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dc.contributor.authorXu, Canen_US
dc.contributor.authorZhao, Wanzhongen_US
dc.contributor.authorLiu, Jinqiangen_US
dc.contributor.authorWang, Chunyanen_US
dc.contributor.authorLv, Chenen_US
dc.identifier.citationXu, C., Zhao, W., Liu, J., Wang, C. & Lv, C. (2022). An integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithm. IEEE Transactions On Vehicular Technology, 71(4), 3621-3632.
dc.description.abstractIn order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time headway (TH) and the probabilistic lane distribution of the surrounding vehicles. With the extracted traffic state, the reinforcement learning (RL) is adopted to learn the optimal state-action pair for specific scenario. Analogously, by mapping millions of traffic scenarios, huge amounts of state-action pairs can be stored in the experience pool. Then the imitation learning (IL) is further employed to memorize the experience pool by deep neural networks. The learning result shows that the accuracy of the decision network can reach 94.17%. Besides, for some imperfect decisions of the network, the rule-based method is taken to rectify by judging the long-term reward. Finally, the IDF is simulated in G25 highway and has promising results, which can always drive the vehicle to the state with high efficiency while ensuring safety.en_US
dc.relation.ispartofIEEE Transactions on Vehicular Technologyen_US
dc.rights© 2022 IEEE. All rights reserved.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleAn integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithmen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.subject.keywordsAutonomous Vehiclesen_US
dc.subject.keywordsHighway Drivingen_US
dc.description.acknowledgementThis work was supported in part by the National Nature Science Foundation of China under Grants 52072175 and 51775007 and in part by the China Scholarship Council under Grant 202006830050.en_US
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