Blind Online Radar Calibration for Autonomous Vehicles
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Solution Overview
Problem
Traditional radar calibration methods for vehicles rely on pre-identified static objects and map data, which can be inaccurate or unavailable due to outdated information or poor GPS reception, limiting dynamic and on-demand calibration capabilities.
Innovation Solution
A blind online calibration method that tunes the radar unit to various positions while the vehicle is in motion, using sensor data scans to identify target points and determine a calibration position based on the trajectory pattern of these points without relying on map data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional map-based calibration is used, then calibration can be performed with pre-identified static objects, but it requires accurate and up-to-date map data and GPS signals which are often unavailable or outdated
Solution Approach 1:
The system performs self-calibration by using its own radar sensor data to automatically identify static objects and determine calibration parameters without external assistance. The processor analyzes radar scan data, identifies target points representing static objects, and computes calibration positions independently, eliminating dependency on external map data and GPS systems.
Solution Approach 2:
The system performs calibration actions in advance during normal vehicle operation before actual navigation tasks require accurate sensing. By continuously calibrating the radar unit during motion, the system ensures readiness for subsequent operations without needing to stop or rely on pre-existing accurate map data.
2Measurement precision
If offline calibration at factory is performed, then initial calibration can be established, but it cannot satisfy on-demand calibration requests while vehicle is in use
Solution Approach 1:
The calibration process continues uninterrupted during vehicle operation rather than being a one-time offline event. The system continuously captures radar sensor data, identifies target points, and updates calibration parameters in real-time while the vehicle is in motion, maintaining both initial accuracy and enabling on-demand recalibration.
Solution Approach 2:
The calibration system transitions from a static offline process to a dynamic online process. The processor continuously adapts calibration parameters based on real-time radar data from multiple positions, allowing the calibration to evolve and update as the vehicle moves through different environments.
3Measurement precision
If static objects are used for calibration, then reference points can be identified, but the objects must be pre-identified in map data which lacks real-time accuracy
Solution Approach 1:
The system replaces the mechanical dependency on external map data with a sensor-based identification system. Instead of relying on pre-stored map information about static objects, the radar sensor directly detects and identifies target points in the current environment, eliminating the information loss associated with outdated maps.
Solution Approach 2:
The system creates a real-time copy of the environment through radar sensing rather than relying on stored map copies. By capturing current radar returns and identifying target points from actual sensor data, the system maintains an up-to-date representation of static objects without needing external map data.
4Reliability
If GPS signal reception is poor or unavailable, then traditional calibration protocol cannot be applied, but calibration is still needed for accurate sensing
Solution Approach 1:
The system becomes self-sufficient by eliminating dependency on GPS infrastructure. The processor uses only the vehicle's own radar sensor data to perform calibration, making the operation simple and reliable regardless of external signal availability. No additional GPS equipment or complex signal processing is required.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media provide a blind online calibration mechanism to calibrate the position of the radar unit while the self-driving vehicle is in motion without the use of any map data. Specifically, a calibration system associated with the self-driving vehicle is configured to adjust the boresight angle of the radar within a calibration range and monitor the convergence or divergence pattern of the resulting clutter locations. The boresight angle of the radar unit may be progressively adjusted in static or dynamic degree increments until the convergence of the clutter curves is observed. In this way, without using any map data or factory settings, radar calibration can be conducted as often as needed while the vehicle is moving. Radar sensing and measurement accuracy is thus improved.