Minimisation of Motion Sickness in Autonomous Vehicles

Objective This study explores the role of anticipation in motion sickness. We compared three conditions varying in motion predictability and assessed the effect of anticipation on subsequent illness ratings using a within-subjects design. Background Anticipation is thought to play a role in motion sickness by reducing the discrepancy between sensed and expected sensory information. However, both the exact role and potential magnitude of anticipation on motion sickness are unknown. Method Participants ( N = 17) were exposed to three 15-min conditions consisting of repeated fore-aft motion on a sled on a 40-m rail (1) at constant intervals and consistent motion direction, (2) at constant intervals but varied motion direction, and (3) at varied intervals but consistent motion direction. Conditions were otherwise identical in motion intensity and displacement, as they were composed of the same repetitions of identical blocks of motion. Illness ratings were recorded at 1-min intervals using an 11-point motion sickness scale. Results Average illness ratings after exposure were significantly lower for the predictable condition, compared with both the directionally unpredictable condition and the temporally unpredictable condition. Conclusion Unpredictable motion is significantly more provocative compared with predictable motion. Findings suggest motion sickness results from a discrepancy between sensed and expected motion, rather than from unpreparedness to motion. Application This study underlines the importance of an individual’s anticipation to motion in motion sickness. Furthermore, this knowledge could be used in domains such as that of autonomous vehicles to reduce carsickness.

Download Full-text

Minimizing Motion Sickness In Autonomous Vehicles; A Hybrid Approach

10.1109/icecie52348.2021.9664709 ◽

2021 ◽

Author(s):

Muhammad Rehan Siddiqi ◽

Reza. N. Jazar ◽

Hormoz Marzbani ◽

Chunyun Fu

Keyword(s):

Motion Sickness ◽

Autonomous Vehicles ◽

Hybrid Approach

Download Full-text

Motion Sickness Minimisation in Autonomous Vehicles Using Optimal Control

Advances in Service and Industrial Robotics - Mechanisms and Machine Science ◽

10.1007/978-3-030-48989-2_30 ◽

2020 ◽

pp. 275-282

Author(s):

Zaw Htike ◽

Georgios Papaioannou ◽

Efstathios Siampis ◽

Efstathios Velenis ◽

Stefano Longo

Keyword(s):

Optimal Control ◽

Motion Sickness ◽

Autonomous Vehicles

Download Full-text

Electrogastrography in Autonomous Vehicles—An Objective Method for Assessment of Motion Sickness in Simulated Driving Environments

Sensors ◽

10.3390/s21020550 ◽

2021 ◽

Vol 21 (2) ◽

pp. 550

Author(s):

Timotej Gruden ◽

Nenad B. Popović ◽

Kristina Stojmenova ◽

Grega Jakus ◽

Nadica Miljković ◽

...

Keyword(s):

Power Spectrum ◽

Motion Sickness ◽

Autonomous Vehicles ◽

Power Spectrum Density ◽

Subjective Perception ◽

Myoelectric Activity ◽

Recording Device ◽

Complete Control ◽

Spectrum Density ◽

Egg Signal

Autonomous vehicles are expected to take complete control of the driving process, enabling the former drivers to act as passengers only. This could lead to increased sickness as they can be engaged in tasks other than driving. Adopting different sickness mitigation techniques gives us unique types of motion sickness in autonomous vehicles to be studied. In this paper, we report on a study where we explored the possibilities of assessing motion sickness with electrogastrography (EGG), a non-invasive method used to measure the myoelectric activity of the stomach, and its potential usage in autonomous vehicles (AVs). The study was conducted in a high-fidelity driving simulator with a virtual reality (VR) headset. There separate EGG measurements were performed: before, during and after the driving AV simulation video in VR. During the driving, the participants encountered two driving environments: a straight and less dynamic highway road and a highly dynamic and curvy countryside road. The EGG signal was recorded with a proprietary 3-channel recording device and Ag/AgCl cutaneous electrodes. In addition, participants were asked to signalize whenever they felt uncomfortable and nauseated by pressing a special button. After the drive they completed also the Simulator Sickness Questionnaire (SSQ) and reported on their overall subjective perception of sickness symptoms. The EGG results showed a significant increase of the dominant frequency (DF) and the percentage of the high power spectrum density (FSD) as well as a significant decrease of the power spectrum density Crest factor (CF) during the AV simulation. The vast majority of participants reported nausea during more dynamic conditions, accompanied by an increase in the amplitude and the RMS value of EGG. Reported nausea occurred simultaneously with the increase in EGG amplitude. Based on the results, we conclude that EGG could be used for assessment of motion sickness in autonomous vehicles. DF, CF and FSD can be used as overall sickness indicators, while the relative increase in amplitude of EGG signal and duration of that increase can be used as short-term sickness indicators where the driving environment may affect the driver.

Download Full-text

A Visual-Vestibular Model to Predict Motion Sickness Response in Passengers of Autonomous Vehicles

10.4271/2021-01-0104 ◽

2021 ◽

Author(s):

Nishant Jalgaonkar ◽

Daniel Sousa Schulman ◽

Sneha Ojha ◽

Shorya Awtar

Keyword(s):

Motion Sickness ◽

Autonomous Vehicles

Download Full-text

Model to predict motion sickness within autonomous vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019879785 ◽

2019 ◽

Vol 234 (5) ◽

pp. 1330-1345

Author(s):

Spencer Salter ◽

Cyriel Diels ◽

Paul Herriotts ◽

Stratis Kanarachos ◽

Doug Thake

Keyword(s):

Motion Sickness ◽

Autonomous Vehicles ◽

Systems Approach ◽

Autonomous Vehicle ◽

Human Motion ◽

Independent Study ◽

Vehicle Design ◽

Specific Method ◽

Motion Vision ◽

Vehicle Motion

Background: Motion sickness is common within most forms of transport; it affects most of the population who experience varied symptoms at some stage in their lives. Thus far, there has been no specific method to quantify the predicted levels of motion sickness for a given vehicle design, task and route. Objective: To develop a motion sickness virtual prediction tool that includes the following inputs: human motion, vision, vehicle motion, occupant task and vehicle design. Method: A time domain analysis using a multi-body systems approach has been developed to provide the raw data for post-processing of vehicle motion, occupant motion and vision, based on a virtual route designed to provoke motion sickness, while the digital occupant undertakes a specific non-driving related task. Results: Predicted motion sickness levels are shared for a simple positional sweep of a vehicle cabin due to a prescribed motion and task. Two additional examples are shared within this study; first, it was found that the model can predict the difference found between sitting forwards and backwards in an autonomous vehicle. Second, analysis of a respected and independent study into auxiliary display height shows that the model can predict both relative and absolute levels between the two display heights congruent to the original physical experiment. Conclusion: It has been shown that the tool has been successful in predicting motion sickness in autonomous vehicles and is therefore of great use in guiding new future mobility solutions in the ability to tune vehicle dynamics and control alongside vision and design attributes.

Download Full-text