Neural Network SVM Hybrid for Drivable Portion Classification
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Solution Overview
Problem
Autonomous systems face challenges in adapting to new environments without the need for time-consuming and resource-intensive retraining of neural networks, which requires offline operations.
Innovation Solution
A system combining a neural network and a support vector machine, where the neural network predicts drivable portions of an environment and the support vector machine refines these predictions using a hyperplane that can be updated online through annotated data, allowing for improved classification without retraining the neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network is retrained with new training data to improve prediction accuracy in new environments, then the classification accuracy is improved, but the system requires offline operations and significant time and computational resources
Solution Approach 1:
The system segments the learning process into two distinct components: (1) an initial offline neural network training phase that establishes baseline prediction capabilities, and (2) an online support vector machine updating phase that refines predictions for new environments. This segmentation allows the time-consuming neural network training to occur once offline, while rapid online adaptation is achieved through lightweight SVM hyperplane updates.
Solution Approach 2:
The support vector machine acts as an intermediary component between the pre-trained neural network and the final classification output. The SVM receives neural network predictions and refines them by learning from annotated corrections in new environments, enabling accurate adaptation without retraining the entire neural network system.
2Adaptability or versatility
If the neural network is retrained with new training data to adapt to new environments, then the adaptability is improved, but the system requires offline operations and computational resources
Solution Approach 1:
The adaptability mechanism is segmented into a static neural network component trained offline and a dynamic support vector machine component that adapts online. This division allows the system to gain environment adaptation capability through simple SVM hyperplane updates rather than complex neural network retraining.
Solution Approach 2:
The system achieves adaptability by changing only the SVM hyperplane parameters (weights and bias) based on annotated data from new environments, while keeping the neural network parameters fixed. This parameter change approach simplifies implementation compared to full neural network retraining.
3Measurement precision
If the neural network is retrained to improve prediction accuracy, then the classification performance is improved, but the system must be taken offline during retraining
Solution Approach 1:
The system separates prediction generation (neural network) from prediction refinement (SVM). The neural network operates offline and remains unchanged, while the SVM performs online refinement, ensuring continuous operational productivity without interruptions for retraining.
Solution Approach 2:
The support vector machine serves as an online intermediary that refines neural network predictions without requiring the neural network itself to be retrained or taken offline. This maintains system productivity while improving prediction accuracy for new environments.
Data Source
AI summary
A system includes a memory module configured to store image data captured by a camera and an electronic controller communicatively coupled to the memory module. The electronic controller is configured to receive image data captured by the camera, implement a neural network trained to predict a drivable portion in the image data of an environment. The neural network predicts the drivable portion in the image data of the environment. The electronic controller is configured to implement a support vector machine. The support vector machine determines whether the predicted drivable portion of the environment output by the neural network is classified as drivable based on a hyperplane of the support vector machine and output an indication of the drivable portion of the environment.