Enabling Technologies and System Architectures for Autonomous Vehicles

Abstract:

The transition of the automotive sector towards autonomous mobility requires a sophisticated amalgamation of cutting-edge hardware, redundant software frameworks, and accurate mathematical modeling. This research examines the technologies that facilitate autonomy in passenger vehicles, highlighting the shift from distributed to centralized zonal systems. The core focus of this study is the development of a unified modeling framework that integrates Kinematic and Dynamic Bicycle Models. Kinematic models prove sufficient for operations at low speeds, while dynamic models are essential for maintaining stability at high speeds, where tire slip and lateral forces gain prominence. This research provides a comprehensive classification of existing designs and introduces a mathematical framework that facilitates transitions between modeling paradigms based on the vehicle's state. A detailed numerical calculus experiment is performed, simulating the transition from a low-speed urban turn to a high-speed interstate lane change. This illustrates the necessity for an integrated modeling approach to ensure safe autonomous navigation.

• Periodical: Advances in Science and Technology (Volume 178)
• Pages: 105-113
• DOI: https://doi.org/10.4028/p-3uKF7f
• Online since: June 2026
• Authors: Theodor George Oprica*, Ilie Dumitru, Daniela Burghilă, Loreta Simniceanu, Lucian Matei
• Keywords: Autonomous Vehicles, Kinematic-Dynamic Modeling, Passenger Car Safety, Sensor Fusion, Software-Defined Vehicles (SDV), Vehicle Dynamics, Zonal Architecture

References

[1] SAE International. (2021). J3016: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles.

DOI: 10.3403/30443080

[2] Beiker, S. (2015). The State of the Art of Automated Driving. Springer.

[3] Eskandarian, A. (Ed.). (2019). Handbook of Intelligent Vehicles. Springer Nature.

[4] Paden, B., Cáp, M., Yong, S. Z., Yershov, D., & Frazzoli, E. (2016). A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles. IEEE Transactions on Intelligent Vehicles, 1(1), 33-55.

DOI: 10.1109/tiv.2016.2578706

[5] Falcone, P., Borrelli, F., Asgari, J., Tseng, H. E., & Hrovat, D. (2007). Predictive Control Approaches for Steering Control of Autonomous Vehicles. IEEE Transactions on Control Systems Technology, 15(3), 528-540.

DOI: 10.1109/tcst.2007.894653

[6] B. Paden, et al., "Next-Generation Zonal E/E Architectures for Software-Defined Vehicles," IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp.112-125, 2024.

[7] J. Chen, X. Wang, and Y. Li, "Autonomous Vehicle Path Tracking Using Event-Triggered MPC With Switching Model: An Experimental Validation," IEEE International Conference on Electro Information Technology, pp.18-24, 2024.

DOI: 10.1109/eit57321.2023.10187304

[8] J. Kabzan, et al., "Comparing Gradient-Based and Blended Kinematic-Dynamic Models for Autonomous Racing," Automotive Innovation, vol. 8, no. 1, pp.45-56, 2025.

[9] Watzenig, D., & Horn, M. (2017). Automated Driving: Safety, Governance, and Testing. Springer.

[10] Rajamani, R. (2011). Vehicle Dynamics and Control. Springer Science & Business Media.

[11] Gillespie, T. D. (1992). Fundamentals of Vehicle Dynamics. SAE.

[12] Kong, J., Pfeiffer, M., Schildbach, G., & Borrelli, F. (2015). Kinematic and Dynamic Vehicle Models for Autonomous Driving Control Design. IEEE Conference on Control Applications (CCA).

DOI: 10.1109/ivs.2015.7225830

[13] Pacejka, H. B., 2005, Tire and Vehicle Dynamics.

DOI: 10.1016/b978-0-08-097016-5.02001-5

[14] https://www.mathworks.com/help/releases/R2025b/sdl/ref/tireroadinteractionmagicformula.html