Profiling drivers based on driver dependent vehicle driving features

Abstract

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.

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Correspondence to Zahid Halim.

Appendix

Appendix

Fig. 7
figure7

Clusters properties using minimum traffic scenario

Fig. 8
figure8

Clusters properties using average traffic scenario

Fig. 9
figure9

Clusters properties using maximum traffic scenario

Fig. 10
figure10

Clusters properties using all traffic scenarios

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Halim, Z., Kalsoom, R. & Baig, A.R. Profiling drivers based on driver dependent vehicle driving features. Appl Intell 44, 645–664 (2016). https://doi.org/10.1007/s10489-015-0722-6

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Keywords

  • Driver behavior modeling
  • Road safety
  • Artificial neural networks
  • Clustering methods
  • Intelligent systems