Abstract: In various examples, techniques for determining perception zones for object detection are described. For instance, a system may use a dynamic model associated with an ego-machine, a dynamic model associated with an object, and one or more possible interactions between the ego-machine and the object to determine a perception zone. The system may then perform one or more processes using the perception zone. For instance, if the system is validating a perception system of the ego-machine, the system may determine whether a detection error associated with the object is a safety-critical error based on whether the object is located within the perception zone. Additionally, if the system is executing within the ego-machine, the system may determine whether the object is a safety-critical object based on whether the object is located within the perception zone.

Abstract: In various examples, a program (e.g., application, algorithm, routine, etc.) may be organized into operational units (e.g., nodes executed by one or more processors), each of which are tasked with executing one or more respective events (e.g., tasks) within the larger program. At least some of the events of the larger program may be successively executed in a flow, one after another, using triggers sent directly from one node to the next. In addition, a manager may exchange communications with the nodes to monitor or assess a status of the system (e.g., determine when a node has completed an event) or to control or trigger a node to initiate an event.

Abstract: The present disclosure provides a water drainage system comprising: a plurality of lengths of drain pipe comprising a channel and a vertical wall, wherein (i) the vertical wall comprises one or more standoffs extending horizontally from the vertical wall and (ii) wherein the drain pipe comprises a top edge and a bottom edge, each of the top and bottom edges being bifurcated by a v-shaped notch.

Abstract: In various examples, techniques for determining perception zones for object detection are described. For instance, a system may use a dynamic model associated with an ego-machine, a dynamic model associated with an object, and one or more possible interactions between the ego-machine and the object to determine a perception zone. The system may then perform one or more processes using the perception zone. For instance, if the system is validating a perception system of the ego-machine, the system may determine whether a detection error associated with the object is a safety-critical error based on whether the object is located within the perception zone. Additionally, if the system is executing within the ego-machine, the system may determine whether the object is a safety-critical object based on whether the object is located within the perception zone.

Abstract: Embodiments of the present disclosure may relate to a method of modifying system behavior based on one or more determined correlations. In some embodiments, the method may include obtaining first data and second data where the first data may include one or more performance indicator values that may correspond to the performance of the system and where the second data may include one or more operational domain parameters that may correspond to the system. In some embodiments, the method may additionally include assembling a data structure based on the first data and the second data. In some embodiments, the method may additionally include determining one or more correlations between individual operational domain parameters and individual performance indicator values based on the assembled data structure. In some embodiments, the method may additionally include modifying one or more aspects of the system based on the determined correlations.

Abstract: In various examples, systems and methods are disclosed that perform sensor fusion using rule-based and learned processing methods to take advantage of the accuracy of learned approaches and the decomposition benefits of rule-based approaches for satisfying higher levels of safety requirements. For example, in-parallel and/or in-serial combinations of early rule-based sensor fusion, late rule-based sensor fusion, early learned sensor fusion, or late learned sensor fusion may be used to solve various safety goals associated with various required safety levels at a high level of accuracy and precision. In embodiments, learned sensor fusion may be used to make more conservative decisions than the rule-based sensor fusion (as determined using, e.g., severity (S), exposure (E), and controllability (C) (SEC) associated with a current safety goal), but the rule-based sensor fusion may be relied upon where the learned sensor fusion decision may be less conservative than the corresponding rule-based sensor fusion.

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 that perform sensor fusion using rule-based and learned processing methods to take advantage of the accuracy of learned approaches and the decomposition benefits of rule-based approaches for satisfying higher levels of safety requirements. For example, in-parallel and/or in-serial combinations of early rule-based sensor fusion, late rule-based sensor fusion, early learned sensor fusion, or late learned sensor fusion may be used to solve various safety goals associated with various required safety levels at a high level of accuracy and precision. In embodiments, learned sensor fusion may be used to make more conservative decisions than the rule-based sensor fusion (as determined using, e.g., severity (S), exposure (E), and controllability (C) (SEC) associated with a current safety goal), but the rule-based sensor fusion may be relied upon where the learned sensor fusion decision may be less conservative than the corresponding rule-based sensor fusion.

Abstract: In various examples, systems and methods are disclosed that perform sensor fusion using rule-based and learned processing methods to take advantage of the accuracy of learned approaches and the decomposition benefits of rule-based approaches for satisfying higher levels of safety requirements. For example, in-parallel and/or in-serial combinations of early rule-based sensor fusion, late rule-based sensor fusion, early learned sensor fusion, or late learned sensor fusion may be used to solve various safety goals associated with various required safety levels at a high level of accuracy and precision. In embodiments, learned sensor fusion may be used to make more conservative decisions than the rule-based sensor fusion (as determined using, e.g., severity (S), exposure (E), and controllability (C) (SEC) associated with a current safety goal), but the rule-based sensor fusion may be relied upon where the learned sensor fusion decision may be less conservative than the corresponding rule-based sensor fusion.

Abstract: In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

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, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

Abstract: A system and method for monitoring conditions in a crawl space is provided. The system generally comprises at least one sensor, computing device, data aggregator operably connected to the at least one sensor, processor operably connected to the computing device, power supply, and non-transitory computer-readable medium coupled to the processor and having instructions stored thereon. The system is designed to collect condition data via the at least one sensor and determine whether the conditions within the crawl space could have a detrimental impact on the building. In particular, the system is designed to monitor settling of a building over time and alert a user if the settling exceeds a predefined threshold.

Abstract: In various examples, systems and methods are disclosed relating to refinement of safety zones and improving evaluation metrics for the perception modules of autonomous and semi-autonomous systems. Example implementations can exclude areas in the state space that are not safety critical, while retaining the areas that are safety-critical. This can be accomplished by leveraging ego maneuver information and conditioning safety zone computations on ego maneuvers. A maneuver-based decomposition of perception safety zones may leverage a temporal convolution operation with the capability to account for collision at any intermediate time along the way to maneuver completion. This provides a significant reduction in zone volume while maintaining completeness, thus optimizing or otherwise enhancing obstacle perception performance requirements by filtering out regions of state space not relevant to a system's route of travel.

Abstract: One or more embodiments of the present disclosure relate to switching between execution schedules related to execution of tasks, or runnables, by multiple compute engines. The execution schedules includes respective sets of commands that dictate timing and order of execution, by the compute engines, of tasks, or runnables, corresponding to computing applications.

Abstract: A system and method for monitoring conditions in a crawl space is provided. The system generally comprises at least one sensor, computing device, data aggregator operably connected to the at least one sensor, processor operably connected to the computing device, power supply, and non-transitory computer-readable medium coupled to the processor and having instructions stored thereon. The system is designed to collect condition data via the at least one sensor and determine whether the conditions within the crawl space could have a detrimental impact on the building. In particular, the system is designed to alert a third-party when a condition might cause damage to a building so that the third-party might correct the cause of the condition.

Abstract: One or more embodiments of the present disclosure relate to executing, by a plurality of compute engines, a plurality of runnables of a computing application based at least on an execution schedule and a set of commands associated with the execution schedule. The execution schedule may be generated using a compiling system to include the set of commands. The set of commands may include one or more individual commands corresponding to one or more timing fences dictating a timing and order of execution of one or more individual runnables of the plurality of runnables.

Abstract: In various examples, a system is provided for monitoring and controlling program flow in an event-triggered system. A program (e.g., application, algorithm, routine, etc.) may be organized into operational units (e.g., nodes executed by one or more processors), each of which tasked with executing one or more respective events (e.g., tasks) within the larger program. At least some of the events of the larger program may be successively executed in a flow, one after another, using triggers sent directly from one node to the next. In addition, the system of the present disclosure may include a manager that may exchange communications with the nodes to monitor or assess a status of the system (e.g., determine when a node has completed an event) or to control or trigger a node to initiate an event.

Abstract: A system is provided for monitoring and controlling program flow in an event-triggered system. A program (e.g., application, algorithm, routine, etc.) may be organized into operational units (e.g., nodes executed by one or more processors), each of which tasked with executing one or more respective events (e.g., tasks) within the larger program. At least some of the events of the larger program may be successively executed in a flow, one after another, using triggers sent directly from one node to the next. In addition, the system of the present disclosure may include a manager that may exchange communications with the nodes to monitor or assess a status of the system (e.g., determine when a node has completed an event) or to control or trigger a node to initiate an event.

Abstract: In various examples, techniques for determining perception zones for object detection are described. For instance, a system may use a dynamic model associated with an ego-machine, a dynamic model associated with an object, and one or more possible interactions between the ego-machine and the object to determine a perception zone. The system may then perform one or more processes using the perception zone. For instance, if the system is validating a perception system of the ego-machine, the system may determine whether a detection error associated with the object is a safety-critical error based on whether the object is located within the perception zone. Additionally, if the system is executing within the ego-machine, the system may determine whether the object is a safety-critical object based on whether the object is located within the perception zone.