Road Monitoring Sensor Calibration Using Traffic Object Maps
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Solution Overview
Problem
Existing methods for calibrating road monitoring devices require significant effort, are unsuitable for repetitive calibration during normal traffic conditions, and are prone to inaccuracies due to insufficient GPS reception and the need for manual intervention in traffic flow.
Innovation Solution
A method that accumulates object location data during normal traffic to create an object location map, which is then filtered and aligned with a world coordinate map using morphological operations and cross-correlation, allowing for automated calibration without additional reference points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods using reference objects and differential GPS are used, then calibration accuracy can be achieved, but the calibration process requires significant manual effort and cannot be performed during normal traffic operations
Solution Approach 1:
The system uses existing traffic objects (vehicles, pedestrians, cyclists) that naturally pass through the monitoring area to perform calibration. These objects serve themselves as reference points without requiring external calibration equipment or manual intervention. The calibration process leverages the natural flow of traffic to automatically update sensor calibration data during normal operations.
Solution Approach 2:
The system pre-establishes a world coordinate system with known geographic positions and pre-records the sensor coordinate system. By having these reference frameworks prepared in advance, the system can quickly perform calibration by matching object positions between the two coordinate systems without requiring time-consuming field surveys or manual measurements during calibration events.
2Measurement precision
If static reference objects are used for calibration, then positional accuracy can be maintained, but traffic flow must be interrupted and lanes may need to be closed
Solution Approach 1:
Instead of placing static reference objects in the road and interrupting traffic for calibration, the system inverts the approach by using moving traffic objects as the reference. This allows calibration to occur during normal traffic flow without requiring lane closures or traffic interruptions, while still achieving accurate positioning through the matching process.
3Area of stationary object
If multiple sensors with minimal field of view overlap are used, then detection coverage is improved, but the calibration time scales linearly and becomes unsuitable for repeated calibration
Solution Approach 1:
The system merges the calibration process across multiple sensors by using a common world coordinate system and shared traffic objects. Instead of calibrating each sensor independently (which would scale linearly with time), the system combines calibration data from multiple sensors that observe the same traffic objects, allowing parallel calibration that reduces overall calibration time while maintaining comprehensive detection coverage.
4Measurement precision
If GPS reference systems are used for calibration, then position determination can be achieved, but insufficient GPS reception in urban canyons causes inaccuracies
Solution Approach 1:
The system introduces traffic objects (vehicles, pedestrians, cyclists) as intermediary reference points between the sensor and the world coordinate system. Instead of relying directly on GPS signals that may be blocked in urban canyons, the system uses these intermediate objects whose positions can be detected by the sensor and matched against pre-recorded world coordinate positions, providing a reliable calibration path even when direct GPS reception is insufficient.
Data Source
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AI summary
The invention relates to a method for calibrating a road monitoring device. The road monitoring device senses a predefined monitoring region, recognizes objects in said monitoring region, and determines and provides object data regarding said objects, more particularly the position of said objects. For a predefined calibration period, the positions of recognized objects in relation to the road monitoring device are sensed and an accumulated object presence map is formed therefrom. Furthermore, a world coordinate map of the lanes and lane centers for the monitoring region, with their position in a predefined world coordinate system, is provided. In an automated way, by computer-based translation and/or rotation of the accumulated object presence map or of an object presence map derived, more particularly filtered, therefrom and by computer-based determination of a measure of agreement with the world coordinate map, a position and orientation having a predefined measure of agreement are determined and, from said position, the actual position and the actual orientation of the sensor are derived and the position of the objects is calibrated accordingly.