Road abnormal event detection and early warning method and system based on unsupervised lifelong learning

Through the unsupervised lifelong learning method, combined with the autoencoder, principal component analysis method and clustering algorithm, road abnormal event detection without a large amount of labeling data is achieved, solving the problems of high data labeling cost and poor model adaptability in the existing technology, and improving the detection efficiency and accuracy.

CN119889015AActive Publication Date: 2025-04-25GUANGZHOU JIAOXIN INVESTMENT TECH CO LTD
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Patent Information

Application Number
CN202510386896.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-31
Publication Date
2025-04-25
Estimated Expiration
2045-03-31

AI Technical Summary

Technical Problem

The existing technology relies on supervised learning algorithms, requiring a large amount of labeled data, which is difficult to adapt to the dynamic changes in traffic scenarios, resulting in low detection efficiency and high cost.

Method used

The road abnormal event detection and early warning method based on unsupervised lifelong learning is adopted, and feature extraction is performed through the autoencoder and principal component analysis method, combined with K-Means and density clustering algorithm, and the online model update is performed using incremental learning and elastic weight integration methods.

Benefits of technology

It realizes efficient abnormal event detection without a large amount of labeled data, can dynamically adapt to traffic scene changes, reduces the cost of data labeling and model training, and improves detection accuracy and system stability.

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Abstract

The invention discloses a road abnormal event detection and early warning method and system based on unsupervised lifelong learning. The method comprises the following steps: acquiring traffic scene data by using various sensors along a road and at a vehicle end, and preprocessing the traffic scene data; performing unsupervised feature extraction on the image data by using an auto-encoder, and performing feature dimension reduction on non-image data by using a principal component analysis method to extract main features; an anomaly detection model is initialized, a K-Means clustering algorithm is adopted to divide the feature space into clusters, and a normal cluster set of an initial normal traffic mode is formed; receiving new traffic data, and continuously performing online learning and updating on the model by adopting an incremental learning method, a density-based clustering algorithm and an elastic weight integration method; newly collected data are input into the updated model for anomaly detection and road anomaly event alarm; according to the method, efficient and accurate detection and timely early warning of road abnormal events are realized through unsupervised feature extraction, model initialization, lifelong learning and a model updating mechanism.
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Citation Information

Patent Citations

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  • Expressway abnormal event type identification and position estimation method

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  • Road abnormal event timely early warning system and method

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