Quantifying the Effect of Automated Driving on Consumer Acceptance and Motion Sickness Response
Assistant Research Scientist, Biosciences, University of Michigan Transportation Research Institute
Engineering models that control vehicle movement parameters in a manner that meets consumer expectations and mitigate the onset of motion sickness response are needed to guide vehicle design. Current engineering approaches to AV involve control inputs that include at least the path (route) and vehicle speed, but detailed control of the acceleration and jerk profiles is also possible. Control algorithms are potentially capable of producing ride experiences that are outside the range possible with human drivers, i.e., offer superior tradeoffs between efficiency (trip duration) and motion experience. The proposed research will quantify the effect of alternative acceleration profiles in an automated vehicle on passenger acceptability and motion sickness response. Volunteer testing will be conducted on Mcity using the Mcity Lincoln MKZ AV programmed to self-drive the route used in the previous human-driven study. The control of the vehicle will follow the path profile for a range of speed that will result in different jerk profiles, while maintaining approximately the same overall trip duration.
The project will identify metrics that characterize vehicle dynamics that meaningfully relate to passenger’s acceptance and motion sickness response, which can also be modeled in AV control algorithms. Additionally, the project will provide preliminary definitions of acceleration profile limits that are acceptable and minimize motion sickness response that can inform design of AV control algorithms.