Traffic Sensor Automatic Lane Calibration
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Solution Overview
Problem
Current traffic sensors require manual calibration by technicians, which is costly and disrupts traffic, and existing automatic methods are inefficient in defining detection zones accurately, especially in dynamic traffic conditions.
Innovation Solution
A method and sensor system that automatically define and update lane center ranges by detecting vehicles, estimating displacements, and calculating new lane center values using reflected microwave signals, allowing for real-time adjustment of detection zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration by technicians is used, then detection zone accuracy is improved, but cost and traffic disruption increase
Solution Approach 1:
The system performs automatic calibration using vehicles themselves as calibration targets. The processor automatically detects vehicles, determines their positions, and uses this information to self-calibrate the detection zones without requiring external technician intervention, thereby eliminating manual calibration costs and traffic disruptions
Solution Approach 2:
The system continuously monitors vehicle positions and uses this feedback to automatically adjust and refine detection zone definitions. The processor receives real-time data on vehicle locations and automatically updates calibration parameters, creating a closed-loop calibration system that improves accuracy over time without human intervention
2Productivity
If automatic calibration methods are used, then calibration efficiency is improved, but detection zone accuracy deteriorates
Solution Approach 1:
The patent replaces manual mechanical calibration processes with automated electronic detection and calculation systems. The processor uses electronic signal processing to detect vehicles and automatically compute calibration parameters, substituting human expertise with automated algorithms that maintain high accuracy while improving efficiency
Solution Approach 2:
The system dynamically adjusts calibration parameters based on real-time vehicle detection data. The processor continuously updates detection zone definitions by analyzing vehicle positions and modifying calibration parameters accordingly, allowing the system to adapt to changing traffic conditions while maintaining accurate detection zones
3Device complexity
If fixed detection zones are used, then system simplicity is maintained, but adaptability to dynamic traffic conditions deteriorates
Solution Approach 1:
The system transitions from static fixed detection zones to dynamic, automatically adjusting detection zones. The processor continuously updates zone definitions based on real-time vehicle detection, allowing the detection zones to adapt to changing traffic patterns, road conditions, and vehicle positions while maintaining relatively simple system architecture
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
Enables accurate and efficient automatic calibration of traffic sensors, reducing costs and traffic disruptions by continuously adjusting detection zones based on vehicle activity, improving the accuracy and reliability of traffic data collection.
Implementation Method 1
at least one antenna for transmitting radiation to a vehicle and for receiving the radiation reflected back from the vehicle
Data Source
AI summary
A method of operating a traffic sensor to define ranges of centers of traffic lanes from the traffic sensor is described. The method comprises a) providing a set of lane center variables representing the ranges of the centers of the traffic lanes from the traffic sensor; b) initializing each lane center variable in the set of lane center variables to have an associated starting range value; and then, c) updating the set of lane center variables by, for each vehicle in a plurality of vehicles, i) detecting the vehicle, ii) determining an associated lane center variable having an associated lane center range value closest to the vehicle; iii) estimating a vehicle displacement from the associated lane center range value, and iv) calculating a new lane center range value for the associated lane centre variable using the associated lane center range value and the vehicle displacement.