Abstract
With ongoing improvements in vehicle automation, research on automation trust has attracted considerable attention. In order to explore effects of automation trust on drivers’ visual distraction, we designed a three-factor 2 (trust type: high trust group, low trust group) × 2 (video entertainment: variety-show videos, news videos) × 3 (measurement stage: 1–3) experiment. 48 drivers were recruited in Dalian, China for the experiment. With a driving simulator, we used detection-response tasks (DRT) to measure each driver’s performance. Their eye movements were recorded, and automation-trust scale was used to divide participants into high trust group and low trust group. The results show that: (1) drivers in the high trust group has lower mental workload and paid more attention to visual non-driving-related tasks; (2) video entertainment also has an impact on distraction behavior, variety-show videos catch more attention than news videos. The findings of the present study indicate that drivers with high automation trust are more likely to be involved in non-driving-related visual tasks.
Highlights
Most people have heard of automated vehicles and feel positively about them [1]
To verify the influence of simulated automated driving experience on drivers’ automation trust and to explore the effectiveness of high trust group, a repeated measures ANOVA was performed with high trust group as the between-subject variable, driver automation trust measured before and after the experiment as the within-subject variable, and the automation trust score as the dependent variable
The results showed that the main effects of the high trust group were significantly different [F(1, 46) = 16.094, p< .001, ηp2 = 0.259]
Summary
Most people have heard of automated vehicles and feel positively about them [1]. According to the questionnaire survey, nearly 70% respondents believe that automated vehicles will occupy at least 50% of market share in the automotive industry by around 2050 [2]. Taking a cautious stand, some researchers believe that vehicle automation will remain at a low level for a long time [3, 4]. A number of studies have been carried out on human-machine interaction in L2 automated driving [5, 6]. In L2 automated driving, the vehicle is responsible for most of the operations, while the driver needs to monitor the vehicle conditions and hazards on the road and should be ready to take over the vehicle at any time (SAE Level 2, SAE International, 2016). Drivers’ supervising performance is not satisfactory: they either respond slowly to emergencies [7, 8] or are attracted too much by non-driving-related tasks [9, 10]
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