Teaming with Your Car: Redefining the Driver–Automation Relationship in Highly Automated Vehicles

Advances in automated driving systems (ADSs) have shifted the primary responsibility of controlling a vehicle from human drivers to automation. Framing driving a highly automated vehicle as teamwork can reveal practical requirements and design considerations to support the dynamic driver–ADS relatio...

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Veröffentlicht in:Journal of cognitive engineering and decision making 2023-03, Vol.17 (1), p.49-74
Hauptverfasser: Lee, Joonbum, Rheem, Hansol, Lee, John D., Szczerba, Joseph F., Tsimhoni, Omer
Format: Artikel
Sprache:eng
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Zusammenfassung:Advances in automated driving systems (ADSs) have shifted the primary responsibility of controlling a vehicle from human drivers to automation. Framing driving a highly automated vehicle as teamwork can reveal practical requirements and design considerations to support the dynamic driver–ADS relationship. However, human–automation teaming is a relatively new concept in ADS research and requires further exploration. We conducted two literature reviews to identify concepts related to teaming and to define the driver–ADS relationship, requirements, and design considerations. The first literature review identified coordination, cooperation, and collaboration (3Cs) as core concepts to define driver–ADS teaming. Based on these findings, we propose the panarchy framework of 3Cs to understand drivers’ roles and relationships with automation in driver–ADS teaming. The second literature review identified main challenges for designing driver–ADS teams. The challenges include supporting mutual communication, enhancing observability and directability, developing a responsive ADS, and identifying and supporting the interdependent relationship between the driver and ADS. This study suggests that the teaming concept can promote a better understanding of the driver–ADS team where the driver and automation require interplay. Eventually, the driver–ADS teaming frame will lead to adequate expectations and mental models of partially automated vehicles.
ISSN:1555-3434
2169-5032
DOI:10.1177/15553434221132636