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Language of Driving for Autonomous Vehicles

Finest Centre for Smart Cities, Tallinn University of Technology, 19086 Tallinn, Estonia
Department of Mechanical and Industrial Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia
Author to whom correspondence should be addressed.
Academic Editor: Marco Guerrieri
Appl. Sci. 2022, 12(11), 5406;
Received: 4 May 2022 / Revised: 23 May 2022 / Accepted: 24 May 2022 / Published: 26 May 2022
(This article belongs to the Special Issue Human Factors in Transportation Systems)
Environmental awareness and technological advancements for self-driving cars are close to making autonomous vehicles (AV) a reality in everyday scenarios and a part of smart cities’ transportation systems. The perception of safety and trust towards AVs of passengers and other agents in the urban scenario, being pedestrians, cyclists, scooter drivers or car drivers, is of primary importance and the theme of investigation of many research groups. Driver-to-driver communication channels as much as car-to-driver human–machine interfaces (HMI) are well established and part of normal training and experience. The situation is different when users must cope with driverless and autonomous vehicles, both as passengers and as agents sharing the same urban domain. This research focuses on the new challenges of connected driverless vehicles, investigating an emerging topic, namely the language of driving (LoD) between these machines and humans participating in traffic scenarios. This work presents the results of a field study conducted at Tallinn University Technology campus with the ISEAUTO autonomous driving shuttle, including interviews with 176 subjects communicating using LoD. Furthermore, this study combines expert focus group interviews to build a joint base of needs and requirements for AVs in public spaces. Based on previous studies and questionnaire results, we established the hypotheses that we can enhance physical survey results using experimental scenarios with VR/AR tools to allow the fast prototyping of different external and internal HMIs, facilitating the assessment of communication efficacy, evaluation of usability, and impact on the users. The aim is to point out how we can enhance AV design and LoD communications using XR tools. The scenarios were chosen to be inclusive and support the needs of different demographics while at the same time determining the limitations of surveys and real-world experimental scenarios in LoD testing and design for future pilots. View Full-Text
Keywords: AV shuttle; self-driving vehicle; language of driving; simulations; interaction AV shuttle; self-driving vehicle; language of driving; simulations; interaction
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MDPI and ACS Style

Kalda, K.; Pizzagalli, S.-L.; Soe, R.-M.; Sell, R.; Bellone, M. Language of Driving for Autonomous Vehicles. Appl. Sci. 2022, 12, 5406.

AMA Style

Kalda K, Pizzagalli S-L, Soe R-M, Sell R, Bellone M. Language of Driving for Autonomous Vehicles. Applied Sciences. 2022; 12(11):5406.

Chicago/Turabian Style

Kalda, Krister, Simone-Luca Pizzagalli, Ralf-Martin Soe, Raivo Sell, and Mauro Bellone. 2022. "Language of Driving for Autonomous Vehicles" Applied Sciences 12, no. 11: 5406.

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