Cognitive Traffic Signal Control Using Video Analysis
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Solution Overview
Problem
Modern traffic lighting systems are limited by detectors that cannot distinguish different vehicle types, pedestrian types, or conditions, leading to inflexible and non-fully automated adaptive control, which affects pedestrian and vehicle flow optimization.
Innovation Solution
A cognitive traffic signal control system using cameras and object recognition technology to analyze real-time video feeds, incorporating additional sensor data to adapt traffic signal timing based on predefined objectives, thereby optimizing vehicle and pedestrian flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detectors are used to detect traffic presence, then basic traffic signal control is achieved, but the system cannot distinguish different vehicle types, pedestrian types, or conditions
Solution Approach 1:
The patent replaces traditional mechanical and electromagnetic detectors with a vision-based system using cameras and computer vision algorithms. This substitution enables the system to capture visual information about vehicles, pedestrians, and environmental conditions, providing detailed classification capabilities without the limitations of conventional detectors.
Solution Approach 2:
The system changes the detection parameters from simple presence/absence signals to comprehensive visual parameters including vehicle type, pedestrian type, weather conditions, and traffic flow characteristics. This parameter expansion allows the cognitive system to make more informed decisions about traffic signal timing.
2Productivity
If fixed time control is used for traffic signals, then simple timing is maintained, but traffic flow optimization is limited
Solution Approach 1:
The patent implements dynamic traffic signal timing that continuously adapts to real-time traffic conditions. The cognitive system processes video feeds and sensor data to adjust signal phases and durations dynamically, replacing static fixed-time control with flexible, condition-responsive timing that optimizes traffic flow efficiency.
Solution Approach 2:
The system incorporates feedback loops where traffic detection data from cameras and sensors is continuously analyzed, and signal timing is adjusted based on this feedback. This closed-loop control enables the system to respond to changing traffic patterns, improving overall traffic flow efficiency through adaptive timing adjustments.
3Extent of automation
If dynamic control with detectors is implemented, then some adaptability is achieved, but full automation and cognitive decision-making are not realized
Solution Approach 1:
The patent introduces computer vision algorithms and cognitive computing systems as intermediaries between the physical traffic environment and the control system. These intermediaries process raw video and sensor data, extract meaningful information about traffic conditions, and translate it into actionable control decisions, achieving full automation without information loss.
Solution Approach 2:
The system performs preliminary analysis of traffic conditions using cognitive algorithms that predict future traffic patterns based on current and historical data. This preliminary action enables proactive signal timing adjustments before traffic conditions deteriorate, maintaining optimal flow with full automation.
Data Source
AI summary
In an approach for adapting traffic signal timing, a computer receives a streaming video for one or more paths of a first intersection. The computer identifies traffic within the received streaming video. The computer calculates traffic flow for the one or more paths of the first intersection based on the identified traffic. The computer determines whether a change in a state of a traffic signal for the first intersection should occur based at least in part on the identified traffic and the determined traffic flow with respect to predefined objectives. Responsive to determining the change in the state of the traffic signal for the first intersection should occur, the computer calculates a change to a traffic signal timing based on the determined change in the state of the traffic signal. The computer initiates an adaptation to the traffic signal timing based on the determined change to the traffic signal timing.