# Strategy to Increase the Safety of a DNN-based Perception for HAD Systems

arXiv: https://arxiv.org/abs/2002.08935 · HTML: https://mlsanity.com/paper/2002.08935/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Deep Learning Safety Concerns in Automated Driving Perception](https://mlsanity.com/paper/2309.03774/) (2023)
- [Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study](https://mlsanity.com/paper/2405.20876/) (2024)
- [Online Out-of-Domain Detection for Automated Driving](https://mlsanity.com/paper/2310.14675/) (2023)
- [Towards a Systematic Risk Assessment of Deep Neural Network Limitations in Autonomous Driving Perception](https://mlsanity.com/paper/2604.20895/) (2026)

## Abstract (arXiv)

Safety is one of the most important development goals for highly automated driving (HAD) systems. This applies in particular to the perception function driven by Deep Neural Networks (DNNs). For these, large parts of the traditional safety processes and requirements are not fully applicable or sufficient. The aim of this paper is to present a framework for the description and mitigation of DNN insufficiencies and the derivation of relevant safety mechanisms to increase the safety of DNNs. To assess the effectiveness of these safety mechanisms, we present a categorization scheme for evaluation metrics.