Roadside Vehicle Occupancy Imaging with Infrared Weather Adaptation
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Solution Overview
Problem
Existing automated vehicle occupancy detection systems suffer from inaccuracies, latency, high costs, and difficulty in installation and calibration, especially under varying road conditions.
Innovation Solution
A roadside imaging system with a processor that captures multiple images of a vehicle's side from a fixed perspective, using infrared light to illuminate occupants and determine occupancy through machine learning, adaptable to different environments with minimal setup and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated vehicle occupancy systems are used, then occupancy detection is achieved, but accuracy deteriorates under varying road conditions
Solution Approach 1:
The system dynamically adjusts image capture parameters including shutter speed, aperture, and exposure time based on detected road conditions such as rain, snow, or fog. The processor modifies these parameters in real-time to maintain optimal image quality and occupancy detection accuracy despite changing environmental conditions.
Solution Approach 2:
The system changes operational parameters of the imaging device based on detected conditions. When adverse weather is detected, the system adjusts lighting parameters, capture frequency, and processing algorithms to compensate for reduced visibility and maintain high detection accuracy across diverse road conditions.
2Speed
If automated vehicle occupancy detection is implemented, then detection speed is improved, but latency increases
Solution Approach 1:
The system performs preliminary actions by pre-processing images and pre-calculating detection thresholds before actual occupancy determination is needed. This allows the system to maintain fast detection speeds while minimizing latency by having processing algorithms ready and optimized in advance.
Solution Approach 2:
The system implements feedback mechanisms where detection results are immediately fed back to adjust subsequent capture operations. This real-time feedback loop allows the system to optimize detection timing and reduce latency by learning from previous detections and adjusting capture intervals accordingly.
3Extent of automation
If automated occupancy detection systems are deployed, then installation cost increases
Solution Approach 1:
The system performs self-service through automated calibration and self-diagnostic functions. The processor automatically adjusts system parameters and performs quality control without requiring expensive manual calibration equipment or specialized installation personnel, thereby reducing overall installation costs while maintaining high automation levels.
Solution Approach 2:
The system replaces complex mechanical calibration procedures with electronic and software-based solutions. Instead of requiring physical adjustment mechanisms and manual alignment tools, the system uses digital processing and automated software to achieve precise occupancy detection, reducing installation complexity and cost.
4Extent of automation
If traditional automated detection systems are used, then occupancy detection is achieved, but reliability deteriorates
Solution Approach 1:
The system implements beforehand cushioning by incorporating redundant detection mechanisms and error correction algorithms that activate when anomalies are detected. This prepares the system in advance to handle potential failures or adverse conditions, maintaining reliable occupancy detection even when individual components encounter problems.
Solution Approach 2:
The system uses feedback mechanisms to continuously monitor detection confidence and system performance. When uncertainty is detected, the system requests additional information or adjusts detection parameters, creating a self-correcting loop that enhances reliability by compensating for potential errors in real-time.
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 achieves high accuracy (above 95%) and robustness across various conditions, reducing costs and installation complexity while maintaining privacy and operating under adverse weather.
Implementation Method 1
A roadside imaging system with a processor that captures multiple images of a vehicle's side from a fixed perspective, using infrared light to illuminate occupants
Data Source
AI summary
A system for detecting occupancy of a vehicle travelling in a direction of travel along a road. The system includes a roadside imaging device positioned on a roadside, and a first roadside light emitter, and a roadside vehicle detector. A processor is configured to receive a signal from the roadside vehicle detector, command the first roadside light emitter to emit light according to a first pattern for a first duration, command the roadside imaging device to capture images of the side of the vehicle, and compute a vehicle occupancy, in each of the captured images by determining one or more regions of interest in each of the captured images, and determining a number of visible occupants in the one or more regions of interest. The processor determines a most likely number of occupants based on each determined vehicle occupancy.