Mobile Vehicle Emission Measurement Using Roadway Image Analysis
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Solution Overview
Problem
Existing methods for estimating vehicle emissions are inaccurate and require costly infrastructure, especially in large-scale applications.
Innovation Solution
A method and apparatus that utilize image processing and machine learning models to estimate vehicle emissions by analyzing images from a moving vehicle, determining the quantity and type of surrounding vehicles, and calculating emission factors based on these analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If large scale estimation techniques are used, then coverage area is improved, but measurement precision deteriorates
Solution Approach 1:
The system divides the roadway into multiple sections and uses multiple mobile measurement devices (vehicles) to segment the measurement task. Each device measures emissions in its local section with high precision, while the collective data provides broad coverage. This resolves the contradiction by making the measurement system modular and scalable.
Solution Approach 2:
The patent introduces mobile measurement devices as intermediaries between the emission sources (vehicles) and the measurement system. These devices travel along roadways, capturing emission data from surrounding vehicles using sensors and cameras, thereby enabling both precise local measurements and extensive coverage through their mobility.
2Measurement precision
If dedicated infrastructure is established for emission measurement, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system uses mobile measurement devices that leverage existing vehicles and sensors already present in the environment. The devices perform self-contained measurements using onboard cameras and sensors, processing data locally without requiring external infrastructure support. This eliminates the need for complex dedicated measurement infrastructure while maintaining measurement precision.
Solution Approach 2:
Instead of building dedicated physical infrastructure for emission measurement, the system creates virtual measurement points through mobile devices that replicate measurement functions. These devices copy the functionality of fixed measurement stations but with the flexibility of mobility, reducing infrastructure complexity while preserving measurement capabilities.
3Ease of operation
If traffic levels alone are used for emission estimation, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system merges multiple data sources including traffic level information with visual data from cameras and sensor readings from mobile measurement devices. By combining these diverse data types, the system maintains operational simplicity while significantly improving emission estimation accuracy through comprehensive data collection and processing.
Solution Approach 2:
The system implements feedback mechanisms where mobile measurement devices continuously monitor and report emission data, which is then used to refine estimation models. This iterative feedback process allows the system to improve precision over time while maintaining ease of operation through automated data processing and analysis.
Data Source
AI summary
Systems and methods are described for the vehicle emission measurement. An example method may include receiving a plurality of images from a first vehicle traveling on a section of roadway, determining a quantity of surrounding vehicles from the plurality of images, determining a cropped image of at least one of the surrounding vehicles from the plurality of images, identifying a model of the at least one of the surrounding vehicles from the cropped image, and calculating an emission measurement factor for the section of roadway based on at least the quantity of surrounding vehicles for the at least one of the surrounding vehicles.