{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T22:05:47Z","timestamp":1791237947391,"version":"4.1.0"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Graduate Studies and Scientific Research at Qassim University","award":["QU-APC-2025"],"award-info":[{"award-number":["QU-APC-2025"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Self-driving vehicles are envisioned as automated and safety-focused vehicles facilitating smooth movement on roads. This research proposes a novel, robust, and intelligent navigation framework for such vehicles through an integrated fusion of advanced technologies like predictive analytics with remote sensing and detection for accurate obstacle\/object detection. TaskTrek, ViewVerse, and RuleRise form the core of the essential model governing vehicle-environment interaction. TaskTrek handles kinematic trajectory synthesis and space-time traffic modeling, ViewVerse provides LiDAR-based volumetric perception and radar-assisted navigational intelligence, and RuleRise manages topological localization, vehicle actuation, and autonomous decision-making through multimodal sensory fusion. The model applies an iterative Multi-FacBiNet method, which uses the cognitive Fully Convolutional Neural Network (FCNN) method to detect and classify obstacles during vehicle movement on the road. Upon stimulation during vehicle movement, the model provided an encouraging outcome. The fusion of predictive intelligence, Radar, and sensing technologies gave 95.3% proficiency. Minimum obstacle detection, processing, and response delays of 0.116 seconds, 0.105 seconds, and 0.36 seconds, respectively, are recorded. The computed mean obstacle detection accuracy for right, left, front and back camera angles are 88.3%, 83.8%, 91.4%, and 89.9%, respectively. Further, a comprehensive analysis of the model\u2019s performance in different on-road scenarios considering metrics like traffic load, road type, and region density was done. The model generated a very impressive accuracy of obstacle detection on all parameters. The results of this study not only aid in accelerating the development of precise navigation-enabled self-driving vehicles but also in the context of environmentally friendly mobility\/motion tracking\u00a0solutions.<\/jats:p>","DOI":"10.1371\/journal.pone.0330933","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:38:17Z","timestamp":1764873497000},"page":"e0330933","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["AttentionDriveNet: Fusion of deep cognitive network with Attention modeling for robust navigation in Self-driving vehicles"],"prefix":"10.1371","volume":"20","author":[{"given":"Sushruta","family":"Mishra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rishabh","family":"Mohata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hrudaya Kumar","family":"Tripathy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jnyana Ranjan","family":"Mohanty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kshira Sagar","family":"Sahoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8116-4733","authenticated-orcid":true,"given":"N. 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IEEE SoC Design Conf. (ISOCC)"},{"key":"pone.0330933.ref013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/4125865","article-title":"Driver Distraction Identification with an Ensemble of Convolutional Neural Networks","volume":"2019","author":"HM Eraqi","year":"2019","journal-title":"Journal of Advanced Transportation"},{"issue":"3","key":"pone.0330933.ref014","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.3390\/su15032603","article-title":"Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods","volume":"15","author":"T Mazhar","year":"2023","journal-title":"Sustainability"},{"key":"pone.0330933.ref015","volume-title":"Fast R-CNN","author":"RG","year":"2015"},{"key":"pone.0330933.ref016","doi-asserted-by":"crossref","first-page":"111577","DOI":"10.1016\/j.asoc.2024.111577","article-title":"A Moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles","volume":"159","author":"MS Anwar","year":"2024","journal-title":"Applied Soft Computing"},{"key":"pone.0330933.ref017","doi-asserted-by":"crossref","first-page":"103259","DOI":"10.1016\/j.trc.2021.103259","article-title":"Pedestrian intention prediction: A convolutional bottom-up multi-task approach","volume":"130","author":"H Razali","year":"2021","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"pone.0330933.ref018","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/978-981-16-3097-2_9","article-title":"VDNet: Vehicle detection network using computer vision and deep learning mechanism for intelligent vehicle system.","author":"A Ojha","year":"2022","journal-title":"Proceedings of the Emerging Trends and Technologies on Intelligent Systems: ETTIS 2021, Noida, India"},{"key":"pone.0330933.ref019","doi-asserted-by":"crossref","first-page":"107941","DOI":"10.1016\/j.knosys.2021.107941","article-title":"Deep embedded hybrid CNN\u2013LSTM network for lane detection on 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Usman","year":"2025","journal-title":"IEEE Trans Consumer Electron"},{"issue":"11","key":"pone.0330933.ref023","doi-asserted-by":"crossref","first-page":"5007","DOI":"10.3390\/s23115007","article-title":"Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent","volume":"23","author":"A Kutlimuratov","year":"2023","journal-title":"Sensors (Basel)"},{"key":"pone.0330933.ref024","first-page":"66","article-title":"Control de velocidad adaptativo para entornos urbanos congestionados","volume":"6","author":"V Milan\u00e9s","year":"2009","journal-title":"Rev Iberoam Autom\u00e1t Inform\u00e1t Ind"},{"issue":"2","key":"pone.0330933.ref025","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0281294","article-title":"Exploration of the intelligent control system of autonomous vehicles based on edge computing","volume":"18","author":"G Ming","year":"2023","journal-title":"PLoS ONE"},{"issue":"7","key":"pone.0330933.ref026","doi-asserted-by":"crossref","first-page":"297","DOI":"10.3390\/wevj15070297","article-title":"Enhanced Object Detection in Autonomous Vehicles through LiDAR\u2014Camera Sensor Fusion","volume":"15","author":"Z Dai","year":"2024","journal-title":"WEVJ"},{"key":"pone.0330933.ref027","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR42600.2020.01164","article-title":"nuScenes: A Multimodal Dataset for Autonomous Driving.","author":"H Caesar","year":"2020","journal-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"pone.0330933.ref028","unstructured":"Leddar PixSet Dataset. https:\/\/leddartech.com\/solutions\/leddar-pixset-dataset"},{"issue":"7","key":"pone.0330933.ref029","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.jpdc.2012.02.007","article-title":"PHAST: Hardware-accelerated shortest path trees","volume":"73","author":"D Delling","year":"2013","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"pone.0330933.ref030","first-page":"1","article-title":"Efficient constrained path planning via search in state lattices.","author":"M Pivtoraiko","year":"2005","journal-title":"International Symposium on Artificial Intelligence, Robotics, and Automation in Space"},{"issue":"6","key":"pone.0330933.ref031","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1177\/027836499101000604","article-title":"Robot Motion Planning: A Distributed Representation Approach","volume":"10","author":"J Barraquand","year":"1991","journal-title":"The International Journal of Robotics Research"},{"issue":"5","key":"pone.0330933.ref032","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1177\/02783640122067453","article-title":"Randomized kinodynamic planning","volume":"20","author":"SM LaValle","year":"2001","journal-title":"The International Journal of Robotics Research"},{"issue":"7","key":"pone.0330933.ref033","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1177\/0278364911406761","article-title":"Sampling-based algorithms for optimal motion planning","volume":"30","author":"S Karaman","year":"2011","journal-title":"The international journal of robotics research"},{"key":"pone.0330933.ref034","unstructured":"Kavraki L, Svestka P, Overmars MH. \u201cProbabilistic roadmaps for path planning in high-dimensional configuration spaces,\u201d. 1994;1994."},{"key":"pone.0330933.ref035","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/IVS.2014.6856433","article-title":"Trajectory planning for automated parking using multi-resolution state roadmap considering non-holonomic constraints.","author":"H Fuji","year":"2014","journal-title":"2014 IEEE Intelligent Vehicles Symposium Proceedings"},{"issue":"4","key":"pone.0330933.ref036","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1109\/TITS.2014.2303995","article-title":"Modeling and Nonlinear Adaptive Control for Autonomous Vehicle Overtaking","volume":"15","author":"P Petrov","year":"2014","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"2","key":"pone.0330933.ref037","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/TRO.2006.870668","article-title":"Modified Newton\u2019s method applied to potential field-based navigation for mobile robots","volume":"22","author":"Ren Jing","year":"2006","journal-title":"IEEE Trans Robot"},{"issue":"5","key":"pone.0330933.ref038","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1177\/0278364909359210","article-title":"Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments","volume":"29","author":"D Dolgov","year":"2010","journal-title":"The International Journal of Robotics Research"},{"key":"pone.0330933.ref039","first-page":"1","article-title":"Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding.","author":"J Dickmann","year":"2016","journal-title":"Proceedings of the IEEE Radar Conference, Philadelphia, PA, USA"},{"key":"pone.0330933.ref040","first-page":"84","article-title":"A model for constraint-based camera planning.","author":"WH Bares","year":"2000","journal-title":"Proceedings of AAAI spring symposium on smart graphics, Stanford, CA, USA"},{"issue":"2","key":"pone.0330933.ref041","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/56.2085","article-title":"Obstacle avoidance with ultrasonic sensors","volume":"4","author":"J Borenstein","year":"1988","journal-title":"IEEE J Robot Automat"},{"key":"pone.0330933.ref042","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.optlastec.2014.06.001","article-title":"A novel laser Doppler velocimeter and its 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