In recent years, electric scooters (E-Scooters) have rapidly gained popularity worldwide as a key mode of micromobility. Due to their convenience, low carbon emissions, and high adaptability to urban environments, many cities have incorporated them into shared transportation systems. However, with the increasing adoption of E-Scooters, the number of traffic accidents involving them has also risen significantly.
Since electric scooters operate on both roadways and sidewalks, ensuring safety presents a major challenge. Moreover, due to constraints such as the small size of control modules, the types of sensors that can be used, and limited power supply, existing autonomous driving and safety assistance systems designed for automobiles are not directly applicable to E-Scooters.
The primary objective of this research is to develop a system that can perform real-time hazard prediction and route recommendations across diverse riding environments using a compact edge computing device. In the future, this system can be leveraged as a node to build a more advanced smart city traffic support network.
Electric Scooter (E-Scooter), Micromobility Traffic Safety, Edge Computing, Real-Time Hazard Prediction, Route Recommendation, Safety Assistance Systems
Traffic Safety Assistance System for Electric Scooters
Map-Aware Super-Resolution GPS Trajectory Reconstruction via Machine Learning
Mobility Data Reconstruction, Spatial-Temporal Data Processing, Transformer Models, Graph Neural Networks (GNNs), Map-Matching Algorithms
Understanding Visitor Mobility Patterns in Public Spaces: A Case Study of Wakayama Castle Park
Visitor Mobility Patterns, Wakayama Castle Park, Reconstruction of Mobility Trajectories, Statistical Analysis,Counterfactual Analysis,Accessibility Constraints,Environmental Conditions,Decision-Making Processes,Design of Mobility Services
Interpretable Environmental Condition Recognition for Autonomous Driving Using Surrogate Decision Trees
Autonomous Driving, Environmental Condition Recognition, CNN, Surrogate Model, Decision Tree
LLM-Driven Adaptive Autonomous Robot Navigation via Multimodal Fusion for Dynamic Environments
Autonomous Navigation, Multimodal Fusion, FPGA Acceleration, LLM (Large Language Model), Socially Compliant Path Planning, Dynamic Environment Adaptation
Quantum Approximation Method and Neural-Integrated Mathematical Optimization Algorithm for Elderly Care Taxi Dispatch Problem
Quantum Computing, Combinatorial Optimization, QUBO, QAOA, Multi-Person Time-Window Constrained Traveling Salesman Problem, TW-TSP, Quantum Approximate Algorithm, Unconstrained Quadratic Optimization Problem, Graph Neural Network, GCN, Vehicle Routing Problem
Lightweight Merging Point Prediction on Highway On-Ramps Using Regression Techniques
Driver Assistance Systems, Multi-autonomous Vehicle Studies, Models, Techniques and Simulations, Automated Vehicle Operation, Motion Planning, Navigation
Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning
Vehicle Routing Problem, Optimization, Integer Linear Programming, Machine Learning, Supervised Learning, Smart City
Synthesis of Pedestrian Movement Patterns: Combining Sparse Location Data with Multi-Spot Density Measurements
GPS，OD estimation