Driver Assistance Parameter Feedback for Scenario-Specific Reliability
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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 scenarios, resulting in reliability and error issues.
Innovation Solution
A driver assistance system comprising a surroundings sensor system, controller, and assessment module that evaluates assistance function control signals based on predetermined functional relationships between sensor data and system parameters, with an optimization module that adjusts parameters based on success values to improve system performance and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If assistance system parameters are calibrated during development based on limited empirical data and physical models, then the system can be manufactured and deployed, but the system performance becomes suboptimal in various traffic situations due to incomplete representation of real-world scenarios
Solution Approach 1:
The patent implements a feedback mechanism where the driver assistance system continuously monitors its own performance through recorded traffic situations and success values. The system stores actual traffic scenarios and evaluates whether the assistance functions performed correctly, then uses this feedback to optimize parameters. This closed-loop feedback allows the system to improve reliability after deployment without requiring complete prior calibration of all possible scenarios.
Solution Approach 2:
The system performs self-optimization by automatically analyzing its own operational data and adjusting its parameters. The controller records traffic situations, evaluates success values, and optimizes assistance system parameters autonomously without requiring external intervention for each optimization cycle. This self-service capability enables continuous improvement of system performance based on real-world operational experience.
2Reliability
If assistance system parameters are optimized for specific functional scenarios during development, then the system performs well in those scenarios, but the system becomes unreliable in situations not covered by the development data
Solution Approach 1:
The patent creates a universal optimization mechanism that handles multiple functional scenarios through a single integrated system. The controller records and evaluates various types of assistance functions (emergency braking, traffic sign recognition, chassis adjustment) using the same feedback loop and parameter optimization process. This universal approach allows the system to adapt to diverse situations rather than requiring separate optimization for each function, improving both reliability and versatility simultaneously.
Solution Approach 2:
The system transitions from static parameters fixed during development to dynamic parameters that continuously adapt based on recorded performance. The assistance system parameters are no longer fixed but are dynamically optimized through the feedback mechanism using recorded traffic situations and success values. This dynamic adaptation enables the system to maintain reliability across varying scenarios rather than being optimized for specific predetermined conditions.
3Reliability
If more empirical data and functional scenarios are collected during development to improve system performance, then the system becomes more reliable, but the development time and complexity increase
Solution Approach 1:
The patent implements preliminary data collection during normal system operation rather than requiring extensive pre-development data collection. The controller automatically records traffic situations and performance data during regular vehicle operation, building the database needed for optimization incrementally over time. This preliminary action during operation eliminates the need for time-consuming pre-development data collection while maintaining system reliability through continuous data gathering and optimization.
Data Source
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.