AgeLab research associate Bryan Reimer gave the talk “Automotive Technology, Driver Distraction and Demographics: Rethinking interface design to match driver capabilities”, hosted by the Johns Hopkins chapter of the Association for Computing Machinery at the Whiting School of Engineering.
The following is an abstract of the talk:
Vehicle interfaces evolved slowly over most of the last century. In contrast, recent innovations in display technology in combination with advances in sensing systems and computational capabilities, have led to a rapid evolution of the vehicle interface. In the late 20th century, automotive interfaces primarily relied on a set of basic controls. Over the past 10 to 15 years interfaces have transformed into multi-functioned control stations with complex menu systems and multiple methods of accomplishing various activities. Embedded telematics systems, in combination with the increasing presence of nomadic devices, draw upon the driver’s limited attention resources. In addition, semi-autonomous control systems increasingly offer to assume shared control of the vehicle and advanced safety systems warn drivers when they appear to be operating outside of acceptable control limits. While interface technology has recently resulted in a heightened discussion of distraction, shifting demographics have resulted in increased prevalence of older drivers who are potentially less capable of interacting with new technologies. Future vehicle systems will offer the promises of improved connectivity, increased comfort and enhanced safety; however, new methods of understanding the complex demands they place on the driver are critically needed. Highlighted in the talk will be areas of research related to state detection, driver distraction, and new in-vehicle technology evaluations being conducted at the MIT AgeLab. Recent research on physiological reactivity and changes in visual attention in response to graded levels of cognitive workload will also be considered. Finally, novel applications explored by the lab for using state detection as a foundation for alerting / cueing the operator will be discussed.