Road Marking Detection Using Predicted and Corrected State Vectors
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Solution Overview
Problem
Existing road marking detection systems are impaired by external disturbances and noise, affecting the reliability of subsequent vehicle guidance functions.
Innovation Solution
A method for road marking detection that involves generating sensor datasets at two measurement instances, determining motion parameters, and using a computing unit to generate observed and corrected state vectors, minimizing errors through polynomial approximations and non-linear state estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional road marking detection methods are used, then the system can detect road markings, but the detection reliability is impaired by external disturbances and noise
Solution Approach 1:
The system performs preliminary actions by generating a predicted state vector based on the first sensor dataset and motion parameters before comparing it with the second observed state vector. This prediction step prepares the system in advance to filter out noise and disturbances by establishing an expected state against which actual measurements can be validated and corrected.
Solution Approach 2:
The system implements feedback by computing a corrected state vector that combines the predicted state vector with the second observed state vector. The correction mechanism uses the difference between predicted and observed values to iteratively improve detection accuracy, feeding back error information to reduce the impact of external disturbances and noise on subsequent detections.
2Measurement precision
If multiple sensor datasets are processed with motion parameters and state vectors, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The detection process is segmented into distinct computational stages: generating a first sensor dataset, determining motion parameters, computing a predicted state vector, acquiring a second sensor dataset, and calculating a corrected state vector. This segmentation allows the system to process complex information in manageable steps, improving measurement precision while controlling computational complexity through structured, modular processing.
Data Source
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AI summary
According to a method for road marking detection, a sensor datasets depicting a road marking (7) at a first and a second measurement instance are generated by an environmental sensor system (4) and a parameter characterizing a motion of the environmental sensor system (4) is determined. A first and a second observed state vector describing the road marking (17) at the first and the second measurement instance, respectively, are generated based on the sensor datasets. A predicted state vector for the second measurement instance is computed depending on the at least one motion parameter and the first observed state vector and a corrected state vector for the second measurement instance is generated depending on the predicted state vector and the second observed state vector.