Roadside Vehicle Occupancy Imaging With Self-Calibration
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Solution Overview
Problem
Automated vehicle occupancy detection systems face inaccuracies, latency issues, high implementation costs, and reliability challenges, especially under varying road conditions, making them difficult to move, install, or recalibrate.
Innovation Solution
A vehicle occupancy detection system using roadside imaging devices with light emitters and processors that capture images, determine regions of interest, and compute occupancy based on visible occupants, adjusting for vehicle speed and ambient conditions, with optional anonymization and augmented reality guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automated vehicle occupancy detection systems are implemented, then detection speed is improved, but accuracy deteriorates under varying road conditions
Solution Approach 1:
The system continuously monitors road conditions through multiple sensors (LIDAR, cameras, weather sensors) and adjusts detection algorithms in real-time based on feedback from environmental data, maintaining accuracy while preserving speed
Solution Approach 2:
The system dynamically changes detection parameters such as threshold values, processing resolution, and sensor activation levels based on detected road conditions, allowing optimal balance between speed and accuracy for each specific operating scenario
2Extent of automation
If traditional automated occupancy systems are deployed, then detection capability is provided, but implementation cost increases
Solution Approach 1:
The system divides the detection function into separate modular components (LIDAR for distance, cameras for visual confirmation, weather sensors for environmental data) that can be independently selected and combined based on budget requirements
Solution Approach 2:
The system uses multi-functional sensors that serve multiple purposes - for example, LIDAR provides both distance measurement and vehicle detection, while camera images serve both occupancy detection and road condition monitoring, reducing overall system cost
3Extent of automation
If automated occupancy detection systems are installed, then occupancy data is obtained, but system reliability decreases under moving conditions
Solution Approach 1:
The system performs preliminary calibration and environmental adaptation before actual operation, creating a buffer that compensates for potential reliability issues during mobile deployment
Solution Approach 2:
The system includes self-diagnostic and self-calibrating capabilities that automatically adjust to new environments without external intervention, maintaining reliability during installation and relocation
4Extent of automation
If automated occupancy systems are deployed, then detection is provided, but calibration complexity increases
Solution Approach 1:
The system performs automatic self-calibration using onboard sensors and environmental references, eliminating the need for complex manual calibration procedures and reducing overall system complexity
Solution Approach 2:
The system uses dynamic parameter adjustment that automatically adapts to different installation locations and conditions, replacing fixed calibration requirements with flexible, data-driven parameter changes
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 system provides accurate, reliable, and efficient vehicle occupancy detection, enabling faster installation and reduced calibration needs, while ensuring privacy through anonymization and providing real-time monitoring capabilities.
Implementation Method 1
a first roadside light emitter emitting light towards vehicles in the first field of view
Data Source
AI summary
According to an aspect there is provided systems and methods for anonymizing images and/or detecting countability scores. Anonymization can be carried out by extracting anonymized images from image processing techniques, anonymizing the image before image processing, or detecting regions of interest and anonymizing regions of interest. Countability scores can be detected based on the inexistence of people in the image. Countability scores can impact the confidence of the count of an image. Count confidence may be used to automatically enforce, for example, tolls.