E-Scooter Rider Detection and Classification in Dense Urban Environments
This addresses safety-critical requirements for autonomous vehicles by improving detection of vulnerable road users, though it is incremental as it builds on existing detection models.
The paper tackled the problem of detecting and classifying e-scooter riders in dense urban environments, where partial occlusions cause misclassification as pedestrians, and introduced a novel occlusion-aware method that achieved a 15.93% improvement in detection performance over the state of the art.
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.