Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods

TL;DR

This study evaluates machine learning classifiers for detecting overtaking maneuvers in trucks using CAN bus data, highlighting the importance of diverse training data and fusion strategies for improved accuracy in real-world conditions.

Contribution

It compares ANN, RF, and SVM classifiers on real-world truck data, demonstrating how data diversity and fusion improve overtaking detection performance.

Findings

Fusion strategy improves per-truck accuracy

Training with multi-vehicle data enhances generalisation

Achieved 93% TNR and 86.5% TPR accuracy

Abstract

Safe overtaking manoeuvres in trucks are vital for preventing accidents and ensuring efficient traffic flow. Accurate prediction of such manoeuvres is essential for Advanced Driver Assistance Systems (ADAS) to make timely and informed decisions. In this study, we focus on overtake detection using Controller Area Network (CAN) bus data collected from five in-service trucks provided by the Volvo Group. We evaluate three common classifiers for vehicle manoeuvre detection, Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), and analyse how different preprocessing configurations affect performance. We find that variability in traffic conditions strongly influences the signal patterns, particularly in the no-overtake class, affecting classification performance if training data lacks adequate diversity. Since the data were collected under unconstrained, real-world conditions, class diversity cannot be guaranteed a priori. However, training with data from multiple vehicles improves generalisation and reduces condition-specific bias. Our pertruck analysis also reveals that classification accuracy, especially for overtakes, depends on the amount of training data per vehicle. To address this, we apply a score-level fusion strategy, which yields the best per-truck performance across most cases. Overall, we achieve an accuracy via fusion of TNR=93% (True Negative Rate) and TPR=86.5% (True Positive Rate). This research has been part of the BIG FUN project, which explores how Artificial Intelligence can be applied to logged vehicle data to understand and predict driver behaviour, particularly in relation to Camera Monitor Systems (CMS), being introduced as digital replacements for traditional exterior mirrors.