Accelerating DevSafeOps for Autonomous Driving via Generative-AI and Synthetic Data

Doktorsavhandling, 2026

Background: The safety of Autonomous Driving (AD) remains a barrier to its widespread adoption, as evidenced by recent incidents. Factors such as a complex environment, evolving technologies, and shifting regulatory and customer requirements necessitate continuous monitoring and improvement of AD software. This is a process that may favor software and system engineering supported by DevOps. The iterative nature of the DevOps process is crucial, serving two purposes: satisfying customer demands through continuous im- provement of the function and providing a framework for timely responses to unknown bugs or incidents. However, any update to the software must follow rigorous safety processes prescribed by standards, regulations, and the state of the art in industry. Incorporating these safety activities into the DevOps forms an iterative process called DevSafeOps. These necessary activities are vital for safety assurance, and may inherently lead to a compromise in rapidity.
Research Goal: In this work, we identify the challenges of rapid DevSafeOps in AD development and explore existing solutions. Subsequently, we propose multiple approaches for accelerating safety analysis, requirements engineering, code generation, and synthetic data generation in DevSafeOps cycles.
Methods: Diverse research methods are utilized to address each research objective. Interview studies and a systematic literature review are conducted to identify the challenges, research gaps, and existing approaches. Then, design science, interview study, case study, and experimentation are employed to design and evaluate new approaches to address our research goal.
Results: Initially, the challenges and research gaps related to each essential activity for the safety of automated driving are identified (Papers A and B), together with the proposed solutions presented in the literature (Paper B). Two approaches are proposed to accelerate the design phase (i.e., analysis and requirements engineering) as an initial step in DevSafeOps. We adapt System Theoretic Process Analysis (STPA) to enable distributed development within automotive system engineering (Paper C). As an alternative approach, a Large Language Model (LLM)-based multi-agent Hazard Analysis and Risk Assessment (HARA) prototype is proposed and evaluated to enable automation (Papers D and E). The rule-based software-implementation phase is accelerated through LLM-based code generation conducted through a conversation in a simulation environment (Papers F and G). To connect the design phase to operation, a vision-language model (VLM) is employed to enable rapid closed-loop DevSafeOps (Paper H). In parallel, a complementary solution is introduced to address the specific needs of Machine Learning (ML)-based software development. As data act as requirements for ML, it is crucial to generate data in a controlled manner to obtain a su!ciently sized population of critical scenarios for training the expected behavior. Hence, through synthetic data generation using three-dimensional Gaussian Splatting (3DGS), ML-based software development in the DevSafeOps cycle is covered (Paper I).
Conclusions: This thesis first identifies multiple challenges in achieving rapid DevSafeOps in AD development and then proposes several approaches for addressing these challenges across different phases of the DevSafeOps cycle. To accelerate the design phase, we introduce an adaptation of STPA for multiparty distributed development and employ multi-agent LLMs as a parallel approach for HARA. We further examine how LLMs and VLMs can support safety concept design, code generation, and monitoring activities with reduced engineer involvement, while defining necessary safeguarding measures. Finally, we investigate 3DGS as an effective and rapid DataOps technique within DevSafeOps, enabling improved data generation and augmentation for ML-based software development.