E-Scooter Rider Detection and Classification in Dense Urban Environments

TL;DR

This paper presents a new benchmark and an occlusion-aware detection method for e-scooter riders in urban environments, significantly improving detection accuracy crucial for autonomous vehicle safety.

Contribution

It introduces a novel benchmark for partially occluded e-scooter rider detection and a new detection method that outperforms existing approaches by nearly 16%.

Findings

15.93% improvement in detection performance

Effective detection under partial occlusion conditions

Enhanced safety for autonomous vehicles in urban settings

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 45kmph. 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.