Dynamic Object Classification for Autonomous Driving Without Retraining
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Solution Overview
Problem
Existing assisted and autonomous driving systems face challenges in efficiently classifying new objects or scenarios without requiring extensive retraining of classification neural networks.
Innovation Solution
A method and system that utilize catalog samples, representative vectors, and techniques like one-shot and few-shot learning to dynamically classify new classes without retraining the classification neural network, using catalog samples and representative vectors generated through zero-shot or few-shot learning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the classification neural network is retrained to recognize new classes, then the classification accuracy for new objects is improved, but the time consumption and computational resources increase significantly
Solution Approach 1:
The system pre-generates catalog samples and representative vectors for potential new classes before they appear in the driving environment. When a new object class is encountered, the system can quickly retrieve and use pre-prepared representative vectors instead of performing time-consuming retraining, thus maintaining high classification accuracy while minimizing time loss.
Solution Approach 2:
Instead of retraining the entire neural network, the system creates copies or representations (representative vectors) of new object classes based on catalog samples. These representative vectors are generated through zero-shot or few-shot learning processes and stored for rapid comparison and classification, avoiding the need to retrain the classification neural network from scratch.
2Reliability
If extensive training data is collected for new classes, then the classification reliability is improved, but the data collection time and system complexity increase
Solution Approach 1:
The system extracts essential features and characteristics of new object classes from a small number of catalog samples to create representative vectors. This extraction process captures the most important attributes needed for reliable classification without requiring extensive training data collection, thereby maintaining classification reliability while reducing system complexity.
Solution Approach 2:
The classification system is designed to handle both known and unknown object classes using the same architecture and processing pipeline. The representative vector generation mechanism works universally for any new class, eliminating the need for separate data collection and processing systems for different object types, thus reducing overall system complexity while maintaining reliability.
3Adaptability or versatility
If the classification neural network is updated frequently to adapt to new classes, then the adaptability is improved, but the computational resources and energy consumption increase
Solution Approach 1:
The system uses lightweight representative vectors instead of full neural network models for new class classification. These representative vectors are computationally inexpensive to generate and store, allowing frequent updates and adaptations to new classes with minimal energy consumption. The vectors can be quickly discarded and replaced when new classes are encountered.
Solution Approach 2:
Instead of performing complete neural network retraining which consumes significant computational resources, the system applies partial learning by generating representative vectors from catalog samples using zero-shot or few-shot learning. This partial action approach provides sufficient adaptability to new classes while consuming far less energy than full retraining would require.
Data Source
AI summary
A method for dynamic classification for autonomous driving, the method includes (i) producing, by a classification neural network associated with a driving of a vehicle, a catalog sample representative vector representing a catalog sample of a new class for autonomous driving being unidentified by the classification neural network at the time of the driving; (ii) generating, by the classification neural network, a new image-based representative vector representing an image of an entity of the new class; and (iii) classifying, by a classification unit that is associated with the classification neural network, the entity as being associated with the new class.