Driving Behaviour Analysis of Professional Bus Drivers in Indian Traffic Scenario (Ongoing)
Driving behaviour classification:
An unsupervised (Explainable) approach
Supervised (AutoML-based) approach
Traditional driving behaviour recognition algorithms leverage hand-crafted features extracted from raw driving data, and then apply user-defined machine learning models to identify driving behaviours. However, such solutions are limited by the set of selected features and by the chosen model, requiring extensive knowledge of the analyzed signals to perform reasonably. In this work, two data-driven driving behaviour recognition frameworks are developed for professional drivers based on
a simple yet efficient, unsupervised, aggregation based approach
automatic feature extraction and feature selection algorithm and a deep neural network architecture obtained using an Automated Machine Learning (AutoML) approach.
Research Outcome:
Milardo S., Rathore, P., Buteau R., Santi, P., Ratti, C. (2021).. An Unsupervised Approach for Driving Behaviour Analysis of Professional Truck Drivers, in EAI International Conference on Intelligent Transportation Systems (EAI-INTSYS). [Best Paper Award]
