The emergence of automated vehicles (AVs) introduces new challenges to human-vehicle interactions, especially in conditional automated driving.This study presents different head-up display designs as the human-machine interface (HMI) to convey uncertainty to AV users.It investigates the impact of such designs on drivers' physiological responses-via electrodermal activity data-and subjective evaluations of cognitive workload during the automated drive.A between-subjects driving simulator experiment (N=187) was conducted to examine four conditions: baseline (no HMI), a progressive colour-based Guardian Angel display, a text-based interruption, and a combination approach.The results showed significant effects of the presence of the Guardian Angel display interventions on physiological arousal associated with cognitive workload.However, the subjective ratings showed no difference across conditions.These findings indicate that the designed displays can trigger physiological responses without affecting perceived workload.It offers

This paper reports results from a high-fidelity driving simulator study (N=215) about a head-up display (HUD) that conveys a conditional automated vehicle’s dynamic “uncertainty” about the current situation while fallback drivers watch entertaining videos. We compared (between-group) three design interventions: display (a bar visualisation of uncertainty close to the video), interruption (interrupting the video during uncertain situations), and combination (a combination of both), against a baseline (video-only). We visualised eye-tracking data to conduct a heatmap analysis of the four groups’ gaze behaviour over time. We found interruptions initiated a phase during which participants interleaved their attention between monitoring and entertainment. This improved monitoring behaviour was more pronounced in combination compared to interruption, suggesting pre-warning interruptions have positive effects. The same addition had negative effects without interruptions (comparing baseline & display). Intermittent interruptions may have safety benefits over placing additional peripheral displays without compromising usability.

Automated driving raises new human factors challenges. There is a paradox that allows drivers to perform non-driving related tasks (NDRTs), while benefiting from a driver who regularly attends to the driving task. Systems that aim to better manage a driver's attention, encouraging task switching and interleaving, may help address this paradox. However, a better understanding of how drivers self-interrupt while engaging in NDRTs is required to inform such systems. This paper presents a counterbalanced within-subject simulator study with N=42 participants experiencing automated driving in a familiar driving environment. Participants chose a TV show to watch on a HUD and mobile display during two 15min drives on the same route. Eye and head tracking data revealed more self-interruptions in the HUD condition, suggesting a higher likelihood of a higher situation awareness. Our results may benefit the design of future attention management systems by informing the visual and temporal integration of the driving and non-driving related task.

The lack of automated cars above SAE level 3 raises challenges for conducting User Experience Design (UXD) and behaviour research for automated driving. User-centred methods are critical to ensuring a human-friendly progress of vehicle automation. This work introduces the Immersive Video-based Automated Driving (IVAD) Simulator. It uses carefully recorded 180/360° videos that are played back in a driving simulator. This provides immersive driving experiences in visually realistic and familiar environments. This paper reports learnings from an iterative development of IVAD, and findings of two user studies: One simulator study (N=15) focused on the immersive experience; and one VR study (N=16) focused on rapid prototyping and the evaluation of Augmented Reality (AR) concepts. Overall, we found the method to be a useful, versatile and low budget UXD tool with a high level of immersion that is uniquely aided by the familiarity of the environment. IVAD's limitations and future improvements are discussed in relation to research applications within AutoUI.