Recent Advances in Autonomous Driving Safety

This paper reviews recent advancements in autonomous driving safety, focusing on the evolution of autonomous driving systems from modular pipelines to end-to-end (E2E) frameworks and emerging vision-language-action (VLA) models. For modular systems, this paper analyzes how to mitigate error propagation between decoupled modules using multi-sensor redundancy and formal verification. For endto- end systems, this paper delves into learning-based motion planning. It emphasizes safety innovations to address the lack of transparency in deep learning, such as interpretable cost maps and world-model-based simulations. For VLA models, this paper investigates integrating large language models (LLMs) to enhance high-level semantic reasoning and understanding of long-tail driving scenarios. It discusses safety guardrail technologies, such as chain of thought (CoT) prompts, to ensure that AI logic aligns with driving regulations. Finally, this paper summarizes current challenges and outlines future research directions, providing a systematic reference for building safe and reliable autonomous driving systems.