{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:41:34Z","timestamp":1770457294200,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,5,31]],"date-time":"2020-05-31T00:00:00Z","timestamp":1590883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chaozhong Wu","award":["U1764262"],"award-info":[{"award-number":["U1764262"]}]},{"name":"Chaozhong Wu","award":["51775396"],"award-info":[{"award-number":["51775396"]}]},{"name":"Chaozhong Wu","award":["2017CFA008"],"award-info":[{"award-number":["2017CFA008"]}]},{"name":"Zhijun Chen","award":["61703319"],"award-info":[{"award-number":["61703319"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>To date, automatic driving technology has become a hotspot in academia. It is necessary to provide a personalization of automatic driving decision for each passenger. The purpose of this paper is to propose a self-learning method for personalized driving decisions. First, collect and analyze driving data from different drivers to set learning goals. Then, Deep Deterministic Policy Gradient algorithm is utilized to design a driving decision system. Furthermore, personalized factors are introduced for some observed parameters to build a personalized driving decision model. Finally, compare the proposed method with classic Deep Reinforcement Learning algorithms. The results show that the performance of the personalized driving decision model is better than the classic algorithms, and it is similar to the manual driving situation. Therefore, the proposed model can effectively learn the human-like personalized driving decisions of different drivers for structured road. Based on this model, the smart car can accomplish personalized driving.<\/jats:p>","DOI":"10.3390\/info11060295","type":"journal-article","created":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T11:49:21Z","timestamp":1591012161000},"page":"295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Method of Personalized Driving Decision for Smart Car Based on Deep Reinforcement Learning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5684-8292","authenticated-orcid":false,"given":"Xinpeng","family":"Wang","sequence":"first","affiliation":[{"name":"Intelligent Transportation Systems Center (ITSC), Wuhan University of Technology, Wuhan 430000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaozhong","family":"Wu","sequence":"additional","affiliation":[{"name":"Intelligent Transportation Systems Center (ITSC), Wuhan University of Technology, Wuhan 430000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4819-9886","authenticated-orcid":false,"given":"Jie","family":"Xue","sequence":"additional","affiliation":[{"name":"Intelligent Transportation Systems Center (ITSC), Wuhan University of Technology, Wuhan 430000, China"},{"name":"Faculty of Technology, Policy and Management, Safety and Security Science Group (S3G), Delft University of Technology, 2628BX Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijun","family":"Chen","sequence":"additional","affiliation":[{"name":"Intelligent Transportation Systems Center (ITSC), Wuhan University of Technology, Wuhan 430000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MITS.2019.2903525","article-title":"Understanding Individualization Driving States via Latent Dirichlet Allocation Model","volume":"11","author":"Chen","year":"2019","journal-title":"IEEE Intell. Trans. Syst. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MITS.2014.2306552","article-title":"Making Bertha Drive-An Autonomous Journey on a Historic Route","volume":"6","author":"Ziegler","year":"2014","journal-title":"Intell. Trans. Syst. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.ssci.2019.07.019","article-title":"Multi-attribute decision-making method for prioritizing maritime traffic safety influencing factors of autonomous ships\u2019 maneuvering decisions using grey and fuzzy theories","volume":"120","author":"Xue","year":"2019","journal-title":"Saf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112753","DOI":"10.1016\/j.eswa.2019.06.041","article-title":"A novel sparse representation model for pedestrian abnormal trajectory understanding","volume":"138","author":"Chen","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1002\/rob.20258","article-title":"Junior: The Stanford entry in the Urban Challenge","volume":"25","author":"Montemerlo","year":"2008","journal-title":"J. Field Robot"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1002\/rob.20262","article-title":"A Perception-driven Autonomous Urban Vehicle","volume":"25","author":"Leonard","year":"2008","journal-title":"J. Field Robot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TIV.2016.2551545","article-title":"Challenges in Perception and Decision Making for Intelligent Automotive Vehicles: A Case Study","volume":"1","author":"Okumura","year":"2016","journal-title":"Trans. Intell. Veh."},{"key":"ref_8","unstructured":"Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., and Zhang, J. (2016, January 26). End to End Learning for Self-Driving Cars. Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, C., Seff, A., and Kornhauser, A.L. (2015, January 7\u201313). DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. Proceedings of the 15th Annual Meeting of the International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.312"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hubmann, C., Becker, M., Althoff, D., Lenz, D., and Stiller, C. (2017, January 11\u201314). Decision Making for Autonomous Driving Considering Interaction and Uncertain Prediction of Surrounding Vehicles. Proceedings of the 28th Annual Meeting of the Intelligent Vehicles Symposium, Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995949"},{"key":"ref_11","unstructured":"Tan, B., Xu, N., and Kong, B. (2018, January 19\u201321). Autonomous Driving in Reality with Reinforcement Learning and Image Translation. Proceedings of the 31th IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Loiacono, D., Prete, A., Lanzi, P.L., and Cardamone, L. (2010, January 18\u201323). Learning to overtake in TORCS using simple reinforcement learning. Proceedings of the Congress on Evolutionary Computation, Barcelona, Spain.","DOI":"10.1109\/CEC.2010.5586191"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.eswa.2018.07.044","article-title":"Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees","volume":"115","author":"Xue","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_15","unstructured":"Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Roth\u00f6rl, T., Lampe, T., and Riedmiller, M. (2017, January 17\u201320). Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. Proceedings of the Artificial Intelligence Conference, San Francisco, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tai, L., Paolo, G., and Liu, M. (2017, January 24\u201328). Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. Proceedings of the 30th International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202134"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Filev, D., Lu, J., Tseng, F., and Kwaku, P.A. (2011, January 9\u201312). Real-time driver characterization during car following using stochastic evolving models. Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA.","DOI":"10.1109\/ICSMC.2011.6083810"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.4028\/www.scientific.net\/AMM.505-506.1225","article-title":"Construction and Application of 3D Traffic Environment Build Platform Based on UC-Win\/Road","volume":"505","author":"Deng","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.trc.2015.02.007","article-title":"Personalised assistance for fuel-efficient driving","volume":"58","author":"Gilman","year":"2015","journal-title":"Trans. Res. Part C Emerg Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, J., Sim, H., and Oh, J. (2012, January 24\u201326). The flexible EV\/HEV and SOC band control corresponding to driving mode, driver\u2019s driving style and environmental circum-stances. Proceedings of the SAE 2012 World Congress and Exhibition, Detroit, MI, USA.","DOI":"10.4271\/2012-01-1016"},{"key":"ref_21","first-page":"1","article-title":"Modeling and recognizing driver behavior based on driving data: A survey","volume":"1","author":"Wang","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dorr, D., Grabengiesser, D., and Gauterin, F. (2014, January 8\u201311). Online driving style recognition using fuzzy logic. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957822"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sutton, R.S. (1998). Reinforcement Learning: An Introduction, MIT.","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, Perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_26","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015, January 7\u20139). Continuous control with deep reinforcement learning. Proceedings of the 3th Annual Meeting of the International Conference on Learning Representations, San Juan, PR, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TCIAIG.2010.2050590","article-title":"The 2009 simulated car racing championship","volume":"2","author":"Loiacono","year":"2010","journal-title":"IEEE Trans. Comput. Intell. AI Games"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neucom.2011.06.034","article-title":"Generalization of TORCS car racing controllers with artificial neural networks and linear regression analysis","volume":"88","author":"Kim","year":"2012","journal-title":"Neurocomputing"},{"key":"ref_29","unstructured":"Bhatnagar, S., Ghavamzadeh, M., Lee, M., and Sutton, R.S. (2007, January 3\u20136). Incremental Natural Actor-Critic Algorithms. Proceedings of the 21th Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_30","unstructured":"Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T.P., Harley, T., Silver, D., and Kavukcuoglu, K. (2016, January 19\u201324). Asynchronous methods for deep reinforcement learning. Proceedings of the 33th International Conference on Machine Learning, New York, NY, USA."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/6\/295\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:34:22Z","timestamp":1760175262000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/6\/295"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,31]]},"references-count":30,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["info11060295"],"URL":"https:\/\/doi.org\/10.3390\/info11060295","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,31]]}}}