This study examined the effects of age and driving experience on the ability to detect hazards while driving; namely, hazard perception. Studies have shown that young-inexperienced drivers are more likely than experienced drivers to suffer from hazard perception deficiencies. However, it remains to be determined if this skill deteriorates with advancing age. Twenty-one young-inexperienced, 19 experienced, and 16 elderly drivers viewed six hazard perception movies while connected to an eye tracking system and were requested to identify hazardous situations. Four movies embedded planned, highly hazardous, situations and the rest were used as control. Generally, experienced and older-experienced drivers were equally proficient at hazard detection and detected potentially hazardous events (e.g., approaching an intersection, pedestrians on curb) continuously whereas young-inexperienced drivers stopped reporting on hazards that followed planned, highly hazardous situations. Moreover, while approaching T intersections older and experienced drivers fixated more towards the merging road on the right while young-inexperienced drivers fixated straight ahead, paying less attention to potential vehicles on the merging road. The study suggests that driving experience improves drivers' awareness of potential hazards and guides drivers' eye movements to locations that might embed potential risks. Furthermore, advanced age hardly affects older drivers' ability to perceive hazards, and older drivers are at least partially aware of their age-related limitations.

While substantial effort has been invested in making robots more reliable, experience demonstrates that robots operating in unstructured environments are often challenged by frequent failures. Despite this, robots have not yet reached a level of design that allows effective management of faulty or unexpected behavior by untrained users. To understand why this may be the case, an in-depth literature review was done to explore when people perceive and resolve robot failures, how robots communicate failure, how failures influence people's perceptions and feelings toward robots, and how these effects can be mitigated. Fifty-two studies were identified relating to communicating failures and their causes, the influence of failures on human-robot interaction (HRI), and mitigating failures. Since little research has been done on these topics within the HRI community, insights from the fields of human computer interaction (HCI), human factors engineering, cognitive engineering and experimental psychology are presented and discussed. Based on the literature, we developed a model of information processing for robotic failures (Robot Failure Human Information Processing, RF-HIP), that guides the discussion of our findings. The model describes the way people perceive, process, and act on failures in human robot interaction. The model includes three main parts: (1) communicating failures, (2) perception and comprehension of failures, and (3) solving failures. Each part contains several stages, all influenced by contextual considerations and mitigation strategies. Several gaps in the literature have become evident as a result of this evaluation. More focus has been given to technical failures than interaction failures. Few studies focused on human errors, on communicating failures, or the cognitive, psychological, and social determinants that impact the design of mitigation strategies. By providing the stages of human information processing, RF-HIP can be used as a tool to promote the development of user-centered failure-handling strategies for HRIs.

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