Overview
Lately, both at international and national level, concerns about the development of on-board systems to monitor driver's physical conditions raised sensibly. The reason behind this growing attention is due to the number of car accidents, which normally peak on weekends. Potential solutions are related to the introduction of driver's physical state control systems and driving style to report any anomalies and to take actions to keep them within the limits of environmental sustainability and accident risk prevention.
Therefore, there is the need to train technicians with a high degree of specialization, in order to facilitate a more concrete application of such modern technologies.
The DRIVE IN2 project focused on the definition of methodologies, technologies and advanced and innovative systems on the interaction between the driver and the vehicle, in order to prevent road accidents and reduce the emissions of polluting agents.
The research was aimed at analyzing the driver’s characteristics and behaviour. The following issues were investigated:
- methods and techniques for the direct and indirect monitoring of vehicle and
- analysis of the behavioural driver’s variables that may impact on driving behaviour.
One of the key-factor of the project was its multidisciplinary approach; during the project the following issues were investigated:
- the analysis of the driver from a psychological point of view, in order to properly identify his behavioural/cognitive aspects;
- the monitoring of psycho-physical conditions of the driver (i.e. vital parameters such as blood pressure, temperature, glucose, drowsiness, etc..) and/or the monitoring of alcohol and drugs abuse, through the use of research paradigms and technologies as applied in the medical field;
- the application of data fusion/data mining techniques for the combined analysis of the variables of the vehicle;
- the monitoring of the driver’s driving style in order to seek the best efficiency of the vehicle (analysis of the trips, instantaneous fuel consumption, CO2 emissions, engine performance, rev, gear, driving cycles).
Funding
Results
Simulations are increasingly adopted as solutions for automotive research, especially for developing driver monitoring systems and measuring acceptability of on-board devices. There are at least two ways in which neuroscientific techniques can be used to provide support research in virtual environments. First, biofeedback methods are general useful to assess reliability and ecological validity of simulation-based approaches. Furthermore, these methods are suitable to enhance the virtual environment adaptivity to the user by dynamically collecting brain activity and then use it a feedback to allowing the virtual scenario to be adapted to the user’s interactions. Regarding the enhancement of simulation adaptivity, different ranges of Human-Computer Interaction (HCI) technologies and methods can be effectively integrated within Virtual Reality (VR) researches. Amongst these biofeedback methods, neurosciences provide a new and relevant approach allowing for identifying the user’s mind state. However, how neuro-feedback can be introduced into the simulation adaptivity is an emerging line of VR-related research, that not only increases measure reliability and controls of experimental conditions, but also provides a consistent solution for implementing adaptive agents in simulation studies.
The neuro-feedback most frequently used in VR environment is electroencephalography (EEG), mainly in due to reason its lightweight, noninvasive and unobtrusive nature. However, other neurophysiological methods, such as Functional Near-Infrared Spectroscopy (fNIRS), are also progressively used as a technique to assess and study brain activity in virtual environments. At the same time as these techniques are becoming better understood, their use is becoming increasingly prevalent in addressing important factors affecting the road safety, as human factor.
Neuro-feedback driven Virtual Reality Adaptive Stimulation could be extremely useful in developing of psychophysical model of the driver behaviour or to investigate the neurological sources of dangerous behaviour on the road.