Driver Assistance Functional Quality Prediction
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Solution Overview
Problem
Driver assistance systems frequently deactivate unexpectedly, leading to unpleasant surprises for drivers who must intervene manually, especially in situations where the system's performance is compromised by environmental conditions or vehicle limitations, resulting in reduced safety and comfort.
Innovation Solution
A method and system that predict the functional quality of driver assistance functions by acquiring and analyzing data from vehicles, including environmental and vehicle-specific information, to provide drivers with advance warnings and enable proactive control adjustments, such as automatic deactivation before reaching sections where the system's performance is insufficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the driver assistance system operates continuously without prediction, then the system is simple to operate, but the system frequently deactivates unexpectedly reducing reliability
Solution Approach 1:
The system performs preliminary actions by predicting functional quality issues before they occur. The prediction module analyzes current operating conditions against historical data and environmental models to forecast potential deactivations, allowing the system to prepare drivers in advance rather than reacting to failures when they happen.
Solution Approach 2:
The patent introduces an environmental model as an intermediary between the driver assistance system and actual environmental conditions. This model pre-characterizes route sections for suitability, acting as a mediator that translates complex environmental factors into actionable predictions about system performance without requiring direct real-time analysis of all environmental variables.
2Reliability
If the system provides real-time feedback about functional quality, then the reliability improves, but the information processing requirements increase
Solution Approach 1:
The environmental model is created and stored in advance for different route sections, containing pre-analyzed suitability characteristics. This preliminary preparation eliminates the need for real-time computation of environmental suitability, as the prediction module can directly query pre-computed data when navigating through stored route information.
Solution Approach 2:
The system uses digital copies of environmental characteristics stored in the environmental model rather than processing actual real-time environmental data. These digital representations capture the essential suitability features of route sections, allowing rapid prediction without analyzing complex raw sensor data or environmental conditions in real-time.
3Reliability
If the driver assistance system deactivates when conditions are unsuitable, then the safety improves, but the ease of operation deteriorates due to frequent manual intervention
Solution Approach 1:
The system provides preliminary information to drivers about upcoming route sections where manual intervention may be needed, allowing drivers to prepare mentally and physically before deactivation occurs. This advance notice reduces the shock and frequency of unexpected manual takeovers by enabling smoother transitions.
Solution Approach 2:
The prediction module provides continuous feedback to drivers about the current and forecasted functional quality of the driver assistance system. This feedback mechanism keeps drivers informed about system limitations and upcoming challenges, improving their ability to anticipate and prepare for manual intervention while maintaining trust in the system's safety decisions.
Data Source
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AI summary
The invention relates to a method for predicting the functional quality of a driver assistance function, having the following steps: detecting (31) information by means of a first vehicle (1), said information characterizing the functional quality of a driver assistance function or being relevant to the functional quality of the driver assistance function; determining (32) the functional quality of the driver assistance function for a second vehicle (2) which is identical to the first vehicle (1) or differs therefrom on the basis of the detected information and using a computing device (61), said functional quality being predicted for a route section, and outputting (33) information relating to the predicted functional quality by means of an output device (21) in a manner which is perceivable to a vehicle occupant of the second vehicle (2). In the process, the information is detected (31) by accessing a control system (10) of the first vehicle (1) and/or the predicted functional quality is determined (32) by accessing data which has been provided by a control system (20) of the second vehicle (2).