Abstract
The human-environment-vehicle triad and how it relates to crashes has long been a topic of discussion, in which the human factor is consistently seen as the leading cause. Recently, more sophisticated approaches to Road Safety have advocated for a road-driver interaction view, in which human characteristics influence road perception and road environment affects driver behavior. This study focuses on road-driver interaction by using a driving simulator. The objective is to investigate how the driver profile influences driving performance and the effects of three countermeasures (peripheral transverse lines before and after the beginning of the curves and roadside poles in the curves). Fifty-six middle-aged male participants drove a non-challenging rural highway simulated scenario based on a real road where many single-vehicle crashes occurred. The drivers' profiles were assessed through their behavioral history measured by a validated version of the Driver Behavior Questionnaire (DBQ) comprising three dimensions: Errors (E), Ordinary Violations (OV), and Aggressive Violations (AV). The relationship between speed and trajectory measures and drivers' profiles was investigated using randomparameter models with heterogeneity in the means. The models' results showed that the DBQ subscale scores in OV explained a considerable part of the heterogeneity found in drivers' performance. Furthermore, the heterogeneity in the means caused by the DBQ subscale scores in OV and E in the presence of peripheral transverse lines indicates a difference in how drivers react to the countermeasures. The peripheral lines were more efficient than roadside poles to moderate speed but did not positively influence all drivers' trajectories. Although the peripheral lines could be seen as an alternative to change driver behavior in a non-challenging or monotonous road environment, the design used in this study should be reviewed.

Abstract
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.

Abstract

Abstract
Conducting scientific experiments in driving simulators requires the modeling of reliable and complete road environments. These environments must provide extensive landscapes with the artifacts and natural element that can be usually found in the real world. This paper presents a method to efficiently produce models of natural vegetation. The produced models are then applied to populate existing terrain definitions, allowing the fast preparation of extensive environments with realistic landscapes. The human supervisor can interact in this generation process, in order to obtain custom landscapes definitions. After the landscape generation process, the road network definition can be then generated, producing a complete driving environment, in an integrated modeling process. The proposed method allows modeling a wide range of drive environments, with the realism and quality required to the realization of virtual training or experimental work in many terrain based activities, such driving simulators.

Abstract
The aim of the present study is to analyse the effect of the alcohol on driving considering distinct blood alcohol concentration up to 0.49 g/l, which is the legal limit in some countries such as Portugal. Additionally, a comparison between the ascending with the descending phases is considered. A driving simulator is used to ensure the safety of the participants. Two questionnaires were applied to analyse the perception of the 20 participants about the alcohol sensation and confidence to drive as well as the driving performance facing two critical events. Also, the number of correct answers to simple questions was analysed to evaluate the cognitive ability. The study results indicate that the cognitive ability and perception of the participants are affected while the driving performance is less affected.