Adaptive Cruise Control Personalization via Driver Behavior Learning
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Solution Overview
Problem
Current driver assistance systems use static tunings for vehicle control thresholds and warnings, which may not accommodate the unique driving habits of individual drivers, leading to a need for a tunable system that adapts to specific driver preferences.
Innovation Solution
A driver assistance system that determines if driver assistance features are associated with stored learned driver-specific data, using this data to adjust vehicle control settings such as following distances and collision warnings, or defaults to vehicle-specific data if no learned data is available, incorporating sensors and a controller to implement personalized adaptive cruise control and forward collision warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static tunings are used for vehicle control thresholds and warnings, then the system is simple to operate, but the system cannot adapt to individual driver preferences
Solution Approach 1:
The system dynamically adjusts control thresholds and warning parameters based on learned driver behavior patterns. The controller continuously monitors driver inputs and automatically modifies system settings to match individual driving habits, transforming static parameters into dynamic, adaptive values that evolve with driver preferences
Solution Approach 2:
The driver assistance system performs self-tuning by automatically learning from driver behavior without requiring manual configuration. The controller autonomously analyzes driving patterns and adjusts system parameters, eliminating the need for drivers to manually program their preferences while maintaining system simplicity
2Adaptability or versatility
If static tunings are used for vehicle control thresholds, then the device complexity is low, but the adaptability to different drivers is limited
Solution Approach 1:
The system performs preliminary learning during the driver acclimation period, collecting and analyzing driving behavior data before full adaptive control is activated. This preliminary action allows the system to pre-establish personalized parameters, reducing the complexity of real-time adaptation while maintaining high tunability
Solution Approach 2:
The system implements continuous feedback loops where driver inputs are monitored and used to adjust control thresholds. The controller compares actual driver behavior against system responses and automatically refines parameters, creating a self-correcting adaptive system that maintains simplicity through automated feedback mechanisms
Data Source
AI summary
Methods and systems for implementing personalized driver assistance features such as adaptive cruise control, adaptive cruise control with stop and go, and forward collision warning. The methods and systems collect specific vehicle data and then calculate values that populate a histogram representing driver habits and tendencies regarding following and stopping distances in relation to objects ahead of the vehicle. The methods and systems utilize the histogram data to provide the driver with a personalized driver assistance features.