The “Out-of-the-Loop” concept in automated driving: proposed definition, measures and implications

Abstract

Despite an abundant use of the term "Out of the loop" (OOTL) in the context of automated driving and human factors research, there is currently a lack of consensus on its precise definition, how it can be measured, and the practical implications of being in or out of the loop during automated driving. The main objective of this paper is to consider the above issues, with the goal of achieving a shared understanding of the OOTL concept between academics and practitioners. To this end, the paper reviews existing definitions of OOTL and outlines a set of concepts, which, based on the human factors and driver behaviour literature, could serve as the basis for a commonly-agreed definition. Following a series of working group meetings between representatives from academia, research institutions and industrial partners across Europe, North America, and Japan, we suggest a precise definition of being in, out, and on the loop in the driving context. These definitions are linked directly to whether or not the driver is in physical control of the vehicle, and also the degree of situation monitoring required and afforded by the driver. A consideration of how this definition can be operationalized and measured in empirical studies is then provided, and the paper concludes with a short overview of the implications of this definition for the development of automated driving functions.

On-Road Evaluation of Autonomous Driving Training

“…The out-of-the-loop problem is a well known potential negative consequence of automation, which leaves "operators of automated systems handicapped in their ability to take over manual operations in the event of automation failure" [4]. In recent years, the behavior of drivers when they resume control of automated vehicles has been extensively studied [5]- [7]. Results from this research have led to important findings about take-over performance [7] depending on the criticality of the situation [8] and the secondary activity [9], [10].…”

Applicability of risky decision-making theory to understand drivers' behaviour during transitions of control in vehicle automation

“…Merat et al (2018) further complement this logic for the context of vehicle automation (based on the model purposed by Michon, 1985), by stating that there are two distinct loops in manual driving, which can be affected by ceding control to automation: one for motor-control coordination, and another for the several decision-making processes that need to be performed while driving. They suggest "(…) that "being in the loop" can be understood in terms of (1) the driver's physical control of the vehicle, and (2) monitoring the current driving situation (…)" (Merat et al, 2018). It must be noted that both loops continually interact with each other, and drivers must be aware of both their visual-motor coordination (see Wilkie et al, 2008 for a more descriptive definition of the term) and the surrounding environment, to safely maintain control of the task.…”

Using Markov Chains to Understand the Sequence of Drivers' Gaze Transitions During Lane-Changes in Automated Driving

“…It is generally agreed that the lack of a need for manual control of the vehicle, as imposed, for instance, by highly automated vehicles (AVs), removes the driver from the decision-making and control loops (Louw & Merat, 2017), requiring them to scan the environment and acquire appropriate situation awareness (Endsley, 1995) when they are required to reengage with the driving task (Merat et al, 2018). In complex environments, and when drivers are not continually monitoring the road, but are required to resume manual control, this rebuilding of situation awareness is normally required within a short time window (Louw & Merat, 2017).…”