Abstract: Systems and methods are disclosed that curate synthetic datasets using a stereo model. For example, embodiments of the present disclosure may include an iterative process that generates a first synthetic dataset using a replicator composer, uses the generated dataset to train a stereo model such as a Foundational Stereo model, and curates a second synthetic dataset using the trained stereo model. Additionally, and/or alternatively, the synthetic datasets that were generated by the replicator composer may include multiple categories of realism such as realistic style synthetic data and chaotic style synthetic data as well as multiple categories of use cases such as navigation, driving, and manipulation. Additionally, and/or alternatively, the replicator composer may generate a scene based on determining a center of mass of objects that are generated for the scene and using the center of mass to orient the camera.

Abstract: In various examples, behavior-based mission task management for mobile autonomous machine systems and applications are provided. A mission controller may generate a mission behavior model logic framework for a mission that accounts for the actions of a set of multiple task agents that play a role in completing the mission. The mission controller may assemble a framework starting from a baseline task sequence, correlate tasks defined by the baseline task sequence with pre-defined behavior models, and customize those behavior models based on mission task customization parameters. The mission controller may provide the mission behavior model logic framework to a mission dispatch function. Individual autonomous mobile task agents may then proceed to execute their assigned portions of local task sequences in accordance with the customized behavior models distributed to them by the mission dispatch function.

Abstract: In various examples, sets of correlated timestamps are sampled from clock sources. The sets of correlated timestamps are used to compute translation data, such as offsets and/or rates of change of the clock sources. The offsets and/or rates of change may be used to translate a timestamp to a reference time domain. The sampled clock sources may be frequency locked and the translation may be performed without using the rates of change. For example, a running average of the offsets may be used to perform the translation. The translated timestamps and corresponding sensor measurements may be provided to one or more applications for use in performing one or more operations for a machine, such as perception and/or control operations.

Abstract: In various examples, sets of correlated timestamps are sampled from clock sources. The sets of correlated timestamps are used to compute translation data, such as offsets and/or rates of change of the clock sources. The offsets and/or rates of change may be used to translate a timestamp to a reference time domain. The sampled clock sources may be frequency locked and the translation may be performed without using the rates of change. For example, a running average of the offsets may be used to perform the translation. The translated timestamps and corresponding sensor measurements may be provided to one or more applications for use in performing one or more operations for a machine, such as perception and/or control operations.

Abstract: A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

Abstract: In various examples, systems and methods are disclosed relating to generating realistic and diverse simulated scenes of people for updating/training artificial intelligence models. A configuration file can be received that that specifies randomization for a semantic layer of a model for a scene. A distribution can be sampled according to the randomization to select data for the semantic layer of the model. The scene can be generated to include the model having the data selected for the semantic layer. The scene, including the model, can be rendered to generate an image for updating a neural network.

Abstract: In various examples, synthetic dataset regeneration for AI systems and applications is described herein. For instance, systems and methods described herein may use a simulator to generate a synthetic dataset along with data (referred to, in some examples, as “log data”) representing information associated with the generation of the synthetic dataset by the simulator. For instance, the log data may represent at least parameters used to generate synthetic dataset, values for the parameters, assets associated with the parameters, and/or values representing results associated with the synthetic dataset. The systems and methods may then use the log data to recreate, modify, and/or enhance the synthetic dataset. For example, the synthetic dataset may be recreated by providing at least the log data as input to the simulator such that the simulator regenerates the dataset using the same parameters, values, and/or assets.

Abstract: Systems and techniques to improve a robotics control application are described herein. In at least one embodiment, event information is generated by one or more robots performing a mission. The event information indicates one or more determinations made by the one or more robots. The event information is stored in a log, and the stored event information can be used to simulate the generating of decisions to control the one or more robots.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: Embodiments of the present disclosure relate to a system and method used to schedule and initiate execution of one or more tasks. The system may include processing units that may perform operations that may include obtaining execution information that may correspond to a first task and a second task. In some embodiments, the first task may include first operations and where the second task may include second operations. In some embodiments, the operations may further include determining a time to initialize execution of the first task and the second task based at least on the execution information. In some embodiments, execution of the first task may be executed on a first computing system and the second task may be executed on a second computing system where the execution of the first operations and the second operations is interdependent.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

Abstract: The present disclosure relates to detecting objects in detection zones using multiple analysis techniques. The multiple analysis techniques may be used to analyze sensor data corresponding to the detection zones. The multiple analysis techniques may be selected based at least on at least two of the analysis techniques of the multiple analysis techniques having a computational diversity by performing different types of computational analyses on the sensor data with respect to each other, and at least two analysis techniques of the multiple analysis techniques having implementation diversity by being implemented on different types of computing platforms with respect to each other.

Abstract: In various examples, sets of correlated timestamps are sampled from clock sources. The sets of correlated timestamps are used to compute translation data, such as offsets and/or rates of change of the clock sources. The offsets and/or rates of change may be used to translate a timestamp to a reference time domain. The sampled clock sources may be frequency locked and the translation may be performed without using the rates of change. For example, a running average of the offsets may be used to perform the translation. The translated timestamps and corresponding sensor measurements may be provided to one or more applications for use in performing one or more operations for a machine, such as perception and/or control operations.

Abstract: In various examples, behavior-based mission task management for mobile autonomous machine systems and applications are provided. A mission controller may generate a mission behavior model logic framework for a mission that accounts for the actions of a set of multiple task agents that play a role in completing the mission. The mission controller may assemble a framework starting from a mission template that defines a baseline task sequence, correlate tasks defined by the baseline task sequence with pre-defined behavior models from a task library, and customize those behavior models based on mission task customization parameters. The mission controller may provide the mission behavior model logic framework to a mission dispatch function. Individual autonomous mobile task agents may then proceed to execute their assigned portions of local task sequences in accordance with the customized behavior models distributed to them by the mission dispatch function.

Abstract: In various examples, behavior-based mission task management for mobile autonomous machine systems and applications are provided. A mission controller may generate a mission behavior model logic framework for a mission that accounts for the actions of a set of multiple task agents that play a role in completing the mission. The mission controller may assemble a framework starting from a baseline task sequence, correlate tasks defined by the baseline task sequence with pre-defined behavior models, and customize those behavior models based on mission task customization parameters. The mission controller may provide the mission behavior model logic framework to a mission dispatch function. Individual autonomous mobile task agents may then proceed to execute their assigned portions of local task sequences in accordance with the customized behavior models distributed to them by the mission dispatch function.

Abstract: A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

Abstract: Systems and methods for efficient lossless compression of captured raw image information are presented. A method can comprise: receiving raw image data from an image capture device, segregating the pixel data into a base layer portion and an enhanced layer portion, reconfiguring the base layer portion expressed in the first color space values from a raw capture format into a pseudo second color space compression mechanism compatible format, and compressing the reconfigured base layer portion of first color space values. The raw image data can include pixel data are expressed in first color space values. The segregation can be based upon various factors, including a compression benefits analysis of a boundary location between the base layer portion and enhanced layer portion. The reconfiguring the base layer portion can include separating the base layer portion based upon multiple components within the raw data; and forming base layer video frames from the multiple components.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.