Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided this study into two distinct lines. First, we wanted to find if any driver’s physiological characteristic, habit, or recent event could interfere with the results. Second, we aimed to analyze the effects of subjective sleepiness on driving behavior. On driving simulator experiments, the driver information and driving performance were collected, and responses to the Karolinska Sleepiness Scale (KSS) were compared with these parameters. The results showed that drowsiness increases when the driver has suffered a recent stress situation, has taken medication, or has slept fewer hours. An increasing driving time is also a strong factor in drowsiness development. On the other hand, robustness, smoking habits, being older, and being a man were revealed to be factors that make the participant less prone to getting drowsy. From another point of view, the speed and lane departures increased with the sleepiness feeling. Subjective drowsiness has a great correlation to drivers’ personal aspects and the driving behavior. In addition, the KSS shows a great potential to be used as a predictor of drowsiness.

Objective: The National Highway Traffic Safety Administration in the USA estimated that the effects of drowsiness while driving led to approximately 72,000 crashes, 44,000 injuries, and 800 deaths in 2013. Keeping this in mind, the risk and injuries of drowsy driving remain a major safety issue that clearly needs to be studied. Our purpose was to conduct a systematic review of international literature including studies on driving behavior associated to drowsy and fatigued drivers. The research focused on the prediction and effects of drowsiness, and particularly on studies based on driving in simulated environments. Additionally, we searched for studies related to driving simulators, in general, to better understand the tool’s efficacy and its advantages and disadvantages. Methods: This review was made in accordance with PRISMA statement guidelines. After conducting in-depth research in targeted databases, 23 studies met the inclusion criteria; the papers were analyzed regarding the type of experiment and procedures and driving performance of 690 participants was studied. Results: Studies revealed that drowsiness have effects on driving performance and these effects become more relevant with time-on-task and in monotonous scenarios and landscapes. In addition, some documents include validations of several technologies to detect and predict sleepiness. Conclusions: Overall, we can conclude that drowsiness and fatigue impair driving performance, resulting in drivers who are more exposed to risky situations.

Driver distraction is a major problem nowadays, contributing to many deaths, injuries, and economic losses. Despite the effort that has been made to minimize these impacts, considering the technological evolution, distraction at the wheel has tended to increase. Not only tech-related tasks but every task that captures a driver’s attention has impacts on road safety. Moreover, driver behavior and characteristics are known to be heterogeneous, leading to a distinct driving performance, which is a challenge in the road safety perspective. This study aimed to capture the effects of drivers’ personal aspects and habits on their distraction behavior. Following a within-subjects approach, a convenience sample of 50 drivers was exposed to three unexpected events reproduced in a driving simulator. Drivers’ reactions were evaluated through three distinct models: a Lognormal Model to make analyze the visual distraction, a Binary Logit Model to explore the adopted type of reaction, and a Parametric Survival Model to study the reaction times. The research outcomes revealed that drivers’ behavior and perceived workload were distinct when they were engaged in specific secondary tasks and for distinct drivers’ personal attributes and habits. Age and type of distraction showed statistical significance regarding the visual behavior. Moreover, reaction times were consistently related to gender, BMI, sleep patterns, speed, habits while driving, and type of distraction. The habit of engaging in secondary tasks while driving resulted in a cumulative better performance.