A multi-time scale driving style analysis method

By using multi-timescale fusion and deep neural networks, the problems of fragmented time scales and low scene sensitivity in driving style analysis are solved, achieving a full-cycle profile of driving style and accurate driving behavior analysis.

CN121388800BActive Publication Date: 2026-05-22JILIN UNIVERSITY
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Patent Information

Application Number
CN202511990725.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-26
Publication Date
2026-05-22
Estimated Expiration
2045-12-26

AI Technical Summary

Technical Problem

Existing driving style analysis methods suffer from fragmented time scales, lack of fusion across multiple time scales, weak dynamic modeling capabilities, low scene sensitivity, difficulty in revealing the intrinsic connection between short, rapid driving behaviors and stable, long-term driving habits, and a high rate of misjudgment.

Method used

By fusing multiple time scales and using multi-resolution analysis methods to split driving time-series data, and combining deep neural networks and clustering algorithms, short-term, medium-term, and long-term driving style models are constructed. By integrating rule engines and intelligent algorithms, feature representation and condition identification of multi-dimensional time-series data are achieved.

Benefits of technology

It achieves a full-cycle profile of driving style, reduces the misjudgment rate of operating conditions, improves the accuracy and generalization ability of the classification model, and can predict the changing trend of driving style, providing advance warning for risk.

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Abstract

The present application relates to the technical field of intelligent auxiliary driving, and specifically provides a multi-time-scale driving style analysis method, which obtains driving time sequence segments in seconds through a multi-resolution analysis method and classifies working conditions; short-time driving style labels are marked through a driving behavior clustering method, a short-time driving style classification model is constructed and trained; a medium-long-time driving task window in minutes or hours is set, short-time driving styles are extracted using the trained short-time driving style classification model, distribution vectors are obtained according to short-time driving style sequences in the medium-long-time driving task window and mapped to a predefined medium-long-time style mode space, and a corresponding medium-long-time style mode is identified through a clustering model; a long-time style window in weeks, months, seasons or years is set, distribution vectors are obtained by counting medium-long-time style mode sequences in the long-time style window and mapping to a predefined long-time style evolution space, and long-time style evolution analysis of a driver is realized through a clustering classification model.
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