Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations

Abstract

Progress enables the creation of more automated and intelligent machines with increasing abilities that open up new roles between humans and machines. Only with a proper design for the resulting cooperative human-machine systems, these advances will make our lives easier, safer and enjoyable rather than harder and miserable. Starting from examples of natural cooperative systems, the paper investigates four cornerstone concepts for the design of such systems: ability, authority, control and responsibility, as well as their relationship to each other and to concepts like levels of automation and autonomy. Consistency in the relations between these concepts is identified as an important quality for the system design. A simple graphical tool is introduced that can help to visualize the cornerstone concepts and their relations in a single diagram. Examples from the automotive domain, where a cooperative guidance and control of highly automated vehicles is under investigation, demonstrate the application of the concepts and the tool. Transitions in authority and control, e.g. initiated by changes in the ability of human or machine, are identified as key challenges. A sufficient consistency of the mental models of human and machines, not only in the system use but also in the design and evaluation, can be a key enabler for a successful dynamic balance between humans and machines.

Driver-automation indirect shared control of highly automated vehicles with intention-aware authority transition

“…Now, if the driver input u D is perfectly known to the automation and the model (11) has no model error, λ * can be immediately recovered from (11) by nonlinear optimization. This premise is however unrealistic and we have to assume a certain level of uncertainty in the observed u D .…”

“…To select an appropriate method to estimate λ * from (14), we have to consider that: 1) the measurement function h x(k), r D (k), w A (k), λ * is highly nonlinear according to (11)- (13); (2) λ * is time-varing and the variation is quite unpredictable. For such nonlinear estimation problems, blockwise methods (such as nonlinear least-squares regression) generally outperform recursive methods (such as extended Kalman filter) at the cost of a higher computational load [24].…”

“…However, Mars et al [10] argue that the automation's authority should depend on the traffic context. In addition, Abbink et al [4] and Flemisch et al [11] suggest that the control authority allocation should adapt to the driver's intention. For example, the driver may imply a higher control authority to secure the vehicle control if the automation's control ability is impaired, and a lower control authority if the situation can be well handled by the automation.…”

Driving automation interface design : supporting drivers' changing role

“…Based on existing research (Engström & Hollnagel, 2007;Flemisch et al, 2012;Hollnagel, 2011), four base principles to overcome OOTL problems are distinguished:…”

“…While the ultimate authority is still at the driver, the interface should then serve as a means for cooperating and sharing the authority (Young et al, 2007). Attuning the cooperation might also be a dynamic process that could change over time (Flemisch et al, 2012). Other researchers have proposed similar ideas, like Norman (1990) who reflects on automation acting as a human co-driver and Schutte (1999) refers to the roles of driver and automation as 'complemation'.…”