When assisted and autonomous vehicles are released, the driver will no longer influence the driving style. The vehicle itself will accomplish this task, and, thus, safety and comfort should be guaranteed by the overall system. This paper presents a methodology for the tuning of a Model Predictive Control (MPC) for assisted and autonomous car to maximize the comfort of the passengers. The comfort is evaluated through ISO 2631 and Motion Sickness Dose Value (MSDV) in order to consider frequencies that cause discomfort and nausea. The controller provides front wheel steering angle and acceleration/deceleration command for both lateral guidance and speed regulation. The signiﬁcant parameters of the controller, as output weighting parameters of the cost function, are varied one at a time to obtain at the same time the most satisfying comfort evaluation and the maximum vehicle performances in terms of lateral deviation, tracking velocity and relative yaw angle. The most fitting parameters set highlights desirable vehicle performances and satisfying comfort evaluation. The tuned controller is tested on a simulated scenario resulting from real GPS data.


Comfort-Oriented Design of Model Predictive Control in Assisted and Autonomous Driving

Division / Sponsors: 
Design Engineering Division and Computers & Information In Engineering Division


Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2020/volume/83907

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