Psychological constructs in driving automation: a consensus model and critical comment on construct proliferation

Heikoop, D.D.; Winter, Joost de; Arem, Bart van; Stanton, Neville A.

doi:10.1080/1463922x.2015.1101507

Cited by 41 publications

(22 citation statements)

References 87 publications

(102 reference statements)

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“…For example, in order to ensure that a system is commercially viable, it must be accurate, reliable, predictable and dependable (Donmez et al 2006;Eriksson & Stanton, 2017). However, a system that is reliable for 99.9% of the time actually has a negative effect on the formation of mental models relating to its capabilities (Groeger, 1997;Stanton & Young, 2000;Heikoop et al 2016). In this instance, the drivers cognitive map of Autopilot would have been developed and therefore skewed because of their experiences in using an otherwise seemingly accurate, reliable, predictable and dependable system.…”

Section: What We Think Happenedmentioning

confidence: 99%

Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016

2018

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Section: What We Think Happenedmentioning

confidence: 99%

Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016

2018

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“…Examples of similar classifications of human behaviour are the Markov dynamic model of driver action (Pentland and Liu 1999), the conceptualisation of a driver's task (De Winter et al 2014), the human-machine cooperation framework of Hoc, Young, and Blosseville (2009), or the hierarchical structure of the road user task (Michon 1985). Michon (1985) further summarises several more in-depth models of human behaviour (see also Heikoop et al 2016;Stanton and Young 2000). Although the classification used in this paper provided valuable insights that could help increase safety in driving with ADS, we would encourage the construction of frameworks with different categorisations, as those could potentially highlight other bottlenecks and design issues related to human behaviour.…”

Section: Limitations Of This Researchmentioning

confidence: 99%

Human behaviour with automated driving systems: a quantitative framework for meaningful human control

Heikoop

Hagenzieker

Mecacci

et al. 2019

Theoretical Issues in Ergonomics Science

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Automated driving systems (ADS) with partial automation are currently available for the consumer. They are potentially beneficial to traffic flow, fuel consumption, and safety, but human behaviour whilst driving with ADS is poorly understood. Human behaviour is currently expected to lead to dangerous circumstances as ADS could place human drivers 'out-of-the-loop' or cause other types of adverse behavioural adaptation. This article introduces the concept of 'meaningful human control' to better address the challenges raised by ADS, and presents a new framework of human control over ADS by means of literature-based categorisation. Using standards set by European authorities for driver skills and road rules, this framework offers a unique, quantified perspective into the effects of ADS on human behaviour. One main result is a rapid and inconsistent decrease in required skill-and rule-based behaviour mismatching with the increasing amount of required knowledge-based behaviour. Furthermore, the development of higher levels of automation currently requires different human behaviour than feasible, as a mismatch between supply and demand in terms of behaviour arises. Implications, discrepancies and emerging mismatches this framework elicits are discussed, and recommendations towards future design strategies and research opportunities are made to provide a meaningful transition of human control over ADS. Relevance to human factors/Relevance to ergonomics theoryHuman Factors in automated driving systems (ADS) are currently poorly understood. The relevance of this paper is that it adds to the understanding in a way that it introduces the innovative concept of "meaningful human control", and applies it to the domain of Human Factors in ADS, as well as that it presents a new framework of human control over ADS. With it, this paper elicits several mismatches between what is currently demanded from a driver of an ADS, and what such a driver is actually capable of doing. Furthermore, the discussion of these implications raises directions to future design strategies and research opportunities in the fields of Human Factors. ARTICLE HISTORY

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“…Many studies have investigated all of these factors over the years. The model was most recently updated by Heikoop et al (2016), which has extended the list of factors and interaction between factors. Stanton (2002, 2004) in particular, investigated the effects of mental demand on the driver's attentional resources.…”

Section: Introduction To Driving Automation and Autonomymentioning

confidence: 99%

Thematic issue: driving automation and autonomy

Stanton

2019

Theoretical Issues in Ergonomics Science

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Automated driving has the potential to support drivers, freeing them up to do other things, such as work, rest or play. The problem is that in the present instantiation, automated driving requires the driver to perform a monitoring function and be ready to intervene if required. This is the worst of all worlds of automation. The monitoring task can be (if performed properly) more demanding than manual driving and the driver is not freed up to do other things. Worse still, is that the monitoring task cannot be sustained for long and, on occasions, led to a vehicle collisions because the driver cannot intervene in a timely manner. One of the first studies conducted over twenty years shows this to be the case and there really have not been any improvements since. This special issue reports on the latest development in vehicle automation and points to future directions that research should be directed. Relevance to human factors/Relevance to ergonomics theoryDriving Automation and Autonomy is already upon us and the problems that were predicted twenty years ago are beginning to appear. These problems include shortfalls in expected benefits, equipment unreliability, driver skill fade, and error-inducing equipment designs. In addition, the driver becomes both physically and mentally detached from the task of driving and may engage with other non-driving tasks. Ironically, if the driver does not engage with other tasks then can suffer from reduced attentional resources (making them less able to regain control from the vehicle in an emergency). If the driver does engage with other non-driving tasks then their attentional resource pool do not deplete to the same extent (offering a protective effect) but the distraction of the other task(s) can slow down the reclaim of vehicle control from automation. This is one of the main dilemmas with automated driving, which the papers in this special issue address to a greater or lesser extent.

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Psychological constructs in driving automation: a consensus model and critical comment on construct proliferation

Cited by 41 publications

References 87 publications

Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016

Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016

Human behaviour with automated driving systems: a quantitative framework for meaningful human control

Thematic issue: driving automation and autonomy

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