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 Article Faculty Publications -Articles 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 Kumar, Sanjay; Ranjan, Anand; Saxena, Surabhi; Ashish Title A name given to the resource ANFIS-Based Multi-Sensor Data Fusion Model for Optimized Autonomous Vehicle Navigation Using Big Data and Filtering Techniques 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 Mobile Networks and Applications;Volume;30;Issue;2026-04-03 00:00:00;pp.341-355 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11036-026-02489-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11036-026-02489-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030688008?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030688008?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 Kumar S., KLEF College of Engineering, Department of Computer Science and Information Technology, KLEF, K L Deemed to be University, Green Fields, Vaddeswaram, Guntur District, 522302, India; Ranjan A., Department of Electronics and Communication Engineering, Faculty of Engineering andTechnology, University of Lucknow, Uttar Pradesh, Lucknow, 226021, India; Saxena S., Department of Computer Science, CHRIST University, 560 029, Karnataka, Bengaluru, India; Ashish, KLEF College of Engineering, Department of Computer Science and Information Technology, KLEF, K L Deemed to be University, Green Fields, Vaddeswaram, Guntur District, 522302, India Description An account of the resource The navigation of an autonomous vehicle depends mostly on the integration of multi-sensor data from sources such as LiDAR, GPS, radar, and cameras. Issues like sensor noise, data asynchrony, and fusion inaccuracies hamper reliable real-time decision-making. This paper proposes an optimized multi-sensor data fusion framework integrating big data analytics with modern filtering techniques to increase navigation accuracy and system robustness. The proposed model integrates Kalman Filter (KF), Extended Kalman Filter (EKF), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for dynamic state estimation and adaptive noise accommodation. In addition, sensor reliability and position tracking are enhanced via Bayesian data fusion and Particle Filter. Simulation results show that the proposed technique is evidently superior to existing models in accuracy (1.5 RMSE), convergence time (0.98s), and latency (50 ms). The fusion system enhances stability and responsiveness in autonomous navigation and offers an intelligent transportation framework that can be deployed efficiently at a real-time scale. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. Subject The topic of the resource Adaptive Neuro-Fuzzy inference system (ANFIS); Autonomous vehicle; Big data; Filtering technology; Kalman filter; Multi sensor Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 1383469X; Language A language of the resource English Type The nature or genre of the resource Article 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