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: 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: 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.