Adaptive Driver Modelling for Forward Collision Warning Systems

Abstract

In this work, driving behaviour is analysed with the purpose of finding connections between a drivers routine driving and their behaviour in collision and near-collision situations. The ambition is to improve the Forward Collision Warning (FCW) system on Volvo cars by taking information from previous driving situations of the current driver into account when determining the best timing for issuing a collision warning. The analysis is performed by means of feature extraction on multivariate time series data, containing measurements from various sensors. Using principal component analysis (PCA) and clustering methods such as k-means and DBSCAN, no connections relevant to the formulated aim could be found in the investigation. The conclusion drawn is that a more thorough evaluation of the available data is required. Removing parts of drive sequences that are not of interest or categorise the sequences into different scenarios can make the information more comparable and hence yield a better result. A more careful data cleaning of the available time series could also lead to an improvement.