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Proceedings of the AAAI Conference on Artificial Intelligence

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Predict and Resist: Long-Term Accident Anticipation Under...
Xingcheng Li · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Xingcheng Liu University of Macau
  • Bin Rao University of Macau
  • Yanchen Guan University of Macau
  • Chengyue Wang University of Macau
  • Haicheng Liao University of Macau
  • Jiaxun Zhang University of Macau
  • Chengyu Lin Zhejiang University
  • Meixin Zhu Southeast University
  • Zhenning Li University of Macau

DOI:

https://doi.org/10.1609/aaai.v40i1.37045

Abstract

Accident anticipation is essential for proactive and safe autonomous driving, where even a brief advance warning can enable critical evasive actions. However, two key challenges hinder real-world deployment: (1) noisy or degraded sensory inputs from weather, motion blur, or hardware limitations, and (2) the need to issue timely yet reliable predictions that balance early alerts with false-alarm suppression. We propose a unified framework that integrates diffusion-based denoising with a time-aware actor-critic model to address these challenges. The diffusion module reconstructs noise-resilient image and object features through iterative refinement, preserving critical motion and interaction cues under sensor degradation. In parallel, the actor-critic architecture leverages long-horizon temporal reasoning and time-weighted rewards to determine the optimal moment to raise an alert, aligning early detection with reliability. Experiments on three benchmark datasets (DAD, CCD, A3D) demonstrate state-of-the-art accuracy and significant gains in mean time-to-accident, while maintaining robust performance under Gaussian and impulse noise. Qualitative analyses further show that our model produces earlier, more stable, and human-aligned predictions in both routine and highly complex traffic scenarios, highlighting its potential for real-world, safety-critical deployment.

How to Cite

Liu, X., Rao, B., Guan, Y., Wang, C., Liao, H., Zhang, J., … Li, Z. (2026). Predict and Resist: Long-Term Accident Anticipation Under Sensor Noise. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 782–790. https://doi.org/10.1609/aaai.v40i1.37045

Issue

Section

AAAI Technical Track on Application Domains I

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