Predict and Resist: Long-Term Accident Anticipation under Sensor Noise

• URL: http://arxiv.org/abs/2511.08640v1
• Date: Thu, 13 Nov 2025 01:01:18 GMT
• Title: Predict and Resist: Long-Term Accident Anticipation under Sensor Noise
• Authors: Xingcheng Liu, Bin Rao, Yanchen Guan, Chengyue Wang, Haicheng Liao, Jiaxun Zhang, Chengyu Lin, Meixin Zhu, Zhenning Li,
• Abstract summary: Accident anticipation is essential for proactive and safe autonomous driving.<n>Two key challenges hinder real-world deployment: noisy or degraded sensory inputs from weather, motion blur, or hardware limitations.<n>We propose a unified framework that integrates diffusion-based denoising with a time-aware actor-critic model to address these challenges.
• Score: 14.759047709239788
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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