Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shankar, Jaya; Kavitha, V.K.; Mohan, Chippy; Gouri, M.S. Title A name given to the resource Deep Q-Learning for Autonomous Vehicle Navigation in Smart Mobility Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 8th International Conference on Circuit, Power and Computing Technologies, ICCPCT 2025;pp.1052-1057 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCPCT65132.2025.11176723" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCPCT65132.2025.11176723</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020186403?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020186403?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shankar J., Baselios Mathews II College of Engineering, Department of Computer Science and Enginering, Sasthamcotta, India; Kavitha V.K., Baselios Mathews II College of Engineering, Department of Computer Science and Enginering, Sasthamcotta, India; Mohan C., Christ University, School of Business &amp; Management, Bangalore, India; Gouri M.S., Baselios Mathews II College of Engineering, Department of Computer Science and Enginering, Sasthamcotta, India Description An account of the resource The proposed system leverages Deep Q-Learning to enhance autonomous vehicle navigation in smart mobility environments. By integrating reinforcement learning with deep neural networks, the system enables vehicles to make real-time decisions while adapting to dynamic traffic conditions. The framework employs a reward-based learning mechanism to optimize path selection, collision avoidance, and efficient maneuvering in complex urban scenarios. To improve decisionmaking accuracy, the proposed approach incorporates an experience replay mechanism, preventing overfitting and ensuring stable learning. Additionally, a target network is utilized to enhance training convergence, allowing the model to generalize effectively across varying road conditions. The system is further optimized through adaptive explorationexploitation strategies, enabling vehicles to balance learning new routes while prioritizing safe and efficient navigation. The proposed methodology demonstrates significant improvements in autonomous mobility, offering a scalable and robust solution for next-generation smart transportation systems. 2025 IEEE. Subject The topic of the resource autonomous vehicle navigation; collision avoidance; Deep Q-Learning; experience replay; exploration-exploitation; intelligent transportation systems; path optimization; reinforcement learning; smart mobility; target network Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154317-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online