Recent Advances in Autonomous Driving Safety

Abstract

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 end- to-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 vision language models (VLMs) to enhance high-level semantic reasoning and understanding of long-tail driving scenarios. It discusses safety guardrail technologies, such as chain of thought (CoT) reasoning, to ensure that 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.

Keywords

autonomous driving / safety validation / end-to-end / vision-language-action (VLA)

Author Contributions

S.W.: Conceptualization, investigation, writing, and revision; H.L.: Investigation, writing, and revision; G.Z: Writing, and revision. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Use of AI and AI-Assisted Technologies

During the preparation of this work, the authors used Gemini for language polishing. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

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