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

TL;DR

This paper introduces a unified framework combining diffusion-based denoising and a time-aware actor-critic model to improve long-term accident anticipation in autonomous driving, especially under sensor noise, enabling earlier and more reliable alerts.

Contribution

It presents a novel integrated approach that enhances accident prediction robustness and timing accuracy under sensor noise conditions, advancing prior methods.

Findings

Achieves state-of-the-art accuracy on benchmark datasets.

Significantly increases mean time-to-accident in tests.

Maintains robustness under Gaussian and impulse noise.

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.