In this talk, we explore the potential of generative AI, specifically Large Language Models (LLMs) and Vision-Language Models (VLMs), to support the development and operation of safe autonomous driving.
The process begins by identifying critical scenarios in real-world data, then generating safety concepts to mitigate them, and ultimately implementing them in code. To enable this "from concept to code" workflow, we first examine the strengths and limitations of LLMs in automotive system engineering tasks. Building on these insights, we design multi-agent systems that enhance robustness, consistency, and validity of the generated artifacts. We then showcase a set of tailored pipelines, each supporting a different activity in the DevSafeOps cycle, from field data to code generation, refinement, and evaluation. This approach is not limited to rule-based software; it also extends to ML-based software units, enabled by integrating 3D Gaussian Splatting for synthetic data generation and VLMs to trigger data collection.
These AI-enabled pipelines accelerate automotive system and software engineering activities, illustrate how generative AI can integrate into the continuous software development process in automotive, and highlight the emerging role of LLMs in this field.
Ali Nouri is an AI expert at Volvo Cars, working on autonomous driving, and a researcher at Chalmers University of Technology. He has more than ten years of experience in autonomous driving systems. He represents Sweden in international ISO standardization efforts, including ISO 8800 (Safety of AI). His research focuses on accelerating DevOps for autonomous driving software development through generative AI.
