Environmental Sensor Measurement Weighting for Robust Object Tracking
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Solution Overview
Problem
Existing methods for assigning radar measurements to objects in driver assistance systems lack robustness due to insufficient model knowledge and systematic deviations, leading to inaccuracies in object parameter estimation.
Innovation Solution
A method that adjusts object state parameters using weighted fitting, incorporating association probabilities and measurement uncertainties, and utilizes unscented transformation to iteratively update object states, enhancing robustness against deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position-finding measurements from multiple environmental sensors are used to determine the position of an object, then the position determination accuracy is improved, but the reliability deteriorates when sensors are misplaced or malfunctioning
Solution Approach 1:
The system performs preliminary checks during an initialization phase before actual position finding operations. It determines expected measurement values based on pre-stored sensor characteristics and installation positions, then compares actual measurements against these expected values to identify misplaced or malfunctioning sensors in advance.
Solution Approach 2:
The system implements a feedback mechanism where measurement plausibility is continuously checked by comparing actual sensor measurements against expected values derived from sensor characteristics. When deviations are detected, the system identifies problematic sensors and excludes them from position calculations, feeding this information back to improve overall measurement reliability.
2Reliability
If sensor installation position is precisely controlled during manufacturing, then measurement reliability is improved, but manufacturing complexity increases
Solution Approach 1:
Instead of requiring precise installation control during manufacturing, the system performs preliminary characterization of each sensor's actual installation position during an initialization phase. This allows the system to adapt to variations in installation positions without requiring complex manufacturing processes.
Solution Approach 2:
The system stores and utilizes installation position information as a parameter for each sensor. By using these stored position parameters to calculate expected measurement values, the system compensates for variations in actual installation positions, effectively decoupling measurement reliability from manufacturing precision requirements.
3Reliability
If sensor characteristics are stored and used for measurement plausibility checks, then false position determinations are reduced, but device complexity increases
Solution Approach 1:
Each sensor's characteristics and installation position information are stored in the control unit, enabling the system to perform self-verification of measurements. The system uses its own stored sensor data to check measurement plausibility, eliminating the need for external verification systems.
Solution Approach 2:
The system creates and stores digital copies of sensor characteristics and installation position information in a database. These copied data representations are then used for comparison and plausibility checks, allowing the system to verify measurements without requiring additional physical sensors or complex hardware.
Data Source
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AI summary
The invention relates to the evaluating of position-finding measurements of an environmental sensor for a motor vehicle, comprising the steps: associating position-finding measurements (10) with an object (14) described by an estimated object state, wherein an association probability (p) is determined for each of the position-finding measurements (10) for the association of the position-finding measurement (10) with the object (14); estimating current state parameters (P) of the object (14), comprising a modification of the state parameters to the position-finding measurements (10) associated with the object (14), wherein weighting of the position-finding measurements (10) associated with the object (14) are considered in the modification, wherein the weighting is dependent for each of the position-finding measurements (10) on the determined association probability (p) for the association of the respective position-finding measurement (10) to the object (14); transferring the estimated current state parameter (P) of the object (14) to a state estimator (30) for updating the estimated object state of the object (14). The invention further relates to a sensor system for carrying out the method.