Vehicular Radar Calibration Using Overlapping Fields for Azimuth Alignment
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Solution Overview
Problem
Radar sensors in vehicle sensing systems often experience misalignment due to imperfect mounting and changes over time, leading to inaccuracies in object detection, especially for distant objects, and require efficient calibration methods to ensure accurate perception.
Innovation Solution
A method for calibrating vehicular radar sensors using overlapping fields of sensing, involving radar-to-radar and radar-to-vehicle calibration, where one sensor is assumed calibrated, and others are aligned based on overlapping fields, utilizing iterative optimization and kinematic models to correct azimuth misalignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radar sensors are mounted on the vehicle, then object detection capability is provided, but azimuth misalignment occurs due to imperfect mounting and changes over time
Solution Approach 1:
The system changes the calibration parameter (azimuth alignment) by detecting objects in overlapping fields of sensing from multiple radar sensors and calculating detection errors. The calibration data is adjusted based on these errors to correct misalignment, thereby maintaining measurement precision without requiring physical remounting of sensors.
2Area of stationary object
If multiple radar sensors with overlapping fields of sensing are used, then object detection coverage is improved, but detection errors and ghost objects increase due to azimuth misalignment
Solution Approach 1:
The system uses feedback by continuously monitoring detection errors from multiple radar sensors detecting the same object in overlapping fields. The calibration process adjusts azimuth alignment based on these feedback errors, reducing ghost objects and improving detection accuracy while maintaining extended sensing coverage.
3Measurement precision
If calibration of radar sensors is performed manually, then mounting precision can be adjusted, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-calibration by automatically detecting objects in overlapping fields, calculating detection errors, and adjusting calibration data without manual intervention. This self-service approach maintains high alignment precision while significantly reducing calibration time and operational complexity compared to manual methods.
Data Source
AI summary
A method for calibrating a vehicular radar sensing system includes providing a plurality of radar sensors at a vehicle that includes a first radar sensor and a second radar sensor, the first radar sensor having a field of sensing that at least partially overlaps a field of sensing of the second radar sensor. An object in the field of sensing of the first radar sensor is detected via processing sensor data captured by the first radar sensor. The object in the field of sensing of the second radar sensor is detected via processing sensor data captured by the second radar sensor. Based on detection of the object with the first radar sensor and with the second radar sensor, a detection error is determined. Calibration of the second radar sensor is adjusted based on the detection error.