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Research on Driving Style Recognition Based on Unsupervised Machine Learning
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Boru Zhao1, Xiang Li1, Chenyue Wang1, Xin Wang2, 3, Zongqin Zhao2, 3
Automotive Digest | 2025, (8) : 25 - 33
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Automotive Digest | 2025, (8): 25-33
• Special Topic on Scenario Perception and Intelligent Experience Technologies for Intelligent Connected Vehicles •
Research on Driving Style Recognition Based on Unsupervised Machine Learning
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Boru Zhao1, Xiang Li1, Chenyue Wang1, Xin Wang2, 3, Zongqin Zhao2, 3
Affiliations
  • 1 National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331
  • 2 Chongqing Changan Automobile Co., Ltd., Chongqing 400023
  • 3 State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 401133
Published: 2025-08-05 doi: 10.19822/j.cnki.1671-6329.20240243
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Driving style recognition plays a crucial role in enhancing personalized driving experiences and optimizing energy utilization in smart connected vehicles. Considering the relationship between different road environments and driving styles, a cascade delivery framework is designed to fully utilize real-world natural driving data and segment the data into events with distinct physical meanings. Using driver IDs as pseudo-labels, an XGBoost model learns differences in driving styles, identifying the key features and weights critical for recognition. Following the principles of a hybrid expert system, the WK-means algorithm clusters driving styles under varying conditions, ultimately generating driving scores to evaluate driver performance. The statistical analysis of the clustered data shows that this method effectively recognizes drivers with diverse driving styles, which lays the foundation for the further development of intelligent networked vehicle technology.

Driving style  /  Unsupervised cluster  /  Feature selection  /  Machine learning
Boru Zhao, Xiang Li, Chenyue Wang, Xin Wang, Zongqin Zhao. Research on Driving Style Recognition Based on Unsupervised Machine Learning[J]. Automotive Digest, 2025 , (8) : 25 -33 . DOI: 10.19822/j.cnki.1671-6329.20240243
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doi: 10.19822/j.cnki.1671-6329.20240243
  • Online Date：2025-10-28
  • Published：2025-08-05
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    1 National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331
    2 Chongqing Changan Automobile Co., Ltd., Chongqing 400023
    3 State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 401133
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