Search Results (2)

Autonomous driving is anticipated to increase safety, efficiency, and accessibility of passenger transportation. Passengers are given freedom in the use of travel time through the potential to conduct non-driving related tasks (NDRTs). However, factors such as trust and motion sickness pose challenges to the widespread adoption of this technology. Human–machine interfaces (HMIs) have shown potential in mitigating motion sickness and fostering trust calibration in autonomous vehicles (AVs), e.g., by visualizing upcoming or current maneuvers of the vehicle. The majority of research on such HMIs relies on the passengers’ attention, preventing uninterrupted NDRT execution and thus impeding the automation’s usefulness. In this paper, we present a visual HMI, providing AV passengers with information about current driving maneuvers through their peripheral fields of view. This method of information transmission is compared to conventional in-vehicle displays and LED strips regarding perceptibility and distraction. In a controlled laboratory setting, N = 34 participants experienced each HMI condition, indicating their perception of the maneuver visualizations using joystick input while either focusing on a fixation cross to measure perceptibility or solving math tasks to measure distraction. The peripheral HMIs caused better maneuver perception (${{\eta }_{\mathrm{p}}}^2=0.12$) and lower distraction (${{\eta }_{\mathrm{g}}}^2=0.16$) from a visual NDRT than the conventional displays. These results yield implications for the design of HMIs for motion sickness mitigation and trust calibration in AVs. Full article

As the development of autonomous vehicles (AVs) progresses, new seating arrangements are emerging. Face-to-face seating is common in SAE L4 AV concepts and is intended to facilitate social interaction during autonomous driving, enabling previously unfeasible non-driving related tasks (NDRTs). However, this is countered by the unpopularity of rearward seating orientations, which is particularly pronounced in cars. In order to develop countermeasures to address this unpopularity, a deeper understanding of the underlying mechanisms is required. This study validates a model that predicts the acceptance of AVs and takes seating orientation into account. To this end, a study with N = 46 participants was conducted to investigate the influence of seating orientation on AV acceptance and related factors such as transparency, trust, and motion sickness. Additionally, internal human–machine interfaces (iHMIs) were evaluated in regard to their ability to compensate for the disadvantages of a rearward seating orientation. To achieve a realistic implementation of a fully functional SAE L4 AV, an experimental vehicle was equipped with a steering and pedal robot, performing self-driven journeys on a test track. The iHMIs provided information about upcoming maneuvers and detected road users. While engaged in a social NDRT, participants experienced a total of six journeys. Seating orientation and iHMI visualization were manipulated between journeys. Rearward-facing passengers showed lower levels of trust and higher levels of motion sickness than forward-facing passengers. However, the iHMIs had no effect on acceptance or related factors. Based on these findings, an updated version of the model is proposed, showing that rearward-facing passengers in autonomous vehicles pose a particular challenge for trust calibration and motion sickness mitigation. During NDRTs, iHMIs which depend on the attention of AV occupants for information transfer appear to be ineffective. Implications for future research and design of iHMIs to address this challenge are discussed. Full article