Every year approximately more than one million people die on world's road. Human factors are the largest contributing factors to the traffic crashes and fatality, and recent researches have identified drivers' cognitive aspect as the major cause of human errors in 80% of crash events. Thus, the development of countermeasures to manage drivers' cognitive aspect is an important challenge to address. Driver-Assistance Systems have been developed and integrated into vehicles to acquire data about the environment and the driver, and to communicate information to the driver, usually via the senses of vision and hearing. Unfortunately, these senses are already subjected to high demands, and the visual and auditory stimuli can be underestimate or considered as annoying. However, other sensory channels could be used to elicit the drivers' cognitive aspect. In particular, smell can impact on various aspects of humans' psychological state, such as people's attention level, and can induce activation states in people. The research presented in this paper aims at investigating whether olfactory stimuli, instead of auditory ones, can be used to influence the cognitive aspect of the drivers. For this purpose, an experimental framework has been set up and experimental testing sessions have been performed. The experimental framework is a multisensory environment consisting of an active stereo-projector and a screen used for displaying a video that reproduces a very monotonous car trip, a seating-buck for simulating the car environment, a wearable Olfactory Display, in-ear earphones and the BioGraph Infiniti system for acquiring the subjects' physiological data. The analysis of the data collected in the testing sessions shows that, in comparison to the relaxation state, olfactory stimuli are effective in increasing subjects' attention level more than the auditory ones.

Investigating the use of smell in vehicle-driver interaction

BORDEGONI, MONICA;CARULLI, MARINA;SHI, YUAN
2016-01-01

Abstract

Every year approximately more than one million people die on world's road. Human factors are the largest contributing factors to the traffic crashes and fatality, and recent researches have identified drivers' cognitive aspect as the major cause of human errors in 80% of crash events. Thus, the development of countermeasures to manage drivers' cognitive aspect is an important challenge to address. Driver-Assistance Systems have been developed and integrated into vehicles to acquire data about the environment and the driver, and to communicate information to the driver, usually via the senses of vision and hearing. Unfortunately, these senses are already subjected to high demands, and the visual and auditory stimuli can be underestimate or considered as annoying. However, other sensory channels could be used to elicit the drivers' cognitive aspect. In particular, smell can impact on various aspects of humans' psychological state, such as people's attention level, and can induce activation states in people. The research presented in this paper aims at investigating whether olfactory stimuli, instead of auditory ones, can be used to influence the cognitive aspect of the drivers. For this purpose, an experimental framework has been set up and experimental testing sessions have been performed. The experimental framework is a multisensory environment consisting of an active stereo-projector and a screen used for displaying a video that reproduces a very monotonous car trip, a seating-buck for simulating the car environment, a wearable Olfactory Display, in-ear earphones and the BioGraph Infiniti system for acquiring the subjects' physiological data. The analysis of the data collected in the testing sessions shows that, in comparison to the relaxation state, olfactory stimuli are effective in increasing subjects' attention level more than the auditory ones.
2016
Proceedings of the ASME Design Engineering Technical Conference
9780791850077
Mechanical Engineering; Computer Graphics and Computer-Aided Design; Computer Science Applications1707 Computer Vision and Pattern Recognition; Modeling and Simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1018895
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