Traffic automobile image processing system and method based on deep learning

Through a deep learning-based traffic car image processing system, combined with multi-source data, real-time prediction of the safety of the surrounding environment when drivers and passengers get off, the problem of difficulty in checking the traffic conditions behind the rear during the departure of the car by autonomous driving is solved, and traffic safety is improved.

CN119693891AInactive Publication Date: 2025-03-25CHINACHEM PUHUI (CHANGCHUN) DATA SERVICE CO LTD
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Patent Information

Application Number
CN202510192485.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-21
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

When driving drivers and passengers get off the car, the line of vision is blocked due to the obstruction between the rear seat and the rearview mirror, making it difficult to view the traffic conditions behind the car, and there are traffic safety hazards.

Method used

The traffic vehicle image processing system based on deep learning is adopted to collect image data through roadside perception, combine weather, seasons and driver and passenger information data, and train image processing models to predict the safety of the surrounding environment when driver and passenger get off the bus in real time.

Benefits of technology

It has achieved the realization of ensuring that drivers and passengers can complete the get-off task safely in autonomous driving online car-hailing, improving traffic safety and reducing potential safety hazards.

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Abstract

The invention discloses a traffic automobile image processing system and method based on deep learning, and relates to the technical field of image processing.The traffic automobile image processing method comprises the steps that L2 + intelligent driving automobile multi-source data are collected and analyzed through roadside perception, and the multi-source data comprise automobile surrounding environment images, current weather, seasons and driver and conductor information data; the roadside perception expands the visual field of the surrounding environment of the automobile, improves the road area coverage, obtains more comprehensive automobile environment data, obtains external interference image data about automobile parking through the roadside perception, and processes and analyzes the external interference image data for training an image processing model; and data privacy protection is ensured through encryption and a security protocol. Carrying out preprocessing, graph structure coding and time sequence analysis on the newly added data, and capturing dynamic interference characteristics; the model predicts the safe getting-off time of the driver and the conductor, and reminds the driver and the conductor to get off safely within the specified time through voice, so that the personal safety is ensured, and the traffic safety is improved.
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