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Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification

Publication Type:

Conference/Workshop Paper


IEEE International Conference on System Engineering and Technology


Vehicle manoeuvre analysis is vital for road safety as it helps understand driver behaviour, traffic flow, and road conditions. However, classifying data from in-vehicle acquisition systems or simulators for manoeuvre recognition is complex, requiring data fusion and machine learning (ML) algorithms. This paper proposes a hybrid approach that combines multivariate multiscale entropy (MMSE) and one-dimensional convolutional neural networks (1D-CNNs). MMSE is utilised for early feature extraction and data fusion, and the extracted features are classified using 1D-CNNs, achieving an impressive 87% test accuracy in multiclass classification. This paper provides insights into improving vehicle manoeuvre classification using advanced ML techniques and data fusion methods to handle complex data sets effectively. Ultimately, this approach can enhance the understanding of driver behaviour, inform policy decisions, and develop more effective strategies to enhance road safety.


author = {Arnab Barua and Mobyen Uddin Ahmed and Shahina Begum},
title = {Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification},
month = {November},
year = {2023},
booktitle = {IEEE International Conference on System Engineering and Technology },
url = {}