Traffic Data Analysis for AI Navigation
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Solution Overview
Problem
Current vehicles can only acquire limited traffic data for AI analysis, restricting their ability to perform comprehensive traffic assessments, such as congestion analysis, due to insufficient data processing capabilities.
Innovation Solution
A traffic data analysis method and apparatus that categorize initial traffic data and send targeted data to AI analysis models based on category information, enabling efficient and accurate AI analysis for applications like automatic driving and Internet of Vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicles acquire limited traffic data from servers, then data acquisition is simple, but AI analysis comprehensiveness deteriorates
Solution Approach 1:
The patent segments traffic data into multiple categories (congestion data, accident data, weather data, road condition data, etc.) and processes each category separately through dedicated processing modules. This segmentation allows the system to handle diverse data types efficiently while maintaining comprehensive analysis capabilities across different traffic aspects.
Solution Approach 2:
The patent introduces a traffic data processor as an intermediary component between data acquisition and AI analysis. This processor categorizes, filters, and prepares traffic data before sending it to AI analysis models, enabling comprehensive analysis without requiring vehicles to directly handle all raw data, thus balancing simplicity and comprehensiveness.
2Reliability
If vehicles process all initial traffic data, then analysis completeness improves, but processing efficiency deteriorates
Solution Approach 1:
The patent extracts only the relevant target traffic data from the complete set of initial traffic data based on analysis requirements. The traffic data processor identifies and extracts specific categories of data needed for particular AI analysis tasks, sending only this extracted subset to the AI models. This extraction approach maintains analysis completeness for required aspects while significantly improving processing efficiency by avoiding unnecessary data handling.
3Quantity of substance
If AI analysis models receive unfiltered traffic data, then data availability improves, but analysis accuracy deteriorates
Solution Approach 1:
The patent applies different processing qualities to different categories of traffic data. Each data category (congestion, accident, weather, etc.) receives appropriate filtering, validation, and formatting specific to its characteristics and analysis requirements. This local quality approach ensures that each data type is optimized for its specific analysis purpose, improving overall analysis accuracy while maintaining broad data availability across multiple categories.
Data Source
AI summary
A traffic data analysis method, an electronic device, a vehicle and a storage medium are provided, and relate to the technical field of artificial intelligence, in particular to the fields of large data processing, automatic driving and vehicle networking, and can be applied to AI navigation. The method includes: acquiring a plurality of initial traffic data; determining a category of each of the plurality of initial traffic data; receiving a search instruction from an AI analysis model, wherein the search instruction includes target category information; determining target traffic data corresponding to the target category information from the respective initial traffic data according to categories of the respective initial traffic data; and sending the target traffic data to the AI analysis model so that the AI analysis model performs an AI analysis according to the target traffic data.