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

被引:21
作者
Goncalves, Rafael C. [2 ]
Louw, Tyron L. [1 ]
Madigan, Ruth [1 ]
Quaresma, Manuela [2 ]
Romano, Richard [1 ]
Merat, Natasha [1 ]
机构
[1] Univ Leeds, Inst Transport Studies, Leeds, England
[2] Pontif Catholic Univ Rio de Janeiro, Rio De Janeiro, Brazil
关键词
Vehicle automation; Gaze patterns; Transition of control; HMI design; Lane change; Eye-tracking; Decision-making time; VISUAL-ATTENTION; EYE-MOVEMENTS; BEHAVIOR; MODEL; LOOP; AWARENESS;
D O I
10.1016/j.aap.2022.106726
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
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 indi-cated 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 be-tween 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.
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页数:12
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