Coordinated Control of Autonomous Electric Vehicles With Lateral and Longitudinal Control Using a Hybrid Approach

ABSTRACT The rise of Autonomous Electric Vehicles (AEVs) has presented formidable challenges in the automotive sector, demanding advanced sensor technology, intricate control systems, and sophisticated decision‐making algorithms. Due to the inherently nonlinear dynamics and uncertainties associated with these vehicles, conventional control methods fall short of providing robust solutions. This study proposes a hybrid approach for coordinated longitudinal and lateral control in autonomous driving scenarios. Addressing lateral and longitudinal control, the research integrates road geometry and lateral dynamics considerations. Utilizing a Proportional Integral Derivative (PID) controller with Fire Hawk Optimizer (FHO) algorithm. This study optimizes controller gains for Nonlinear longitudinal dynamics, ensuring reliable speed tracking. Additionally, a Linear Parameter Varied‐Models Predictive Controller (LPV‐MPC) addresses the challenges related to time‐varying longitudinal speeds and distance impact on vehicle lateral stability. Implementation in the matrix laboratory demonstrates the approach's superiority in terms of speed, precision, stability, trajectory tracking, and achieving a minimal lateral error of 0.0526 and mean error, mean absolute error and root mean squared error of 0.193, 0.087 and 0.108 respectively.