{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T11:03:30Z","timestamp":1773140610141,"version":"3.50.1"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB0106000"],"award-info":[{"award-number":["2018YFB0106000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Veh. Technol."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tvt.2024.3358299","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T18:28:28Z","timestamp":1706207308000},"page":"4621-4635","source":"Crossref","is-referenced-by-count":20,"title":["Deep Reinforcement Learning Based Integrated Eco-Driving Strategy for Connected and Automated Electric Vehicles in Complex Urban Scenarios"],"prefix":"10.1109","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0553-520X","authenticated-orcid":false,"given":"Jiawei","family":"Fan","sequence":"first","affiliation":[{"name":"SJTU-ParisTech Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0096-3830","authenticated-orcid":false,"given":"Xiaodong","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1326-6257","authenticated-orcid":false,"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5737-2217","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2319812"},{"key":"ref2","first-page":"12","article-title":"Evaluating the safety impact of connected and autonomous vehicles on motorways","volume-title":"Accident Anal. Prevention","volume":"124","author":"Papadoulis","year":"2019"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2006.884615"},{"issue":"1","key":"ref4","article-title":"IntelliDrive: Safer, smarter, greener","volume-title":"Public Roads","volume":"74","author":"Row","year":"2010"},{"key":"ref5","article-title":"Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections","volume-title":"Transp. Res. Part D: Transport Environ.","volume":"92","author":"Ma","year":"2021"},{"key":"ref6","article-title":"A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles","volume-title":"Expert Syst. Appl.","volume":"141","author":"Peng","year":"2020"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2016.2574740"},{"key":"ref8","first-page":"20","article-title":"A methodology for assessing ECO-cruise control for passenger vehicles","volume-title":"Transp. Res. Part D: Transport Environ.","volume":"19","author":"Saerens","year":"2013"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11768-015-4058-x"},{"key":"ref10","first-page":"110","article-title":"Stochastic Eco-routing in a signalized traffic network","volume-title":"Transp. Res. Procedia","volume":"7","author":"Sun","year":"2015"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3134372"},{"key":"ref13","article-title":"Velocity control in car-following behavior with autonomous vehicles using reinforcement learning","volume-title":"Accident Anal. Prevention","volume":"174","author":"Wang","year":"2022"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CAVS.2019.8887764"},{"key":"ref15","article-title":"Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness","volume-title":"Transp. Res. Part C: Emerg. Technol.","volume":"134","author":"Li","year":"2022"},{"key":"ref16","first-page":"1112","article-title":"Reliable and efficient lane changing behaviour for connected autonomous vehicle through deep reinforcement learning","volume-title":"Procedia Comput. Sci.","volume":"218","author":"Alagumuthukrishnan","year":"2023"},{"key":"ref17","article-title":"Integrated Eco-driving automation of intelligent vehicles in multi-lane scenario via model-accelerated reinforcement learning","volume-title":"Transp. Res. Part C: Emerg. Technol.","volume":"144","author":"Gu","year":"2022"},{"key":"ref18","article-title":"Hybrid deep reinforcement learning based Eco-driving for low-level connected and automated vehicles along signalized corridors","volume-title":"Transp. Res. Part C: Emerg. Technol.","volume":"124","author":"Guo","year":"2021"},{"key":"ref19","article-title":"Modeling drivers acceleration and lane changing behavior","author":"Ahmed","year":"1999"},{"key":"ref20","article-title":"CLACD: A complete LAne-Changing decision modeling framework for the connected and traditional environments","volume-title":"Transp. Res. Part C: Emerg. Technol.","volume":"128","author":"Ali","year":"2021"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1080\/19427867.2021.1919469"},{"key":"ref22","article-title":"Agent-time attention for sparse rewards multi-agent reinforcement learning","volume-title":"Proc. Auton. Agents Multi-Agent Syst.","author":"She","year":"2022"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/9.580874"},{"key":"ref24","first-page":"1587","article-title":"Addressing function approximation error in actor -critic methods","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fujimoto","year":"2018"},{"issue":"2","key":"ref25","first-page":"232","article-title":"Contributions to the theory of optimal control. A general procedure for the computation of switching manifolds","volume-title":"Trans. Amer. Math. Soc.","volume":"110","author":"Lewis","year":"1964"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/icait.2014.7019528"},{"key":"ref27","first-page":"278","article-title":"Policy invariance under reward transformations: Theory and application to reward shaping","volume-title":"Proc. 16th Int. Conf. Mach. Learn.","author":"Ng","year":"1999"},{"key":"ref28","first-page":"105","article-title":"Lane-changing model in SUMO","volume-title":"Proc. 2nd SUMO Conf. Model. Mobility Open Data","volume":"24","author":"Erdmann","year":"2014"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.55.5597"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2008.4621143"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.81.3797"},{"key":"ref32","first-page":"362","article-title":"A critical evaluation of the next generation simulation (NGSIM) vehicle trajectory dataset","volume-title":"Transp. Res. Part B: Methodological","volume":"105","author":"Coifman","year":"2017"}],"container-title":["IEEE Transactions on Vehicular Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/25\/10504682\/10414122.pdf?arnumber=10414122","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T17:32:14Z","timestamp":1713461534000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10414122\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":32,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tvt.2024.3358299","relation":{},"ISSN":["0018-9545","1939-9359"],"issn-type":[{"value":"0018-9545","type":"print"},{"value":"1939-9359","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}