image: The passenger motion sickness binary classification model includes EEG and ECG inputs, EEG temporal and spatial convolution modules, multi-attention module, ECG feature extraction, fusion, and final binary classification.
Credit: Bin Ren, Xiaoyuchen Wang, Pengyu Ren, Menghan Wu / Shanghai University
Shanghai, September 2025 — Motion sickness is a major barrier to passenger comfort, particularly during nighttime journeys when visual cues are limited. A new study published in Artificial Intelligence and Autonomous Systems (AIAS) provides experimental evidence that warm red in-vehicle lighting can significantly reduce motion sickness, compared with cool blue light or driving in darkness. The article, “Passenger motion sickness binary classification model and analysis of vehicle lighting intervention effect,” authored by Bin Ren*, Xiaoyuchen Wang, Pengyu Ren and Menghan Wu from Shanghai University, investigates how different ambient lighting conditions affect passenger comfort during real-road nighttime driving.
Why Motion Sickness Matters
As vehicles evolve into mobile living spaces, motion sickness—triggered by mismatches between sensory systems—remains a serious obstacle to passenger acceptance. Effective, practical countermeasures are essential for enhancing comfort.
The Experimental Study
The research team conducted nighttime road tests in a SAIC Feifan F7 electric vehicle under three distinct lighting conditions:
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Warm red light (620–650 nm)
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Cool blue light (450–470 nm)
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No additional lighting (darkness)
Volunteer participants experienced the driving scenarios as passengers. The study combined subjective ratings on a Quick Motion Sickness Scale with simultaneous recordings of multimodal physiological signals (EEG and ECG) to objectively assess the symptoms.
Key Findings
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Red light was most effective: The proportion of "no motion sickness" instances was 77.8% under red light, significantly higher than with blue light (38.9%) or in darkness (27.8%). The average subjective motion sickness score was also lowest under red light.
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Blue light's effect was intermediate: The incidence of motion sickness under blue light (61.1%) was lower than in darkness (72.2%), but still substantially higher than under red light.
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Physiological signals revealed the mechanism: EEG analysis showed that red light significantly enhanced alpha wave power (associated with neurological relaxation) and reduced delta wave power, providing a neurophysiological basis for its mitigating effect.
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A novel AI model for detection: The researchers developed a deep learning fusion model that integrated EEG and ECG signals, achieving a high accuracy of 92.5% in classifying motion sickness states, outperforming traditional machine learning models.
Why Red Light Works
The study demonstrates that warm red light exerts a neuro-relaxation effect by enhancing alpha brain wave activity and reducing delta wave activity, thereby helping to alleviate the neurological stress associated with motion sickness.
Implications for Future Vehicle Design
These findings highlight the potential of wavelength-specific adaptive lighting systems in intelligent vehicle cabins. Incorporating such systems could offer passengers a simple yet powerful tool to mitigate motion sickness, especially during nighttime travel.
As corresponding author Bin Ren explains: “Our study shows that in-vehicle lighting is more than a matter of visibility or aesthetics. The spectral characteristics of light, particularly warm red light, have a significant impact on passengers' physiological state and can effectively enhance comfort.”
Ren B, Wang X, Ren P, Wu M. Passenger motion sickness binary classification model and analysis of vehicle lighting intervention effect. Artif. Intell. Auton. Syst. 2025(2):0007, https://doi.org/10.55092/aias20250007
Journal
Artificial Intelligence and Autonomous Systems
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Passenger motion sickness binary classification model and analysis of vehicle lighting intervention effect
Article Publication Date
30-Sep-2025