Optimizing Lane Change Precision in Autonomous Vehicles: Smooth Trajectory Execution With MPC-Based State Estimation

Autonomous vehicle lane change maneuvers are an intricate task that requires multiple subsystems to work together to ensure the maneuver’s safety and efficiency. This research paper investigates lane change maneuvers of autonomous vehicles, utilizing a trajectory generated using a sinusoidal function considering the ISO 3888 standards. The driving maneuver steering angle input is derived from the generated trajectory, which is adjusted using model predictive control (MPC) to calculate an optimal steering angle for lateral movement and manage throttle input to maintain longitudinal stability. Throughout lane change maneuvers, the vehicle’s states are estimated using model-based Kalman filters, relying on input and measurement data from inertial sensors. The paper compares four state estimator filters: Standard Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Unscented Kalman Filter. The implementation of the lane change trajectory generator, MPC algorithm, and state estimators within MATLAB/Simulink, validated through IPG CarMaker, highlights the Adaptive Unscented Kalman Filter as the optimal choice for lane change state estimation. Its adaptive covariance adjustment sets it apart from the other filters under examination.