Road Element Signatures Using Correlated Neural Identifiers
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Solution Overview
Problem
Existing assisted and autonomous driving systems face challenges in efficiently classifying road elements due to the need for improved classification systems and methods.
Innovation Solution
A method and system utilizing neural networks to generate dynamic in-correlation signatures through iterative processes, where identifiers are generated based on relative occurrences in true and false positive signature sets, allowing for accurate and robust classification of road elements by associating multiple signatures per element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification systems are used for road elements, then the system structure is simple, but the classification accuracy is insufficient
Solution Approach 1:
The patent segments the classification task into multiple iterative processes, where each process generates signatures with identifiers that capture different aspects of road elements. Instead of using a single complex classifier, the system divides the problem into multiple signature generation steps, each contributing to the overall classification accuracy through incremental refinement of identifying features.
Solution Approach 2:
The patent introduces a new dimensional approach by generating multiple signatures per road element, where each signature contains identifiers representing different features. This transforms the traditional single-classification-dimension approach into a multi-dimensional signature space, allowing more nuanced differentiation of road elements through correlated identifiers across multiple signature dimensions.
2Measurement precision
If multiple signatures are generated per road element, then the classification accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-generating multiple signatures and their associated identifiers before the actual classification decision. Each signature is prepared in advance with correlated identifiers that encode specific features, allowing the system to have multiple pre-computed options ready for comparison and selection during classification, thereby reducing real-time computational burden.
Solution Approach 2:
The iterative signature generation process incorporates feedback mechanisms where each signature generation step learns from previous true positive and false positive signature sets. This feedback loop refines the identifier selection criteria iteratively, improving detection precision while avoiding redundant computational efforts by learning from past classification outcomes.
3Measurement precision
If iterative signature generation is used, then the identification accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies partial action by generating signatures iteratively until a sufficient level of identification accuracy is achieved, rather than exhaustively generating all possible signatures. The process can be stopped once the desired precision is reached, balancing the trade-off between identification accuracy and processing time by performing just enough iterations to meet performance requirements.
Data Source
AI summary
A method that includes (i) generating, in a first iterative process, a first signature comprising first identifiers that are indicative of at least one of (a) a feature of a road element associated with the first signature or (b) a feature of a generation of the first signature, the first identifiers being generated in correlation to each other, and (ii) generate, in a second iterative process, a second signature comprising second identifiers that are indicative of (a) a feature of a road element associated with the second signature or (b) a feature of a generation of the second signature, the second identifiers being generated in correlation to each other; wherein the second identifiers of the second signature are generated in de-correlation to the first identifiers of the first signature, and wherein the first signature and the second signature collectively represent a cluster of sensed information.