# Safe Perception -- A Hierarchical Monitor Approach

arXiv: https://arxiv.org/abs/2208.00824 · HTML: https://mlsanity.com/paper/2208.00824/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach](https://mlsanity.com/paper/2111.12360/) (2021)

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

- [Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation](https://mlsanity.com/paper/2407.14306/) (2024)

## Abstract (arXiv)

Our transportation world is rapidly transforming induced by an ever increasing level of autonomy. However, to obtain license of fully automated vehicles for widespread public use, it is necessary to assure safety of the entire system, which is still a challenge. This holds in particular for AI-based perception systems that have to handle a diversity of environmental conditions and road users, and at the same time should robustly detect all safety relevant objects (i.e no detection misses should occur). Yet, limited training and validation data make a proof of fault-free operation hardly achievable, as the perception system might be exposed to new, yet unknown objects or conditions on public roads. Hence, new safety approaches for AI-based perception systems are required. For this reason we propose in this paper a novel hierarchical monitoring approach that is able to validate the object list from a primary perception system, can reliably detect detection misses, and at the same time has a very low false alarm rate.