TY - JOUR
T1 - Collection and comparison of driver/passenger physiologic and behavioural data in simulation and on-road driving
AU - Ruscio, Daniele
AU - Bascetta, Luca
AU - Gabrielli, Alessandro
AU - Matteucci, Matteo
AU - Ariansyah, Dedy
AU - Bordegoni, Monica
AU - Caruso, Giandomenico
AU - Mussone, Lorenzo
PY - 2017
Y1 - 2017
N2 - The i.Drive Lab has developed inter-disciplinary methodology for the analysis and modelling of behavioral and physiological responses related to the interaction between driver, vehicle, infrastructure, and virtual environment. The present research outlines the development of a validation study for the combination of virtual and real-life research methodologies. i.Drive driving simulator was set up to replicate the data acquisition of environmental and physiological information coming from an equipped i.Drive electric vehicle with same sensors. i.Drive tests are focused on the identification of driver's affective states that are able to define recurring situations and psychophysical conditions that are relevant for road-safety and drivers' comfort. Results show that it is possible to combine different research paradigms to collect low-level vehicle control behavior and higher-level cognitive measures, in order to develop data collection and elaboration for future mobility challenges.
AB - The i.Drive Lab has developed inter-disciplinary methodology for the analysis and modelling of behavioral and physiological responses related to the interaction between driver, vehicle, infrastructure, and virtual environment. The present research outlines the development of a validation study for the combination of virtual and real-life research methodologies. i.Drive driving simulator was set up to replicate the data acquisition of environmental and physiological information coming from an equipped i.Drive electric vehicle with same sensors. i.Drive tests are focused on the identification of driver's affective states that are able to define recurring situations and psychophysical conditions that are relevant for road-safety and drivers' comfort. Results show that it is possible to combine different research paradigms to collect low-level vehicle control behavior and higher-level cognitive measures, in order to develop data collection and elaboration for future mobility challenges.
KW - Artificial Intelligence
KW - Computer Networks and Communications
KW - Human Factors
KW - Modeling and Simulation
KW - driving assessment
KW - physiological measures
KW - Artificial Intelligence
KW - Computer Networks and Communications
KW - Human Factors
KW - Modeling and Simulation
KW - driving assessment
KW - physiological measures
UR - http://hdl.handle.net/10807/107291
UR - http://ieeexplore.ieee.org/abstract/document/8005705/
U2 - 10.1109/MTITS.2017.8005705
DO - 10.1109/MTITS.2017.8005705
M3 - Conference article
SP - 403
EP - 408
JO - Models and Technologies for Intelligent Transportation Systems
JF - Models and Technologies for Intelligent Transportation Systems
T2 - 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017
Y2 - 26 June 2017 through 28 June 2017
ER -