# Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

arXiv: https://arxiv.org/abs/2304.12583 · HTML: https://mlsanity.com/paper/2304.12583/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence](https://mlsanity.com/paper/2106.01883/) (2021)
- [Maximum Mean Discrepancy Gradient Flow](https://mlsanity.com/paper/1906.04370/) (2019)
- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [Open-world Machine Learning: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2105.13448/) (2021)
- [Active Learning for Human-in-the-Loop Customs Inspection](https://mlsanity.com/paper/2010.14282/) (2020)
- [CADM: Confusion Model-based Detection Method for Real-drift in Chunk Data Stream](https://mlsanity.com/paper/2303.16906/) (2023)

## Built on by (most cited first, 4 of 4)

- [VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer](https://mlsanity.com/paper/2512.11891/) (2025)
- [CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement](https://mlsanity.com/paper/2310.18215/) (2023)
- [Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift](https://mlsanity.com/paper/2506.08063/) (2025)
- [Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios](https://mlsanity.com/paper/2601.21173/) (2026)

## Abstract (arXiv)

Real-time safety assessment (RTSA) of dynamic systems is a critical task that has significant implications for various fields such as industrial and transportation applications, especially in non-stationary environments. However, the absence of a comprehensive review of real-time safety assessment methods in non-stationary environments impedes the progress and refinement of related methods. In this paper, a review of methods and techniques for RTSA tasks in non-stationary environments is provided. Specifically, the background and significance of RTSA approaches in non-stationary environments are firstly highlighted. We then present a problem description that covers the definition, classification, and main challenges. We review recent developments in related technologies such as online active learning, online semi-supervised learning, online transfer learning, and online anomaly detection. Finally, we discuss future outlooks and potential directions for further research. Our review aims to provide a comprehensive and up-to-date overview of real-time safety assessment methods in non-stationary environments, which can serve as a valuable resource for researchers and practitioners in this field.