Gaze entropy metrics for mental workload estimation are heterogenous during hands-off level 2 automation.

As the level of vehicle automation increases, drivers are more likely to engage in non-driving related tasks which take their hands, eyes, and/or mind away from the driving task. Consequently, there has been increased interest in creating Driver Monitoring Systems (DMS) that are valid and reliable for detecting elements of driver state. Workload is one element of driver state that has remained elusive within the literature. Whilst there has been promising work in estimating mental workload using gaze-based metrics, the literature has placed too much emphasis on point estimate differences. Whilst these are useful for establishing whether effects exist, they ignore the inherent variability within individuals and between different drivers. The current work builds on this by using a Bayesian distributional modelling approach to quantify the within and between participants variability in Information Theoretical gaze metrics. Drivers (N = 38) undertook two experimental drives in hands-off Level 2 automation with their hands and feet away from operational controls. During both drives, their priority was to monitor the road before a critical takeover. During one drive participants had to complete a secondary cognitive task (2-back) during the hands-off Level 2 automation. Changes in Stationary Gaze Entropy and Gaze Transition Entropy were assessed for conditions with and without the 2-back to investigate whether consistent differences between workload conditions could be found across the sample. Stationary Gaze Entropy proved a reliable indicator of mental workload; 92 % of the population were predicted to show a decrease when completing 2-back during hands-off Level 2 automated driving. Conversely, Gaze Transition Entropy showed substantial heterogeneity; only 66 % of the population were predicted to have similar decreases. Furthermore, age was a strong predictor of the heterogeneity of the average causal effect that high mental workload had on eye movements. These results indicate that, whilst certain elements of Information Theoretic metrics can be used to estimate mental workload by DMS, future research needs to focus on the heterogeneity of these processes. Understanding this heterogeneity has important implications toward the design of future DMS and thus the safety of drivers using automated vehicle functions. It must be ensured that metrics used to detect mental workload are valid (accurately detecting a particular driver state) as well as reliable (consistently detecting this driver state across a population).

The effect of information from dash-based human-machine interfaces on drivers' gaze patterns and lane-change manoeuvres after conditionally automated driving.

The goal of this paper was to measure the effect of Human-Machine Interface (HMI) information and guidance on drivers' gaze and takeover behaviour during transitions of control from automation. The motivation for this study came from a gap in the literature, where previous research reports improved performance of drivers' takeover based on HMI information, without considering its effect on drivers' visual attention distribution, and how drivers also use the information available in the environment to guide their response. This driving simulator study investigated drivers' lane-changing behaviour after resumption of control from automation. Different levels of information were provided on a dash-based HMI, prior to each lane change, to investigate how drivers distribute their attention between the surrounding environment and the HMI. The difficulty of the lane change was also manipulated by controlling the position of approaching vehicles in drivers' offside lane. Results indicated that drivers' decision-making time was sensitive to the presence of nearby vehicles in the offside lane, but not directly influenced by the information on the HMI. In terms of gaze behaviour, the closer the position of vehicles in the offside lane, the longer drivers looked in that direction. Drivers looked more at the HMI, and less towards the road centre, when the HMI presented information about automation status, and included an advisory message indicating it was safe to change lane. Machine learning techniques showed a strong relationship between drivers' gaze to the information presented on the HMI, and decision-making time (DMT). These results contribute to our understanding of HMI design for automated vehicles, by demonstrating the attentional costs of an overly-informative HMI, and that drivers still rely on environmental information to perform a lane-change, even when the same information can be acquired by the HMI of the vehicle.

The effect of motor control requirements on drivers' eye-gaze pattern during automated driving.

This driving simulator study compared drivers' eye movements during a series of lane-changes, which required different levels of motor control for their execution. Participants completed 12 lane-changing manoeuvres in three drives, categorised by degree of manual engagement with the driving task: Fully Manual Drive, Manual Intervention Required, Fully Automated Drive (Manual drive, Partial automation, Full automation). For Partial automation, drivers resumed control from the automated system and changed lane manually. For Full automation, the automated system managed the lane change, but participants initiated the manoeuvre by pulling the indicator lever. Results were compared to the Manual drive condition, where drivers controlled the vehicle at all times. For each driving condition, lane changing was initiated by drivers, at their discretion, in response to a slow-moving lead vehicle, which entered their lane. Failure to change lane did not result in a collision. To understand how different motor control requirements affected driver visual attention, eye movements to the road centre, and drivers' vertical and horizontal gaze dispersion were compared during different stages of the lane change manoeuvre, for the three drives. Results showed that drivers' attention to the road centre was generally lower for drives with less motor control requirements, especially when they were not engaged in the lane change process. However, as drivers moved closer to the lead vehicle, and prepared to change lane, the pattern of eye movements to the road centre converged, regardless of whether drivers were responsible for the manual control of the lane change. While there were no significant differences in horizontal gaze dispersion between the three drives, vertical dispersion for the two levels of automation was quite different, with higher dispersion during Partial automation, which was due to a higher reliance on the HMI placed in the centre console.