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Guidance for Autonomous Driving Data Framework on AWS

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Overview

This Guidance demonstrates how customers can process and search high-accuracy, scenario-based data with the Autonomous Driving Data Framework (ADDF). Automotive teams who want to implement common tasks for autonomous vehicles (AV) and advanced driver-assistance systems (ADAS) can share, modify, or create fully customizable modules that reduce the amount of effort required to create and deploy this Guidance.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This Guidance offers a secure-by-default setup to allow users to safely operate and respond to incidents and events. If you decide to move into a production-like environment, the ADDF security and operations guide outlines best practices for securely deploying and operating ADDF in the AWS Cloud. 

Read the Operational Excellence whitepaper 

ADDF was built with security in mind. Before release to the public, AWS performed an initial, internal security review of ADDF and resolved any identified security issues. Both AWS and the open-source community contribute to ongoing security reviews of the framework. 

Interfaces to the public internet are not exposed by the core modules. Services are only reachable as an authenticated user in the context of an AWS account.

Various built-in security features in ADDF are designed to help you set up a secure framework and help your organization meet common enterprise security requirements. AWS defined an ADDF shared responsibility model, as well as a secure setup and operation guide, to help you on your ADDF journey from a secure start through to production. 

Read the Security whitepaper 

To implement a reliable architecture, each individual module is designed to cover module-specific throttling and limit-issues based on current experience. The default deployment options offer the end-user a sensible working baseline with common account limits. If the end-user decides to scale out, that user is responsible for considering any newly hit constraints or limits.

ADDF is an open-source project. The ADDF community constantly improves features based on customer or community input.

Read the Reliability whitepaper 

ADDF provides best-practice patterns that have been proven in challenging enterprise environments with customers. All selected services reflect the learnings from real-life customer use-cases. Amazon EKS hosts high-performance, on-demand visualization applications for engineers. For developer instances, Amazon EC2 and Amazon DCV stage and share files using FSx for Lustre. Both patterns have proven to work at scale in enterprise environments.

The default deployment options offer the end-user a sensible working baseline. The user is free to change the default configuration of modules to scale up or down based on the use case.

Read the Performance Efficiency whitepaper 

This Guidance uses resources based on workload data and resource characteristics to keep up with demand.

Read the Cost Optimization whitepaper 

In this Guidance, the ADDF modules describe patterns for running ADAS and AV workloads at an enterprise scale, containing common best-practices for scaling traffic and data access patterns. 

Any compute intensive workload should have a default value that balances between a high baseline utilization and end-user usability. The ADDF modules provide a reference implementation, and all deployed resources are set to the minimum resources needed to support the ADDF models. This ensures a high baseline utilization. 

Read the Sustainability whitepaper 

Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

Go to sample code

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.