Application of Self-Organizing Maps on Time Series Data for identifying interpretable Driving Manoeuvres

Abstract

Driving manoeuvre identification with Self-organizing Maps: Understanding the usage of a product is essential for any manufacturer in particular for further development. Driving style of the driver is a significant factor in the usage of a city bus. This work proposes a new method to observe various driving manoeuvres in regular operations and identify the patterns in these manoeuvres. The significant advance in this method over other engineering approaches is the use of uncompressed data instead of transformations into certain Performance indicators. Here, the time series inputs were preserved and prepared as 10-second-frames using a sliding window technique and fed into Kohonen’s Self-organizing Map (SOM) algorithm. This produced a high accuracy in the identification and classification of maneuvres and at the same time to a highly interpretable solution that can be readily used for suggesting improvements. Driving Style Comparison: The proposed method is applied to comparing the driving styles of two drivers driving in a similar environment; the differences are illustrated using frequency distributions of identified manoeuvres and then interpreted for the amelioration of fuel consumption.