Christian Purucker

Clustered Insights : Improving Eye Tracking Data Analysis using Scan Statistics

Analysis of eye-tracking data in marketing research has traditionally relied upon regions of interest (ROIs) methodology or the use of heatmaps. Clear disadvantages exist for both methods. Addressing this gap, the current research applies spatiotemporal scan statistics to the analysis and visualisation of eye tracking data. Results of a sample experiment using anthropomorphic car faces demonstrate several advantages provided by the new method. In contrast to traditional approaches, scan statistics provide a means to scan eye tracking data automatically in space and time with differing gaze clusters, with results able to be comprehensively visualised and statistically assessed.

Validity of Research Environments – Comparing Criticality Perceptions Across Research Environments

According to standards such as ISO 26262, system controllability for human drivers must be ensured with new driver assistance systems. The choice of an appropriate research environment is a central issue in controllability research and precedes questions such as the criticality of test scenarios and deduction of pass-fail-criteria. The methodological trade-off between research on test tracks and with driving simulators cannot be resolved easily. Although test track research allows for the analysis of human interactions with real vehicle dynamics, the investigation of critical situations with other traffic agents requires considerable effort and is sometimes not feasible. For example, the complexities of real-life urban scenarios cannot be readily replicated on test tracks. Driving simulations do not underlie these restrictions, but limitations concerning visual and proprioceptive feedback raise questions of validity. Within the UR:BAN MV KON project, a study was performed on criticality perceptions towards various metrics (longitudinal and lateral distances and decelerations) across four research environments. A total of five experiments were performed using a dynamic driving simulator, a static driving simulator, a test track vehicle, and a Vehicle-In-the-Loop (VIL), which is a hybrid between a test track vehicle and a driving simulator. In the present chapter, we present results from the series of experiments and show how the results relate to existing validity research.