Mitigating Motion Sickness in Online Motion Planning by Means of Linear Quadratic Optimization
Manuel Hess, Jan Riffel, Christopher A. Bohn, Sören Hohmann · 2025
This paper presents a method for parameterizing a linear quadratic (LQ) motion planning algorithm for automated vehicles. The presented method approximates the behavior of a planning algorithm that optimizes for a multi-objective optimization (MOO) objective function. The MOO objective function captures the Pareto-conflicting objectives of mitigating motion sickness and reducing travel time. Both objectives cannot be considered directly in the LQ motion planning algorithm due to the complex formulation of the motion sickness objective and the limited prediction horizon, which prevents the algorithm from considering total travel time explicitly. Nevertheless, we use an LQ approach because it allows for online planning. Our method uses Bayesian optimization to tune the parameters of the LQ objective function so that the resulting state trajectory is optimal with respect to the MOO objective function. Additionally, we employ a normalized weighted-sum method to assign varying importance to the MOO objectives, resulting in a convex Pareto front of the MOO objectives. The presented results demonstrate that the LQ motion planning algorithm is online capable and can be effectively tuned to balance the trade-off between motion sickness and travel time according to passenger susceptibility.