# Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems

arXiv: https://arxiv.org/abs/2109.08599 · HTML: https://mlsanity.com/paper/2109.08599/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2021.

## Built on by (most cited first, 3 of 3)

- [Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models](https://mlsanity.com/paper/2111.03201/) (2021)
- [Multimodal Detection of Unknown Objects on Roads for Autonomous Driving](https://mlsanity.com/paper/2205.01414/) (2022)
- [When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems](https://mlsanity.com/paper/2606.29115/) (2026)

## Abstract (arXiv)

Corner cases for driving automation systems can often be detected by the system itself and subsequently resolved by remote humans. There exists a wide variety of technical approaches on how remote humans can resolve such issues. Over multiple domains, no common taxonomy on those approaches has developed yet, though. As the scaling of automated driving systems continues to increase, a uniform taxonomy is desirable to improve communication within the scientific community, but also beyond to policymakers and the general public. In this paper, we provide a survey on recent terminologies and propose a taxonomy for remote human input systems, classifying the different approaches based on their complexity.