Overview
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Scene Intelligence with Rosbag on AWS is purpose-built to help streamline the development process for Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV). The solution features modules for sensor extraction and object detection, helping machine learning engineers and data scientists to accelerate scene search for model training.
You can use this solution to stage sample rosbag files, extract rosbag sensor data such as metadata and images, apply object detection and lane detection models to the extracted images, as well as apply and store scene detection business logic.
Benefits
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Scalable, flexible data pipelines that reliably ingest, transform, label, and catalog billions of miles of real or simulated data.
Greater accessibility for global teams to search, identify, and analyze automotive data.
Reduce the number of dependencies and prerequisites with open-source configuration options.
Technical details
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You can automatically deploy this architecture using the implementation guide and the accompanying AWS CloudFormation template.
Step 1
The AV uploads the rosbag file to Amazon Simple Storage Service (Amazon S3). The end user invokes the workflow to start processing through Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and a directed acyclic graph (DAG).
Related content
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This Guidance demonstrates how customers can process and search high-accuracy, scenario-based data with the Autonomous Driving Data Framework (ADDF).
Total results: 1
- Publish Date
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- Version: 1.0.2
- Released: 5/2024
- Author: AWS
- Est. deployment time: 100-120
mins - Estimated cost: See details