Novel Approaches to Cognitive Load Estimation in Automated Driving Systems

Automation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. However, the present-day ADS requires the human driver to remain vigilant at all times and be ready to take over whenever the driving task requires. Thus, continuous monitoring of the drivers is important for adopting ADS. Such monitoring can be done in ADS by measuring the cognitive load experienced by the drivers. Studies show various methods to estimate cognitive load, however, the state of the art in cognitive load estimation, particularly, the non-invasive ones suitable for ADS, still suffer from significant deficiencies. Thus, more research to improve the accuracy of cognitive load estimators is crucial for allowing the safe adoption of ADS. This thesis contains the analysis of non-invasive metrics that can be used as reliable indicators of cognitive load. Eye-tracking measures such as pupil size, eye-gaze, and eye-blinks from low-cost eye-trackers are analyzed. In addition to eye-tracking data, heart rate is also studied as an estimator of cognitive load. Furthermore, this thesis introduces a novel model-based approach to filter noisy physiological measurements for the real-time monitoring of cognitive load. The proposed measures will be beneficial to the development of more accurate metrics for cognitive load estimation, thereby contributing to the advancement of ADS. The thesis also contains a detailed description of two datasets collected at the HSLab.These datasets will be helpful to researchers interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine automation

Eye Metrics for Task-Dependent Automation

Future air traffic is expected to grow increasingly, opening up a gap for task dependent automation and adaptive interfaces, helping the Air Traffic Controller to cope with fluctuating workloads. One of the challenging factors in the application of such intelligent systems concerns the question what the operator is doing in order to optimize support and minimize automation surprises. This study questions whether eye metrics are able to determine what task the operator is engages in. We therefore examined A) if the eye-path would differ for three different ATC tasks and B) whether this effect can be quantified with six eye-metrics. In an experiment, the six eye-metrics were calculated and used as a dependent variable. The results show that some tasks can be inferred by eye movement metrics and other metrics infer workload, although none inferred by both task and workload. Copyright 2014 ACM