A Deep Learning Framework for Time Series Prediction of Passenger Motion Sickness Based on Vehicle Dynamics Data University of Michigan

Format:

Book

Conference/Event

Author/Creator:

Kolachalama, Srikanth, author.

Contributor:

Awtar, Shorya

Jalgaonkar, Nishant M.

Kerr, Bradley

Pienkny, Jedidiah Ethan Shapiro

Sousa Schulman, Daniel

Wachsman, Michael Ben

Yin, Siyuan

Conference Name:

WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Technology development for enhancing passenger experience has gained attention in the field of autonomous vehicle (AV) development. A new possibility for occupants of AVs is performing productive tasks as they are relieved from the task of driving. However, passengers who execute non-driving-related tasks are more prone to experiencing motion sickness (MS). To understand the factors that cause MS, a tool that can predict the occurrence and intensity of MS can be advantageous. However, there is currently a lack of computational tools that predict passenger's MS state. Furthermore, the lack of real-time physiological data from vehicle occupants limits the types of sensory data that can be used for estimation under realistic implementations. To address this, a computational model was developed to predict the MS score for passengers in real time solely based on the vehicle's dynamic state. The model leverages self-reported MS scores and vehicle dynamics time series data from a previous study performed under realistic driving conditions. The data comprises 66 trials (1 trial = 1350s) and includes MS score (1-10 scale), static parameters (e.g., age, gender, MS susceptibility, et cetera), cabin parameters (id est, temperature, humidity), and vehicle's dynamic state (e.g., acceleration, angular velocity etc). The deep-learning models presented here include long short-term memory (LSTM) and nonlinear auto-regressive (NARX), which can create a time series mapping between vehicle sensor data, static parameters, and MS score. The predictive models were optimized in terms of their hyperparameters, and their results were validated by analyzing the conformance between actual and predicted MS scores. The NARX and LSTM models produced mean RMSE of 2.2 and 2.3, respectively. Both results are deemed acceptable based on the range of MS scores in the scale used. The developed model framework is a promising solution for estimating MS when only vehicle data is available

Notes:

Vendor supplied data

Publisher Number:

2025-01-8104

Access Restriction:

Restricted for use by site license