AI Traffic Control for Multi-Branch Intersections
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Solution Overview
Problem
Existing traffic control systems at intersections are inefficient in managing multi-branch intersections, leading to right-angle collisions and uneven traffic flow, and often rely on costly in-ground inductive position sensors.
Innovation Solution
A method utilizing RGB cameras and LiDAR systems to capture image data, process it with AI and ML algorithms, and adjust traffic light timings to optimize traffic flow and prioritize emergency vehicles, pedestrians, and bicycles, while reducing collisions and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If in-ground inductive position sensors are used for traffic control, then traffic flow management capability is improved, but system cost increases significantly
Solution Approach 1:
The patent replaces mechanical in-ground inductive position sensors with an optical system consisting of cameras and AI-based image processing. This substitution eliminates the need for expensive physical sensors embedded in the road while achieving equivalent or superior traffic detection and control capabilities through computer vision technology.
Solution Approach 2:
The system creates a virtual representation of the physical traffic environment by capturing images with cameras and processing them through AI algorithms. This digital copy of the traffic scene enables traffic control decisions without requiring physical sensors, reducing manufacturing costs while maintaining functionality.
2Reliability
If traditional traffic control systems are used at multi-branch intersections, then right-angle collisions occur, but implementing AI-based camera systems increases device complexity
Solution Approach 1:
The system changes the detection parameters from simple presence detection to multi-attribute analysis including vehicle position, speed, direction, and type. By processing multiple parameters simultaneously through AI algorithms, the system achieves superior collision prevention while the modular architecture keeps device complexity manageable.
Solution Approach 2:
The patent divides the intersection monitoring into multiple camera units positioned at different locations, each capturing specific zones. The AI system processes images from multiple segments independently and integrates the results, improving collision detection reliability while distributing system complexity across multiple independent components.
3Productivity
If static traffic light timings are used, then system simplicity is maintained, but traffic flow efficiency decreases
Solution Approach 1:
The patent transitions from static, fixed-time traffic light control to dynamic control that adapts in real-time based on detected traffic conditions. The AI system continuously analyzes current traffic flow, vehicle queues, and intersection conditions to adjust signal timings dynamically, maximizing traffic flow efficiency while maintaining manageable system complexity through software-based control.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring traffic conditions through cameras, analyzing the data with AI algorithms, and adjusting traffic light timings based on the analysis results. This feedback mechanism enables automatic optimization of traffic flow without requiring complex manual intervention or pre-programmed schedules.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution reduces right-angle collisions, lowers costs compared to traditional systems, and enhances traffic flow by dynamically adjusting traffic light timings based on real-time data from multiple camera units, improving safety and efficiency.
Implementation Method 1
capturing image data by a plurality of camera units positioned at the intersection
Implementation Method 2
capturing image data by a plurality of camera units positioned at the intersection
Data Source
AI summary
A method controls traffic at an intersection having three or more branches. The method comprising the steps of: capturing image data by a plurality of camera units positioned at the intersection; sending the image data to a control unit; determining from the image data a plurality of variables; and based on the plurality of variables, setting orders and durations of lights of a plurality of traffic lights positioned at the intersection. A system performs the method.