This case study presents Capgemini's innovative approach to modernizing automotive software development through the integration of LLMs and cloud technologies. The presentation features multiple speakers from Capgemini, including their Managing Delivery Architect, Enterprise Architecture Director, and Head of Software Defined Vehicle, who collectively outline their solution to a significant industry challenge. # Problem Context and Industry Challenges The automotive industry has traditionally faced several significant challenges in software development: * Extremely long development cycles compared to web or cloud development * Late-stage integration testing that often reveals issues when they're expensive to fix * Complex regulatory and safety certification requirements * Need to maintain consistency across multiple vehicle variants and model years * Limited access to physical testing resources Traditional automotive development processes could take months or even years to go from concept to testable implementation, creating a significant barrier to innovation and efficient development. # The LLM-Powered Solution Capgemini developed an accelerator called "amplifier" that leverages LLMs deployed on AWS Bedrock to transform the development process. The solution consists of several key components and capabilities: ## Requirements Generation and Processing The system begins with the digitization of whiteboard ideation sessions. Instead of letting valuable ideas fade away after meetings, the solution: * Captures whiteboard content through photographs * Uses specifically engineered prompts with LLMs to extract information from these images * Converts unstructured ideas into formal, consistent requirements * Tests requirements for ambiguity automatically * Generates ready-to-use user stories for development teams The solution can process around 30 requirements in approximately 15 minutes, dramatically reducing the traditional timeframe of days or weeks. ## Virtual Development Environment The solution incorporates a sophisticated virtualization layer that runs on AWS Cloud, providing: * Virtualized ECUs (Electronic Control Units) with real software * Complete simulation environment for testing and development * Access to necessary components like climate control and battery level systems * Ability to test integration points early in the development cycle ## Test Automation and Quality Assurance The LLM system also supports comprehensive testing capabilities: * Automated generation of test cases from requirements * Early integration of tests into the development lifecycle ("shift-left" testing) * Consistent testing structure across different components * Ability to scale testing from single vehicles to large fleets # Implementation and Technical Architecture The solution is built on a modern technical stack that includes: * AWS Bedrock for LLM deployment and management * Pre-trained and fine-tuned AI models (including versions of LLAMA) * AWS Engineering Workbench for developer tools * Virtual ECU Builder for simulating vehicle components * Automated CI/CD pipelines for deployment and testing * Cloud infrastructure for scalable testing and simulation The architecture maintains compliance with the traditional V-model development process required in automotive while enabling more iterative and agile development practices. # Results and Benefits The implementation has delivered several significant improvements to the automotive software development process: ## Speed and Efficiency * Reduction in requirements processing time from weeks to hours * Immediate access to development and testing environments * Faster iteration cycles for feature development * Early problem detection and resolution ## Quality and Consistency * Standardized requirement generation * Automated ambiguity checking * Consistent test case generation * Comprehensive integration testing before physical deployment ## Scalability and Resources * Ability to simulate thousands of vehicles for testing * Efficient resource utilization through virtualization * Reduced dependency on physical testing hardware * Cloud-based scaling for large-scale testing scenarios # Critical Analysis While the solution presents significant advantages, it's important to note several considerations: * The system still requires physical hardware testing for certain aspects like timing tests and safety certifications * The effectiveness of the LLM-generated requirements and test cases would likely depend heavily on the quality of the prompt engineering and training data * The solution represents a significant change in workflow that would require careful change management and training * The deployment of such a system would need to maintain strict compliance with automotive industry regulations and safety standards # Future Implications This approach represents a significant shift in automotive software development, potentially: * Enabling faster innovation cycles in vehicle software development * Reducing the cost and time of bringing new features to market * Improving the quality of software through more comprehensive testing * Supporting the industry's move toward software-defined vehicles The solution demonstrates how LLMs can be practically applied in highly regulated industries while maintaining necessary quality and safety standards. It shows the potential for AI to transform traditional development processes without compromising the stringent requirements of automotive software development.