# Towards Scenario-based Safety Validation for Autonomous Trains with Deep Generative Models

arXiv: https://arxiv.org/abs/2310.10635 · HTML: https://mlsanity.com/paper/2310.10635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [SoK: Certified Robustness for Deep Neural Networks](https://mlsanity.com/paper/2009.04131/) (2020)
- [Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications](https://mlsanity.com/paper/2008.02793/) (2020)
- [DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers](https://mlsanity.com/paper/2103.01629/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Comparative Analysis of Visual Odometry in Virtual and Real-World Railways Environments](https://mlsanity.com/paper/2403.17084/) (2024)

## Abstract (arXiv)

Modern AI techniques open up ever-increasing possibilities for autonomous vehicles, but how to appropriately verify the reliability of such systems remains unclear. A common approach is to conduct safety validation based on a predefined Operational Design Domain (ODD) describing specific conditions under which a system under test is required to operate properly. However, collecting sufficient realistic test cases to ensure comprehensive ODD coverage is challenging. In this paper, we report our practical experiences regarding the utility of data simulation with deep generative models for scenario-based ODD validation. We consider the specific use case of a camera-based rail-scene segmentation system designed to support autonomous train operation. We demonstrate the capabilities of semantically editing railway scenes with deep generative models to make a limited amount of test data more representative. We also show how our approach helps to analyze the degree to which a system complies with typical ODD requirements. Specifically, we focus on evaluating proper operation under different lighting and weather conditions as well as while transitioning between them.