Impact of Smart Phones’ Interaction Modality on Driving Performance for Conventional and Autonomous Vehicles

Distracted driving related to cell phone usage ranks among the top three causes of fatal crashes on the road. Although forty-eight of 50 U.S. states allow the use of personal devices if operated hands-free and secured in the vehicle, scientific studies have yet to quantify the safety improvement presumed to be introduced by voice-to-text interactions. Thus, this study investigated how different modes of interaction of drivers with a smart phone (i.e., manual texting vs. vocal input) affect drivers’ distraction and performance in both conventional and semi-autonomous vehicles. The study was executed in a full-car integrated simulator and tested a population of 32 drivers. The study considered two scenarios: (1) conventional manual driving in a suburban environment with intersection stops; and (2) control takeover from an engaged autonomous vehicle that reverted to manual driving at a highway exit. The quality of execution of maneuvers as well as timing and tracking of eye-gaze focus areas were assessed in both scenarios. Results demonstrated that while participants perceived an increased level of safety while using the hands-free interface, response times and drift did not significantly differ from those manually texting. Furthermore, even though participants perceived a greater effort in accomplishing the text reply through the manual interface, none of the measured quantities for driving performance or eye-gaze focus revealed statistical difference between the two interfaces, ultimately calling into question the assumption of greater safety implicit in the laws allowing hands-free devices

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

PhD ThesisThe 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 HAV