A machine learning module may generate a probability distribution from training data including labeled modeling data correlated with reflection data. Modeling data may include data from a LIDAR system, camera, and/or a GPS for a target environment/object. Reflection data may be collected from the same environment/object by a radar and/or an ultrasonic system. The probability distribution may assign reflection coefficients for radar and/or ultrasonic systems conditioned on values for modeling data. A mapping module may create a reflection model to overlay a virtual environment assembled from a second set of modeling data by applying the second set to the probability distribution to assign reflection values to surfaces within the virtual environment. Additionally, a test bench may evaluate an algorithm, for processing reflection data to generate control signals to an autonomous vehicle, with simulated reflection data from a virtual sensor engaging reflection values assigned within the virtual environment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a set of training data, stored on a physical media set, comprising: three-dimensional modeling data from at least one of a camera system, a LIght-Detection-And-Ranging (LIDAR) system, and a locational-awareness system; and physics modeling data from at least one of a radar system and an ultrasonic system; and a machine learning module, implemented on a processor set, operable to learn a mapping to physical modeling characteristics from three-dimensional modeling information. 2. The system of claim 1, further comprising: a mapping module, on the processor set, operable to create a physical model for the virtual environment by applying the mapping to the three-dimensional modeling information, the physical model providing reflection values for at least one of a virtual radar and a virtual ultrasonic system in the virtual environment; and wherein: the three-dimensional modeling information, stored on the physical media set, carries information with which to generate a three-dimensional model of a virtual environment. 3. The system of claim 2, further comprising: a model generation module, on the processor set, operable to assemble the three-dimensional modeling information into the three-dimensional model of the virtual environment; and wherein the mapping module is further operable to apply the mapping to the three-dimensional modeling information by assigning reflectivity coefficients to surfaces in the three-dimensional model of the virtual environment. 4. The system of claim 2, further comprising a test bench, implemented on the processor set, operable to implement a test algorithm over a path through the virtual environment, the test algorithm processing reflection values received from the virtual environment throughout the path and providing outcomes in the form of at least one of detections, classifications, and determinations. 5. The system of claim 2, further comprising a set of test benches, implemented on the processor set, comprising: at least one Hardware-In-Loop (HIL) system operable to provide mathematical models for mechanical dynamics of a virtual vehicle as it travels a path through the virtual environment and dynamics for the at least one of the virtual radar and the virtual ultrasonic system; and a Software-In-Loop (SIL) system communicably coupled to the at least one HIL system and operable to implement a test algorithm over the path through the virtual environment, the test algorithm processing reflection values received from the virtual environment throughout the path and providing control signals to the virtual vehicle. 6. The system of claim 1, further comprising: a model generation module, on the processor set, operable to assemble the three-dimensional modeling information into a three-dimensional model of a virtual object; and wherein the mapping module is further operable to apply the mapping to the three-dimensional modeling information by assigning reflectivity coefficients to surfaces of the three-dimensional model of the virtual object, creating the virtual object for positioning in a virtual environment with a reflection cross-section for at least one of the virtual radar and the virtual ultrasonic system relative to the virtual object. 7. The system of claim 1, wherein: the set of training data is indexed to labels for a set of target categories; and the machine learning module comprises a deep neural network operable to be trained with supervised learning using the labels indexed to the set of training data. 8. The system of claim 7, wherein the deep neural network further comprises a classifier implemented with a convolution neural network. 9. The system of claim 1, further comprising a heuristic module within the machine learning module operable to identify aspects of the three-dimensional modeling data subject to a set of heuristics informing the machine learning module. 10. A method for physical modeling, further comprising: applying a machine learning algorithm to a set of training data to create a probability distribution for reflection coefficients conditioned on modeling data acquired by at least one of a camera system, a LIght-Detection-And-Ranging (LIDAR) system, and a position system; assigning reflection-coefficient values to a set of modeling data by applying the set of modeling data to the probability distribution. 11. The method of claim 10, further comprising: assembling a set of surface regions from the set of modeling data to model a simulation environment simulating an environment from which the set of modeling data is captured; the step of assigning reflection-coefficient values further comprises: assigning reflection-coefficient values to the set of surface regions; collecting reflection data from transmissions from a virtual sensor placed within the simulation environment, as reflected in accordance with reflection-coefficient values assigned to the set of surface regions in the simulation environment; and testing a perception algorithm on the reflection data. 12. The method of claim 11, wherein testing the perception algorithm further comprises: placing a virtual vehicle within the simulation environment, coupled to the virtual sensor and operable to travel a path through the simulation environment; and sending control signals output by the perception algorithm to the virtual vehicle. 13. The method of claim 10, further comprising: assembling a set of surface regions from the set of modeling data to form a virtual object; the step of assigning reflection-coefficient values further comprises assigning reflection-coefficient values to the set of surface regions; placing the virtual object within a virtual environment; collecting reflection data from transmissions from a virtual sensor placed within the virtual environment, as reflected in accordance with reflection-coefficient values assigned to the set of surface regions in the simulation environment; and testing a perception algorithm on the reflection data. 14. The method of claim 10, wherein: the step of applying a machine learning algorithm to a set of training data further comprises performing supervised learning on a deep neural network with the training data; and after training, the deep neural network implements the probability distribution. 15. The method of claim 10, further comprising tagging portions of at least one of the set of training data and the set of modeling data, which portions correspond to regions in at least one environment from which the at least one of the set of training data and the set of modeling data is collected, and which regions are correlated with ranges of reflection-coefficient values. 16. The method of claim 10, further comprising collecting the set of training data, which further comprises: collecting a training set of modeling data for a set of target areas; collecting a training set of reflection data from the set of target areas correlated with the training set of modeling data; identifying aspects of the set of target areas in the training data with a set of labels for supervised learning with the set of training data. 17. A system for modeling reflections, comprising: a set of virtual-environment data, stored on a physical media set, captured by at least one of a camera system, a Light-Detection-And-Ranging (LIDAR) system, and a locational-awareness system, sufficient to generate a three-dimensional model of a virtual environment; and a mapping module, implemented on a processer set, operable to create a reflection model for the virtual environment by applying the virtual-environment data to a distribution function assigning reflection values to surfaces within the three-dimensional model of the virtual environment, for at least one of a virtual radar system and a virtual ultrasonic system. 18. The system of claim 17, further comprising a machine learning module operable to generate the probability distribution from training data comprising labeled modeling data correlated with reflection data from at least one of a radar system and an ultrasonic system. 19. The system of claim 18, further comprising a heuristic model operable to provide information to the machine learning module about a location relative to an overall field of view of a sensor, used in capturing the training data, from which a portion of the training data originates. 20. The system of claim 17, further comprising a test bench operable to evaluate a reflection-processing algorithm with simulated reflection data obtained from a virtual sensor engaging reflection values assigned to surfaces of the three-dimensional model encountered by the virtual sensor traveling within the virtual environment.