Personalized Driver Assistance Control via State Correlation
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Solution Overview
Problem
Existing driver assistance systems in vehicles lack personalized and automatic control of operating parameters, failing to adapt effectively to individual drivers' behaviors and vehicle conditions, which can lead to suboptimal performance and safety.
Innovation Solution
A computer-implemented method using machine learning classifiers to create and update personalized driver and vehicle state models based on sensed data, correlating driver and vehicle states to predict potential risks and adjust operating parameters of driver assistance components, such as brake assist and adaptive cruise control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver assistance systems use fixed operating parameters, then system simplicity is maintained, but performance and safety are suboptimal due to lack of personalization
Solution Approach 1:
The system dynamically adjusts operating parameters of driver assistance components based on real-time driver state classification and vehicle state classification. The predictor engine continuously updates predictions of driver behavior parameters (alertness, mood, distraction level) and automatically modifies system behavior accordingly, transforming a static system into an adaptive one that personalizes assistance levels for each driver.
Solution Approach 2:
The system performs self-adjustment by automatically classifying driver and vehicle states, correlating these states through machine learning models, and modifying operating parameters without requiring manual intervention. The predictor engine and correlation engine work autonomously to personalize the driver assistance experience, eliminating the need for manual system configuration while improving safety.
2Adaptability or versatility
If driver assistance systems collect and process extensive sensed data for personalization, then adaptability to individual drivers improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The system segments data processing into distinct classification tasks: driver state classification (separating driver-related parameters) and vehicle state classification (separating vehicle-related parameters). This segmentation allows the correlation engine to focus on finding relationships between specific driver behaviors and vehicle states, reducing overall computational complexity while maintaining personalization capability.
Solution Approach 2:
The correlation engine acts as an intermediary between the classifier outputs and the predictor engine. It receives classified driver and vehicle states, applies correlation logic to identify meaningful relationships, and passes refined information to the predictor engine. This intermediary layer simplifies the data flow and reduces computational burden on the final prediction system.
3Productivity
If the system uses machine learning classifiers to create personalized models, then responsiveness to individual driving behaviors improves, but system complexity and computational resources increase
Solution Approach 1:
The system performs preliminary classification of driver and vehicle states before correlation and prediction. By pre-processing sensed data through trained classifiers to extract meaningful features (driver alertness, vehicle conditions), the system reduces the computational burden on subsequent correlation and prediction stages, improving overall responsiveness while managing complexity.
Solution Approach 2:
The system implements feedback loops where classifier outputs feed into the correlation engine, which updates the predictor engine's understanding of driver behavior patterns. The predictor engine continuously refines its predictions based on correlated data, creating a feedback mechanism that improves responsiveness over time while the system learns from accumulated driving patterns.
Data Source
AI summary
Embodiments are directed to a computer-implemented method of operating a driver assistance component (DAC) of a vehicle. The method includes receiving sensed operator state data and sensed vehicle state data that represents a vehicle state of the vehicle. Based at least in part on the sensed operator state data, an operator state model is created, trained, and updated. Based at least in part on the sensed vehicle state data, a vehicle state model is created, trained, and updated. Based at least in part on new sensed operator state data, an operator state model classification output is created. Based at least in part on new sensed vehicle state data, a vehicle state model classification output is created. The operator state model classification output and the vehicle state model classification output are correlated, and operating parameters for the DAC are predicted.