Abstract: A computer-implemented method is provided for creating a velocity grid using a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects. The method may also include recording image data collected from a camera sensor, the image data comprising a first 2D image frame comprising the simulated objects made up of a plurality of pixels. The method may also include identifying a first 3D point on the first simulated object in a 3D view of the simulated road scenario, wherein the first 3D point corresponds to the first pixel in the first 2D image frame. The method may also include generating a velocity of the first point based upon a velocity of the first simulated object, and projecting the velocity back into the first 2D image frame.

Abstract: A computer-implemented method is provided for training a machine-learning (ML) algorithm that contributes to piloting an autonomous vehicle using a velocity grid generated from a simulation. The method may include simulating a scene. The scene includes a simulated autonomous vehicle and at least one simulated object moving in the scene. The method may also include predicting a velocity of at least one point on the simulated object as it moves in the simulated scene using a ML algorithm. The method may also include comparing the predicted velocity of the at least one point on the simulated object with velocities in the velocity grid generated from a simulation. The method may further include adjusting the ML algorithm to more accurately predict velocity of at least one point on the simulated object as it moves in the scene based on a difference in the predicted velocity compared to the velocity grid, which yields a trained ML algorithm.

Abstract: A computed-implemented method is provided for creating a velocity grid using a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects, collecting first LiDAR data from simulated LiDAR sensors in the simulated road scenario, wherein the collected first LiDAR data comprises a first plurality of points that are representative of a first simulated object at a first 3D location and a first time. The method may also include transforming the first plurality of points from a simulated-scene frame-of-reference to a first simulated object frame-of-reference, and simulating the first simulated object to move from the first 3D location to a second 3D location within the simulated road scenario between the first time and a second time.

Abstract: A computer-implemented method is provided for creating a velocity grid of a simulated object in a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects. The method may also include tracking a point representative of a portion of one of the simulated objects in the simulated road scenario, wherein the point representative of the portion of one of the simulated objects moves from a first location to a second location in a time period, wherein the velocity of the point is generated from the first location to the second location. The method may further include storing the velocity of the point representative of the portion of one of the simulated objects in a velocity grid for the simulated objects in a memory device in an electrical communication with a processor.

Abstract: A computer-implemented method is provided for training a machine-learning (ML) algorithm that contributes to piloting an autonomous vehicle using a velocity grid generated from a simulation. The method may include simulating a scene. The scene includes a simulated autonomous vehicle and at least one simulated object moving in the scene. The method may also include predicting a velocity of at least one point on the simulated object as it moves in the simulated scene using a ML algorithm. The method may also include comparing the predicted velocity of the at least one point on the simulated object with velocities in the velocity grid generated from a simulation. The method may further include adjusting the ML algorithm to more accurately predict velocity of at least one point on the simulated object as it moves in the scene based on a difference in the predicted velocity compared to the velocity grid, which yields a trained ML algorithm.

Abstract: A computer-implemented method is provided for creating a velocity grid of a simulated object in a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects. The method may also include tracking a point representative of a portion of one of the simulated objects in the simulated road scenario, wherein the point representative of the portion of one of the simulated objects moves from a first location to a second location in a time period, wherein the velocity of the point is generated from the first location to the second location. The method may further include storing the velocity of the point representative of the portion of one of the simulated objects in a velocity grid for the simulated objects in a memory device in an electrical communication with a processor.

Abstract: A computed-implemented method is provided for creating a velocity grid using a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects, collecting first LiDAR data from simulated LiDAR sensors in the simulated road scenario, wherein the collected first LiDAR data comprises a first plurality of points that are representative of a first simulated object at a first 3D location and a first time. The method may also include transforming the first plurality of points from a simulated-scene frame-of-reference to a first simulated object frame-of-reference, and simulating the first simulated object to move from the first 3D location to a second 3D location within the simulated road scenario between the first time and a second time.

Abstract: Methods and systems are provided for augmenting simulations with machine learning data. In some aspects, a process can include steps for detecting a lack of data relating to a scenario in a real world environment, generating a simulation of the real world environment based on the detecting of the lack of data relating to the scenario, adding at least one asset to the simulation to satisfy the lack of data relating to the scenario, generating output data based on the simulation with the at least one asset, and updating a machine learning model based on the output data relating to the simulation with the at least one asset.

Abstract: A computer-implemented method is provided for creating a velocity grid using a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects. The method may also include recording image data collected from a camera sensor, the image data comprising a first 2D image frame comprising the simulated objects made up of a plurality of pixels. The method may also include identifying a first 3D point on the first simulated object in a 3D view of the simulated road scenario, wherein the first 3D point corresponds to the first pixel in the first 2D image frame. The method may also include generating a velocity of the first point based upon a velocity of the first simulated object, and projecting the velocity back into the first 2D image frame.