Radar Sensor Magnitude Calibration Using Known-Object Radar Maps
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Solution Overview
Problem
Autonomous vehicles rely on accurate sensor data from radar systems for safe operation, but sensor measurements can be inaccurate due to differences between sensors, obstructed views, manufacturing defects, and environmental exposure, necessitating frequent calibration.
Innovation Solution
Calibrate radar sensors using a radar map of known static objects in the operating environment, adjusting radar returns based on expected reflectance values to ensure accuracy, utilizing a conversion function to align actual with estimated values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar sensor calibration is performed frequently to ensure accuracy, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system pre-stores radar map data containing reflectance values of known static objects in the environment. During calibration, instead of performing full sensor characterization, the system quickly compares current radar returns against these pre-existing maps to determine calibration parameters, significantly reducing calibration time while maintaining precision
Solution Approach 2:
The system uses a pre-created radar map (a copy of expected environmental radar signatures) to represent the true reflectance values of static objects. By comparing actual sensor readings against this map copy, the system can quickly assess and correct sensor drift without requiring physical reference targets or lengthy calibration procedures
2Adaptability or versatility
If multiple radar sensors are used to expand field of view, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system merges data from multiple radar sensors by comparing their respective radar returns against the same pre-stored radar map of known static objects. This unified approach to calibration across multiple sensors simplifies the overall system complexity while maintaining the expanded field of view capability, as all sensors are calibrated against a common reference framework
Solution Approach 2:
The pre-stored radar map serves as a universal calibration reference that can be used by multiple different radar sensors simultaneously. This single radar map provides calibration data for entire sensor arrays, making the calibration system universal and multi-functional rather than requiring separate calibration mechanisms for each sensor
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and reliability of radar sensor data by continuously calibrating based on known objects, mitigating errors and ensuring safe navigation for autonomous vehicles.
Implementation Method 1
transmitting a radar signal into an operating environment and receiving radar returns from a plurality of objects in the operating environment based on the radar signal sent
Data Source
AI summary
A method of calibrating a radar sensor includes receiving radar returns from a plurality of objects based on a radar signal sent from the radar sensor, each of the radar returns having a magnitude, at least a subset of the objects are known static objects, identifying a location and orientation of the radar sensor when the signal was sent, identifying expected reflectance values for each of the plurality of known static objects, calculating a conversion function configured to adjust the magnitudes of each of the radar returns for the known static objects to an estimated reflectance value based on the expected reflectance values for each of the known static objects, and adjusting an output of the radar sensor based on the conversion function.