Measuring information demand of a dynamic in-vehicle display while driving – A study evaluating the MARS (Masking Action Relevant Stimuli) method

The MARS (Masking Action Relevant Stimuli) method assesses information demand for dynamic stimuli while driving. An action relevant stimulus is masked and the driver presses a button to unmask the stimulus for a limited period. We interpreted button presses as information demand. Following our previous research (Rittger, Kiesel, Schmidt, & Maag, 2014), the current study further evaluates the method. We applied the MARS method to a dynamic in-vehicle display containing recommendations from a traffic light assistant. In a driving simulator, drivers approached intersections with different traffic light phasing. The display either presented simple or complex information. In half of the drives, the participants used the MARS method. The study had a full within subjects design and fixations were recorded in all conditions. The results showed that the information demand varied according to the information in the display and the traffic light phase. A comparison of button presses with fixations showed that one unmasking interval came along with one fixation on the display. As a conclusion, the MARS method can distinguish between conditions with high and low information demand for the display. Button presses relate to fixations on the display. Hence, the MARS method is a promising tool to assess the information demand in dynamic environments and can be applied as an extension or alternative for eye tracking.     

Are situation awareness and decision-making in driving totally conscious processes? Results of a hazard prediction task

Detecting danger in the driving environment is an indispensable task to guarantee safety which depends on the driver’s ability to predict upcoming hazards. But does correct prediction lead to an appropriate response? This study advances hazard perception research by investigating the link between successful prediction and response selection. Three groups of drivers (learners, novices and experienced drivers) were recruited, with novice and experienced drivers further split into offender and non-offender groups. Specifically, this works aims to develop an improved Spanish Hazard Prediction Test and to explore the differences in Situation Awareness, (SA: perception, comprehension and prediction) and Decision-Making (DM) among learners, younger inexperienced and experienced drivers and between driving offenders and non-offenders. The contribution of the current work is not only theoretical; the Hazard Prediction Test is also a valid way to test Hazard Perception. The test, as well as being useful as part of the test for a driving license, could also serve a purpose in the renewal of licenses after a ban or as a way of training drivers. A sample of 121 participants watched a series of driving video clips that ended with a sudden occlusion prior to a hazard. They then answered questions to assess their SA (“What is the hazard?” “Where is it located?” “What happens next?”) and DM (“What would you do in this situation?”). This alternative to the Hazard Perception Test demonstrates a satisfactory internal consistency (Alpha = 0.750), with eleven videos achieving discrimination indices above 0.30. Learners performed significantly worse than experienced drivers when required to identify and locate the hazard. Interestingly, drivers were more accurate in answering the DM question than questions regarding SA, suggesting that drivers can choose an appropriate response manoeuvre without a totally conscious knowledge of the exact hazard.