Electric two-wheeled vehicle high-risk accident early warning system and method based on roadside unit

By installing a display screen and image recognition system on the roadside unit, combined with the vehicle terminal and communication transmission module, using radar speed measurement and machine learning algorithms to identify and warn the high-risk behavior of electric two-wheelers in real time, the problem of difficulty in effectively warning and reducing high-risk accidents of electric two-wheelers in the existing technology is solved, and effective early warning and safety improvement of high-risk accidents of electric two-wheelers is achieved.

CN119694126APending Publication Date: 2025-03-25TSINGHUA UNIVERSITY +1
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Patent Information

Application Number
CN202411862951.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-17
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

The prior art is difficult to effectively warn and reduce the occurrence of high-risk accidents in electric two-wheelers, especially in complex traffic environments.

Method used

The electric two-wheeler high-risk accident warning system is adopted based on roadside units. The system includes a display screen, roadside unit, vehicle terminal, communication transmission module and early warning information display module. Through image recognition, radar speed measurement and machine learning algorithms, high-risk behavior of electric two-wheeler is recognized and warned in real time.

Benefits of technology

Real-time identification and early warning of high-risk accidents for electric two-wheelers has been achieved, reducing the probability of accidents and personnel damage, and improving traffic safety.

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Abstract

The invention discloses an electric two-wheeled vehicle high-risk accident early warning system and method based on a roadside unit. The method comprises the following steps: acquiring an electric two-wheeled vehicle related image of the roadside unit; identifying related images of the electric two-wheeled vehicle by using a deep learning algorithm to obtain related state information; performing non-contact measurement on the speed of the electric two-wheeled vehicle by using a radar sensor and an image processing technology to obtain a speed measurement result; and utilizing a machine learning algorithm to early warn a future driving path of the electric two-wheeled vehicle according to the speed measurement result and the related state information. High-risk traffic accidents of the electric two-wheeled vehicle are reduced through real-time monitoring and early warning, and personnel injury is reduced.
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