{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:35:52Z","timestamp":1787160952194,"version":"build-2736575974"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["52202495"],"award-info":[{"award-number":["52202495"]}]},{"name":"National Science Foundation of China","award":["52202494"],"award-info":[{"award-number":["52202494"]}]},{"name":"National Science Foundation of China","award":["ZDK1202402"],"award-info":[{"award-number":["ZDK1202402"]}]},{"name":"Open Fund Project of the Key Laboratory of Automotive Power Train and Electronics","award":["52202495"],"award-info":[{"award-number":["52202495"]}]},{"name":"Open Fund Project of the Key Laboratory of Automotive Power Train and Electronics","award":["52202494"],"award-info":[{"award-number":["52202494"]}]},{"name":"Open Fund Project of the Key Laboratory of Automotive Power Train and Electronics","award":["ZDK1202402"],"award-info":[{"award-number":["ZDK1202402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Path tracking is an essential task for autonomous driving (AD), for which controllers are designed to issue commands so that vehicles will follow the path of upper-level decision planning properly to ensure operational safety, comfort, and efficiency. Current path-tracking methods still face challenges in balancing tracking accuracy with computational overhead, and more critically, lack consideration for Motion Sickness (MS) mitigation. However, as AD applications divert occupants\u2019 attention to non-driving activities at varying degrees, MS in self-driving vehicles has been significantly exacerbated. This study presents a novel framework, the Hybrid Supervised\u2013Reinforcement Learning (HSRL), designed to reduce passenger discomfort while achieving high-precision tracking performance with computational efficiency. The proposed HSRL employs expert data-guided supervised learning to rapidly optimize the path-tracking model, effectively mitigating the sample efficiency bottleneck inherent in pure Reinforcement Learning (RL). Simultaneously, the RL architecture integrates a passenger MS mechanism into a multi-objective reward function. This design enhances model robustness and control performance, achieving both high-precision tracking and passenger comfort optimization. Simulation experiments demonstrate that the HSRL significantly outperforms Proportional\u2013Integral\u2013Derivative (PID) and Model Predictive Control (MPC), achieving improved tracking accuracy and significantly reducing passengers\u2019 cumulative Motion Sickness Dose Value (MSDV) across several test scenarios.<\/jats:p>","DOI":"10.3390\/s25123695","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T11:47:07Z","timestamp":1749728827000},"page":"3695","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hybrid Supervised and Reinforcement Learning for Motion-Sickness-Aware Path Tracking in Autonomous Vehicles"],"prefix":"10.3390","volume":"25","author":[{"given":"Yukang","family":"Lv","sequence":"first","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8826-2404","authenticated-orcid":false,"given":"Ziguo","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8309-7396","authenticated-orcid":false,"given":"Yuze","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchao","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1597-1961","authenticated-orcid":false,"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4195-5033","authenticated-orcid":false,"given":"Fei","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"},{"name":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G.P., and Cho, W. 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