Neural Network Traffic Control for Adaptive Intersection Flow
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Solution Overview
Problem
Current traffic light control systems struggle to accurately recognize incoming traffic and comprehend driver intentions, leading to inefficient traffic flow and increased wait times, while existing sensors often fail to detect non-conductive vehicles and require costly and miscalibrated installations.
Innovation Solution
Implementing a neural network-based traffic control system with real-time adaptive learning capabilities, utilizing high-density solid-state neural networks to recognize and prioritize traffic flow, incorporating various sensors for comprehensive traffic object detection and decision-making without external synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors are used for traffic detection, then installation costs are high and calibration is complex, but detection accuracy for non-conductive vehicles is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/electromagnetic sensors with a neural network-based vision system that uses cameras and image processing to detect vehicles. This substitution eliminates the need for costly sensor installations and complex calibrations while achieving superior detection accuracy for all vehicle types including non-conductive ones.
Solution Approach 2:
The system changes the detection parameters from physical sensor signals to visual image data processed by neural networks. By transforming the detection approach from direct physical sensing to computational vision, the system achieves better detection accuracy without the installation and calibration problems of traditional sensors.
2Productivity
If fixed-time traffic control is used, then system complexity is low, but traffic flow optimization is insufficient and wait times are increased
Solution Approach 1:
The patent implements dynamic traffic control by continuously monitoring traffic conditions through neural network vision systems and adjusting signal timing in real-time. This dynamic adaptation to changing traffic patterns optimizes flow and reduces wait times, despite the increased system complexity from continuous processing and learning.
Solution Approach 2:
The system uses feedback from continuous traffic condition monitoring and neural network analysis to adjust traffic light timing. By learning from historical data and real-time conditions, the system optimizes traffic flow while managing complexity through automated decision-making algorithms.
3Loss of information
If basic traffic detection is used, then device complexity is low, but recognition of driver intentions and traffic prioritization is insufficient
Solution Approach 1:
The patent adds the dimension of intent recognition by analyzing driver behavior patterns, vehicle positioning, and contextual information through neural networks. This dimensional expansion from basic detection to intent understanding enables better traffic prioritization while managing processing complexity through hierarchical analysis.
Solution Approach 2:
The system performs preliminary analysis of traffic conditions and driver intentions before making control decisions. By pre-processing and analyzing traffic patterns, the neural network prepares prioritization decisions in advance, improving response accuracy without requiring maximum processing power for every decision moment.
Data Source
AI summary
Systems and method are disclosed for adaptive and/or autonomous traffic control. In one illustrative implementation, there is provided a method for processing traffic information. Moreover, the method may include receiving data regarding travel of vehicles associated with an intersection, using neural network technology to recognize types and/or states of traffic, and using the neural network technology to process/determine/memorize optimal traffic flow decisions as a function of experience information. Exemplary implementations may also include using the neural network technology to achieve efficient traffic flow via recognition of the optimal traffic flow decisions.