Lightweight Safety Assistance System for E-Scooters: Current Results and Future Directions

Abstract

Electric scooters (e-scooters) are reshaping urban mobility, providing eco-friendly and cost-effective transport. However, the surge in their usage in mixed-traffic environments poses significant safety risks, especially for vulnerable road users (VRUs). This paper presents a lightweight vision-based safety assistance system optimized for real-time inference on edge devices. The core modules include semantic segmentation enhanced by semi-supervised learning, accurate bird’s-eye view (BEV) transformation, real-time motion prediction, and adaptive path planning. Extensive evaluations demonstrate high segmentation accuracy and low-latency execution suitable for resource-constrained hardware. Future research directions include knowledge distillation for model adaptability, cooperative multi-scooter perception, intersection-based AI infrastructure, and the application of large language models (LLMs) to optimize urban traffic flows.