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

arXiv: https://arxiv.org/abs/2507.00593 · HTML: https://mlsanity.com/paper/2507.00593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters](https://mlsanity.com/paper/2308.08469/) (2023)
- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections](https://mlsanity.com/paper/1901.05105/) (2019)
- [Attention-based Lane Change Prediction](https://mlsanity.com/paper/1903.01246/) (2019)
- [Data and Knowledge for Overtaking Scenarios in Autonomous Driving](https://mlsanity.com/paper/2305.19421/) (2023)
- [Predicting Overtakes in Trucks Using CAN Data](https://mlsanity.com/paper/2404.05723/) (2024)

## Abstract (arXiv)

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.