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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
Streamline data management
Scalable, flexible data pipelines that reliably ingest, transform, label, and catalog billions of miles of real or simulated data.
Increase developer efficiency
Greater accessibility for global teams to search, identify, and analyze automotive data.
Simplify deployment
Reduce the number of dependencies and prerequisites with open-source configuration options.
Step 3 Amazon SageMaker applies object detection and lane detection models to the extracted data. SageMaker then writes the data and labels to another S3 bucket.
Step 4 Amazon EMR Serverless (with an Apache Spark job) applies business logic to the data and labels in Amazon S3. This generates metadata related to the object detection and lane detection. Amazon EMR Serverless then writes the metadata to Amazon DynamoDB and another S3 bucket.
Step 5 An AWS Lambda function publishes new incoming DynamoDB data (metadata) to the Amazon OpenSearch Service cluster. The end user accesses the OpenSearch Service cluster, through a proxy on Amazon Elastic Compute Cloud (Amazon EC2), to submit queries against the metadata.
Step 2 AWS Batch pulls the rosbag file from Amazon S3, parses and extracts the sensor and image data, and writes this data to another S3 bucket.
Step 3 Amazon SageMaker applies object detection and lane detection models to the extracted data. SageMaker then writes the data and labels to another S3 bucket.
Step 4 Amazon EMR Serverless (with an Apache Spark job) applies business logic to the data and labels in Amazon S3. This generates metadata related to the object detection and lane detection. Amazon EMR Serverless then writes the metadata to Amazon DynamoDB and another S3 bucket.
Step 5 An AWS Lambda function publishes new incoming DynamoDB data (metadata) to the Amazon OpenSearch Service cluster. The end user accesses the OpenSearch Service cluster, through a proxy on Amazon Elastic Compute Cloud (Amazon EC2), to submit queries against the metadata.
Step 2 AWS Batch pulls the rosbag file from Amazon S3, parses and extracts the sensor and image data, and writes this data to another S3 bucket.
Step 3 Amazon SageMaker applies object detection and lane detection models to the extracted data. SageMaker then writes the data and labels to another S3 bucket.
Step 3 Amazon SageMaker applies object detection and lane detection models to the extracted data. SageMaker then writes the data and labels to another S3 bucket.
Step 4 Amazon EMR Serverless (with an Apache Spark job) applies business logic to the data and labels in Amazon S3. This generates metadata related to the object detection and lane detection. Amazon EMR Serverless then writes the metadata to Amazon DynamoDB and another S3 bucket.
Step 5 An AWS Lambda function publishes new incoming DynamoDB data (metadata) to the Amazon OpenSearch Service cluster. The end user accesses the OpenSearch Service cluster, through a proxy on Amazon Elastic Compute Cloud (Amazon EC2), to submit queries against the metadata.
Step 2 AWS Batch pulls the rosbag file from Amazon S3, parses and extracts the sensor and image data, and writes this data to another S3 bucket.
Step 3 Amazon SageMaker applies object detection and lane detection models to the extracted data. SageMaker then writes the data and labels to another S3 bucket.
Step 4 Amazon EMR Serverless (with an Apache Spark job) applies business logic to the data and labels in Amazon S3. This generates metadata related to the object detection and lane detection. Amazon EMR Serverless then writes the metadata to Amazon DynamoDB and another S3 bucket.
Step 5 An AWS Lambda function publishes new incoming DynamoDB data (metadata) to the Amazon OpenSearch Service cluster. The end user accesses the OpenSearch Service cluster, through a proxy on Amazon Elastic Compute Cloud (Amazon EC2), to submit queries against the metadata.
Step 2 AWS Batch pulls the rosbag file from Amazon S3, parses and extracts the sensor and image data, and writes this data to another S3 bucket.
Step 3 Amazon SageMaker applies object detection and lane detection models to the extracted data. SageMaker then writes the data and labels to another S3 bucket.
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Guidance for Autonomous Driving Data Framework on AWS
This Guidance demonstrates how customers can process and search high-accuracy, scenario-based data with the Autonomous Driving Data Framework (ADDF).
