{"id":40,"date":"2025-06-02T17:36:22","date_gmt":"2025-06-02T17:36:22","guid":{"rendered":"https:\/\/webpublishing.oit.ncsu.edu\/icel-ise-ncsu-edu\/?page_id=40"},"modified":"2026-01-21T20:03:24","modified_gmt":"2026-01-21T20:03:24","slug":"explainable-ai-algorithm-for-driving-decision-making","status":"publish","type":"page","link":"https:\/\/webpublishing.oit.ncsu.edu\/icel-ise-ncsu-edu\/research\/explainable-ai-algorithm-for-driving-decision-making\/","title":{"rendered":"Explainable AI Algorithm for Driving Decision-Making"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n

Last Updated<\/strong>: 01\/21\/2026 | All information is accurate and up-to-date<\/p>\n\n\n\n

Explainable AI Algorithm for Driving Decision-Making<\/h2>\n\n\n\n

Key Features<\/strong><\/p>\n\n\n\n

    \n
  1. Implicit visual-semantic module captures the region-based action-inducing components (implicit visual semantics) to provide a human-understandable explanation;<\/li>\n\n\n\n
  2. Explicit reasoning module is developed to jointly align the human-annotated explanation and intention prediction in a multi-task fashion.<\/li>\n<\/ol>\n\n\n\n
    \"A<\/figure>\n\n\n\n

    Multi-modal Feedback for Explainable Driving Decision-making<\/strong><\/p>\n\n\n\n

    \"A<\/figure>\n\n\n\n

    Key Features<\/strong><\/p>\n\n\n\n

      \n
    1. Multi-head-attention-based self-learning scene graph;<\/li>\n\n\n\n
    2. Adjustable balance between global and local features, which determines automatic and human-guided feature weighting.<\/li>\n<\/ol>\n\n\n\n
      \"A<\/figure>\n\n\n\n
      \"A<\/figure>\n\n\n\n
        \n
      • Action prediction accuracy is optimal at balanced \u03bb;<\/li>\n\n\n\n
      • Explanation accuracy always increases.<\/li>\n<\/ul>\n\n\n\n
        \"Several
        Multi-task learning boosting<\/figcaption><\/figure>\n\n\n\n

        <\/p>\n","protected":false},"excerpt":{"rendered":"

        Last Updated: 01\/21\/2026 | All information is accurate and up-to-date Explainable AI Algorithm for Driving Decision-Making Key Features Multi-modal Feedback for Explainable Driving Decision-making Key Features<\/p>\n","protected":false},"author":12816,"featured_media":0,"parent":36,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"source":"","ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"ncst\/floating-box-header","ncst_dynamicHeaderData":"{\"backgroundColor\":\"custombg_one\",\"boxPosition\":\"left\",\"ctaNum\":\"one\",\"useCTA\":false,\"imageID\":102,\"imageURL\":\"https:\/\/webpublishing.oit.ncsu.edu\/icel-ise-ncsu-edu\/wp-content\/uploads\/sites\/372\/2025\/06\/cognitive-ergonomics-intelligent-systems-research-featured-image-10-2024-01.jpg\",\"imageAlt\":\"A one-armed, research robot working in the Intelligent Cognitive Ergonomics Lab.\",\"pageIntro\":\"ICEL's research in AI 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