Proposals

The Eclipse SDV Landscape project provides a comprehensive, visual, and continuously maintained overview of all Software-Defined Vehicle (SDV)–related projects hosted under the Eclipse Foundation. The project delivers a website that categorizes and presents Eclipse SDV projects in a clear, structured, and easily navigable way, supporting discover-ability, orientation, and communication across the SDV ecosystem.

The project builds on existing information of the Eclipse Foundation PMI and relies on existing official Eclipse Project APIs.

The Eclipse SDV Landscape is the official, community-maintained reference for the SDV Working Group and its stakeholders.

The project is providing 

1. Eclipse SDV Landscape public Website
   • Interactive visual overview of SDV projects
   • Hosted via Eclipse infrastructure or approved hosting
   • Organize projects by functional categories (e.g., tooling, middleware, RTOS, AI, digital twin, orchestration, etc.)
   • Exportable views (e.g., PNG, SVG) for presentations and documentation
   • Allow easy updates as projects are added, retired, or reclassified
2. Open Source Repository
   • Source code for generating and maintaining the landscape
   • Configuration and metadata describing SDV projects
   • Documentation for contributors and maintainers
3. Contribution Guidelines
   • Clear process for adding, updating, or removing projects
   • Alignment with Eclipse Foundation branding and policies

Out of Scope

The project will not:

• Replace official project documentation or websites
• Provide technical integration or dependencies between projects
• Act as a governance or compliance tool
• Host or mirror project artifacts or code beyond the landscape itself

The Eclipse Connector Fabric Manager includes:

- A tenant management and provisioning system that can be extended to support a variety of dataspace-related services.
 - A multi-role user interface that supports operators, B2B resellers, and end users for tenant management and service provisioning.
- A demonstrator sample

Eclipse VOStack is an open source software stack for IoT virtualization and convergence with edge/cloud computing technologies. It is aligned with the W3C Web of Things Standard.

The core of Eclipse VOStack is a python implementation building on wot-py (asynchronous Python implementation of a W3C Web of Things runtime). It extends wot-py with features of Virtual Objects (VO): 

• Extension of Protocol Bindings: HTTP(S), CoAP, WebSockets, MQTT, Zenoh
• Periodic Functions: Repeat Functions in irregular intervals
• InfluxDB: Automatic saving of Property values on read/write operations, logging of Action invocations and Event emission
• Automatic configuration of the VO: Script runner that takes a Web of Things Thing Description, a VO descriptor and a python code file and configures the VO

Additionally, VO Stack provides the following functionalities:

• Automatic orchestration/virtualization in Kubernetes deployments
• RTSP server for video streams
• Proxy mode to turn a cVO into a proxy to other VOs

The project provides an API to facilitate the execution of AI agents on Jakarta EE runtimes. It defines the API contract between the runtime and agent implementation. This is similar to what Servlet did for HTTP processing, Jakarta REST did for RESTful web services, or perhaps most appropriately, Jakarta Batch did for batch processing. The annotation-based API will define common usage patterns and life cycles that make it as understandable, consistent, and easy as possible to implement different types of agents. CDI is used as a core component model for the API. The project will likely also define integrations with other key Jakarta EE APIs such as Validation, REST, JSON Binding, Persistence, Data, Transactions, NoSQL, Concurrency, Security, Messaging, and so on. The project aims for the broadest industry consensus possible by engaging as many relevant subject matter experts and API consumers as possible, from within the Java/Jakarta EE ecosystem as well as externally.

Industrial and academic projects requiring access to either 32- or 64-bit, embedded or application class cores. The CVA6 can be synthesized for both FPGA and ASIC targets.

The objective of this project is to propose a means to support the due diligence responsibilities of manufacturers who rely on F/OSS components in a way that, rather than burdening F/OSS maintainers or stewards, helps to sustain F/OSS projects and facilitates interaction with both market surveillance and vulnerability coordination functions at the national and ENISA levels.

Voluntary security attestations offer an opportunity to proactively strengthen the security posture of F/OSS by enabling a wide range of stakeholders, from developers and stewards to integrators and public authorities, to participate in a structured and trustworthy process of security validation.

The Eclipse Data Plane Core project provides:

• Data plane SDKs for Go, Java, .NET, Rust, and Typescript. Other languages may be added in the future based on community feedback.
• A Rust-based data plane implementation for transferring data over HTTP compatible with the Data Plane Signaling Specification.

Data Plane Signaling will consist of the following:

- A protocol state machine definition
- JSON message types and schemas
- HTTP RESTful APIs

The initial specification proposal is here: https://github.com/Metaform/dataplane-signaling.

Eclipse SageTea Runtime contains:

• Smalltalk based source code for the development of Object-Oriented software written primarily in Squeak Smalltalk

• Smalltalk based source code for the development of Low Code AI Software to be included in other Eclipse Software Projects

• Smalltalk based source code for the SAGETEA Postgresql database (for new RAG implementations)

In the scope of the project: 

Adoption of WASM and associated APIs and tools

WASM is the new industry standard portable virtual machine which supports multiple languages with interoperability. It provides a robust security and language neutral component model addressing a major concern with todays browser-based applications . Google, Microsoft, Cloudflare have made significant investments in WASM and WAS.
 

Leveraging AI to Solve Historical Challenges

AI's advancements unlock solutions to challenges previously unsolvable for SageTea. Notably:
1. AI-Based Agents for Actions: The long-standing challenge of implementing Actions within
SageTea can now be addressed through AI-powered Agents. These agents, a burgeoning trend in AI, provide intelligent decision-making and process execution capabilities that complement
SageTea's framework.

2. LLM Integration for Code Generation: Recent experiments demonstrate that Large Language
Models (LLMs) can generate Smalltalk code directly from English statements. This capability
transforms SageTea into a low-code AI platform, making it more accessible and versatile for
developers and end-users alike.

3. SageTea as a RAG Database: The SageTea database's high performance and adaptability make it well-suited for Retrieval-Augmented Generation (RAG) systems. RAG combines traditional database querying with AI-driven reasoning, offering applications in industries such as knowledge management, customer service, and advanced analytics.

Eclipse Automotive Integration for AutoSD uses an AutoSD image, built and tailored for its community, to run and test both Eclipse SDV projects and blueprints.

Eclipse Automotive Integration for AutoSD offers a foundation for projects to build, run and test their stack, components and services, including blueprints.

The image setup will expose projects into thinking how their projects or blueprints would work in an Mixed Critical Orchestration[0]  architecture.

Several upstream tools from the CentOS Automotive SIG can used, from building images[1] to performance testing[2], but this integration project could also provide its own set of specialized tools for Eclipse SDV, to ease the process of deploying blueprints into this reference AutoSD image and so on.

This integration project gets several supported platforms (read boards) for free[3], including virtual ones, such as AWS and Azure, allowing the possibility of running blueprints tests in said cloud providers.

[0] - https://sigs.centos.org/automotive/features-and-concepts/con_mixed-criticality/
[1] - https://gitlab.com/CentOS/automotive/src/automotive-image-builder
[2] - https://sigs.centos.org/automotive/performance_monitoring_with_pcp/#arcaflow-workflow
[3] - https://sigs.centos.org/automotive/provisioning/