Active Collision Avoidance System for E-Scooters in Pedestrian Environment

TL;DR

This paper presents a novel collision avoidance system for e-scooters that predicts pedestrian trajectories using an advanced LSTM network, enhancing safety and path planning in crowded urban environments.

Contribution

The study introduces an innovative e-scooter collision avoidance system combining pedestrian trajectory prediction with scooter motion control, validated on public datasets.

Findings

Effective pedestrian trajectory prediction demonstrated on ETH and UCY datasets.

Enhanced scooter path planning in dense pedestrian areas.

Potential to improve urban scooter safety and integration.

Abstract

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced LSTM network integrated with a state refinement module. This proactive model is designed to ensure unobstructed movement in areas with substantial pedestrian traffic without collisions. Results are validated on two public datasets, ETH and UCY, providing encouraging outcomes. Our model demonstrated proficiency in anticipating pedestrian paths and augmented scooter path planning, allowing for heightened adaptability in densely populated locales. This study shows the potential of melding pedestrian trajectory prediction with scooter motion planning. With the ubiquity of electric scooters in urban environments, such advancements have become crucial to safeguard all participants in urban transit.