The rapid development of sensor and tracking technology enables deployment of new Advanced Driver Assistance Systems (ADAS) that support the driver not just on highways but in urban areas as well. Intersections particularly present critical traffic scenarios where almost 35% of accidents occur, partially due to the present lack of in-depth research about human errors and their determinants. The first step in ergonomic design of ADAS is to identify the specific situations in which drivers require support. To contribute to identification of such spots, situation awareness of 20 drivers in four critical intersection scenarios was explored. The study was conducted in the fixed-base driving simulator. The applied approach consisted of assessing drivers' expectations and mental workload and of comparing theoretically correct cognitive behavior to experimentally collected data. Intersection scenarios were divided into five segments, and for each segment a task analysis was made. The study has shown that the driving simulator environment can be successfully deployed to provoke and explore various driver errors. The results have revealed that, in scenarios in which information is objectively missing, the majority of errors happened because the drivers had inaccurate mental models of particular scenarios. To the contrary, in the complex scenario the major cause of accidents was information overload. Furthermore, the task analysis disclosed applicable areas of intersection assistance. (Author/publisher)
Abstract