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Research

Main Facts

  • Risk perception
  • Cognitive workload
  • Systems usefulness
  • Technology user acceptance

Human Factors

The innovation potential of XCYCLE lies in the focus of the technology and its interface towards the communication to all users, cyclists included, of risk and hazard avoidance, based on a behavioral evaluation in terms of trust, reactions to false alarms, risk perception, with consideration also of cognitive workload and attention issues.

XCYCLE investigates user experience and user acceptance of technology developed for the project. Road user acceptance of technology is the precondition that will permit innovative systems to achieve their forecasted benefit levels. It is unproductive to invest effort in designing and building an intelligent co-driver if the system is never switched on, or even disabled.

Using the extended technology acceptance model, XCYCLE will assess the use of technology and its predictors. The use of automation technology is predicted by attitude toward use. The influence of task-technology compatibility (e.g., the match among the road user, the technology, the task to perform, and the situation) and trust (e.g., a generalized expectancy held by the road user that the system can be relied on) on the attitude toward use is mediated by perceived usefulness and perceived ease of use.

XCYCLE will explore the dynamic adoption process by considering the feedback mechanisms. We can assume a mutual influence of perceptions, beliefs, and use, rather than a simple cause-effect relationship in which user attitudes influence automation usage. Attitudes influence use, and use influences attitudes.

TAM model
Extended Technology Acceptance Model to Assess Automation (Ghazizadeh, Lee, Boyle – 2012)

There are some systems and technologies available on the market aimed at car detection built on bicycle. In the last years several prototypes of detection devices on bicycle have been tested, and most of them (e.g., Backtracker or the Cyber-Physical Bicycle of Rutgers University) are designed for the detection of vehicles that approaching the bike from behind. However, these on-bike devices, unlike the in-vehicle devices, are not particularly widespread in the market yet. This is mainly due to two reasons: the weight and the dimension of the sensing devices with related batteries. In fact, due to the needed resolution, ultrasonic sensors must be very large, while radar technology requires unwieldy directional antennas. Therefore, these types of equipment are not practical for being installed on a bike.

One major concern is the actual usefulness of these systems. Existing devices detect and report to the cyclist all vehicles in a given radius. This involves communication of information that is irrelevant with numerous false positive warnings, with the result of diverting the cyclist’s attention away from the primary task of riding, then resulting, in this way, a detriment to safety.