Personal Mobility Load Profiling for Tandem Ride Detection
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Solution Overview
Problem
Existing personal mobility vehicles lack a cost-effective method to detect tandem riding, which can lead to wear and tear of components and increased costs due to the need for additional hardware like camera and weight sensors.
Innovation Solution
A method using existing vehicle sensors to estimate rider load and compare it against a user profile to detect tandem riding, warning the rider through existing vehicle components without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional hardware components (camera sensors, weight sensors) are installed to detect tandem riding, then detection accuracy is improved, but vehicle cost increases
Solution Approach 1:
The patent enables the vehicle to detect tandem riding using its own existing sensors (accelerometer, gyroscope, motor current sensor) without requiring external detection hardware. The vehicle self-monitors its operational parameters and autonomously determines when tandem riding is occurring by analyzing patterns in its own sensor data, thereby eliminating the need for additional detection components.
Solution Approach 2:
The patent repurposes existing vehicle sensors originally designed for other functions (motion sensing for navigation, current sensing for power management) to also detect tandem riding. This multi-functional use of existing components allows accurate detection without adding specialized hardware, resolving the contradiction between detection capability and device complexity.
2Difficulty of detecting and measuring
If additional sensors are added to detect tandem riding, then detection capability is improved, but manufacturing cost increases
Solution Approach 1:
The vehicle uses its own existing sensor suite to perform tandem riding detection, eliminating the need for additional detection hardware during manufacturing. The system leverages sensors already installed for other purposes (accelerometers for motion tracking, gyroscopes for orientation, motor current sensors for power monitoring) to also identify tandem riding conditions through pattern recognition algorithms.
Solution Approach 2:
The patent implements multi-functional use of existing vehicle components, allowing the same sensors to serve both their original purposes and tandem riding detection. This approach maintains ease of manufacture by not requiring additional sensor installation while still achieving accurate detection capability through sophisticated data analysis of existing sensor inputs.
3Device complexity
If existing sensors are used to detect tandem riding, then vehicle cost is reduced, but detection accuracy may be compromised
Solution Approach 1:
The vehicle autonomously analyzes its own operational data from existing sensors to detect tandem riding with high accuracy. By monitoring patterns in motor current consumption, acceleration profiles, vibration characteristics, and rider position data, the system can distinguish between single and multiple riders without needing specialized detection hardware, thereby maintaining both simplicity and accuracy.
Solution Approach 2:
The system continuously monitors sensor data and provides feedback to the control system, which adjusts its analysis based on observed patterns. The feedback mechanism allows the vehicle to learn from its sensor data and improve its tandem riding detection accuracy over time, compensating for the lack of specialized detection hardware through intelligent data processing and pattern recognition.
Data Source
AI summary
In particular embodiments, a computing system may collect, for each ride of a plurality of rides, first ride data associated with a user riding a personal mobility vehicle during a certain time period. The system may calculate, for each ride, an approximate load on the personal mobility vehicle based on at least the first ride data. The system may calculate a user profile of the user based on the approximate loads. The system may collect, for a subsequent ride, second ride data associated with the user riding a current personal mobility vehicle after the certain time period. The system may calculate, for the subsequent ride, a second approximate load on the current personal mobility vehicle based on at least the second ride data. The system may compare the second approximate load to the user profile and classify the subsequent ride as individual or tandem based on the comparison.