Using Glance Behaviour to Inform the Design of Adaptive HMI for Partially Automated Vehicles

Abstract

This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

The notorious B.I.T: The effects of a ransomware and a screen failure on distraction in automated driving

“…As expected, the explicit failure was more conspicuous than the silent one, as shown by the increased number of fixations and longer duration. This new empirical finding is in line with recent evidence suggesting that drivers allocate some attention towards a HMI when provided with an explanation for the occurrence of a failure (Kraft et al, 2020;Ulahannan et al, 2021).…”

Collection, utilization, protection and compliance governance of personal data in the vehicle in the development of auto-drive system

“…The map supporting unmanned driving is not two-dimensional abstract data, but requires high-resolution and high-precision data, so the amount of data stored and transmitted will be large. Once the data is connected to the system center of the vehicle, the intelligent automatic driving algorithm of AI technology can be used to identify the environment and realize intelligent automatic control [7][8] .…”

A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature Review

“…An in-vehicle system could also detect glances, which occur when a user briefly looks at something, e.g., at secondary screens while driving [341]. In this context, Ulahannan et al [368] used glance behavior to inform adaptive UIs in AVs. A 6-second sequence of glances was used by Fridman et al [108] to predict the driver state.…”