Foundational Analysis of Safety Engineering Requirements (SAFER)

2026-01-09 · Noga Chemo, Yaniv Mordecai, Yoram Reich arxiv

We introduce a framework for Foundational Analysis of Safety Engineering Requirements (SAFER), a model-driven methodology supported by Generative AI to improve the generation and analysis of safety requirements for complex safety-critical systems. Safety requirements are often specified by multiple stakeholders with uncoordinated objectives, leading to gaps, duplications, and contradictions that jeopardize system safety and compliance. Existing approaches are largely informal and insufficient for addressing these challenges. SAFER enhances Model-Based Systems Engineering (MBSE) by consuming requirement specification models and generating the following results: (1) mapping requirements to system functions, (2) identifying functions with insufficient requirement specifications, (3) detecting duplicate requirements, and (4) identifying contradictions within requirement sets. SAFER provides structured analysis, reporting, and decision support for safety engineers. We demonstrate SAFER on an autonomous drone system, significantly improving the detection of requirement inconsistencies, enhancing both efficiency and reliability of the safety engineering process. We show that Generative AI must be augmented by formal models and queried systematically, to provide meaningful early-stage safety requirement specifications and robust safety architectures.

📄 PDF Abstract BibTeX arXiv:2601.06335

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OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research

2023-05-16 · Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai 외

AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns. Particularly in safety-c…

Philosophy reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

DriveSafer: End-to-End Autonomous Driving with Safety Guidance

2026-05-16 · Shounak Sural, Raj Rajkumar arxiv

End-to-End (E2E) autonomous driving models have shown growing capability in recent years, with performance improving on increasingly challenging benchmarks. However, modern generative E2E planners still suffer from a sub…

Autonomous Driving

SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition

2021-09-29 · Dylan Z Slack, Yinlam Chow, Bo Dai, Nevan Wichers

Though many reinforcement learning (RL) problems involve learning policies in settings that are difficult to specify safety constraints and sparse rewards, current methods struggle to rapidly and safely acquire successfu…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Robotic Grasping+1

RECALL-MM: A Multimodal Dataset of Consumer Product Recalls for Risk Analysis using Computational Methods and Large Language Models

2025-03-29 · Diana Bolanos, Mohammadmehdi Ataei, Daniele Grandi, Kosa Goucher-Lambert

Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains underutilized. In this study, we curate data from the United States Consu…

Large Language Model

Learning Safe Autonomous Driving Policies Using Predictive Safety Representations

2025-12-19 · Mahesh Keswani, Raunak Bhattacharyya arxiv

Safe reinforcement learning (SafeRL) is a prominent paradigm for autonomous driving, where agents are required to optimize performance under strict safety requirements. This dual objective creates a fundamental tension, …

Reinforcement Learning Autonomous Driving