TY - JOUR
T1 - E-Scooter Rider detection and classification in dense urban environments
AU - Gilroy, Shane
AU - Mullins, Darragh
AU - Jones, Edward
AU - Parsi, Ashkan
AU - Glavin, Martin
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85139311453&partnerID=8YFLogxK
U2 - 10.1016/j.rineng.2022.100677
DO - 10.1016/j.rineng.2022.100677
M3 - Article
AN - SCOPUS:85139311453
SN - 2590-1230
VL - 16
JO - Results in Engineering
JF - Results in Engineering
M1 - 100677
ER -