Training method and device of model for controlling vehicle horn, vehicle and storage medium
By receiving data from the sound source vehicle and the evaluation vehicle, a target neural network model is obtained through neural network training. This solves the problem of inaccurate vehicle horn adjustment, thereby reducing noise pollution and improving the driving experience.
CN120980406APending Publication Date: 2025-11-18GUANGZHOU AUTOMOBILE GROUP CO LTD
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Patent Information
- Application Number
- CN202410573566.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-09
- Publication Date
- 2025-11-18
AI Technical Summary
Technical Problem
In existing technologies, vehicle horns cause serious noise pollution, and their adjustment is inaccurate in fixed scenarios, failing to adapt to the uncertainties of the driving environment.
Method used
By receiving data from the sound source vehicle and the evaluation vehicle, a target neural network model is obtained by training a preset neural network model. Combining the vehicle data and the evaluation data, precise control of the vehicle horn is achieved.
Benefits of technology
It improves the accuracy of vehicle horn adjustment, reduces noise pollution, and enhances the driving experience.
✦ Generated by Eureka AI based on patent content.
Abstract
The embodiment of the invention discloses a training method and device of a model for controlling a vehicle horn, a vehicle and a storage medium. According to the scheme, vehicle data sent by a sound source vehicle are received, the vehicle data comprise horn data and driving data when the sound source vehicle is in a driving state, then evaluation data for the vehicle data are obtained from an evaluation vehicle, the distance between the evaluation vehicle and the sound source vehicle is within a preset distance range, and finally the evaluation data are sent to the sound source vehicle. And training a preset neural network model based on the vehicle data and the evaluation data to obtain a target neural network model for controlling the vehicle horn. Therefore, the accuracy of vehicle horn adjustment can be improved.
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