Device initialization and authentication for computing systems and applications

Abstract

The invention relates to device initialization and authentication for computing systems and applications. Various embodiments relate to applications, platforms, architectures, and the like for authenticating and initializing peripheral devices. For example, before the peripheral device is authenticated, one or more initialization operations with respect to the peripheral device may be performed. In these and other embodiments, authentication may be based on information maintained during initialization, such as error detection code values and / or message counter values. Performing the authentication and initialization operations in the disclosed manner may reduce the amount of time used to authenticate and initialize the peripheral device as compared to as typically done to authenticate first and then initialize.

Description

Background Technology

[0001] A computing system can perform one or more operations (e.g., processing operations, presentation operations, application operations, etc.) based on sensor data obtained using one or more sensors. For example, a computing system can display information based on sensor data (e.g., displaying a camera or video feed), or process sensor data for decision-making or to determine a specific state of the system.

[0002] In these and other embodiments, certain computing systems that use sensor data can be configured to initialize one or more sensors used to acquire sensor data for the computing system. For example, the computing system can be configured to perform one or more initialization operations on one or more sensors to initialize the sensors such that the sensors are configured in a specific manner.

[0003] Computing systems that utilize sensor data can also be configured to perform authentication operations on the sensors used to obtain such data. Authentication operations help verify the reliability of the sensor data obtained from the sensors.

[0004] In some instances, the authentication operation may include authenticating the sensor itself. Additionally or alternatively, the authentication operation may include verifying whether the sensor was initialized in the manner indicated by the computing system.

[0005] In some traditional implementations, authentication can be performed before and / or during sensor initialization. For example, before performing one or more initialization operations, a particular sensor can be authenticated by the corresponding computing system to verify that the sensor is the expected type of sensor capable of meeting certain criteria. In these and other instances, once the sensor itself has completed authentication, the sensor and computing system can communicate with each other based on the sensor authentication using secure communication (e.g., via encrypted communication) for the initialization operations. An operation corresponding to securing the communication can be part of the authentication process, ensuring that the initialization operations are performed as instructed by the computing system.

[0006] However, this process can lead to delays in the initialization process. For example, initialization may not begin before the sensor itself has completed authentication. Sensor authentication may also involve running and initializing other hardware and / or software (e.g., cryptographic engines, keystores, etc.), which can further delay initialization. Additionally, using secure communication regarding initialization can introduce additional computational overhead, which may further delay initialization.

[0007] Such initialization delays can make it difficult for some systems to meet certain criteria. For example, in automotive applications, a specific key performance indicator (KPI) related to a reversing camera might be the display of the image captured by the camera on the screen within a certain timeframe. Delays in initializing the reversing camera can make it difficult to meet the specific criteria associated with this KPI. Summary of the Invention

[0008] According to one or more embodiments of this disclosure, systems and methods can allow for faster initialization of peripheral devices of a computing system (e.g., sensors, such as camera sensors), while authenticating these devices and verifying the correctness of device configurations obtained via initialization. For example, according to one or more embodiments, one or more initialization operations regarding the peripheral device can be performed before authentication. In these and other embodiments, the peripheral device can be fully initialized before performing any authentication operations regarding verifying the peripheral device itself and / or verifying the initialization. Performing authentication and initialization operations in the disclosed manner can reduce the amount of time required to authenticate and initialize the peripheral device compared to the usual practice of authenticating before initialization. Attached Figure Description

[0009] The system and method for device initialization and authentication for autonomous and semi-autonomous systems and applications are described in detail below with reference to the accompanying drawings, wherein:

[0010] Figure 1A The illustration shows an example system related to authentication corresponding to a peripheral device according to one or more embodiments of the present invention;

[0011] Figure 1B The illustrations depict one or more embodiments of the present disclosure that can be used in a device and Figure 1A An example process of device initialization and authentication performed between computing systems;

[0012] Figure 2 The illustration shows a flowchart illustrating a method for performing device authentication according to one or more embodiments of the present disclosure;

[0013] Figure 3A This is an illustration of an example autonomous vehicle according to one or more embodiments of the present disclosure;

[0014] Figure 3B According to one or more embodiments of this disclosure Figure 3A Examples of camera positions and fields of view for autonomous vehicles;

[0015] Figure 3C According to one or more embodiments of this disclosure Figure 3AA block diagram of an example system architecture for an example autonomous vehicle;

[0016] Figure 3D A cloud-based server according to one or more embodiments of this disclosure and Figure 3A A system diagram illustrating communication between autonomous vehicles;

[0017] Figure 4 This is a block diagram of an example computing device suitable for implementing one or more embodiments of the present disclosure; and

[0018] Figure 5 This is a block diagram of an example data center applicable to implementing one or more embodiments of this disclosure. Detailed Implementation

[0019] The systems and methods disclosed herein relate to the authentication of peripheral devices corresponding to a computing system. For example, a peripheral device (e.g., a sensor) may provide data (referred to herein as "device data") to the computing system, and the computing system may perform one or more operations based on the received device data. For example, the computing system may display information based on the device data (e.g., display camera or video feed), and / or may process the device data for decision-making or to determine a specific state of another system (e.g., a vehicle) to which the computing system corresponds (e.g., as part of, controlling, monitoring, etc. of, it).

[0020] In these and other embodiments, the computing system can be configured to configure one or more peripheral devices. For example, the computing system can be configured to perform one or more initialization operations with respect to the peripheral devices (e.g., upon power-on or startup), such that the peripheral devices are configured to operate in a certain manner during operation—e.g., to provide device data to the computing system in a specific way (e.g., format, timeframe, etc.).

[0021] For example, the computing system may perform one or more write operations on a peripheral device as part of an initialization operation (e.g., writing data to one or more registers of the peripheral processing system), which may configure the peripheral device in a specific manner. Additionally or alternatively, the computing system may perform one or more read operations as part of an initialization operation, wherein information may be read from the peripheral device (e.g., reading information from one or more registers of the peripheral device processing system).

[0022] In these and other embodiments, the computing system may authenticate the peripheral devices themselves. For example, in some embodiments, authentication may include the computing system verifying whether the peripheral device from which device data can be obtained is the device from which the computing system expects to provide such device data (e.g., for a certain type of device, a certain model of device, etc.). For example, in machine-related (e.g., vehicle) embodiments, the peripheral device may include one or more sensors that generate sensor data that can be used by the relevant computing system. Such sensors may have certain performance requirements associated with them (e.g., resolution, data collection frequency, range, etc.). These performance requirements can help ensure that the information provided by such sensor data meets certain criteria associated with using such sensor data. Thus, verification of the sensors themselves can allow the computing system to verify that these sensors are indeed sensors that meet the specified performance requirements and are therefore capable of producing sensor data that meets the corresponding criteria.

[0023] In these and other embodiments, and as another example, authentication may include verifying whether the configuration of the peripheral device is performed in a manner indicated during the corresponding initialization of such a device. For example, it may be verified whether the data written to the peripheral device during initialization is the same data that the computing system is instructed to write to the peripheral device. In this disclosure, the reference to "authentication of a peripheral device" generally refers to the authentication or verification of the device itself and / or the authentication or verification of the device's configuration via an initialization operation.

[0024] As described in more detail in this disclosure, according to one or more embodiments of this disclosure, one or more initialization operations can be performed on one or more peripheral devices (e.g., sensors) before authentication is completed. In these and other embodiments, the peripheral devices can be fully initialized before any authentication operations concerning the verification of the peripheral devices themselves and / or the verification initialization are performed. This process can be achieved by utilizing certain operations performed during initialization as further described herein. In contrast, conventional methods typically involve authenticating the peripheral devices before initialization and performing certain operations during initialization (e.g., using a secure communication protocol) to assist in authenticating the initialization.

[0025] Compared to authentication before initialization, performing authentication operations after initialization in the disclosed manner can reduce the amount of time required to authenticate and initialize peripheral devices. For example, a peripheral device may be able to perform initialization operations while other hardware and / or software used to perform one or more authentication operations are being initialized. In contrast, in instances where authentication is performed first, such operations are typically performed serially. Additionally or alternatively, the amount of time required to perform the initialization operation itself can be reduced by reducing or eliminating the processing overhead traditionally required to secure the transmission of initialization data (which may include up to hundreds of transmissions). In contrast, as discussed further in detail in this disclosure, the transmission of information used to verify the correctness of the initialization after initialization may consist of only one to a few secure communications.

[0026] One or more embodiments of this disclosure may relate to device authentication (e.g., sensor authentication) associated with self-machines and / or components of one or more self-machines, which may include any applicable machine or system capable of performing one or more autonomous or semi-autonomous operations. Example self-machines may include, but are not limited to, vehicles (land, sea, space, and / or air), robots, robotic platforms, etc. By way of example, self-machine computing applications may include one or more applications that can be performed by autonomous or semi-autonomous vehicles, such as regarding... Figures 3A to 3D The described example autonomous vehicle 300 (which may alternatively be referred to herein as "vehicle 300" or "self-machine 300"). In this disclosure, references to "autonomous vehicle" or "semi-autonomous vehicle" can include any vehicle that can be configured to perform one or more autonomous or semi-autonomous navigation or driving operations. Thus, such vehicles may also include those in which an operator is required or in which an operator can also perform such operations.

[0027] Additionally or alternatively, the systems and methods described herein may be used by, but not limited to, the following: 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, manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, spacecraft, ships, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, engineering vehicles, submarines, drones and / or other vehicle types. Furthermore, the systems and methods described herein can be used for a variety of purposes, including but not limited to machine control, machine motion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and supervision, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actor simulation and / or digital twins, generative AI, data center processing, conversational AI (such as by employing one or more language models, such as one or more large language models (LLM), visual language models (VLM), multimodal language models, etc.), optical transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing and / or any other suitable application.

[0028] The disclosed embodiments can comprise 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, aviation systems, medical systems, boating systems, smart area surveillance systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems containing one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations (e.g., systems implementing one or more LLMs), systems implementing one or more visual language models (VLMs), systems implementing one or more multimodal language models, systems for performing one or more generative AI operations, systems for hosting real-time streaming applications, systems for presenting one or more of virtual reality content, augmented reality content, or mixed reality content, systems for performing optical transmission simulations, systems for performing collaborative content creation for 3D assets, systems implemented at least partially using cloud computing resources, and / or other types of systems.

[0029] Embodiments of this disclosure will be explained with reference to the accompanying drawings. It should be understood that these drawings are illustrative and schematic representations of such exemplary embodiments and are not limiting, nor are they necessarily drawn to scale. In the various drawings, features with the same numbers indicate the same structure and function, unless otherwise described.

[0030] Figure 1A An example system relating to authentication of a peripheral device 102 (“Peripheral Device 102”) is illustrated according to one or more embodiments of the present invention. Generally, system 100 may include device 102 and computing system 104. It should be understood that such and other arrangements described herein are merely illustrative. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used to supplement or replace the illustrated arrangements and elements, and some elements may be omitted entirely.

[0031] For example, system 100 may include any number of peripheral devices, with authentication operations performed on these peripheral devices. Additionally or alternatively, system 100 may include multiple controllers configured to perform authentication operations such as those described herein, individually and / or collectively. Furthermore, many of the elements described herein are functional entities that can 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 entities may be performed by hardware, firmware, and / or software. For example, a processor executing instructions stored in memory can be used to perform these functions.

[0032] Furthermore, system 100 can be implemented in any applicable system, apparatus, or device in which device authentication can be performed. By way of example, but not limited to, system 100 can be implemented in vehicle settings, such as those concerning... Figures 3A to 3D 300 vehicles.

[0033] In some embodiments, device 102 may include any suitable device that may be configured to provide data (referred to as “device data”) to computing system 104 for use by computing system 104. For example, device 102 may include a separate computing system configured to acquire data and / or provide data to computing system 104.

[0034] As another example, device 102 may include a sensor system (generally referred to herein as a “sensor”) configured to capture data (generally referred to herein as “sensor data”) and provide such sensor data to computing system 104. The sensor may include any kind of sensor configured to capture sensor data. Additionally or alternatively, device 102 may correspond to a machine, such as a self-contained machine. For example, device 102 may include any of the sensors described with respect to vehicle 300. Figures 3A to 3D The following description is provided. For example, in some embodiments, device 102 may include a camera (e.g., a reversing camera) configured to provide video feeds to computing system 104.

[0035] In these and other embodiments, device 102 may include one or more processing systems configured to perform one or more operations relating to device 102. For example, the processing system may be configured to format data in a certain manner, generate data (e.g., based on detections performed by sensors), control the acquisition and / or transmission of data, etc. In these and other embodiments, the processing system of device 102 may be implemented by one or more computing devices and / or implemented as one or more computing devices (such as those relating to...). Figure 4 (The computing device described in further detail).

[0036] The computing system 104 may include any suitable system configured to perform operations based on device data that may be received from the device 102. In some embodiments, the computing system 104 may be a localized system, wherein its components may be located in relatively similar locations. Additionally or alternatively, the computing system 104 may be a distributed system, wherein one or more components of the computing system 104 may be located remotely to each other.

[0037] In some embodiments, computing system 104 may include code and routines configured to cause the execution of operations described with respect to computing system 104. Additionally or alternatively, computing system 104 may be implemented using hardware including one or more processors, CPUs, graphics processing units (GPUs), data processing units (DPUs), parallel processing units (PPUs), microprocessors (e.g., performing one or more operations or controlling the execution of one or more operations), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), accelerators (e.g., deep learning accelerators (DLAs)), one or more programmable vision accelerators (PVAs), and / or other processor types, wherein the one or more programmable vision accelerators (PVAs) may include one or more vector processing units (VPUs), one or more direct memory access (DMA) systems, one or more pixel processing engines (PPEs), etc. In these and other embodiments, computing system 104 may be implemented using a combination of hardware and software. In this disclosure, operations described as being performed by computing system 104 may include operations that computing system 104 may perform itself or cause to be performed by another device.

[0038] In these or other embodiments, the computing system 104 may be implemented by one or more computing devices and / or implemented as one or more computing devices (such as regarding Figure 4 The computing device described in further detail (as described in the following description). Additionally or alternatively, the computing system 104 may correspond to, for example, regarding... Figures 3A to 3D The described vehicle 300 or similar machine (e.g., included in the machine, controlling the machine, etc.). In these or other embodiments, the computing system 104 may correspond to, for example, regarding... Figure 5 The data center described is a data center such as data center 500 (e.g., included in the data center, controlling the data center, etc.).

[0039] In some embodiments, the computing system 104 may be configured to perform one or more initialization operations concerning the device 102. Additionally or alternatively, the computing system 104 may be configured to perform one or more authentication operations concerning the authentication of the device 102. In these and other embodiments, the computing system 104 may be configured to perform the authentication operation after performing one or more initialization operations (e.g., after initializing the device 102). This process can be implemented by utilizing certain operations performed during sensor initialization.

[0040] For example, during sensor initialization, one or more error-checking operations can be performed on the initialization data transmitted between the initialization computing system and the sensor. Error-checking operations can be used to detect whether changes have occurred in the initialization data, and are typically used to determine whether any unintended changes have occurred to the initialization data that could corrupt it (e.g., during communication).

[0041] Example mechanisms that can be used for error checking may include generating one or more error detection codes—such as Cyclic Redundancy Check (CRC) codes—based on initialization data transmitted between device 102 and computing system 104. For example, both device 102 and computing system 104 can determine a CRC code and its corresponding value based on initialization data transmitted during the initialization of device 102. An instance of a CRC code mismatch indicates that the initialization data expected to be stored on one side differs from the initialization data actually stored, thus indicating data corruption. Conversely, an instance of a CRC code match indicates that the initialization data expected to be stored on one side is the same as the initialization data actually stored, thereby verifying data integrity.

[0042] Another verification mechanism related to initialization may include maintaining a message counter. Generally, a message counter may indicate a running tally of the number of messages that can be transmitted during initialization. For example, a write message counter may be maintained to count the number of written messages. Additionally or alternatively, a read message counter may be maintained to count the number of read messages. In these and other embodiments, a read / write message counter may be maintained to count both read and written messages. In some instances, such a message counter may be used to identify whether messages between device 102 and computing system 104 are lost during initialization. Additionally or alternatively, a message counter may be used to identify whether device 102 receives messages that do not originate from computing system 104, and vice versa.

[0043] According to one or more embodiments of this disclosure, such error detection codes and / or message counters (generally referred to as "trace codes") can also be used as a mechanism that allows authentication operations to be performed after sensor initialization. For example, in some embodiments, authentication operations can be performed to authenticate the device 102 itself after device initialization. In some embodiments, authentication of the device itself can be performed in the same or similar manner as in instances where the device 102 is authenticated before initialization.

[0044] Based on device authentication, secure communication can be established between device 102 and computing system 104. Additionally or alternatively, device 102 may transmit one or more trace code values ​​to computing system 104, which are determined by device 102 based on initialization data transmitted during device initialization. For example, in some embodiments, device 102 may transmit a write CRC value to computing system 104 via secure communication, based on data written to device 102 based on a write operation performed during device initialization. Additionally or alternatively, in some embodiments, device 102 may transmit a read CRC value to computing system 104 via secure communication, based on data read from a sensor based on a read operation performed during device initialization. In these and other embodiments, device 102 may transmit one or more message counter values ​​to computing system 104.

[0045] The computing system 104 can compare the received tracing code value with an expected value of the received tracing code value—for example, the expected value can be determined at the computing system 104. In response to a match between the received tracing code value and the expected tracing code value, the computing system 104 can verify whether the device 102 has been initialized as expected, thus verifying initialization. Conversely, in response to a mismatch between the received tracing code value and the expected tracing code value, the computing system 104 cannot verify whether the device 102 has been initialized as expected, thus failing to verify initialization. In some embodiments, it can be done according to... Figure 1B The execution of one or more operations corresponding to the authentication and verification of device 102.

[0046] Figure 1B An example process 150 for initializing and authenticating device 102, which can be performed between device 102 and computing system 104 according to one or more embodiments of the present disclosure, is illustrated. Process 150 describes a plurality of operations that can be performed; however, it should be understood that individual implementations may include additional operations and / or omit one or more operations.

[0047] In some embodiments, process 150 may include one or more initialization operations 110. In some embodiments, initialization operation 110 may include: transferring data between computing system 104 and device 102 to configure device 102 in a certain way.

[0048] For example, in some embodiments, initialization operation 110 may include one or more write operations. In some embodiments, a write operation may include instructions from computing system 104 to device 102 for writing certain information to device 102. In these and other embodiments, the write instructions may include data to be written to the processing system of device 102 (e.g., to the memory of the processing system of device 102, such as registers). The data to be written may be configured in a particular manner for device 102. In these and other embodiments, device 102 may write such information in response to receiving a write instruction.

[0049] Additionally or alternatively, initialization operation 110 may include one or more read operations. Read operations may relate to data read from device 102 (e.g., from the memory of device 102). In these and other embodiments, read operations may include instructions from computing system 104 to device 102 for reading information from device 102. In these and other embodiments, read operations may include: device 102 reading the instructed data. Additionally or alternatively, read operations may include: device 102 transferring the read data to computing system 104.

[0050] In these and other embodiments, device 102 may perform operation 112 regarding initialization operation 110. Operation 112 may include maintaining one or more first trace codes. Additionally or alternatively, computing device 104 may perform operation 114 regarding initialization operation 110. Operation 114 may include maintaining one or more second trace codes.

[0051] In some embodiments, the first trace code may correspond to one or more error-checking operations that can be performed with respect to initialization data (e.g., writing and / or reading data), which may be transmitted and / or accessed during initialization. In some embodiments, one or more different error detection codes may be maintained with respect to initialization operation 110. For example, in some embodiments, write error detection codes (e.g., write CRC codes) and corresponding values ​​may be maintained based on write operations performed during initialization operation 110. In these and other embodiments, read error detection codes (e.g., read CRC codes) and corresponding values ​​may be maintained based on read operations performed during initialization operation 110. Additionally or alternatively, read / write error detection codes (e.g., read / write CRC codes) and corresponding values ​​may be maintained based on both read and write operations performed during initialization operation 110. In some embodiments, the first trace code and the second trace code may include error detection codes (e.g., CRC codes).

[0052] In these and other embodiments, one or more message counters and their respective values ​​may be maintained by device 102 and system 104 regarding the initialization operation 110. In some embodiments, the first trace code and the second trace code may also include message counters in addition to error detection code.

[0053] In some embodiments, after initialization operation 110, process 150 may include one or more authentication operations. In some embodiments, authentication operations may include device verification operation 116. Verification operation 116 is used to verify the device 102 itself. For example, verification operation 116 may be used to verify whether the device 102 is a device of a certain type with the ability to meet certain criteria. For example, verification operation 116 may be used to verify whether the device 102 meets certain performance requirements by verifying whether the device 102 is a certain type of device capable of meeting such requirements.

[0054] In some embodiments, verification operation 116 includes any suitable operation or process that can be performed to verify device 102 itself. For example, in some embodiments, a secret session key can be generated by device 102 and computing system 104 according to any suitable process—for example, before or as part of performing verification operation 116. In these and other embodiments, verification operation 116 may include: the exchange of a secret session key between device 102 and computing system 104 to authenticate device 102 and computing system 104 with respect to each other using any suitable mechanism.

[0055] For example, in some embodiments, public key certification can be used to verify device 102. For instance, device 102 can provide access to its public key certificate via an insecure link, and computing system 104 can use a known public key (which is not private) of a signature management authority to verify the certificate covertly.

[0056] As another example, the device 102 can be verified based on a private key mechanism. For instance, the same private key supply device 102 and computing system 104 can be used in a secure (trusted) environment so that they can verify each other using a known private key when operating in an insecure environment.

[0057] In some embodiments, process 150 may include operation 118. At operation 118, a secure communication session may be established between device 102 and computing system 104. In these and other embodiments, a secret session key used as part of the authentication operation 116 may be used to establish the secure communication session. Any suitable protocol may be used to establish the secure communication session, in which secure communication (e.g., encrypted communication) may be exchanged between device 102 and computing system 104.

[0058] In some embodiments, process 150 may further include operation 120. Operation 120 may include one or more device authentication operations associated with verifying whether device 102 has been initialized (e.g., verifying whether device 102 is configured as instructed by computing system 104). In some embodiments, computing system 104 may request device 102 to transmit one or more values ​​corresponding to one or more trace codes to computing system 104. In these and other embodiments, the request may be performed via secure communication corresponding to a secure communication session.

[0059] For example, in some embodiments, computing system 104 may request device 102 to transmit a first write CRC code value, a first read CRC code value, a first read / write CRC code value, a first write counter value, a first read counter value, and / or a first read / write counter value.

[0060] During operation 122, device 102 may transmit the requested first trace code value to computing system 104. In these and other embodiments, device 102 may transmit the requested first trace code value via secure communication. Secure communication helps ensure that the information received at computing system 104 is identical to the information transmitted by device 102. Thus, secure communication can maintain the integrity of the information being transmitted to verify initialization (e.g., maintain the integrity of the first trace code value).

[0061] At operation 124, the computing system 104 can compare the received first trace code value with a corresponding second trace code value. The second trace code value may be a value that the computing system 104 has already determined based on write and / or read operations, and may be an expected value of the first trace code value received from the device 102.

[0062] For example, computing system 104 may compare the second write CRC value maintained by computing system 104 with the first write CRC value received from device 102, compare the second read CRC value maintained by computing system 104 with the first read CRC value received from device 102, compare the second read / write CRC value maintained by computing system 104 with the first read / write CRC value received from device 102, compare the second write message counter value maintained by computing system 104 with the first write message counter value received from device 102, compare the second read message counter value maintained by computing system 104 with the first read message counter value received from device 102, and / or compare the second read / write message counter value maintained by computing system 104 with the first read / write message counter value received from device 102.

[0063] At operation 126, the computing system can verify the initialization based on comparison. For example, an instance where a particular first trace code value does not match its corresponding second trace code value indicates that the initialization data corresponding to such a value, which was expected to be stored and / or read on one side, is different from the initialization data that was actually stored and / or read, thus indicating corruption of the data and the initialization. Thus, in such instances where one or more of the first trace code values ​​do not match their corresponding second trace code values, it can be determined that the initialization was not performed as expected—for example, it was corrupted and / or compromised in some way—and the initialization cannot be verified or certified.

[0064] In contrast, instances where a specific first tracing code value matches its corresponding second tracing code value indicate that the corresponding initialization data expected to be stored and / or read on one side is the same as the initialization data actually stored and / or read, thereby verifying data integrity. Thus, in such instances where one or more first tracing code values ​​match their corresponding second tracing code values, it can be determined that initialization was performed as expected, and the initialization can be verified or authenticated. In some embodiments, initialization cannot be verified unless all first tracing code values ​​match their corresponding second tracing code values.

[0065] Therefore, performing this process 150 can be used to authenticate device 102 (e.g., the device itself and its initialization) after initialization, while also maintaining the integrity of the authentication. Furthermore, compared to the traditional approach of authenticating first and then initializing, this method... Figure 1A and Figure 1B Performing authentication operations in the disclosed manner can reduce the amount of time spent authenticating and initializing device 102.

[0066] For example, device 102 may be able to perform initialization operations while other hardware and / or software used to perform one or more authentication operations are being initialized. In contrast, in instances where authentication is performed first, such operations are typically performed serially. Additionally or alternatively, the amount of time required to perform the initialization operation itself can be reduced by decreasing or eliminating the processing overhead traditionally required to secure the transmission of initialization data (which may involve up to hundreds of transmissions). In contrast, transmitting trace code after initialization may involve only a single, less secure communication.

[0067] Without departing from the scope of this disclosure, [the following can be done] Figure 1A and Figure 1B The description may be modified, added to, or deleted. For example, in some embodiments, system 100 may include any number of devices that can be initialized and authenticated and / or any number of computing systems that may correspond to initialization and / or authentication. Alternatively or additionally, in some embodiments, the order of operations described with respect to process 150 may be different. In these and other embodiments, one or more operations of process 150 may be deleted or changed. For example, in some embodiments, device 102 may transmit a first trace code value even if computing system 104 has not received a request to do so. As another example, although the description indicates that authentication operations are performed after device 102 has been fully initialized, some embodiments may include performing partial initialization, and then performing authentication operations after partial initialization but before full initialization.

[0068] Figure 2 This is a flowchart illustrating a method 200 for performing device authentication according to one or more embodiments of the present disclosure. One or more operations of the method 200 can be performed by any suitable system, apparatus, or device (such as, for example, those described in this disclosure). Figure 1A One or more components of system 100, about Figures 3A to 3D The described one or more autonomous vehicle systems, about Figure 4 The described one or more computing devices and / or about Figure 5 The described one or more data systems are executed.

[0069] Method 200 may include box B202. At box B202, actions can be performed regarding peripheral devices (such as...) Figure 1A One or more initialization operations can be performed on the peripheral device (102). In these and other embodiments, the peripheral device and the computing system (such as...) can perform one or more initialization operations. Figure 1A Initialization operations are performed between the computing system 104). In some embodiments, one or more initialization operations may include those described in this disclosure. Figure 1A and Figure 1BOne or more initialization operations described in the initialization operations—for example, regarding Figure 1A One or more of the initialization operations described in initialization operation 110.

[0070] At box B204, one or more authentication operations concerning the peripheral device can be performed. Authentication operations can be performed after the peripheral device has been initialized or at least after the peripheral device has been at least partially initialized. Authentication operations may include: verifying the device itself, such as, for example, regarding... Figure 1A and Figure 1B The device described in this disclosure. In these and other embodiments, the authentication operation may include: verifying the initialization of the device, such as, for example, regarding... Figure 1A and Figure 1B The device described in this disclosure.

[0071] Additionally or alternatively, authentication operations can be based on trace code that can be maintained during initialization, such as, for example, regarding... Figure 1A and Figure 1B The tracing code described in this disclosure. In some embodiments, one or more initialization operations may include those described in this disclosure regarding... Figure 1A and Figure 1B One or more authentication operations described in the authentication operation—for example, regarding Figure 1B One or more of the operations described are 116, 118, 120, 122, 124 or 126.

[0072] Without departing from the scope of this disclosure, method 200 may be modified, added to, or deleted. For example, although illustrated as discrete boxes, the various boxes of method 200 may be divided into additional boxes, combined into fewer boxes, or deleted, depending on a specific implementation. Furthermore, in some embodiments, method 200 may be used to perform multiple different authentications on multiple different peripheral devices.

[0073] Example autonomous vehicles

[0074] Figure 3AThis is an illustration of an example autonomous vehicle 300 according to some embodiments of the present disclosure. The autonomous vehicle 300 (or, alternatively, referred to herein as “vehicle 300”) may include, but is not limited to, passenger vehicles such as automobiles, trucks, buses, ambulances, shuttles, electric or motorized bicycles, motorcycles, fire trucks, police cars, ambulances, boats, engineering vehicles, underwater vessels, drones, and / or other types of vehicles (e.g., driverless and / or capable of accommodating one or more passengers). Autonomous vehicles are generally described according to the level of automation 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, published June 15, 2018; Standard No. J3016-201609, published September 30, 2016; and previous and future versions of this standard). Vehicle 300 is capable of performing one or more functions that meet Level 3-5 of autonomous driving standards. Vehicle 300 is capable of performing one or more functions that meet Level 1-5 of automated driving standards. For example, depending on the embodiment, vehicle 300 is capable of driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5). The term "autonomy" as used herein can include any and / or all types of autonomy for vehicle 300 or other machines, such as full autonomy, high autonomy, conditional autonomy, partial autonomy, providing assisted autonomy, semi-autonomy, primary autonomy, or other names.

[0075] Vehicle 300 may include components such as chassis, body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other vehicle components. Vehicle 300 may include a propulsion system 350, such as an internal combustion engine, a hybrid power plant, an all-electric motor, and / or another type of propulsion system. Propulsion system 350 may be connected to the drivetrain of vehicle 300, which may include a transmission, to enable propulsion of vehicle 300. Propulsion system 350 may be controlled in response to receiving a signal from throttle / accelerator 352.

[0076] A steering system 354, which may include a steering wheel, can be used to steer the vehicle 300 (e.g., along a desired path or route) when the propulsion system 350 is operating (e.g., when the vehicle is in motion). The steering system 354 may receive signals from the steering actuator 356. For fully automatic (level 5) functionality, the steering wheel may be optional.

[0077] The brake sensor system 346 can be used to operate the vehicle brakes in response to receiving signals from the brake actuator 348 and / or the brake sensor.

[0078] It may include one or more CPUs, System-on-a-Chip (SoC) 304 ( Figure 3C One or more controllers 336, including and / or one or more GPUs, may provide signals (e.g., signals representing commands) to one or more components and / or systems of vehicle 300. For example, one or more controllers may send signals to operate vehicle brakes via one or more brake actuators 348, to operate steering system 354 via one or more steering actuators 356, and / or to operate propulsion system 350 via one or more throttles / accelerators 352. One or more controllers 336 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 enable autonomous driving and / or assist a human driver in driving vehicle 300. One or more controllers 336 may include a first controller 336 for autonomous driving functions, a second controller 336 for functional safety functions, a third controller 336 for artificial intelligence functions (e.g., computer vision), a fourth controller 336 for infotainment functions, a fifth controller 336 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 336 can handle two or more of the functions described above, and two or more controllers 336 can handle a single function, and / or any combination thereof.

[0079] One or more controllers 336 may provide signals for controlling one or more components and / or systems of vehicle 300 in response to sensor data (e.g., sensor inputs) received from one or more sensors. Sensor data may be received from, for example, but not limited to, global navigation satellite system sensors 358 (e.g., GPS sensors), RADAR sensors 360, ultrasonic sensors 362, LIDAR sensors 364, inertial measurement unit (IMU) sensors 366 (e.g., accelerometers, gyroscopes, magnetic compasses, magnetometers, etc.), microphones 396, stereo cameras 368, wide-angle cameras 370 (e.g., fisheye cameras), infrared cameras 372, surround cameras 374 (e.g., 360-degree cameras), long-range and / or medium-range cameras 398, speed sensors 344 (e.g., for measuring the rate of vehicle 300), vibration sensors 342, steering sensors 340, braking sensors (e.g., as part of braking sensor system 346), and / or other sensor types.

[0080] One or more of the controllers 336 may receive input (e.g., represented by input data) from the instrument panel 332 of the vehicle 300 and provide output (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display 334, an auditory signaling device, a speaker, and / or via other components of the vehicle 300. These outputs may include information such as vehicle speed, rate, time, map data (e.g., [missing information]). Figure 3C Information such as the HD map 322, location data (e.g., the location of vehicle 300 on the map), direction, and the location of other vehicles (e.g., occupying a grid), as well as information about objects and their states perceived by controller 336, etc. For example, HMI display 334 may display information about the existence 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.).

[0081] The vehicle 300 further includes a network interface 324, which can communicate via one or more networks using one or more wireless antennas 326 and / or a modem. For example, the network interface 324 may be able to communicate via LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The one or more wireless antennas 326 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 LE, Z-Wave, ZigBee, etc., and / or one or more low-power wide area networks (LPWANs) such as LoRaWAN, SigFox, etc.

[0082] Figure 3B For use in accordance with some embodiments of this disclosure Figure 3A This is an example of the camera position and field of view of an example autonomous vehicle 300. The camera and its respective field 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 these cameras may be located at different positions on the vehicle 300.

[0083] The camera type used for the camera may include, but is not limited to, a digital camera suitable for use with components and / or systems of vehicle 300. The camera may operate at Automotive Safety Integrity Level (ASIL) B and / or another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc., depending on the embodiment. The camera may be able to use 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 sharp-pixel camera, such as a camera with RCCC, RCCB, and / or RBGC color filter arrays, may be used in efforts to improve light sensitivity.

[0084] 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 cameras) can simultaneously record and provide image data (e.g., video).

[0085] One or more of the cameras can be mounted in mounting components such as custom-designed (3-D printed) parts to cut off stray light and reflections from inside the vehicle (e.g., reflections from the dashboard in the windshield mirror) that may interfere with the camera's image data capture capabilities. Regarding the wing mirror mounting components, the wing mirror components can be custom-3-D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras can be integrated into the wing mirror. For side-view cameras, one or more cameras can also be integrated into the four pillars at each corner of the cab.

[0086] A camera with a field of view that includes the environment in front of the vehicle 300 (e.g., a front-facing camera) can be used for surround view to help identify forward paths and obstacles, and, with the assistance of one or more controllers 336 and / or control SoCs, to provide information crucial for generating an occupancy grid and / or determining the preferred vehicle path. 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 in ADAS functions and systems, including Lane Departure Warning (LDW), Autonomous Cruise Control (ACC), and / or other functions such as traffic sign recognition.

[0087] A variety of cameras can be used in front-facing configurations, including, for example, monocular camera platforms including CMOS (Complementary Metal-Oxide-Semiconductor) color imagers. Another example could be a wide-angle camera 370, which can be used to perceive objects entering the field of view from the periphery (such as pedestrians, traffic at intersections, or bicycles). Although Figure 3B The middle image shows only one wide-angle camera, but any number of wide-angle cameras 370 can be present on the vehicle 300. Furthermore, remote cameras 398 (e.g., a pair of long-view stereo cameras) can be used for depth-based object detection, especially for objects for which neural networks have not yet been trained. Remote cameras 398 can also be used for object detection and classification, as well as basic object tracking.

[0088] One or more stereo cameras 368 may also be included in a front-mounted configuration. The stereo camera 368 may include an integrated control unit comprising a scalable processing unit that can provide a multi-core microprocessor and programmable logic (FPGA) with an integrated 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. Alternative stereo cameras 368 may include a compact stereo vision sensor that may include two camera lenses (one on each side) and an image processing chip capable of measuring the distance from the vehicle to a target object and using the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. Other types of stereo cameras 368 may be used in addition to those described herein, or alternatively.

[0089] Cameras with a field of view including the side portion of the vehicle 300 (e.g., side-view cameras) can be used for surround view, providing information for creating and updating occupancy grids and generating side-impact collision warnings. For example, surround camera 374 (e.g., ... Figure 3B The four surround cameras 374 shown can be mounted on the vehicle 300. The surround cameras 374 can include wide-angle cameras 370, fisheye cameras, 360-degree cameras, and / or the like. For example, four fisheye cameras can be positioned at the front, rear, and sides of the vehicle. In an alternative arrangement, the vehicle can use three surround cameras 374 (e.g., left, right, and rear) and can utilize one or more other cameras (e.g., forward-facing cameras) as a fourth surround-view camera.

[0090] A camera with a field of view that includes the environment behind the vehicle 300 (e.g., a rear-view camera) can be used for parking assistance, surround view, rear collision warning, and creating and updating occupancy grids. A wide variety of cameras can be used, including but not limited to those also suitable as front-facing cameras as described herein (e.g., long-range and / or mid-range camera 398, stereo camera 368, infrared camera 372, etc.).

[0091] Figure 3C For use in accordance with some embodiments of this disclosure Figure 3A The example autonomous vehicle 300 is illustrated in the block diagram of an example system architecture. It should be understood that this arrangement, and other arrangements described herein, are merely illustrative. 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. Furthermore, many of the elements described herein are functional entities, which 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 these entities can be implemented via hardware, firmware, and / or software. For example, the various functions can be implemented by a processor executing instructions stored in memory.

[0092] Figure 3C Each component, feature, and system in vehicle 300 is illustrated as being connected via bus 302. Bus 302 may include a Controller Area Network (CAN) data interface (or, alternatively, referred to herein as the "CAN bus"). CAN may be a network within vehicle 300 used to assist in the control of various features and functions of vehicle 300, such as the actuation of brakes, acceleration, braking, steering, windshield wipers, etc. The CAN bus may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). The CAN bus can be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button positions, and / or other vehicle status indicators. The CAN bus may be ASIL B compliant.

[0093] Although bus 302 is described herein as a CAN bus, this is not intended to be limiting. For example, FlexRay and / or Ethernet may be used in addition to or alternatively to a CAN bus. Furthermore, although bus 302 is represented by a single line, this is not intended to be limiting. For example, any number of buses 302 may exist, 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 302 may be used to perform different functions and / or may be used for redundancy. For example, a first bus 302 may be used for a collision avoidance function, and a second bus 302 may be used for drive control. In any example, each bus 302 may communicate with any component of vehicle 300, and two or more buses 302 may communicate with the same component. In some examples, each SoC 304, each controller 336, and / or each computer within the vehicle may have access to the same input data (e.g., input from sensors of vehicle 300) and may be connected to a common bus such as a CAN bus.

[0094] Vehicle 300 may include one or more controllers 336, such as those described herein. Figure 3A The controllers described herein. Controller 336 can be used for a wide variety of functions. Controller 336 can be coupled to any other different components and systems of vehicle 300 and can be used for the control of vehicle 300, artificial intelligence of vehicle 300, infotainment and / or the like for vehicle 300.

[0095] Vehicle 300 may include one or more System-on-a-Chip (SoC) 304. SoC 304 may include a CPU 306, GPU 308, processor 310, cache 312, accelerator 314, data storage 316, and / or other components and features not shown. SoC 304 can be used to control vehicle 300 across a wide variety of platforms and systems. For example, one or more SoCs 304 may be combined with an HD map 322 in a system (e.g., the system of vehicle 300), the HD map being transmitted via a network interface 324 from one or more servers (e.g., [server name missing]). Figure 3D One or more servers (378) receive map refresh and / or updates.

[0096] CPU 306 may include a CPU cluster or CPU complex (or, alternatively, referred to herein as "CCPLEX"). CPU 306 may include multiple cores and / or L2 cache. For example, in some embodiments, CPU 306 may include eight cores in a coherent multiprocessor configuration. In some embodiments, CPU 306 may include four dual-core clusters, each cluster having a dedicated L2 cache (e.g., 2MB L2 cache). CPU 306 (e.g., CCPLEX) may be configured to support simultaneous cluster operation, such that any combination of clusters of CPU 306 can be active at any given time.

[0097] CPU 306 can implement power management capabilities including one or more of the following features: automatic clock gating of hardware blocks when idle to conserve dynamic power; clock gating of each core when the core is not actively executing instructions due to the execution of WFI / WFE instructions; independent power gating of each core; independent clock gating of each core cluster when all cores are clock-gated or power-gated; and / or independent power gating of each core cluster when all cores are power-gated. CPU 306 can further implement enhanced algorithms for managing power states, wherein allowed power states and desired wake-up times are specified, and the hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core can support simplified power state entry sequences in software, with this work offloaded to the microcode.

[0098] GPU 308 may include an integrated GPU (or, alternatively, referred to herein as an "iGPU"). GPU 308 may be programmable and efficient for parallel workloads. In some examples, GPU 308 may use an enhanced tensor instruction set. GPU 308 may include one or more streaming microprocessors, wherein each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96KB of storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB of storage capacity). In some embodiments, GPU 308 may include at least eight streaming microprocessors. GPU 308 may use a computation application programming interface (API). Furthermore, GPU 308 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0099] In automotive and embedded applications, the GPU 308 can be power-optimized for optimal performance. For example, the GPU 308 can be fabricated on FinFETs. However, this is not intended to be limiting, and the GPU 308 can be fabricated using other semiconductor manufacturing processes. Each streaming microprocessor can combine several mixed-precision processing cores divided into multiple blocks. For example, and without limitation, 64 FP32 cores and 32 FP64 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, an L0 instruction cache, a warp scheduler, dispatch units, and / or a 64KB register file. Furthermore, the streaming microprocessor can include independent parallel integer and floating-point data paths to provide efficient execution of workloads by leveraging the mixture of computation and addressing computation. The streaming microprocessor can include independent thread scheduling capabilities to allow for finer-grained synchronization and cooperation between parallel threads. Streaming microprocessors can include a combination of L1 data cache and shared memory units to improve performance while simplifying programming.

[0100] The GPU 308 may include, in some examples, a High Bandwidth Memory (HBM) and / or a 16GB HBM2 memory subsystem providing a peak memory bandwidth of approximately 900GB / s. In some examples, in addition to HBM memory or alternatively, Synchronous Graphics Random Access Memory (SGRAM), such as Generation 5 Graphics Double Data Rate Synchronous Random Access Memory (GDDR5), may be used.

[0101] The GPU 308 may include unified memory technology, which includes access counters to allow memory pages to be migrated more precisely to the processors that access them most frequently, thereby improving the efficiency of shared memory ranges between processors. In some examples, Address Translation Service (ATS) support can be used to allow the GPU 308 to directly access the CPU 306 page tables. In such examples, when the GPU 308 Memory Management Unit (MMU) experiences a miss, an address translation request can be transferred to the CPU 306. In response, the CPU 306 can look up the virtual-physical mapping for the address in its page tables and transfer the translation back to the GPU 308. Thus, unified memory technology can allow a single unified virtual address space for the memory of both the CPU 306 and the GPU 308, simplifying GPU 308 programming and porting applications to the GPU 308.

[0102] In addition, the GPU 308 may include access counters that track how frequently the GPU 308 accesses the memory of other processors. These access counters help ensure that memory pages are moved to the physical memory of the processor that accesses those pages most frequently.

[0103] SoC 304 may include any number of caches 312, including those described herein. For example, cache 312 may include an L3 cache available to both CPU 306 and GPU 308 (e.g., it is connected to both CPU 306 and GPU 308). Cache 312 may include a write-back cache, which can track the state of rows, for example, using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4 MB or more, but a smaller cache size may also be used.

[0104] SoC 304 may include one or more arithmetic logic units (ALUs) that can be used to perform processing of any of a variety of tasks or operations relating to vehicle 300, such as processing a DNN. Furthermore, SoC 304 may include a floating-point unit (FPU) or other mathematical coprocessor or digital coprocessor type for performing mathematical operations within the system. For example, SoC 304 may include one or more FPUs integrated as execution units within CPU 306 and / or GPU 308.

[0105] SoC 304 may include one or more accelerators 314 (e.g., hardware accelerators, software accelerators, or combinations thereof). For example, SoC 304 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. This large on-chip memory (e.g., 4MB SRAM) can enable the hardware acceleration cluster to accelerate neural networks and other computations. The hardware acceleration cluster can be used to supplement GPU 308 and offload some tasks from GPU 308 (e.g., freeing up more cycles of GPU 308 to perform other tasks). As an example, accelerator 314 can be used for targeted workloads (e.g., perceptrons, convolutional neural networks (CNNs), etc.) that are stable enough to be easily controlled for acceleration. When used herein, the term "CNN" can include all types of CNNs, including region-based or region convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0106] Accelerator 314 (e.g., a hardware acceleration cluster) may include a Deep Learning Accelerator (DLA). The DLA may include one or more Tensor Processing Units (TPUs) that can be configured to provide an additional 10 trillion operations per second for deep learning applications and inference. The TPU may be an accelerator configured to perform image processing functions (e.g., for CNNs, RCNNs, 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 as well as inference. The DLA is designed to provide higher performance per millimeter than a general-purpose GPU and significantly outperform CPUs. The TPU can perform several functions, including single-instance convolution functions, support for INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0107] DLA can execute neural networks, especially CNNs, quickly and efficiently on processed or unprocessed data for any function across a wide variety of applications, such as, but not limited to: CNNs for object recognition and detection using data from camera sensors; CNNs for distance estimation using data from camera sensors; CNNs for emergency vehicle detection and recognition using data from microphones; CNNs for face recognition and vehicle owner recognition using data from camera sensors; and / or CNNs for safety and / or safety-related events.

[0108] The DLA can perform any function of the GPU 308, and by using inference accelerators, for example, a designer can make either the DLA or the GPU 308 target any function. For example, a designer can focus the CNN processing and floating-point operations on the DLA and leave other functions to the GPU 308 and / or other accelerators 314.

[0109] Accelerator 314 (e.g., a hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may alternatively be referred to herein as a computer vision accelerator. 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 can provide a balance between performance and flexibility. For example, each PVA may include, for example, but not limited to, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0110] RISC cores can interact with image sensors (such as the image sensor of any camera described herein), image signal processors, and / or the like. Each of these RISC cores may include any amount of memory. Depending on the embodiment, the RISC core may use any of several protocols. In some examples, the RISC core may execute a real-time operating system (RTOS). RISC cores may be implemented using one or more integrated circuit devices, application-specific integrated circuits (ASICs), and / or memory devices. For example, a RISC core may include an instruction cache and / or tightly coupled RAM.

[0111] DMA enables PVA components to access system memory independently of the CPU 306. DMA can support any number of features to provide optimizations to the PVA, including but not limited to support for multidimensional addressing and / or circular addressing. In some examples, DMA can support addressing in up to six or more dimensions, which can include block width, block height, block depth, horizontal block step, vertical block step, and / or depth step.

[0112] A vector processor can be a programmable processor designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, a PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, one or more DMA engines (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem may operate as the main processing engine of the PVA and may include a vector processing unit (VPU), an instruction cache, and / or vector memory (e.g., VMEM). The VPU core may include a digital signal processor, such as, for example, a Single Instruction Multiple Data (SIMD) or Very Long Instruction Word (VLIW) digital signal processor. The combination of SIMD and VLIW can enhance throughput and speed.

[0113] Each of the vector processors may include an instruction cache and may be coupled to dedicated memory. Consequently, in some examples, each of the vector processors may be configured to execute independently of 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, vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even different algorithms on a sequence of images or portions of an image. Among other things, any number of PVAs may be included in a hardware-accelerated cluster, and any number of vector processors may be included in each of these PVAs. Furthermore, the PVA may include additional error-correcting code (ECC) memory to enhance overall system security.

[0114] Accelerator 314 (e.g., a hardware acceleration cluster) may include an on-chip computer vision network and SRAM to provide high-bandwidth, low-latency SRAM for accelerator 314. In some examples, on-chip memory may include at least 4MB of SRAM consisting of, for example, but not limited to, eight field-configurable memory blocks, accessible by both the PVA and 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 memory via a backbone that provides high-speed memory access to the PVA and DLA. The backbone may include (e.g., using an APB) an on-chip computer vision network that interconnects the PVA and DLA to memory.

[0115] On-chip computer vision networks can include interfaces that ensure both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such interfaces can provide separate phases and channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface can conform to ISO 26262 or IEC 61508 standards, but other standards and protocols can also be used.

[0116] In some examples, SoC 304 may include, for example, a real-time ray tracing hardware accelerator as described in U.S. Patent Application No. 16 / 101,232, filed August 10, 2018. This real-time ray tracing hardware accelerator can be used to quickly and efficiently determine the location and extent of objects (e.g., within a world model) to generate real-time visualization simulations for RADAR signal interpretation, sound propagation synthesis and / or analysis, SONAR system simulation, general wave propagation simulation, comparison with LiDAR data for localization and / or other functional purposes, and / or other uses. In some embodiments, one or more Tree Traversal Units (TTUs) may be used to perform one or more ray tracing-related operations.

[0117] Accelerators 314 (e.g., hardware accelerator clusters) have broad applications in autonomous driving. PVAs can be programmable vision accelerators used in critical processing stages of ADAS and autonomous vehicles. The capabilities of PVAs are a good match for algorithmic domains requiring predictable processing, low power, and low latency. In other words, PVAs perform well in semi-dense or dense rule computation, even on small datasets requiring predictable runtimes with low latency and low power. Therefore, in the context of platforms for autonomous vehicles, PVAs are designed to run classical computer vision algorithms because they are efficient in object detection and integer mathematical operations.

[0118] For example, according to one embodiment of this technology, PVA is used to perform computer stereo vision. In some examples, semi-global matching-based algorithms may be used, but this is not intended to be limiting. Many applications for Level 3–5 autonomous driving require instantaneous motion estimation / stereo matching (e.g., from moving structures, pedestrian recognition, lane detection, etc.). PVA can perform computer stereo vision functions on input from two monocular cameras.

[0119] In some examples, PVA can be used to perform intensive optical flow, processing 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, for example, involves processing raw time-of-flight data to provide processed time-of-flight data.

[0120] DLA can be used to run any type of network to enhance control and driving safety, including, for example, neural networks that output a confidence metric for each object detection. Such a confidence value can be interpreted as a probability or as providing a relative “weight” for each detection compared to other detections. This confidence value allows the system to make further decisions about which detections should be considered true positives rather than false positives. For example, the system can set a threshold for the confidence and only consider detections exceeding the threshold as true positives. In an Automatic Emergency Braking (AEB) system, false positives can cause the vehicle to automatically perform emergency braking, which is clearly undesirable. Therefore, only the most confident detections should be considered as triggers for AEB. DLA can run a neural network to regress the confidence value. This neural network can take at least a subset of parameters as input, such as bounding box dimensions, ground plane estimates (e.g., from another subsystem), inertial measurement unit (IMU) sensor 366 outputs related to vehicle orientation and distance, 3D position estimates of objects obtained from the neural network and / or other sensors (e.g., LiDAR sensor 364 or RADAR sensor 360), etc.

[0121] SoC 304 may include one or more data storage units 316 (e.g., memory). The data storage unit 316 may be on-chip memory of SoC 304, which may store neural networks to be executed on the GPU and / or DLA. In some examples, for redundancy and security, the data storage unit 316 may be large enough to store multiple instances of the neural network. The data storage unit 316 may include L2 or L3 cache 312. References to the data storage unit 316 may include references to memory associated with PVA, DLA, and / or other accelerators 314 as described herein.

[0122] SoC 304 may include one or more processors 310 (e.g., embedded processors). Processor 310 may include a startup and power management processor, which may be a dedicated processor and subsystem for handling startup power and management functions, as well as safety implementation. The startup and power management processor may be part of the SoC 304 startup sequence and may provide runtime power management services. The startup power and management processor may provide clock and voltage programming, auxiliary system low-power state transitions, SoC 304 thermal and temperature sensor management, and / or SoC 304 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC 304 may use the ring oscillator to detect the temperature of CPU 306, GPU 308, and / or accelerator 314. If it is determined that the temperature exceeds a threshold, the startup and power management processor may enter a temperature fault routine and place SoC 304 into a lower power state and / or place vehicle 300 into a driver-safe parking mode (e.g., safely stop vehicle 300).

[0123] Processor 310 may further 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 for full hardware support for multi-channel audio via multiple interfaces, as well as a wide and flexible range of audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core with a digital signal processor and dedicated RAM.

[0124] The processor 310 may further include an always-on-processor engine that can provide the necessary hardware features to support low-power sensor management and wake-up use cases. This always-on-processor engine may include a processor core, tightly coupled RAM, support for peripherals (such as timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0125] Processor 310 may further include a secure cluster engine, which includes a dedicated processor subsystem for handling security management of automotive applications. The secure cluster engine may include two or more processor cores, tightly coupled RAM, support for peripheral devices (e.g., timers, interrupt controllers, etc.), and / or routing logic. In secure mode, the two or more cores may operate in lockstep mode and function as a single core with comparison logic that detects any differences between their operations.

[0126] The processor 310 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0127] The processor 310 may further 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.

[0128] Processor 310 may include a video image compositer, which may be (e.g., implemented on a microprocessor) a processing block, implementing video post-processing functions required by the video playback application to generate the final image for the player window. The video image compositer may perform lens distortion correction on the wide-angle camera 370, the surround camera 374, and / or the in-cabin monitoring camera sensor. The in-cabin monitoring camera sensor is preferably monitored by a neural network running on another instance of an advanced SoC, configured to recognize in-cabin events and respond accordingly. The in-cabin system may perform lip reading to activate mobile phone services and make calls, dictate emails, change vehicle destinations, activate or change the vehicle's infotainment system and settings, or provide voice-activated web browsing. Some functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other situations.

[0129] Video image compositers can include enhanced temporal denoising for both spatial and temporal noise reduction. For example, in the case of motion in the video, denoising appropriately weights spatial information, reducing the weight of information provided by neighboring frames. In cases where the image or part of the image does not contain motion, the temporal denoising performed by the video image compositer can use information from previous images to reduce noise in the current image.

[0130] The video image compositer can also be configured to perform stereo correction on input stereo camera frames. When the operating system desktop is in use and the GPU 308 does not need to continuously render new surfaces, the video image compositer can be further used for user interface components. Even when the GPU 308 is powered on and activated, performing 3D rendering, the video image compositer can be used to offload the GPU 308 to improve performance and responsiveness.

[0131] SoC 304 may further include a Mobile Industry Processor Interface (MIPI) camera serial interface, a high-speed interface, and / or a video input block that can be used for camera and related pixel input functions for receiving video and input from a camera. SoC 304 may further include an input / output controller that can be software-controlled and can be used to receive I / O signals not assigned to a specific role.

[0132] SoC 304 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management and / or other devices. SoC 304 can be used to process data from cameras and sensors (e.g., LIDAR sensor 364, RADAR sensor 360, etc., which can be connected via Gigabit Multimedia Serial Link and Ethernet), data from bus 302 (e.g., vehicle 300 speed, steering wheel position, etc.), and data from GNSS sensor 358 (connected via Ethernet or CAN bus). SoC 304 may further include a dedicated high-performance, high-capacity memory controller, which may include its own DMA engine, and which can be used to free up CPU 306 from routine data management tasks.

[0133] SoC 304 can be an end-to-end platform with a flexible architecture spanning Automation Levels 3-5, providing a comprehensive functional safety architecture that leverages and efficiently utilizes computer vision and ADAS technologies for diversity and redundancy, along with deep learning tools to deliver a flexible and reliable driving software stack. SoC 304 can be faster, more reliable, and even more energy- and space-efficient than conventional systems. For example, when combined with CPU 306, GPU 308, and data storage 316, accelerator 314 can provide a fast and efficient platform for Level 3-5 autonomous vehicles.

[0134] Therefore, this technology offers capabilities and functionalities that cannot be achieved through conventional systems. For example, computer vision algorithms can be executed on CPUs, which can be configured using high-level programming languages ​​such as C to execute a wide variety of processing algorithms across a diverse range of visual data. However, CPUs often cannot meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption. In particular, many CPUs cannot execute complex object detection algorithms in real time, which is a requirement for automotive ADAS applications and practical Level 3-5 autonomous vehicles.

[0135] In contrast to conventional systems, the techniques described in this paper, by providing CPU complexes, GPU complexes, and hardware acceleration clusters, allow multiple neural networks to be executed simultaneously and / or sequentially, and the results combined to achieve Level 3–5 autonomous driving capabilities. For example, a CNN executed on a DLA or dGPU (e.g., GPU 320) could include text and word recognition, allowing a supercomputer to read and understand traffic signs, including those for which neural networks have not yet been specifically trained. The DLA could further include a neural network capable of recognizing, interpreting, and providing semantic understanding of the signs, and passing that semantic understanding to a path planning module running on a CPU complex.

[0136] As another example, multiple neural networks can operate simultaneously, as required for Level 3, 4, or 5 driving. For instance, a warning sign consisting of "Caution: Flashing lights indicate icy conditions," along with a light, can be interpreted independently or jointly by several neural networks. The sign itself can be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing lights indicate icy conditions" can be interpreted by a deployed second neural network, which informs the vehicle's path planning software (preferably executing on a CPU complex) that icy conditions exist when the flashing lights are detected. The flashing lights can be identified by a deployed third neural network operating across multiple frames, which informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks can operate simultaneously, for example, within a DLA and / or on a GPU 308.

[0137] In some examples, the CNN used for facial recognition and owner identification can use data from camera sensors to identify the presence of an authorized driver and / or owner of vehicle 300. A processing engine always on the sensors can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's door, and in safe mode, to disable the vehicle when the owner leaves. In this way, SoC 304 provides security against theft and / or carjacking.

[0138] In another example, the CNN used for emergency vehicle detection and identification can use data from microphone 396 to detect and identify emergency vehicle siren. In contrast to conventional systems that use a general classifier to detect siren and manually extract features, SoC 304 uses a CNN to classify environmental and urban sounds as well as visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative shut-off rate of emergency vehicles (e.g., by using the Doppler effect). The CNN can also be trained to identify emergency vehicles specific to the localized area in which the vehicle operates, as identified by GNSS sensor 358. Thus, for example, when operating in Europe, the CNN will seek to detect European siren, and when operating in the United States, the CNN will seek to identify siren only in North America. Once an emergency vehicle is detected, with the assistance of ultrasonic sensor 362, the control program can be used to execute emergency vehicle safety routines, causing the vehicle to slow down, pull over to the side of the road, stop, and / or idle until the emergency vehicle passes.

[0139] The vehicle may include a CPU 318 (e.g., a discrete CPU or dCPU) that can be coupled to the SoC 304 via a high-speed interconnect (e.g., PCIe). The CPU 318 may include, for example, an x86 processor. The CPU 318 can be used to perform any of a wide variety of functions, including, for example, arbitrating the results of potential inconsistencies between ADAS sensors and the SoC 304, and / or monitoring the status and health of the controller 336 and / or the infotainment SoC 330.

[0140] Vehicle 300 may include a GPU 320 (e.g., a discrete GPU or dGPU) that can be coupled to SoC 304 via a high-speed interconnect (e.g., NVIDIA's NVLINK). GPU 320 may provide additional artificial intelligence capabilities, for example by executing redundant and / or different neural networks, and can be used to train and / or update neural networks based on inputs from sensors of vehicle 300 (e.g., sensor data).

[0141] Vehicle 300 may further include a network interface 324, which may include one or more wireless antennas 326 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). Network interface 324 can be used to enable wireless connectivity via the Internet to the cloud (e.g., with server 378 and / or other network devices), with other vehicles, and / or with computing devices (e.g., a passenger's client device). For communication 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 via the Internet). A direct link can be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link can provide vehicle 300 with information about vehicles approaching vehicle 300 (e.g., vehicles in front, to the side, and / or behind vehicle 300). This functionality can be part of vehicle 300's cooperative adaptive cruise control function.

[0142] Network interface 324 may include a SoC that provides modulation and demodulation functions and enables controller 336 to communicate via a wireless network. Network interface 324 may include an RF front-end for up-conversion from baseband to RF and down-conversion from RF to baseband. Frequency conversion can be performed using known processes and / or using a superheterodyne process. In some examples, the RF front-end functionality may be provided by a separate chip. The network interface may include wireless functions for communication via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0143] Vehicle 300 may further include data storage 328, which may include off-chip (e.g., outside of SoC 304) storage devices. Data storage 328 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, hard disk, and / or other components and / or devices capable of storing at least one bit of data.

[0144] Vehicle 300 may further include a GNSS sensor 358. The GNSS sensor 358 (e.g., GPS, assisted GPS sensor, differential GPD (DGPS) sensor, etc.) is used for auxiliary mapping, sensing, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 358 can be used, including, for example, but not limited to, GPS using a USB connector with an Ethernet-to-serial (RS-232) bridge.

[0145] Vehicle 300 may further include a RADAR sensor 360. The RADAR sensor 360 can be used by vehicle 300 for remote vehicle detection even in dark and / or inclement weather conditions. The RADAR functional safety level may be ASIL B. The RADAR sensor 360 can use CAN and / or bus 302 (e.g., to transmit data generated by the RADAR sensor 360) for control and access to object tracking data, and in some examples, Ethernet access for accessing raw data. A wide variety of RADAR sensor types can be used. For example, and without limitation, the RADAR sensor 360 can be adapted for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0146] RADAR sensor 360 can 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, etc. In some examples, long-range RADAR can be used for adaptive cruise control functions. A long-range RADAR system can provide a wide field of view (e.g., within 250m) achieved through two or more independent scans. RADAR sensor 360 can help distinguish between stationary and moving objects and can be used by ADAS systems for emergency braking assist and forward collision warning. Long-range RADAR sensors can include a single-site multi-mode 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 four central antennas can create a focused beam pattern designed to record the vehicle 300's surroundings at higher rates with minimal traffic interference from adjacent lanes. The other two antennas can extend the field of view, enabling rapid detection of vehicles entering or leaving the vehicle 300's lane.

[0147] As an example, a mid-range RADAR system can include a range of up to 160m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 150 degrees (rear). Short-range RADAR systems can include, but are not limited to, RADAR sensors designed to be mounted at both ends of the rear bumper. When mounted at both ends of the rear bumper, such a RADAR sensor system can create two beams that continuously monitor blind spots behind and beside the vehicle.

[0148] Short-range RADAR systems can be used in ADAS systems for blind spot detection and / or lane change assistance.

[0149] Vehicle 300 may further include ultrasonic sensors 362. Ultrasonic sensors 362, which may be positioned at the front, rear, and / or sides of vehicle 300, can be used for parking assistance and / or creating and updating occupancy grids. A wide variety of ultrasonic sensors 362 can be used, and different ultrasonic sensors 362 can be used for different detection ranges (e.g., 2.5m, 4m). Ultrasonic sensors 362 can operate at functional safety level ASIL B.

[0150] Vehicle 300 may include a LIDAR sensor 364. The LIDAR sensor 364 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 364 may be of functional safety level ASIL B. In some examples, vehicle 300 may include multiple LIDAR sensors 364 (e.g., two, four, six, etc.) that can use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0151] In some examples, the LiDAR sensor 364 may be able to provide a list of objects and their distances within a 360-degree field of view. Commercially available LiDAR sensors 364 may have an advertising range of, for example, approximately 100m, with an accuracy of 2cm-3cm, and support for 100Mbps Ethernet connectivity. In some examples, one or more non-protruding LiDAR sensors 364 may be used. In such examples, the LiDAR sensor 364 may be implemented as a small device that can be embedded in the front, rear, sides, and / or corners of a vehicle 300. In such examples, the LiDAR sensor 364 may provide a horizontal field of view of up to 120 degrees and a vertical field of view of 35 degrees, even for low-reflectivity objects, with a range of 200m. Front-mounted LiDAR sensors 364 may be configured for a horizontal field of view between 45 degrees and 135 degrees.

[0152] In some examples, LiDAR technologies such as 3D flash LiDAR can also be used. 3D flash LiDAR uses a flash of laser light as the emission source to illuminate the vehicle's surroundings up to approximately 200 meters. A flash LiDAR unit includes a receiver that records the laser pulse propagation time and reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LiDAR allows for the generation of highly accurate and distortion-free images of the surrounding environment using each laser flash. In some examples, four flash LiDAR sensors can be deployed, one on each side of the vehicle. Available 3D flash LiDAR systems include solid-state 3D staring array LiDAR cameras (e.g., non-browsing LiDAR devices) without moving parts other than a fan. Flash LiDAR devices can use 5 nanosecond Class I (eye-safe) laser pulses per frame and can capture 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, LiDAR sensor 364 is less susceptible to motion blur, vibration, and / or shock.

[0153] The vehicle may further include an IMU sensor 366. In some examples, the IMU sensor 366 may be located at the center of the rear axle of the vehicle 300. The IMU sensor 366 may include, for example, but not limited to, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, such as in a six-axis application, the IMU sensor 366 may include an accelerometer and a gyroscope, while in a nine-axis application, the IMU sensor 366 may include an accelerometer, a gyroscope, and a magnetometer.

[0154] In some embodiments, the IMU sensor 366 can be implemented as a miniature, high-performance GPS-assisted inertial navigation system (GPS / INS) that combines a microelectromechanical 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 366 can enable the vehicle 300 to estimate heading by directly observing and correlating velocity changes from GPS to the IMU sensor 366 without input from a magnetic sensor. In some examples, the IMU sensor 366 and the GNSS sensor 358 can be combined into a single integrated unit.

[0155] The vehicle may include a microphone 396 placed in and / or around the vehicle 300. Among other things, the microphone 396 may be used for emergency vehicle detection and identification.

[0156] The vehicle may further include any number of camera types, including stereo cameras 368, wide-angle cameras 370, infrared cameras 372, surround cameras 374, long-range and / or mid-range cameras 398, and / or other camera types. These cameras can be used to capture image data around the entire perimeter of the vehicle 300. The types of cameras used depend on the embodiment and the requirements of the vehicle 300, and any combination of camera types can be used to provide the necessary coverage around the vehicle 300. Furthermore, the number of cameras may 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 without limitation, these cameras may support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras is described herein with respect to... Figure 3A and Figure 3B It was described in more detail.

[0157] Vehicle 300 may further include vibration sensor 342. Vibration sensor 342 can measure vibrations of vehicle components such as axles. For example, changes in vibration can indicate changes in the road surface. In another example, when two or more vibration sensors 342 are used, differences between vibrations can be used to determine friction or slippage on the road surface (e.g., when there is a vibration difference between a power drive shaft and a freely rotating shaft).

[0158] Vehicle 300 may include ADAS system 338. In some examples, ADAS system 338 may include SoC. ADAS system 338 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.

[0159] ACC systems can utilize RADAR sensors 360, LIDAR sensors 364, and / or cameras. ACC systems can include longitudinal ACC and / or lateral ACC. Longitudinal ACC monitors and controls the distance to vehicles immediately in front of vehicle 300 and automatically adjusts the vehicle speed to maintain a safe distance. Lateral ACC performs distance holding and, if necessary, advises vehicle 300 to change lanes. Lateral ACC is associated with other ADAS applications such as LCA and CWS.

[0160] CACC uses information from other vehicles, which can be received indirectly from other vehicles via a wireless link or network connection (e.g., via the Internet) through network interface 324 and / or wireless antenna 326. Direct links can be provided by vehicle-to-vehicle (V2V) communication links, while indirect links can be infrastructure-to-vehicle (I2V) communication links. Typically, the V2V communication concept provides information about vehicles immediately ahead (e.g., vehicles immediately in front of vehicle 300 and in the same lane), while the I2V communication concept provides information about traffic further ahead. A CACC system can include either or both of these I2V and V2V information sources. Given information about vehicles ahead of vehicle 300, CACC can be more reliable, and it has the potential to improve traffic flow and reduce road congestion.

[0161] The Forward-Looking Warning (FCW) system is designed to alert the driver to hazards, enabling the driver to take corrective action. The FCW system uses a front-facing camera and / or RADAR sensor 360 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, audible, visual, haptic, and / or rapid braking pulses.

[0162] An AEB (Autonomous Emergency Braking) 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 a specified time or distance parameter. The AEB system can use a front-facing camera and / or RADAR sensor 360 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 a collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes to attempt 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 proximity braking.

[0163] The Lane Departure Warning (LDW) system provides visual, auditory, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle crosses a lane marking. When the driver indicates intentional lane departure, the LDW system is deactivated by activating a turn signal. The LDW system can utilize a front-facing camera 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.

[0164] The Lane Keeping Assist (LKA) system is a variant of the Lane Departure Warning (LDW) system. If vehicle 300 begins to leave its lane, the LKA system provides corrective steering input or braking to vehicle 300. The Blind Spot Warning (BSW) system detects and warns the driver of vehicles in the vehicle's blind spot. The BSW system can provide visual, audible, and / or tactile warnings to indicate that merging or changing lanes is unsafe. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use one or more rear-facing cameras and / or one or more RADAR sensors.

[0165] RCTW systems can provide visual, auditory, and / or tactile notifications when an object is detected outside the range of a rear-view camera while the vehicle is reversing. Some RCTW systems include AEB (Autonomous Emergency Braking) to ensure the application of the vehicle's brakes to avoid a collision. RCTW systems can use one or more rear-view RADAR sensors 360 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which is electrically coupled to driver feedback such as displays, speakers, and / or vibrating components.

[0166] Conventional ADAS systems can be prone to false positives, which can be annoying and distracting for the driver, but typically not catastrophic, as they alert the driver and allow them to determine whether a safe condition truly exists and take appropriate action. However, in an autonomous vehicle 300, in the event of conflicting results, the vehicle 300 itself must decide whether to heed the results from the main computer or auxiliary computer (e.g., the first controller 336 or the second controller 336). For example, in some embodiments, the ADAS system 338 may be a backup and / or auxiliary computer for providing perception information to a backup computer rationality module. The backup computer rationality monitor may run redundant and varied software on hardware components to detect faults in perception and dynamic driving tasks. Outputs from the ADAS system 338 may be provided to a supervisory MCU. If outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to reconcile the conflict to ensure safe operation.

[0167] In some examples, the master computer can be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence level in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the master computer's direction regardless of whether the auxiliary computer provides conflicting or inconsistent results. If the confidence score does not meet the threshold and the master and auxiliary computers indicate different results (e.g., conflict), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0168] The supervisory MCU can be configured to run a neural network trained and configured to determine the conditions under which the auxiliary computer provides a false alarm based on outputs from both the host and auxiliary computers. Thus, the neural network in the supervisory MCU can learn when the output of the auxiliary computer can be trusted and when it cannot. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU can learn when the FCW system is identifying a metallic object that is not actually dangerous, such as a drain grid 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 can learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In embodiments that include a neural network running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or GPU suitable for running the neural network using associated memory. In a preferred embodiment, the supervisory MCU may include a component of SoC 304 and / or be included as a component of SoC 304.

[0169] In other examples, ADAS system 338 may include an auxiliary computer that performs ADAS functions using conventional computer vision rules. This allows the auxiliary computer to use classic computer vision rules (if-then), and the presence of neural networks in the supervising 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) functionality. For instance, if a software vulnerability or bug exists in the software running on the host computer and non-identical software code running on the auxiliary computer provides the same overall result, the supervising MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware on the host computer does not cause a substantial error.

[0170] In some examples, the output of ADAS system 338 can be fed to the perception block and / or the dynamic driving task block of the main computer. For example, if ADAS system 338 issues a forward collision warning because an object is immediately in front, the perception block can use this information when identifying the object. In other examples, the assistance computer can have its own neural network, which is trained and thus reduces the risk of false positives as described herein.

[0171] Vehicle 300 may further include an infotainment SoC 330 (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 330 may include a combination of hardware and software that can be used to provide vehicle 300 with audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming media, etc.), telephone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.) and / or information services (e.g., navigation system, rear parking assistance, radio data system, vehicle-related information such as fuel level, total coverage distance, brake fuel level, fuel level, door opening / closing, air filter information, etc.). For example, the infotainment SoC 330 may include a radio, disc player, navigation system, video player, USB and Bluetooth connectivity, in-vehicle computer, in-vehicle entertainment, Wi-Fi, steering wheel audio controls, hands-free voice controls, head-up display (HUD), HMI display 334, telematics device, control panel (e.g., for controlling and / or interacting with various components, features, and / or systems) and / or other components. The infotainment SoC 330 may further be used to provide information (e.g., visual and / or auditory) to the vehicle's users, such as information from ADAS system 338, 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.

[0172] The infotainment SoC 330 may include GPU functionality. The infotainment SoC 330 can communicate with other devices, systems, and / or components of the vehicle 300 via bus 302 (e.g., CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 330 may be coupled to a supervisory MCU, allowing the GPU of the infotainment system to perform autonomous driving functions in the event of a failure of the main controller 336 (e.g., the primary and / or backup computer of the vehicle 300). In such an example, the infotainment SoC 330 may place the vehicle 300 into a driver-safe parking mode as described herein.

[0173] Vehicle 300 may further include instrument cluster 332 (e.g., digital instrument panel, electronic instrument cluster, digital instrument panel, etc.). Instrument cluster 332 may include a controller and / or supercomputer (e.g., a discrete controller or supercomputer). Instrument cluster 332 may include a set of instruments such as speedometer, fuel level, oil pressure, tachometer, odometer, turn indicator, shift position indicator, seatbelt warning light, parking brake warning light, engine malfunction indicator, airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and / or shared between infotainment SoC 330 and instrument cluster 332. In other words, instrument cluster 332 may be included as part of infotainment SoC 330, or vice versa.

[0174] Figure 3D For cloud-based servers and according to some embodiments of this disclosure Figure 3A This is a schematic diagram of a system for communication between example autonomous vehicles 300. System 376 may include server 378, network 390, and vehicles including vehicle 300. Server 378 may include multiple GPUs 384(A)-384(H) (collectively referred to herein as GPU 384), PCIe switches 382(A)-382(H) (collectively referred to herein as PCIe switch 382), and / or CPUs 380(A)-380(B) (collectively referred to herein as CPU 380). GPUs 384, CPUs 380, and PCIe switches may be interconnected with high-speed interconnects and / or PCIe connections 386, such as, but not limited to, NVLink interface 388 developed by NVIDIA. In some examples, GPUs 384 are connected via NVLink and / or NVSwitch SoCs, and GPUs 384 and PCIe switches 382 are connected via PCIe interconnects. Although eight GPUs 384, two CPUs 380, and two PCIe switches are shown in the diagram, this is not intended to be limiting. Depending on the embodiment, each of the servers 378 may include any number of GPUs 384, CPUs 380, and / or PCIe switches. For example, each of the servers 378 may include eight, sixteen, thirty-two, and / or more GPUs 384.

[0175] Server 378 can receive image data from vehicles via network 390, representing images of unexpected or changed road conditions such as recently commenced roadworks. Server 378 can also transmit neural network 392, updated neural network 392, and / or map information 394, including information about traffic and road conditions, to vehicles via network 390. Updates to map information 394 may include updates to HD map 322, such as information about construction sites, potholes, bends, floods, or other obstacles. In some examples, neural network 392, updated neural network 392, and / or map information 394 may have been generated from new training and / or data received from any number of vehicles in the environment, and / or based on experience gained from training performed at a data center (e.g., using server 378 and / or other servers).

[0176] Server 378 can be used to train machine learning models (e.g., neural networks) based on training data. Training data can be generated by the vehicle and / or generated in a simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., where 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., where the neural network does not require supervised learning). Training can be performed according to any one or more classes of machine learning techniques, including but not limited to: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including principal component analysis and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternative dictionary learning), rule-based machine learning, anomaly detection, and any variations or combinations thereof. Once the machine learning model is trained, it can be used by the vehicle (e.g., transmitted to the vehicle via network 390), and / or the machine learning model can be used by server 378 to remotely monitor the vehicle.

[0177] In some examples, server 378 can receive data from vehicles and apply that data to state-of-the-art real-time neural networks for real-time intelligent inference. Server 378 may include a deep learning supercomputer powered by GPU 384 and / or a dedicated AI computer, such as the DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 378 may include a deep learning infrastructure in a data center that uses only CPU power.

[0178] The deep learning infrastructure of server 378 may be capable of rapid real-time inference and can be used to assess and verify the health status of the processor, software, and / or associated hardware in vehicle 300. For example, the deep learning infrastructure may receive periodic updates from vehicle 300, such as image sequences and / or objects located in those image sequences that vehicle 300 has already 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 with objects identified by vehicle 300. If the results do not match and the infrastructure concludes that the AI ​​in vehicle 300 has malfunctioned, then server 378 may transmit a signal to vehicle 300 instructing the vehicle 300's fail-safe computer to take control, notify passengers, and complete a safe stopping operation.

[0179] For inference, server 378 may include GPU 384 and one or more programmable inference accelerators (such as NVIDIA's TensorRT 3). The combination of GPU-powered servers and inference acceleration enables real-time response. In other examples, such as where performance is less critical, CPU, FPGA, and other processor-powered servers can be used for inference.

[0180] Example computing device

[0181] Figure 4 The diagram below is a block diagram of an example computing device 400 suitable for implementing some embodiments of this disclosure. The computing device 400 may include an interconnect system 402 directly or indirectly coupled to the following devices: memory 404, one or more central processing units (CPUs) 406, one or more graphics processing units (GPUs) 408, a communication interface 410, input / output (I / O) ports 412, input / output components 414, a power supply 416, one or more presentation components 418 (e.g., displays), and one or more logic units 420. In at least one embodiment, the computing device 400 may include one or more virtual machines (VMs), and / or any component thereof may include virtual components (e.g., virtual hardware components). For a non-limiting example, one or more GPUs 408 may include one or more vGPUs, one or more CPUs 406 may include one or more vCPUs, and / or one or more logic units 420 may include one or more virtual logic units. Therefore, computing device 400 may include discrete components (e.g., a complete GPU dedicated to computing device 400), virtual components (e.g., a portion of the GPU dedicated to computing device 400), or a combination thereof.

[0182] although Figure 4The various boxes are shown connected via an interconnect system 402 with wiring, but this is not intended to be limiting and is merely for clarity. For example, in some embodiments, a presentation component 418, such as a display device, can be considered an I / O component 414 (e.g., if the display is a touchscreen). As another example, CPU 406 and / or GPU 408 may include memory (e.g., memory 404 may represent a storage device other than the memory of GPU 408, CPU 406, and / or other components). In other words, Figure 4 The computing devices mentioned are merely illustrative. No distinction is made between categories such as "workstation," "server," "laptop," "desktop," "tablet," "client device," "mobile device," "handheld device," "game console," "electronic control unit (ECU)," "virtual reality system," and / or other device or system types, as all of these are considered within the same category. Figure 4 Within the scope of computing devices.

[0183] Interconnect system 402 may represent one or more links or buses, such as address buses, data buses, control buses, or combinations thereof. Interconnect system 402 may include one or more link or bus types, such as Industry Standard Architecture (ISA) bus, Extended Industry Standard Architecture (EISA) bus, Video Electronics Standards Association (VESA) bus, Peripheral Component Interconnect (PCI) bus, 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 406 may be directly connected to memory 404. Furthermore, CPU 406 may be directly connected to GPU 408. In cases where there is a direct or point-to-point connection between components, interconnect system 402 may include a PCIe link to perform the connection. In these examples, a PCI bus is not required in computing device 400.

[0184] Memory 404 can include any medium of a wide variety of computer-readable media. Computer-readable media can be any available medium that can be accessed by computing device 400. Computer-readable media can include volatile and non-volatile media, as well as removable and non-removable media. For example and without limitation, computer-readable media can include computer storage media and communication media.

[0185] Computer storage media may include volatile and non-volatile media and / or removable and non-removable media, implemented in any way or by any method or technique for storing information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 404 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system). Computer storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other storage technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage devices, magnetic tape cassettes, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by computing device 400. As used herein, computer storage media does not include the signal itself.

[0186] Computer storage media may contain computer-readable instructions, data structures, program modules, and / or other data types in modulated data signals such as carrier waves or other transmission mechanisms, and include any information transport medium. The term "modulated data signal" can refer to a signal whose characteristics are set or altered in a manner that encodes information into that signal. For example and without limitation, computer storage media may include wired media such as wired networks or direct wired connections, and wireless media such as sound, RF, infrared, and other wireless media. Any combination of the above should also be included within the scope of computer-readable media.

[0187] CPU 406 may be configured to execute at least some of computer-readable instructions to control one or more components of computing device 400 to perform one or more of the methods and / or processes described herein. Each of CPUs 406 may include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of processing a large number of software threads simultaneously. CPU 406 may include any type of processor and may include different types of processors depending on the type of computing device 400 implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device 400, the processor may be an advanced RISC mechanism (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). In addition to one or more microprocessors or supplementary coprocessors such as math coprocessors, computing device 400 may also include one or more CPUs 406.

[0188] In addition to or replacing CPU 406, GPU 408 may also be configured to execute at least some computer-readable instructions to control one or more components of computing device 400 to perform one or more of the methods and / or processes described herein. One or more GPUs 408 may be integrated GPUs (e.g., having one or more CPUs 406) and / or one or more GPUs 408 may be discrete GPUs. In embodiments, one or more GPUs 408 may be coprocessors of one or more CPUs 406. Computing device 400 may use GPU 408 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, GPU 408 may be used for general-purpose computing on a GPU (GPGPU). GPU 408 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. GPU 408 may generate pixel data for outputting an image in response to rendering commands (e.g., rendering commands received via a host interface from CPU 406). GPU 408 may include graphics memory, such as display memory, for storing pixel data or any other suitable data (e.g., GPGPU data). Display memory may be included as part of memory 404. GPU 408 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 via a switch (e.g., using NVSwitch). When combined, each GPU 408 may generate different portions of pixel data or GPGPU data for different outputs (e.g., the first GPU for the first image, the second GPU for the second image). Each GPU may include its own memory or may share memory with other GPUs.

[0189] In addition to or replacing CPU 406 and / or GPU 408, logic unit 420 may be configured to execute at least some computer-readable instructions to control one or more components of computing device 400 to perform one or more methods and / or processes described herein. In embodiments, CPU 406, GPU 408, and / or logic unit 420 may execute any combination of methods, processes, and / or portions thereof discretely or jointly. One or more logic units 420 may be part of and / or integrated into one or more CPUs 406 and / or one or more GPUs 408, and / or one or more logic units 420 may be discrete components of CPU 406 and / or GPU 408 or otherwise external thereto. In embodiments, one or more logic units 420 may be processors of one or more CPUs 406 and / or one or more GPUs 408.

[0190] Examples of logic unit 420 include one or more processing cores and / or components thereof, such as a data processing unit (DPU), tensor core (TC), tensor processing unit (TPU), pixel vision core (PVC), vision processing unit (VPU), graphics processing cluster (GPC), texture processing cluster (TPC), streaming multiprocessor (SM), tree traversal unit (TTU), artificial intelligence accelerator (AIA), deep learning accelerator (DLA), arithmetic logic unit (ALU)), application-specific integrated circuit (ASIC), floating-point unit (FPU), input / output (I / O) element, peripheral component interconnect (PCI) or peripheral component interconnect fast (PCIe) element, etc.

[0191] Communication interface 410 may include one or more receivers, transmitters, and / or transceivers that enable computing device 400 to communicate with other computing devices via electronic communication networks, including wired and / or wireless communications. Communication interface 410 may include components and functions that enable communication via any of several different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communication via Ethernet or InfiniBand), low-power wide area networks (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, logic unit 420 and / or communication interface 410 may include one or more data processing units (DPUs) to directly transmit data received via a network and / or via interconnect system 402 to one or more GPUs 408 (e.g., memory within GPU 408).

[0192] I / O port 412 enables computing device 400 to be logically coupled to other devices, including I / O component 414, presentation component 418, and / or other components, some of which may be built into (e.g., integrated into) computing device 400. Illustrative I / O component 414 includes microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dish antennas, browsers, printers, wireless devices, and so on. I / O component 414 can provide a Natural User Interface (NUI) for processing user-generated air gestures, voice, or other physiological input. In some instances, the input may be transmitted to appropriate network elements for further processing. The NUI can implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, on-screen and adjacent-screen gesture recognition, air gestures, head and eye tracking, and touch recognition associated with the display of computing device 400 (as described in more detail in this disclosure). Computing device 400 may include depth cameras such as stereo camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations thereof for gesture detection and recognition. In addition, computing device 400 may include an accelerometer or gyroscope that enables motion detection (e.g., as part of an inertial measurement unit (IMU)). In some examples, the output of the accelerometer or gyroscope may be used by computing device 400 to render immersive augmented reality or virtual reality.

[0193] Power supply 416 may include a hard-wired power supply, a battery power supply, or a combination thereof. Power supply 416 may supply power to computing device 400 so that components of computing device 400 can operate.

[0194] The presentation component 418 may include a display (such as a monitor, touch screen, television screen, head-up display (HUD), other display types, or combinations thereof), speakers, and / or other presentation components. The presentation component 418 may receive data from other components (such as GPU 408, CPU 406, DPU, etc.) and output that data (e.g., as images, videos, sounds, etc.).

[0195] Example Data Center

[0196] Figure 5 An example data center 500 is shown, which can be used in at least one embodiment of this disclosure. The data center 500 may include a data center infrastructure layer 510, a framework layer 520, a software layer 530, and an application layer 540.

[0197] like Figure 5As shown, the data center infrastructure layer 510 may include a resource coordinator 512, grouped computing resources 514, and node computing resources (“nodes CR”) 516(1)-516(N), where “N” represents any complete positive integer. In at least one embodiment, nodes CR 516(1)-516(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 processing units or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state drives or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules, and cooling modules, etc. In some embodiments, one or more nodes CR 516(1)-516(N) may correspond to servers having one or more of the aforementioned computing resources. In addition, in some embodiments, nodes CR516(1)-516(N) may include one or more virtual components, such as vGPU, vCPU, etc., and / or one or more of nodes CR516(1)-516(N) may correspond to virtual machines (VMs).

[0198] In at least one embodiment, the grouped computing resources 514 may include individual groups (not shown) of nodes CR516 housed in one or more racks, or a plurality of racks (also not shown) housed in data centers in various geographic locations. Individual groups of nodes CR516 within the grouped computing resources 514 may include computing, networking, memory, or storage resources that can be configured or allocated to support groups of one or more workloads. In at least one embodiment, several nodes CR516, including CPUs, GPUs, DPUs, and / or other processors, may be grouped within 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.

[0199] Resource coordinator 512 may be configured or otherwise controlled to control one or more nodes CR516(1)-516(N) and / or grouped computing resources 514. In at least one embodiment, resource coordinator 512 may include a Software Design Infrastructure (SDI) management entity for data center 500. Resource coordinator 512 may include hardware, software, or some combination thereof.

[0200] In at least one embodiment, such as Figure 5As shown, framework layer 520 may include a job scheduler 533, a configuration manager 534, a resource manager 536, and a distributed file system 538. Framework layer 520 may include a framework of software 532 supporting software layer 530 and / or one or more applications 542 of application layer 540. Software 532 or application 542 may respectively include web-based service software or applications, such as service software or applications provided by Amazon Web Services, Google Cloud, and Microsoft Azure. Framework layer 520 may be, but is not limited to, a free and open-source software web application framework, such as Apache Spark, which can utilize distributed file system 538 for large-scale data processing (e.g., "big data"). TM (Hereinafter referred to as "Spark"). In at least one embodiment, the job scheduler 533 may include a Spark driver for facilitating the scheduling of workloads supported by various layers of data center 500. In at least one embodiment, the configuration manager 534 may be able to configure different layers, such as software layer 530 and framework layer 520 including Spark and a distributed file system 538 for supporting large-scale data processing. The resource manager 536 is able to manage cluster or group computing resources mapped to or allocated for supporting distributed file system 538 and job scheduler 533. In at least one embodiment, cluster or group computing resources may include grouped computing resources 514 at data center infrastructure layer 510. The resource manager 536 may coordinate with resource coordinator 512 to manage these mapped or allocated computing resources.

[0201] In at least one embodiment, the software 532 included in the software layer 530 may include software used by at least a portion of the nodes CR516(1)-516(N), the grouped computing resources 514, and / or the distributed file system 538 of the framework layer 520. One or more types of software may include, but are not limited to, Internet web page search software, email virus browsing software, database software, and streaming video content software.

[0202] In at least one embodiment, the application layer 540 may include one or more applications 542 that can be used by at least a portion of nodes CR516(1)-516(N), grouped computing resources 514, and / or the distributed file system 538 of the framework layer 520. The one or more types of applications may include, but are not limited to, any number of genomics 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.

[0203] In at least one embodiment, any of the configuration manager 534, resource manager 536, and resource coordinator 512 can perform any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. Self-modification actions can alleviate the risk of data center operators of data center 500 making potentially poor configuration decisions and can prevent underutilization and / or skewed portions of the data center.

[0204] Data center 500 may include tools, services, software, or other resources for training one or more machine learning models or using one or more machine learning models to predict or infer information according to one or more embodiments described herein. For example, a machine learning model can be trained by calculating weight parameters based on a neural network architecture using the software and computing resources described herein with respect to data center 500. In at least one embodiment, by using weight parameters calculated through one or more training techniques, the resources described herein with respect to data center 500 can be used to infer or predict information using trained machine learning models corresponding to one or more neural networks, such as, but not limited to, those described herein.

[0205] In at least one embodiment, the data center 500 may use a CPU, application-specific integrated circuit (ASIC), GPU, FPGA, and / or other hardware (or corresponding virtual computing resources) to perform training and / or inference. Furthermore, one or more software and / or hardware resources described in this disclosure may be configured as a service to allow a user to train or perform information inference, such as image recognition, speech recognition, or other artificial intelligence services.

[0206] Example network environment

[0207] A network environment suitable for implementing embodiments of this disclosure may include one or more client devices, servers, network-attached storage (NAS), other backend devices, and / or other device types. Client devices, servers, and / or other device types (e.g., each device) may... Figure 4 The implementation is carried out on one or more instances of computing device 400—for example, each device may include similar components, features, and / or functions of computing device 400. Furthermore, in the case of implementing backend devices (e.g., servers, NAS, etc.), the backend devices may be included as part of data center 500, examples of which are described herein. Figure 5 To describe in more detail.

[0208] Components of a network environment can communicate with each other via a network, which can be wired, wireless, or both. A network can include multiple networks, or networks within multiple networks. For example, a 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 the Public Switched Telephone Network (PSTN)), and / or one or more private networks. In cases 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 connectivity.

[0209] A compatible network environment 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 server functionality described herein can be implemented on any number of client devices.

[0210] In at least one embodiment, the network environment may include one or more cloud-based network environments, distributed computing environments, combinations thereof, etc. 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, which may include one or more core network servers and / or edge servers. The framework layer may include a framework for supporting software at the software layer and / or one or more applications at the application layer. The software or applications may respectively include network-based service software or applications. In embodiments, one or more client devices may use the network-based service software or applications (e.g., by accessing the service software and / or applications via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a type of free and open-source software network application framework, such as one that can use a distributed file system for large-scale data processing (e.g., "big data").

[0211] A cloud-based network environment can provide cloud computing and / or cloud storage for any combination of the computing and / or data storage functions (or one or more portions thereof) described herein. Any of these various functions can be distributed across multiple locations from a central or core server (e.g., distributed across one or more data centers at the state, region, country, global, etc.). If the connection to a user (e.g., a 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).

[0212] Client devices may include those described in this article. Figure 4 The example computing device 400 described includes at least some components, features, and functions. By way of example and not limitation, the client device may be a personal computer (PC), laptop computer, mobile device, smartphone, tablet computer, smartwatch, wearable computer, personal digital assistant (PDA), MP3 player, virtual reality headset, global positioning system (GPS) or device, video player, camera, surveillance equipment or system, vehicle, ship, aircraft, virtual machine, drone, robot, handheld communication device, hospital equipment, gaming equipment or system, entertainment system, in-vehicle computer system, embedded system controller, remote control, electrical appliance, consumer electronics device, workstation, edge device, any combination of these described devices, or any other suitable device.

[0213] This disclosure can be described in the general context of machine-usable instructions or computer code, including computer-executable instructions such as program modules, which are executed by a computer or other machine such as a personal digital assistant or other handheld device. Typically, a program module, including routines, programs, objects, components, data structures, etc., refers to code that performs a specific task or implements a specific abstract data type. This disclosure can be practiced in a wide variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. This disclosure can also be practiced in distributed computing environments where tasks are performed by remote processing devices linked via a communication network.

[0214] As used herein, the phrase “and / or” relating 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” could 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 element A, B, and C. Furthermore, “at least one of element A or element B” could 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” could 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. Moreover, the use of the term “based on” should not be interpreted as “based on only” or “based on only”. Rather, a first element “based on” a second element includes instances where the first element is based on the second element but may also be based on one or more additional elements.

[0215] This document describes in detail the subject matter of this disclosure to satisfy legal requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have envisioned that the claimed subject matter may also be embodied in other ways to include steps different from or similar combinations of steps described herein in conjunction with other current or future techniques. 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 construed as implying any particular order among or between the various steps disclosed herein, unless the order of the steps is explicitly described.

[0216] For example, the subject matter of this disclosure is illustrated with respect to various aspects described below. For convenience, various examples of aspects of this disclosure are described as numbered examples (1, 2, 3, etc.). These are provided by way of example only and do not limit this disclosure. Unless the context otherwise indicates, aspects of various implementations described herein may be omitted, replaced with aspects of other implementations, or combined with aspects of other implementations. For example, one or more aspects of Example 1 below may be omitted, replaced with one or more aspects of another example (e.g., Example 2) or more examples, or combined with aspects of another example. The following is a non-limiting overview of some example implementations presented herein.

[0217] Example 1: A method that includes:

[0218] The computing system performs multiple initialization operations related to initializing the sensor; and

[0219] The computing system performs one or more authentication operations on authenticating the sensor after the sensor is initialized.

[0220] Example 2: According to the method of Example 1, the one or more authentication operations include one or more of the following:

[0221] The sensor itself is verified by the computing system; or

[0222] The computing system verifies whether the initialization of the sensor follows the plurality of initialization operations.

[0223] Example 3: According to the method of Example 2, the verification of the initialization of the sensor is based at least on one or more first tracking codes maintained by the sensor during initialization.

[0224] Example 4: According to the method of Example 3, the verification of the sensor initialization is further based at least on a comparison between a first tracking code value corresponding to a specific first tracking code in one or more first tracking codes and a second tracking code value that is the expected tracking code value of the specific first tracking code.

[0225] Example 5: According to the method of Example 4, the second tracking code value is determined by the computing system based at least on data transmitted with respect to one or more of the plurality of initialization operations.

[0226] Example 6: According to the method of Example 5, the first tracking code value is transmitted from the sensor to the computing system via secure communication.

[0227] Example 7: According to the method of Example 6, the secure communication is based at least on the sensor itself being verified via the one or more authentication operations.

[0228] Example 8: According to the method of Example 3, the first tracing code includes one or more of the following:

[0229] Cyclic Redundancy Check (CRC) code; or

[0230] Message counter.

[0231] Example 9: According to the method of Example 1, the first tracing code includes one or more of the following:

[0232] One or more write operations, wherein the computing system instructs data to be written to the sensor; or

[0233] One or more read operations, wherein the computing system instructs the reading of data from the sensor.

[0234] Example 10: According to the method of Example 1, the sensor includes a camera.

[0235] Example 11: A computing system comprising:

[0236] One or more processors for performing operations, said operations including:

[0237] Perform multiple initialization operations related to initializing peripheral devices associated with the computing system; and

[0238] After the peripheral device is initialized, one or more authentication operations are performed to authenticate the peripheral device.

[0239] Example 12: According to the computing system of Example 11, the one or more authentication operations include one or more of the following:

[0240] Verify the peripheral device itself; or

[0241] Verify whether the initialization of the peripheral device follows the multiple initialization operations.

[0242] Example 13: The computing system according to Example 12, wherein the verification of the initialization of the peripheral device is based at least on one or more first trace codes maintained by the peripheral device during initialization.

[0243] Example 14: According to the computing system of Example 13, the verification of the initialization of the peripheral device is further based at least on a comparison between a first trace code value corresponding to a specific first trace code in one or more first trace codes and a second trace code value that is the expected trace code value of the specific first trace code.

[0244] Example 15: According to the computing system of Example 14, the second tracking code value is determined by the computing system based at least on data transmitted with respect to one or more of the plurality of initialization operations.

[0245] Example 16: The computing system according to Example 11, wherein the peripheral device includes a sensor corresponding to the self-machine.

[0246] Example 17: The computing system according to Example 11, wherein the system is included in at least one of the following:

[0247] Control systems for autonomous or semi-autonomous machines;

[0248] Sensing systems for autonomous or semi-autonomous machines;

[0249] A system used to perform simulation operations;

[0250] Systems used to perform digital twin operations;

[0251] A system for performing optical transmission simulation;

[0252] A system for performing collaborative content creation for 3D assets;

[0253] A system used to perform deep learning operations;

[0254] A system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;

[0255] A system used to host one or more real-time streaming applications;

[0256] Systems implemented using edge devices;

[0257] Systems implemented using robots;

[0258] Systems used to perform conversational AI operations;

[0259] A system for performing one or more generative AI operations;

[0260] A system that implements one or more large language models (LLMs);

[0261] A system that implements one or more visual language models (VLMs);

[0262] A system that implements one or more multimodal language models;

[0263] A system for generating synthetic data;

[0264] A system containing one or more virtual machines (VMs);

[0265] A system that is at least partially implemented in a data center; or

[0266] A system that utilizes cloud computing resources at least in part.

[0267] Example 18: One or more processors, including:

[0268] Processor circuitry for performing operations, said operations including:

[0269] The computing system performs multiple initialization operations related to initializing the sensor; and

[0270] The computing system performs one or more authentication operations on the sensor after the sensor has been at least partially initialized, the one or more authentication operations being based at least on one or more first tracking codes maintained during the initialization of the sensor.

[0271] Example 19. According to one or more processors in Example 18, wherein the one or more authentication operations are based at least on a comparison between a first trace code value corresponding to a particular first trace code in the one or more first trace codes and a second trace code value that is the expected trace code value of the particular first trace code.

[0272] Example 20. According to one or more processors in Example 19, wherein the second tracking code value is determined by the computing system based at least on data transmitted with respect to one or more of the plurality of initialization operations.

Claims

1. A method comprising: The computing system performs multiple initialization operations to initialize the sensor; as well as The computing system performs one or more authentication operations on authenticating the sensor after the sensor is initialized.

2. The method of claim 1, wherein the one or more authentication operations comprise one or more of the following: The sensor itself is verified by the computing system; or The computing system verifies whether the initialization of the sensor follows the plurality of initialization operations.

3. The method of claim 2, wherein the verification of the initialization of the sensor is based at least on one or more first tracking codes maintained by the sensor during initialization.

4. The method of claim 3, wherein the verification of the sensor initialization is further based at least on a comparison between a first tracking code value corresponding to a particular first tracking code in one or more first tracking codes and a second tracking code value that is an expected tracking code value of the particular first tracking code.

5. The method of claim 4, wherein the second tracking code value is determined by the computing system based at least on data transmitted with respect to one or more of the plurality of initialization operations.

6. The method of claim 5, wherein the first tracking code value is transmitted from the sensor to the computing system via secure communication.

7. The method of claim 6, wherein the secure communication is based at least on the sensor itself being verified via the one or more authentication operations.

8. The method of claim 3, wherein the first tracing code comprises one or more of the following: Cyclic Redundancy Check (CRC) code; or Message counter.

9. The method of claim 1, wherein the first tracing code comprises one or more of the following: One or more write operations, wherein the computing system instructs data to be written to the sensor; or One or more read operations, wherein the computing system instructs the reading of data from the sensor.

10. The method of claim 1, wherein the sensor comprises a camera.

11. A computing system, comprising: One or more processors for performing operations, said operations including: Perform multiple initialization operations related to initializing peripheral devices associated with the computing system; and After the peripheral device is initialized, one or more authentication operations are performed to authenticate the peripheral device.

12. The computing system of claim 11, wherein the one or more authentication operations include one or more of the following: Verify the peripheral device itself; or Verify whether the initialization of the peripheral device follows the multiple initialization operations.

13. The computing system of claim 12, wherein the verification of the initialization of the peripheral device is based at least on one or more first trace codes maintained by the peripheral device during initialization.

14. The computing system of claim 13, wherein the verification of the initialization of the peripheral device is further based at least on a comparison between a first trace code value corresponding to a particular first trace code in one or more first trace codes and a second trace code value that is an expected trace code value of the particular first trace code.

15. The computing system of claim 14, wherein the second tracking code value is determined by the computing system based at least on data transmitted with respect to one or more of the plurality of initialization operations.

16. The computing system of claim 11, wherein the peripheral device includes a sensor corresponding to the self-machine.

17. The computing system of claim 11, wherein the system is included in at least one of the following: Control systems for autonomous or semi-autonomous machines; Sensing systems for autonomous or semi-autonomous machines; A system used to perform simulation operations; Systems used to perform digital twin operations; A system for performing optical transmission simulation; A system for performing collaborative content creation for 3D assets; A system used to perform deep learning operations; A system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; A system used to host one or more real-time streaming applications; Systems implemented using edge devices; Systems implemented using robots; A system for performing conversational AI operations; A system for performing one or more generative AI operations; A system that implements one or more large language models (LLMs); A system that implements one or more visual language models (VLMs); A system that implements one or more multimodal language models; A system for generating synthetic data; A system containing one or more virtual machines (VMs); A system that is at least partially implemented in a data center; or A system that utilizes cloud computing resources at least in part.

18. One or more processors, comprising: Processor circuitry for performing operations, said operations including: The computing system performs multiple initialization operations related to initializing the sensor; and The computing system performs one or more authentication operations on the sensor after the sensor has been at least partially initialized, the one or more authentication operations being based at least on one or more first tracking codes maintained during the initialization of the sensor.

19. One or more processors according to claim 18, wherein the one or more authentication operations are based at least on a comparison between a first trace code value corresponding to a particular first trace code in the one or more first trace codes and a second trace code value that is an expected trace code value of the particular first trace code.

20. The processor of claim 19, wherein the second tracking code value is determined by the computing system based at least on data transmitted with respect to one or more of the plurality of initialization operations.

Images