Traffic Information Quantization Using CNN and Entropy Analysis
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Solution Overview
Problem
Existing technologies fail to quantify the amount of driving information, including both visible and invisible information, in the intelligent network environment, which hinders driving efficiency and safety optimization.
Innovation Solution
A traffic information quantization method involving image classification using a convolutional neural network (CNN) and ResNet50 framework, followed by information theory calculations to determine state sets and probability distributions, enabling the quantification of driving information sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traffic information is collected and transmitted to drivers, then driving safety and travel efficiency are improved, but the information remains abstract and unquantified, making it difficult to prioritize and process
Solution Approach 1:
The patent applies parameter changes by introducing information entropy as a quantitative parameter to measure traffic information. This transforms abstract traffic information into measurable data with specific entropy values, allowing the system to prioritize and process information based on quantified uncertainty levels rather than treating all information equally.
2Quantity of substance
If existing technologies calculate information amount of transportation infrastructure, then basic infrastructure data is available, but the amount of information of driving scenes is not specifically calculated
Solution Approach 1:
The patent segments traffic information into distinct categories: visible information (directly observable) and invisible information (occluded, long-distance, auditory). This segmentation allows the system to apply different measurement approaches to different information types, making the measurement of driving scene information feasible by breaking down the complex task into manageable components.
Solution Approach 2:
The patent introduces information entropy as an intermediary concept to bridge the gap between raw traffic data and meaningful measurement. By using entropy as a mediator, the system can quantify the uncertainty and information content of diverse driving scene elements without requiring direct measurement of each individual element.
3Loss of information
If all traffic information is provided to drivers, then comprehensive awareness is achieved, but the uncertainty of real-time traffic conditions cannot be eliminated
Solution Approach 1:
The patent changes the parameter of information representation from qualitative descriptions to quantitative entropy measurements. This allows the system to identify and prioritize high-entropy (high uncertainty) information that requires immediate attention, while filtering out low-entropy information, thereby reducing processing complexity while maintaining uncertainty elimination effectiveness.
Data Source
AI summary
A traffic information quantization method in an intelligent network environment includes: step S1: extracting traffic information sources according to collected videos and classifying the traffic information sources; step S2: obtaining a state set and a probability distribution of each traffic information source through actual observation and calculation; step S3: using information theory to quantify the traffic information sources. Further, a traffic information quantization system in the intelligent network environment is provided, and the traffic information quantization method in the intelligent network environment and its system are adopted to quantify the driving information of the driving process and calculate the amount of information. The method and system can transform uncertain information into certain information, improve driving efficiency, help drivers optimize the driving process, and ensure driving safety.