Abstract: A system for converting global shutter image data into simulated rolling shutter image data may include a global-to-rolling shutter image converter. The converter may be configured to receive global shutter image data associated with a plurality of global shutter images. The global shutter image data may include dynamic global shutter image data representative of a moving object in the plurality of global shutter images. The converter may also be configured to determine an optical flow field based at least in part on two or more of the plurality of global shutter images and generate the simulated rolling shutter image based at least in part on the optical flow field. The converter may also be configured to generate high dynamic range (HDR) images based at least in part on the optical flow field and the global shutter image data.

Abstract: A system for converting global shutter image data into simulated rolling shutter image data may include a global-to-rolling shutter image converter. The converter may be configured to receive global shutter image data associated with a plurality of global shutter images. The global shutter image data may include dynamic global shutter image data representative of a moving object in the plurality of global shutter images. The converter may also be configured to determine an optical flow field based at least in part on two or more of the plurality of global shutter images and generate the simulated rolling shutter image based at least in part on the optical flow field. The converter may also be configured to generate high dynamic range (HDR) images based at least in part on the optical flow field and the global shutter image data.

Abstract: An object model for a real-world object is constructed using a parameter-based object generator, where the parameters for the object model are determined using sensor data such as photographic images, radar images, or LIDAR images obtained from the real-world object. Conversion of the sensor data to appropriate object parameters is accomplished using a machine learning system. The machine learning system is calibrated by generating a plurality of object parameters, generating a corresponding plurality of learning objects from the plurality of object parameters, generating a plurality of simulated sensor return data for the learning objects, and then providing the simulated sensor return data and object parameters to the machine learning system.

Abstract: Procedural generation of simulated environments is described herein. In an example, a computing device can access road network data associated with a real environment. Further, the computing device can generate, based on the sensor data, a road mesh associated with the real environment. The computing device can associate the road network data with the road mesh to generate a simulated environment and can procedurally render at least one object into the simulated environment based at least in part on at least one rule. The computing device can output the simulated environment for at least one of testing, validating, or training an algorithm used by an autonomous vehicle for at least one of navigating, planning, or decision making.

Abstract: Procedural world generation using tertiary data is described. In an example, a computing device can receive road network data associated with the real environment and a road mesh associated with a real environment. The computing device can associate the road network data with the road mesh to generate a simulated environment. Additionally, the computing device can associate supplemental data with the road network data and the road mesh to enhance the simulated environment (e.g., supplementing information otherwise unavailable to the sensor data due to an occlusion). The computing device can output the simulated environment for at least one of testing, validating, or training an algorithm used by an autonomous robotic computing device for at least one of navigating, planning, or decision making.