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Volume 15
Issue 10
10.3390/electronics15102031
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Path Tracking for Autonomous Vehicles Integrating HOCBF-Based Reinforcement Learning and Model Predictive Control

by
Zhengyu Song
1,
Wenxin Wen
1,
Junze Li
1,
Junjie Wang
1,
Minghui Ye
2,
Mengna Li
2,
Bowen Li
2,
Zhuo Wang
2,
Changqun Sun
2,
Aidong Luan
2,
Meng Zhang
2,
Changpeng Liu
2,
Yantao Si
2 and
Bo Leng
1,*
1
College of Automotive and Energy Engineering, Tongji University, Shanghai 201804, China
2
Chongqing Changan Automobile Company Limited, Chongqing 400023, China
*
Author to whom correspondence should be addressed.
Electronics 2026, 15(10), 2031; https://doi.org/10.3390/electronics15102031
Submission received: 13 March 2026 / Revised: 23 April 2026 / Accepted: 28 April 2026 / Published: 10 May 2026
(This article belongs to the Special Issue Advances in Electrical and Autonomous Vehicles: Trends, Challenges and Prospects)
Versions Notes

Abstract

High-precision path tracking is crucial for the safe operation of autonomous vehicles. However, the performance of Model Predictive Control (MPC) depends heavily on the accuracy of the vehicle dynamics model, whereas Deep Reinforcement Learning (DRL) generally lacks formal safety guarantees during training and exploration. To address this issue, this paper proposes a hybrid path-tracking framework, termed CRL-MPC, which integrates High-Order Control Barrier Function (HOCBF)-based reinforcement learning feedforward control with model predictive feedback control. Specifically, a Deep Deterministic Policy Gradient (DDPG) agent generates nominal feedforward steering commands, which are then corrected online by a High-Order Control Barrier Function (HOCBF)-based safety filter through a Quadratic Programming (QP) problem. During training on a high-fidelity CarSim–Simulink–Python co-simulation platform, the HOCBF-based safety filter constrains exploration within physically feasible regions, thereby preventing simulator failure caused by dynamically unsafe actions and improving training stability and sample efficiency. Meanwhile, the MPC controller provides feedback correction to compensate for residual errors. Comparative simulations were conducted against two baseline architectures: a standalone conventional MPC controller and a reinforcement-learning-based MPC(RL-MPC) hybrid architecture without the HOCBF-based safety filter. The results show that CRL-MPC achieves superior overall performance in path-tracking accuracy, control smoothness, and lateral dynamic stability. Compared with conventional MPC, CRL-MPC reduces the maximum lateral displacement error and its root mean square error (RMSE) by 54.1% and 62.7%, respectively, and reduces the maximum heading-angle error and its RMSE by 18.1% and 27.1%, respectively.
Keywords: path tracking; autonomous vehicles; safe reinforcement learning; model predictive control; control barrier function path tracking; autonomous vehicles; safe reinforcement learning; model predictive control; control barrier function

Share and Cite

MDPI and ACS Style

Song, Z.; Wen, W.; Li, J.; Wang, J.; Ye, M.; Li, M.; Li, B.; Wang, Z.; Sun, C.; Luan, A.; et al. Path Tracking for Autonomous Vehicles Integrating HOCBF-Based Reinforcement Learning and Model Predictive Control. Electronics 2026, 15, 2031. https://doi.org/10.3390/electronics15102031

AMA Style

Song Z, Wen W, Li J, Wang J, Ye M, Li M, Li B, Wang Z, Sun C, Luan A, et al. Path Tracking for Autonomous Vehicles Integrating HOCBF-Based Reinforcement Learning and Model Predictive Control. Electronics. 2026; 15(10):2031. https://doi.org/10.3390/electronics15102031

Chicago/Turabian Style

Song, Zhengyu, Wenxin Wen, Junze Li, Junjie Wang, Minghui Ye, Mengna Li, Bowen Li, Zhuo Wang, Changqun Sun, Aidong Luan, and et al. 2026. "Path Tracking for Autonomous Vehicles Integrating HOCBF-Based Reinforcement Learning and Model Predictive Control" Electronics 15, no. 10: 2031. https://doi.org/10.3390/electronics15102031

APA Style

Song, Z., Wen, W., Li, J., Wang, J., Ye, M., Li, M., Li, B., Wang, Z., Sun, C., Luan, A., Zhang, M., Liu, C., Si, Y., & Leng, B. (2026). Path Tracking for Autonomous Vehicles Integrating HOCBF-Based Reinforcement Learning and Model Predictive Control. Electronics, 15(10), 2031. https://doi.org/10.3390/electronics15102031

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