Motion Sickness Alleviation via Anticipatory Control of Active Seats in Autonomous Vehicles

The goal of this integrative research project is to develop and demonstrate a passenger motion sickness mitigation solution that employs preemptive or anticipatory control of Active Seats in autonomous vehicles. The resulting proof of concept will enable implementation and deployment of the proposed technology. Motion sickness when traveling in a vehicle is a common condition that afflicts one in three adults in the US. Moreover, passengers who are not driving the vehicle experience such motion sickness more acutely compared to the driver of the vehicle. This is due to the driver’s ability to make anticipatory corrections when initiating a driving action that involves acceleration (e.g. speeding up, breaking, or taking turns). These anticipatory corrections by the driver (such as tightening their abdominal core muscles when braking or leaning their body/head into the direction of the turn when turning) help prepare the driver for the accelerations associated with the driving actions slightly ahead of time, whereas the passenger ends up passively reacting to these driving actions. With the impending transformation in ground transportation due to autonomous vehicles, where every occupant is a passive passenger, the deleterious effects of motion sickness on the passenger comfort and productivity during their commute is expected to be significant. The proposed solution strategy leverages the existing science on the causes of motion sickness (including the sensory conflict, neural mismatch, and postural instability theories), and the well-known benefits of anticipatory corrective action in mitigating the same. In this project, we will develop a test vehicle equipped with Active Seats capable of roll, pitch, and yaw motions that can be controlled preemptively based on apriori knowledge of the driving conditions in a closed-track testing facility (M-City). These driving conditions include vehicle path/route (including turns and stop and go events), vehicle speed and acceleration profiles, and vehicle parameters and dynamics. Based on this apriori knowledge of driving conditions, we will develop algorithms that preemptively control the Active Seat, for example starting to tilt the seat towards the direction of a turn slightly before the turn happens. Our hypothesis is that such preemptive correction will provide anticipation and reduce body movement, thereby lowering the incidence of passenger motion sickness. Thus, the passenger of an autonomous vehicle equipped with the proposed technology will no longer be entirely passive and instead be more like the driver of a traditional vehicle.

• Record URL:
  ccat.umtri.umich.edu

Language

• English

Project

• Status: Active
• Funding: $250,000
• Contract Numbers:

  69A3551747105

• Sponsor Organizations:

  University Transportation Center Program

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Managing Organizations:

  University of Michigan, Ann Arbor

  Department of Civil and Environmental Engineering
  2350 Hayward
  Ann Arbor, MI  United States  48109-2125
• Project Managers:

  Tucker-Thomas, Dawn

  Bezzina, Debra

• Performing Organizations:

  University of Michigan Transportation Research Institute

  2901 Baxter Road
  Ann Arbor, Michigan  United States  48109
• Principal Investigators:

  Awtar, S

• Start Date: 20201001
• Expected Completion Date: 20211231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Subprogram: Research

Subject/Index Terms

• TRT Terms: Automated guided vehicle systems; Control systems; Human factors engineering; Motion sickness; Safety and security
• Subject Areas: Design; Passenger Transportation; Safety and Human Factors; Transportation (General); Vehicles and Equipment;

Filing Info

• Accession Number: 01754279
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3551747105
• Files: UTC, RiP
• Created Date: Oct 6 2020 2:07PM