Vehicle Control System Using Driving Characteristic Segmentation
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Solution Overview
Problem
Existing vehicle control methods fail to accurately predict driving environments and driver tendencies, leading to unnecessary vehicle control adjustments and reduced fuel efficiency and drivability due to misclassification of driving environments.
Innovation Solution
A method that collects driving data, extracts ordinary and distinguishing characteristics, classifies driving tendencies, and controls vehicle operations based on these tendencies, including engine state and creep torque adjustments, to optimize energy efficiency and prevent unnecessary mode transitions between hybrid electric vehicle and electric vehicle modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicle control methods use simple driving environment classification, then device complexity is reduced, but measurement precision of driving environment prediction deteriorates
Solution Approach 1:
The patent segments driving characteristics into multiple dimensions: ordinary driving characteristics (average speed, acceleration, braking) and distinguishing driving characteristics (standard deviation of speed, standard deviation of acceleration). This segmentation allows the system to analyze different aspects of driving behavior separately and combine them for accurate driving environment prediction, resolving the contradiction between system complexity and prediction accuracy.
2Adaptability or versatility
If vehicle control adjusts frequently based on predicted driving environment, then adaptability to driving conditions is improved, but fuel efficiency deteriorates due to unnecessary control adjustments
Solution Approach 1:
The patent performs preliminary classification of driving tendencies by analyzing historical driving characteristics before executing control adjustments. By pre-classifying drivers into types (e.g., aggressive, economical, normal) based on their characteristic patterns, the system can predict appropriate control strategies in advance and avoid frequent unnecessary adjustments, thereby maintaining adaptability while improving fuel efficiency.
3Measurement precision
If vehicle control uses multiple driving characteristics for classification, then driving tendency classification accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts specific key characteristics from large amounts of driving data, focusing on ordinary driving characteristics (average speed, average acceleration, average braking) and distinguishing driving characteristics (standard deviation of speed, standard deviation of acceleration). By extracting only the most relevant features rather than processing all available data, the system achieves accurate driving tendency classification while keeping data processing complexity manageable.
Data Source
AI summary
The present disclosure provides a method for controlling driving of a vehicle using driving information of the vehicle including: collecting, by a collector, driving data of the vehicle; extracting, by an extractor, ordinary driving characteristics and distinguishing driving characteristics of the vehicle from the collected driving data; classifying, by a classifier, driving tendency of the vehicle based on the extracted driving characteristics; and controlling, by a controller, driving of the vehicle based on the classified driving tendency. The ordinary driving characteristics includes an average speed of the vehicle, the distinguishing driving characteristics includes standard deviation of speed of the vehicle, and the driving tendency of the vehicle includes driving environment of the vehicle and driving propensity of a driver of the vehicle.