Human factors researchers are well familiar with Sheridan and Verplank’s (1978) ‘levels of automation’. Although this automation dimension has proved useful, the last decade has seen a vast increase of automation in different forms, especially in transportation domains. To captur
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Human factors researchers are well familiar with Sheridan and Verplank’s (1978) ‘levels of automation’. Although this automation dimension has proved useful, the last decade has seen a vast increase of automation in different forms, especially in transportation domains. To capture these and future developments, we propose an extended automation taxonomy via additional dimensions. Specifically, we propose a 4D LINT representation for vehicle operation regarding control across multiple simultaneous dimensions of (1) Location (from local to remote), (2) Identity (between human and computer), (3) Number of agents (degree of centralization of control), as well as (4) adaptive optimization over Time. Our model aims to provide guidance and support in communicable ways to allocation authority agents (whether human or computer) in optimized supervisory outer loop control of complex and intelligent dynamic systems for more efficient, safe, and robust transportation operations
