Hyper Graph Scene Representation for Traffic Incident Detection
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Solution Overview
Problem
Conventional methods for detecting and localizing traffic incidents rely on static image analysis, which fail to effectively capture dynamic and structured information such as moving traffic flow and pedestrians, limiting their accuracy and reliability in real-time traffic management and autonomous driving.
Innovation Solution
The system generates and compares hyper graphs based on images captured by connected vehicles, allowing for unique appearance modeling of individual objects, dynamic information representation, and structured scene layout, enabling faster communication and 3D scene localization through multiple view integration and matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static image analysis methods are used, then the system is simple to implement, but the accuracy of detecting dynamic traffic incidents is insufficient
Solution Approach 1:
The patent transforms static image analysis into dynamic scene interpretation by introducing temporal dimensions through video sequences and motion detection. The system analyzes changes over time to detect traffic incidents, converting a static approach into a dynamic one that captures moving traffic flow and pedestrian activities, thereby improving detection accuracy without excessive complexity increase
Solution Approach 2:
The patent adds spatial and temporal dimensions to traditional 2D image analysis by constructing 3D scene representations and analyzing multi-view images from different vehicles. This dimensional expansion enables more accurate incident detection by providing depth information and multiple perspectives, resolving the contradiction between simplicity and accuracy
2Productivity
If hyper graph-based scene representation is used, then communication efficiency is improved, but the complexity of processing and generating hyper graphs increases
Solution Approach 1:
The patent extracts only the essential relational information from complex traffic scenes and represents it in hyper graph format. By selecting and extracting only the most relevant spatial and temporal relationships rather than processing all possible data, the system achieves efficient communication while managing processing complexity through selective information extraction
Solution Approach 2:
The patent transforms raw image and sensor data into hyper graph representations by changing the parameter format from pixel-level or point-cloud data to relational structures. This parameter transformation compresses information while preserving essential incident characteristics, improving communication efficiency without requiring proportional increases in processing complexity
3Reliability
If multiple view integration is used, then the reliability of incident detection is improved, but the amount of data to be processed increases
Solution Approach 1:
The patent merges multiple view images and their corresponding hyper graphs into a unified scene representation. By combining information from multiple vehicles' perspectives and integrating their hyper graphs, the system achieves more reliable incident detection through cross-validation and complementary information, while the merging process itself manages data volume through efficient integration algorithms
4Measurement precision
If 3D vision geometry is applied for scene localization, then the precision of incident localization is improved, but the computational requirements increase
Solution Approach 1:
The patent performs preliminary construction of hyper graph representations and extraction of geometric constraints before applying 3D vision geometry algorithms. By preparing data structures and identifying key geometric relationships in advance, the system reduces the computational burden during actual 3D localization, achieving high precision while managing energy consumption through pre-processing
Data Source
AI summary
A vehicle for interpreting a traffic scene is provided. The vehicle includes one or more sensors configured to capture an image of an external view of the vehicle, and a controller. The controller is configured to obtain the captured image of the external view of the vehicle from the one or more sensors, segment a plurality of instances from the captured image, determine relational information among the plurality of instances, and generate a hyper graph including a plurality of nodes representing the plurality of instances and a plurality of edges representing the relational information among the plurality of instances.