Neural Networks ML Reproducibility

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Graph neural networks are increasingly applied to road-level crash prediction, but the stability of their reported gains has received little scrutiny. We independently reconstruct the data pipeline of a recent uncertainty-aware model and evaluate eleven of its design decisions across three London boroughs under an expanding-window protocol. Four survive replication on a second borough; seven do not, and four of those reverse sign rather than attenuate. Multi-seed evaluation is decisive: one effect reverses sign between random seeds within a single borough, and the reference architecture exhibits per-borough seed spreads of up to 35.7 points against 4 points for ours. We further compare both networks against a parameter-free baseline that ranks segments by cumulative past crash count. At matched history depth our model is statistically indistinguishable from that baseline (

-0.90

points,

p=0.61

), and the reference architecture loses to it on 18 of 18 held-out windows (

-17.37

p<10^{-6}

). Sweeping the baseline's lookback horizon shows it spans 22.71% to 83.94% accuracy on that variable alone, and that every published figure in this line of work is matched by the baseline at a horizon of one to five years. We argue that the apparent margin of graph networks over historical baselines in this task is substantially an artefact of the short horizons those baselines were computed over, and recommend horizon-matched baselines and multi-seed reporting as minimum practice.