Sensor Misalignment Detection via Cross-Principle Signal Comparison
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Solution Overview
Problem
Misalignment of sensors in motor vehicles can lead to reduced usable range, false detection, or non-detection of objects, compromising the reliability of driver assistance systems, due to factors like improper adhesion, damage, or manufacturing tolerances.
Innovation Solution
A method that compares measurement signals from sensors with different physical principles to detect misalignment by creating a surroundings map, identifying objects detected by one sensor but not another, and assessing detection-relevant properties to determine if a misalignment is present, even without overlapping detection areas, using a probability-based approach to confirm misalignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors with different measuring principles are used for redundancy and automation, then the reliability of driver assistance systems is improved, but the complexity of sensor alignment and calibration increases
Solution Approach 1:
The system performs self-diagnosis by automatically comparing measurement signals from multiple sensors to detect misalignment. The evaluation device autonomously identifies sensor errors without requiring manual calibration, allowing the sensor group to self-correct alignment issues through continuous monitoring and comparison of overlapping detection areas.
Solution Approach 2:
The system continuously monitors measurement signals from multiple sensors and provides feedback when misalignment is detected. The evaluation device compares data from sensors with overlapping detection areas and generates alerts or correction signals when deviations exceed threshold values, enabling real-time adjustment and maintaining system reliability.
2Reliability
If sensor misalignment is not monitored, then the device complexity remains low, but false detection or non-detection of objects occurs
Solution Approach 1:
The system combines measurement signals from multiple sensors with overlapping detection areas into a unified evaluation process. By merging data from ultrasonic, radar, lidar, or optical sensors, the system creates a comprehensive view of the detection zone, allowing cross-validation of object positions and reducing false detections through correlated analysis.
Solution Approach 2:
The system monitors only the overlapping detection areas between sensors rather than the entire detection space. This partial monitoring approach focuses computational resources on critical regions where misalignment would have the greatest impact, detecting errors without requiring complete analysis of all sensor data.
3Manufacturing precision
If manual calibration of each sensor is performed, then manufacturing precision can be ensured, but the productivity and time required for setup increases
Solution Approach 1:
The system automatically performs alignment verification by comparing measurement signals from sensors with overlapping detection areas. Instead of requiring manual calibration during manufacturing or installation, the system self-verifys alignment through continuous operation, detecting misalignment when it occurs and enabling rapid deployment without time-consuming manual procedures.
Data Source
AI summary
The invention relates to a method for establishing the presence of a misalignment of at least one sensor within a sensor group with two or more sensors which detects objects in the surroundings of a motor vehicle, wherein at least two of the sensors differ from each other in their measuring principle and the measurement signals from the sensors are compared with each other.