Traffic Signal Phase Control Using RL and Intersection Image Encoding
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Solution Overview
Problem
Current traffic control systems struggle to adapt to complex and fluctuating traffic conditions, particularly in urban environments, as they rely on predetermined time tables or high-cost installations that do not effectively optimize traffic signal phases in real-time.
Innovation Solution
A system and method utilizing a reinforcement learning (RL) model to analyze intersection images, identify and encode vehicle and pedestrian positions and speeds, dynamically determining traffic signal phases to optimize traffic flow and adapt to real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predetermined time table is used for traffic control, then the system is simple to implement, but it cannot effectively adapt to complex or fluctuated traffic conditions
Solution Approach 1:
The system employs reinforcement learning to enable the traffic control system to automatically learn and adapt to varying traffic conditions without human intervention. The RL agent continuously optimizes signal phases by learning from observed traffic patterns, achieving self-improving adaptability while maintaining relatively simple infrastructure
Solution Approach 2:
The system dynamically changes traffic signal parameters (phase timing, duration, sequence) based on real-time traffic conditions observed by cameras. The reinforcement learning model adjusts these parameters continuously to optimize traffic flow, transforming the static predetermined timetable into a dynamic adaptive system
2Reliability
If induction coils are installed by digging the road, then traffic signal phase adjustment can be achieved, but the installation cost and complexity increase significantly
Solution Approach 1:
The patent replaces the mechanical induction coil system (requiring road digging and physical installation) with an optical-based camera system. The cameras capture traffic images that are processed by AI algorithms, eliminating the need for invasive road construction while maintaining reliable traffic detection and signal control
Solution Approach 2:
Instead of using physical induction coils embedded in the road, the system creates optical copies (images) of traffic conditions using cameras. These visual copies are then processed to extract traffic flow information, providing a non-invasive alternative that maintains detection reliability without physical road modifications
3Productivity
If real-time traffic condition optimization is implemented, then traffic flow efficiency improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system implements continuous feedback loops where cameras monitor traffic conditions, the reinforcement learning model processes this information, and signal phases are adjusted in real-time. This closed-loop feedback mechanism enables dynamic optimization of traffic flow while managing system complexity through iterative learning rather than complex predetermined rules
Data Source
AI summary
A method for controlling traffic signal provided by the present disclosure, comprises: obtaining an intersection image; identifying types of multiple objects in the intersection image; counting the types of the multiple objects, to generate at least one vehicle position layer, a pedestrian layer and an object speed layer corresponding to the types of the multiple objects; encoding the foresaid multiple layers, to respectively generate multiple encodings; inputting the foresaid multiple encodings to a reinforcement learning (RL) model; and the RL model determining a phase of the traffic signal according to the input foresaid multiple encodings.