- aws
- ml
- ai
- cloud
- autonomous-driving
- llm
- agentic-ai
- rag
- multi-tenant
•
•
•
•
•
•
•
•
-
How Continental uses Mountpoint for Amazon S3 in autonomous driving development – accelerating simulation performance by 20%
Replace complex caching and prefetching logic with Mountpoint for Amazon S3 to simplify Continental's simulation architecture. By mounting S3 buckets as local file systems, simulation workloads access petabytes of driving data directly, boosting throughput by 20% while reducing operational complexity.
-
Guidance for Autonomous Driving Data Framework on AWS
Modular, reusable framework for building autonomous driving data pipelines on AWS. Provides customizable modules covering the full lifecycle — from data ingestion and labeling to scenario search, simulation, and analytics — following AWS Well-Architected best practices.
-
Autonomous Driving Data Framework (ADDF) security and operations guide
Best practices for securing ADDF deployments on AWS — covering IAM policies, network isolation, encryption at rest and in transit, CI/CD pipeline hardening, and operational monitoring. Helps DevOps and security teams confidently deploy autonomous driving workloads in production.
-
BMW Cloud Data Hub: A reference implementation of the modern data architecture on AWS
How BMW Group built a modern data mesh on AWS with advanced dataset discovery, automated data quality checks, and self-service analytics. The Cloud Data Hub enables teams across the enterprise to find trusted datasets via intelligent search and query data to generate new insights.
-
Developing a Platform for Software-defined Vehicles with Continental Automotive Edge (CAEdge)
How Continental built CAEdge — a modular middleware framework connecting vehicle ECUs to the cloud. Enables OTA updates, remote diagnostics, and CI/CD pipelines for automotive software, bringing cloud-native development practices to software-defined vehicles.
-
Develop and deploy a customized workflow using Autonomous Driving Data Framework (ADDF) on AWS
Build and deploy custom autonomous driving workflows with ADDF — a fully automated reference solution offering pre-built data pipelines, visualization dashboards, annotation interfaces, and extraction classifiers. ADDF reduces deployment time and ensures AD workloads follow AWS Well-Architected best practices out of the box.
-
How to Run Massively Scalable ADAS Simulation Workloads on CAEdge
Orchestrate massively parallel ADAS simulation workloads using CAEdge with Amazon MWAA and Amazon EKS. The CAEdge Framework delegates high-performance computing jobs to EKS clusters, enabling automotive teams to run thousands of concurrent simulation scenarios while leveraging specialization and parallel processing at scale.
-
Field Notes: Deploy and Visualize ROS Bag Data on AWS using rviz and Webviz for Autonomous Driving
Set up cloud-based 3D visualization of autonomous driving sensor data using two complementary tools — rviz for point cloud rendering and Webviz for browser-based replay of lidar, camera, and radar recordings. Enables remote teams to inspect and debug driving recordings without local infrastructure.