Passive fatigue during conditional automated driving can compromise driver readiness and safety. This paper presents findings from a test-track study with 40 participants in a real-world automated driving scenario. In this scenario, a Large Language Model (LLM) based conversational agent (CA) was designed to check in with drivers and re-engage them with their surroundings. Drawing on in-car video recordings, sleepiness ratings and interviews, we analysed how drivers interacted with the agent and how these interactions shaped alertness. Results show the CA is helpful for supporting vigilance during passive fatigue. Thematic analysis of acceptability further revealed three user preference profiles that implicate future intention to use CAs. Positioning empirically observed profiles within existing CA archetype frameworks highlights the need for adaptive design sensitive to diverse user groups. This work underscores the potential of CAs as proactive Human–Machine Interface (HMI) interventions, demonstrating how natural language can support context-aware interaction during automated driving.

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

Figure 1: Using vestibular cues as a subtle awareness "nudge" modality in L3 automated driving scenario.

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.