Hazard Guidance for Personal Mobility Devices
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Solution Overview
Problem
The increasing use of personal mobility devices, especially in shared economies, has led to a rise in accidents due to the lack of effective hazard prediction and guidance systems, particularly for users, especially teenagers, who may not be aware of potential hazards on their driving routes.
Innovation Solution
A method and server system that predicts hazards on a personal mobility device's driving route by analyzing historical driving records and providing real-time guidance to users, recommending optimal destinations and alerting them to hazards before and during use, thereby enhancing safety awareness and reducing accident risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hazard prediction and guidance systems are implemented for personal mobility devices, then safety awareness and accident prevention are improved, but device complexity and system resource requirements increase
Solution Approach 1:
The server system automatically extracts historical driving records, predicts destinations, and identifies hazard elements without requiring user input. The system serves itself by utilizing existing operational data to generate safety guidance, eliminating the need for additional sensors or user interaction while maintaining high reliability
Solution Approach 2:
A server acts as an intermediary between the personal mobility device and the user, processing historical data and generating hazard predictions remotely. This mediator approach transfers computational complexity from the device to the server, reducing on-device resources while maintaining safety functionality
2Reliability
If real-time hazard guidance is provided during personal mobility device operation, then accident prevention is improved, but information processing time and computational resources increase
Solution Approach 1:
The system performs destination prediction and hazard element identification in advance based on historical driving records before the user actually needs the guidance. By pre-processing data and preparing safety information ahead of time, the system minimizes real-time processing delays while maintaining accurate accident prevention capabilities
3Measurement precision
If historical driving records are analyzed to predict destinations and hazards, then guidance accuracy is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The server extracts only the essential features and patterns from historical driving records that are necessary for destination prediction and hazard identification. By selecting and extracting only relevant data elements rather than storing and processing complete raw datasets, the system achieves high prediction accuracy while minimizing data storage requirements
Data Source
AI summary
A method for predicting a hazard element on a driving route for a personal mobility device, and guiding the predicted hazard element on the driving route to a customer is provided A hazard element guide server includes a server communication unit that communicates with a customer terminal possessed by a first customer, and receives an application message including information on at least one of a first departure point and a first driving time for use of a first personal mobility. A server controller extracts a first historical driving record corresponding to at least one of a second departure point positioned within a predetermined radius, and estimates a destination included in the extracted first historical driving record as a final destination, and the server controller guides the first customer away from a hazard element that is predicted on a first driving route from the first departure point to the final destination.