Ergonomic Path Planning for Autonomous Vehicles-An Investigation on the Effect of Transition Curves on Motion Sickness

Abstract

Motion sickness in self-driving cars is a key human factor that aggravates the passengers’ health in autonomous vehicles and is investigated in the following pages. As drivers turn into passengers and passengers dwell into other activities, the probability of car sickness is inevitable in self-driving cars. Path planning could serve an important role in reducing sickness. The present study establishes thresholds that contribute to motion sickness from a vehicle’s dynamic point of view to generate at first the most susceptible reference track to motion sickness, then redesigned using B-spline, Bezier, and Hermite curves to investigate the thresholds. Trajectory tracking of an eight degree of freedom vehicle model within the Autodriver algorithm is then studied using curvature dependent and curvature independent controllers to draw a comparison. Results are then compared and evaluated to find the optimal transition curve to minimize motion sickness probability. Furthermore, the findings are applied to lane changing maneuvers using various transition curves. Results indicate that four out of five of the motion sickness thresholds were successfully addressed in this investigation. Further research is recommended to address the fifth motion sickness threshold by utilizing the transition curve’s key characteristics like local control and non-uniformity.