Enhancing Autonomous Driving through Dual-Process Learning with Behavior and Reflection Integration

Enhancing Autonomous Driving through Dual-Process Learning with Behavior and Reflection Integration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Autonomous Driving through Dual-Process Learning with Behavior and Reflection Integration Xiao Zhang, Kangsheng Wang, Tianyu Hu, Huimin Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6223799/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Contemporary autonomous driving (AD) methodologies, which predominantly convert visual features into control directives, face long-tail challenges due to constraints imposed by limited data distribution. Conversely, human drivers exhibit proficiency in such conditions, underscoring the significance of emulating human cognition in AD systems. Therefore, we introduce Dual-Process Learning (D-PL) approach for cognitiveenhanced decision-making. Inspired by dual-process theory, the D-PL method combines Behavior Pattern Learning (BPL) and Self-Reflective Learning (SRL) to integrate quick, intuitive decisions with deliberate, analytical reasoning, constructing a hierarchical decision model for sophisticated trajectory planning. Our approach improves decision-making, enhances adaptability, and tackles the crucial open-world generalization challenge encountered by current AD methods. Comprehensive evaluations on the nuScenes dataset validate the robustness of our method, demonstrating its superior performance in navigating the intricacies of real-world contrasting with conventional models. Cognitive Autonomous Driving Large Language Model Dual-Process Theory Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6223799","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428660991,"identity":"05003361-05bd-49e2-abfe-0c3700a865f5","order_by":0,"name":"Xiao Zhang","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Zhang","suffix":""},{"id":428660992,"identity":"f79f2aa2-7308-49cb-93d6-a94dc01a84d0","order_by":1,"name":"Kangsheng Wang","email":"","orcid":"https://orcid.org/0009-0009-8392-4148","institution":"University of 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