# Open-World Active Learning with Stacking Ensemble for Self-Driving Cars

arXiv: https://arxiv.org/abs/2109.06628 · HTML: https://mlsanity.com/paper/2109.06628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [Open-world Learning and Application to Product Classification](https://mlsanity.com/paper/1809.06004/) (2018)

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

- [Evaluating Uncertainty Calibration for Open-Set Recognition](https://mlsanity.com/paper/2205.07160/) (2022)

## Abstract (arXiv)

The environments, in which autonomous cars act, are high-risky, dynamic, and full of uncertainty, demanding a continuous update of their sensory information and knowledge bases. The frequency of facing an unknown object is too high making hard the usage of Artificial Intelligence (AI) classical classification models that usually rely on the close-world assumption. This problem of classifying objects in this domain is better faced with and open-world AI approach. We propose an algorithm to identify not only all the known entities that may appear in front of the car, but also to detect and learn the classes of those unknown objects that may be rare to stand on an highway (e.g., a lost box from a truck). Our approach relies on the DOC algorithm from Lei Shu et. al. as well as on the Query-by-Committee algorithm.