Driver Assistance Feedback Control for Real-World Parameter Adaptation
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Solution Overview
Problem
Existing driver assistance systems rely on limited empirical data and physical models, leading to suboptimal performance in various traffic situations due to incomplete representation of real-world events, resulting in reliability and error issues.
Innovation Solution
A driver assistance system comprising an environment sensor system, control device, and evaluation module that determines assistance function control signals based on environmental data and adjusts assistance system parameters dynamically to optimize performance, using a feedback loop and machine learning methods to improve system reliability and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If assistance system parameters are tuned during development based on limited empirical data and physical models, then the system can be developed with reasonable resources, but the system performance remains suboptimal in individual driving or environmental situations
Solution Approach 1:
The patent implements a feedback mechanism where the evaluation module continuously monitors system performance by determining success values based on environmental data and vehicle reactions. When success values fall below thresholds, the system automatically triggers re-evaluation and optimization of assistance system parameters, creating a closed-loop feedback system that continuously improves reliability without requiring complex manual re-tuning
Solution Approach 2:
The system performs self-optimization through the evaluation module that automatically evaluates success values and triggers parameter re-evaluation when performance degrades. This self-service mechanism eliminates the need for external expert intervention to optimize parameters for different driving situations, allowing the system to maintain optimal performance autonomously across varying conditions
2Adaptability or versatility
If assistance system parameters are optimized for specific functional scenarios during development, then the system can achieve good performance in those scenarios, but the system fails to adapt to real-life traffic conditions that were not considered during development
Solution Approach 1:
The patent transforms static assistance system parameters into dynamic ones that can be re-evaluated and adjusted during operation. The evaluation module enables parameters to adapt to different driving situations by continuously monitoring success values and triggering re-optimization when performance thresholds are not met, allowing the system to maintain reliability across diverse and changing traffic conditions
Solution Approach 2:
The system implements parameter changes by allowing assistance system parameters to be re-evaluated and modified based on actual system performance. The evaluation module detects when success values fall below thresholds and triggers parameter optimization, enabling the system to adapt parameters dynamically to match real-life traffic conditions rather than relying on fixed development-stage settings
3Measurement precision
If physical models and empirical data are used to represent real-world traffic conditions, then the system can be developed with available information, but the models only provide a more or less good approximation of physical reality leading to systematic errors
Solution Approach 1:
The evaluation module provides continuous feedback on actual system performance by determining success values based on real environmental data and vehicle reactions. This feedback loop identifies discrepancies between model predictions and actual outcomes, enabling the system to detect and correct systematic errors by re-evaluating and optimizing parameters when performance degradation is detected
Solution Approach 2:
The system performs preliminary evaluation of success values and proactively triggers parameter optimization before systematic errors can cause safety issues. By continuously monitoring performance and initiating re-optimization when thresholds are approached, the system prevents the accumulation of modeling errors rather than reacting only after failures occur
Data Source
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AI summary
The invention relates to a driver assistance system for a motor vehicle, comprising a surroundings sensor system, a controller, and an analysis module. The controller is equipped and designed so as to ascertain an assistance function control signal on the basis of surroundings data detected by the surroundings sensor system, said assistance function control signal being in a specified functional relationship with at least one assistance system parameter and the detected surroundings data, and to actuate the vehicle so as to provide an assistance function by means of the assistance function control signal. The analysis module is equipped and designed so as to ascertain a success value for the assistance function control signal using reference information and to initiate an optimization of the at least one assistance system parameter if the success value falls below a success threshold. Fig.