# Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems

arXiv: https://arxiv.org/abs/2411.14374 · HTML: https://mlsanity.com/paper/2411.14374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Safety Verification of Deep Neural Networks](https://mlsanity.com/paper/1610.06940/) (2016)
- [Reachability Analysis of Deep Neural Networks with Provable Guarantees](https://mlsanity.com/paper/1805.02242/) (2018)
- [The Probabilistic Model Checker Storm](https://mlsanity.com/paper/2002.07080/) (2020)
- [Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study](https://mlsanity.com/paper/2302.04634/) (2023)
- [Systematic Literature Review of Validation Methods for AI Systems](https://mlsanity.com/paper/2107.12190/) (2021)
- [TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation](https://mlsanity.com/paper/2302.14486/) (2023)
- [Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles](https://mlsanity.com/paper/2104.06178/) (2021)
- [Certified Control for Train Sign Classification](https://mlsanity.com/paper/2311.09778/) (2023)

## Abstract (arXiv)

The certification of autonomous systems is an important concern in science and industry. The KI-LOK project explores new methods for certifying and safely integrating AI components into autonomous trains. We pursued a two-layered approach: (1) ensuring the safety of the steering system by formal analysis using the B method, and (2) improving the reliability of the perception system with a runtime certificate checker. This work links both strategies within a demonstrator that runs simulations on the formal model, controlled by the real AI output and the real certificate checker. The demonstrator is integrated into the validation tool ProB. This enables runtime monitoring, runtime verification, and statistical validation of formal safety properties using a formal B model. Consequently, one can detect and analyse potential vulnerabilities and weaknesses of the AI and the certificate checker. We apply these techniques to a signal detection case study and present our findings.