{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:25:54Z","timestamp":1773800754827,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1609\/aaai.v40i1.37045","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:45:07Z","timestamp":1773787507000},"page":"782-790","source":"Crossref","is-referenced-by-count":0,"title":["Predict and Resist: Long-Term Accident Anticipation Under Sensor Noise"],"prefix":"10.1609","volume":"40","author":[{"given":"Xingcheng","family":"Liu","sequence":"first","affiliation":[]},{"given":"Bin","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Yanchen","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Chengyue","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haicheng","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Jiaxun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chengyu","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Meixin","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Zhenning","family":"Li","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37045\/41007","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37045\/41007","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:45:08Z","timestamp":1773787508000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i1.37045","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}