Intelligent Roadside Unit Radar Calibration Beyond GPS Accuracy
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Solution Overview
Problem
Current GPS-based location methods for intelligent roadside units are insufficient in accuracy for autonomous driving applications, and there is a need to calibrate the position of radar systems within these units.
Innovation Solution
A method involving an intelligent driving vehicle within a preset range of the roadside unit, using point clouds from both vehicle and roadside unit radars to determine the location of the roadside unit's radar, improving positioning accuracy through alignment and rotation of point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based location method is used for intelligent roadside unit, then the system complexity is low, but the positioning accuracy is insufficient for autonomous driving requirements
Solution Approach 1:
The patent uses point cloud data as an intermediary to establish a relationship between the roadside unit and vehicle radar. The point cloud serves as a mediator that enables indirect positioning by matching spatial features from both radars, thereby achieving high-precision positioning without directly increasing the complexity of the roadside unit itself.
Solution Approach 2:
The patent replaces the traditional GPS mechanical positioning system with a radar-based point cloud matching system. By substituting GPS with a more sophisticated radar point cloud analysis method, the system achieves higher positioning accuracy suitable for autonomous driving, while the complexity is managed through algorithmic processing rather than additional hardware.
2Measurement precision
If radar calibration is performed using point cloud alignment between vehicle and roadside unit, then the positioning accuracy is improved, but the calibration process complexity increases
Solution Approach 1:
The patent performs preliminary actions by collecting and storing point cloud data from both the vehicle radar and roadside unit radar before calibration. The system pre-processes these point clouds to identify common spatial features, which simplifies the actual calibration process by having the matching work partially done in advance.
Solution Approach 2:
The patent creates a virtual copy of the spatial environment through point cloud representation. By working with copied point cloud data rather than directly calibrating physical radar components, the system achieves precise positioning through digital matching while keeping the physical calibration process simpler and non-invasive.
3Adaptability or versatility
If multiple sensor detectors are added to intelligent roadside unit to improve active sensing ability, then the sensing capability is enhanced, but the device complexity increases
Solution Approach 1:
The patent makes the radar system multi-functional by enabling it to perform both traditional detection and calibration functions. The same radar hardware is used for both sensing objects and for self-positioning through point cloud matching, eliminating the need for separate dedicated calibration devices and reducing overall system complexity.
Solution Approach 2:
The roadside unit performs self-calibration and self-positioning using its own radar and the vehicle's radar data. The system serves itself by using the interaction between vehicle and roadside unit radars to automatically determine the roadside unit's position and orientation, without requiring external calibration equipment or complex manual configuration.
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 of positioning for intelligent roadside units by determining the location of the roadside unit's radar relative to the vehicle, allowing for precise calibration and integration with other sensors.
Implementation Method 1
obtaining a first point cloud detected by a first radar in the intelligent driving vehicle, and obtaining location information of the intelligent driving vehicle; obtaining a second point cloud detected by a second radar in the intelligent roadside unit
Data Source
AI summary
The present disclosure proposes a method, an apparatus, a device, and a medium for calibrating an intelligent roadside unit. The method includes: obtaining an intelligent driving vehicle within a preset range of the intelligent roadside unit; obtaining a first point cloud detected by a first radar in the intelligent driving vehicle, and obtaining location information of the intelligent driving vehicle; obtaining a second point cloud detected by a second radar in the intelligent roadside unit; and obtaining location information of the second radar based on the first point cloud, the second point cloud, and the location information of the intelligent driving vehicle.