Incremental guidance of functionality for autonomous and semi-autonomous systems and applications

Abstract

The invention discloses incremental guidance of functionality for autonomous and semi-autonomous systems and applications. In various examples, incremental guidance of functionality for autonomous systems and applications is described herein. Systems and methods are disclosed that use different stages to direct functionality of a machine in order to more quickly provide at least a portion of the functionality without compromising the safety of the machine. For example, during a first phase, resources associated with a machine may be allocated when initializing a function. Further, at least a first portion of the functionality, such as a functionality that does not compromise the security of the machine if an error occurs, may be made available for use by one or more users of the machine. Subsequently, during the second phase, at least a second portion of the functionality, such as a functionality that may compromise security of the machine if an error occurs, may be made available for use by a user of the machine.

Description

Background Art

[0001] Designing a system that drives a vehicle semi-autonomously or autonomously without supervision while actually accepting the required safety level is extremely difficult and requires different types of functions with various safety levels. For example, an autonomous vehicle may include functions that are critical to the safety of the autonomous vehicle, such that if an associated error (e.g., a safety-critical function) occurs, the safety of the machine may be compromised, such as fully automated driving functions and minimal risk maneuvering functions. As another example, some functions of the autonomous vehicle (e.g., non-safety-related functions) may not involve or endanger the safety of the vehicle - such as entertainment functions (e.g., radio, display, etc.), parking assistance functions, and lane assistance functions. Therefore, in order to maintain a high level of safety, the autonomous vehicle may operate in various stages when guiding the function. For example, during a first operating stage (such as an initialization stage), the autonomous vehicle may at least allocate resources (e.g., processing resources, memory resources, network resources, etc.) and initialize only safety-related functions while making non-safety-related functions unavailable. Next, during a second operating stage, such as an operating stage that occurs when resource allocation is completed (e.g., the entire system is ready), the autonomous vehicle may make all functions available to the user of the autonomous vehicle. Finally, during a third operating phase, such as during a deinitialization phase that occurs when the autonomous vehicle completes navigation, the autonomous vehicle may terminate (eg, shut down) functions and / or resources.

[0002] As described herein, the autonomous vehicle may operate in these three operating phases for safety reasons. For example, the autonomous vehicle may wait until the second operating phase to make all functions available to the user, because interference may occur to one or more functions while resources are still being allocated. Therefore, if interference with a safety-critical function occurs, there may be a safety risk to the autonomous vehicle. Alternatively, if interference occurs to a function that is not safety-critical (e.g., a failsafe function), there will be no safety risk to the autonomous vehicle. However, even if there is no safety risk if such a function fails, the user of the autonomous vehicle still needs to wait until the second operating phase when the function is available for use. Summary of the invention

[0003] Embodiments of the present disclosure relate to incremental guidance of functions for autonomous and semi-autonomous systems and applications. Systems and methods are disclosed for guiding the functions of a vehicle (and / or other type of machine) using different operating phases so as to provide at least a portion of the functions more quickly without compromising the safety of the vehicle. For example, during a first operating phase (e.g., an initialization phase), resources associated with the vehicle may be allocated when initializing the functions. In addition, at least a first portion of the functions (such as functions associated with one or more security levels) may be made available to one or more users of the vehicle. Next, during a second operating phase (e.g., an operation phase), at least a second portion of these functions (such as functions associated with one or more additional higher security levels) may be made available to one or more users of the vehicle. In some cases, the second operating phase is initialized at least based on the readiness of the entire system (e.g., resource allocation is complete). Next, and during a third operating phase (e.g., a deinitialization phase), functions and / or resources may be terminated (e.g., shut down).

[0004] Compared to conventional systems (such as those described above), in some embodiments, the current system can provide certain functions to the user more quickly, such as a first part of the function associated with one or more security levels, while still maintaining the same security level of the vehicle. In some examples, the current system can provide such functions faster based on making the function available to the user (even when resources are still allocated and / or other functions are still initialized). In addition, the current system can maintain the same security level of the vehicle by refraining from making the second part of the function available until the entire system is ready (e.g., resource allocation is completed). In this way, there will be no interference with the second part of the function, such as interference caused by resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The present system and method for incremental guidance of functionality of autonomous or semi-autonomous systems and applications is described in detail below with reference to the accompanying drawings, wherein:

[0006] Figure 1 An example data flow diagram illustrating a process of using various operational phase boot functions according to some embodiments of the present disclosure;

[0007] Figure 2 An example of initializing and providing functions associated with a vehicle over a period of time according to some embodiments of the present disclosure is shown;

[0008] Figure 3 shows an example architecture of one or more systems that perform one or more of the processes described herein according to some embodiments of the present disclosure;

[0009] Figure 4A flow chart showing a method for incrementally directing a function associated with a vehicle according to some embodiments of the present disclosure;

[0010] Figure 5 A flow chart showing a method for directing functions associated with a vehicle using various operating phases according to some embodiments of the present disclosure;

[0011] Figure 6 A flow chart showing a method for incrementally directing functions associated with a vehicle using configuration data according to some embodiments of the present disclosure;

[0012] Figure 7 A flow chart showing a method for rebooting a function where an error has occurred using various stages of operation according to some embodiments of the present disclosure;

[0013] Fig. 8A is an illustration of an example autonomous vehicle according to some embodiments of the present disclosure;

[0014] Figure 8B According to some embodiments of the present disclosure Fig. 8A Examples of autonomous vehicle camera positions and fields of view;

[0015] Figure 8C According to some embodiments of the present disclosure Fig. 8A a block diagram of an example system architecture for an example autonomous vehicle;

[0016] Fig.8D According to some embodiments of the present disclosure, a method for Fig. 8A System diagram of an example of communication between autonomous vehicles;

[0017] Fig. 9 is a block diagram of an example computing device suitable for implementing some embodiments of the present disclosure; and

[0018] Fig.10 is a block diagram of an example data center suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Systems and methods are disclosed that relate to incremental guidance of functionality for semi-autonomous and autonomous systems and applications. Although the present disclosure may be described with respect to an example autonomous or semi-autonomous vehicle 800 (alternatively referred to herein as “vehicle 800,” “ego vehicle 800,” “machine 800,” or “ego machine 800”), the examples are described with respect to FIG. 8A to FIG. 8DThe present disclosure may be described with respect to path planning, but this is not intended to be limiting. For example, the systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, driven and undriven robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying boats, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, airplanes, construction vehicles, underwater vehicles, drones, and / or other vehicle types. Furthermore, while the present disclosure may be described with respect to path planning, this is not intended to be limiting, and the systems and methods described herein may be used in augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and / or any other technology space where path planning may be used.

[0020] For example, one or more systems may use one or more operating phases to incrementally guide various functions of a vehicle or other machine type so as to provide a first portion of these functions to one or more users more quickly without jeopardizing the safety of the vehicle or machine. As described herein, functions associated with a vehicle may include: (1) functions associated with a first safety level such that failures and / or errors will not affect safety, which may be referred to as "non-safety-related functions" (e.g., because in some examples, during a "failsafe phase", they are available), (2) functions associated with a second safety level such that failures and / or errors may cause a temporary reduction in safety, which may be repaired by stopping the function and / or verifying that one or more conditions are met, which may be referred to as "failsafe functions" (e.g., because in some examples, during a "failsafe phase", they are available), and / or (3) functions associated with a third safety level such that failures and / or errors are not tolerated (e.g., to a certain extent) because the function must guarantee reliability, which may also be referred to as "availability functions" (e.g., in some examples, during an "availability phase", they become available). Although three security levels are described, this is not intended to be limiting and any number of security levels may be included in the system.

[0021] For example, usability functions may include, but are not limited to, fully autonomous driving functions (e.g., fully autonomous mode), minimal risk maneuvering functions (e.g., safely stopping if a problem occurs with the vehicle), and the like. In addition, failsafe functions may include, but are not limited to, assisted parking functions, assisted driving functions, functions for summoning vehicles, and the like. In some examples, a function may be considered failsafe because the vehicle is able to provide control to a user (e.g., driver) if an error occurs in the function. Nevertheless, non-safety-related functions may include, but are not limited to, entertainment functions (e.g., radio, display, etc.), environmental control functions, and the like. Therefore, in some examples, the higher the safety level, the more important the system may be to ensure that failures associated with the function do not occur. For example, although failures associated with non-safety-related functions may cause some problems, such as annoying the user, such failures may be more acceptable than failures associated with usability functions, which may require reliable vehicle navigation.

[0022] In some examples, the one or more systems may initially operate in a first operating phase (which may be divided into additional operating phases, as described in more detail below), which may also be referred to as an "initialization phase," in which hardware, software, operating systems, etc. are started or booted. In addition, during the first operating phase, one or more systems may, such as assigning resources associated with one or more systems to a function (e.g., processing resources, memory resources, network resources, etc.), while also using the assigned resources to begin initialization of the function. In order to more quickly provide the first portion of the function to the user, the system may also make the first portion of the function available based at least on completion of initialization of the first portion of the function. As described herein, in some examples, the first portion of the function may include non-safety-related functions and / or fail-safe functions. In addition, in order to maintain the safety of the vehicle, one or more systems may avoid making the second portion of the function available during the first operating phase, even at the completion of initialization of the second portion of the function. As described herein, the second portion of the function may include an availability function.

[0023] Next, upon the occurrence of one or more first events, the one or more systems may be operated in a second operating phase, which may be referred to as a "running phase". As described, the one or more first events may include, but are not limited to, completion of allocation of resources, readiness of the entire one or more systems, safety checks indicating that there are no errors in one or more systems and / or functions, and the like. During the second operating phase, the one or more systems may then make the second portion of the functionality available for use. As described herein, by waiting to render the second portion of the functionality available during the second operating phase, problems that may occur during system initialization (e.g., interference that may occur based on the allocation of resources) may not interfere with the second portion of the functionality.

[0024] Next, one or more systems may operate in a third operating phase, which may be referred to as a "deinitialization phase," such as upon the occurrence of one or more second events. As described herein, one or more second events may include, but are not limited to, the vehicle being parked, the vehicle being turned off, one or more users ending use of the vehicle, etc. During the third operating phase, one or more systems may at least terminate (e.g., turn off) functions and / or resources associated with the vehicle.

[0025] In some examples, if the function does go wrong, one or more systems can perform one or more safety processes. For the first example, if the function fails, such as non-safety-related functions and / or failsafe functions, the one or more systems can operate in the fourth operating stage, which can also be referred to as the "reinitialization stage", in which the one or more systems cause the second part of these functions to be unavailable. One or more systems can then make the failed function initialized again and available. In addition, once the function is available again, one or more systems can switch back to the second operating stage and / or make the second part of the function available again. For the second example, and again if the function (such as the failsafe function) fails, the one or more systems can switch back to the first operating stage in order to initialize the function and / or one or more additional functions again. Once the function and / or one or more additional functions are available, one or more systems can then switch back to the second operating stage again.

[0026] Additionally or alternatively, in some examples, one or more systems may perform one or more safety procedures based on when a function fails. For the first example, if a function fails during a failsafe phase, which is described in more detail herein, the vehicle may be stopped and / or the failed function may be inhibited by terminating the failed function. For the second example, if a function fails during an available phase (which is described in more detail herein), resources associated with the vehicle may be blocked (which may cause delays), the vehicle may be stopped, and / or one or more functions may be terminated.

[0027] In some examples, in order to allow the use of the various operating stages described herein to guide the function, developers, suppliers, etc. associated with the vehicle can provide additional information associated with the function. For example, the information can indicate whether the function is at least a non-safety-related or non-critical function, a failsafe function, or an availability or critical function. In this way, one or more systems can determine which functions one or more systems may use during the first operating stage and which functions one or more systems may not use before the second operating stage. For example, if the system determines that the first initialization of the first function and the second initialization of the second function are both completed during the first operating stage, and the first function includes a failsafe function and the second function includes an availability function, the system can make the first function available for use during the first operating stage. However, one or more systems can also determine to wait until the second operating stage to make the second function available for use, so as to ensure that the second function does not fail, such as interference with the second function.

[0028] The systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, driven and undriven robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying boats, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, airplanes, construction vehicles, underwater vehicles, drones, and / or other vehicle types. Further, the systems and methods described herein may be used for a variety of purposes, by way of example and not limitation, for machine control, machine motion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, generative AI, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actor simulation and / or digital twins, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing, and / or any other suitable application.

[0029] The disclosed embodiments may be included in a variety of different systems, such as automotive systems (e.g., control systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aerial systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems including one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least in part in a data center, systems implementing a large language model (LLM), systems for performing conversational AI operations, systems for performing light transport simulations, systems for performing collaborative content creation of 3D assets, systems implementing one or more language models such as a large language model (LLM), systems implemented at least in part using cloud computing resources, and / or other types of systems.

[0030] refer to Figure 1 , Figure 1 An example data flow diagram of a process 100 using various operational phases to guide functions according to some embodiments of the present disclosure is shown. It should be understood that this and other arrangements described herein are set forth by way of example only. Other arrangements and elements (e.g., machines, interfaces, functions, orders, functional groupings, etc.) may be used in addition to or in place of the arrangements and elements shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any suitable combination and location. The various functions described herein as being performed by an entity may be performed by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory. In some embodiments, the same may be used. FIG. 8A to FIG. 8D Examples of autonomous or semi-autonomous vehicles 800, Fig. 9 The example computing device 900 of Fig.10 Components, features, and / or functionality similar to those of the example data center 1000 may be used to implement the systems, methods, and processes described herein.

[0031] Process 100 may include a system (eg, vehicle 800, computing device 900, data center 1000, etc.) operating in an initialization phase 102. Figure 1 As shown in the example of FIG. 1 , in order to provide the improvements described herein, such as providing a portion of the functionality to the user more quickly while also maintaining the safety of the vehicle, the initialization phase 102 can be broken down into two additional operation phases, such as a setup phase 104 and a failsafe phase 106. During the setup phase 104, one or more systems can boot up hardware, software, operating systems, etc., which are at least Figure 3. In addition, during the setup phase 104, the one or more systems may initiate infrastructure, such as one or more monitors, that ensures that any errors associated with the one or more systems will be detected. For example, the one or more systems may perform one or more hardware checks, one or more software checks, etc. to detect any errors. If an error is detected, the one or more systems may move to operate in a safe state until the error is no longer detected. However, if no error is detected, the one or more systems may move to the failsafe phase 106.

[0032] In the failsafe phase 106, one or more systems may begin to allocate resources 108 (such as processing resources, memory resources, network resources, and / or any other type of resources) to at least these functions. In addition, when resources 108 are allocated to functions, one or more systems may also begin to initialize functions. For example, one or more systems may allocate a first portion of resources 108 to a first function, and based at least on the first portion of the allocated resources 108, a first initialization associated with the first function may occur. One or more systems may also allocate a second portion of resources 108 to a second function, and based at least on the second portion of the allocated resources 108, a second initialization associated with the second function may occur. One or more systems may then continue to perform these processes for one or more additional functions (e.g., each additional function) during the failsafe phase 106.

[0033] As described herein, in order to more quickly provide at least a first portion of the functionality 110 to the user, such as one or more fail-safe functions, due to which the safety of the vehicle is not compromised if an error occurs, the one or more systems may also make the first portion of the functionality 110 available to the user during the fail-safe phase 106. For example, and using the above example where the system initializes two functions, if the first function includes a non-safety-related function and / or a fail-safe function, the one or more systems may make the first function available to the user during the fail-safe phase 106. As described herein, the one or more systems may make the function available by starting the function and / or providing the user with functionality associated with the function. For the first example, if the function includes an entertainment function (e.g., a non-safety-related function), such as a radio, the user may be able to listen to the radio based on the system making the function available. For the second example, if the function includes an assisted parking function (e.g., a fail-safe function), the user may use the assisted parking based on the system making the function available. Further, for the third example, if the functionality includes a fully autonomous functionality (e.g., an availability functionality), the user may place the vehicle in a fully autonomous mode based on the system making the functionality available.

[0034] However, to maintain the safety of the vehicle, the one or more systems can refrain from making the second portion of the functionality 112, such as one or more usability functions, available during the failsafe phase 106. For example, and using the above example where the system initializes two functions, if the second functionality includes a usability function, the one or more systems can refrain from making the second functionality available to the user even though initialization of the second functionality is completed during the failsafe phase 106. In some examples, the one or more systems refrain from making the second functionality available because interference with the second functionality may still occur during the failsafe phase 106 based on resources still allocated by the one or more systems.

[0035] For example, Figure 2 An example of initializing and providing functions associated with a vehicle at time period 202 according to some embodiments of the present disclosure is shown. Figure 2 As shown in the example of , at a first time T(1) that may occur during the failsafe phase 106, one or more systems may begin allocating resources to functions. The one or more systems may then begin initializing the first function 204 at a second time T(2), such as when a first resource associated with the first function has been allocated, initializing the second function 206 at a third time T(3), such as when a second resource associated with the second function has been allocated, initializing the third function 208 at a fourth time T(4), such as when a third resource associated with the third function has been allocated, and initializing the fourth function 210 at a fifth time T(5), such as when a fourth resource associated with the fourth function has been allocated. In some examples, the one or more systems may perform one or more additional processes for one or more additional functions.

[0036] like Figure 2 As further shown in the example of , initialization of the first function may be completed 212 at a sixth time T(6), initialization of the third function may be completed 214 at a seventh time T(7), initialization of the second function may be completed 216 at an eighth time T(8), and initialization of the fourth function may be completed 218 at a ninth time T(9). As such, one or more systems may determine that the first function comprises a non-safety-related function, and based at least on this determination, the one or more systems may make the first function available to the user at a tenth time T(10) 220. The one or more systems may also determine that the third function comprises a failsafe function, and based at least on this determination, the one or more systems may make the third function available to the user at an eleventh time T(11) 222. However, the system may determine that the second function and the fourth function comprise availability functions. As such, the one or more systems may avoid making the second function and the fourth function available while resources are still being allocated.

[0037] Return to reference Figure 1 In an example of the process 100, the process 100 may include one or more systems switching to operate in the run phase 114. As described herein, the one or more systems may switch to operate in the run phase 114 based on the occurrence of one or more first events. The first event may include, but is not limited to, the completion of the allocation of the resource 108, the readiness of the entire one or more systems, a safety check indicating that one or more systems and / or functions are free of errors, and the like. Figure 1 As shown in the example of, in order to provide the improvements described herein, such as providing the first portion of functionality 110 to the user more quickly while also maintaining the safety of the vehicle, the operating phase 114 may include at least an availability phase 116 in which one or more systems now make the second portion of functionality 112 available to the user.

[0038] For example, and returning a reference Figure 2 In the example of , the one or more systems may at least complete the allocation of resources at the twelfth time T(12). As such, based at least on the occurrence of the event, the one or more systems may switch from operating in the failsafe phase 106 to operating in the availability phase 116. The one or more systems may then make the second function available to the user at the thirteenth time T(13) 224, and make the fourth function available to the user at the fourteenth time T(14) 226. Thus, by executing Figure 2 , the system is able to make the first function and the third function available to the user before the resource allocation is completed, the first function and the third function again respectively including the non-safety related function and the fail-safe function. In this way, and compared to conventional vehicle systems, one or more systems are able to provide these functions to the user more quickly. In addition, by waiting to make the second function and the fourth function (which again includes the availability function) available until after the resource allocation is completed, one or more systems are still able to provide the same level of safety as conventional vehicle systems.

[0039] Return to see Figure 1 In some examples, the operation phase 114 may include a reinitialization phase 118, which one or more systems use if one or more errors still occur in one or more functions 120. For example, if an error occurs in a function 120, such as a non-safety-related function and / or a fail-safe function or an availability function, the system may initially terminate (e.g., shut down) the function 120, which is relative to Figure 3The example of is described in more detail. The system may then determine whether to reboot function 120 and / or whether to perform one or more additional operations based at least on function 120 being terminated. For the first example, if function 120 includes one or more non-safety-related functions and / or fail-safe functions that the system can safely reboot, one or more systems may perform one or more operations to safely reboot the function. For the second example, if function 120 includes an availability function, the system may begin activating and / or executing one or more backup functions (e.g., function 110 and / or function 112) in order to maintain the safety level of the vehicle. In addition, in either example, depending on the type of function, one or more systems may provide control back to the user. For example, if function 120 includes a parking assist function, the system may notify the user that there is an error in function 120, enabling the user to control the vehicle for parking.

[0040] As such, in some examples, if the function 120 fails, one or more systems can switch from operating in the running phase 114 and / or the availability phase 116 to operating in the reinitialization phase 118. When operating the reinitialization phase 118, one or more systems can cause one or more functions to be temporarily unavailable to the user (e.g., suspend the function), such as the second part of the function 112, so as to prevent one or more functions from erroneously occurring. One or more systems can then allocate resources to the failed function 120 and / or reinitialize the failed function 120. In addition, once the reinitialization of the failed function 120 is completed, one or more systems can again make the function 120 available to the user. After the function 120 is available to the user again, one or more systems can then switch from operating in the reinitialization phase 118 to operating in the running phase 114 and / or the availability phase 116. One or more systems can then make the temporarily suspended function available to be used by the user again. By performing such a process using the reinitialization phase 118 , one or more systems may be able to once again make disabled functionality 120 available to the user while also maintaining the safety level of the vehicle.

[0041] Additionally or alternatively, in some examples, one or more systems can switch from operating in the running phase 114 and / or the availability phase 116 to operating in the initialization phase 102 (e.g., the failsafe phase 106). When operating in the initialization phase 102, one or more systems can again make one or more functions temporarily unavailable to the user (e.g., suspend one or more functions), such as the second part of the function 112, in order to prevent one or more functions from malfunctioning. One or more systems can then allocate resources to the failed function 120 and / or reinitialize the failed function 120. In addition, once the reinitialization of the failed function 120 is completed, one or more systems can again make the function 120 available to the user. After the function 120 is available to the user again, one or more systems can then switch from operating in the initialization phase 102 to operating in the running phase 114 and / or the availability phase 116. One or more systems can then make the temporarily suspended function available to the user again. Again, by performing such a process using the initialization phase 102 , one or more systems can once again make disabled functionality 120 available to the user while also maintaining the safety level of the vehicle.

[0042] As described herein, in some examples, if function 120 fails, one or more systems may cause one or more backup functions to be activated and / or executed in order to maintain the safety level of the vehicle. For the first example, if function 120 includes a failsafe function, such as an assisted parking function that automatically parks the vehicle, one or more systems may stop the vehicle and / or terminate function 120. One or more systems may then perform one or more additional processes, such as notifying the user of the fault and / or providing the user with control of the parked vehicle. For the second example, if function 120 includes an availability function, such as a minimum risk maneuvering function that safely stops the vehicle under a specific environment (such as when a potential collision with another vehicle is detected), one or more additional functions may be performed based on the failure of the function. For example, one or more systems may cause one or more additional functions associated with safely navigating the vehicle to occur, such as navigating along different routes until the user of the vehicle is able to take control of the vehicle. In this way, one or more systems are again able to maintain the safety level of the vehicle and / or the user.

[0043] In some examples, one or more systems may still perform one or more additional operations based on the failure of the function 120. For example, if another function (e.g., function 110, function 116, etc.) depends on the failed function 120, the system may terminate the other function in order to maintain the safety level. For example, if the failed function 120 includes a perception function associated with detecting the location of an object within the environment, one or more systems may terminate one or more other functions that depend on the location of the object to be operated. For example, one or more systems may terminate a parking assist function because the parking assist function may not operate as expected without initially determining the location of the object within the environment.

[0044] In some examples, one or more systems can perform additional processes based on when a function fails. For the first example, if function 110 fails during failsafe phase 106, one or more systems can stop the vehicle and / or perform one or more other safety processes. In addition, one or more systems can notify the user of the failure and / or provide the user with control of the vehicle. For the second example, if function 110 and / or function 112 fails during availability phase 116, one or more systems can terminate function 110 or 112 and / or one or more additional functions 110 or 112, stop the vehicle, and / or perform one or more additional safety processes.

[0045] like Figure 1 As further shown in the example of , process 100 may include the system switching to operate in a deinitialization phase 122. As described herein, one or more systems may switch to operate in the deinitialization phase 122 based on the occurrence of one or more second events. The second event may include, but is not limited to, the vehicle being parked, the vehicle being turned off, the user's use of the vehicle is finished, and the like. In some examples, while operating in the deinitialization phase 122, one or more systems may begin to terminate (e.g., shut down) functions, such as the first portion of function 110 and / or the second portion of function 112. Additionally, in some examples, while operating in the deinitialization phase 122, one or more systems may begin to shut down resources 108 and / or save information 124 that one or more systems may use later, such as location information indicating the current location of the vehicle, preference information indicating one or more preferences associated with the user, and / or any other information.

[0046] Figure 3An example architecture 302 of a system for performing one or more of the processes described herein according to some embodiments of the present disclosure is shown. As shown, the architecture 302 may include hardware, such as one or more processing units 304 (e.g., one or more central processing units, one or more graphics processing units, one or more deep learning accelerators (DLAs), one or more programmable vision accelerators (PVAs) - which may include one or more vector processing units and / or direct memory access units (DMAs), one or more data processing units (DPUs), etc.) and a functional safety island 306. In some examples, the functional safety island 306 may include a collection of one or more small or discrete processing units. For example, the functional safety island may be implemented so that common cause failures between, for example, the functional safety island 306 and the processing unit 304 are avoided. In some examples, the architecture may include additional hardware components, such as in Figure 8C One or more hardware components shown in the example of . The architecture 302 may further include software such as a hypervisor 308, functional safety island software 310 executed by the functional safety island 306, and virtual machines 312(1)-(3) (also referred to as "virtual machines 312" in the singular or "virtual machines 312" in the plural) that execute processes 314(1)-(6) (also referred to as "process 314" in the singular or "processes 314" in the plural) associated with the functions. Although Figure 3 The example of FIG. 30 shows architecture 302 as including three virtual machines 312 , but in other examples, architecture 302 may include any other number of virtual machines.

[0047] In some examples, hypervisor 308 virtualizes access to different resources (e.g., resource 108) and / or accelerators. In addition, hypervisor 308 provides an abstraction for hardware resources and a set of virtual machines 312 running different functions (e.g., function 110, function 116, etc.). In some examples, functional decisions are made by software executing on processing unit 304. For example, in the event of an error (e.g., a problem with the function), the error is typically reported to functional safety island 306. Functional safety island 306 can then perform processing to determine how to proceed based on at least the error.

[0048] For example, the hardware (e.g., one or more processing units 304, functional safety island 306, etc.) may take local action by terminating a virtual machine 312 associated with the function in which the error exists and / or injecting a fault into the virtual machine 312. In some examples, the virtual machine 312 may then take local action, such as terminating one or more processes 314 associated with the function. In addition, the manager 308 and / or the virtual machine 312 may notify the functional safety island software 310 of the action taken, such as terminating the virtual machine 312 and / or the process 314. The functional safety island software 310 may then make a decision on how to proceed, for example, by using one or more of the processes described herein. For example, the functional safety island software 310 may determine whether to stop the function, stop all functions, continue running, restart the function, and / or perform any other process.

[0049] See now Figures 4 to 7 , each block of methods 400, 500, 600, and 700 described herein includes a computing process that can be performed using any combination of hardware, firmware, and / or software. For example, the various functions can be implemented by a processor executing instructions stored in a memory. Methods 400, 500, 600, and 700 can also be embodied as computer usable instructions stored on a computer storage medium. Methods 400, 500, 600, and 700 can be provided by a stand-alone application, a service, or a hosted service (standalone or in combination with other hosted services), or a plug-in to another product, to name a few. In addition, by way of example, with respect to Figure 1 Methods 400, 500, 600, and 700 are described. However, these methods 400, 500, 600, and 700 may additionally or alternatively be performed by any one system or any combination of systems, including but not limited to those described herein.

[0050] Figure 4A flow chart of a method 400 for incrementally directing functions associated with a vehicle according to some embodiments of the present disclosure is shown. At box B402, the method 400 may include causing a first initialization of a first function of the vehicle and a second initialization of a second function of the vehicle during the allocation of one or more resources associated with the vehicle. For example, one or more systems may cause a first initialization of a first function 110 and a second initialization of a second function 112 during the allocation of one or more resources 108. As described herein, one or more systems may operate in a specific operating phase during the allocation of one or more resources 108, such as an initialization phase 102 and / or a failsafe phase 106. In addition, the first function 110 may include non-safety-related functions and / or failsafe functions associated with one or more safety levels, and the second function 112 may include an availability function associated with one or more additional higher safety levels.

[0051] At block B404, the method 400 may include making the first function available for use during the allocation of the one or more resources. For example, the one or more systems may determine that the first initialization of the first function 110 is complete. Thus, the one or more systems may make the first function 110 available for use by the user even if the one or more resources 108 are still being allocated, such as during the initialization phase 102 and / or the failsafe phase 106. In some examples, the one or more systems make the first function 110 available so as to provide the user with access to features of the first function 110 more quickly.

[0052] At frame B406, method 400 may include making the second function available for use based at least on the allocation of one or more resources. For example, the system may determine that the allocation of resource 108 is complete. In some examples, one or more systems may then monitor to ensure that no error has occurred in one or more systems and / or any function. In some examples, one or more systems may then switch from operating in initialization phase 102 and / or failure protection phase 106 to operating in operation phase 114 and / or availability phase 116. The system may then make the second function 112 available for use by the user. In some examples, one or more systems wait until the allocation of one or more resources 108 is complete, so as to avoid any interference with the operation of the second function 112.

[0053] At box B408, the method 400 may include causing the vehicle to perform one or more operations based at least on the second function. For example, the system may cause the vehicle to perform an operation based at least on the second function 112. For the first example, if the second function 112 includes a fully autonomous function, the system may cause the vehicle to navigate based at least on the output from the second function 112. For the second example, if the second function 112 is a minimum risk maneuver function, one or more systems may cause the vehicle to navigate according to one or more minimum risk maneuvers output by the second function 112, such as to safely stop on one side of the road. Although these are only two examples of operations that may be performed by the vehicle, in other examples, the vehicle may perform additional and / or alternative operations.

[0054] Figure 5 A flow chart of a method 500 for guiding functions associated with a vehicle using various operating stages according to some embodiments of the present disclosure is shown. At box B502, the method 500 may include: when operating in the first operating stage, causing a first initialization of a first function of the vehicle and a second initialization of a second function of the vehicle. For example, when operating in the first operating stage, one or more systems may cause a first initialization of the first function 110 and a second initialization of the second function 112. As described herein, the first operating stage associated with the system may include an initialization stage 102 and / or a failsafe stage 106. In addition, one or more systems may perform additional processes when operating in the first operating stage, such as starting the vehicle's hardware, starting the vehicle's software, starting the vehicle's operating system, allocating resources 108 of the vehicle, monitoring one or more systems to find one or more errors, etc.

[0055] At box 504, the method 500 may include: when operating in the first operating stage, making the first function available for use. For example, one or more systems may determine that the first initialization of the first function 110 is complete. In this way, one or more systems can make the first function 110 available for use by the user, even when one or more systems are still operating in the first operating stage. In some examples, one or more systems make the first function 110 available to provide the user with access to the features of the first function 110 more quickly. In addition, even if an error may occur in the first function 110 while operating in the first operating stage, the first function 110 may include non-safety-related functions and / or fail-safe functions so that the error does not compromise the safety of the vehicle and / or can minimally compromise the safety of the vehicle.

[0056] At block 506, method 500 may include making the second function available for use based at least on switching from operating in the first operating stage to operating in the second operating stage. For example, the system may determine that an event has occurred, such as completion of allocation of resources 108, readiness of the entire system, a safety check indicating that the system and / or function has no errors, etc. Based at least on the event, one or more systems may switch from operating in the first operating stage to operating in the second operating stage, such as the operation stage 114 and / or the availability stage 116. The system may then make the second function 112 available for use by the user. In some examples, one or more systems wait until operating in the second operating stage to avoid any interference with the operation of the second function 112.

[0057] At box B508, method 500 may include causing the vehicle to perform one or more operations based at least on the second function. For example, the system may cause the vehicle to perform an operation based at least on the second function 112. For the first example, if the second function 112 includes a fully autonomous function, the system may cause the vehicle to navigate based at least on the output from the second function 112. For the second example, if the second function 112 is a minimum risk maneuver function, one or more systems may cause the vehicle to navigate according to one or more minimum risk maneuvers output by the second function 112, such as to safely stop on one side of the road. While these are only two examples of operations that may be performed by the vehicle, in other examples, the vehicle may perform additional and / or alternative operations.

[0058] Figure 6 A flow chart of a method 600 for incrementally directing functions associated with a vehicle using configuration data according to some embodiments of the present disclosure is shown. At box B602, the method 600 may include storing first data indicating that the first function includes a first type of function. For example, one or more systems may store first data indicating that the first function 110 includes a first type of function. As described herein, the first type of function may be associated with a function 110 that includes one or more non-safety-related functions and / or one or more fail-safe functions. In some examples, one or more systems store the first data based at least on receiving input data from one or more devices indicating that the first function 110 includes a first type of function.

[0059] At block B604, method 600 may include storing second data indicating that the second function includes a second type of function. For example, one or more systems may store second data indicating that the second function 112 includes a second type of function. As described herein, the second type of function may be associated with function 112 including an availability function. In some examples, one or more systems store the second data based at least on receiving input data from one or more devices indicating that the second function 112 includes a second type of function.

[0060] At block B606, the method 600 may include determining, based at least on the first data, to make the first function available for use during the first operating phase. For example, one or more systems may use the first data to determine to make the first function 110 available for use during the first operating phase (such as the initialization phase 102 and / or the failsafe phase 106). For example, the one or more systems may make the first function 110 available based at least on the completion of initialization of the first function 110 so that the first function 110 is ready to be used. As described herein, the one or more systems may make the first function 110 available by starting the first function 110 and / or allowing the user to use features associated with the first function 110.

[0061] At block B608, the method 600 may include determining to make the second function available for use during the second operating phase based at least on the second data. For example, one or more systems may use the second data to determine to make the second function 112 available during the second operating phase (such as the operation phase 114 and / or the availability phase 116). For example, even if initialization of the second function 112 is completed during the first operating phase, the system may still wait to make the second function 112 available for use until the second operating phase. As described herein, one or more systems may make the second function 112 available by starting the second function 112 and / or allowing the user to use features associated with the second function 112.

[0062] Figure 7 A flow chart of a method 700 for redirecting a function where an error occurred using various operating stages according to some embodiments of the present disclosure is shown. At block B702, the method 700 may include: while operating in a first operating stage, determining that an error occurred in a first function. For example, one or more systems may operate in the operation stage 114 and / or the availability stage 116. While operating in the operation stage 114 and / or the availability stage 116, the one or more systems may determine that an error occurred in the first function, which may include Figure 7 Function 110 and / or function 112 in the example.

[0063] At box B704, method 700 may include determining whether to reboot the first function. For example, the system may use one or more factors to determine whether to reboot the first function. One or more factors may include, but are not limited to, whether the first function is a safety function and / or a failsafe function, whether the first function is an availability function, whether the hardware needs to be restarted, whether the software needs to be restarted, the current driving scene of the vehicle, and / or any other factors. If it is determined at box B704 that the first function is not restarted, then at box B706, method 700 may include causing the first function to remain terminated. For example, if the system determines not to restart the first function, the system may cause the first function to remain terminated. The system may also determine whether to terminate one or more additional functions and / or whether to continue navigation.

[0064] However, if it is determined at block B704 to restart the first function, then at block B708, the method 700 may include switching from operating in the first operating phase to operating in the second operating phase. For example, if the system(s) determine to restart the first function, then the system(s) may switch to operating in the initialization phase 102 (e.g., the failsafe phase 106) and / or the reinitialization phase 118.

[0065] At block B710, method 700 may include making the second function unavailable for use, and at block B712, method 700 may include rebooting the first function. For example, one or more systems may then cause the second function (such as function 112) to be unavailable for use. In some examples, one or more systems may make the second function unavailable to prevent errors in the second function upon reboot, such as from a reboot that interferes with the first function. One or more systems may then reboot the first function.

[0066] At block B714, the method 700 may include switching from operating in the second operating phase to operating in the first operating phase, and at block B716, the method 700 may include making the second function available for use. For example, the one or more systems may then switch back to the operating phase 114 and / or the availability phase 116. In addition, the one or more systems may again make the second function available for use. In some examples, the one or more systems may make the second function available for use because, when operating in the operating phase 114 and / or the availability phase 116, the second function does not experience errors such as from interference.

[0067] Example Autonomous Vehicle

[0068] Fig. 8Ais an illustration of an example autonomous or semi-autonomous vehicle 800 according to some embodiments of the present disclosure. Autonomous vehicle 800 (alternatively referred to herein as "vehicle 800") may include, but is not limited to, a passenger vehicle such as a car, a truck, a bus, a first response vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police vehicle, an ambulance, a boat, a construction vehicle, an underwater vessel, a robotic vehicle, a drone, an airplane, a vehicle coupled to a trailer (e.g., a semi-tractor truck for hauling cargo), and / or another type of vehicle (e.g., a vehicle that is unmanned and / or accommodates one or more passengers). Autonomous vehicles are generally described in terms of automation levels as defined by the National Highway Traffic Safety Administration (NHTSA), a division of the U.S. Department of Transportation, and the Society of Automotive Engineers (SAE), “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201806, issued on June 15, 2018, Standard No. J3016-201609, issued on September 30, 2016, and previous and future versions of the standard). The vehicle 800 may be capable of implementing one or more functions of Level 3-5 that conform to the autonomous driving level. The vehicle 800 may be capable of implementing one or more functions of Level 1-5 according to the autonomous driving level. For example, depending on the embodiment, the vehicle 800 may be capable of driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5). As used herein, the term “autonomy” may include any and / or all types of autonomy of a vehicle 800 or other machine, such as fully autonomous, highly autonomous, conditionally autonomous, partially autonomous, assisted autonomous, semi-autonomous, primarily autonomous, or other names.

[0069] The vehicle 800 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. The vehicle 800 may include a propulsion system 850, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. The propulsion system 850 may be connected to a drive train of the vehicle 800, which may include a transmission, to achieve propulsion of the vehicle 800. The propulsion system 850 may be controlled in response to receiving a signal from a throttle / accelerator 852.

[0070] A steering system 854, which may include a steering wheel, may be used to steer the vehicle 800 (e.g., along a desired path or route) when the propulsion system 850 is operating (e.g., when the vehicle is in motion). The steering system 854 may receive signals from a steering actuator 856. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0071] Brake sensor system 846 may be used to operate vehicle brakes in response to receiving signals from brake actuator 848 and / or brake sensors.

[0072] May include one or more system on chip (SoC) 804 ( Figure 8C ) and / or one or more controllers 836 of one or more GPUs can provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 800. For example, the one or more controllers can send signals to operate vehicle brakes via one or more brake actuators 848, operate the steering system 854 via one or more steering actuators 856, and operate the propulsion system 850 via one or more throttles / accelerators 852. The one or more controllers 836 may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals and output operating commands (e.g., signals representing commands) to achieve autonomous driving and / or assist a human driver in driving the vehicle 800. The one or more controllers 836 may include a first controller 836 for autonomous driving functions, a second controller 836 for functional safety functions, a third controller 836 for artificial intelligence functions (e.g., computer vision), a fourth controller 836 for infotainment functions, a fifth controller 836 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 836 may handle two or more of the above functions, two or more controllers 836 may handle a single function, and / or any combination thereof.

[0073] The one or more controllers 836 may provide signals for controlling one or more components and / or systems of the vehicle 800 in response to sensor data (e.g., sensor input) received from one or more sensors. The sensor data may be received from, for example and without limitation, a global navigation satellite system ("GNSS") sensor 858 (e.g., a global positioning system sensor), a RADAR sensor 860, an ultrasonic sensor 862, a LIDAR sensor 864, an inertial measurement unit (IMU) sensor 866 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 896, a stereo camera 868, a wide angle camera 870 (e.g., a fisheye camera), an infrared camera 872, a surround camera 874 (e.g., a 360 degree camera), a long-range and / or mid-range camera 898, a speed sensor 844 (e.g., for measuring the velocity of the vehicle 800), a vibration sensor 842, a steering sensor 840, a brake sensor (e.g., as part of a brake sensor system 846), and / or other sensor types.

[0074] One or more of the controllers 836 may receive inputs (e.g., represented by input data) from the instrument cluster 832 of the vehicle 800 and provide outputs (e.g., represented by output data, display data, etc.) via a human machine interface (HMI) display 834, an audible annunciator, a speaker, and / or via other components of the vehicle 800. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Figure 8C The HMI display 834 may include information such as a high-definition (“HD”) map 822 of the vehicle 800 , location data (e.g., the location of the vehicle 800 on the map), directions, locations of other vehicles (e.g., an occupancy grid), information about objects and states of objects as sensed by the controller 836 , and the like. For example, the HMI display 834 may display information about the presence of one or more objects (e.g., street signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that the vehicle has made, is making, or will make (e.g., changing lanes now, leaving 34B in two miles, etc.).

[0075] The vehicle 800 also includes a network interface 824 that can communicate via one or more networks using one or more wireless antennas 826 and / or a modem. For example, the network interface 824 may be capable of communicating via Long Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile Communications (“GSM”), IMT-CDMA Multi-Carrier (“CDMA2000”), etc. The one or more wireless antennas 826 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.) using one or more local area networks such as Bluetooth, Bluetooth Low Energy (“LE”), Z-wave, ZigBee, etc., and / or one or more low power wide area networks (“LPWAN”) such as LoRaWAN, SigFox, etc.

[0076] Figure 8B For use according to some embodiments of the present disclosure Fig. 8A 800. The cameras and respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or replaceable cameras may be included and / or the cameras may be located at different locations on the vehicle 800.

[0077] The camera type for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 800. The camera may operate at an automotive safety integrity level (ASIL) B and / or at another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120fps, 240fps, etc., depending on the embodiment. The camera may be capable of using a rolling shutter, a global shutter, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red-white-white-white (RCCC) color filter array, a red-white-white-blue (RCCB) color filter array, a red-blue-green-white (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensor (RGGB) color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In some embodiments, a clear pixel camera such as a camera with an RCCC, RCCB, and / or RBGC color filter array may be used in an effort to improve light sensitivity.

[0078] In some examples, one or more of the cameras can be used to perform advanced driver assistance system (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a multi-function monocular camera can be installed to provide functions including lane departure warning, traffic sign assistance, and intelligent headlight control. One or more of the cameras (e.g., all of the cameras) can simultaneously record and provide image data (e.g., video).

[0079] One or more of the cameras may be mounted in a mounting assembly such as a custom designed (three-dimensional ("3D") printed) assembly to cut out stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirror) that may interfere with the camera's image data capture capabilities. With respect to the wing mirror mounting assembly, the wing mirror assembly may be custom 3D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras may be integrated into the wing mirror. For the side view cameras, one or more cameras may also be integrated into the four pillars at each corner of the cab.

[0080] A camera (e.g., a front-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 800 can be used for surround view to help identify the forward path and obstacles, as well as assist in providing information critical to generating an occupancy grid and / or determining a preferred vehicle path with the help of one or more controllers 836 and / or control SoCs. The front-facing camera can be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. The front-facing camera can also be used for ADAS functions and systems, including lane departure warning ("LDW"), autonomous cruise control ("ACC"), and / or other functions such as traffic sign recognition.

[0081] A variety of cameras may be used in the front-facing configuration, including, for example, a monocular camera platform including a complementary metal oxide semiconductor ("CMOS") color imager. Another example may be a wide-angle camera 870, which may be used to sense objects (e.g., pedestrians, intersection traffic, or bicycles) entering the field of view from the periphery. Figure 8B Only one wide-angle camera is shown in the figure, but there may be any number (including zero) of wide-angle cameras 870 on the vehicle 800. In addition, any number of long-range cameras 898 (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not been trained. Long-range cameras 898 may also be used for object detection and classification and basic object tracking.

[0082] Any number of stereo cameras 868 may also be included in the front configuration. In at least one embodiment, one or more stereo cameras 868 may include an integrated control unit including a scalable processing unit that may provide a multi-core microprocessor and programmable logic ("FPGA") with an integrated controller area network ("CAN") or Ethernet interface on a single chip. Such a unit can be used to generate a 3D map of the vehicle environment, including distance estimates for all points in the image. An alternative stereo camera 868 may include a compact stereo vision sensor that may include two camera lenses (one on the left and one on the right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. Other types of stereo cameras 868 may be used in addition to or alternatively to those described herein.

[0083] A camera having a field of view that includes a portion of the environment to the side of the vehicle 800 (e.g., a side view camera) can be used for surround viewing, providing information used to create and update the occupancy grid and generate side impact collision warnings. Figure 8B The four surround cameras 874 shown in FIG. 8 may be placed on the vehicle 800. The surround cameras 874 may include a wide-angle camera 870, a fisheye camera, a 360-degree camera, and / or the like. For example, the four fisheye cameras may be placed in front, behind, and on the sides of the vehicle. In an alternative arrangement, the vehicle may use three surround cameras 874 (e.g., left, right, and rear), and may utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0084] A camera having a field of view that includes a portion of the environment behind the vehicle 800 (e.g., a rear view camera) can be used to assist with parking, surround view, rear collision warning, and creating and updating occupancy grids. A variety of cameras can be used, including but not limited to cameras that are also suitable as front cameras as described herein (e.g., long-range and / or mid-range cameras 898, stereo cameras 868, infrared cameras 872, etc.).

[0085] Figure 8C For use according to some embodiments of the present disclosure Fig. 8ABlock diagram of an example system architecture for an example autonomous vehicle 800. It should be understood that this arrangement and other arrangements described herein are set forth merely as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any appropriate combination and location. The various functions described herein as being performed by an entity may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory.

[0086] Figure 8C Each of the components, features, and systems of the vehicle 800 is illustrated as being connected via a bus 802. The bus 802 may include a controller area network (CAN) data interface (alternatively, referred to herein as a "CAN bus"). The CAN may be a network inside the vehicle 800 that assists in controlling various features and functions of the vehicle 800, such as the actuation of brakes, acceleration, braking, steering, windshield wipers, and the like. The CAN bus may be configured to have tens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compatible.

[0087] Although bus 802 is described as a CAN bus here, this is not intended to be limiting. For example, in addition to or alternatively to the CAN bus, FlexRay and / or Ethernet can be used. In addition, although bus 802 is represented by a single line, this is not intended to be limiting. For example, there can be any number of buses 802, which may include one or more CAN buses, one or more FlexRay buses, one or more Ethernet buses, and / or one or more other types of buses using different protocols. In some examples, two or more buses 802 can be used to perform different functions, and / or can be used for redundancy. For example, a first bus 802 can be used for a collision avoidance function, and a second bus 802 can be used for drive control. In any example, each bus 802 can communicate with any component of the vehicle 800, and two or more buses 802 can communicate with the same component. In some examples, each SoC 804, each controller 836, and / or each computer in the vehicle can have access to the same input data (e.g., input from a sensor of the vehicle 800), and can be connected to a common bus such as a CAN bus.

[0088] The vehicle 800 may include one or more controllers 836, such as those described herein. Fig. 8A Those controllers described. Controller 836 can be used for a variety of functions. Controller 836 can be coupled to any other different components and systems of vehicle 800, and can be used for control of vehicle 800, artificial intelligence of vehicle 800, infotainment for vehicle 800, and / or the like.

[0089] The vehicle 800 may include one or more system on chip (SoC) 804. The SoC 804 may include a CPU 806, a GPU 808, a processor 810, a cache 812, an accelerator 814, a data store 816, and / or other components and features not shown. The SoC 804 may be used to control the vehicle 800 in a variety of platforms and systems. For example, one or more SoCs 804 may be combined with an HD map 822 in a system (e.g., a system of the vehicle 800), and the HD map may be downloaded from one or more servers (e.g., a server) via a network interface 824. Fig.8D one or more servers 878) to obtain map refreshes and / or updates.

[0090] The CPU 806 may include a CPU cluster or CPU complex (alternatively, referred to herein as a "CCPLEX"). The CPU 806 may include multiple cores and / or L2 caches. For example, in some embodiments, the CPU 806 may include eight cores in a coherent multiprocessor configuration. In some embodiments, the CPU 806 may include four dual-core clusters, each of which has a dedicated L2 cache (e.g., a 2MB L2 cache). The CPU 806 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, such that any combination of clusters of the CPU 806 can be active at any given time.

[0091] CPU 806 may implement power management capabilities including one or more of the following features: each hardware block may be automatically clock gated when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to the execution of WFI / WFE instructions; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. CPU 806 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wake-up times are specified, and hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core may support a simplified power state entry sequence in software, with the work being offloaded to the microcode.

[0092] The GPU 808 may include an integrated GPU (alternatively, referred to herein as an "iGPU"). The GPU 808 may be programmable and efficient for parallel workloads. In some examples, the GPU 808 may use an enhanced tensor instruction set. The GPU 808 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB storage capacity). In some embodiments, the GPU 808 may include at least eight streaming microprocessors. The GPU 808 may use a computing application programming interface (API). In addition, the GPU 808 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0093] In the case of automotive and embedded use, GPU 808 can be power optimized to achieve optimal performance. For example, GPU 808 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 808 can be manufactured using other semiconductor manufacturing processes. Each streaming microprocessor can merge several mixed precision processing cores divided into multiple blocks. For example and without limitation, 64 PF32 cores and 32 PF64 cores can be divided into four processing blocks. In such an example, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed precision NVIDIA tensor cores for deep learning matrix arithmetic, L0 instruction cache, thread beam (warp) scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor can include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of calculations and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. A streaming microprocessor may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0094] The GPU 808 may include a high bandwidth memory (HBM) and / or 16GB HBM2 memory subsystem that provides a peak memory bandwidth of approximately 900 GB / s in some examples. In some examples, synchronous graphics random access memory (SGRAM), such as fifth generation graphics double data rate synchronous random access memory (GDDR5), may be used in addition to or in lieu of HBM memory.

[0095] The GPU 808 may include unified memory technology that includes access counters to allow memory pages to be more accurately migrated to the processor that accesses them most frequently, thereby improving the efficiency of memory ranges shared between processors. In some examples, address translation service (ATS) support can be used to allow the GPU 808 to directly access the CPU 806 page table. In such an example, when the GPU 808 memory management unit (MMU) experiences a miss, an address translation request can be transmitted to the CPU 806. In response, the CPU 806 can look up the virtual-physical mapping for the address in its page table and transmit the translation back to the GPU 808. In this way, unified memory technology can allow a single unified virtual address space to be used for the memory of both the CPU 806 and the GPU 808, thereby simplifying GPU 808 programming and porting applications to the GPU 808.

[0096] In addition, GPU 808 may include access counters that can track how often GPU 808 accesses the memory of other processors. Access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0097] SoC 804 may include any number of caches 812, including those described herein. For example, cache 812 may include an L3 cache available to both CPU 806 and GPU 808 (e.g., connected to both CPU 806 and GPU 808). Cache 812 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4MB or more, although smaller cache sizes may also be used.

[0098] The SoC 804 may include an arithmetic logic unit (ALU) that may be utilized in performing any of a variety of tasks or operations with respect to the vehicle 800, such as processing a DNN. Additionally, the SoC 804 may include a floating point unit (FPU) (or other math coprocessor or digital coprocessor type) for performing mathematical operations within the system. For example, the SoC 104 may include one or more FPUs integrated as execution units within the CPU 806 and / or GPU 808.

[0099] SoC 804 may include one or more accelerators 814 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, SoC 804 may include a hardware accelerator cluster, which may include optimized hardware accelerators and / or large on-chip memory. The large on-chip memory (e.g., 4MB SRAM) may enable the hardware accelerator cluster to accelerate neural networks and other calculations. The hardware accelerator cluster may be used to supplement GPU 808 and offload some tasks of GPU 808 (e.g., free up more cycles of GPU 808 for performing other tasks). As an example, accelerator 814 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are sufficiently stable to be easily controlled for acceleration. When used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0100] The accelerator 814 (e.g., a hardware accelerator cluster) may include a deep learning accelerator (DLA). The DLA may include one or more tensor processing units (TPUs) that may be configured to provide an additional 10 trillion operations per second for deep learning applications and reasoning. The TPU may be an accelerator configured to perform image processing functions (e.g., for CNN, RCNN, etc.) and optimized for performing image processing functions. The DLA may be further optimized for a specific set of neural network types and floating point operations and reasoning. The design of the DLA may provide higher performance per millimeter than a general-purpose GPU, and far exceeds the performance of the CPU. The TPU may perform several functions, including a single instance convolution function, support, for example, INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0101] The DLA can quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a wide variety of functions, such as, but not limited to: a CNN for object recognition and detection using data from a camera sensor; a CNN for distance estimation using data from a camera sensor; a CNN for emergency vehicle detection and identification and detection using data from a microphone; a CNN for facial recognition and vehicle owner identification using data from a camera sensor; and / or a CNN for safety and / or security-related events.

[0102] The DLA can perform any function of the GPU 808, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 808. For example, the designer can focus the processing of CNNs and floating point operations on the DLA, and leave other functions to the GPU 808 and / or other accelerators 814.

[0103] The accelerator 814 (e.g., a hardware accelerator cluster) may include a programmable vision accelerator (PVA), which may be alternatively referred to as a computer vision accelerator herein. The PVA may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (ADAS), autonomous driving, and / or augmented reality (AR) and / or virtual reality (VR) applications. The PVA may provide a balance between performance and flexibility. For example, each PVA may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0104] The RISC core can interact with an image sensor (e.g., an image sensor of any camera described herein), an image signal processor, and / or the like. Each of these RISC cores can include any amount of memory. Depending on the embodiment, the RISC core can use any of a number of protocols. In some examples, the RISC core can execute a real-time operating system (RTOS). The RISC core can be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and / or storage devices. For example, the RISC core can include an instruction cache and / or a tightly coupled RAM.

[0105] The DMA may enable components of the PVA to access system memory independently of the CPU 806. The DMA may support any number of features used to provide optimizations for the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support addressing in up to six or more dimensions, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0106] The vector processor can be a programmable processor that can be designed to efficiently and flexibly perform programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA can include a PVA core and two vector processing subsystem partitions. The PVA core can include a processor subsystem, one or more DMA engines (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem can operate as the main processing engine of the PVA, and can include a vector processing unit (VPU), an instruction cache, and / or a vector memory (e.g., VMEM). The VPU core can include a digital signal processor, such as, for example, a single instruction multiple data (SIMD), a very long instruction word (VLIW) digital signal processor. The combination of SIMD and VLIW can enhance throughput and rate.

[0107] Each of the vector processors may include an instruction cache and may be coupled to a dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, multiple vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on sequence images or portions of an image. Among other things, any number of PVAs may be included in a hardware accelerator cluster, and any number of vector processors may be included in each of these PVAs. In addition, the PVA may include additional error correction code (ECC) memory to enhance overall system security.

[0108] The accelerator 814 (e.g., a hardware accelerator cluster) may include an on-chip computer vision network and SRAM to provide high bandwidth, low latency SRAM for the accelerator 814. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and not limited to, eight field-configurable memory blocks that can be accessed by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides high-speed memory access to the PVA and DLA. The backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using APB).

[0109] The on-chip computer vision network may include an interface that determines that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such an interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface may comply with ISO 26262 or IEC 61508 standards, but other standards and protocols may also be used.

[0110] In some examples, SoC 804 may include a real-time ray tracing hardware accelerator such as described in U.S. Patent Application No. 16 / 101,232 filed on August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for SONAR system simulations, for general wave propagation simulations, for comparison with LIDAR data for the purpose of positioning and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0111] The accelerator 814 (e.g., a cluster of hardware accelerators) has a wide range of uses in autonomous driving. The PVA can be a programmable visual accelerator that can be used in key processing stages in ADAS and autonomous vehicles. The capabilities of the PVA are a good match for algorithmic domains that require predictable processing, low power, and low latency. In other words, the PVA performs well on semi-dense or dense rule calculations, and even on small data sets that require predictable runtimes with low latency and low power. Therefore, in the context of a platform for autonomous vehicles, the PVA is designed to run classic computer vision algorithms because they are effective at object detection and integer math.

[0112] For example, according to one embodiment of the technology, PVA is used to perform computer stereo vision. In some examples, algorithms based on semi-global matching can be used, but this is not intended to be limiting. Many applications for level 3-5 autonomous driving require instant motion estimation / stereo matching (e.g., structure from motion, pedestrian recognition, lane detection, etc.). PVA can perform computer stereo vision functions on input from two monocular cameras.

[0113] In some examples, PVA can be used to perform dense optical flow. According to the process raw RADAR data (e.g., using 4D fast Fourier transform) to provide processed RADAR. In other examples, PVA is used for time-of-flight depth processing, which is for example by processing raw time-of-flight data to provide processed time-of-flight data.

[0114] DLA can be used to run any type of network to enhance control and driving safety, including, for example, a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability, or as providing a relative "weight" of each detection compared to other detections. This confidence value enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positive detections. In an automatic emergency braking (AEB) system, a false positive detection will cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detection should be considered a trigger for AEB. DLA can run a neural network for regressing confidence values. The neural network can take at least some subset of parameters as its input, such as bounding box dimensions, ground plane estimates obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 866 output related to vehicle 800 orientation and distance, 3D position estimates of objects obtained from neural networks and / or other sensors (e.g., LIDAR sensor 864 or RADAR sensor 860), etc.

[0115] SoC 804 may include one or more data stores 816 (e.g., memory). Data store 816 may be on-chip memory of SoC 804 that may store neural networks to be executed on the GPU and / or DLA. In some examples, data store 816 may be large enough to store multiple instances of the neural network for redundancy and safety. Data store 812 may include an L2 or L3 cache 812. References to data store 816 may include references to memory associated with PVA, DLA, and / or other accelerators 814 as described herein.

[0116] SoC 804 may include one or more processors 810 (e.g., embedded processors). Processor 810 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management functions and related safety implementations. The boot and power management processor may be part of the SoC 804 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, auxiliary system low power state transitions, SoC 804 thermal and temperature sensor management, and / or SoC 804 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC 804 may use the ring oscillator to detect the temperature of CPU 806, GPU 808, and / or accelerator 814. If it is determined that the temperature exceeds a threshold, the boot and power management processor may enter a temperature fault routine and place SoC 804 in a lower power state and / or place the vehicle 800 in a driver safety parking mode (e.g., parking the vehicle 800 safely).

[0117] The processor 810 may also include a set of embedded processors that can be used as an audio processing engine. The audio processing engine can be an audio subsystem that allows full hardware support for multi-channel audio through multiple interfaces and a wide range of flexible audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core having a digital signal processor with dedicated RAM.

[0118] The processor 810 may also include an always-on processor engine that can provide the necessary hardware features to support low-power sensor management and wake-up use cases. The always-on processor engine may include a processor core, tightly coupled RAM, supporting peripherals (such as timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0119] The processor 810 may also include a safety cluster engine that includes a dedicated processor subsystem that handles safety management of automotive applications. The safety cluster engine may include two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.), and / or routing logic. In safety mode, the two or more cores may operate in lockstep mode and act as a single core with comparison logic to detect any differences between their operations.

[0120] Processor 810 may also include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0121] Processor 810 may also include a high dynamic range signal processor, which may include an image signal processor, which is a hardware engine that is part of the camera processing pipeline.

[0122] The processor 810 may include a video image compositer, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required for the video playback application to produce a final image for the player window. The video image compositer may perform lens distortion correction for the wide-angle camera 870, the surround camera 874, and / or for an in-cab surveillance camera sensor. The in-cab surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC, configured to recognize in-cab events and respond accordingly. The in-cab system may perform lip reading to activate mobile phone service and place calls, dictate emails, change vehicle destinations, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other circumstances.

[0123] The video image compositer may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, in the case of motion in the video, the noise reduction appropriately weights the spatial information and reduces the weight of information provided by adjacent frames. In the case where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositer may use information from previous images to reduce noise in the current image.

[0124] The video image compositor may also be configured to perform stereoscopic rectification on the input stereoscopic footage frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use and the GPU 808 does not need to continuously render new surfaces. Even when the GPU 808 is powered on and active for 3D rendering, the video image compositor may be used to offload the GPU 808 to improve performance and responsiveness.

[0125] The SoC 804 may also include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from a camera, a high-speed interface, and / or a video input block that may be used for camera and related pixel input functions. The SoC 804 may also include an input / output controller that may be controlled by software and may be used to receive I / O signals that are not committed to a specific role.

[0126] SoC 804 may also include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management and / or other devices. SoC 804 may be used to process data from cameras (connected via Gigabit multimedia serial links and Ethernet), sensors (e.g., LIDAR sensor 864, RADAR sensor 860, etc., which may be connected via Ethernet), data from bus 802 (e.g., speed of vehicle 800, steering wheel position, etc.), data from GNSS sensor 858 (connected via Ethernet or CAN bus). SoC 804 may also include dedicated high-performance mass storage controllers, which may include their own DMA engines, and which may be used to free up CPU 806 from routine data management tasks.

[0127] SoC 804 can be an end-to-end platform with a flexible architecture that spans automation levels 3-5, providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS technologies to achieve diversity and redundancy, together with deep learning tools to provide a platform for a flexible and reliable driving software stack. SoC 804 can be faster, more reliable, and even more energy efficient and space efficient than conventional systems. For example, when combined with CPU 806, GPU 808, and data storage 816, accelerator 814 can provide a fast and efficient platform for level 3-5 autonomous vehicles.

[0128] The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms can be executed on CPUs, which can be configured using high-level programming languages ​​such as the C programming language to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs often fail to meet the performance requirements of many computer vision applications, such as those related to, for example, execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, which is a requirement for in-vehicle ADAS applications and a requirement for practical Level 3-5 autonomous vehicles.

[0129] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a cluster of hardware accelerators, the techniques described herein allow multiple neural networks to be executed simultaneously and / or sequentially, and the results to be combined together to achieve Level 3-5 autonomous driving functions. For example, a CNN executed on a DLA or dGPU (e.g., GPU 820) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which a neural network has not been specifically trained. The DLA may also include a neural network that can recognize, interpret, and provide semantic understanding of the signs, and pass that semantic understanding to a path planning module running on the CPU complex.

[0130] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks may be running simultaneously. For example, a warning sign consisting of "Caution: Flashing Lights Indicate Icing Conditions" along with electric lights may be interpreted by several neural networks independently or collectively. The sign itself may be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing Lights Indicate Icing Conditions" may be interpreted by a deployed second neural network that informs the vehicle's path planning software (preferably executing on a CPU complex) that icing conditions exist when the flashing lights are detected. The flashing lights may be identified by operating a deployed third neural network over multiple frames that informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks may be running simultaneously, for example, within the DLA and / or on the GPU 808.

[0131] In some examples, a CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 800. The always-on sensor processing engine can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's door, and in security mode, disable the vehicle when the owner leaves the vehicle. In this way, the SoC 804 provides security against theft and / or carjacking.

[0132] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 896 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 804 uses CNN to classify environmental and urban sounds and to classify visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing rate of emergency vehicles (e.g., by using the Doppler effect). The CNN can also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by the GNSS sensor 858. Thus, for example, when operating in Europe, the CNN will seek to detect European sirens, and when in the United States, the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, with the assistance of the ultrasonic sensor 862, the control program can be used to execute emergency vehicle safety routines to slow the vehicle, drive to the side of the road, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0133] The vehicle may include a CPU 818 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 804 via a high-speed interconnect (e.g., PCIe). The CPU 818 may include, for example, an X86 processor. The CPU 818 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 804, and / or monitoring the status and health of the controller 836 and / or the infotainment SoC 830.

[0134] The vehicle 800 may include a GPU 820 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 804 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 820 may provide additional artificial intelligence functionality, for example, by executing redundant and / or different neural networks, and may be used to train and / or update the neural network based at least in part on input from sensors of the vehicle 800 (e.g., sensor data).

[0135] The vehicle 800 may also include a network interface 824, which may include one or more wireless antennas 826 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). The network interface 824 can be used to enable wireless connections to the cloud (e.g., with a server 878 and / or other network devices), to other vehicles, and / or to computing devices (e.g., a passenger's client device) via the Internet. In order to communicate with other vehicles, a direct link can be established between the two vehicles, and / or an indirect link can be established (e.g., across a network and through the Internet). The direct link can be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link can provide the vehicle 800 with information about vehicles approaching the vehicle 800 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 800). This functionality can be part of the collaborative adaptive cruise control functionality of the vehicle 800.

[0136] The network interface 824 may include a SoC that provides modulation and demodulation functions and enables the controller 836 to communicate over a wireless network. The network interface 824 may include a radio frequency front end for up-conversion from baseband to radio frequency and down-conversion from radio frequency to baseband. The frequency conversion may be performed by a known process and / or may be performed using a super-heterodyne process. In some examples, the radio frequency front end function may be provided by a separate chip. The network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0137] The vehicle 800 may also include data storage 828, which may include off-chip storage (e.g., outside the SoC 804). The data storage 828 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, hard disk, and / or other components and / or devices that can store at least one bit of data.

[0138] The vehicle 800 may also include a GNSS sensor 858. The GNSS sensor 858 (e.g., GPS, assisted GPS sensor, differential GPS (DGPS) sensor, etc.) is used to assist in mapping, perception, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 858 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0139] The vehicle 800 may also include a RADAR sensor 860. The RADAR sensor 860 may be used by the vehicle 800 for remote vehicle detection even in darkness and / or inclement weather conditions. The RADAR functional safety level may be ASIL B. The RADAR sensor 860 may use CAN and / or bus 802 (e.g., to transmit data generated by the RADAR sensor 860) for control and access to object tracking data, accessing Ethernet in some examples to access raw data. A variety of RADAR sensor types may be used. For example and without limitation, the RADAR sensor 860 may be suitable for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0140] The RADAR sensor 860 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, and the like. In some examples, the long-range RADAR may be used for adaptive cruise control functions. The long-range RADAR system may provide a wide field of view (e.g., within a range of 250m) achieved by two or more independent scans. The RADAR sensor 860 may help distinguish between static objects and moving objects, and may be used by the ADAS system for emergency braking assistance and forward collision warnings. The long-range RADAR sensor may include a single-station multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In an example with six antennas, the central four antennas may create a focused beam pattern designed to record the surroundings of the vehicle 800 at a higher rate with minimal traffic interference from adjacent lanes. The other two antennas may expand the field of view, making it possible to quickly detect vehicles entering or leaving the lane of the vehicle 800.

[0141] As an example, a medium-range RADAR system may include a range of up to 860m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 850 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on both ends of a rear bumper. When mounted on both ends of a rear bumper, such a RADAR sensor system may create two beams that continuously monitor the blind spots behind and beside the vehicle.

[0142] Short-range RADAR systems can be used in ADAS systems for blind spot detection and / or lane change assistance.

[0143] The vehicle 800 may also include ultrasonic sensors 862. Ultrasonic sensors 862, which may be placed on the front, rear, and / or sides of the vehicle 800, may be used for parking assistance and / or creating and updating occupancy grids. A variety of ultrasonic sensors 862 may be used, and different ultrasonic sensors 862 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensors 862 may operate at a functional safety level of ASIL B.

[0144] The vehicle 800 may include a LIDAR sensor 864. The LIDAR sensor 864 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 864 may be ASIL B for functional safety level. In some examples, the vehicle 800 may include multiple LIDAR sensors 864 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0145] In some examples, the LIDAR sensor 864 may be able to provide a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensors 864 may have, for example, an advertised range of approximately 800m, an accuracy of 2cm-3cm, and support for 800Mbps Ethernet connections. In some examples, one or more non-protruding LIDAR sensors 864 may be used. In such examples, the LIDAR sensor 864 may be implemented as a small device that can be embedded in the front, back, side, and / or corner of the vehicle 800. In such examples, the LIDAR sensor 864 may provide a field of view of up to 120 degrees horizontally and 35 degrees vertically, with a range of 200m, even for low reflectivity objects. The front-mounted LIDAR sensor 864 may be configured for a horizontal field of view between 45 and 135 degrees.

[0146] In some examples, LIDAR technologies such as 3D flash LIDAR may also be used. 3D flash LIDAR uses a flash of laser as an emission source to illuminate the vehicle's surroundings up to about 200m. The flash LIDAR unit includes a receiver that records the laser pulse transmission time and the reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LIDAR can allow highly accurate and distortion-free images of the surrounding environment to be generated with each laser flash. In some examples, four flash LIDAR sensors can be deployed, one on each side of the vehicle 800. Available 3D flash LIDAR systems include solid-state 3D staring array LIDAR cameras (e.g., non-scanning LIDAR devices) with no moving parts other than fans. The flash LIDAR device can use 5 nanosecond Class I (eye-safe) laser pulses per frame, and can capture the reflected laser light in the form of a 3D range point cloud and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor 864 may be less susceptible to motion blur, vibration, and / or shock.

[0147] The vehicle may also include an IMU sensor 866. In some examples, the IMU sensor 866 may be located at the center of the rear axle of the vehicle 800. The IMU sensor 866 may include, for example and without limitation, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, for example, in a six-axis application, the IMU sensor 866 may include an accelerometer and a gyroscope, while in a nine-axis application, the IMU sensor 866 may include an accelerometer, a gyroscope, and a magnetometer.

[0148] In some embodiments, the IMU sensor 866 can be implemented as a miniature high-performance GPS-assisted inertial navigation system (GPS / INS) that combines a micro-electromechanical system (MEMS) inertial sensor, a high-sensitivity GPS receiver, and an advanced Kalman filter algorithm to provide estimates of position, velocity, and attitude. Thus, in some examples, the IMU sensor 866 can enable the vehicle 800 to estimate heading by directly observing and correlating velocity changes from the GPS to the IMU sensor 866 without the need for input from a magnetic sensor. In some examples, the IMU sensor 866 and the GNSS sensor 858 can be combined into a single integrated unit.

[0149] The vehicle may include microphones 896 positioned in and / or around the vehicle 800. The microphones 896 may be used for, among other things, emergency vehicle detection and identification.

[0150] The vehicle may also include any number of camera types, including a stereo camera 868, a wide-angle camera 870, an infrared camera 872, a surround camera 874, a long-range and / or mid-range camera 898, and / or other camera types. These cameras can be used to capture image data around the entire periphery of the vehicle 800. The type of camera used depends on the embodiment and the requirements of the vehicle 800, and any combination of camera types can be used to provide the necessary coverage around the vehicle 800. In addition, the number of cameras can vary depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or another number of cameras. As an example and not limitation, the cameras can support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may be capable of supporting Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Fig. 8A and Figure 8B Described in more detail.

[0151] The vehicle 800 may also include a vibration sensor 842. The vibration sensor 842 may measure vibrations of components of the vehicle, such as axles. For example, changes in vibration may indicate changes in the road surface. In another example, when two or more vibration sensors 842 are used, the difference between the vibrations may be used to determine friction or slippage of the road surface (e.g., when there is a vibration difference between a powered drive shaft and a freely rotating shaft).

[0152] The vehicle 800 may include an ADAS system 838. In some examples, the ADAS system 838 may include a SoC. The ADAS system 838 may include autonomous / adaptive / automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward collision warning (FCW), automatic emergency braking (AEB), lane departure warning (LDW), lane keeping assist (LKA), blind spot warning (BSW), rear cross traffic warning (RCTW), collision warning system (CWS), lane centering (LC), and / or other features and functions.

[0153] The ACC system may use a RADAR sensor 860, a LIDAR sensor 864, and / or a camera. The ACC system may include a longitudinal ACC and / or a lateral ACC. The longitudinal ACC monitors and controls the distance to the vehicle immediately in front of the vehicle 800, and automatically adjusts the vehicle speed to maintain a safe distance from the vehicle in front. The lateral ACC performs distance keeping and suggests that the vehicle 800 change lanes when necessary. The lateral ACC is related to other ADAS applications such as LCA and CWS.

[0154] CACC uses information from other vehicles, which can be received from other vehicles indirectly via a wireless link or through a network connection (e.g., through the Internet) via a network interface 824 and / or a wireless antenna 826. A direct link can be provided by a vehicle-to-vehicle (V2V) communication link, while an indirect link can be an infrastructure-to-vehicle (I2V) communication link. Typically, the V2V communication concept provides information about the vehicle immediately ahead (e.g., the vehicle immediately ahead of the vehicle 800 and in the same lane as it), while the I2V communication concept provides information about traffic farther ahead. The CACC system may include either or both of the I2V and V2V information sources. Given information about the vehicle ahead of the vehicle 800, CACC can be more reliable, and it is possible to improve the smoothness of traffic flow and reduce road congestion.

[0155] The FCW system is designed to alert the driver to hazards so that the driver can take corrective action. The FCW system uses a front camera and / or RADAR sensor 860 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibrating components. The FCW system can provide warnings in the form of, for example, sound, visual warnings, vibrations and / or rapid brake pulses.

[0156] The AEB system detects an impending forward collision with another vehicle or other object and can automatically apply the brakes if the driver does not take corrective action within specified time or distance parameters. The AEB system can use a front camera and / or RADAR sensor 860 coupled to a dedicated processor, DSP, FPGA and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes in an effort to prevent or at least mitigate the effects of the predicted collision. The AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0157] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 800 crosses a lane marking. When the driver indicates an intention to leave the lane, by activating a turn signal, the LDW system is not activated. The LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0158] The LKA system is a variation of the LDW system. If the vehicle 800 begins to leave the lane, the LKA system provides steering input or braking to correct the vehicle 800.

[0159] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, auditory and / or tactile alerts to indicate that it is unsafe to merge or change lanes. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use a rear-facing camera and / or RADAR sensor 860 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibration components.

[0160] The RCTW system may provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 800 is in reverse. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. The RCTW system may use one or more rear RADAR sensors 860 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0161] Conventional ADAS systems may be prone to false positive results, which may annoy and distract the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether the safety condition really exists and take action accordingly. However, in the autonomous vehicle 800, in the case of conflicting results, the vehicle 800 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 836 or the second controller 836). For example, in some embodiments, the ADAS system 838 can be a backup and / or auxiliary computer for providing perception information to the backup computer rationality module. The backup computer rationality monitor can run redundant and diverse software on hardware components to detect faults in perception and dynamic driving tasks. The output from the ADAS system 838 can be provided to the supervisory MCU. If the outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to coordinate the conflict to ensure safe operation.

[0162] In some examples, the master computer can be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the direction of the master computer, regardless of whether the auxiliary computer provides conflicting or inconsistent results. In the case where the confidence score does not meet the threshold and the master computer and the auxiliary computer indicate different results (e.g., conflicts), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0163] The supervisory MCU may be configured to run a neural network that is trained and configured to determine conditions under which the auxiliary computer provides a false alarm based, at least in part, on outputs from the primary computer and the auxiliary computer. Thus, the neural network in the supervisory MCU may learn when the output of the auxiliary computer may be trusted and when it may not. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU may learn when the FCW system is identifying a metal object that is not actually dangerous, such as a drainage grate or manhole cover that triggers an alarm. Similarly, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In an embodiment that includes a neural network running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or a GPU suitable for running the neural network with associated memory. In a preferred embodiment, the supervisory MCU may include a component of the SoC 804 and / or be included as a component of the SoC 804.

[0164] In other examples, the ADAS system 838 may include an auxiliary computer that uses traditional computer vision rules to perform ADAS functions. In this way, the auxiliary computer can use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or error in the software running on the main computer and the non-identical software code running on the auxiliary computer provides the same overall result, the supervisory MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware on the main computer does not cause a substantial error.

[0165] In some examples, the output of the ADAS system 838 can be fed to the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, if the ADAS system 838 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information in identifying the object. In other examples, the auxiliary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0166] The vehicle 800 may also include an infotainment SoC 830 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as an SoC, the infotainment system may not be an SoC and may include two or more discrete components. The infotainment SoC 830 may include a combination of hardware and software that can be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, WiFi, etc.), and / or information services (e.g., navigation system, rear parking assistance, radio data system, vehicle-related information such as fuel level, total distance covered, brake fuel level, oil level, door open / closed, air filter information, etc.) to the vehicle 800. For example, the infotainment SoC 830 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connectivity, an onboard computer, onboard entertainment, WiFi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 834, a telematics device, a control panel (e.g., for controlling and / or interacting with various components, features, and / or systems), and / or other components. The infotainment SoC 830 may further be used to provide information (e.g., visual and / or auditory) to a user of the vehicle, such as information from an ADAS system 838, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0167] The infotainment SoC 830 may include GPU functionality. The infotainment SoC 830 may communicate with other devices, systems, and / or components of the vehicle 800 via a bus 802 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 830 may be coupled to a supervisory MCU so that in the event of a failure of a master controller 836 (e.g., a main and / or backup computer of the vehicle 800), the GPU of the infotainment system may perform some self-driving functions. In such an example, the infotainment SoC 830 may place the vehicle 800 in a driver safe parking mode as described herein.

[0168] The vehicle 800 may also include an instrument cluster 832 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 832 may include a controller and / or a supercomputer (e.g., a discrete controller or a supercomputer). The instrument cluster 832 may include a set of instruments, such as a speedometer, a fuel level, an oil pressure, a tachometer, an odometer, a turn indicator, a shift position indicator, a seat belt warning light, a parking brake warning light, an engine fault light, an airbag (SRS) system information, lighting controls, safety system controls, navigation information, and the like. In some examples, information may be displayed and / or shared between the infotainment SoC 830 and the instrument cluster 832. In other words, the instrument cluster 832 may be included as part of the infotainment SoC 830, or vice versa.

[0169] Fig.8D For a cloud-based server and Fig. 8A 800. System 876 may include a server 878, a network 890, and a vehicle including the vehicle 800. The server 878 may include a plurality of GPUs 884(A)-884(H) (collectively referred to herein as GPUs 884), PCIe switches 882(A)-882(H) (collectively referred to herein as PCIe switches 882), and / or CPUs 880(A)-880(B) (collectively referred to herein as CPUs 880). The GPUs 884, CPUs 880, and PCIe switches may be interconnected with high-speed interconnects and / or PCIe connections 886 such as, for example and without limitation, the NVLink interface 888 developed by NVIDIA. In some examples, the GPUs 884 are connected via NVLink and / or NVSwitch SoCs, and the GPUs 884 and PCIe switches 882 are connected via PCIe interconnects. Although eight GPUs 884, two CPUs 880, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the servers 878 may include any number of GPUs 884, CPUs 880, and / or PCIe switches. For example, each of the servers 878 may include eight, sixteen, thirty-two, and / or more GPUs 884.

[0170] Server 878 may receive image data over network 890 and from a vehicle, the image data representing images showing unexpected or changed road conditions, such as recently begun road work. Server 878 may transmit neural network 892, updated neural network 892, and / or map information 894, including information about traffic and road conditions, over network 890 and to the vehicle. Updates to map information 894 may include updates to HD map 822, such as information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, neural network 892, updated neural network 892, and / or map information 894 may have been generated from new training and / or data received from any number of vehicles in the environment and / or based on experience from training performed at a data center (e.g., using server 878 and / or other servers).

[0171] Server 878 can be used to train a machine learning model (e.g., a neural network) based on training data. The training data can be generated by the vehicle, and / or can be generated in a simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., when the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other examples, the training data is not labeled and / or preprocessed (e.g., when the neural network does not require supervised learning). Training can be performed according to any one or more categories of machine learning techniques, including but not limited to the following categories: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including main components and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternate dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations thereof. Once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via network 890), and / or the machine learning model can be used by server 878 to remotely monitor the vehicle.

[0172] In some examples, server 878 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. Server 878 may include a deep learning supercomputer and / or a dedicated AI computer powered by GPU 884, such as DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 878 may include a deep learning infrastructure of a data center powered only by CPUs.

[0173] The deep learning infrastructure of server 878 may be capable of rapid real-time inference, and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 800. For example, the deep learning infrastructure may receive periodic updates from vehicle 800, such as a sequence of images and / or objects located in the sequence of images that vehicle 800 has located (e.g., via computer vision and / or other machine learning object classification techniques). The deep learning infrastructure may run its own neural network to identify objects and compare them to the objects identified by vehicle 800, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 800 has failed, then server 878 may transmit a signal to vehicle 800 instructing the fail-safe computer of vehicle 800 to take control, notify passengers, and complete a safe parking maneuver.

[0174] For reasoning, the server 878 may include a GPU 884 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and reasoning acceleration can make real-time responses possible. In other examples, such as when performance is not so important, CPU, FPGA, and other processor-powered servers can be used for reasoning.

[0175] Example computing device

[0176] Fig. 9 900 is a block diagram of an example computing device 900 suitable for implementing some embodiments of the present disclosure. The computing device 900 may include an interconnect system 902 that directly or indirectly couples the following devices: a memory 904, one or more central processing units (CPUs) 906, one or more graphics processing units (GPUs) 908, a communication interface 910, an input / output (I / O) port 912, an input / output component 914, a power supply 916, one or more presentation components 918 (e.g., (one or more) displays) and one or more logic units 920. In at least one embodiment, the (one or more) computing device 900 may include one or more virtual machines (VMs), and / or any of its components may include virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs 908 may include one or more vGPUs, one or more of the CPUs 906 may include one or more vCPUs, and / or one or more of the logic units 920 may include one or more virtual logic units. As such, computing device(s) 900 may include discrete components (e.g., a full GPU dedicated to computing device 900), virtual components (e.g., a portion of a GPU dedicated to computing device 900), or a combination thereof.

[0177] although Fig. 9 The various blocks of are shown as being connected via interconnect system 902 using wires, but this is not intended to be limiting and is for clarity only. For example, in some embodiments, presentation component 918 (such as a display device) may be considered to be I / O component 914 (e.g., if the display is a touch screen). As another example, CPU 906 and / or GPU 908 may include memory (e.g., memory 904 may represent a storage device in addition to the memory of GPU 908, CPU 906, and / or other components). In other words, Fig. 9 The computing devices described herein are illustrative only. No distinction is made between such categories as "workstations," "servers," "laptops," "desktop computers," "tablet computers," "client devices," "mobile devices," "handheld devices," "game consoles," "electronic control units (ECUs)," "virtual reality systems," and / or other device or system types, as all are considered Fig. 9 within the range of computing devices.

[0178] The interconnection system 902 may represent one or more links or buses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnection system 902 may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect fast (PCIe) bus, and / or another type of bus or link. In some embodiments, there is a direct connection between components. As an example, CPU 906 may be directly connected to memory 904. Further, CPU 906 may be directly connected to GPU 908. In the case where there is a direct or point-to-point connection between components, the interconnection system 902 may include a PCIe link to perform the connection. In these examples, the PCI bus does not need to be included in the computing device 900.

[0179] The memory 904 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by the computing device 900. Computer-readable media may include volatile and non-volatile media, as well as removable and non-removable media. By way of example and not limitation, computer-readable media may include computer storage media and communication media.

[0180] Computer storage media may include volatile and nonvolatile media and / or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 904 may store computer-readable instructions (e.g., representing (one or more) programs and / or (one or more) program elements, such as an operating system). Computer storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by computing device 900. As used herein, computer storage media does not include the signals themselves.

[0181] Computer storage media may embody computer readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transport mechanism, and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in a manner that encodes the information in the signal. By way of example, and not limitation, computer storage media may include wired media (such as a wired network or a direct wired connection) and wireless media (such as acoustic, RF, infrared, and other wireless media). Combinations of any of the above should also be included within the scope of computer readable media.

[0182] The CPU 906 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and / or processes described herein. The CPUs 906 may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of handling numerous software threads simultaneously. The CPU 906 may include any type of processor, and may include different types of processors (e.g., a processor with fewer cores for a mobile device and a processor with more cores for a server) depending on the type of computing device 900 implemented. For example, depending on the type of computing device 900, the processor may be an Advanced RISC Machine (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 900 may also include one or more CPUs 906 in addition to one or more microprocessors or supplementary coprocessors (such as a math coprocessor).

[0183] In addition to or in place of (one or more) CPUs 906, (one or more) GPUs 908 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and / or processes described herein. One or more of the GPUs 908 may be an integrated GPU (e.g., with one or more of the CPUs 906) and / or one or more of the GPUs 908 may be a discrete GPU. In an embodiment, one or more of the GPUs 908 may be a coprocessor of one or more of the CPUs 906. The GPU 908 may be used by the computing device 900 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, the GPU 908 may be used for general-purpose computing on a GPU (GPGPU). The GPU 908 may include hundreds or thousands of cores capable of handling hundreds or thousands of software threads simultaneously. The GPU 908 may generate pixel data for an output image in response to a rendering command (e.g., a rendering command received from the CPU 906 via a host interface). The GPU 908 may include graphics memory (e.g., display memory) for storing pixel data or any other suitable data (e.g., GPGPU data). The display memory may be included as part of the memory 904. The GPU 908 may include two or more GPUs operating in parallel (e.g., via a link). The link may connect the GPUs directly (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU 908 may generate pixel data or GPGPU data for different parts of the output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs.

[0184] In addition to or in lieu of the CPU 906 and / or GPU 908, the logic unit 920 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and / or processes described herein. In embodiments, the CPU(s) 906, the GPU(s) 908, and / or the logic unit(s) 920 may discretely or jointly perform any combination of methods, processes, and / or portions thereof. One or more of the logic units 920 may be a part of and / or integrated into one or more of the CPU 906 and / or GPU 908 and / or one or more of the logic units 920 may be discrete components or otherwise external to the CPU 906 and / or GPU 908. In embodiments, one or more of logic units 920 may be co-processors of one or more of CPUs 906 and / or one or more of GPUs 908 .

[0185] Examples of logic unit 920 include one or more processing cores and / or components thereof, such as a data processing unit (DPU), a tensor core (TC), a tensor processing unit (TPU), a pixel vision core (PVC), a vision processing unit (VPU), a graphics processing cluster (GPC), a texture processing cluster (TPC), a streaming multiprocessor (SM), a tree transverse unit (TTU), an artificial intelligence accelerator (AIA), a deep learning accelerator (DLA), an arithmetic logic unit (ALU), an application specific integrated circuit (ASIC), a floating point unit (FPU), an input / output (I / O) element, a peripheral component interconnect (PCI) or a peripheral component interconnect express (PCIe) element, etc.

[0186] The communication interface 910 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 900 to communicate with other computing devices via an electronic communication network (including wired and / or wireless communications). The communication interface 910 may include components and functions that implement communication through any of a plurality of different networks, such as a wireless network (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), a wired network (e.g., via Ethernet or unlimited band communication), a low power wide area network (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, the one or more logic units 920 and / or the communication interface 910 may include one or more data processing units (DPUs) to transmit data received through the network and / or through the interconnect system 902 directly to one or more GPUs 908 (e.g., memory of).

[0187] The I / O ports 912 may enable the computing device 900 to be logically coupled to other devices including I / O components 914, (one or more) presentation components 918, and / or other components, some of which may be built into (e.g., integrated into) the computing device 900. Illustrative I / O components 914 include microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dishes, scanners, printers, wireless devices, and the like. The I / O components 914 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some cases, the input may be transmitted to an appropriate network element for further processing. The NUI may implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition on and near the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with the display of the computing device 900. The computing device 900 may include a depth camera for gesture detection and recognition, such as a stereo camera system, an infrared camera system, an RGB camera system, touch screen technology, and combinations of these. Additionally, computing device 900 may include an accelerometer or gyroscope that enables detection of motion (e.g., as part of an inertial measurement unit (IMU)). In some examples, computing device 900 may use the output of the accelerometer or gyroscope to render an immersive augmented reality or virtual reality.

[0188] The power supply 916 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 916 may provide power to the computing device 900 to enable the components of the computing device 900 to operate.

[0189] The presentation component 918 may include a display (e.g., a monitor, a touch screen, a television screen, a head-up display (HUD), other display types, or a combination thereof), a speaker, and / or other presentation components. The presentation component 918 may receive data from other components (e.g., GPU 908, CPU 906, DPU, etc.) and output the data (e.g., as an image, video, sound, etc.).

[0190] Sample Data Center

[0191] Fig.10 An example data center 1000 is shown that can be used in at least one embodiment of the present disclosure. The data center 1000 can include a data center infrastructure layer 1010, a framework layer 1020, a software layer 1030, and / or an application layer 1040.

[0192] like Fig.10As shown, the data center infrastructure layer 1010 may include a resource coordinator 1012, grouped computing resources 1014, and node computing resources ("node CRs") 1016(1)-1016(N), where "N" represents any complete positive integer. In at least one embodiment, the node CRs 1016(1)-1016(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memories), storage devices (e.g., solid-state or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules and / or cooling modules, etc. In some embodiments, one or more of the node CRs from the node CRs 1016(1)-1016(N) may correspond to servers having one or more of the above-mentioned computing resources. Furthermore, in some embodiments, nodes CR1016(1)-10161(N) may include one or more virtual components, such as vGPU, vCPU, etc., and / or one or more of nodes CR1016(1)-1016(N) may correspond to a virtual machine (VM).

[0193] In at least one embodiment, the grouped computing resources 1014 may include separate groups of nodes CR1016 housed in one or more racks (not shown), or many racks housed in data centers at different geographical locations (also not shown). The separate groups of nodes CR1016 within the grouped computing resources 1014 may include grouped computing, networks, memory, or storage resources that can be configured or allocated to support one or more workloads. In at least one embodiment, several nodes CR1016 including CPUs, GPUs, DPUs, and / or other processors may be grouped in one or more racks to provide computing resources to support one or more workloads. One or more racks may also include any number of power modules, cooling modules, and / or network switches in any combination.

[0194] Resource coordinator 1012 may configure or otherwise control one or more node CRs 1016(1)-1016(N) and / or grouped computing resources 1014. In at least one embodiment, resource coordinator 1012 may include a software design infrastructure (SDI) management entity for data center 1000. Resource coordinator 1012 may include hardware, software, or some combination thereof.

[0195] In at least one embodiment, Fig.10As shown, the framework layer 1020 may include a job scheduler 1033, a configuration manager 1034, a resource manager 1036, and / or a distributed file system 1038. The framework layer 1020 may include a framework that supports software 1032 of the software layer 1030 and / or one or more applications 1042 of the application layer 1040. The software 1032 or the application 1042 may include network-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 1020 may be, but is not limited to, a free and open source software network application framework (such as Apache Spark) that can utilize the distributed file system 1038 for large-scale data processing (e.g., "big data"). TM (hereinafter referred to as "Spark")). In at least one embodiment, the job scheduler 1033 may include a Spark driver to facilitate scheduling workloads supported by different layers of the data center 1000. The configuration manager 1034 may be able to configure different layers, such as the software layer 1030 and the framework layer 1020 (which includes Spark and a distributed file system 1038 for supporting large-scale data processing). The resource manager 1036 may be able to manage clustered or grouped computing resources mapped to the distributed file system 1038 and the job scheduler 1032 or allocated to support the distributed file system 1038 and the job scheduler 1033. In at least one embodiment, the clustered or grouped computing resources may include grouped computing resources 1014 at the data center infrastructure layer 1010. The resource manager 1036 may coordinate with the resource coordinator 1012 to manage these mapped or allocated computing resources.

[0196] In at least one embodiment, the software 1032 included in the software layer 1030 may include software used by at least a portion of the node CRs 1016(1)-1016(N), the grouped computing resources 1014, and / or the distributed file system 1038 of the framework layer 1020. The one or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0197] In at least one embodiment, the applications 1042 included in the application layer 1040 may include one or more types of applications used by at least a portion of the node CRs 1016(1)-1016(N), the grouped computing resources 1014, and / or the distributed file system 1038 of the framework layer 1020. The one or more types of applications may include, but are not limited to, any number of genomic applications, cognitive computing, and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and / or other machine learning applications used in conjunction with one or more embodiments.

[0198] In at least one embodiment, any of configuration manager 1034, resource manager 1036, and resource coordinator 1012 can implement any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. The self-modification actions can save a data center operator of data center 1000 from making potentially poor configuration decisions and potentially avoiding underutilized and / or poorly performing portions of the data center.

[0199] According to one or more embodiments described herein, data center 1000 may include tools, services, software, or other resources to train one or more machine learning models or use one or more machine learning models to predict or infer information. For example, the (one or more) machine learning models may be trained by calculating weight parameters according to a neural network architecture using the software and / or computing resources described above with respect to data center 1000. In at least one embodiment, a trained or deployed machine learning model corresponding to one or more neural networks may be used to infer or predict information using the resources described above with respect to data center 1000 using weight parameters calculated by one or more training techniques, such as, but not limited to, those described herein.

[0200] In at least one embodiment, the data center 1000 may use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, and / or other hardware (or virtual computing resources corresponding thereto) to perform training and / or inference using the above resources. In addition, one or more software and / or hardware resources described above may be configured to allow a user to train or perform inference services on information, such as image recognition, speech recognition, or other artificial intelligence services.

[0201] Example network environment

[0202] A network environment suitable for implementing embodiments of the present disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and / or other device types. The client devices, servers, and / or other device types (e.g., each device) may be configured to communicate with the client device, server, or other device types. Fig. 9 The backend device 1000 may be implemented on one or more instances of the computing device 900 of the data center 1000 - for example, each device may include similar components, features and / or functions of the computing device 900 of the data center 1000. In addition, in the case of implementing a backend device (e.g., a server, a NAS, etc.), the backend device may be included as part of the data center 1000, and examples of the data center 1000 are described herein. Fig.10 Describe in more detail.

[0203] The components of the network environment can communicate with each other via a network, which can be wired, wireless, or both. The network can include multiple networks or one of multiple networks. For example, the network can include one or more wide area networks (WANs), one or more local area networks (LANs), one or more public networks (such as the Internet and / or a public switched telephone network (PSTN)), and / or one or more private networks. In the case where the network includes a wireless telecommunications network, components such as base stations, communication towers, or even access points (and other components) can provide wireless connections.

[0204] Compatible network environments may include one or more peer-to-peer network environments (in which case the server may not be included in the network environment) and one or more client-server network environments (in which case one or more servers may be included in the network environment). In a peer-to-peer network environment, the functionality described herein for the server may be implemented on any number of client devices.

[0205] In at least one embodiment, the network environment may include one or more cloud-based network environments, distributed computing environments, combinations thereof, and the like. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more servers, and the server may include one or more core network servers and / or edge servers. The framework layer may include a framework for software supporting the software layer and / or one or more applications of the application layer. The software or application may include network-based service software or applications, respectively. In an embodiment, one or more client devices may use network-based service software or applications (e.g., by accessing the service software and / or application via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a free and open source software network application framework that can use a distributed file system for large-scale data processing (e.g., "big data").

[0206] A cloud-based network environment can provide cloud computing and / or cloud storage that performs any combination of the computing and / or data storage functions (or one or more portions thereof) described herein. Any of these different functions can be distributed across multiple locations from a central or core server (e.g., one or more data centers that can be distributed across a state, region, country, global, etc.). If the connection to the user (e.g., client device) is relatively close to an edge server, the core server can assign at least a portion of the functionality to the edge server. A cloud-based network environment can be private (e.g., limited to a single organization), public (e.g., available to many organizations), and / or a combination thereof (e.g., a hybrid cloud environment).

[0207] The client device(s) may include the Fig. 9 At least some of the components, features, and functionality of the described (one or more) example computing device(s) 900. By way of example and not limitation, the client device may be implemented as a personal computer (PC), a laptop computer, a mobile device, a smart phone, a tablet computer, a smart watch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality headset, a global positioning system (GPS) or device, a video player, a camera, a surveillance device or system, a vehicle, a boat, a spacecraft, a virtual machine, a drone, a robot, a handheld communication device, a hospital device, a gaming device or system, an entertainment system, a vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of the depicted devices, or any other suitable device.

[0208] The present disclosure may be described in the general context of machine-usable instructions or computer codes executed by a computer or other machine such as a personal digital assistant or other handheld device, including computer-executable instructions such as program modules. Typically, program modules including routines, programs, objects, components, data structures, etc. refer to codes that perform specific tasks or implement specific abstract data types. The present disclosure may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communication network.

[0209] As used herein, the statement of "and / or" with respect to two or more elements should be interpreted as referring to only one element or a combination of elements. For example, "element A, element B, and / or element C" may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B, and C. In addition, "at least one of element A or element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, "at least one of element A and element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B.

[0210] The subject matter of the present disclosure is described in detail herein to meet statutory requirements. However, the description itself is not intended to limit the scope of the present disclosure. On the contrary, the present inventors have contemplated that the claimed subject matter may also be embodied in other ways to include steps different from the steps described herein in conjunction with other current or future technologies or combinations of similar steps. Moreover, although the terms "step" and / or "block" may be used herein to imply different elements of the method employed, these terms should not be interpreted as implying any particular order among or between the various steps disclosed herein, unless the order of the steps is explicitly described.

Claims

1. A method comprising: causing, during allocation of one or more resources associated with a machine, a first initialization of a failsafe function associated with the machine and a second initialization of an availability function associated with the machine; making available the fail-safe functionality associated with the machine during allocation of the one or more resources; Based at least on the completion of the allocation of the one or more resources, making available for use the availability function associated with the machine; as well as Based at least on the second function, the machine is caused to perform one or more control operations for navigating the machine through at least a portion of an environment.

2. The method of claim 1, wherein: The failsafe functionality associated with the machine includes a first type of functionality associated with a first safety level; and The availability functions associated with the machine include a second type of functionality associated with a second security level greater than the first security level.

3. The method of claim 1, further comprising: causing the machine to operate in a first operating phase based at least on the allocation of the one or more resources occurring, wherein making available the failsafe functionality associated with the machine occurs while the machine is operating in the first operating phase; and Based at least on the completion of the allocation of the one or more resources, the machine is caused to operate in a second operating phase, wherein making available the availability function associated with the machine occurs while the machine is operating in the second operating phase.

4. The method of claim 3, further comprising: When the machine is operating in the third operation stage, at least one of the following is performed: hardware associated with the machine is started, software associated with the machine is started, an operating system associated with the machine is started, or monitoring of the one or more resources associated with the machine is started, The first operating phase and the third operating phase are associated with initialization of the machine.

5. The method of claim 1, further comprising: Based at least on detecting an error associated with the failsafe function, rendering the availability function unavailable for use; causing a third initialization associated with the failsafe function when the availability function is no longer available; making the failsafe function available again when the availability function is no longer available; as well as After the failsafe function has become available again, the availability function is made available again.

6. The method of claim 1, further comprising: storing first data indicating that the failsafe functionality includes a first type of functionality, wherein making the failsafe functionality available for use during allocation of the one or more resources is based at least on the failsafe functionality including the first type of functionality; as well as Second data indicating that the availability function includes a second type of function is stored, wherein making the availability function available based at least on the completion of the allocation of the resource is based at least on the availability function including the second type of function.

7. The method of claim 1, further comprising: determining during allocation of the one or more resources that the second initialization of the availability function is complete; as well as Based at least on the type of the availability function, a determination is made to wait until allocation of the one or more resources is complete before making the availability function available for use.

8. The method of claim 1, further comprising: determining that an event associated with navigating the machine has occurred; as well as Based at least on the occurrence of the event, a first termination of the fail-safe function and a second termination of the availability function are caused.

9. A system comprising: One or more processing units, the one or more processing units being configured to: causing, during a first operating phase associated with a machine, a first initialization of a first function associated with the machine and a second initialization of a second function associated with the machine; making available, during said first operating phase, said first function associated with said machine; making available, during a second operating phase associated with the machine, the second function associated with the machine; as well as Based at least on the second function, the machine is caused to perform one or more operations.

10. The system of claim 9, wherein: The first functionality associated with the machine comprises a first type of functionality associated with a first security level; and The second functionality associated with the machine includes a second type of functionality associated with a second security level greater than the first security level.

11. The system of claim 9, wherein the one or more processing units are further configured to: causing the machine to operate in the first operating phase during allocation of one or more resources associated with the machine; and Based at least on the completion of the allocation of the one or more resources, the machine is caused to operate in the second operating stage.

12. The system of claim 9, wherein the one or more processing units are further configured to: causing, during a third operating phase associated with the machine, at least one of: starting hardware associated with the machine, starting software associated with the machine, starting an operating system associated with the machine, or starting monitoring of one or more resources associated with the machine, The first operating phase and the third operating phase are associated with initialization of the machine.

13. The system of claim 9, wherein the one or more processing units are further configured to: Based at least on detecting an error associated with the first functionality, rendering the second functionality no longer available for use; When the second function is no longer available, causing a third initialization associated with the first function; When the second function is no longer available, making the first function available again; and After the first function is available again, the second function is made available again.

14. The system of claim 9, wherein the one or more processing units are further configured to: storing first data indicating that the first functionality comprises a first type of functionality, wherein making the first functionality available for use during the first operating phase is based at least on the first functionality comprising the first type of functionality; and Second data indicating that the second function includes a second type of function is stored, wherein making the second function available during the second operating phase is based at least on the second function including the second type of function.

15. The system of claim 9, wherein the one or more processing units are further configured to: determining during the first operating phase that the second initialization of the second function is complete; and Based at least on the type of the second functionality, it is determined to wait until the second operating phase before making the second functionality available.

16. The system of claim 9, wherein the one or more processing units are further configured to: determining that an event associated with navigating the machine has occurred; and Based at least on the event occurrence, a first termination of the first function and a second termination of the second function are caused during a third operating phase associated with the machine.

17. The system of claim 9, wherein the system is included in at least one of the following: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing simulation operations; Systems for performing digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; Systems for performing deep learning operations; Systems implemented using edge devices; Systems implemented using robots; A system implementing one or more large language models (LLMs); A system for performing one or more generative AI operations; Systems for performing conversational AI operations; Systems for generating synthetic data; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

18. A processor, comprising: One or more processing units, the one or more processing units being used to make non-safety-related functions available during a first operating phase in which one or more resources associated with a machine are being allocated, and to make availability functions available during a second operating phase in which the one or more resources associated with the machine are allocated, wherein a first initialization of the first resource and a second initialization of the second resource occur during the first operating phase.

19. The processor of claim 18, wherein the one or more processing units are further configured to: storing first data indicating that the failsafe functionality includes a first type of functionality, wherein making the failsafe functionality available during the first operating phase is based at least on the failsafe functionality including the first type of functionality; and Second data indicating that the availability function includes a second type of functionality is stored, wherein making the availability function available during the second operating phase is based at least on the availability function including the second type of functionality.

20. The processor of claim 18, wherein the processor is included in at least one of: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing simulation operations; Systems for performing digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; Systems for performing deep learning operations; Systems implemented using edge devices; Systems implemented using robots; A system implementing one or more large language models (LLMs); A system for performing one or more generative AI operations; Systems for performing conversational AI operations; Systems for generating synthetic data; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources.

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