Autonomous Vehicle ML Data Pipeline for Faster Model Development
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Solution Overview
Problem
Developing machine learning models for autonomous vehicles is a time-consuming and complex process due to the manual nature of data ingestion, processing, and model development, often requiring multiple disconnected tools and leading to errors and increased complexity.
Innovation Solution
A data science system that provides an end-to-end platform for ingesting, processing, and visualizing data, automating tasks, and provisioning resources, allowing for seamless transition through development phases and integration of disparate tools within a single ecosystem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for data ingestion, processing, and model development, then flexibility and control are maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service through automated data ingestion, processing, and model development workflows. The platform automatically performs data cleaning, transformation, and preparation tasks without requiring manual intervention at each step, while still allowing data scientists to control key decisions and parameters through the interface.
2Adaptability or versatility
If multiple disconnected tools are used for different development tasks, then specialized functionality is achieved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent merges multiple previously disconnected tools and processes into a single integrated platform. The system combines data ingestion, processing, visualization, model development, and deployment capabilities into one unified ecosystem, eliminating the need to switch between multiple separate tools while maintaining specialized functionality for each task.
Solution Approach 2:
The platform achieves universality by designing a single system that performs multiple functions across the entire machine learning workflow. The same platform handles data ingestion from various sources, processes different data types, provides visualization capabilities, develops models, and deploys them - making the system adaptable to diverse needs without requiring separate specialized tools.
3Manufacturing precision
If manual data cleaning and processing is performed, then data quality can be controlled, but the process becomes tedious and time-consuming
Solution Approach 1:
The system performs preliminary action by automatically executing data cleaning, transformation, and processing tasks before the data reaches the analysis stage. The platform pre-processes incoming data streams, applies cleaning rules, and prepares datasets for modeling automatically, eliminating the need for manual data preparation while ensuring consistent quality standards are met.
4Productivity
If automated processes are implemented for data processing, then efficiency increases, but the system becomes more complex and requires more resources
Solution Approach 1:
The patent applies segmentation by dividing the automated processing system into modular components that handle specific tasks independently. The data processing pipeline is segmented into distinct stages (ingestion, cleaning, transformation, validation) that can be executed separately and independently, reducing overall system complexity while maintaining high automation efficiency. Each module can be optimized and managed independently.
Data Source
AI summary
In one embodiment, a method is provided. The method includes receiving sensor data generated by a set of vehicles. The method also includes performing a first set of processing operations on the sensor data. The method further includes providing an exploration interface configured to allow one or more of browsing, searching, and visualization of the sensor data. The method further includes selecting a subset of the sensor data. The method further includes performing a second set of processing operations on the subset of the sensor data. The method further includes provisioning one or more of computational resources and storage resources for developing an autonomous vehicle (AV) model based on the subset of the sensor data.