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. 

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Farkostteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)

Keyword

Data Extraction

Data Fusion

Multivariate Multiscale Entropy (MMSE)

Vehicle Manoeuvre

Accident prevention

Classification (of information)

Complex networks

Data mining

Entropy

Extraction

Learning algorithms

Machine learning

Motor transportation

Roads and streets

Driver's behavior

Flow condition

Machine-learning

Multi-scale datum

Multivariate multiscale entropies

Multivariate multiscale entropy

Road safety

Traffic flow

Vehicle maneuver

Vehicles

Publication and Content Type

ref (subject category)

kon (subject category)