Analysing Behavioural Data from On-Road Driving Studies: Handling the Challenges of Data Processing

The analysis of real world driving data entails numerous challenges. In this chapter, several strategies are proposed to meet challenges that surface in data storage, data extraction, data correction and data enrichment. The strategies are illustrated with examples from a study that had been conducted as part of the UR:BAN research project ”Behaviour Prediction and Intention Detection” (VIE), which aimed at investigating driving behaviour when approaching intersections under real environmental conditions in order to predict turning manoeuvres at urban intersections. It was demonstrated that with the proposed data infrastructure, correction procedures and extracted filters for potentially confounding variables, it is possible to establish a “clean” data basis to implement and adjust a prediction algorithm for turning manoeuvres according to individual driver characteristics.