# Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot

arXiv: https://arxiv.org/abs/2211.11520 · HTML: https://mlsanity.com/paper/2211.11520/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications](https://mlsanity.com/paper/2107.11750/) (2021)
- [Embedded out-of-distribution detection on an autonomous robot platform](https://mlsanity.com/paper/2106.15965/) (2021)
- [Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems](https://mlsanity.com/paper/2207.14694/) (2022)
- [Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems](https://mlsanity.com/paper/2103.14172/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems](https://mlsanity.com/paper/2307.13419/) (2023)

## Abstract (arXiv)

In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle's operation. However, ML models are unlikely to make accurate decisions when presented with data outside their training distribution. Out-of-distribution (OOD) detection can act as a safety monitor for ML models by identifying such samples at run time. However, in safety critical systems like AVs, OOD detection needs to satisfy real-time constraints in addition to functional requirements. In this demonstration, we use a mobile robot as a surrogate for an AV and use an OOD detector to identify potentially hazardous samples. The robot navigates a miniature town using image data and a YOLO object detection network. We show that our OOD detector is capable of identifying OOD images in real-time on an embedded platform concurrently performing object detection and lane following. We also show that it can be used to successfully stop the vehicle in the presence of unknown, novel samples.