{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T11:52:58Z","timestamp":1791201178551,"version":"4.2.0"},"reference-count":20,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T00:00:00Z","timestamp":1791072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional reinforcement learning for autonomous driving faces challenges such as slow convergence, control instability, and limited adaptability. To address these issues, this paper proposes a hybrid decision-making framework that integrates V2X communication, human\u2013machine interaction experience, and deep reinforcement learning. Based on the Actor\u2013Critic architecture, the framework incorporates a human-like driving tendency network to constrain the policy search space and integrates a Kalman filter-based V2X communication anomaly handling module. The model constructs the vehicle\u2019s action space and state representation. Comparative experiments with a method lacking the driving tendency module and the hierarchical planning method DDPG-RRT* show that DDPG-HP achieves faster convergence and better scenario adaptability than these baselines. It effectively avoids static and dynamic obstacles, demonstrates anticipatory and gradual deceleration, and initiates braking 1.7 s earlier than the DDPG-RRT* method, thereby reducing collision risks and enhancing pedestrian safety. Furthermore, the communication anomaly compensation mechanism effectively improves the system\u2019s adaptability and overall robustness under unstable communication conditions.<\/jats:p>","DOI":"10.3390\/s26196295","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:57:46Z","timestamp":1791197866000},"page":"6295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Human\u2013Machine Interaction\u2013Experience\u2013Learning Algorithm for Integrated Lateral\u2013Longitudinal Decision-Making and Control in Intelligent Connected Vehicles"],"prefix":"10.3390","volume":"26","author":[{"given":"Wenxuan","family":"Shen","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Cunyue","family":"Yan","sequence":"additional","affiliation":[{"name":"Shanghai Xizhong Suo Heavy Machinery Complete Set Co., Ltd., Shanghai 201900, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Zhaoqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Zeng","family":"Huang","sequence":"additional","affiliation":[{"name":"The 704th Research Institute of China State Shipbuilding Co., Ltd., Shanghai 200030, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"Shanghai Hunter Hydraulic Control Technology Co., Ltd., Shanghai 201612, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60519","DOI":"10.1109\/ACCESS.2025.3557089","article-title":"Improvement of Adaptive Cruise Control System Performance on Sloped Roads Based on Adaptive Neuro-Fuzzy Inference System","volume":"13","author":"Choi","year":"2025","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1177\/09544070231193187","article-title":"Collaborative adaptive cruise control and energy management strategy for extended-range electric logistics van platoon","volume":"238","author":"Wang","year":"2023","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1049\/iet-its.2019.0457","article-title":"Driving policies of V2X autonomous vehicles based on reinforcement learning methods","volume":"14","author":"Wu","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qiu, X., Pan, Y., Zhu, M., Yang, L., and Zheng, X. (2024, January 2\u20135). Driving Style-aware Car-following Considering Cut-in Tendencies of Adjacent Vehicles with Inverse Reinforcement Learning. Proceedings of the 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Republic of Korea.","DOI":"10.1109\/IV55156.2024.10588574"},{"key":"ref_5","first-page":"702","article-title":"Vehicle longitudinal speed planning based on deep reinforcement learning CLPER-DDPG","volume":"15","author":"Liu","year":"2024","journal-title":"J. Automot. Saf. Energy"},{"key":"ref_6","first-page":"1165","article-title":"Impact of driving style on rear-end crash risk in leading vehicle emergency braking situations","volume":"240","author":"Wei","year":"2026","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"30765","DOI":"10.1109\/ACCESS.2024.3368435","article-title":"Enhancing longitudinal velocity control with attention mechanism-based deep deterministic policy gradient (DDPG) for safety and comfort","volume":"12","author":"Islam","year":"2024","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1109\/LRA.2025.3640975","article-title":"Deep Reinforcement Learning Based Autonomous Drift System for Abrupt Obstacle Avoidance","volume":"11","author":"Liu","year":"2026","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1177\/09544070221075423","article-title":"Human-like driving decision at unsignalized intersections based on game theory","volume":"237","author":"Li","year":"2023","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_10","first-page":"1505","article-title":"A decision-making of autonomous driving method based on DDPG with pretraining","volume":"239","author":"Ma","year":"2024","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/TIV.2022.3185303","article-title":"End-to-End Autonomous Driving with Semantic Depth Cloud Mapping and Multi-Agent","volume":"8","author":"Natan","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3648","DOI":"10.1109\/TVT.2022.3141732","article-title":"Integrated vehicle-following control for four-wheel-independent-drive electric vehicles against non-ideal V2X communication","volume":"71","author":"Liu","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"125094","DOI":"10.1109\/ACCESS.2023.3330665","article-title":"An Empirical Study of DDPG and PPO-Based Reinforcement Learning Algorithms for Autonomous Driving","volume":"11","author":"Siboo","year":"2023","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Du, L., Zhang, J., and Sun, W. (2020, January 12\u201314). Design of Interacting Multiple Model with Unscented Kalman Filter for V2X Test. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.","DOI":"10.1109\/ITNEC48623.2020.9085155"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, D., Kim, K., Kwon, D., and Choi, J.-W. (2021, January 27\u201330). V2X-based Vehicle Speeding Enforcement System. Proceedings of the 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), Norman, OK, USA.","DOI":"10.1109\/VTC2021-Fall52928.2021.9625284"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"You, C., Lu, J., Filev, D., and Tsiotras, P. (2018, January 26\u201330). Highway Traffic Modeling and Decision Making for Autonomous Vehicle Using Reinforcement Learning. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500675"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11466","DOI":"10.1109\/TITS.2025.3576372","article-title":"A Safe and Efficient Self-Evolving Algorithm for Decision-Making and Control of Autonomous Driving Systems","volume":"26","author":"Yang","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Elsagheer Mohamed, S.A., AlShalfan, K.A., Al-Hagery, M.A., and Ben Othman, M.T. (2022). Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles. Sensors, 22.","DOI":"10.3390\/s22187051"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1177\/09544070221121835","article-title":"Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication","volume":"238","author":"Song","year":"2022","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7170","DOI":"10.1109\/TITS.2021.3067048","article-title":"Success Probability Analysis of Cooperative C-V2X Communications","volume":"23","author":"Pan","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6295\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T11:01:13Z","timestamp":1791198073000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6295"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,4]]},"references-count":20,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196295"],"URL":"https:\/\/doi.org\/10.3390\/s26196295","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,4]]}}}