Top View Sensor Traffic Density Estimation
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Solution Overview
Problem
Current methods for estimating traffic density are error-prone and lack precision, particularly in dynamic traffic environments, due to the misinterpretation of multiple passengers as multiple vehicles and insufficient accuracy in locating vehicles on individual lanes, which affects the control of traffic infrastructure and autonomous vehicle navigation.
Innovation Solution
A method and system that utilize dynamically acquired image data from air or space-based sensors to estimate traffic density by monitoring areas, generating trigger data for image acquisition, and processing this data to provide enhanced traffic information for controlling traffic infrastructure and vehicles, allowing for lane-level resolution and accurate vehicle classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mobile device position information is used to estimate traffic density, then extensive data basis is available, but measurement precision deteriorates due to misinterpretation of multiple passengers as multiple vehicles and insufficient lane-level accuracy
Solution Approach 1:
The patent introduces an aerial imaging device as an intermediary to capture top-view images of traffic. These images serve as a mediator between mobile device data (which lacks precision) and traffic density estimation (which requires precision). The image data provides accurate visual confirmation of vehicle positions and counts, resolving the ambiguity of multiple passengers being miscounted as multiple vehicles.
Solution Approach 2:
The patent replaces the indirect mechanical/GNSS-based position sensing system with an optical imaging system. Instead of relying on mobile device GPS coordinates that cannot reliably distinguish vehicles from passengers, the system uses aerial cameras to directly visualize and count vehicles from top-view images, achieving superior measurement precision.
2Measurement precision
If satellite images are processed to detect traffic changes, then traffic volume detection is enhanced, but processing resources are excessively consumed
Solution Approach 1:
The patent segments the image processing task by focusing only on relevant areas. Instead of processing entire satellite images, the system identifies areas of interest (roads, intersections) and processes only those segments. This segmentation dramatically reduces computational resources while maintaining traffic detection accuracy.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of image data. Rather than performing exhaustive processing on complete satellite imagery, the system processes only the minimal required image segments containing traffic information, achieving efficient resource utilization while maintaining detection precision.
3Measurement precision
If traditional traffic infrastructure devices are deployed, then measurement precision is high, but device complexity and cost increase
Solution Approach 1:
The patent makes the aerial imaging device multi-functional. The same device that captures images for traffic density estimation can also be used for traffic volume detection, vehicle classification, and infrastructure monitoring. This universality replaces multiple specialized devices (satellite imagery systems, ground-based cameras, sensors) with a single versatile platform, reducing overall system complexity.
Solution Approach 2:
The patent uses aerial imaging to create a visual copy or representation of the traffic scene from above. This top-view image copy provides all necessary information (vehicle positions, velocities, densities) that would traditionally require multiple ground-based sensors, thereby simplifying the physical infrastructure while maintaining measurement precision.
4Measurement precision
If comprehensive image data processing is performed, then traffic density accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying and marking areas of interest before detailed processing. The system first scans image data to locate roads and traffic regions, then focuses intensive processing only on those pre-identified areas. This preliminary segmentation prepares the data structure in advance, enabling faster subsequent processing while maintaining comprehensive analysis accuracy.
Data Source
AI summary
A method and a system for controlling at least one of a traffic infrastructure device, an actuator of a traffic participant based on enhanced traffic data is provided. The method comprises steps of monitoring, by a trigger processor, an area; generating, by the trigger processor, trigger data in case a trigger event is determined in the monitored area and providing the generated trigger data indicating an area-of-interest to an image data source; obtaining image data on the area-of-interest based on the transmitted trigger data and transmitting the obtained image data to a traffic evaluation processor; evaluating, by the traffic evaluation processor, the obtained image data to generate enhanced traffic data on the area-of-interest; and outputting, by the traffic evaluation processor, the generated enhanced traffic data in a control signal, wherein the control signal is configured to control the traffic infrastructure device and/or the actuator of the traffic participant.