Learning Adaptive Control for Autonomous Vehicles
Keywords:
Adaptive Control ,Autonomous Vehicle , Dynamic Environment , Model-Free Reinforcement Learning , Safety , Traffic Efficiency.Abstract
Adaptive control of autonomous vehicles in dynamic mixed-traffic environments has emerged as a trendy research direction. Most studies achieve adaptive control through planning, control, or generalization. Unlike planning-based methods, control methods rely on a predefined driving policy, which limits adaptation capability. Moreover, the decision horizon remains static and is not related to the surrounding driving conditions. Therefore, training of target driving policies based on previous traffic samples for similar traffic conditions is essential for potential improvement. Reinforcement learning is one of the most applicable techniques to solve such tasks.
The development of model-based reinforcement learning requires a simulator. High-fidelity driving simulators, such as SUMMIT and CARLA, exhibit unknown dynamic properties, and thus they are mainly used to develop and test planning-based algorithms. They are not appropriate training simulators because trained networks do not perform well in the real world. However, perception and planning-based modules of autonomous driving systems are usually implemented using symbolic or engineering solutions. Based on such symbolic planners, sampling-based planners, or pre-planned trajectories, a driving-policy learning framework is formulated and a driving policy is learned in the context of supervised learning. This driving policy can serve as a training vehicle for potential future driving-policy adaptation in real vehicles using reinforcement learning. In addition, the proposed learning framework enables other adaptive control methods, such as reinforcement-learning-based methods.
References
1. Amini, A., Gilitschenski, I., Phillips, J., Moseyko, J., Banerjee, R., Karaman, S., & Rus, D. (2020). Learning robust control policies for end-to-end autonomous driving from data-driven simulation. IEEE Robotics and Automation Letters, 5(2), 1143–1150.
2. Likmeta, A., Metelli, A. M., Tirinzoni, A., Giol, R., Restelli, M., & Romano, D. (2020). Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving. Robotics and Autonomous Systems, 131, 103568.
3. Morais, G. A. P. de, Marcos, L. B., Bueno, J. N. A. D., Resende, N. F. de, Terra, M. H., & Grassi Júnior, V. (2020). Vision-based robust control framework based on deep reinforcement learning applied to autonomous ground vehicles. Control Engineering Practice, 104, 104630.
4. Mangalampalli, B. M., Bandi, V. D. V. K., Kolla, S. K., & Kumar, M. V. K. (2025). Towards Self-Evolving Healthcare Intelligence: Integrating Advanced Learning Systems with Real-Time Clinical Data Pipelines. Cultura: International Journal of Philosophy of Culture and Axiology, 22(12s), 464-486.
5. Wasala, A., Byrne, D., Miesbauer, P., O’Hanlon, J., Heraty, P., & Barry, P. (2020). Trajectory based lateral control: A reinforcement learning case study. Engineering Applications of Artificial Intelligence, 94, 103799.
6. Zhu, M., Wang, Y., Pu, Z., Hu, J., Wang, X., & Ke, R. (2020). Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving. Transportation Research Part C: Emerging Technologies, 117, 102662.
7. Hewing, L., Wabersich, K. P., Menner, M., & Zeilinger, M. N. (2020). Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems, 3, 269–296.
8. Kolla, S. K., & Reddy, V. A. R. (2024). Evaluating Cloud-Native vs. Hybrid Architectures for Health Benefit Administration Systems. International Journal of Medical Toxicology and Legal Medicine, 27(5), 1042-1053.
9. Zhang, K., Sun, Q., & Shi, Y. (2021). Trajectory tracking control of autonomous ground vehicles using adaptive learning MPC. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5554–5564.
10. Zhang, L., Zhang, R., Wu, T., Weng, R., Han, M., & Zhao, Y. (2021). Safe reinforcement learning with stability guarantee for motion planning of autonomous vehicles. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5435–5444.
11. Das, L. C., & Won, M. (2021). SAINT-ACC: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. Proceedings of the 38th International Conference on Machine Learning, 139, 2445–2455.
12. Dong, J., Chen, S., Li, Y., Du, R., Steinfeld, A., & Labi, S. (2021). Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range assessment. Transportation Research Part C: Emerging Technologies, 128, 103192.
13. Pan, T., Guo, R., Lam, W. H. K., Zhong, R., Wang, W., & He, B. (2021). Integrated optimal control strategies for freeway traffic mixed with connected automated vehicles: A model-based reinforcement learning approach. Transportation Research Part C: Emerging Technologies, 123, 102987.
14. Mangala, N., & Kolla, S. H. (2025). Real-Time Feature Engineering for Streaming AI Workloads Using PySpark and Azure Event Hubs. International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management, 1(6), 93-105.
15. Wegener, M., Koch, L., Eisenbarth, M., & Andert, J. (2021). Automated eco-driving in urban scenarios using deep reinforcement learning. Transportation Research Part C: Emerging Technologies, 126, 102967.
16. Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2022). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909–4926.
17. Zhu, Z., & Zhao, H. (2022). A survey of deep reinforcement learning and imitation learning for autonomous driving policy learning. IEEE Transactions on Intelligent Transportation Systems, 23(9), 14043–14065.
18. Mattaparthi, R. (2025). GenAI-Augmented Diagnostic Reasoning for Diesel Engine Fault Triage: A Large Language Model Framework for Technician Decision Support at Scale. Journal of Material Sciences & Manufacturing Research, 6(12), 1.
19. Ye, F., Zhang, S., Wang, P., & Chan, C.-Y. (2021). A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles. In 2021 IEEE Intelligent Vehicles Symposium (IV) (pp. 1073–1080). IEEE.
20. Dinneweth, J., Boubezoul, A., Mandiau, R., & Espié, S. (2022). Multi-agent reinforcement learning for autonomous vehicles: A survey. Autonomous Intelligent Systems, 2, Article 27.
21. Wang, W., Jiang, L., Lin, S., Fang, H., & Meng, Q. (2022). Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts. Multimedia Tools and Applications, 81, 39873–39889.
22. Jurj, S. L., Grundt, D., Werner, T., Borchers, P., Rothemann, K., & Möhlmann, E. (2021). Increasing the safety of adaptive cruise control using physics-guided reinforcement learning. Energies, 14(22), 7572.
23. Nguyen, H. D., & Han, K. (2022). Safe reinforcement learning-based driving policy design for autonomous vehicles on highways. International Journal of Automotive Technology, 23, 1145–1157.
24. Mangalampalli, B. M., Kolla, S. K., Bandi, V. D. V. K., Yandamuri, U. S., & Rani, P. S. (2025). Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems. Vascular and Endovascular Review, 8(20s), 330-347.
25. Du, Y., Chen, J., Zhao, C., Liu, C., Liao, F., & Chan, C.-Y. (2022). Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning. Transportation Research Part C: Emerging Technologies, 134, 103489.
26. Alighanbari, S., & Azad, N. L. (2023). Deep reinforcement learning with NMPC assistance Nash switching for urban autonomous driving. IEEE Transactions on Intelligent Vehicles, 8(3), 2604–2615.
27. Cao, Z., Xu, S., Jiao, X., Peng, H., & Yang, D. (2022). Trustworthy safety improvement for autonomous driving using reinforcement learning. Transportation Research Part C: Emerging Technologies, 138, 103656.
28. Al-Sharman, M. K., Dempster, R., Daoud, M. A., Nasr, M., Rayside, D., & Melek, W. (2023). Self-learned autonomous driving at unsignalized intersections: A hierarchical reinforced learning approach for feasible decision-making. IEEE Transactions on Intelligent Transportation Systems, 24(11), 12341–12354.
29. Cai, Y., Zhou, R., Wang, H., Sun, X., Chen, L., Li, Y., Liu, Q., & He, Y. (2023). Rule-constrained reinforcement learning control for autonomous vehicle left turn at unsignalized intersection. IET Intelligent Transport Systems, 17(11), 2143–2153.
30. Yu, X. (2023). DGPR-MPC: Learning-based model predictive controller for autonomous vehicle path following. IET Intelligent Transport Systems, 17(11), 2220–2233.
31. Mahadevan, S. (2024). Intelligent Serverless Process Automation for Scalable Cloud Operations and Dynamic Resource Optimization. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 4207-4221.
32. Li, Y., Zhao, K., Zhang, Z., & colleagues. (2023). Robust learning-based lateral tracking control for autonomous driving with input constraints. International Journal of Robust and Nonlinear Control, 33, 10070–10090.
33. Wang, B., Bai, F., & Zhang, K. (2024). Actor-critic objective penalty function method: An adaptive strategy for trajectory tracking in autonomous driving. Complex & Intelligent Systems, 10, 1715–1732.
34. He, X., & Lv, C. (2023). Towards safe autonomous driving: Decision making with observation-robust reinforcement learning. Automotive Innovation, 6, 509–520.
35. Cao, Z., Jiang, K., Zhou, W., Xu, S., Peng, H., & Yang, D. (2023). Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning. Nature Machine Intelligence, 5, 145–158.
36. Sivashangaran, S., & Eskandarian, A. (2023). Integration of robust control with reinforcement learning for safe autonomous vehicle motion. IFAC-PapersOnLine, 56(2), 1101–1106.
37. Fayyazi, M., Abdoos, M., Phan, D., Golafrouz, M., Jalili, M., Jazar, R. N., Langari, R., & Khayyam, H. (2023). Real-time self-adaptive Q-learning controller for energy management of conventional autonomous vehicles. Expert Systems with Applications, 222, 119770.
38. Wang, Y., Chen, Z., Sun, M., & Sun, Q. (2024). Enhancing active disturbance rejection design via deep reinforcement learning and its application to autonomous vehicle. Expert Systems with Applications, 239, 122433.
39. Loganathan, R. (2025). AGENTIC AI FRAMEWORKS FOR AUTONOMOUS RISK DETECTION AND COMPLIANCE REMEDIATION IN ENTERPRISE DATA CENTER OPERATIONS. Lex Localis-Journal of Local Self-Government, 23 (S6), 9672–9697.
40. Zhao, S., Zhang, J., He, C., Ji, Y., Huang, H., & Hou, X. (2024). Autonomous vehicle extreme control for emergency collision avoidance via reachability-guided reinforcement learning. Advanced Engineering Informatics, 62, 102801.
41. Qi, X., Zhang, L., Wang, P., Yang, J., Zou, T., & Liu, W. (2024). Learning-based MPC for autonomous motion planning at freeway off-ramp diverging. IEEE Transactions on Intelligent Vehicles, 9(10), 6183–6194.
42. Xia, Q., Chen, P., Xu, G., Sun, H., Li, L., & Yu, G. (2025). Adaptive path-tracking controller embedded with reinforcement learning and preview model for autonomous driving. IEEE Transactions on Vehicular Technology, 74(3), 3736–3750.
43. Lin, X., Huang, J., Zhang, B., Zhou, B., & Chen, Z. (2025). A velocity adaptive steering control strategy of autonomous vehicle based on double deep Q-learning network with varied agents. Engineering Applications of Artificial Intelligence, 139, 109655.
44. Bódis, G., & colleagues. (2024). Supervised reinforcement learning based trajectory tracking control for autonomous vehicles. IFAC-PapersOnLine, 58(10), 140–145.
45. Zhou, X., Hu, C., Duo, R., Xiong, H., & colleagues. (2022). Learning-based MPC controller for drift control of autonomous vehicles. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE.
46. Irshayyid, A., Chen, J., & Xiong, G. (2024). A review on reinforcement learning-based highway autonomous vehicle control. Green Energy and Intelligent Transportation, 3(4), 100156.
47. Wu, J., Huang, C., Huang, H., Lv, C., Wang, Y., & Wang, F.-Y. (2024). Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey. Transportation Research Part C: Emerging Technologies, 164, 104654.
