Predicting Overtakes in Trucks Using CAN Data
Talha Hanif Butt Prayag Tiwari Fernando Alonso-Fernandez
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 extract CAN features. We observe
that the prediction scores of the overtake class tend to increase as we
approach the overtake trigger, while the no-overtake class remain stable or
oscillates depending on the classifier. Thus, the best accuracy is achieved
when approaching the trigger, making early overtaking prediction challenging.
The classifiers show good accuracy in classifying overtakes (Recall/TPR > 93%),
but accuracy is suboptimal in classifying no-overtakes (TNR typically 80-90%
and below 60% for one SVM variant). We further combine two classifiers (Random
Forest and linear SVM) by averaging their output scores. The fusion is observed
to improve no-overtake classification (TNR > 92%) at the expense of reducing
overtake accuracy (TPR). However, the latter is kept above 91% near the
overtake trigger. Therefore, the fusion balances TPR and TNR, providing more
consistent performance than individual classifiers.