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, a safety decomposition architecture for autonomous machine applications is presented that uses two or more individual safety assessments to satisfy a higher safety integrity level (e.g., ASIL D). For example, a behavior planner may be used as a primary planning component, and a collision avoidance feature may be used as a diverse safety monitoring component—such that both may redundantly and independently prevent violation of safety goals. In addition, robustness of the system may be improved as single point and systematic failures may be avoided due to the requirement that two independent failures—e.g., of the behavior planner component and the collision avoidance component—occur simultaneously to cause a violation of the safety goals.

Abstract: In various examples, a safety decomposition architecture for autonomous machine applications is presented that uses two or more individual safety assessments to satisfy a higher safety integrity level (e.g., ASIL D). For example, a behavior planner may be used as a primary planning component, and a collision avoidance feature may be used as a diverse safety monitoring component—such that both may redundantly and independently prevent violation of safety goals. In addition, robustness of the system may be improved as single point and systematic failures may be avoided due to the requirement that two independent failures—e.g., of the behavior planner component and the collision avoidance component—occur simultaneously to cause a violation of the safety goals.

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: 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, 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 method for network hardware table management includes: obtaining, by a network device table manager of a network device, a first feature table entry of a first feature table, where the first feature table entry comprises a first prefix and a first action to take for a first feature; obtaining, by the network device table manager, a second feature table entry of a second feature table, where the second feature table entry comprises a second prefix and a second action to take for a second feature; making a first determination that the first prefix and the second prefix include a common portion and that the common portion is an entirety of each of the first and second prefixes; and in response to the first determination, adding a combined feature table entry to a combined feature table.

Abstract: In various examples, a safety decomposition architecture for autonomous machine applications is presented that uses two or more individual safety assessments to satisfy a higher safety integrity level (e.g., ASIL D). For example, a behavior planner may be used as a primary planning component, and a collision avoidance feature may be used as a diverse safety monitoring component—such that both may redundantly and independently prevent violation of safety goals. In addition, robustness of the system may be improved as single point and systematic failures may be avoided due to the requirement that two independent failures—e.g., of the behavior planner component and the collision avoidance component—occur simultaneously to cause a violation of the safety goals.

Abstract: Systems and methods for disabling autonomous vehicle input devices are provided. In one example embodiment, a computer implemented method includes identifying an operating mode of an autonomous vehicle. The method includes determining one or more vehicle input devices to be disabled based at least in part on the operating mode of the autonomous vehicle. The vehicle input devices are located onboard the autonomous vehicle. The method includes disabling the one or more vehicle input devices based at least in part on the identified operating mode of the autonomous vehicle such that an input by a user with respect to the one or more vehicle input devices does not affect an operation of the autonomous vehicle.

Abstract: Methods, systems, and computer readable mediums for network hardware table management including obtaining, by a network device table manager of a network device, a first feature table entry published by a first feature; obtaining, by the network device table manager, a second feature table entry published by a second feature; making a first determination that the first feature table entry and the second table feature entry each comprise a common prefix; and based on the first determination, adding a first combined feature table entry to a combined feature table, the first combined feature table entry comprising the common prefix, a first feature action of the first feature table entry and a second feature action of the second feature table entry.

Abstract: Methods, systems, and computer readable mediums for network hardware table management including obtaining, by a network device table manager of a network device, a first feature table entry published by a first feature; obtaining, by the network device table manager, a second feature table entry published by a second feature; making a first determination that the first feature table entry and the second table feature entry each comprise a common prefix; and based on the first determination, adding a first combined feature table entry to a combined feature table, the first combined feature table entry comprising the common prefix, a first feature action of the first feature table entry and a second feature action of the second feature table entry.

Abstract: Methods and systems are described for comparing values using an associative memory. An associative memory lookup is performed based on a key that comprises a first number, a second number, and a third number. The associative memory includes sets of mask rows that are configured such that the associative memory returns a result of true when the sum of the first number and the second number is equal to the third number. The result of the associative memory lookup is outputted. The associative memory configured in this manner may be used, for example, by a packet forwarding device to perform a zero-value boundary condition check or packet sequence check.

Abstract: Methods and systems are described for comparing values using an associative memory. An associative memory lookup is performed based on a key that comprises a first number, a second number, and a third number. The associative memory includes sets of mask rows that are configured such that the associative memory returns a result of true when the sum of the first number and the second number is equal to the third number. The result of the associative memory lookup is outputted. The associative memory configured in this manner may be used, for example, by a packet forwarding device to perform a zero-value boundary condition check or packet sequence check.

Abstract: Methods, systems, and computer readable mediums for network hardware table management including obtaining, by a network device table manager of a network device, a first feature table entry published by a first feature; obtaining, by the network device table manager, a second feature table entry published by a second feature; making a first determination that the first feature table entry and the second table feature entry each comprise a common prefix; and based on the first determination, adding a first combined feature table entry to a combined feature table, the first combined feature table entry comprising the common prefix, a first feature action of the first feature table entry and a second feature action of the second feature table entry.

Abstract: The invention relates to temporary cross-linked cellulose ethers, a process to make them, as well as their use to influence the rheological profile of an aqueous medium in which they are dissolved. The temporary cross-linked cellulose ethers are characterized in that they are cellulose ethers that are Cross-linked with at least one or more compounds of the formula (C1-4 alkyl)-OC(O)CHOHO—(C1-4 alkyl).

Abstract: Systems and methods for disabling autonomous vehicle input devices are provided. In one example embodiment, a computer implemented method includes identifying an operating mode of an autonomous vehicle. The method includes determining one or more vehicle input devices to be disabled based at least in part on the operating mode of the autonomous vehicle. The vehicle input devices are located onboard the autonomous vehicle. The method includes disabling the one or more vehicle input devices based at least in part on the identified operating mode of the autonomous vehicle such that an input by a user with respect to the one or more vehicle input devices does not affect an operation of the autonomous vehicle.

Abstract: Systems and methods for disabling autonomous vehicle input devices are provided. In one example embodiment, a computer implemented method includes identifying an operating mode of an autonomous vehicle. The method includes determining one or more vehicle input devices to be disabled based at least in part on the operating mode of the autonomous vehicle. The vehicle input devices are located onboard the autonomous vehicle. The method includes disabling the one or more vehicle input devices based at least in part on the identified operating mode of the autonomous vehicle such that an input by a user with respect to the one or more vehicle input devices does not affect an operation of the autonomous vehicle.

Abstract: Systems and methods for disabling autonomous vehicle input devices are provided. In one example embodiment, a computer implemented method includes identifying an operating mode of an autonomous vehicle. The method includes determining one or more vehicle input devices to be disabled based at least in part on the operating mode of the autonomous vehicle. The vehicle input devices are located onboard the autonomous vehicle. The method includes disabling the one or more vehicle input devices based at least in part on the identified operating mode of the autonomous vehicle such that an input by a user with respect to the one or more vehicle input devices does not affect an operation of the autonomous vehicle.

Abstract: The invention relates to temporary cross-linked cellulose ethers, a process to make them, as well as their use to influence the rheological profile of an aqueous medium in which they are dissolved. The temporary cross-linked cellulose ethers are characterized in that they are cellulose ethers that are Cross-linked with at least one or more compounds of the formula (C1-4 alkyl)-OC(O)CHOHO—(C1-4 alkyl).