An Active Learning Framework for Constructing High-Fidelity Mobility Maps

Recent workat the U.S. Army CCDC Ground Vehicle Systems Center has shown that machine learning classifiers can quickly construct high-fidelity mobility maps. Training these classifiers, on the other hand, is still a challenge, since each data instance is labeled by performing a computationally inten...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-10, Vol.70 (10), p.9803-9813
Main Authors: Marple, Gary R., Gorsich, David, Jayakumar, Paramsothy, Veerapaneni, Shravan
Format: Article
Language:English
Subjects:
Accuracy
Active learning
Algorithms
Autonomous vehicles
Classifiers
Computational modeling
Computer simulation
Data models
Machine learning
Neural networks
path planning
Prediction algorithms
Predictive models
Random sampling
Soil
Training
Uncertainty
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Summary:Recent workat the U.S. Army CCDC Ground Vehicle Systems Center has shown that machine learning classifiers can quickly construct high-fidelity mobility maps. Training these classifiers, on the other hand, is still a challenge, since each data instance is labeled by performing a computationally intensive, physics-based simulation. In this paper we introduce an active learning framework, based on the query-by-bagging algorithm, that substantially reduces the number of simulations needed to train a classifier. Experimental results suggest that our sampling algorithm can train a neural network, with higher accuracy, using less than half the number of simulations when compared to random sampling.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3107338