Volume 25 Issue 4
Aug.  2025
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QU Da-yi, LI Ao-di, ZHANG Zhi, WEI Chuan-bao, WANG Tao. Multi-vehicle responsive longitudinal and lateral cooperative control method for networked autonomous driving[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 281-295. doi: 10.19818/j.cnki.1671-1637.2025.04.020
Citation: QU Da-yi, LI Ao-di, ZHANG Zhi, WEI Chuan-bao, WANG Tao. Multi-vehicle responsive longitudinal and lateral cooperative control method for networked autonomous driving[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 281-295. doi: 10.19818/j.cnki.1671-1637.2025.04.020
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Multi-vehicle responsive longitudinal and lateral cooperative control method for networked autonomous driving

doi: 10.19818/j.cnki.1671-1637.2025.04.020

  • School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, Shandong, China

Funds:

National Natural Science Foundation of China 52272311

More Information
  • Corresponding author: QU Da-yi (1973-), male, professor, PhD, dayiqu@qut.edu.cn
  • Received Date: 2024-07-04
  • Accepted Date: 2025-03-12
  • Rev Recd Date: 2025-01-06
  • Publish Date: 2025-08-28
    Abstract
  • In order to study multi-vehicle cooperative control in connected environments and improve road traffic efficiency, a hierarchical cooperative control framework for connected autonomous driving was proposed. The framework divided cooperative control for connected autonomous driving into upper and lower layers. At the traffic management layer, the longitudinal driving strategy was determined. A differential game approach was used to control the longitudinal motion of connected vehicle platoons, aiming to optimize vehicle states within the platoon. At the vehicle control layer, to address steering control, a feedback linear quadratic regulator (LQR) considering feed-forward was proposed. Based on this, the LQR parameter matrix was dynamically adjusted using the artificial potential field method to handle obstacles at different distances and improve the vehicle's lateral control accuracy. For the safety of connected autonomous vehicles (CAV) merging into platoons, a virtual platoon was adopted as the basis, by incorporating vehicle dimensions and critical collision constraints to control the vehicle, thereby ensuring safe CAV merging behavior. CarSim/Simulink was used to build a merging scenario for simulation experiments to analyze and verify the feasibility and effectiveness of the proposed longitudinal and lateral cooperative control method for multiple vehicles. Simulation results show that the spacing within the platoon is more than 20 m when the differential game approach is applied at a speed of 16.67 m·s-1, while the spacing within the platoon is below 40 m at 22.22 m·s-1, indicating that the strategy improves traffic efficiency while ensuring safe distances between vehicles. Compared with the traditional LQR, the LQR that considers potential field strength reduces lateral error and heading error by 6.19% and 7.66%, respectively, at a speed of 22.22 m·s-1. Therefore, the proposed hierarchical longitudinal and lateral cooperative control framework achieves multi-vehicle cooperative and safe operation of CAVs merging into connected platoons, while ensuring longitudinal safety control and stability as well as improving lateral control accuracy.

     

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