Spectral Power Analysis of Drivers' Gas Pedal Control during Steady-state Car-following on Freeways. (September 2016)

• Record Type: Journal Article
• Authors: Feng, Fred; Bao, Shan; Sayer, James; LeBlanc, David
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 60:Part 1(2016)
• Page Start: 729
• Page End: 733
• Publication Date: 2016-09
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1541931213601167
• Languages: English
• ISSNs: 1541-9312

Abstract

This paper investigated the frequency characteristics of drivers' gas pedal control in steady-state car-following on freeways by using vehicle sensor data from an existing naturalistic driving study. The main objectives were to examine the frequency range and distributions of a driver operating the gas pedal when following a lead vehicle, and whether the higher and lower frequency components of the gas pedal signal would vary when following a lead vehicle with varying distances. A total of 1, 461 driving segments each with 90-seconds of steady-state freeway car-following were extracted from the naturalistic driving data. Fourier analysis was performed to convert the time series data of drivers' gas pedal control to the frequency domain. The results show that during steady-state freeway car-following, the power of the gas pedal control peaks at around 0.033 Hz or 15 s per pedal movement (derived using the median of the peak frequency), and the upper limit of the frequency is around 0.94 Hz or 0.5 s per pedal movement (derived using the 95th percentile of the cutoff frequency). Further analysis showed that following a lead vehicle with smaller gap was associated with a larger proportion of the higher frequency component ( p < .001), and following a lead vehicle with larger gap was associated with a larger proportion of the lower frequency component ( p < .001). This suggests that the larger gap may allow the driver to relax control of the gas pedal with smoother operation. Potential applications of this paper include developing more realistic driver models that could be used in designing advanced driver assistance systems.