Driving Behaviour Classification : An Eco-driving Approach

Driving behavior plays a vital role in determining road safety and also greatly impacts fuel efficiency. Eco-driving is an efficient and economical way of driving, which contributes to the decrease in fuel consumption and pollution. This thesis work deals with the driving behaviour analysis from the perspective of eco-driving rules. Classification of the driving behavior is based on the features which are extracted from the time-series signals collected from On-Board Diagnostic (OBD-II) port of a vehicle. Two methods of classification are proposed: a scoring algorithm based on fuzzy logic and unsupervised learning method. The scoring algorithm is designed to provide a quantitative feedback. The unsupervised learning methods are explored for classifying the drivers behaviour. In order to evaluate these methods, the real-world driving data collected from different vehicles is used. The results show that there is a high correlation between the calculated score and the fuel consumption. Further, unsupervised learning methods are also employed to distinguish among different driving behaviors.