Unknown Scene Security Level Assessment Using Similarity Matching
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Solution Overview
Problem
Autonomous vehicles face challenges in quickly identifying the security level of unknown scenes due to their inherent uncertainty and unpredictability, limiting the recorded service scenes and compromising traffic security.
Innovation Solution
A method and apparatus that determine the security level of an unknown scene by comparing a target scene's parameters with a predefined scene library, selecting a control scene based on similarity, and generating a parameter subset to assess the target scene's security level, reducing calculation and determination time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use comprehensive scene analysis to ensure security, then security level is improved, but determination time increases
Solution Approach 1:
The scene parameters are segmented into multiple dimensions (environmental parameters, road parameters, traffic parameters, vehicle parameters, etc.), and the comparison is performed dimension by dimension against predefined scenes. This segmentation allows the system to efficiently identify matching scenes without analyzing all parameters simultaneously, thus reducing determination time while maintaining comprehensive security assessment.
Solution Approach 2:
The system pre-establishes a scene library with predefined scenes and their corresponding parameter sets before actual operation. During runtime, the system only needs to compare current scene parameters against these pre-defined templates, rather than analyzing all possible scene combinations from scratch. This preliminary preparation significantly accelerates the security level determination process.
2Measurement precision
If autonomous vehicles process all scene parameters to ensure accuracy, then measurement precision is improved, but calculation complexity increases
Solution Approach 1:
The system extracts only the key parameters that are most relevant to scene similarity and security assessment from the complete set of scene parameters. By identifying and extracting critical parameters (such as environmental conditions, road type, traffic density, etc.), the system maintains measurement precision while significantly reducing calculation complexity compared to processing all available parameters.
Solution Approach 2:
Different parameters are assigned different weights and levels of importance based on their impact on scene similarity and security. The system focuses computational resources on parameters with higher local quality (greater importance) while using simplified analysis for less critical parameters. This differential approach maintains overall accuracy while reducing total calculation complexity.
Data Source
AI summary
The present application provides a scene security level determination method, relates to the technical field of autonomous driving, and is used for determining the security level of an unknown scene. The method comprises: determining, according to a first parameter set and a first parameter value set of the target scene, whether at least one predefined scene with a similarity between the at least one predefined scene and a target scene being larger than a set similarity threshold exists in a predefined scene library; selecting, in response to the at least one predefined scene existing, one predefined scene from the at least one predefined scene as a control scene; generating, according to the parameter subset of the control scene, a parameter value subset of the target scene; and determining, according to the parameter subset and the parameter value subset, a target security level of the target scene.