Case Study
Tuesday, September 30
04:00 PM - 04:30 PM
Live in Berlin
Less Details
The rapid advancements in automated vehicles and cognitive cyber-physical systems demand a rethinking of traditional development models. While the V-model has long been a cornerstone of system engineering, its limitations are becoming increasingly evident in the face of AI-driven autonomy and iterative, data-centric development. In this presentation, we introduce an extension of the V-model that integrates iterative, data-based processes, harmonizing traditional and emerging methodologies into a unified framework. This approach enables continuous refinement through real-world and synthetic data, ensuring a structured yet adaptive path for the development, verification, and validation of complex autonomous systems. The retrospective approach enhances confidence in data completeness, performance metrics, and release criteria, ultimately ensuring trust in both the product and development process while meeting ambitious goals.
In this session, you will learn more about:
Lars Ullrich is a Ph.D. student at the Chair of Automatic Control at Friedrich–Alexander–Universität Erlangen–Nürnberg, where he also earned his M.Sc. in Mechatronics in 2022. For his Master’s thesis, he received the Baumüller and Hanns-Voith-Foundation Award. He holds a B.Eng. in Mechatronics from the Cooperative State University (DHBW). His research focuses on probabilistic trajectory planning for safe and reliable autonomous driving in uncertain dynamic environments, with a particular emphasis on addressing challenges associated with AI systems in automated driving. Since early 2025, he serves as Vice-Chair of the IEEE Intelligent Transportation Systems Society (ITSS) German Chapter.
The Pop in Your Job – What drives you? Why do you love your job?
What drives me is the fusion of technology and innovation, especially in the rapidly evolving field of automated driving. I thrive in dynamic, ambitious, and impactful environments, where cutting-edge research meets real-world application. The challenge of developing AI-driven solutions that enhance safety and scalability excites me, as does the opportunity to bridge the gap between theory, industry, and regulation. I am passionate about solving complex problems, collaborating with experts across disciplines, and shaping the future of autonomous mobility. The constant pursuit of progress—whether through new methodologies, safety frameworks, or breakthrough technologies—is what makes my work so engaging and rewarding.