The paper describes the conceptual model of an emotion-aware car interface able to: map both the driver’s cognitive and emotional states with the vehicle dynamics; adapt the level of automation or support the decision-making process if emotions negatively affecting the driving performance are detected; ensure emotion regulation and provide a unique user experience creating a more engaging atmosphere (e.g. music, LED lighting) in the car cabin. To enable emotion detection, it implements a low-cost emotion recognition able to recognize Ekman’s universal emotions by analyzing the driver’s facial expression from stream video. A preliminary test was conducted in order to determine the effectiveness of the proposed emotion recognition system in a driving context. Results evidenced that the proposed system is capable to correctly qualify the drivers’ emotion in a driving simulation context.

A preliminary investigation towards the application of facial expression analysis to enable an emotion-aware car interface / Ceccacci, S.; Generosi, A.; Giraldi, L.; Carbonara, G.; Castellano, A.; Montanari, R.; Mengoni, M.. - ELETTRONICO. - 12189:(2020), pp. 504-517. [10.1007/978-3-030-49108-6_36]

A preliminary investigation towards the application of facial expression analysis to enable an emotion-aware car interface

Ceccacci S.;Generosi A.;Giraldi L.;Mengoni M.
2020-01-01

Abstract

The paper describes the conceptual model of an emotion-aware car interface able to: map both the driver’s cognitive and emotional states with the vehicle dynamics; adapt the level of automation or support the decision-making process if emotions negatively affecting the driving performance are detected; ensure emotion regulation and provide a unique user experience creating a more engaging atmosphere (e.g. music, LED lighting) in the car cabin. To enable emotion detection, it implements a low-cost emotion recognition able to recognize Ekman’s universal emotions by analyzing the driver’s facial expression from stream video. A preliminary test was conducted in order to determine the effectiveness of the proposed emotion recognition system in a driving context. Results evidenced that the proposed system is capable to correctly qualify the drivers’ emotion in a driving simulation context.
2020
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-303049107-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/286315
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