Traffic Congestion Detection via Selective Image Analysis
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Solution Overview
Problem
Existing traffic congestion detection systems face increased communication and analysis costs due to the need for continuous image transmission and analysis from all locations where congestion is detected.
Innovation Solution
A traffic congestion detection system that selectively uses image information for detection based on a confidence level, reducing the need for continuous image analysis by acquiring and processing only necessary images from vehicles with high confidence levels, and adjusting image specifications accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image analysis is performed for all locations where traffic congestion is detected, then traffic congestion detection accuracy is improved, but communication cost and analysis cost increase
Solution Approach 1:
The patent applies partial action by performing image analysis only for a subset of detected congestion locations rather than all locations. The server selects specific vehicles based on confidence levels from traveling data analysis, and only requests surrounding images from those vehicles. This partial approach maintains adequate detection accuracy while significantly reducing communication and analysis costs compared to universal image analysis.
2Reliability
If image information is acquired from all vehicles, then traffic congestion detection reliability is improved, but communication load increases
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different vehicles based on their individual characteristics and confidence levels. Instead of uniformly acquiring images from all vehicles, the server evaluates traveling data for each vehicle and selectively requests images only from those with appropriate confidence levels. This localized approach ensures reliable detection where needed while minimizing unnecessary communication load.
Solution Approach 2:
The server performs partial action by requesting surrounding images only from a selected subset of vehicles rather than all vehicles. The selection is based on confidence levels derived from traveling data analysis, ensuring that image acquisition is performed only where it adds value to detection reliability, thereby reducing overall communication load.
3Measurement precision
If traveling data analysis is performed for all vehicles, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The server applies partial action by performing detailed traveling data analysis only for a selected subset of vehicles rather than all vehicles. After initial congestion detection, the server identifies specific vehicles of interest and conducts focused analysis on their traveling data. This selective approach maintains detection accuracy for critical cases while significantly reducing overall processing time.
Data Source
AI summary
A traffic congestion detection system including a server device, the server device includes at least one processor, the at least one processor being configured to perform commands: acquiring pieces of traveling data from a plurality of vehicles; generating first traffic congestion detection information based on at least part of the acquired pieces of traveling data; acquiring image information on a surrounding image for at least one vehicle out of the plurality of vehicles; determining whether to use the image information for traffic congestion detection based on a confidence level related to the first traffic congestion detection information; generating second traffic congestion detection information by using the acquired image information when the determining the image information is to be used for the traffic congestion detection; and generating third traffic congestion detection information based on the second traffic congestion detection information.