Biosignal-Based Driving Experience Analysis between Automated Mode and Manual Mode

Author:

Hu Hongyu1,Zhang Guojuan1,Cheng Ming1,Li Zhengyi1,He Lei1,Su Lili2

Affiliation:

1. Jilin University

2. China Automotive Technology and Research

Abstract

<div class="section abstract"><div class="htmlview paragraph">With the rapid development of intelligent driving technology, there has been a growing interest in the driving comfort of automated vehicles. As vehicles become more automated, the role of the driver shifts from actively engaging in driving tasks to that of a passenger. Consequently, the study of the passenger experience in automated driving vehicles has emerged as a significant research area. In order to examine the impact of automatic driving on passengers' riding experience in vehicle platooning scenarios, this study conducted real vehicle experiments involving six participants. The study assessed the subjective perception scores, eye movement, and electrocardiogram (ECG) signals of passengers seated in the front passenger seat under various vehicle speeds, distances, and driving modes. The results of the statistical analysis indicate that vehicle speed has the most substantial influence on passenger perception. The driving mode has a minor effect on the passenger riding experience, while vehicle distance has virtually no impact. Additionally, the study found that average heart rate, average pupil diameter, maximum pupil diameter, and blink frequency can effectively reflect changes in passengers' subjective perception. Furthermore, a stepwise regression analysis was performed on the selected indicators that demonstrated statistical significance. It was discovered that passenger stress levels are positively correlated with average pupil diameter, thus establishing a relationship between passengers' subjective perception and objective physiological indicators. This study contributes to the research on the comfort of automated vehicles and can provide valuable insights for enhancing the acceptance of such vehicles.</div></div>

Publisher

SAE International

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4. Becker , C. , Brewer , J.C. , Yount , L. 2020

5. Alberto , R. , Tiwana , V. , Sergio , I. et al. Surface Electromyography for Risk Assessment in Work Activities Designed Using the "Revised NIOSH Lifting Equation" International Journal of Industrial Ergonomics 68 2018 34 45

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