Autonomous Vehicle Model Training with Pipe-Mode Data Shuffling
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Solution Overview
Problem
Current methods for training machine learning models for autonomous vehicles and advanced driving assistance systems are inadequate due to the need for expensive and complex infrastructure, and existing cloud-based solutions like TensorFlow and Amazon Sagemaker require significant time and effort to achieve scalability, flexibility, and security.
Innovation Solution
Adapting deep neural networks to a pipe mode using Amazon Sagemaker, which decouples data storage from training execution, and implementing multi-level data shuffling and boosting techniques to enhance training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional cloud-based solutions like TensorFlow and Amazon Sagemaker are used for training machine learning models, then model training capability is provided, but training time and infrastructure cost increase significantly
Solution Approach 1:
The training pipeline is segmented into distinct stages: data preprocessing, model training, and evaluation. The data is divided into multiple datasets that can be processed in parallel, and the training process is broken down into manageable steps that can be executed concurrently, significantly reducing total training time while maintaining model accuracy
Solution Approach 2:
Data preprocessing operations are performed in advance before the actual training begins. The system pre-processes and prepares training data, creating ready-to-use datasets that eliminate time-consuming preprocessing during the training phase, thereby accelerating the overall training process
2Productivity
If existing cloud-based infrastructure is used for training, then training capability is achieved, but infrastructure complexity and cost increase
Solution Approach 1:
Multiple training datasets are merged into a unified structure that can be processed efficiently. The system combines data from various sources into a consolidated training pipeline, reducing the need for separate infrastructure components for each data source and simplifying the overall system architecture
Solution Approach 2:
The system creates copies of training datasets that can be distributed across multiple processing units. Instead of requiring complex infrastructure to handle all data simultaneously, the system replicates data subsets that can be processed in parallel, reducing infrastructure complexity while maintaining training efficiency
3Measurement precision
If data is processed sequentially in traditional training pipelines, then processing accuracy is maintained, but training time increases
Solution Approach 1:
The training pipeline dynamically adjusts the processing order and parallelization of data operations. The system monitors training progress and adapts the execution strategy, switching between sequential and parallel processing modes as needed, which maintains model accuracy while minimizing processing time
Solution Approach 2:
The system maintains continuous data processing without idle waiting periods. Preprocessing operations continue in the background during model training, and data is fed continuously to the model in optimized batches, eliminating gaps and delays while preserving training accuracy through consistent data flow
Data Source
AI summary
Techniques are disclosed for the implementation of machine learning model training utilities to generate models for advanced driving assistance system (ADAS), driving assistance, and/or automated vehicle (AV) systems. The techniques described herein may be implemented in conjunction with the utilization of open source and cloud-based machine learning training utilities to generate machine learning trained models. One example of such an open source solution includes TensorFlow, which is a free and open-source software library for dataflow and differentiable programming across a range of tasks. TensorFlow may be used in conjunction with many different types of machine learning utilities, such as Amazon's cloud-based SageMaker utility for instance, which is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.