Tyron Louw

Were they in the loop during automated driving? Links between visual attention and crash potential

Background A proposed advantage of vehicle automation is that it relieves drivers from the moment-to-moment demands of driving, to engage in other, non-driving related, tasks. However, it is important to gain an understanding of drivers’ capacity to resume manual control, should such a need arise. As automation removes vehicle control-based measures as a performance indicator, other metrics must be explored. Methods This driving simulator study, conducted under the European Commission (EC) funded AdaptIVe project, assessed drivers’ gaze fixations during partially-automated (SAE Level 2) driving, on approach to critical and non-critical events. Using a between-participant design, 75 drivers experienced automation with one of five out-of-the-loop (OOTL) manipulations, which used different levels of screen visibility and secondary tasks to induce varying levels of engagement with the driving task: 1) no manipulation, 2) manipulation by light fog, 3) manipulation by heavy fog, 4) manipulation by heavy fog plus a visual task, 5) no manipulation plus an n-back task. Results The OOTL manipulations influenced drivers’ first point of gaze fixation after they were asked to attend to an evolving event. Differences resolved within one second and visual attention allocation adapted with repeated events, yet crash outcome was not different between OOTL manipulation groups. Drivers who crashed in the first critical event showed an erratic pattern of eye fixations towards the road centre on approach to the event, while those who did not demonstrated a more stable pattern. Conclusions Automated driving systems should be able to direct drivers’ attention to hazards no less than 6 seconds in advance of an adverse outcome.

The effect of varying levels of vehicle automation on drivers’ lane changing behaviour

Much of the Human Factors research into vehicle automation has focused on driver responses to critical scenarios where a crash might occur. However, there is less knowledge about the effects of vehicle automation on drivers’ behaviour during non-critical take-over situations, such as driver-initiated lane-changing or overtaking. The current driving simulator study, conducted as part of the EC-funded AdaptIVe project, addresses this issue. It uses a within-subjects design to compare drivers’ lane-changing behaviour in conventional manual driving, partially automated driving (PAD) and conditionally automated driving (CAD). In PAD, drivers were required to re-take control from an automated driving system in order to overtake a slow moving vehicle, while in CAD, the driver used the indicator lever to initiate a system-performed overtaking manoeuvre. Results showed that while drivers’ acceptance of both the PAD and CAD systems was high, they generally preferred CAD. A comparison of overtaking positions showed that drivers initiated overtaking manoeuvres slightly later in PAD than in manual driving or CAD. In addition, when compared to conventional driving, drivers had higher deviations in lane positioning and speed, along with higher lateral accelerations during lane changes following PAD. These results indicate that even in situations which are not time-critical, drivers’ vehicle control after automation is degraded compared to conventional driving.

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

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.

Designing the interaction of automated vehicles with other traffic participants: design considerations based on human needs and expectations

Automated vehicles (AV) are expected to be integrated into mixed traffic environments in the near future. As human road users have established elaborated interaction strategies to coordinate their actions among each other, one challenge that human factors experts and vehicle designers are facing today is how to design AVs in a way that they can safely and intuitively interact with other traffic participants. This paper presents design considerations that are intended to support AV designers in reducing the complexity of the design space. The design considerations are based on a literature review of common human–human interaction strategies. Four categories of information are derived for the design considerations: (1) information about vehicle driving mode; (2) information about AVs’ manoeuvres; (3) information about AVs’ perceptions of the environment; and (4) information about AVs’ cooperation capabilities. In this paper, we apply the four categories to analyse existing research studies of traffic participants’ needs during interactions with AVs and results of the CityMobil2 project. From the CityMobil2 project we present central results from face-to-face interviews, an onsite-survey and two focus groups. To further support the AV designers we describe and rate different design options to present the information of the four categories, including the design of the infrastructure, the vehicle shape, the vehicle manoeuvres and the external human–machine interface of the AV.