E-Scooter Rider detection and classification in dense urban environments

Shane Gilroy, Darragh Mullins, Edward Jones, Ashkan Parsi, Martin Glavin

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. Although similar in physical appearance to pedestrians, e-scooter riders follow distinctly different characteristics of movement and can reach speeds of up to 45 kmph. The challenge of detecting e-scooter riders is exacerbated in urban environments where the frequency of partial occlusion is increased as riders navigate between vehicles, traffic infrastructure and other road users. This can lead to the non-detection or mis-classification of e-scooter riders as pedestrians, providing inaccurate information for accident mitigation and path planning in autonomous vehicle applications. This research introduces a novel benchmark for partially occluded e-scooter rider detection to facilitate the objective characterization of detection models. A novel, occlusion-aware method of e-scooter rider detection is presented that achieves a 15.93% improvement in detection performance over the current state of the art.

Original languageEnglish
Article number100677
JournalResults in Engineering
Volume16
DOIs
Publication statusPublished - Dec 2022

Fingerprint

Dive into the research topics of 'E-Scooter Rider detection and classification in dense urban environments'. Together they form a unique fingerprint.

Cite this