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

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