Zhang, Zhengming (Purdue University), Peng, Juntong (Purdue University), Wang, Shaozhi (North Carolina State University), Chen, Yaobin (Purdue University), Zhou, Jue (Purdue University), Ding, Zhengming (Tulane University), Chien, Stanley (Purdue University Indianapolis), Tian, Renran (North Carolina State University)

Multi-Trajectory Prediction for E-Scooter Riders with Multi-Modal Inputs

Scheduled for presentation during the Regular Session "S39b-Data-Driven Optimization in Intelligent Transportation Systems" (FR-EA-T39), Friday, November 21, 2025, 14:30−14:50, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Electric scooters (e-scooters) have rapidly gained popularity in urban transportation, bringing new safety challenges—particularly in interactions with vehicles. While pedestrian trajectory prediction has been extensively studied, research on e-scooter riders remains limited due to scarce data. This paper presents a multi-trajectory prediction framework for e-scooter riders using multi-modal inputs from cameras, LiDAR, GPS, and high-definition maps. Built on a transformer-based architecture, the model integrates a masked autoencoder-based self-supervised learning framework and incorporates visual and spatial contextual features through BEV modules. The proposed method significantly outperforms baseline and prior single-trajectory models across short-, mid-, and long-term prediction horizons. To support this work, a large-scale naturalistic driving dataset comprising over 6,200 e-scooter cases was collected and annotated. Results demonstrate improved prediction accuracy and the potential to reduce collision risks, advancing more effective autonomous vehicle interaction with e-scooter riders.