Investigating older drivers' takeover performance and requirements to facilitate safe and comfortable human-machine interactions in highly automated vehicles

Abstract

The forthcoming highly automated vehicles (HAVs) may potentially benefit older drivers. However, limited research have investigated the their performance and requirements when interacting with HAVs in order to provide an understanding of what would facilitate a safe and comfortable human-machine interaction with HAVs for them. This thesis fills the research gap using a range of quantitative and qualitative methodologies through four investigations. Firstly, a driving simulator investigation was conducted with 76 participants (39 older and 37 younger drivers) to investigate the effects of age and the state of complete disengagement from driving on the takeover performance. This investigation found that age and complete disengagement from driving negatively affect takeover performance. Then, a second driving simulator investigation was conducted to investigate the effect of age and adverse weather conditions on takeover performance. It was found that age affects takeover performance. And adverse weather conditions, especially snow and fog, lead to a deteriorated takeover performance. Next, a qualitative interview investigation was implemented with 24 older drivers who participated the two driving simulator investigations. This study yielded a wide range of older drivers’ requirements towards the human-machine interactions in HAVs, especially towards the periods of automated driving and taking over control. Lastly, in the third driving simulator investigation, three human-machine interfaces (HMIs) of HAVs were designed based on older drivers’ requirements, their effectiveness on enhancing drivers’ takeover performance were evaluated. It has found that the HMI informing drivers of vehicle status together with the reasons for takeover is the most beneficial HMI to the drivers of HAV. Based on the findings above, the thesis proposed recommendations for facilitating safe and comfortable human-machine interactions in HAVs for older drivers. The thesis concluded the importance of fully considering older adults’ performance, capabilities and requirements during the design of human-machine interactions in HAVs