Low-Confidence Embedding Routing for Vehicle Element Classification
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Solution Overview
Problem
Existing classification systems in assisted and autonomous driving systems face inefficiencies in accurately classifying elements with insufficient confidence levels, leading to potential errors in vehicle operations.
Innovation Solution
A method and system that utilizes multiple embedding-based classification units, arranged in a hierarchical structure, to dynamically reroute embeddings classified with insufficient confidence to more suitable units for accurate classification, employing routing rules and re-evaluation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single embedding-based classification unit is used, then the device complexity is reduced, but the classification accuracy deteriorates for elements with insufficient confidence levels
Solution Approach 1:
The classification system is divided into multiple specialized embedding-based classification units, each trained to handle specific types of elements or scenarios. This segmentation allows each unit to focus on particular classification tasks, improving overall accuracy while managing complexity through modular design
Solution Approach 2:
A routing unit acts as an intermediary that receives embeddings from the embedding generation unit and directs them to the appropriate classification unit based on routing rules. This mediator coordinates between the generation and classification stages, ensuring embeddings are processed by the most suitable unit without requiring a completely restructured system
2Reliability
If multiple embedding-based classification units are deployed, then the classification accuracy is improved, but the processing time increases
Solution Approach 1:
Routing rules are established in advance that contain pre-determined logic for directing specific embedding types to specific classification units. This preliminary organization of routing logic enables rapid decision-making without requiring complex real-time analysis, reducing processing delays
Solution Approach 2:
The routing unit dynamically selects which classification unit processes each embedding based on the characteristics of the embedding and the routing rules. This dynamic routing optimizes processing by directing each item to the most appropriate unit, improving both accuracy and efficiency
3Reliability
If embeddings with insufficient confidence levels are processed by the same classification unit, then the system operation is simplified, but the classification reliability deteriorates
Solution Approach 1:
Different classification units are trained with different specializations and quality standards appropriate for their specific task domains. Each unit has local expertise optimized for particular element types, ensuring high reliability for its designated function while maintaining overall system simplicity through clear division of labor
Data Source
AI summary
A method for downstream processing of embedding information items, the method includes (i) receiving multiple evaluated element embedding information items that represent multiple evaluated elements within an environment of a vehicle; (ii) identifying that the multiple evaluated element embedding information items are classified into an insufficient confidence level; and (iii) for each one of the multiple evaluated embedding information items identified as an being classified into the insufficient confidence level, automatically routing evaluated element information to a corresponding embedding information item-based classification unit that is trained to classify elements represented by the evaluated element embedding information item associated with the corresponding population of embedding information items.