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Developing and deploying Autonomous Vehicles requires the ability to collect, store and manage massive amounts of data, high performance computing capacity and advanced deep learning frameworks, along with the capability to do real-time processing of local rules and events in the vehicle. 

AWS provides a full suite of services to support Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle development and deployment.  AWS' nearly unlimited storage and compute capacity and support for deep learning frameworks such as Apache MXNet and TensorFlow and PyTorch accelerate your algorithm training and testing.  AWS Greengrass provides edge computing with ML inference capabilities for real-time processing of local rules and events in the vehicle while minimizing the cost of transmitting data to the cloud.
Learn how uses AWS to manage their IT infrastructure, so they can focus on building the brains behind the self-driving car.

“Using the AWS Cloud and specifically Amazon EC2 P3 instances, we’re able to build a scalable and highly performant applications stack to efficiently handle and process the huge amount of data that we collect,” says Mike Garrison, Technical Lead, Infrastructure Engineering, Toyota Research Institute."

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DriveAI creates AI software for autonomous vehicles using a deep learning first methodology.  AWS provides them the ability to seamlessly scale as their business grows, and the capability to handle their high performance computing demands.  


nuTonomy makes software to build self-driving cars and autonomous mobile robots.  AWS enables them quickly provision compute resources, deploy new features, and add IT resources on demand.  


TuSimple built their autonomous driving platform using sophisticated deep learning algorithms developed with Apache MXNet on AWS.  TuSimple has used AWS to simulate billions of miles of driving.


Mapillary is using AWS to create an image representation of the world. By connecting images across time and users, Mapillary provides an immersive street-level image view for people to virtually explore places on Earth.  

AWS’ highly scalable storage and compute services and advanced deep learning frameworks allow for the collection, ingestion, storage and anlalysis of autonomous vehicle data to support full-scale autonomous vehicle development.

AWS Deep Learning

AWS Deep Learning AMIs provide the tools to accelerate deep learning in the cloud and create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with general or compute-optimized CPU instances.

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EC2 P3 Instances

Amazon EC2 P3 instances are powerful and scalable to provide GPU-based parallel compute capabilities, ideal for computationally challenging applications like autonomous vehicle development.

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Amazon Sagemaker

Amazon SageMaker is a fully managed end-to-end machine learning service that enables you to quickly build, train, and host machine learning models at scale, drastically accelerating model training and deployment.

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Amazon Snowball Edge

AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities, accelerating data transfer into and out of the AWS Cloud and providing local data analysis and filting.

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Automotive companies of all types and sizes, from global automakers to startups rely on AWS. Contact our experts and start your own AWS Cloud journey today.

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