mlsanityData to 7 Oct 2026

Evaluation of Runtime Monitoring for UAV Emergency Landing

arXiv 2202.03059 ↗First posted 7 Feb 2022cs.RO cs.AI cs.CVcited by 4

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Abstract (arXiv)

To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

12 of 34 references.

References · 12

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  3. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  4. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  5. 2019Misbehaviour Prediction for Autonomous Driving Systemscited by 55
  6. 2018Runtime Monitoring Neuron Activation Patternscited by 34
  7. 2021Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trendscited by 26
  8. 2019Towards Structured Evaluation of Deep Neural Network Supervisorscited by 19
  9. 2021Benchmarking Safety Monitors for Image Classifiers with Machine Learningcited by 14
  10. 2021Certifying Emergency Landing for Safe Urban UAVcited by 6
  11. 2020Robust Autonomous Landing of UAV in Non-Cooperative Environments based on Dynamic Time Camera-LiDAR Fusioncited by 3
  12. 2020Ensemble Transfer Learning for Emergency Landing Field Identification on Moderate Resource Heterogeneous Kubernetes Clustercited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 2.

4 papers.

  1. 2022Out-Of-Distribution Detection Is Not All You Needcited by 23
  2. 2022Unifying Evaluation of Machine Learning Safety Monitorscited by 8
  3. 2024Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forwardcited by 5
  4. 2024Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoringcited by 2