# Vulnerable road user detection: state-of-the-art and open challenges

arXiv: https://arxiv.org/abs/1902.03601 · HTML: https://mlsanity.com/paper/1902.03601/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2019.

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

- [LDFA: Latent Diffusion Face Anonymization for Self-driving Applications](https://mlsanity.com/paper/2302.08931/) (2023)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- ["If you could see me through my eyes": Predicting Pedestrian Perception](https://mlsanity.com/paper/2202.13981/) (2022)

## Abstract (arXiv)

Correctly identifying vulnerable road users (VRUs), e.g. cyclists and pedestrians, remains one of the most challenging environment perception tasks for autonomous vehicles (AVs). This work surveys the current state-of-the-art in VRU detection, covering topics such as benchmarks and datasets, object detection techniques and relevant machine learning algorithms. The article concludes with a discussion of remaining open challenges and promising future research directions for this domain.