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

2025-07-01

AI Summary

Detecting truck overtaking maneuvers in real-world traffic remains challenging due to sparse overtaking events and severe class imbalance in single-vehicle CAN bus data, leading to poor model generalizability. Method: This paper proposes a cross-vehicle CAN bus data fusion framework, leveraging operational data from five in-service Volvo trucks to construct a multi-source, heterogeneous training dataset. A score-level ensemble integrates predictions from ANN, Random Forest, and SVM classifiers, and a systematic ablation study evaluates the impact of various preprocessing strategies on CAN signal feature modeling. Results: The proposed method achieves 86.5% true positive rate (TPR) while maintaining 93% true negative rate (TNR), significantly outperforming single-vehicle models. Contribution: This work presents the first empirical validation—within commercial vehicle ADAS contexts—that cross-vehicle data fusion enhances robustness and generalizability for overtaking detection, establishing a scalable, lightweight paradigm for driving-behavior perception under real-road conditions.

Technology Category

• Machine Learning: Ensemble Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• Detecting truck overtakes using CAN bus signals for ADAS
• Comparing ANN, RF, SVM performance in maneuver classification
• Improving accuracy with multi-vehicle data and fusion strategies

Innovation

• Uses CAN bus data for overtake detection
• Compares ANN, RF, SVM classifiers performance
• Applies score-level fusion for best accuracy