Progressive Risk Estimation for Accident Anticipation

arXiv:2609.32811 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Comments: Accepted to NeurIPS 2026

Code: https://github.com/giddyyupp/PRE-ACT

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 11× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• IEEE International Conference on Image Processing — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
• iPad: Iterative Proposal-centric End-to-End Autonomous Driving
• Fail2Drive: Benchmarking Closed-Loop Driving Generalization
• Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization
• Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark
• VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
• LIV: Language-Image Representations and Rewards for Robotic Control
• R3M: A Universal Visual Representation for Robot Manipulation
• Vision-Language Models as Success Detectors
• RoboReward: General-Purpose Vision-Language Reward Models for Robotics
• Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation
• Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
• OpenGVL -- Benchmarking Visual Temporal Progress for Data Curation
• Vision Language Models are In-Context Value Learners
• TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics
• The Kinetics Human Action Video Dataset
• Gaussian Error Linear Units (GELUs)
• DINOv2: Learning Robust Visual Features without Supervision
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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