Holistic modelling for human-autonomous system interaction

Abstract

For complex systems that embed automation, but also rely on human interaction for guidance and contingency management, holistic models are needed that provide for an understanding of the individual human and computer elements, and address the critical interactions of such complex systems. Discrete event simulation (DES) models and system dynamics (SD) models are two different approaches that can be used to address these requirements. Both modelling approaches can support the designers of future autonomous vehicle (AV) systems by simulating the impact of alternate designs on vehicle, operator, and system performance. However, the DES modelling approach is likely best suited for using probabilistic distributions to accurately model an operator who is a serial processor of discrete tasks, as well as an environment with randomly occurring events. The SD modelling approach is better suited for modelling continuous performance feedback that is temporally dependent and is affected by qualitative variables such as trust.

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

“…Another modelling approach for autonomous systems (e.g., autonomous driving) is proposed in [147]. They combine discrete event simulation with system dynamics models to simulate the effects of different system designs.…”

The privileged sensing framework: A principled approach to improved human-autonomy integration

“…It is unlikely that a generalised, closed-form solution may be derived for all combinations of agents and environments and their dynamics (Cummings 2014;Cummings and Clare 2015), but there is no evidence to suggest that the only way to respond to such challenges is to continually build more and more custom solutions. Alternative approaches may provide a broad framework for general application of guiding principles.…”

“…Treating the human as the ultimate and final authority is a premise that appears to have been based on either an explicit moral mandate (Billings 1991) or on a tacit assumption that human influence would result in optimal behaviour. Either way, in articulating the PSF, we join those who have argued that adherence to this premise has limited how well human inputs have been integrated with autonomous systems (Woods 1985;Woods and Branlat 2010;Cummings and Clare 2015).…”

A System Dynamics Model for Human Trust in Automation under Speed and Accuracy Requirements

“…Human trust in automation is a key factor that plays a pivotal role in the interaction between the human and automation (Lee & See, 2004). Recent technological advances improved automation capabilities, such that human-automation interaction schemes in which the human performs supervisory roles have become common (Cummings & Clare, 2015; J. Gao & Lee, 2006;Xu & Dudek, 2015).…”

“…Many studies were devoted to investigating the role of trust and how it interplays with other human and environmental factors, and subsequent effects on human reliance on automation. A number of conceptual (Jonker & Treur, 1999;Lee & See, 2004;Parasuraman & Riley, 1997) and computational (Akash, Hu, Reid, & Jain, 2017;Cummings & Clare, 2015; F. Gao et al, 2013;J. Gao & Lee, 2006;Xu & Dudek, 2015) models has been proposed to capture these dynamics.…”