Motion Sickness Mitigating Algorithms and Control Strategy for Autonomous Vehicles

Abstract

Autonomous Vehicles (AVs) have the potential to generate socioeconomic benefits by reducing commute times, accidents and energy consumption. Ergonomists have foreseen the future of these vehicles as comfortable pods where the passengers will be watching movies, reading or simply relaxing. Motion Sickness (MS) is thought to be a human comfort issue which needs to be addressed to enable such a vision. The present study endeavours to address this issue by firstly proposing three algorithms for mitigating MS during various manoeuvres, namely: (1) Single Lane Change (SLC), (2) Double Lane Change (DLC) and (3) Cornering. This is done in 3 phases; phase 1 is the identification of coordinates using Particle Swarm Optimization (PSO), phase 2 is the MS-free block that minimizes MS, and the last phase is shaping and symmetry block for minimizing postural instability and maximizing handling comfort. This is believed to be the first time an ultimate motion sickness mitigating algorithm is introduced. The second proposed solution is a control strategy to ensure real-time adaptive MS reduction while the AV is on the move with the help of modern & traditional controllers. Ultimately, The proposed hybrid solution is compared against other recent studies, with results illustrating a substantial MS reduction in AV’s of about 75%.