Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Matthew J Eagon, Setayesh Fakhimi, Adam Pernsteiner, William F. Northrop

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

Original languageEnglish (US)
Title of host publication2022 IEEE Intelligent Vehicles Symposium, IV 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781665488211
StatePublished - 2022
Event2022 IEEE Intelligent Vehicles Symposium, IV 2022 - Aachen, Germany
Duration: Jun 5 2022Jun 9 2022

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


Conference2022 IEEE Intelligent Vehicles Symposium, IV 2022

Bibliographical note

Funding Information:
This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Vehicle Technologies Office Award Number DE-EE0009233.

Publisher Copyright:
© 2022 IEEE.


  • AI
  • Bayesian network
  • Mass prediction
  • machine learning


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