Comfort-Aware Trajectory Planning in Autonomous Driving via Multi-Objective Nonlinear Model Predictive Control

Autonomous passenger vehicles are expected to change mobility as we know it. They can potentially offer many advantages over traditional vehicles, but user acceptance is fundamental to benefit from them. In particular, comfort will be one of the most relevant deciding factors. The lack of control over the vehicle's motion will increase the passengers' inability to predict the path, raising their discomfort. The most straightforward way to maximize comfort is to adopt smooth and slow speed and cornering profiles, but the risk is to penalize too much the total travel time. A preliminary study on the optimal trajectory planning for an autonomous vehicle to find the best user-defined trade-off between comfort and travel time is presented. The problem is approached with a Nonlinear Model-Predictive Control strategy. After describing a suitable vehicle model, the complete formulation of the algorithm is presented. A multi-objective analysis is then carried out to obtain the Pareto Front and the Utopian solution for comfort and travel time. Finally, the algorithm is applied to a case study, assessing the overall performance.