Measuring cognitive load is essential for understanding driver performance. Under- and overload can result in dangerous situations on the road. Cognitive load can be estimated by monitoring the diameter and movements of the pupils, but during measurements external influences such as changes in light intensity affect pupil diameters. In this paper, we present a novel method for quantifying light intensity with a head-mounted eye-tracker by weighting pixel values around the gaze direction. We demonstrate its effectiveness in cognitive load classification systems that use pupil metrics only. 54 participants in two separate studies have carried out n-back tasks during a simple driving task in a driving simulator. The data is classified by cognitive task (baseline, 1-back, 2-back) with the Random Forest algorithm. The resulting systems are 92.5% accurate with and 85.9% accurate without gaze features available, but are unable to generalise to participants unseen in the training phase of the algorithm.

Long before humans will completely trust fully automated vehicles, partial and conditional automation where the human driver is still in the loop will dominate the era of autonomous vehicles. However, more than 90% of traffic accidents are due to human errors, of which approximately half appear to be due to perceptual errors. Especially at busy and complex intersections that have a high density of visual stimuli. This poses a high demand for accurate measurement of the driver's situation awareness, for real-world driver monitoring. Eye tracking seems to be an ideal method to determine what the driver has or has not seen, since people tend to look at what they inquire information from. The main objective of this thesis assignment was therefore to develop a platform that combines the driver's gaze behaviour in combination with the vehicle's road scene perception, to set up a real-world driving experiment to gather such data on the road, and to come up with a proof of concept that gaze behaviour combined with situational knowledge can be predictive of SA. The platform developed consisted of an eye-tracker with four cameras constructed in the available Toyota Prius of the department of Intelligent Vehicles, which is equipped for self-driving. The driver's gaze was layered over the object identification data from the vehicle, to see which objects are looked at or fixated upon and which are not. A real-world driving experiment was then conducted in which participants (N = 14) performed a driving task and a recall task. The driving task consisted of 8 intersection crossings in which mostly left turns were made to manoeuvre the vehicle off a main priority road. After each crossing, the participants performed a recall task in which they had to select images of the object they encountered during the driving task. The results showed that 88.1% of all relevant objects they encountered were seen with central vision, of which 41.8% were recalled. The remainder 11.9% of all relevant objects that were not seen, have only been in peripheral view, of which 18.2% were recalled. These preliminary results indicate that at least 2.2% (18.2% of 11.9%) of relevant objects are perceived by the driver using peripheral vision. The variables seen, first saccade angle and first saccade moment contributed significantly to a prediction model that predicted whether a relevant object would be recalled by the driver. The variables fixation count, total glance duration and saccade count were not significant predictor variables. The conclusion was drawn that the results of this exploratory research confirmed that gaze behaviour combined with situational knowledge can be predictive of driver SA. However, a crucial recommendation for future research is an improved recall task procedure to obtain higher recall rates and therefore more accurate prediction of SA. This outcome could be of great value for future research and development of applications that assist or steer the driver's attention to possible threats or objects the driver is missing or not focusing on.