A Study of Vehicle Driving Condition Recognition Using Supervised Learning Methods

Bin Xu, Junzhe Shi, Sixu Li, Huayi Li

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


In vehicle operation, in order to maximize the fuel economy, the propulsion system control can easily adapt to pressure and temperature variations as these variations can be measured by sensors. However, it is challenging to detect driving cycles. With growing progress made in the artificial intelligence field, pattern recognition gains momentum in various applications. This study presents a study on driving cycle pattern recognition based on supervised learning. Training data 2-D visualization is achieved by the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Ten out of 12 supervised learning algorithms predict driving conditions with an accuracy of 88% or higher, and the extra tree (ET) algorithm leads the recognition accuracy at 90.26%. To improve the recognition accuracy, two hierarchical frameworks are proposed by integrating multiple supervised learning methods using weighted average and vote methods. The two hierarchical frameworks boost the driving condition recognition accuracy from 90.26% to 90.43% (weighted average) and 91.76% (vote). The results are further validated in the holdout test. In addition, a plug-in hybrid electric vehicle simulation shows 3.88%-5.82% fuel economy improvement compared to the baseline method. Two hierarchical methods outperform the ET method by 2% fuel economy. In summary, supervised learning shows great potential to detect driving cycles for vehicle energy saving.

Original languageEnglish
Pages (from-to)1665-1673
Number of pages9
JournalIEEE Transactions on Transportation Electrification
Issue number2
StatePublished - Jun 1 2022

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Driving cycle
  • pattern recognition
  • supervised learning
  • vehicle propulsion system control

ASJC Scopus subject areas

  • Automotive Engineering
  • Transportation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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