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Human-inspired autonomous driving: A survey
University of Trento, Italy.
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Interaction Lab (iLab))ORCID iD: 0000-0003-3129-4892
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Interaction Lab (iLab))ORCID iD: 0000-0003-0093-3655
University of Trento, Italy.
2024 (English)In: Cognitive Systems Research, ISSN 2214-4366, E-ISSN 1389-0417, Vol. 83, article id 101169Article, review/survey (Refereed) Published
Abstract [en]

Autonomous vehicles promise to revolutionize society and improve the daily life of many, making them a coveted aim for a vast research community. To enable complex reasoning in autonomous vehicles, researchers are exploring new methods beyond traditional engineering approaches, in particular the idea of drawing inspiration from the only existing being able to drive: the human. The mental processes behind the human ability to drive can inspire new approaches with the potential to bridge the gap between artificial drivers and human drivers. In this review, we categorize and evaluate existing work on autonomous driving influenced by cognitive science, neuroscience, and psychology. We propose a taxonomy of the various sources of inspiration and identify the potential advantages with respect to traditional approaches. Although these human-inspired methods have not yet reached widespread adoption, we believe they are critical to the future of fully autonomous vehicles. 

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 83, article id 101169
Keywords [en]
Autonomous vehicles, Human cognition, Human driving, Imitation learning, Neuromorphic computing, Autonomous driving, Daily lives, Mental process, Research communities, Traditional engineerings, adoption, autonomous vehicle, cognition, human, human experiment, imitation, learning, neuroscience, psychology, short survey, taxonomy
National Category
Human Computer Interaction Ethics Vehicle Engineering Computer Sciences
Research subject
Interaction Lab (ILAB)
Identifiers
URN: urn:nbn:se:his:diva-23259DOI: 10.1016/j.cogsys.2023.101169ISI: 001080906500001Scopus ID: 2-s2.0-85171333787OAI: oai:DiVA.org:his-23259DiVA, id: diva2:1800857
Note

CC BY-NC-ND 4.0 DEED

© 2023

Corresponding author: E-mail address: alice.plebe@unitn.it (A. Plebe)

Available from: 2023-09-28 Created: 2023-09-28 Last updated: 2023-12-06Bibliographically approved

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Svensson, HenrikMahmoud, Sara

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