Human-Guided Continual Learning for Personalized Decision-Making of Autonomous Driving

Learning-based techniques hold considerable promise in achieving human-like autonomous driving. However, one deployed policy encounters difficulties in satisfying the drivers’ diverse decision-making preferences simultaneously. Meanwhile, training personalized policies for each driver from scratch is time-consuming and resource-intensive. To address these challenges, this paper proposes a human-guided continual learning framework, wherein the human drivers could real-time take over a deployed policy when it performs unsatisfactorily, and the autonomous vehicle (AV) agent would automatically acquire human demonstrations and dynamically alter itself in accordance with personalized decision-making preference. Furthermore, a priority experience memory-enabled elastic weight consolidation (PEM-EWC) mechanism is developed to prevent the AV agent from overfitting to a limited number of human demonstrations and catastrophically forgetting its acquired fundamental driving abilities. Driver-in-the-loop simulations and real-world experiments are conducted in representative autonomous driving decision-making scenarios, and experimental results demonstrate the superior equilibrium of our proposed approach in terms of driving safety, human likeness, and training efficiency, compared to other baselines, which suggests that it provides a promising solution for personalized decision-making in autonomous driving. The supplementary video is available at https://youtu.be/HKF0ayxMycc.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3524609
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2025, IEEE. The contents of this paper reflect the views of the author[s] and do not necessarily reflect the official views or policies of the Transportation Research Board or the National Academy of Sciences.
• Authors:
  • Yang, Haohan
  • Zhou, Yanxin
  • Wu, Jingda
  • Liu, Haochen
  • Yang, Lie
  • Lv, Chen
• Publication Date: 2025-4

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5435-5447
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 26
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Decision making; Human factors engineering; Machine learning
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01980032
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 19 2026 10:53AM