Radar Mount-Angle Calibration via Occupancy Grid Maps
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Solution Overview
Problem
Existing radar sensor calibration methods are time-consuming, require prearranged calibration targets, and are not suitable for real-time calibration in service, leading to potential misalignment and reduced performance in automotive applications.
Innovation Solution
A method and apparatus for calibrating the mount angle of a radar sensor using occupancy grid maps generated from radar data, allowing for automated selection of the optimal mount angle based on minimizing the number of grid cells occupied by detected objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radar sensor calibration methods are used, then calibration accuracy can be achieved, but the calibration process is time-consuming and requires prearranged calibration targets
Solution Approach 1:
The radar sensor performs self-calibration by processing its own radar data to generate occupancy grid maps and determine mount angles without external calibration targets or manual intervention. The system uses real-world environmental data to automatically calibrate itself, eliminating the need for time-consuming traditional calibration procedures while maintaining accuracy.
Solution Approach 2:
The system pre-generates occupancy grid maps for multiple candidate mount angles and compares them to identify the optimal angle before actual calibration is needed. This preliminary processing enables rapid calibration by having candidate solutions ready for comparison, significantly reducing the time required during actual calibration operations.
2Measurement precision
If traditional calibration methods with prearranged targets are used, then accurate mount angle determination is possible, but the method is not suitable for real-time calibration in service
Solution Approach 1:
The calibration system is designed to work in multiple scenarios: during manufacturing, in-service, and with various environmental conditions. It uses universal occupancy grid mapping that can process radar data from any environment, making the calibration method adaptable to different real-world situations without requiring specific calibration targets or controlled conditions.
Solution Approach 2:
The system dynamically adjusts the calibration process by evaluating multiple candidate mount angles in real-time and selecting the optimal angle based on current radar data. The mount angle is not fixed but can be updated dynamically as the system processes new environmental data, enabling real-time calibration adaptation to changing conditions.
3Productivity
If automated calibration without prearranged targets is implemented, then calibration efficiency and repeatability improve, but the complexity of processing radar data increases
Solution Approach 1:
The complex calibration process is segmented into discrete steps: generating occupancy grid maps for each candidate angle, comparing the maps, and selecting the optimal angle. This segmentation breaks down the complex data processing into manageable modules, improving computational efficiency and making the process more manageable while maintaining high calibration speed.
Solution Approach 2:
The patent replaces traditional mechanical calibration methods (physical targets, manual measurement) with computational processing of radar data. By substituting mechanical systems with algorithm-based occupancy grid mapping and comparison, the system achieves higher efficiency and repeatability while the computational complexity is managed through structured processing methods.
Data Source
AI summary
A device includes one or more processors configured to receive radar data, and generate a plurality of occupancy grid maps based on the radar data. Each of the occupancy grid maps corresponds to a respective one of a plurality of candidate angles. The one or more processors is also configured to select one of the candidate angles as a sensor mount angle based on the occupancy grid maps, and trigger an action based on the sensor mount angle and the radar data.