Vehicle Driving Parameter Control for Adaptive Driving Style Matching
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Solution Overview
Problem
Existing automated driving systems struggle to dynamically adjust driving parameters based on individual driving styles and environments, relying on manual settings that fail to adapt in real-time.
Innovation Solution
A method that acquires driving data, extracts driving features such as operation frequencies of vehicle components, and uses a logistic regression model to determine probabilities of different driving styles, thereby dynamically adjusting parameters like longitudinal acceleration and deceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual setting method is used for automated driving parameters, then the parameter setting is simple and easy to implement, but the system cannot dynamically adjust parameters based on driving styles and environments
Solution Approach 1:
The patent implements dynamic adjustment of automated driving parameters by continuously monitoring driving data and automatically modifying parameters such as longitudinal acceleration and deceleration based on detected driving styles, transforming the static manual setting system into a dynamic adaptive system
Solution Approach 2:
The system performs self-adjustment by automatically analyzing driving data, identifying driving styles through pattern recognition, and modifying driving parameters without requiring manual intervention, enabling the system to serve itself in adapting to driver preferences
2Ease of operation
If automated driving parameters are manually set, then the implementation cost is low, but the user experience and responsiveness to driver needs deteriorate
Solution Approach 1:
The system establishes a feedback loop by continuously collecting driving data, analyzing driver behavior patterns, and using this information to automatically adjust driving parameters, creating a closed-loop system that responds to driver needs in real-time
3Adaptability or versatility
If real-time adjustment of driving parameters is implemented, then the system adaptability improves, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary classification of driving styles by analyzing patterns in driving data and pre-establishing parameter sets corresponding to different driving styles, enabling rapid parameter selection and adjustment without extensive real-time computation
Data Source
AI summary
A method for determining driving parameters includes: acquiring driving data of a driver; extracting driving features of the driver based on the driving data, where the driving features include first operation frequency of a first component of a vehicle and second operation frequency of a second component of the vehicle; determining, based on the driving features, a first probability that the driver has a first driving style and a second probability that the driver has a second driving style; and determining the driving parameters based on the first probability and the second probability, where the driving parameters include at least longitudinal acceleration and longitudinal deceleration. The method can dynamically adjust automated driving parameters to meet the driving style of the driver, thereby effectively improving user experience.