Probabilistic FMCW LiDAR Sensor Modeling for Virtual Testing
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Solution Overview
Problem
Current methods for simulating FMCW LiDAR sensors in virtual test environments are computationally intensive and require extensive sensor-specific information, making them complex and inefficient for pre-development and testing in industrial environments, especially for mobile robots and industrial trucks.
Innovation Solution
A computer-implemented method models an FMCW LiDAR sensor using a probabilistic distribution to simulate virtual transmission signals, reducing the need for detailed physical effects and allowing for faster computation by modeling output values based on probabilistic distributions of parameters like azimuth and elevation angles, thereby simplifying the simulation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex simulation of the measurement principle is used to realistically recreate the functionality of a real CW LiDAR sensor, then the realism and accuracy of the sensor simulation is improved, but the computational complexity and time consumption increases significantly
Solution Approach 1:
The patent creates a virtual copy of the LiDAR sensor and test environment that replicates essential measurement behaviors without requiring full physical realism. The virtual sensor generates measurement data that statistically matches real sensor characteristics, enabling algorithm testing without complex physical simulations.
Solution Approach 2:
The patent transforms the simulation approach by changing from detailed physical parameter simulation to statistical parameter generation. Instead of simulating electromagnetic wave propagation and complex measurement physics, the system uses probabilistic distributions to generate measurement values that reflect real sensor behavior, dramatically reducing computational requirements.
2Measurement precision
If detailed sensor-specific information is used to simulate the actual functionality of the CW LiDAR sensor, then the accuracy of the simulation output is improved, but the amount of required information and complexity increases
Solution Approach 1:
The patent extracts only the essential statistical characteristics of LiDAR sensor behavior from the complex physical system. Instead of requiring complete sensor specifications and physical models, the system identifies and uses key statistical parameters (measurement distributions, noise characteristics) that capture the essential measurement behavior needed for algorithm testing.
Solution Approach 2:
The patent replaces expensive, complex physical sensor models with simple computational models that generate equivalent statistical behavior. These simplified virtual sensors require minimal configuration and can be rapidly instantiated for different testing scenarios without the overhead of detailed physical simulations.
3Measurement precision
If ray tracing is used to simulate the virtual transmission signal in the virtual test environment, then the spatial accuracy of signal propagation is improved, but the computational effort increases
Solution Approach 1:
The patent segments the simulation into two parts: spatial geometry (handled efficiently by ray tracing for occlusion and path determination) and measurement generation (handled by statistical models). This division allows ray tracing to focus only on geometric accuracy while statistical methods handle the computationally intensive measurement simulation.
Solution Approach 2:
The patent applies ray tracing selectively only where spatial accuracy is critical (determining signal paths and occlusions) rather than simulating all physical aspects of signal propagation. This partial application of ray tracing maintains necessary spatial accuracy while avoiding the computational cost of full physical simulation.
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
This approach significantly reduces computational effort and time, enabling efficient generation of sensor data for testing algorithms and applications, allowing for diverse scenario simulations and reducing the effort required for testing, while providing realistic output values comparable to real sensor measurements.
Implementation Method 1
at least one parameter of the simulation model and/or the output value are modeled by a probabilistic distribution
Implementation Method 2
a virtual transmission signal sent by the virtual sensor is simulated, in particular by means of ray tracing, in the virtual test environment
Data Source
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Figure 3a~3c
AI summary
The invention relates to a method for modeling a sensor, in particular an FMCW LiDAR sensor, for measuring a distance, in a virtual test environment, especially for testing algorithms or software in an industrial environment and/or for testing software for mobile robots and/or for industrial trucks, wherein: - a simulation model is defined, the simulation model comprising a virtual sensor and the virtual test environment, wherein in the method: a virtual transmission signal emitted by the virtual sensor is simulated in the virtual test environment; - it is determined whether the virtual transmission signal hits a virtual object at an impact point in the virtual test environment; - if this is determined positively, the distance traveled by the virtual transmission signal from the virtual sensor to the impact point in the virtual test environment is calculated;and - at least one output value of the virtual sensor is determined, wherein the determination of the output value is based on at least one parameter of the simulation model, wherein the parameter and/or the output value are modeled by a probabilistic distribution.;