| Time-Efficient Hierarchical Vehicle Control Framework for Path Tracking in Intelligent Vehicles |
| Ming Liu1, Yuanjian Zhang1, Bo Leng1, Jincheng Hu2, Jihao Li2, Lu Xiong1 |
1School of Automotive Studies, Tongji University, Shanghai, 201804, China
2Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK |
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Received: December 9, 2024; Revised: April 3, 2025 Accepted: April 5, 2025. Published online: May 26, 2025. |
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| ABSTRACT |
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This paper proposes a novel time-efficient hierarchical vehicle control framework (THVCF) to meet the real-time and high-accuracy requirements of intelligent vehicle path tracking. The main contribution lies in the integration of three coordinated layers: an offline explicit control layer using EMPC to precompute control laws and reduce online computational cost; an online control layer combining feed-forward and feedback strategies to improve steering accuracy; and a parameter optimization layer that adaptively tunes control weights for different paths. This layered design enhances tracking performance, robustness, and real-time capability. Simulation results under double lane change and circular arc scenarios demonstrate that THVCF improves tracking accuracy and computational efficiency by over 60% and 80%, respectively. Furthermore, steering overshoot under high-curvature conditions is reduced by 56% through feed-forward compensation and adaptive tuning. Ablation studies further verify the contribution of each layer. These results confirm that THVCF offers a practical and scalable solution for real-time vehicle control in complex driving environments. |
| Key Words:
Intelligent vehicle · Path tracking · Model predictive control |
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Corresponding Author.
Bo Leng , Email. 
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