Driver Assistance Lane Guidance Using Stored Sensor Data
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Solution Overview
Problem
Driver assistance systems face disruptions in difficult traffic conditions where lane markings are obscured, leading to the unnecessary deactivation of functions and reduced comfort for the driver, as existing methods fail to effectively utilize stored data for continuous operation.
Innovation Solution
The method employs previously stored data from surroundings sensors to generate estimated values for lane guidance, with limitations based on time or distance, and uses a plausibility factor to weight these estimates, transitioning smoothly between estimation and measurement data, and operates in partial or full modes depending on lane type and data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the driver assistance system uses current data from surroundings sensors for lane guidance, then the guidance is accurate and reliable, but the system must be deactivated when lane markings are obscured or sensors malfunction, reducing operational continuity
Solution Approach 1:
The system performs preliminary actions by storing historical lane data and vehicle state information in memory before sensor failures or obscurations occur. This stored data serves as a foundation for generating estimated values when current sensor data is unavailable, allowing the system to maintain operational continuity without compromising reliability
Solution Approach 2:
The system introduces an intermediary estimation module that generates predicted lane parameters based on stored historical data and vehicle dynamics models. This intermediary component bridges the gap between unavailable sensor data and required guidance information, enabling continuous operation while maintaining reliability through plausibility checks and weighted combinations with current sensor data when available
2Reliability
If the system deactivates functions when lane markings are obscured, then safety is maintained, but driver comfort and system usability are reduced
Solution Approach 1:
The estimation module acts as an intermediary that provides fallback lane guidance when sensor data is obscured or unavailable. By generating plausible estimated values from historical data and vehicle dynamics, the system maintains ease of operation and driver comfort without compromising safety, as the estimation is constrained by physical limits and plausibility checks
Solution Approach 2:
The system dynamically changes operational parameters by switching between different data sources and estimation modes based on sensor availability and data quality. When sensor data is reliable, the system uses direct measurement; when obscured, it transitions to estimation mode with appropriate parameter adjustments, maintaining both safety and comfort across varying conditions
3Duration of action of moving object
If the system uses estimated values from stored data, then operational continuity is maintained, but errors may accumulate over time and distance
Solution Approach 1:
The system performs preliminary filtering and validation of stored historical data before using it for estimation. By pre-processing and storing high-quality reference data, the system reduces potential error accumulation when generating estimated values during sensor failures, maintaining measurement precision while ensuring operational continuity
Solution Approach 2:
The system dynamically adjusts the use of estimated values based on time and distance traveled since the last valid sensor measurement. The plausibility factor decreases with increasing time and distance, causing the system to rely less on older estimates and more on current sensor data when available, thereby preventing error accumulation while maintaining operational continuity during brief outages
4Stability of the object's composition
If the system smoothly transitions between estimated and measured values, then operational stability is improved, but the complexity of the control system increases
Solution Approach 1:
The system uses a plausibility factor as an intermediary parameter to smoothly blend between estimated and measured values. This factor, based on time and distance since last valid measurement, enables stable transitions without abrupt changes, while the blending mechanism adds minimal complexity compared to full control system redesign
Solution Approach 2:
The system changes the weighting parameter (plausibility factor) dynamically based on time and distance to achieve smooth transitions between estimation and measurement modes. This parameter-based approach provides control stability through continuous blending while maintaining relatively simple system architecture compared to more complex adaptive control schemes
Data Source
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AI summary
The method involves capturing surrounding data by a surrounding sensor. The data captured by the surrounding sensor is stored, and with an occurrence of a disturbance during the collection of the surrounding data, the stored data is accessed in order to derive estimated values for current missing data. An independent claim is also included for a driver assistance system, which has a surrounding sensor.