SDV Guide
digital.auto
  • Welcome
  • SDV101
    • Part A: Essentials
      • Smart Phone? No: Habitat on Wheels!
      • Basics: What is a Software-defined Vehicle
      • MHP: Expert Opinion
      • Challenges: What sets automotive software development apart?
      • SDV Domains and Two-Speed Delivery
    • Part B: Lessons Learned
      • Learnings from the Internet Folks
        • Innovation Management
        • Cloud Native Principles
          • DevOps and Continuous Delivery
          • Loose Coupling
            • Microservices & APIs
            • Containerization
            • Building Robust and Resilient Systems
      • Learnings from the Smart Phone Folks
    • Part C: Building Blocks
      • Foundation: E/E Architecture
        • Today`s E/E Architectures
        • Evolving Trends in E/E Architectur
        • Case Study: Rivian
      • Standards for Software-Defined Vehicles and E/E Architectures
      • Building Blocks of an SDV
        • Service-Oriented Architecture
          • The SOA Framework for SDVs
          • Container Runtimes
          • Vehicle APIs
          • Example: Real-World Application of SDV Concepts
          • Ensuring Functional Safety
          • Event Chains in Vehicle SOAs
          • Vehicle SOA Tech Stack
        • Over-the-Air Updates: The Backbone of Software-Defined Vehicles
        • Vehicle App Store: The Holy Grail of Software-Defined Vehicles
      • Summary: Building Blocks for Software-Defined Vehicles
    • Part D: Implementation Strategies
      • #DigitalFirst
      • Hardware vs Software Engineering
        • The Traditional V-Model in Automotive Development
        • Agile V-Model, anybody?
        • Key: Loosely Coupled, Automated Development Pipelines
        • The SDV Software Factory
      • Implementing the Shift Left
        • Simulation and Digital Prototyping
          • Early Validation: Cloud-based SDV Prototyping
          • Detailed Validation: SDVs and Simulation
        • Towards the Virtual Vehicle
          • Case Study: Multi-Supplier Collaboration on Virtual Platform
          • Long-Term Vision
        • Physical test system
        • De-Coupled, Multi-Speed System Evolution
        • Continuous Homologation
        • Summary and Outlook
      • Enterprise Topics
        • Variant Management
        • Engineering Intelligence
        • Enterprise Organization, Processes, and Architecture
        • Incumbent OEMs vs EV Start-ups
  • SDV201
  • ./pulse
    • SDV Culture
    • Lean Sourcing
      • LeanRM
        • Why so many Requirements?
      • SCM for SDVs
    • SDV Systems Engineering
      • LeanSE
      • SDVxMBSE
    • Digital First
    • Loose Coupling
      • API-first
      • Freeze Points
    • Automation and Engineering Intelligence
    • Continuous Homologation
    • Build / Measure / Learn
  • Glossary
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SDV Guide

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(c) 2025 Dirk Slama

On this page
  • Cloning Test Vehicles for Scalability
  • Advanced Configuration and AI-Driven Adaptation
  • Virtual Integration with Hardware-in-the-Loop
  • Managing Complexity in the House of HIL
  • Bridging Virtual and Physical Systems
  • The Future of System Complexity
  1. SDV101
  2. Part D: Implementation Strategies
  3. Implementing the Shift Left
  4. Towards the Virtual Vehicle

Long-Term Vision

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Last updated 6 months ago

The long-term vision for SDVs focuses on a 100% virtualized vehicle in the cloud, enabling engineers to clone test vehicles effortlessly. Instead of spending months replicating complex HIL integration scenarios or building physical test vehicles, virtual vehicles can be copied and pasted with a few clicks. This allows for rapid deployment of identical test setups, accelerating testing timelines and reducing costs.

Cloning Test Vehicles for Scalability

Cloning virtual vehicles provides a significant edge in scalability. Engineers can create unlimited instances of the same test setup, whether for running simultaneous tests, replicating advanced driving scenarios, or supporting globally distributed teams. This capability removes constraints associated with physical vehicle availability, enabling unparalleled flexibility in testing.

Advanced Configuration and AI-Driven Adaptation

Generative AI simplifies reconfiguration of virtual vehicles. For example, converting a left-hand drive model to a right-hand drive becomes seamless as AI identifies and automates adjustments, such as repositioning the steering wheel and associated components. This results in two independent test environments for parallel validation, enhancing efficiency in managing diverse vehicle variants.

Virtual Integration with Hardware-in-the-Loop

Virtual environments pave the way for HIL testing, where real hardware components, such as ECUs, sensors, and actuators, are validated within simulated conditions. Initially, Component HIL validates single hardware modules, while System HIL scales to the entire vehicle, forming a House of HIL for comprehensive testing.

Managing Complexity in the House of HIL

The House of HIL integrates dozens of ECUs, sensors, and actuators in modular test racks, supporting system-level validation. Reconfiguring these physical setups for new variants—like left- versus right-hand drive—requires significant time and resources. However, feeding simulated data into HIL environments ensures robust safety validation without needing operational vehicles.

Bridging Virtual and Physical Systems

Features like “cabin door open” require safety checks, such as vehicle speed and rear-camera inputs. Simulated data replaces real-world inputs, enabling hardware systems to operate and validate functions within the HIL lab seamlessly.

The Future of System Complexity

As SDVs transition toward centralized compute and zone-based architectures, the complexity of HIL systems may reduce. Combined with the ability to clone virtual vehicles, this evolution allows for a modular, scalable testing strategy that enhances speed, cost efficiency, and collaboration.

By integrating virtual cloning, AI-driven configuration, and HIL validation, this approach empowers the industry to accelerate development cycles and streamline the path to software-defined vehicles.