  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.
