Abstract

Automated driving is expected to increase safety and efficiency of road transport. With regard to the implementation of automated driving, we observed that those aspects which need to be further developed especially relate to human capabilities. Based on this observation and the understanding that automation will most likely be applied in terms of partially automated driving, we distinguished 2 major challenges for the implementation of partially automated driving: (1) Defining appropriate levels of automation, and; (2) Developing appropriate transitions between manual control and automation. The Assisted Driver Model has provided a framework for the first challenge, because this model recommends levels of automation dependent on traffic situations. To conclude, this research also provided brief directions on the second challenge, i.e. solutions how to accommodate drivers with partially automation

Abstract

utomated driving is expected to increase safety and efficiency of road transport. With regard to the implementation of automated driving, we observed that those aspects which need to be further developed especially relate to human capabilities. Based on this observation and the understanding that automation will most likely be applied in terms of partially automated driving, we distinguished 2 major challenges for the implementation of partially automated driving: (1) Defining appropriate levels of automation, and; (2) Developing appropriate transitions between manual control and automation. The Assisted Driver Model has provided a framework for the first challenge, because this model recommends levels of automation dependent on traffic situations. To conclude, this research also provided brief directions on the second challenge, i.e. solutions how to accommodate drivers with partially automation

Abstract

Studies show that drivers’ intention to use automated vehicles is strongly modulated by trust. It follows that their benefits are unlikely to be achieved if users do not trust them. To date, most studies of trust in automated vehicles have relied on self-reports. However, questionnaires cannot capture real-time changes in drivers’ trust, and are hard to use in applied settings. In previous work, we found evidence that gaze behaviour could provide an effective measure of trust. In this study we tested whether combining gaze behaviour with Electrodermal Activity could provide a stronger metric. The results indicated a strong relationship between self-reported trust, monitoring behaviour and Electrodermal Activity: The higher participants’ self-reported trust, the less they monitored the road, the more attention they paid to a non-driving related secondary task, and the lower their Electrodermal Activity. We also found evidence that combined measures of gaze behaviour and Electrodermal Activity predict self-reported trust better than either of these measures on its own. These findings suggest that such combined measures have the potential to provide a reliable and objective real-time indicator of driver trust. © 2019 Elsevier Ltd