Print Email Facebook Twitter Mitigating Motion Sickness with Optimization-Based Motion Planning Title Mitigating Motion Sickness with Optimization-Based Motion Planning Author Zheng, Y. (TU Delft Intelligent Vehicles) Shyrokau, B. (TU Delft Intelligent Vehicles) Keviczky, T. (TU Delft Team Tamas Keviczky) Date 2024 Abstract 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. Subject Automated vehiclesmotion planningmotion sicknessreal-time optimization To reference this document use: http://resolver.tudelft.nl/uuid:c944ae79-0b9a-4522-bef4-3a858fc9d4b3 DOI https://doi.org/10.1109/TIV.2023.3289854 ISSN 2379-8858 Source IEEE Transactions on Intelligent Vehicles, 9 (1), 2553-2563 Part of collection Institutional Repository Document type journal article Rights © 2024 Y. Zheng, B. Shyrokau, T. Keviczky Files PDF Mitigating_Motion_Sicknes ... anning.pdf 1.69 MB Close viewer /islandora/object/uuid:c944ae79-0b9a-4522-bef4-3a858fc9d4b3/datastream/OBJ/view