Active Collision Avoidance System for E-Scooters in Pedestrian Environment Oakland University

Format:

Book

Conference/Event

Author/Creator:

Yan, Xuke, author.

Contributor:

Shen, Dan

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

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 Long short-term memory (LSTM) network integrated with a state refinement module. This method predicts future trajectories by considering not just past pedestrian positions but also accounting for the behavior and locations of surrounding individuals, acknowledging the influence of human interactions. Leveraging the pedestrians' estimated trajectories based on their historical behaviors, we have devised an e-scooter path planning system that relies on an interpolating curve planner, which can continuously analyze the driving scene, understand the behavior of other road users, evaluate the risk assessment, and predict its future trajectory. 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

Notes:

Vendor supplied data

Publisher Number:

2024-01-2555

Access Restriction:

Restricted for use by site license