No abstract
Complex traffic environments can affect driver stress and driving performance negatively. Driving through tunnels can be particularly stressful, and these segments have been associated with higher crash rates. However, the constraints of the natural environment (e.g., mountains, waterways) often restrict the flexibility to make major modifications in tunnels. The purpose of this study was to evaluate stress as drivers traversed along an interstate route that included tunnel and nontunnel segments, as well as a 75-m transition period before the tunnels. Data from 50 drivers, including information from electrocardiogram recordings such as heart rate and standard deviation of interbeat intervals (SDNN), were collected. Driving performance measures included vehicle speed and braking. Multivariate and univariate analyses of variance were used to identify increases in drivers' stress during several road segments: the transition to a tunnel entrance, within a tunnel, and open road segments. The largest variations in performance measures were observed in tunnels, followed by the periods in transition segments. Evaluation of continuous speed profiles along the route showed that drivers tended to decrease their speed before entering a tunnel and increase speed just before exiting a tunnel. The highest level of stress (denoted by the largest positive change in heart rate and lowest variability in SDNN) was observed along the transition segments, followed by the tunnel segments. Identifying situations in which drivers may experience higher levels of stress and the corresponding impact on driving performance is important for future road and tunnel designs.
Objective: A driving simulator study was conducted to evaluate the longitudinal effects of an intervention and withdrawal of a lane keeping system on driving performance and cognitive workload. Background: Autonomous vehicle systems are being implemented into the vehicle fleet. However, limited research exists in understanding the carryover effects of long-term exposure. Methods: Forty-eight participants (30 treatment, 18 control) completed eight drives across three separate days in a driving simulator. The treatment group had an intervention and withdrawal of a lane keeping system. Changes in driving performance (standard deviation of lateral position [SDLP] and mean time to collision [TTC]) and cognitive workload (response time and miss rate to a detection response task) were modeled using mixed effects linear and negative binomial regression. Results: Drivers exposed to the lane keeping system had an increase in SDLP after the system was withdrawn relative to their baseline. Drivers with lane keeping had decreased mean TTC during and after system withdrawal compared with manual drivers. There was an increase in cognitive workload when the lane keeping system was withdrawn relative to when the system was engaged. Conclusion: Behavioral adaptations in driving performance and cognitive workload were present during automation and persisted after the automation was withdrawn. Application: The findings of this research emphasize the importance to consider the effects of skill atrophy and misplaced trust due to semi-autonomous vehicle systems. Designers and policymakers can utilize this for system alerts and training.
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.
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