Predicting Overtakes in Trucks Using CAN Data

arXiv:2404.05723v1 Announce Type: new
Abstract: Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM), are employed for the task. Our analysis covers up to 10 seconds before the overtaking event, using an overlapping sliding window of 1 second to …

abstract accidents ann artificial artificial neural networks arxiv classifiers congestion cs.lg data decisions detection driving flow making networks neural networks prediction random reduce safe support traffic truck trucks type vector