Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. Conclusions: the present study confirms the possibility of continuously monitoring the driver’s status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.

In recent years, driving simulators have been widely used by automotive manufacturers and researchers in human-in-the-loop experiments, because they can reduce time and prototyping costs, and provide unlimited parametrization, more safety, and higher repeatability. Simulators play an important role in studies about driver behavior in operating conditions or with unstable vehicles. The aim of the research is to study the effects that the force feedback (f.f.b.), provided to steering wheel by a lane-keeping-assist (LKA) system, has on a driver’s response in simulators. The steering’s force feedback system is tested by reproducing the conditions of criticality of the LKA system in order to minimize the distance required to recover the driving stability as a function of set f.f.b. intensity and speed. The results, obtained in three specific criticality conditions, show that the behaviour of the LKA system, reproduced in the simulator, is not immediately understood by the driver and, sometimes, it is in opposition with the interventions performed by the driver to ensure driving safety. The results also compare the performance of the subjects, either overall and classified into subgroups, with reference to the perception of the LKA system, evaluated by means of a questionnaire. The proposed experimental methodology is to be regarded as a contribution for the integration of acceptance tests in the evaluation of automation systems.

Nowadays, driving simulation devices represent a continuously evolving and developing area in the world of virtual reality. One of the fundamental elements in the design of driving software is the track model. This work aims to study the use of advanced technologies for the three-dimensional modeling of a racing track in a driving simulator. Specifically, it employs the LiDAR methodology to acquire the three-dimensional coordinates of a 1 km long circuit located on the Fisciano campus of the University of Salerno. The purpose of this work is to explain and present a novel track acquisition and modeling methodology within the realm of simulated driving reality. Following the study, the Virtual Reality Laboratory’s driving simulator at the Department of Industrial Engineering conducted tests to validate the proposed circuit model. The test rides analyzed the realism of the driving experience, thereby validating the proposed track model. This phase was complemented by a series of proposals for possible future developments in the field of three-dimensional modeling applied to driving simulation and beyond. In the end, the 3D model obtained demonstrated the high definition of the acquired result and the speed with which multiple data were obtained simultaneously, thanks to the laser scanner used.