Onboard Visual Violation Inspection for Complex Traffic Scenes
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Solution Overview
Problem
Conventional self-driving vehicles lack the ability to accurately and diversely determine violation events in real-time using camera and ranging devices, relying on manual video analysis and image recognition methods that struggle with complex scenarios, resulting in insufficient accuracy and diversity in violation detection.
Innovation Solution
A violation inspection system installed on a self-driving vehicle's onboard computer, utilizing a microprocessor, databases, and AI-based processors to continuously identify and analyze traffic images, detect object dynamics, and match situations against templates to determine violations, improving accuracy and diversity in violation detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional image recognition methods are used to automatically identify violation events, then automation is improved, but accuracy and diversity in violation determination deteriorate due to inability to handle complex images with multiple violation events
Solution Approach 1:
The patent segments the violation detection process into multiple independent detection modules, each specialized for detecting specific types of violation events. The system divides complex images into multiple analysis streams, with each stream handling particular violation patterns (e.g., lane crossing, pedestrian violations, vehicle violations). This segmentation allows parallel processing of multiple violation types simultaneously, improving both accuracy and diversity of detection without compromising automation.
2Measurement precision
If manual video analysis is used to determine violations, then accuracy can be maintained through human judgment, but productivity deteriorates due to time-consuming manual review processes
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated multi-module detection system that uses computer vision algorithms and AI models. The system automatically analyzes video frames, identifies objects, detects violation patterns, and generates determination results without human intervention. This substitution maintains high accuracy through sophisticated algorithmic analysis while dramatically improving productivity by eliminating time-consuming manual review processes.
3Device complexity
If a single violation event is identified per screen image using conventional methods, then device complexity is kept simple, but adaptability deteriorates due to inability to recognize multiple violation events simultaneously
Solution Approach 1:
The patent designs a universal detection framework where multiple detection modules share common infrastructure (image preprocessing, object detection backbone, post-processing pipeline). Each module is multi-functional, capable of detecting various violation types through configurable parameters and templates. The system uses a unified architecture that can adapt to different violation scenarios by loading appropriate detection templates, achieving high versatility without proportionally increasing overall system complexity.
Data Source
AI summary
A violation inspection system based on visual sensing of a self-driving vehicle and a method thereof are disclosed. The violation inspection system receives traffic images captured by the self-driving vehicle, identifies image objects by artificial intelligence, continuously detects a movement status of each of the image objects, to generate image object dynamics; when the image object matches one of a trigger object factor and an association object factor, the image object and the image object dynamics thereof are inputted to an AI-based situation recognition model, to output a current situation, and when the current situation matches a violation situation template, the image objects corresponding to the trigger object factor and the association object factor corresponding to the violation situation template, and a violation item are outputted, so as to achieve the technical effect of improving accuracy and diversity in violation behavior determination.