{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T10:34:49Z","timestamp":1762079689269,"version":"3.41.2"},"reference-count":34,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2019,5,20]],"date-time":"2019-05-20T00:00:00Z","timestamp":1558310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2019,5,20]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of <jats:italic>R<\/jats:italic>.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-07-2018-0154","type":"journal-article","created":{"date-parts":[[2019,5,3]],"date-time":"2019-05-03T08:00:54Z","timestamp":1556870454000},"page":"444-452","source":"Crossref","is-referenced-by-count":23,"title":["Research on decision-making of autonomous vehicle following based on reinforcement learning method"],"prefix":"10.1108","volume":"46","author":[{"given":"Hongbo","family":"Gao","sequence":"first","affiliation":[]},{"given":"Guanya","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Kelong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guotao","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Yuchao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"2","key":"key2019090415305604000_ref001","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1287\/opre.6.2.165","article-title":"Traffic dynamics: studies in car following","volume":"6","year":"1958","journal-title":"Operations Research"},{"issue":"9","key":"key2019090415305604000_ref002","first-page":"1","article-title":"Object classification using CNN-Based fusion of vision and LIDAR in autonomous vehicle environment","volume":"14","year":"2018","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"key2019090415305604000_ref003","first-page":"1","article-title":"Cloud model approach for lateral control of intelligent vehicle systems","volume":"2016","year":"2016","journal-title":"Scientific Programming"},{"journal-title":"International Journal of Advanced Robotic Systems","article-title":"A Car-Following method based on inverse reinforcement learning for autonomous vehicles decision-making","year":"2019","key":"key2019090415305604000_ref004"},{"key":"key2019090415305604000_ref005","first-page":"1","article-title":"Longitudinal control for mengshi autonomous vehicle via gauss cloud model","volume-title":"Sustainability","year":"2017"},{"key":"key2019090415305604000_ref006","first-page":"365","article-title":"Research of intelligent vehicle variable granularity evaluation based on cloud model","volume":"44","year":"2016","journal-title":"Acta Electronica Sinica"},{"issue":"4","key":"key2019090415305604000_ref007","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1287\/opre.7.4.499","article-title":"Car-following theory of steady-state traffic flow","volume":"7","year":"1959","journal-title":"Operations Research"},{"issue":"1","key":"key2019090415305604000_ref008","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1287\/opre.7.1.86","article-title":"Traffic dynamics: analysis of stability in car following","volume":"7","year":"1959","journal-title":"Operations Research"},{"article-title":"Limitation of previous models on car-following behaviours and research needs","volume-title":"The 82th Transportation Research Board Annual Meeting","year":"2003","key":"key2019090415305604000_ref009"},{"key":"key2019090415305604000_ref010","first-page":"270","article-title":"Development and applications of traffic simulation models at the Karlsruhe Institut f\u00fcr Verkehrswesen","volume":"27","year":"1986","journal-title":"Traffic Engineering & Control"},{"key":"key2019090415305604000_ref011","unstructured":"Li, G.F. 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