Personal Mobility Vehicle Path Training for Geofenced Assistance
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Solution Overview
Problem
Existing personal-mobility vehicles (PMVs) for individuals with reduced mobility face challenges in safe navigation due to user interaction difficulties with control interfaces, leading to potential safety risks, especially in urban environments, and self-driving solutions are not sufficiently reliable for these users.
Innovation Solution
An assistance system for PMVs that includes sensors and a processing system to monitor vehicle state, learn allowed zones during a training phase, and adjust actuator control to keep the vehicle within these zones, providing real-time assistance and alerts to maintain safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If self-driving solutions are implemented in PMVs, then autonomous navigation capability is improved, but reliability and safety are insufficient for users with reduced mobility
Solution Approach 1:
The patent introduces a geofencing system as an intermediary layer between the user and the autonomous driving system. This virtual boundary system mediates navigation by allowing autonomous operation only within predefined safe zones, thereby maintaining reliability while enabling automation. The geofence acts as a mediator that constrains autonomous behavior to approved areas.
Solution Approach 2:
The system performs preliminary action by pre-defining geofenced areas and safe zones before autonomous navigation begins. These virtual boundaries are established in advance based on safety considerations, and the autonomous system operates within these pre-approved parameters, ensuring reliability is maintained while automation is enabled.
2Reliability
If geofencing systems are implemented to restrict movement to safe zones, then safety is improved, but user freedom of movement and adaptability are reduced
Solution Approach 1:
The geofencing system implements dynamics by allowing the definition of multiple types of zones with different characteristics (safe zones, restricted zones, transition zones). The system can dynamically adjust navigation permissions based on the user's location within these zones, providing adaptability while maintaining safety constraints. Users can be granted temporary permissions to exit geofenced areas when needed.
Solution Approach 2:
The patent segments the operational environment into distinct geofenced zones with different safety characteristics and access permissions. By dividing the space into safe zones, restricted zones, and transition zones, the system provides granular control over autonomous navigation, balancing safety requirements with user freedom and adaptability.
3Reliability
If virtual boundaries and geofences are implemented to prevent deviations, then safety control is improved, but device complexity increases
Solution Approach 1:
The patent replaces physical mechanical boundary systems with virtual geofenced boundaries implemented through software and GPS technology. Instead of physical barriers or mechanical constraint systems, the solution uses digital maps, coordinate-based zones, and software logic to define and enforce safe areas, significantly reducing mechanical complexity while maintaining safety control.
Solution Approach 2:
The system creates a virtual copy of the physical environment through digital mapping and geofencing. Rather than physically marking or constraining boundaries, the patent uses digital representations of safe zones that mirror the physical safe areas, enabling safety control through information processing rather than physical complexity.
Data Source
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AI summary
Described herein is an assistance system for an electric personal-mobility vehicle (1a) for a person with reduced mobility. The assistance system comprises sensors (50a) configured for supplying data on the current state of the electric vehicle (1a) and a processing system (60, 2, 3). During a path training step, the processing system monitors a control signal (S1; Da, Db) supplied by a user interface (10a) of the electric vehicle (1a) and drives at least one actuator (30) of the electric vehicle (1a) as a function of the control signal (S1; Da, Db). Moreover, the processing system acquires a plurality of positions of the electric vehicle (1a), processes such position data to generate data that define at least one allowed zone, and stores the at least one allowed zone in a memory (62). Instead, during a normal operating step, the processing system determines the current state of the electric vehicle (1a) as a function of the data supplied by the sensors (50a) and monitors the control signal (S1; Da, Db) supplied by the user interface (10a). On the basis of these data, the processing system estimates a future state of the electric vehicle (1a) and determines whether the future position of the vehicle is within at least one allowed zone stored in the memory (62). In the case where the future position is within at least one allowed zone, the processing system drives the at least one actuator (30) of the electric vehicle (1a) as a function of the control signal (S1; Da, Db). Instead, in the case where the future position is not within at least one allowed zone, the processing system may control the actuators for keeping the electric vehicle (1a) within at least one allowed zone and/or generate an alert signal.