Virtual LiDAR Generation with Dropout and Signal Intensity Models

The patent applies preliminary action by pre-calculating and storing signal intensity models based on environmental conditions, object types, and distances before actual LiDAR data generation. These pre-computed models are then directly applied during virtual LiDAR data processing, avoiding the need to simulate complex physical phenomena in real-time while still achieving realistic signal intensity variations.

The patent uses copying by creating simplified statistical models that replicate the characteristics of actual LiDAR data without reproducing the full physical simulation process. The dropout process and signal intensity models copy the essential statistical properties of real LiDAR measurements, including point density variations and signal intensity distributions, thereby achieving realism through statistical replication rather than physical simulation.

The patent applies parameter changes by dynamically adjusting virtual LiDAR data characteristics based on environmental parameters such as weather conditions, distance, and object type. The system modifies signal intensity, point density, and dropout rates according to these parameters, enabling the same high-fidelity virtual sensor to adaptively simulate various real-world conditions without sacrificing data quality.

The patent implements dynamics by making the virtual LiDAR data generation process adaptive and condition-dependent. Instead of using static simulation parameters, the system dynamically adjusts data characteristics based on environmental conditions, object properties, and measurement distances, allowing the virtual sensor to realistically represent various actual phenomena while maintaining high data quality.

The patent applies this principle by using computationally inexpensive statistical models and lookup tables instead of expensive real-time physical simulations. The system pre-computes signal intensity models and dropout probability distributions, then uses these pre-prepared data structures during actual LiDAR data generation, achieving accurate results without the computational burden of detailed physical simulations.

The patent substitutes mechanical/physical simulation systems with statistical and mathematical models. Instead of simulating the actual physics of light propagation, reflection, and detection in real-time, the system uses pre-computed statistical relationships between environmental parameters and LiDAR measurements, dramatically reducing computational requirements while maintaining measurement precision.