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February 14, 2026Automotive Innovation

Can Driving Style be Accurately Inferred from Short Trajectories? A Comparative Study for Driving Style Classification Approaches

Kehua Chen Yan Luo Meixin Zhu

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Key Points

Driving style classification accurately infers using various machine learning algorithms and short trajectories.
Key evidence shows certain methods outperform others with an efficiency improvement of over 20% in accuracy.
Comparative analysis using driving data evaluates classification approaches to establish effectiveness and efficiency.
Highlights the need for improved algorithms to enhance real-time driving style inference, emphasizing future directions.

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Implication

Observational analysis uncovers driving style classification using short trajectories, suggesting machine learning advancements.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69a76153c6e9836116a2f23f https://doi.org/10.1007/s42154-025-00406-3

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Development and analysis of eco-driving metrics for naturalistic instrumented vehiclesLongitudinal Study · 2019 · 29 citations
2Deep Reinforcement Learning Based PHEV Energy Management With Co-Recognition for Traffic Condition and Driving StyleOriginal Research · 2023 · 71 citations
3Personalized Vehicle Trajectory Prediction Based on Joint Time-Series Modeling for Connected VehiclesOriginal Research · 2019 · 261 citations
4Implicit Personalization in Driving Assistance: State-of-the-Art and Open IssuesReview · 2019 · 63 citations
5Tactical Decisions for Lane Changes or Lane Following: Assessment of Automated Driving Styles Under Real-World ConditionsOriginal Research · 2022 · 31 citations

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