Overview of Chassis Domain Control System Architecture, Algorithms, and Verification Techniques: -- A Survey of Chassis Domain Control System Architecture, Algorithms, and Verification Techniques for Autonomous Vehicles

Ke Xie

doi:10.62051/ijmee.v8n2.03

Authors

Ke Xie

DOI:

https://doi.org/10.62051/ijmee.v8n2.03

Keywords:

Autonomous Driving, Chassis Domain Control, System Architecture, Model Predictive Control, Deep Reinforcement Learning, AUTOSAR

Abstract

This work systematically reviews the current status and development trends of autonomous vehicle chassis domain control systems across three dimensions: architecture, algorithms, and verification techniques. During the transformation toward intelligent vehicles, the limitations of traditional distributed electronic/electrical architectures in enhancing advanced autonomous driving capabilities have become apparent, prompting both industry and academia to shift focus toward the evolution of centralized domain control architectures. This study delves into the technical drivers and challenges underlying the evolution of chassis domain control systems from distributed to domain-centralized architectures. Building upon this foundation, it further explores the emerging trend toward zonal architecture. The study emphasizes hardware-software co-design strategies compliant with AUTOSAR standards. Through in-depth investigation and comprehensive evaluation, it conducts a holistic comparative analysis of traditional control methods—such as model-based predictive control and sliding mode control—alongside data-driven deep reinforcement learning strategies, examining their performance limits and application prospects within integrated vehicle chassis control systems. Finally, it systematically summarizes verification methodologies based on high-fidelity simulation (e.g., CARLA) and hardware-in-the-loop testing. This research aims to fill the current gap in integrating system-level architecture theory, providing a comprehensive technical blueprint and theoretical foundation for developing next-generation high-performance, high-safety intelligent chassis control systems.

References

[1] Jin, Y., Li, Y., Zheng, L., Li, G., & Huang, X. (2025). Design and implementation process of an intelligent automotive chassis domain controller system based on AUTOSAR. Sensors, 25(16), 5056. https:// doi.org/ 10. 3390/ s25165056.

[2] Tan, H. B. (2024). Role of Intelligent Chassis Control Systems in Vehicle Safety Performance. Automotive Maintenance Technician, (18), 132.

[3] Wu, J. Y. (2008). Design and methodology research of integrated chassis control systems for enhancing vehicle maneuvering stability [Doctoral dissertation, Shanghai Jiao Tong University].

[4] Cui, C., Park, C., & Park, S. (2023). Physical length and weight reduction of humanoid in-robot network with zonal architecture. Sensors, 23(5), 2627.https://doi.org/10.3390/s23052627.

[5] Wang, Y., Sun, Y., Luo, Z., & Zhang, C. (2025). Review of autonomous driving behavior decision-making based on deep reinforcement learning. Control and Decision. Advance online publication. https:// doi.org/ 10. 13195/ j. kzyjc.2025.0441.

[6] HE Xiang-Kun, ZHAO Yang, FANG Jian-Wu, CHENG Hong, LYU Chen. (2025). Toward trustworthy policy optimization for autonomous driving: An adversarial robust reinforcement learning approach. Acta Automatica Sinica, 51(11): 1–13. https://doi.org/10.16383/j.aas.c250193.

[7] Fu, G., Lu, Z., Ma, L., & Wang, B. (2025). A survey of end-to-end autonomous driving algorithms based on CARLA simulation. Journal of Zhejiang University (Science Edition). Advance online publication. https:// doi. org/ 10. 3785/ 1008-9497.2025.106.

[8] Shan, Z. (2025). Research on decision planning for autonomous vehicles in hazardous scenarios[Master’s thesis, Jilin University]. Jilin University. DOI:10.27162/d.cnki.gjlin.2025.000228.

[9] LI Jing, YU Chun-xian. (2013). Vehicle chassis integrated control system based on fuzzy and PID. Journal of Jilin University (Engineering and Technology Edition), 43 Sup.: 509–513. https://www.cnki.net.

[10] Niu, L.M., Chen, L., Zhao, Y.Q., et al. (2008) Motor Control in Integrated Vehicle Chassis Control Systems. Transactions of the Chinese Society of Agricultural Machinery, 39(12): 27-30, 44.

[11] Hima, S., Glaser, S., Chaibet, A., & Vanholme, B. (2011). Controller Design for Trajectory Tracking of Autonomous Passenger Vehicles. In: IEEE Intelligent Transportation Systems Conference (ITSC 2011). Washington DC, United States. DOI: 10.1109/ITSC.2011.6083126.

[12] Alcala, E., Sellart, L., Puig, V., et al. Comparison of two non-linear model-based control strategies for autonomous vehicles. Sensors, 2023, 23(5): 2627.

[13] Goren. (2025). Research on reinforcement learning-based visual navigation for autonomous driving [Master’s thesis, Beijing University of Posts and Telecommunications]. Beijing University of Posts and Telecommunications. DOI:10. 26969/d.cnki.gbydu.2025.000065.

[14] Lü, P., Liu, Y., & Wang, Y. (2025). The prospect on human-machine-object interaction mode and development trend in intelligent society: An analysis based on passenger data from autonomous ride-hailing services. Journal of Beijing University of Technology (Social Sciences Edition), 25(6), 116–134.https://link.cnki.net/ urii/d/ 11. 4558.G.20251015.1040.002.

[15] Ren, R.-H., Yang, C., Yang, K., Zhang, B.-D., Zhang, X.-D., Wang, L.-J., & Ma, J.-F. (2024). Survey on vehicle system safety testing research for autonomous driving. Journal of Software.http://www.jos.org.cn/1000-9825/ 7486.htm.