Driver Habit Recognition for Adaptive Vehicle Assistance Control
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Solution Overview
Problem
Current driver assistance systems, such as adaptive cruise control, require frequent driver intervention due to their inability to adapt to individual driving habits, leading to a suboptimal driving experience.
Innovation Solution
A method and device that recognize vehicle working conditions and status using a combination of vehicle speed information, map information, positioning information, camera information, and radar information, and employ a moving average method to determine and associate driver habits, allowing for adaptive vehicle control based on these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed time interval is used for calculating safe vehicle-following distance, then the calculation is simple, but it cannot adapt to actual daily driving habits of the driver
Solution Approach 1:
The system dynamically adjusts the time interval parameter based on recognized driver habits and current driving conditions. Instead of using a fixed time interval, the system learns from historical driving data and adapts the time interval to match the specific driver's preferences and behaviors, making the parameter dynamic rather than static.
Solution Approach 2:
The system automatically learns and adapts to driver habits without requiring manual configuration or intervention. The driver assistance system serves itself by continuously monitoring driving patterns and autonomously adjusting control parameters to match the driver's preferences, eliminating the need for drivers to manually set or adjust time intervals.
2Reliability
If driver assistance systems use simple algorithms, then the system is easy to operate, but frequent driver intervention is required
Solution Approach 1:
The system implements a feedback loop where driver actions during intervention events are captured and used to refine the control algorithms. When drivers intervene in automatic control operations, the system learns from these corrections and adjusts future control decisions to better anticipate driver intentions, continuously improving reliability through experience.
Solution Approach 2:
The system performs preliminary learning and adaptation during normal driving operations before full automatic control is engaged. By continuously monitoring and analyzing driving patterns in advance, the system prepares optimized control parameters and predictions that reduce the likelihood of requiring driver intervention when automatic control is active.
3Measurement precision
If multiple recognition methods are used for vehicle working condition, then the accuracy is improved, but the system complexity increases
Solution Approach 1:
The recognition system is divided into multiple independent modules, each responsible for analyzing specific types of data (speed information, map information, positioning information, camera information). Each module processes its specific data type and contributes to the overall working condition determination, allowing for modular design and independent optimization of each recognition component.
Solution Approach 2:
The system merges results from multiple independent recognition methods to determine the final vehicle working condition. By combining speed-based recognition, map-based recognition, positioning-based recognition, and camera-based recognition, the system achieves higher accuracy through data fusion while maintaining modular architecture that manages complexity.
Data Source
AI summary
The invention relates to a method and device for driver assistance for determining habits of a driver, a computer storage medium, and a vehicle. The method for driver assistance for determining the habits of the driver includes: recognizing a vehicle working condition; recognizing a vehicle status; and determining the habits of the driver based on the vehicle working condition and the vehicle status, where the recognizing a vehicle working condition includes: performing first vehicle working condition recognition based on vehicle speed information; and performing second vehicle working condition recognition based on a combination of map information and positioning information and/or camera information. By performing working condition recognition twice, accuracy of vehicle working condition recognition can be improved and a vehicle working condition recognition result can be prevented from incorrectly and frequently switching between various vehicle working conditions. This achieves efficient and accurate vehicle working condition recognition.