Bicycle Automatic Control Reset for Rider Preference Switching
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Solution Overview
Problem
Existing human-powered vehicles with automatic control systems struggle to adapt to individual rider preferences and intentions, particularly in ride-sharing scenarios where multiple riders with different preferences use the same vehicle.
Innovation Solution
A human-powered vehicle control device and method that allows resetting of automatic control settings based on rider interventions, using a processor to learn and adjust parameters according to predetermined conditions, such as specific operations or travel speed changes, and notifying the rider of the reset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automatic control parameters are continuously optimized through learning rider interventions, then the control system becomes more personalized and adaptive to individual rider preferences, but the system cannot adapt quickly when riders change (ride-sharing scenario)
Solution Approach 1:
The system pre-stores multiple rider profiles with their respective optimized control parameters in the memory unit. When a rider identifies themselves (e.g., through authentication), the system immediately retrieves and applies their pre-optimized parameters without requiring real-time learning, thus avoiding time loss while maintaining personalization.
Solution Approach 2:
The system dynamically switches between different rider profiles based on rider identification. The control parameters are not fixed but adaptively change according to which rider profile is active, enabling quick adaptation to different riders in ride-sharing scenarios while maintaining individualized optimization for each rider.
2Measurement precision
If the system learns and changes control parameters based on rider interventions, then the automatic control becomes more accurate to rider intentions, but the system complexity increases
Solution Approach 1:
The system monitors rider interventions (manual overrides of automatic control) and uses this feedback to refine and update the control parameters for each rider profile. This continuous feedback loop improves accuracy of control alignment with rider intentions while maintaining a manageable complexity through iterative learning rather than complex initial design.
Solution Approach 2:
The system creates and stores copies of optimized control parameters for different rider profiles in memory. Instead of maintaining one complex adaptive system, it replicates the learned parameters across multiple rider-specific profiles, reducing the complexity of real-time decision-making while maintaining high accuracy for each rider.
3Adaptability or versatility
If the system resets parameters to predetermined data when predetermined conditions are satisfied, then the system can quickly adapt to rider changes, but the personalized optimization is lost
Solution Approach 1:
The system pre-prepares multiple rider profiles with their personalized parameters stored in memory. When a rider change is detected, the system switches to the appropriate pre-prepared profile rather than resetting to default, thus maintaining both adaptability to rider changes and reliability of personalized settings simultaneously.
Solution Approach 2:
The system changes the active parameter set based on rider identification rather than resetting parameters. It selects from pre-stored parameter sets corresponding to different riders, enabling quick adaptation while preserving the reliability of personalized optimizations for each identified rider.
Data Source
AI summary
A human-powered vehicle control device includes a processor configured to read information from a memory and execute processing. The processor is further configured to execute processing of acquiring input information related to traveling of a human-powered vehicle, performing automatic control on a controlled device provided to the human-powered vehicle by control data of the controlled device, the control data being decided based on the input information acquired, changing, based on the input information, a parameter related to automatic control of the controlled device through learning an intervening operation performed on the automatic control by a rider, and resetting the parameter related to the automatic control, which is changed through learning, to predetermined data in a case where a predetermined condition is satisfied.