Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
- 2021R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensorscited by 9
- 2024Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiingcited by 8
- 2022RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networkscited by 4
- 2025Proof Minimization in Neural Network Verificationcited by 4
- 2023Metric Tools for Sensitivity Analysis with Applications to Neural Networkscited by 0
- 2026Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modelingcited by 0
- 2026Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling22 Jul 2026
- 2025Proof Minimization in Neural Network Verification11 Nov 2025
- 2024Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing8 Jan 2024
- 2023Metric Tools for Sensitivity Analysis with Applications to Neural Networks3 May 2023
- 2022RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks11 Oct 2022
- 2021R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors1 Sep 2021
- 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks1 Jul 2021