Fuzzy Logic Driving Tendency Detection
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Solution Overview
Problem
Existing methods for determining a driver's short-term driving tendency are limited, as they rely mainly on accelerator pedal position and change rate, failing to accurately capture the overall driving intention, especially in varying conditions such as mood, road conditions, and sudden changes in driving intention, leading to dissatisfaction with vehicle performance.
Innovation Solution
An apparatus and method that detect vehicle acceleration, speed, and inter-vehicle distance, calculate relative speed, and use fuzzy logic with membership functions to determine short-term driving tendency, allowing for customized shift control by differentiating between aggressive, defensive, and normal driving styles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If accelerator pedal position and change rate are used to determine driving tendency, then the determination process is simple, but the accuracy of capturing overall driving intention is insufficient
Solution Approach 1:
The patent combines multiple parameters (accelerator pedal position, accelerator pedal change rate, vehicle acceleration, vehicle speed, and inter-vehicle distance) into a comprehensive driving tendency determination system. This merging of multiple data sources resolves the contradiction by achieving high measurement precision through multi-parameter fusion while managing complexity through systematic integration rather than isolated parameter analysis.
Solution Approach 2:
The patent introduces fuzzy logic as an intermediary mechanism that processes multiple input parameters and transforms them into a comprehensive driving tendency determination. The fuzzy logic system acts as a mediator that handles the complexity of multi-parameter analysis while providing accurate output, thus resolving the contradiction between simplicity and precision.
2Reliability
If learning-based driving tendency control is implemented, then long-term driver preferences are captured, but short-term changes in driving intention are not reflected
Solution Approach 1:
The patent implements a dynamic driving tendency determination system that continuously updates its assessment based on real-time parameter changes. By using current vehicle acceleration, speed, and inter-vehicle distance data combined with historical learning data, the system adapts to sudden changes in driving intention while maintaining reliability through consistent fuzzy logic processing of both short-term and long-term patterns.
Solution Approach 2:
The patent incorporates feedback mechanisms where the determined driving tendency is used to control shift patterns, and this control outcome feeds back into the determination system. The system continuously monitors whether the learned driving tendency matches actual driving behavior and adjusts accordingly, resolving the contradiction between consistency and adaptability through closed-loop control.
3Device complexity
If uniform driving tendency assumption is made, then the control system is simplified, but individual driver variations and environmental factors are not accounted for
Solution Approach 1:
The patent applies local quality by determining driving tendency separately for different driving conditions and situations rather than using a single uniform assumption. The system adjusts shift control patterns based on specific local conditions (acceleration state, inter-vehicle distance, road conditions) while maintaining an overall learned driving tendency profile, thus achieving adaptability without excessive complexity.
Data Source
AI summary
An apparatus and a method for determining a short-term driving tendency of a driver may include a data detector configured to detect an acceleration of a vehicle, a vehicle speed, and an inter-vehicle distance, and a controller configured to calculate a relative speed with respect to a forward vehicle from the vehicle speed and the inter-vehicle distance, extract fuzzy result values for the vehicle speed and the inter-vehicle distance, respectively, by setting a membership function that corresponds to each of the acceleration of the vehicle and the relative speed with respect to the forward vehicle, and determine the short-term driving tendency of the driver using the fuzzy result values.