This paper presents a method based on a Model Predictive Control (MPC) aiming to optimize the passenger comforts in assisted and autonomous vehicles. The controller works on the lateral and longitudinal dynamics of the car, providing front wheel steering angle and acceleration/deceleration command. The comfort is evaluated through two indexes extracted from the ISO 2631: an equivalent acceleration a eq and a Motion Sickness Dose Value (MSDV) index. The MPC weighting parameters are designed according to the values assumed by these indexes. Specifically, each weighting parameter is changed until the most satisfying comfort evaluation and the maximum vehicle performances, in terms of lateral deviation, tracking velocity and relative yaw angle, are reached. The controller is tested numerically on a simulated scenario resulting from real GPS data obtained in a highway. The method is compared with an alternative control strategy based on the combination of a PID and a Stanley control for the longitudinal and lateral dynamics, respectively. The results demonstrate the effectiveness of the approach, leading to a low percentage of passengers can experience motion sickness.

This paper presents a technique for the lane keeping and the longitudinal speed control of an autonomous vehicle with the combination of an MPC and a PID control. The goal of the proposed control method is to minimize the lateral deviation and relative yaw angle with respect to the planned trajectory, while driving the vehicle at the highest acceptable longitudinal speed. The reference profile of the longitudinal speed is computed considering both the lateral and longitudinal dynamic of the vehicle. The vehicle is represented by means of a linear 3-DoF bicycle model. The control algorithm takes the road lane boundaries as the only external input. The proposed strategy is validated in simulation on three distinct driving scenarios.