On driver behavior recognition for increased safety: A roadmap

Luca Davoli; Marco Martalò; Antonio Cilfone; Laura Belli; Gianluigi Ferrari; Roberta Presta; Roberto Montanari; Maura Mengoni; Luca Giraldi; Elvio G. Amparore; Marco Botta; Idilio Drago; Giuseppe Carbonara; Andrea Castellano; Johan Plomp

doi:10.3390/safety6040055

On driver behavior recognition for increased safety: A roadmap

Luca Davoli (Corresponding Author), Marco Martalò, Antonio Cilfone, Laura Belli, Gianluigi Ferrari, Roberta Presta, Roberto Montanari, Maura Mengoni, Luca Giraldi, Elvio G. Amparore, Marco Botta, Idilio Drago, Giuseppe Carbonara, Andrea Castellano, Johan Plomp

Research output: Contribution to journal › Review Article › peer-review

30 Citations (Scopus)

306 Downloads (Pure)

Abstract

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: We consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human-Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

Original language	English
Article number	55
Number of pages	33
Journal	Safety
Volume	6
Issue number	4
DOIs	https://doi.org/10.3390/safety6040055
Publication status	Published - 12 Dec 2020
MoE publication type	A2 Review article in a scientific journal

Funding

This work received funding from the European Union’s Horizon 2020 research and innovation program ECSEL Joint Undertaking (JU) under grant agreement No. 876487, NextPerception project—“Next Generation Smart Perception Sensors and Distributed Intelligence for Proactive Human Monitoring in Health, Wellbeing, and Automotive Systems”. The JU received support from the European Union’s Horizon 2020 research and innovation programme and the nations involved in the mentioned projects. The work of L.D. is partially funded by the University of Parma, under “Iniziative di Sostegno alla Ricerca di Ateneo” program, “Multi-interface IoT sYstems for Multi-layer Information Processing” (MIoTYMIP) project.

Keywords

Advanced Driver-Assistance System (ADAS)
Artificial Intelligence (AI)
Driver Complex State (DCS)
Driver safety and comfort
Emotion recognition

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/safety6040055Licence: CC BY

safety6040055Final published version, 3.52 MBLicence: CC BY

Cite this

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title = "On driver behavior recognition for increased safety: A roadmap",

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AU - Montanari, Roberto

AU - Mengoni, Maura

AU - Giraldi, Luca

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AB - Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: We consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human-Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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On driver behavior recognition for increased safety: A roadmap

Abstract

Funding

Keywords

UN SDGs

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