Signal Light Control Using AI Strategy Data
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Solution Overview
Problem
Existing signal light control methods face inaccuracies and inefficiencies due to manual data determination in both simulated and application scenarios, leading to low reliability and time-consuming processes.
Innovation Solution
A signal light control method that acquires control strategy data, determines light state information, and establishes association relationships between light colors to automatically control signal lights, enhancing accuracy and reliability by integrating AI technology for intelligent control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual determination of traffic light data is used, then the control process can be implemented, but the accuracy and efficiency of signal light control deteriorates
Solution Approach 1:
The system enables automated determination of traffic light data through AI technology, where the control device automatically processes traffic flow information and generates control strategies without manual intervention. This self-service mechanism eliminates the time-consuming manual data determination process while improving accuracy and reliability of signal light control.
Solution Approach 2:
The patent replaces the manual mechanical process of reading and determining traffic light data with an automated AI-based system. The control device uses machine learning models and algorithms to automatically analyze traffic flow data and generate optimal signal light control strategies, substituting human manual operations with intelligent automated systems that operate faster and more accurately.
2Measurement precision
If manual determination of traffic light data is used, then the control process can be implemented, but the accuracy of signal light control deteriorates
Solution Approach 1:
The patent replaces manual determination processes with AI-based automated systems that use machine learning models to analyze traffic flow data and generate precise control strategies. This substitution significantly improves measurement precision and accuracy in determining traffic light data, as the AI system can process multiple parameters simultaneously with high precision without human error.
Solution Approach 2:
The system creates a virtual simulation environment that copies real-world traffic scenarios, allowing for automated testing and validation of control strategies. This virtual copying enables the AI system to learn from simulated data and improve accuracy without requiring complex manual calibration processes, thereby improving precision while managing complexity.
3Productivity
If automated AI control is implemented, then accuracy and efficiency improve, but the system complexity increases
Solution Approach 1:
The patent divides the complex AI control system into modular functional components: data acquisition modules, processing modules, model training modules, and execution modules. Each module performs a specific function and can be independently developed, tested, and maintained. This segmentation manages system complexity while enabling high productivity through automated parallel processing of traffic data and control strategy generation.
Solution Approach 2:
The AI control system is designed as a universal platform that can handle multiple traffic scenarios, intersection types, and control strategies through a single integrated system. The multi-functional AI model can adapt to different traffic patterns and requirements without requiring separate manual configuration for each scenario, thereby improving productivity while managing complexity through code reusability and standardized interfaces.
Data Source
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AI summary
The present disclosure provides a signal light control method, apparatus and system, relating to the field of intelligent transportation technology in the field of artificial intelligence technology. The specific implementation includes: acquiring control strategy data for a signal light at an intersection, where the control strategy data represents a control rule for controlling the signal light, and the signal light have a plurality of light heads, and determining light state information of each phase of the signal light according to the control strategy data, where a phase represents a traffic flow of a light head corresponding to the phase within a preset time period, and determining control-related information of each phase according to the light state information of each phase, where the control-related information represents an association relationship between respective light colors of the light head corresponding to the phase, and controlling, according to the control-related information of each phase, the light head corresponding to each phase to display a light color, which achieves a high degree of fit between the simulation test and the actual application, achieves the technical effect of improving test flexibility and reliability, and improves intelligence and accuracy of control.