Transportation Systems Engineering, Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Powai, Mumbai, 400 076, India.
Transportation Systems Engineering, Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Powai, Mumbai, 400 076, India.
Accid Anal Prev. 2021 Jun;156:106123. doi: 10.1016/j.aap.2021.106123. Epub 2021 Apr 13.
Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG'), safety critical time headway and speed variability with respect to leading vehicle's speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG' by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG') significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.
睡眠剥夺驾驶员的安全评估是一个具有挑战性的研究领域，只有少数与睡眠相关的研究调查了跟车时的安全性能。因此，本研究旨在测量部分睡眠剥夺对跟车时驾驶员安全的影响。50 名健康男性驾驶员，无任何睡眠相关障碍史，在三种不同睡眠持续时间的条件下驾驶驾驶模拟器：基础条件（无睡眠剥夺）、经历一个晚上部分睡眠剥夺后的测试 1（TS1）（睡眠时间≤4.5 小时/夜）和连续两个晚上部分睡眠剥夺后的测试 2（TS2）。PSD 期间的睡眠时间减少通过 Actiwatch 进行监测。使用 Karolinska 嗜睡量表（KSS）来指示驾驶员警觉性的丧失。每次驾驶都包括一个跟车任务，以根据速度和车头时距管理来测量纵向安全指标：标准化暴露于临界间隙的时间（TECG'）、安全关键车头时距和相对于前车速度的速度变异性（SPV）。根据跟车时驾驶员的减速率，还确定了碰撞潜在指数（CPI），并且发现该指数与其他指标相关。因此，为了确定 PSD 对跟车时安全的综合影响，CPI 是根据 TECG、SPV、THW 和其他协变量来建模的。所有安全指标都使用广义混合效应回归模型进行建模。结果表明，与基础驾驶相比，在 TS1 和 TS2 期间，关键车头时距分别减少了 0.65 倍和 1.08 倍，而速度变异性分别增加了 1.34 倍和 1.28 倍，这表明碰撞风险更高。然而，在 TS1 和 TS2 期间，TECG'分别减少了 64%和 56%，表明存在因睡眠缺失而导致风险的补偿措施。碰撞潜在的分数回归模型表明，低车头时距、更高的速度变异性和高暴露于临界间隙的时间（TECG'）显著导致更高的 CPI 值，表明更高的安全风险。其他协变量，如睡眠时间、专业驾驶经验和交通违法史，也与安全指标和 CPI 相关，但研究中没有注意到年龄的显著影响。研究结果表明，在特定的 PSD 条件下，安全指标对追尾碰撞敏感，可用于设计避撞系统和策略，以提高整体交通安全。
