# Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives

arXiv: https://arxiv.org/abs/1709.00911 · HTML: https://mlsanity.com/paper/1709.00911/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks](https://mlsanity.com/paper/2107.00415/) (2021)
- [R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors](https://mlsanity.com/paper/2109.00533/) (2021)
- [Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing](https://mlsanity.com/paper/2402.00035/) (2024)
- [RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks](https://mlsanity.com/paper/2210.05276/) (2022)
- [Proof Minimization in Neural Network Verification](https://mlsanity.com/paper/2511.08198/) (2025)
- [Metric Tools for Sensitivity Analysis with Applications to Neural Networks](https://mlsanity.com/paper/2305.02368/) (2023)
- [Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling](https://mlsanity.com/paper/2607.20339/) (2026)

## Abstract (arXiv)

We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards. We apply the concept in a concrete case study in designing a high-way ANN-based motion predictor to guarantee safety properties such as impossibility for the ego vehicle to suggest moving to the right lane if there exists another vehicle on its right.