Variations in Road Conditions on Driver Stress

Abstract

A number of studies have used heart rate variability (HRV) measures to estimate driver stress across different driving conditions. Understanding the constructs of driver stress can provide insights regarding the underlying reasons for safety-critical events. The intent of this study is to evaluate the electrocardiogram (ECG) activities of drivers along a pre-defined route with varying roadway conditions. Heart rate, standard deviation of selected RR interval series (SDNN) and low frequency to high frequency ratio (LF/HF) were examined. An examination of short interval stress at evenly distributed points along the route indicates that some impact was detected from long duration driving and the statistical model was adjusted accordingly to reflect this. The findings suggest that temporal and frequency domain parameters generated similar characteristics regardless of the direction of travel (clockwise or counterclockwise direction). That is, these two parameters provided fairly consistent outcomes regarding driver workload. However, heart rate was not as good an indicator suggesting that this measure may be picking up some other unexplained effects.

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

“…Some studies also show that HR value has an indirect relationship to driving time and a direct relationship to vehicle maneuvering, traffic volume, and road direction (Simonson et al 1968;Platt 1970). Compared with HR, HRV signals (both in time and frequency domains) retain their characteristics related to driver stress regardless of the direction of travel (Miller and Boyle 2013). However, HRV is shown to increase with driving time and decrease after alerting events.…”

“…HRV signals are often analyzed in time and frequency domains to detect driver stress levels. In the time domain, HRV signals are indirectly related to driver stress (Eilebrecht et al 2012;Miller and Boyle 2013). Mean normal-to-normal intervals (MNN), standard deviation of normal-to-normal intervals (SDNN), square root of the mean squared difference of successive normal-to-normal intervals (RMSSD), and number of pairs of successive normal-to-normal intervals that differ by more than 50ms (PNN50) have been used by several studies as HRV time-domain features.…”

“…In the frequency domain, high-frequency (HF) and low-frequency (LF) ranges between 0.2 and 0.4Hz, and 0.05 and 0.2Hz, respectively, are the most common power spectrum parameters used for detecting driver stress levels. The ratio of power in these two frequency bands (LF/HF) can be considered an indicator of sympathovagal balance (Montano et al 1994), which has a direct correlation with a driver's stress and mental workload level (Healey and Picard 2005;Miller and Boyle 2013;Heikoop et al 2017). As a driver's stress and mental workload levels increase, HF values decrease while LF values increase.…”

An Evaluation Method of Safe Driving for Senior Adults Using ECG Signals

“…A considerable amount of research has been conducted to develop ways to detect threats during driving, with the main foci being on stress [17,22,23,24,25], fatigue [26,27,28], and aggressiveness [29,30,31,32,33]. These factors are linked to stressful driving situations and are associated with increased risk of fatal accidents [6].…”

“…Hayano et al [38] compared a driver’s heart rate to their estimated manipulations, such as accelerating, and found that it corresponded to the vehicle’s speed. These results suggest that heart rate is a strong indicator of physical state in situations on the road [24]. Healey et al [22] analyzed data from 24 driving sessions that were at least 50 min in length.…”