{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:28:12Z","timestamp":1773800892505,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Precise detection of driver mental fatigue is critical for reducing traffic accidents and enhancing road safety. Compared with vision-based detection\u2014which is susceptible to  illumination and  occlusion\u2014multimodal physiological\u2011signal-based approaches integrate complementary information from diverse biosignals,  delivering more faithful and objective fatigue assessments. However, adverse factors such as motion artifacts and environmental noise induce   ceaseless   deterioration  to physiological signals, which markedly  degrade the performance of existing multimodal fusion methods. To address this challenge, we propose Multimodal Uncertainty-based Self-driven Evolution, MUSE,  reallocating modality contributions in real time via  overall uncertainty minimization, thereby enabling efficient collaborative fusion of multi\u2010source predictions. Theoretically, MUSE guarantees  a provably bounded cumulative error,  and its generalization error approaches  the Bayesian\u2011optimal fusion as iterations progress.  Operating in a closed loop without labels or manual recalibration,  MUSE presents superior suitability for real\u2011world driving scenarios compared to supervised algorithms.  On the large\u2011scale driving fatigue dataset  SEED\u2011VIG, MUSE  outperforms existing models in both classification and regression tasks, substantiating its robustness and practicality as a promising driving fatigue detection solution.<\/jats:p>","DOI":"10.1609\/aaai.v40i2.37085","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:50:30Z","timestamp":1773787830000},"page":"1141-1149","source":"Crossref","is-referenced-by-count":0,"title":["MUSE: Multimodal Uncertainty-Based Self-Driven Evolution for Robust Physiological-Signal\u2013Based Driver Fatigue Detection"],"prefix":"10.1609","volume":"40","author":[{"given":"Jiaheng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Si","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honglin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"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\/37085\/41047","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37085\/41047","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:50:31Z","timestamp":1773787831000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i2.37085","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]]}}}