In-Vehicle Edge Computing for Real-Time Traffic Image Analysis
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Solution Overview
Problem
Current traffic condition systems for Internet of vehicles lack the ability to efficiently gather and transmit dense, real-time traffic information from moving sources, relying on manpower or fixed sources which are prone to delays and errors, and fail to provide comprehensive information about non-detectable conditions outside a driver's visual range.
Innovation Solution
A traffic condition system utilizing in-vehicle devices with edge computing to capture and analyze traffic images, classify information into static and dynamic categories, and transmit it to a backend platform for cross-validation and prediction, creating a low-bandwidth, automated network that provides drivers with comprehensive traffic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional radio broadcast method is used to provide traffic information, then drivers can receive traffic condition updates, but the information is delayed and lacks real-time accuracy
Solution Approach 1:
The system enables vehicles to autonomously capture, process, and share traffic condition information through onboard cameras and edge computing devices. Each vehicle acts as both information consumer and provider, eliminating reliance on centralized radio broadcast systems and manual reporting, thereby achieving real-time, accurate traffic information exchange without human intervention or time delays
Solution Approach 2:
The patent replaces the mechanical radio broadcast system with an automated image recognition and data processing system. Instead of using radio waves to transmit pre-recorded traffic updates, the system uses computer vision algorithms to automatically detect, classify, and share traffic conditions between vehicles through wireless communication, achieving real-time information exchange with superior accuracy
2Loss of information
If in-vehicle devices capture and transmit detailed traffic images continuously, then comprehensive traffic information is provided, but bandwidth consumption increases significantly
Solution Approach 1:
The system extracts only the essential traffic condition information from captured images using edge computing devices. Instead of transmitting entire images or video streams, the system processes images locally to identify and extract key elements such as traffic signs, road conditions, and hazard locations, then transmits only this processed data to the backend platform and other vehicles, dramatically reducing bandwidth consumption while maintaining information completeness
Solution Approach 2:
The patent segments traffic information into distinct categories (e.g., traffic signs, road conditions, hazards, weather) and processes each segment independently through image recognition algorithms. This segmentation allows the system to transmit only relevant information segments based on current driving conditions and vehicle location, optimizing bandwidth usage while ensuring comprehensive coverage of all traffic conditions
3Reliability
If cross-validation mechanism is implemented to confirm traffic conditions, then information reliability is improved, but system complexity increases
Solution Approach 1:
The system implements a cross-validation mechanism where the backend platform receives traffic condition reports from multiple vehicles, compares the data using feedback loops, and confirms or refutes conditions based on consistency across sources. This feedback-driven verification process improves reliability by filtering out false positives while maintaining manageable system complexity through automated comparison algorithms
4Loss of time
If dynamic prediction is used to forecast traffic conditions, then drivers receive early warnings of hazards, but computational requirements increase
Solution Approach 1:
The system performs preliminary computational actions by training machine learning models offline to predict traffic conditions based on historical data and patterns. During real-time operation, the trained models require minimal computational energy to generate predictions, allowing the system to provide early warnings of hazards while maintaining low energy consumption. The heavy computational lifting is done in advance rather than in real-time
Data Source
AI summary
The invention discloses a traffic condition system for Internet of vehicles based on image recognition, comprising a plurality of in-vehicle devices and a backend platform; using edge computing to carry out the traffic condition identification in the image by each in-vehicle device. Each in-vehicle device captures images and analyzes the images to obtain the traffic condition information on the road. The information is divided into static easily-detectable information, dynamic easily-detectable information and static not-easily-detectable information and filed to the backend platform via wireless transmission. The backend platform is to verify the validity of the traffic condition information by means of cross-validation mechanism and supplemented by the dynamic prediction mechanism, to establish a traffic condition map and transmit the traffic condition information to each in-vehicle device according to the position of each driver for the attention, so as to form a low-bandwidth, automated traffic condition system for Internet of vehicles.