Vehicle motion planning for ride comfort without passenger feedback using linear MPC

Abstract

Ride comfort is an emerging focus in autonomous driving, yet integrating passenger behavior into vehicle motion planning remains challenging due to the impracticality of real-time passenger state feedback and high computational demands. This paper proposes a solution that approximates Ideal Motion Planning (IMP)—which traditionally requires nonlinear model predictive control (NMPC) and passenger state feedback—using linear model predictive control (LMPC) with weight tuning via Bayesian optimization. Our method eliminates the need for passenger state feedback and reduces the computation time, allowing real-time implementation. Although simplifying the cost function to prioritize tracking and stability, we achieve vehicle motion that maintains ride comfort equivalent to IMP, as demonstrated in simulations. This approach offers a practical pathway to improve passenger comfort in autonomous vehicles without additional sensory input.