Designing an AI-companion to support the driver in highly autonomous cars

De Salis, Emmanuel (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Capallera, Marine (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Meteier, Quentin (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Angelini, Leonardo (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Abou Khaled, Omar (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Mugellini, Elena (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Widmer, Marino (University of Fribourg, Fribourg, Switzerland) ; Carrino, Stefano (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland)

In this paper, we propose a model for an AI-Companion for conditionally automated cars, able to maintain awareness of the driver regarding the environment but also to able design take-over requests (TOR) on the fly, with the goal of better support the driver in case of a disengagement. Our AI-Companion would interact with the driver in two ways: first, it could provide feedback to the driver in order to raise the driver Situation Awareness (SA), prevent them to get out of the supervision loop and so, improve takeover during critical situations by decreasing their cognitive workload. Second, in the case of TOR with a smart choice of modalities for convey the request to the driver. In particular, the AI-Companion can interact with the driver using many modalities, such as visual messages (warning lights, images, text, etc.), auditory signals (sound, speech, etc.) and haptic technologies (vibrations in different parts of the seat: back, headrest, etc.). The ultimate goal of the proposed approach is to design smart HMIs in semi-autonomous vehicles that are able to understand 1) the user state and fitness to drive, 2) the current external situation (vehicle status and behavior) in order to minimize the automation surprise and maximizing safety and trust, and 3) leverage AI to provide adaptive TOR and useful feedback to the driver.


Note: This work is part of a project funded by the Hasler Foundation. We would also like to thank our colleagues who helped us during this project.


Keywords:
Conference Type:
published full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
HE-Arc Ingénierie
Institute:
HumanTech - Technology for Human Wellbeing Institute
Publisher:
Copenhagen, Denmark, 19-24 July 2020
Date:
2020-07
Copenhagen, Denmark
19-24 July 2020
Cham
Springer
Pagination:
16 p.
Published in:
Proceedings of HCII 2020 : 22nd International Conference on Human-Computer Interaction, 19-24 July 2020, Copenhagen, Denmark ; Lecture Notes in Computer Science
DOI:
ISSN:
0302-9743
ISBN:
978-3-030-49061-4
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2021-01-12, last modified 2021-01-20

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