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

2025-01-8104

04/01/2025

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
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, etc.), cabin parameters (i.e., 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.
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DOI
https://doi.org/10.4271/2025-01-8104
Pages
19
Citation
Kolachalama, S., Sousa Schulman, D., Kerr, B., Yin, S. et al., "A Deep Learning Framework for Time Series Prediction of Passenger Motion Sickness Based on Vehicle Dynamics Data," SAE Technical Paper 2025-01-8104, 2025, https://doi.org/10.4271/2025-01-8104.
Additional Details
Publisher
Published
Apr 01
Product Code
2025-01-8104
Content Type
Technical Paper
Language
English