Distributed cognition for collaboration between human drivers and self-driving cars
Frontiers in Artificial Intelligence, ISSN: 2624-8212, Vol: 5, Page: 910801
2022
- 2Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
This paper focuses on the collaboration between human drivers and intelligent vehicles. We propose a collaboration mechanism grounded on the concept of distributed cognition. With distributed cognition, intelligence does not lie just in the single entity but also in the interaction with the other cognitive components in a system. We apply this idea to vehicle intelligence, proposing a system distributed into two cognitive entities—the human and the autonomous agent—that together contribute to drive the vehicle. This account of vehicle intelligence differs from the mainstream research effort on highly autonomous cars. The proposed mechanism follows one of the paradigm derived from distributed cognition, the rider-horse metaphor: just like the rider communicates their intention to the horse through the reins, the human influences the agent using the pedals and the steering wheel. We use a driving simulator to demonstrate the collaboration in action, showing how the human can communicate and interact with the agent in various ways with safe outcomes.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85137910211&origin=inward; http://dx.doi.org/10.3389/frai.2022.910801; http://www.ncbi.nlm.nih.gov/pubmed/36092977; https://www.frontiersin.org/articles/10.3389/frai.2022.910801/full; https://dx.doi.org/10.3389/frai.2022.910801; https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.910801/full
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