{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:23Z","timestamp":1761176243095,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Deep learning has revolutionized autonomous driving; nevertheless, its inherent opacity hinders explainability, an essential requirement for public trust and regulatory approval. Existing explainable autonomous driving research typically employs a multi-task framework, simultaneously generating driving actions and their corresponding explanations (collectively called categories). Most methods use a two-stage approach: extracting category-related features and modeling category correlations separately. This separation overlooks the potential synergy between these two processes. Moreover, existing approaches often rely on simple linear combinations of task-specific losses, which may fail to optimally balance action and explanation objectives. To address these limitations, we propose Dual-Attention with Multi-Objective optimization (DAMO). DAMO introduces a dual-attention mechanism that alternates between cross-attention for category representation learning and self-attention for category correlation modeling, fostering mutual enhancement. Additionally, we devise a multi-objective optimization algorithm that dynamically balances tasks and achieves Pareto optimality with theoretical guarantees. Extensive evaluations on two benchmarks show that DAMO surpasses state-of-the-art baselines and a large vision-language model, delivering up to 13.9% performance improvement and enhanced generalization across diverse driving scenarios.<\/jats:p>","DOI":"10.3233\/faia251218","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:55:00Z","timestamp":1761126900000},"source":"Crossref","is-referenced-by-count":0,"title":["DAMO: Dual-Attention with Multi-Objective Optimization for Explainable Autonomous Driving"],"prefix":"10.3233","author":[{"given":"Chengtai","family":"Cao","sequence":"first","affiliation":[{"name":"City University of Hong Kong"}]},{"given":"Shenglin","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}]},{"given":"Xinhong","family":"Chen","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}]},{"given":"Yung-Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Foxconn Research"}]},{"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251218","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:55:00Z","timestamp":1761126900000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251218","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}