Containerized Design of Services in Software Define Vehicles for Vehicle and in Cloud [ Part -2 ]

Off-Board /Cloud Services

Understand the Off-Board / Cloud services of SDVs in relation to Up/Re-Skilling Required for Organizations from Traditional SW Application to Containerized Application Development for Centralized Domain / Zonal Controller E/E Architecture

In this Series of Articles we discuss the Key Characteristics of SDVs , Architecture of SDVs and its components by leveraging cloud-native application design process and the next-gen skills required for organizations to upskill/Reskill its workforce for successful enablement of Design, Develop , Test and Deploy the applications in an SDV.

Architecture of On-Board & Off-board services — Containerized Design in SDV

Components of Off-Board Cloud Services in Software-Defined Vehicles (SDV)

Off-board cloud services are crucial for the functionality and management of Software-Defined Vehicles (SDVs). These services extend the computational capabilities of vehicles by leveraging cloud infrastructure to process data, run complex algorithms, and manage applications. Here’s an overview of the key components and how they interconnect:

Virtual Compute Environment: Virtual compute environments in the cloud provide the necessary infrastructure for running vehicle-related applications and processing data. AWS Graviton, for instance, offers Arm-based processors that deliver high performance and energy efficiency. These processors power various Amazon EC2 instances, offering tailored compute power for different types of applications These virtual environments can scale up or down based on demand, providing the computational power needed for tasks such as data analytics, machine learning, and simulation. [More Examples — Google Cloud’s Compute Engine , Microsoft Azure Virtual Machines , IBM Cloud Virtual Servers etc..]

Cloud APIs [Amazon EC2 API , AWS IoT Core API , AWS Lambda API ,Amazon S3 API ] : Cloud APIs facilitate communication between the vehicle and the cloud. These APIs allow the vehicle to send data to the cloud and receive commands, updates, and configurations. Cloud APIs enable various functionalities, including telemetry data collection, remote diagnostics, and over-the-air (OTA) updates. They also support real-time data exchange, which is critical for applications such as predictive maintenance and autonomous driving.

Receiving and Orchestrating Containers: Cloud services receive containers from in-vehicle systems and orchestrate their deployment and management using AWS-EKS (Elastic Kubernettes Services). Containers encapsulate applications and their dependencies, ensuring consistent performance regardless of the underlying infrastructure. When a vehicle sends a container to the cloud, orchestration tools like Kubernetes manage the deployment, scaling, and operation of these containers, ensuring they run efficiently and can interact with other cloud services as needed.

OS/Containers: In the cloud, containers run on top of operating systems that provide the necessary environment for execution. Examples include AWS Fargate, which abstracts the underlying infrastructure, allowing developers to focus on application development rather than infrastructure management. This approach ensures that containerized applications from vehicles run smoothly in the cloud, benefiting from the cloud’s scalability and reliability.

Mixed Critical Orchestrator: A mixed critical orchestrator in the cloud manages both safety-critical and non-critical applications. By leveraging virtualization and containerization, the orchestrator can ensure that critical applications (such as those related to vehicle safety and navigation) are isolated from non-critical ones (such as infotainment and user interfaces). This isolation is crucial to prevent faults in non-critical applications from affecting the performance and reliability of critical systems.

Integration and Usage

Data Processing and Analytics:

1. Data Collection: Vehicles collect data from various sensors and systems and send it to the cloud using Cloud APIs.
2. Data Storage and Processing: In the cloud, virtual compute environments like those powered by AWS Graviton process this data. The cloud provides the necessary storage and computational resources to analyze the data in real-time or batch mode, depending on the application.
3. Machine Learning and AI: The processed data is used to train machine learning models that can improve vehicle performance, predict maintenance needs, and enhance autonomous driving algorithms.

Application Management:

1. Container Reception: Vehicles package updates, new features, and bug fixes into containers and send them to the cloud. The cloud infrastructure receives these containers and uses orchestration tools like Kubernetes to manage their deployment.
2. Resource Allocation: Kubernetes ensures that each container receives the appropriate resources (CPU, memory, storage) and manages scaling based on demand. This dynamic resource allocation helps in handling peak loads efficiently.
3. Execution Environment: Containers run on top of operating systems like AWS Fargate, which abstracts the underlying infrastructure, ensuring consistency and reliability in application performance.

Security and Reliability:

1. Isolation: The mixed critical orchestrator ensures that safety-critical applications are isolated from non-critical ones. This isolation is crucial for maintaining the reliability and safety of the vehicle’s operations.
2. Monitoring and Management: Cloud services continuously monitor the health and performance of running applications. Any anomalies or potential issues are detected and addressed promptly to ensure uninterrupted service.

Updates and Maintenance:

1. Over-the-Air (OTA) Updates: The cloud can push OTA updates to vehicles, ensuring that the latest features and security patches are deployed without requiring physical intervention.
2. Remote Diagnostics: Cloud APIs facilitate remote diagnostics, allowing for the identification and resolution of issues without needing the vehicle to visit a service center.

Off-board cloud services in SDVs involve a synergistic interplay of virtual compute environments, cloud APIs, container orchestration, and mixed critical orchestrators. AWS Graviton provides scalable and efficient processing power, while Kubernetes orchestrates container deployment and management. Cloud APIs enable seamless data exchange between the vehicle and the cloud, supporting a range of applications from real-time analytics to remote diagnostics. The mixed critical orchestrator ensures the isolation and reliability of safety-critical applications, maintaining the integrity and safety of vehicle operations. This integrated approach enhances the capabilities, safety, and reliability of Software-Defined Vehicles, driving the evolution of modern automotive technology.

Link to Part-1 : https://medium.com/@jujjavarapurpratap/containerized-design-of-services-in-software-define-vehicles-for-vehicle-and-in-cloud-part-1-566c49b13ff1

To Be Continued in Part-3 ………

Pratap R Jujjavarapu is a Technical Evangelist , Director | Enterprise Management -B2B , working to explore Software Defined vehicles (SDV) in terms of Content Curation and Curriculum management for Upskilling and Reskilling of Employees across Automotive Organizations in Skill Lync. We provide paid services in terms of skilling Employees for Automotive SW OEMs , TIER-1s , GCCs & ER&Ds across the Globe. Reach me at pratap.jujjavarapu@skill-lync.com