Increase in rear-end collision risk by acute stress-induced fatigue in on-road truck driving

Abstract

Increasing road crashes related to occupational drivers’ deteriorating health has become a social problem. To prevent road crashes, warnings and predictions of increased crash risk based on drivers’ conditions are important. However, in on-road driving, the relationship between drivers’ physiological condition and crash risk remains unclear due to difficulties in the simultaneous measurement of both. This study aimed to elucidate the relationship between drivers’ physiological condition assessed by autonomic nerve function (ANF) and an indicator of rear-end collision risk in on-road driving. Data from 20 male truck drivers (mean ± SD, 49.0±8.2 years; range, 35–63 years) were analyzed. Over a period of approximately three months, drivers’ working behavior data, such as automotive sensor data, and their ANF data were collected during their working shift. Using the gradient boosting decision tree method, a rear-end collision risk index was developed based on the working behavior data, which enabled continuous risk quantification. Using the developed risk index and drivers’ ANF data, effects of their physiological condition on risk were analyzed employing a logistic quantile regression method, which provides wider information on the effects of the explanatory variables, after hierarchical model selection. Our results revealed that in on-road driving, activation of sympathetic nerve activity and inhibition of parasympathetic nerve activity increased each quantile of the rear-end collision risk index. The findings suggest that acute stress-induced drivers’ fatigue increases rear-end collision risk. Hence, in on-road driving, drivers’ physiological condition monitoring and ANF-based stress warning and relief system can contribute to promoting the prevention of rear-end truck collisions.

Relationship between Autonomic Nervous Function and Brain Functions Such as Memory and Attention

“…Recently, we conducted a study using autonomic nervous function evaluation to predict accident risks in truck drivers and to utilize it for accident prevention. We have identi ed the following correlations: In onroad driving, the activation of sympathetic nerve activity and inhibition of parasympathetic nerve activity increased the rear-end collision risk index in each quantile [21]. The rear-end collision risk index of the shift-day was positively correlated with the sympathetic nerve activity index of the post-shift condition on the previous day [22].…”

Effects of Placing a CMS Monitor to Present Side and Rear View at the Driver-centered Position on Drivers’ Rearward Visual Behavior, Cognitive Load, and Mental Stress

“…Because the number of irregular events, such as accidents when performing the free-driving task, was similar for all conditions (Table 4), the mental stress reduction was not so influential in immediate accident prevention. However, when the cognitive load is high, the mental stress is high, increasing the risk of collisions (25) . Therefore, the side and rear views at the driver-centered position may yield positive effects, such as stress reduction and accident prevention in practical longduration driving scenarios.…”

Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function

“…However, if the time window is too short, the reliability of the HRV analysis may be reduced, and a trade-off could exist between the reliability and resolution. In a previous study [9], we found that the appropriate analysis window width for calculating the ANF is 2 min under several different conditions during driving. Here, following the previous study, we divided the measured RRI into 2 min segments without overlapping intervals.…”

“…We defined the accident risks to be predicted by the model. The crashes considered in this study was limited to rear-end collisions, as this type of collision risk can be estimated from the driver's level of fatigue [8] [9]. Because the data on actual crashes are rare and insufficient for analysis, this study focuses on "near-miss situations," which may lead to rear-end collisions [9].…”

“…Previously, we developed a prediction method for the collision risk from heartbeat data to realize safe truck driver operations [7][8][9] [10]. In these previous studies, we discovered that several indices from heartbeat data are associated with the risk of rear-end collision by truck drivers [8] [9].…”