  The acceptance of automated driving is under the potential threat of motion
sickness. It hinders the passengers' willingness to perform secondary
activities. In order to mitigate motion sickness in automated vehicles, we
propose an optimization-based motion planning algorithm that minimizes the
distribution of acceleration energy within the frequency range that is found to
be the most nauseogenic. The algorithm is formulated into integral and
receding-horizon variants and compared with a commonly used alternative
approach aiming to minimize accelerations in general. The proposed approach can
reduce frequency-weighted acceleration by up to 11.3% compared with not
considering the frequency sensitivity for the price of reduced overall
acceleration comfort. Our simulation studies also reveal a loss of performance
by the receding-horizon approach over the integral approach when varying the
preview time and nominal sampling time. The computation time of the
receding-horizon planner is around or below the real-time threshold when using
a longer sampling time but without causing significant performance loss. We
also present the results of experiments conducted to measure the performance of
human drivers on a public road section that the simulated scenario is actually
based on. The proposed method can achieve a 19\% improvement in general
acceleration comfort or a 32% reduction in squared motion sickness dose value
over the best-performing participant. The results demonstrate considerable
potential for improving motion comfort and mitigating motion sickness using our
approach in automated vehicles.
