Sensor System Coordinate Transformation via Road User Association
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Solution Overview
Problem
Current sensor systems for traffic infrastructure require extensive manual configuration and high-quality, cost-intensive calibration methods to associate data from cameras and radars, lacking an efficient method for automatic transformation of coordinate systems for accurate object detection and localization.
Innovation Solution
A method that automatically determines a transformation rule between camera and radar coordinate systems by associating road users detected by video cameras and radar devices, using cumulative position information to identify lane profiles and stopping positions, eliminating the need for manual reference objects and precise calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual positioning of reference objects or manual marking of positions in sensor data is used for calibration, then measurement precision can be achieved, but device complexity and time consumption increase significantly
Solution Approach 1:
The system automatically identifies and associates road users detected by both camera and radar, performing calibration without manual intervention. The control unit autonomously determines correspondence between sensor data using detected road users as natural reference points, eliminating the need for manual positioning of reference objects or marking of positions in sensor data.
Solution Approach 2:
Road users serve as intermediary objects that bridge the calibration process between camera and radar systems. Instead of requiring manual reference objects, the system uses naturally occurring road users (vehicles, cyclists, pedestrians) as mediators to establish correspondence between the two coordinate systems through automatic detection and association.
2Measurement precision
If manual positioning of reference objects is used for calibration, then measurement precision can be achieved, but loss of time increases due to traffic flow interruption
Solution Approach 1:
The calibration process is fully automated, with the control unit independently identifying road users in both camera and radar data, determining their correspondence, and calculating the transformation rule without requiring manual intervention or traffic interruption. This self-service approach eliminates time loss while maintaining calibration precision.
Solution Approach 2:
The system continuously detects and accumulates road user data in advance, building up a database of detected objects and their positions. This preliminary accumulation of data allows the calibration to be performed rapidly when needed, rather than requiring time-consuming manual setup at the moment of calibration.
3Measurement precision
If high-quality systems like differential GPS are used for determining reference object positions, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
Road users detected by the sensor systems themselves serve as intermediary reference points, replacing the need for external high-precision positioning systems like differential GPS. The correspondence between camera and radar data is established through these natural mediators, simplifying the system architecture while maintaining sufficient calibration precision.
Solution Approach 2:
Instead of using expensive external positioning systems, the system creates virtual copies or representations of road users from the sensor data itself. These digital representations serve as sufficient reference points for calibration, replacing the need for physical reference objects or external GPS infrastructure.
4Measurement precision
If extensive manual support is used for configuration and marking, then measurement precision can be achieved, but productivity decreases
Solution Approach 1:
The control unit automatically performs the entire calibration process including identifying road users, determining correspondence between sensor detections, and calculating transformation rules. This self-service automation eliminates manual configuration steps while maintaining calibration precision, dramatically improving system setup efficiency and productivity.
Solution Approach 2:
The system changes the approach from manual parameter input to automatic parameter extraction. Instead of manually entering reference object positions and marking sensor data, the system automatically extracts position and identification parameters from detected road users, transforming the calibration process into an automated parameter extraction and matching operation.
Data Source
AI summary
A method of controlling a sensor system for a traffic infrastructure facility, by way of which a transformation rule for a coordinate transformation of radar data acquired by way of a radar device and of video data acquired by way of a video camera is determined based on an association of road users detected by way of the video camera with road users detected by way of the radar device.