The research in the design of self-driving vehicles has been boosted, in the last decades, by the developments in the fields of artificial intelligence. Despite the growing number of industrial and research initiatives aimed at implementing autonomous driving, none of them can claim, yet, to have reached the same driving performance of a human driver. In this paper, we will try to build upon the reasons why the human brain is so effective in learning tasks as complex as the one of driving, borrowing explanations from the most established theories on sensorimotor learning in the field of cognitive neuroscience. The contribution of this work would like to be a new point of view on how the known capabilities of the brain can be taken as an inspiration for the implementation of a more robust artificial driving agent. In this direction, we consider the Convergencedivergence Zones (CDZs) as the most prominent proposal in explaining the simulation process underlying the human sensorimotor learning. We propose to use the CDZs as a “template” for the implementation of neural network models mimicking the phenomenon of mental imagery, which is considered to be at the heart of the human ability to perform sophisticated sensorimotor controls such driving.

Mental Imagery for Intelligent Vehicles / Plebe, Alice; Donà, Riccardo; Rosati Papini, Gastone Pietro; Da Lio, Mauro. - (2019), pp. 43-51. ((Intervento presentato al convegno 5th International Conference on Vehicle Technology and Intelligent Transport Systems tenutosi a Heraklion, Crete, Greece nel 3-5 May 2019 [10.5220/0007657500430051].

Mental Imagery for Intelligent Vehicles

Plebe, Alice;Donà, Riccardo;Rosati Papini, Gastone Pietro;Da Lio, Mauro
2019-01-01

Abstract

The research in the design of self-driving vehicles has been boosted, in the last decades, by the developments in the fields of artificial intelligence. Despite the growing number of industrial and research initiatives aimed at implementing autonomous driving, none of them can claim, yet, to have reached the same driving performance of a human driver. In this paper, we will try to build upon the reasons why the human brain is so effective in learning tasks as complex as the one of driving, borrowing explanations from the most established theories on sensorimotor learning in the field of cognitive neuroscience. The contribution of this work would like to be a new point of view on how the known capabilities of the brain can be taken as an inspiration for the implementation of a more robust artificial driving agent. In this direction, we consider the Convergencedivergence Zones (CDZs) as the most prominent proposal in explaining the simulation process underlying the human sensorimotor learning. We propose to use the CDZs as a “template” for the implementation of neural network models mimicking the phenomenon of mental imagery, which is considered to be at the heart of the human ability to perform sophisticated sensorimotor controls such driving.
Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
Heraklion, Crete, Greece
SciTePress
978-989-758-374-2
Plebe, Alice; Donà, Riccardo; Rosati Papini, Gastone Pietro; Da Lio, Mauro
Mental Imagery for Intelligent Vehicles / Plebe, Alice; Donà, Riccardo; Rosati Papini, Gastone Pietro; Da Lio, Mauro. - (2019), pp. 43-51. ((Intervento presentato al convegno 5th International Conference on Vehicle Technology and Intelligent Transport Systems tenutosi a Heraklion, Crete, Greece nel 3-5 May 2019 [10.5220/0007657500430051].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/242379
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