On-Device ML Network Learning for Faster Autonomous Vehicle Updates
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Solution Overview
Problem
Existing on-device learning methods for autonomous vehicles are hindered by slow update cycles and limited accessibility due to reliance on cloud servers and Over-The-Air (OTA) connections, which are restricted in certain environments.
Innovation Solution
The method employs multi-stage learning with adaptive hyper-parameter sets, allowing the on-device learning device to divide the learning process into stages, generate adaptive hyper-parameter sets, and train the machine learning network using both previous and new training data without cloud connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based training with OTA connection is used, then machine learning network can be updated with new data, but update cycle becomes slow and accessibility is limited
Solution Approach 1:
The training process is segmented into multiple stages (first stage training, second stage training, etc.) where each stage uses different hyperparameter sets and different proportions of new versus previous training data. This allows incremental adaptation without requiring complete retraining or cloud connectivity, reducing update time while maintaining adaptability.
Solution Approach 2:
The hyperparameter sets are dynamically adjusted across different training stages. The learning rate, batch size, and other hyperparameters change adaptively based on the training stage and the proportion of new training data, enabling efficient on-device learning without fixed configuration constraints.
2Productivity
If multi-stage learning with adaptive hyperparameter sets is used, then learning speed improves, but system complexity increases
Solution Approach 1:
Multiple hyperparameter sets are prepared in advance for different training stages. The first hyperparameter set is used for initial training with a higher proportion of new data, while subsequent hyperparameter sets are used for fine-tuning with varying data proportions. This preliminary preparation enables faster learning without requiring complex real-time optimization during training.
3Adaptability or versatility
If on-device training with limited computing power is used, then cloud connectivity dependency is reduced, but training efficiency decreases
Solution Approach 1:
The system changes key training parameters including the proportion of new versus previous training data, learning rates, batch sizes, and number of training epochs across different stages. These parameter adjustments optimize training efficiency for on-device computation with limited power, achieving effective adaptation without cloud dependency while maintaining reasonable training speed.
Data Source
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AI summary
A method for performing on-device learning of embedded machine learning network of autonomous vehicle by using multi-stage learning with adaptive hyper-parameter sets is provided. The processes include: (a) dividing the current learning into a 1-st stage learning to an n-th stage learning, assigning 1-st stage training data to n-th stage training data, generating a 1_1-st hyper-parameter set candidate to a 1_h-th hyper-parameter set candidate, training the embedded machine learning network in the 1-st stage learning, and determining a 1-st adaptive hyper-parameter set; (b) generating a k_1-st hyper-parameter set candidate to a k_h-th hyper-parameter set candidate, training the (k-1)-th stage-completed machine learning network in the k-th stage learning, and determining a k-th adaptive hyper-parameter set; and (c) generating an n-th adaptive hyper-parameter set, and executing the n-th stage learning, to thereby complete the current learning.