Advanced Driver Assistance Systems (ADAS) are revolutionizing the automotive industry by enhancing vehicle safety and the driving experience, with scalable solutions powered by Amazon Elastic Kubernetes Service (Amazon EKS). Car manufacturers often find themselves faced with significant challenges with ADAS, such as high infrastructure costs, scalability issues, and the need for rapid deployment of new features. Moreover, ADAS systems often face sensor limitations, inconsistent performance in adverse weather conditions, and the complexity of integrating various technologies.

Renova Cloud’s RenoCube addresses these challenges by utilizing Amazon EKS to create a scalable, cost-efficient ADAS platform. Our expertise in cloud transformation, DevOps, and automation technologies allows us to deliver solutions that reduce lead times, optimize costs, and support global expansion. By implementing dynamic resource provisioning and serverless architectures, RenovaCube ensures that ADAS systems are robust, reliable, and future-ready.

In this post, you’ll learn how Renova Cloud helped an Electronic Vehicle (EV) manufacturer achieve global efficiency by leveraging Amazon EKS in designing a scalable Advanced Driver Assistance System. Renova Cloud is the leading provider of strategic cloud consulting in Vietnam, recognized for its AWS Migration & Modernization Consulting Competency, Amazon EC2 for Windows Server Delivery, Amazon RDS Delivery and Glue Delivery Competency, and specializing in transitioning legacy workloads to cloud, DevOps, and automation technologies.

Solution Overview

RenoCube consists of the following two key workloads, which are integrated with the AWS Landing Zone:

  • Data Processing Pipeline. This workload ensures efficient data processing across different markets. By creating separate AWS accounts for Vietnam and Overseas, scalability is enabled based on market demand. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) plays a pivotal role, maximizing efficiency through auto-scaling and stability features, minimizing unnecessary expense. Moreover, each market would have independent infrastructure to ensure that data processing aligns with local regulations, language, and business practices.
  • Central Management. This workload consists of containerized core applications deploying on Amazon Elastic Container Service (Amazon ECS) cluster with AWS Fargate – a serverless compute engine. This workload is responsible for managing and triggering events for the data pipelines under Airflow orchestrator using RESTful API with internal authentication mechanism. The serverless architecture eliminates the need for provisioning and managing servers, resulting in further cost savings.

                                                                                                                              Figure 1. High-level Proposed Solution Architecture

 

Services Used

RenoCube offers pre-built integrations with the following AWS services to accelerate the speed and efficiency at which ADAS system operates:

  • Karpenter: an AWS open-source node lifecycle management project for Kubernetes that enables automated and dynamic EKS node management. By automatically provisioning nodes based on specific pod requirements and retiring them when no longer needed, Karpenter optimizes workloads with compute resources categorized into general-purpose, memory-intensive, and GPU-intensive groups. Leveraging a 4:6 ratio of EC2 Spot and On-Demand instances, RenoCube tailors infrastructure to client needs, ensuring efficient resource allocation and cost savings.
  • Amazon FSx for Lustre: a scalable, high-performance file system that supports compute-intensive workloads and enables efficient data processing. Effective ADAS systems usually rely on real-time data from sensors like cameras, radar, and lidar, which could sum up to a huge amount. FSx for Lustre supports simultaneous data read and write operations, reducing data processing lead times from days to just 2.5 hours through a warm-up strategy that prioritizes Amazon Simple Storage Service (Amazon S3) bucket data using “lazy loading”. This optimization minimizes worker pod delays and prevents data processing crashes, ensuring swift, reliable data transitions and enhanced efficiency for ADAS system development.
  • Amazon Simple Storage Service (Amazon S3): used in conjunction with FSx for Lustre to optimize data retrieval and processing times.

Benefits to Client

RenoCube has revolutionized the EV Manufacturer’s ADAS system. Handling a whopping 45 terabytes of daily data, the system’s scalability efficiently processes road data from EVs equipped with cameras and sensors.

The significant reduction in lead time—from days to just 2.5 hours—demonstrates optimized data retrieval and processing capabilities. This technical enhancement supports global expansion efforts across Asia, the EU, and the US, enabling rapid deployment of new features and updates.

RenoCube achieves up to 90% cost savings through Infrastructure as Code (IaC), containerized workloads on Amazon EKS, dynamic resource provisioning with Karpenter, and Spot instances. Streamlined deployment and resource efficiency drive significant cost reductions.

Conclusion

Renova Cloud’s RenoCube leverages AWS technologies to optimize the EV manufacturer’s ADAS system, achieving significant cost savings, improved efficiency, and scalability for global expansion.

This particular project not only strengthens the relationship between the EV manufacturer, AWS, and Renova, but also enables us to jointly up-skill the client’s internal engineering team on AWS technologies. This collaboration ensures confident expansion of migration support for the ADAS system across other EV series in the future. Our commitment to customer obsession, bias for action, and deep dive best practices drives all opportunities to enhance the client’s business value.

For more information about RenoCube, contact Renova Cloud. You can also learn more about Renova Cloud in AWS Marketplace.