Research on Automotive Vibration Comfort Based on Psychological Indicators

In recent years, the vibration comfort of automobiles has become a key consideration for consumers when purchasing vehicles. This study introduces human electrocardiogram (ECG) signals and blood pressure, and proposes a comfort prediction model based on physiological indicators. The research steps include: obtaining riding indicators and subjective feelings on flat and bumpy roads, and analyzing the differences in heart rate variability indicators and blood pressure under different road conditions through paired sample tests; playing different sound signals on bumpy roads, and using repeated measures analysis of variance to explore their impacts on physiological indicators and subjective evaluations; conducting data validity tests on the subjective evaluation results, and constructing a comfort prediction model based on correlation analysis and support vector regression algorithm. The results show that there are significant differences in indicators such as the average RR interval and standard deviation of normal-to-normal intervals (SDNN) under different riding environments; music in the frequency band of 200Hz to 600Hz can significantly improve comfort, and the average relative error of the prediction model is 8.209%. This study can provide data support for automobile manufacturers to optimize the design of suspension systems and seats. At the same time, by monitoring the physiological indicators of passengers, the vehicle system can adjust the sound signals in real time to alleviate the discomfort caused by bumps and enhance the driving experience.