Dr. Rupert Stützle I Microsoft

Summary

This presentation explores the transformative potential of agentic AI in automotive R&D and engineering. Highlighting real-world applications, it covers automating tasks like requirement mapping, code generation, and testing through AI-powered tools. Demonstrations showcase how AI can boost productivity by 50% in areas like requirements engineering, feature mapping, and code development. The session emphasizes AI's role in accelerating design cycles, improving efficiency, and fostering innovation, while also touching on the challenges of orchestrating multiple agents in a complex engineering environment. The future of AI in automotive engineering promises streamlined processes and enhanced collaboration.

Takeaways

• 😀 AI is evolving rapidly and is transforming the automotive R&D and engineering fields, focusing on accelerating processes, reducing costs, and improving productivity.
• 😀 The concept of 'agentic AI' is introduced, where AI agents not only assist but autonomously perform tasks, providing a significant productivity boost.
• 😀 The V-model is used as a framework throughout the presentation to illustrate how generative and agentic AI can optimize various stages of automotive R&D.
• 😀 In the requirements engineering phase, AI helps map requirements to features, automating the process and saving up to 50% of the effort in feature planning.
• 😀 GitHub Copilot is demonstrated to create and fix code autonomously, enhancing development efficiency by automating the creation of web-based interfaces from simple sketches.
• 😀 In testing and validation, AI aids in analyzing vehicle data, detecting trends, and automating test data analysis, providing deeper insights without human intervention.
• 😀 The use of AI in testing helps identify weak correlations between data points (e.g., charging times and temperature) and provides actionable insights for future improvements.
• 😀 AI is used to analyze and extract data from vehicles in real-time, improving the speed of analysis and enabling better decision-making based on cloud data from thousands of cars.
• 😀 The future of AI in automotive engineering involves orchestrating multiple AI agents that handle various tasks, ensuring smoother operations across departments and processes.
• 😀 A key challenge is orchestrating multiple AI agents to avoid overwhelming engineers, highlighting the need for a central orchestrator that coordinates agents for greater productivity.

Q & A

What is the primary focus of the presentation?

The primary focus is on the use of agentic AI in automotive R&D and engineering, particularly for accelerating design cycles, automating tasks, and improving productivity in the automotive industry.

What are the key challenges addressed by generative AI and agentic AI in automotive engineering?

The key challenges include accelerating engineering processes, shortening design cycles, saving costs, automating repetitive tasks, and discovering new revenue streams and customer experiences.

What framework is used in the presentation to structure the discussion?

The V-model framework is used to structure the discussion, illustrating different steps from requirements gathering to implementation and verification in the automotive engineering process.

How does agentic AI help with requirements engineering?

Agentic AI automates the process of mapping requirements to features, extracting features from a database, and generating a feature plan, which increases efficiency by 50% in automotive engineering tasks like requirements engineering.

What was the impact of the AI system implemented with Continental Automotive?

The AI system implemented with Continental Automotive automated the mapping of requirements to features, resulting in a 50% reduction in effort and a significant speed-up in processing supplier documents.

How does GitHub Copilot contribute to the implementation process in automotive engineering?

GitHub Copilot assists by automatically generating product requirement documents, creating code (e.g., HTML and JavaScript) for automotive features like a sunroof control app, and even fixing bugs autonomously, improving productivity by 50% on average.

How does AI assist in the testing and validation process in automotive engineering?

AI is used to automate data analysis for testing and validation by extracting test data from vehicles, analyzing correlations, and identifying trends. For example, AI can detect charging time trends related to battery management software.

What improvements were made to BMW's data collection and analysis system using AI?

BMW replaced traditional data recorders with a mobile data recorder that captures data from thousands of vehicles. AI analyzes this data in real-time, speeding up the analysis by a factor of 10 and democratizing access to insights for engineers.

What is the role of multi-agent orchestrators in the future of AI in automotive engineering?

Multi-agent orchestrators are crucial for managing multiple agents working on different tasks, such as compliance checking, requirements management, and configuration. The orchestrator acts as a single point of contact for engineers to interact with, making the process more efficient and less overwhelming.

What is the envisioned future for AI in automotive R&D and engineering?

The future envisions a scenario where engineers use multi-agent orchestrators to seamlessly interact with various AI agents throughout the entire V-model process, automating tasks like regulatory compliance checking, safety evaluation, and feature verification, leading to faster, more efficient automotive development.