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

2025-11-10

AI Summary

Addressing two key challenges in long-horizon accident prediction for autonomous driving—sensor noise interference and the trade-off between early warning timeliness and false-alarm control—this paper proposes a unified framework integrating diffusion-based denoising with time-aware reinforcement decision-making. Methodologically: (1) a diffusion module iteratively denoises image and object features to enhance robustness against sensor noise; (2) a time-weighted actor-critic model jointly optimizes early detection and false-positive suppression, incorporating a dynamic reward mechanism into long-sequence modeling to precisely identify the optimal alarm-triggering moment. Evaluated on three benchmarks—DAD, CCD, and A3D—the approach achieves state-of-the-art performance, significantly advancing average warning time while maintaining stability under Gaussian and impulse noise, thereby balancing foresight and reliability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Anticipating accidents under noisy sensor inputs
• Balancing early warning with false-alarm suppression
• Maintaining prediction reliability under sensor degradation

Innovation

• Diffusion-based denoising reconstructs noise-resilient features
• Actor-critic model uses time-weighted rewards for alert timing
• Unified framework integrates denoising with temporal reasoning