Vehicle Shift Control Adaptation to Driver Tendency
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Solution Overview
Problem
Current vehicle shift control systems fail to accurately reflect a driver's intention, particularly in sporty modes, as they do not consider the driver's long-term driving tendency, leading to dissatisfaction with driving performance.
Innovation Solution
A device and method that utilize a data detector and controller to determine both short-term and long-term driving tendencies, adjusting shift patterns based on the vehicle's running mode and long-term tendency, incorporating sensors for vehicle speed, acceleration, shift-speed, steering angle, and distance to the forward vehicle, and calculating indices to adapt shift control accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If shift control is based only on existing sporty mode pattern, then sporty mode performance is improved, but driver's individual tendency is not reflected
Solution Approach 1:
The system performs preliminary learning of driver tendencies over time, storing driving patterns and preferences in advance. This pre-acquired knowledge is then applied during sporty mode operation, allowing the system to adapt to individual drivers without adding complexity to the real-time control logic.
Solution Approach 2:
The system continuously monitors driver behavior during sporty mode operation and uses this feedback to refine the shift control strategy. By incorporating real-time feedback loops that adjust shift timing and patterns based on actual driver responses, the system achieves better adaptability while maintaining manageable complexity through iterative learning.
2Measurement precision
If shift control considers long-term driving tendency, then driver satisfaction is improved, but calculation complexity increases
Solution Approach 1:
The system performs preliminary learning of driver tendencies over time, storing driving patterns and preferences in advance. This pre-acquired knowledge is then applied during sporty mode operation, allowing the system to adapt to individual drivers without adding complexity to the real-time control logic.
Solution Approach 2:
The system creates simplified representations or models of complex driver behavior patterns. By copying essential tendency characteristics into compact data structures that can be quickly referenced during control decisions, the system achieves high measurement precision while avoiding the computational burden of analyzing complete historical driving data in real-time.
3Ease of operation
If shift pattern is fixed for sporty mode, then control simplicity is maintained, but driver intention accuracy deteriorates
Solution Approach 1:
The system transitions from a static, fixed shift pattern to a dynamic adaptive pattern that evolves based on learned driver tendencies. The shift control parameters are no longer fixed but dynamically adjusted according to the driver's individual preferences and behavior patterns, maintaining simplicity through automation while improving accuracy.
Solution Approach 2:
The system modifies key shift control parameters such as shift timing, RPM thresholds, and gear selection criteria based on learned driver tendencies. By changing these parameters adaptively rather than maintaining fixed values, the system achieves better driver intention accuracy while keeping the overall control structure simple through parameter-based adaptation.
Data Source
AI summary
A device for controlling shift in a vehicle may include a data detector configured to detect data for a shift control and a controller configured to determine a short-term driving tendency of a driver using the data, determine a long-term driving tendency of the driver using the short-term driving tendency of the driver, and control shift in the vehicle in consideration of a running mode of the vehicle and the long-term driving tendency which are determined from the data.