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The transformer-based model, with multimodal data fusion, achieved the best performance with an accuracy of 93.02%, significantly surpassing EEG-only (80.80%) and eye movement and operational data-only models (78.46%). This highlights the transformer\u2019s superior ability to capture complex spatiotemporal correlations in multimodal data. Additionally, pre-extracted EEG frequency domain features could improve model performance, though less significantly than changes in model architecture. Embedding this system into autonomous driving systems is expected to enhance their ability to quickly and accurately recognize and respond to dangerous scenarios.<\/jats:p>","DOI":"10.1142\/s2301385025500864","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T23:09:38Z","timestamp":1730848178000},"page":"143-156","source":"Crossref","is-referenced-by-count":0,"title":["Equipping with Human Cognition: Driver Intention Recognition with Multimodal Information Fusion"],"prefix":"10.1142","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8916-3419","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, P.\u00a0R.\u00a0China"},{"name":"National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, P. R. 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