{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:58:53Z","timestamp":1771275533673,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Society for the Promotion of Science","award":["20K04531"],"award-info":[{"award-number":["20K04531"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Uncoordinated driving behavior is one of the main reasons for bottlenecks on freeways. This paper presents a novel cyber-physical framework for optimal coordination of connected and automated vehicles (CAVs) on multi-lane freeways. We consider that all vehicles are connected to a cloud-based computing framework, where a traffic coordination system optimizes the target trajectories of individual vehicles for smooth and safe lane changing or merging. In the proposed framework, the vehicles are coordinated into groups or platoons, and their trajectories are successively optimized in a receding horizon control (RHC) approach. Optimization of the traffic coordination system aims to provide sufficient gaps when a lane change is necessary while minimizing the speed deviation and acceleration of all vehicles. The coordination information is then provided to individual vehicles equipped with local controllers, and each vehicle decides its control acceleration to follow the target trajectories while ensuring a safe distance. Our proposed method guarantees fast optimization and can be used in real-time. The proposed coordination system was evaluated using microscopic traffic simulations and benchmarked with the traditional driving (human-based) system. The results show significant improvement in fuel economy, average velocity, and travel time for various traffic volumes.<\/jats:p>","DOI":"10.3390\/s23020611","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T04:51:31Z","timestamp":1672894291000},"page":"611","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Cyber-Physical Framework for Optimal Coordination of Connected and Automated Vehicles on Multi-Lane Freeways"],"prefix":"10.3390","volume":"23","author":[{"given":"Yuta","family":"Sakaguchi","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0799-3140","authenticated-orcid":false,"given":"A. S. M.","family":"Bakibillah","sequence":"additional","affiliation":[{"name":"Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo 152-8552, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3150-0510","authenticated-orcid":false,"given":"Md Abdus Samad","family":"Kamal","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kou","family":"Yamada","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.tranpol.2017.07.002","article-title":"Is there more traffic congestion in larger cities?-Scaling analysis of the 101 largest US urban centers","volume":"59","author":"Chang","year":"2017","journal-title":"Transp. 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