Every year, more than one million people die from road accidents. Between 10 and 30% of these accidents are due to human factors, especially drowsiness, stress, and inattention. Determining the psychophysical condition of the driver is important to help prevent those accidents, limit damage to things and people, and even reduce the number of deaths. Today's technology is moving in the analysis of the physiological data necessary to determine these states. However, one of the major problems concerning data collection is conducting the tests in a real-world environment. For this reason, designing and employing a virtual environment that recreates a simulation of real-world’s conditions would enable researchers to obtain reliable and realistic data without putting users at risk. The paper describes the development and validation of a configurable and scalable virtual environment, conceived to help researchers to set up the environment freely according to their research needs. The in-depth analysis explored a normal and an altered environmental condition to investigate and assess the negative parameters responsible for increasing driving inattention. Finally, the outcomes have become the first experimentation to create an AI-based simulator for diagnosing the driver's state of health.
Longhin J., Amidei A., Giacobone G.A. (2023). Exploring a Configurable Virtual Environment for the Assessment and Diagnosis of the Driver's Psychophysical State. Singapore : Springer [10.1007/978-981-99-3989-3_11].
Exploring a Configurable Virtual Environment for the Assessment and Diagnosis of the Driver's Psychophysical State
Amidei A.;
2023
Abstract
Every year, more than one million people die from road accidents. Between 10 and 30% of these accidents are due to human factors, especially drowsiness, stress, and inattention. Determining the psychophysical condition of the driver is important to help prevent those accidents, limit damage to things and people, and even reduce the number of deaths. Today's technology is moving in the analysis of the physiological data necessary to determine these states. However, one of the major problems concerning data collection is conducting the tests in a real-world environment. For this reason, designing and employing a virtual environment that recreates a simulation of real-world’s conditions would enable researchers to obtain reliable and realistic data without putting users at risk. The paper describes the development and validation of a configurable and scalable virtual environment, conceived to help researchers to set up the environment freely according to their research needs. The in-depth analysis explored a normal and an altered environmental condition to investigate and assess the negative parameters responsible for increasing driving inattention. Finally, the outcomes have become the first experimentation to create an AI-based simulator for diagnosing the driver's state of health.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
