Roadside Vehicle Occupancy Detection with Multi-Image Infrared Imaging
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Solution Overview
Problem
Existing automated vehicle occupancy detection systems suffer from inaccuracies, latency, high cost, and difficulty in installation and recalibration, 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 can be performed, but accuracy is insufficient and latency occurs
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of the vehicle side before final occupancy determination. The processor captures a plurality of images at different times as the vehicle passes through the field of view, enabling accurate occupancy detection without latency by having advance visual data ready for analysis
Solution Approach 2:
The imaging device continuously captures images throughout the vehicle's passage through the field of view rather than taking single snapshots. This continuous capture ensures no occupancy events are missed and provides multiple data points for accurate real-time occupancy determination
2Reliability
If traditional automated occupancy systems are implemented, then detection can occur, but cost is high and installation is difficult
Solution Approach 1:
The system extracts only the necessary functional elements for occupancy detection: a single roadside imaging device, light emitter, and processor. By removing unnecessary complex components from traditional multi-sensor systems, the patent achieves high reliability through minimalistic design focused on capturing side views of vehicles
Solution Approach 2:
The roadside imaging device serves multiple functions: capturing vehicle side views, detecting occupancy, and providing data for occupancy determination. This multi-functionality reduces system complexity by consolidating what would traditionally require separate sensors and processing units
3Ease of operation
If traditional automated systems are used, then occupancy detection is possible, but calibration and repair are difficult
Solution Approach 1:
The system performs self-calibration by automatically determining vehicle occupancy based on captured images without requiring manual intervention. The processor analyzes the plurality of captured images and determines occupancy status automatically, eliminating the need for complex calibration procedures while maintaining high detection accuracy
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, requiring fewer components, lower maintenance, and rapid deployment, 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
Implementation Method 2
command the first roadside imaging device to capture one or more images of the side of the vehicle
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.