Dynamic Vehicle Sensor Calibration via Sinogram Correction
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Solution Overview
Problem
Current methods for calibrating vehicle sensors in motor vehicles are time-consuming, costly, and difficult to implement, especially in multisensor systems, as they require static measurements and alignment with the travel axis, which can be problematic and lead to increased complexity and downtimes.
Innovation Solution
A dynamic calibration method that subdivides the total measuring time period into partial periods, computes partial sinograms, and corrects them using a factor to determine the orientation of the vehicle sensor relative to the travel axis, allowing calibration without downtimes or alignment, and is applicable to various sensors like radar, LIDAR, and ultrasonic sensors without relying on fixed objects or additional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static calibration methods are used with the vehicle on a chassis stand, then measurement precision can be achieved, but the calibration process becomes time-consuming and increases downtime
Solution Approach 1:
The patent transitions from static calibration (vehicle stationary on chassis stand) to dynamic calibration (vehicle moving during calibration). The sensor calibration is performed while the vehicle is in motion, using dynamic sensor data collected during normal driving or transport, thereby eliminating calibration downtime while maintaining measurement precision through motion-based reference frame establishment.
Solution Approach 2:
The patent performs preliminary actions by collecting sensor data continuously during vehicle motion before final calibration computation is required. The system pre-processes sensor measurements and establishes reference orientations during normal operation, so that calibration results are already available when needed, eliminating waiting time for calibration processes.
2Measurement precision
If static calibration methods are used, then measurement precision can be achieved, but device complexity increases linearly with each sensor
Solution Approach 1:
The patent creates a universal calibration method that works for multiple sensor types (radar, LIDAR, ultrasonic sensors, cameras) simultaneously. The dynamic calibration approach using vehicle motion as a reference applies to all these sensor types through a unified processing framework, reducing overall system complexity compared to separate calibration procedures for each sensor type.
Solution Approach 2:
The patent merges the calibration processes for multiple sensors into a single integrated dynamic calibration system. By combining sensor data from different types and using a unified computation approach based on vehicle motion, the system reduces complexity that would otherwise increase linearly with each additional sensor requiring separate calibration.
3Loss of time
If dynamic calibration with stationary objects is used, then calibration can be performed during motion, but it becomes difficult to monitor object positions and track objects
Solution Approach 1:
Instead of using stationary objects as references and tracking them from a moving vehicle, the patent inverts the approach by using the moving vehicle's own motion and known trajectory as the reference frame. The vehicle's position and orientation are determined through inertial sensors and navigation systems, and stationary features in the environment are then used to validate and refine sensor calibration, eliminating the need to track moving objects.
Data Source
AI summary
A method for calibrating a vehicle sensor of a motor vehicle. The method includes: ascertaining sensor data for a plurality of measuring points in time during a total measuring time period, the total measuring time period being subdivided into partial measuring time periods, and the motor vehicle moving relative to objects in surroundings of the motor vehicle; for each partial measuring time period, computing positions of the objects based on the ascertained sensor data; for each partial measuring time period, computing a partial measuring time period sinogram based on the computed positions; computing a total measuring time period sinogram by adding the partial measuring time period sinograms and correcting using a factor that is a function of the partial measuring time period sinograms; ascertaining an orientation of the vehicle sensor based on the total measuring time period sinogram; and calibrating the vehicle sensor based on the ascertained orientation.