Eclipse SDV AI SIG – Proposal Detail
SIG Proposal Backup – John Stenlake (Microsoft)
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Context (can be skipped)
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Generative AI�is changing the automotive industry
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Generative AI�
Generative AI engine
Memory & Context
Universal interface
Ask me anything…
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Agents are an extension of GenAI models, giving it the ability to interact with its surroundings and providing additional context for its role
A2A
Tools Interface
Agent
Tool Control
Tool Control
Profile, Goals and Instructions
Model based Reasoning & Planning
Local Memory
User
Main building blocks
Memory interaction Context
Model based Reasoning & Planning
Profiles, Specific Know How
Utilizing industry standards
Agent
MCP
A2A
User interaction
MCP
Agent Interface
Agent
Agent
A2A
Flexible Data structures
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Generative AI supports every stage of the V-Model
data architecture
Use Cases
Agents can collaborate on tasks
Agents support within existing development tools
Agents support across tool boundaries
Service
Production
Configuration Vehicle / Product Structure
Regulations & Standards
Homologation
Mechanical
E/E
Software
Requirementsmgmt.
Architecture
Integration & Testing
Ideation
Build on emerging Standards
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Multi Agents can collaborate on complex Tasks
Single Agent
Agent
Tools
User
Single Tasks / Department
Agent
Agent
Tools
Agent
Tools
Agent
Tools
User
Agent
User
Multiple Tasks / Departments Interaction
MCP
A2A
User interaction
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Single Focus Area Use Cases
data architecture
Activities
Coding
Requirement Level
Modeling
Service
Production
Configuration Vehicle / Product Structure
Regulations & Standards
Homologation
Mechanical
E/E
Software
Requirementsmgmt.
Architecture
Integration & Testing
Ideation
Issue Tracking
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Agent Collaboration across multiple Focus Areas
data architecture
Main building blocks
Integration Agent
Ticket Agent
Architecture Agent
Service
Production
Configuration Vehicle / Product Structure
Regulations & Standards
Homologation
Mechanical
E/E
Software
Requirementsmgmt.
Architecture
Integration & Testing
Ideation
Triage Agent
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AI Assistant support in Requirement Management
AI Agent use case
Requirement Management
System Level
Requirement Level
Extraction and Processing
Quality & Compliance Improvement
Classification and Organization
Traceability Management
Gap Analysis
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Stakeholder Communication
Reuse and Pattern Recognition
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Change Impact Assessment
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Extraction and Processing
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Quality & Compliance Improvement
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…
Gap Analysis
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Stakeholder Communication
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Sub Process - Ticket Triage
Triage Agent
Initial Data Analysis & Hypothesis Forming
Process Flow
Symptom Analysis, match historical data, recommend additional data collection
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Architecture Agent, provide system topology and communication path
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Data Analysis filter relevant data from noise
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Integration Agent sub system changes & known issues
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Hypothesis formation ranked cause analysis, recommend responsible team
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Architecture agent
System Architecture Analysis
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Integration Agent
System Integration
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Ticket Agent
Symptom Collection & Analysis
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Eclipse SDV AI SIG
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Proposal: Rationale and Objectives for Forming the AI SIG
Unifying AI Experts
The AI SIG would bring together experts and enthusiasts to foster collaboration and innovation in AI technologies.
Knowledge Sharing
The group would promote sharing of insights and research to advance understanding and application of AI to SDV
Developing Best Practices
AI SIG focuses on creating frameworks, standards and best practices to address AI deployment challenges effectively.
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Why Now
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Why a SIG?�
Need a Focus point for AI-related topics
While individual projects might also look at aspects of AI a joined-up approach can help accelerate AI benefits across the whole Eclipse SDV domain
Broad Scope
There are multiple aspects to AI relating to SDV to be considered – AI for Toolchains, AI in-vehicle stacks, Autonomous functionality, distributed Agents – which go beyond the scope of individual projects.�Initial focus is on Toolchains.
Flexible Governance
A SIG allows us to meet with individuals who are outside Eclipse membership (although they cannot vote or direct meetings). This enables close cooperation with other communities as appropriate. A cooperative approach with COVESA is proposed due to common interest and the ability to build broader support.
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Vision Statement
Enable interoperable, agent-augmented automotive development tooling across the V-Model through open, practical service capability specifications and reference implementations.
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Proposed Initial Focus: AI Enabled SDV Toolchains�
AI Toolchain Design Vision
Focus on designing AI toolchains that integrate seamlessly to support complex workflows efficiently. Specifically:��Enable interoperable, agent-augmented automotive development tooling across the V-Model through open, practical service capability specifications and reference implementations.
Implementation Strategies
Explore practical approaches for implementing AI toolchains to maximize performance and scalability. Seek collaborative opportunities to substantiate and demonstrate these approaches including:�� - collaboration with COVESA on the above goal
- collaboration with Digital.auto and their SDV lab
- potential collaboration with EU funded projects
Impact on AI Adoption
Examine how AI toolchains accelerate SDV and AI adoption across diverse industries, driving innovation and efficiency.
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Foundational Principles
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V-Model Focus areas (simplified)
Stage | Why It Matters | Agent / AI Leverage |
Requirements | Alignment & traceability | Summaries, gap suggestions |
Architecture | Consistency checks | Model ↔ requirement validation |
Implementation | Artifact generation | Code/model scaffolds, impact hints |
Integration & Validation | Test & assembly | Test gap clustering, planning |
Homologation | Compliance evidence | Automated collation |
main focus areas
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Initial Focus Area (Pilot)
Pilot Scope:
Establish the working pattern
Three pilot phases (repeated for each V-Model stage):
Then: review, revise, repeat
Three-Phase Pilot Pattern
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Sample Timeline (Illustrative)
Month | Milestone |
Nov 2025 | Kickoff + finalize pilot scope (Requirements Management??) |
Dec 2025 | Use case documentation & consolidation |
Jan 2025 | Draft service (pilot) capability spec v0.1 |
Feb 2026 | Reference implementation alpha |
Mar 2026 | Vendor feedback + spec v0.2 |
Apr 2026 | Broader outreach + 2nd stage selection |
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Deliverables (Foundation)
The initial phase deliverables aim to establish a solid foundation for the working group:
Ongoing the working group will maintain and grow a set of living outputs:
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Success Indicators
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Request for Steering Committee Approval�
Community Support and Interest
We’ve received statements of support from: Bosch, ETAS, AVL, Elektrobit, Kinnovia, T-Systems, Harman, UL Solutions, TTTech, useblocks, and Microsoft.
In addition, the COVESA parallel group has support from Rivian VW, VXLabs, Ropix, SDVerse, Modernize.
Kick off SIG Activity
Subject to Steering Committee approval, we propose to initiate workshops as soon as possible to agree scope and action plans and initialize SIG activity.
Steering Committee Request
I’d like to thank the committee for their attention and consideration, and would like to formally request approval of the proposed SIG.
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Plans for Participation, Collaboration, and Next Steps
Community Participation and Interest
Encourage active community involvement to build engagement and shared ownership in the AI SIG.
Finalize Proposal
Outline clear goals and deliverables and finalize written proposal for distribution in mailing list, and propose to the Technical Advisory and Steering Committees
Kick off SIG Activity
Subject to support, initiate workshops to foster knowledge exchange and initialize SIG activity.
Interested? Please get in touch!
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Thank You
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