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

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.