Neural Network Map Data Validation for Invalid Road Information
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Solution Overview
Problem
Existing map data updating methods do not effectively detect and prevent malicious destruction of On board devices, leading to the reporting of invalid road information, which can result in errors in map data and incorrect navigation routes, increasing travel costs for users.
Innovation Solution
A method utilizing a pre-trained neural network model to recognize valid road information by analyzing sequences of road data from roadside units, featuring a training process that distinguishes between real and false data, ensuring accurate map data updates by filtering out invalid information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If road information is directly updated without validation, then map data updating speed is improved, but data accuracy deteriorates due to malicious destruction and invalid information
Solution Approach 1:
The patent applies preliminary action by training a neural network model in advance to recognize valid road information. The model is pre-trained with sample data including both valid and invalid road information, enabling it to automatically filter and validate incoming road information before map data updating, thus maintaining both high updating speed and data accuracy
Solution Approach 2:
The patent introduces a neural network model as an intermediary between road information collection and map data updating. This intermediary validates and filters road information by recognizing patterns of valid versus invalid data, preventing malicious or erroneous information from corrupting the map database while allowing legitimate updates to proceed efficiently
2Reliability
If neural network validation is implemented, then data accuracy is improved, but system complexity increases due to model training and inference requirements
Solution Approach 1:
The patent applies self-service by enabling the system to automatically train and validate road information using its own accumulated data. The neural network model learns from sample data and autonomously performs validation without requiring manual intervention, reducing operational complexity while maintaining high reliability in distinguishing valid from invalid road information
Data Source
AI summary
The present application discloses a map data updating method, an apparatus, a device and a readable storage medium. The specific implementation solution is: after receiving road information reported by an electronic device, a server obtains multiple sequences according to the road information, and each road information belonging to the same sequence has the same type and location. After that, the server inputs each road information contained in the sequences to a pre-trained neural network model, so that the neural network model outputs a recognition result according to the sequences. The server updates map data according to the recognition result. With such solution, valid road information is recognized by combining context of each road information in the sequences and the neural network technology, and the map data is updated, which achieves the purpose of accurately updating the map data.