# Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach

arXiv: https://arxiv.org/abs/2111.12360 · HTML: https://mlsanity.com/paper/2111.12360/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2021.

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

- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [Unifying Evaluation of Machine Learning Safety Monitors](https://mlsanity.com/paper/2208.14660/) (2022)
- [SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2308.02594/) (2023)
- [Safety Monitoring of Machine Learning Perception Functions: a Survey](https://mlsanity.com/paper/2412.06869/) (2024)
- [Safe Perception -- A Hierarchical Monitor Approach](https://mlsanity.com/paper/2208.00824/) (2022)

## Abstract (arXiv)

While the most visible part of the safety verification process of automated vehicles concerns the planning and control system, it is often overlooked that safety of the latter crucially depends on the fault-tolerance of the preceding environment perception. Modern perception systems feature complex and often machine-learning-based components with various failure modes that can jeopardize the overall safety. At the same time, a verification by for example redundant execution is not always feasible due to resource constraints. In this paper, we address the need for feasible and efficient perception monitors and propose a lightweight approach that helps to protect the integrity of the perception system while keeping the additional compute overhead minimal. In contrast to existing solutions, the monitor is realized by a well-balanced combination of sensor checks -- here using LiDAR information -- and plausibility checks on the object motion history. It is designed to detect relevant errors in the distance and velocity of objects in the environment of the automated vehicle. In conjunction with an appropriate planning system, such a monitor can help to make safe automated driving feasible.