End-to-end indicator verification for automotive systems and applications

Abstract

The invention discloses end-to-end indicator verification for automotive systems and applications. In various examples, techniques for end-to-end indicator verification for automotive systems and applications include receiving, from a buffer, a set of commands associated with frames to be displayed on a screen. The technique further includes determining, based at least on the set of commands, (i) an expected checksum of the indicator to be included in the frame, and (ii) at least a portion of the frame associated with the indicator. The technique further includes calculating a checksum for at least a portion of the frame and causing an alert associated with the indicator to be generated based at least on a comparison of the calculated checksum to an expected checksum.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 544,094, filed on October 13, 2023, which is incorporated herein by reference in its entirety. Background Art

[0003] In automobiles, machines, and / or other systems, telltales (also known as warning lights, false lights, indicator lights, fault lights, engine lights, instrument lights, etc.) include images and / or other visual representations that are used to alert the operator of the vehicle or machine to important safety-related events or conditions. For example, the telltales may be displayed as indications of potential dangers related to nearby objects, weather, road conditions, traffic conditions, faults, failures, and / or other types of conditions. To address such potential dangers, a virtual machine running within a display pipeline may program a display controller to display a corresponding telltale within a frame on the screen. The display controller typically pre-processes and combines multiple input channels ("windows") into a single image, and then further post-processes the single image to create a final frame to be displayed on the screen. A given telltale is typically contained in a corresponding window so that it is blended on top of other windows to ensure visibility. The guest virtual machine is used to control this window channel and related settings.

[0004] However, errors caused by software and / or hardware may prevent indicators from displaying as intended, thus causing safety issues. For example, failure to display an indicator and / or incorrect display of an indicator may prevent the operator of a machine or vehicle from becoming aware of a corresponding safety-related event or condition.

[0005] To reduce the potential for errors when displaying the indicator, a checksum (e.g., a cyclic redundancy check (CRC)) may be calculated for the pixels in the area where the indicator is to be displayed. The calculated checksum may be compared to the expected checksum for the area. Any mismatch in the two checksums may then be used to generate an alarm, security error, and / or other indication of a problem associated with displaying the indicator on the screen.

[0006] A simple approach uses a guest virtual machine to perform this checksum verification in the corresponding window channel. However, this approach cannot detect errors in the compositing and post-compositing stages that occur before display in the display pipeline, because a given guest virtual machine is typically allowed to control the corresponding input (e.g., window) channel processed using one or more previous pre-compositing stages in the display pipeline.

[0007] To detect indicator display errors in the composition processing stage and the post-composition processing stage of the display pipeline, conventional methods implement checksum logic in the host virtual machine and fully access the display hardware behind the display pipeline. However, this creates complex communications between the guest virtual machine and the host virtual machine that operate the window channel. In addition, the host virtual machine may be unable to synchronize with the guest virtual machine because the host virtual machine may not be able to match the changes made by the guest virtual machine with the corresponding scan output frame to be displayed on the screen. If the host virtual machine becomes out of sync with the guest virtual machine, the host virtual machine may erroneously identify a checksum mismatch when there is no mismatch, and / or determine that there is a checksum match when the area of ​​the frame associated with the checksum match does not contain a corresponding indicator.

[0008] Therefore, more effective techniques are needed to improve the detection of indicator display errors in the display pipeline. Summary of the invention

[0009] Embodiments of the present disclosure relate to end-to-end indicator verification for automotive systems and applications. The technology described herein includes receiving a set of commands associated with a frame to be displayed on a screen from a buffer. The technology also includes determining (i) an expected checksum of an indicator to be included in the frame and (ii) at least a portion of the frame associated with the indicator based on the set of commands. The technology also includes calculating a checksum of at least a portion of the frame and causing an alarm associated with the indicator to be generated based on a comparison of the calculated checksum with the expected checksum.

[0010] One technical advantage of the disclosed technology relative to previous approaches is the ability to detect errors in indicators to be displayed on the screen after multiple windows and / or input channels containing indicators and / or other visual content have been composited into a frame. Therefore, the disclosed technology is able to detect indicator errors that occur in the composition processing stage and the post-composition processing stage immediately before the frame is displayed on the screen. Another technical advantage of the disclosed technology is the synchronous transmission and execution of commands for verifying indicators using pixel values ​​of indicators and / or frames. Therefore, the disclosed technology avoids potential mismatch errors and / or reduces synchronization complexity compared to traditional methods, which involve a large amount of communication between the guest virtual machine operating the window channel and the host virtual machine performing indicator verification in the composition processing stage and the post-composition processing stage of the display pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present system and method for end-to-end indicator verification of automotive systems and applications is described in detail below with reference to the accompanying drawings, wherein:

[0012] Figure 1 A computing device configured to implement one or more aspects of various embodiments is shown;

[0013] Figure 2 According to various embodiments Figure 1 A more detailed diagram of the virtual machine and display pipeline;

[0014] Figure 3A shows an example portion of an output frame including an indicator according to various embodiments;

[0015] Figure 3B According to various embodiments Figure 2 A more detailed diagram of the checker;

[0016] Figure 4 A flow chart illustrating a method for validating an output frame associated with one or more indicators according to various embodiments is shown;

[0017] Figure 5A is an illustration of an example autonomous vehicle according to some embodiments of the present disclosure;

[0018] Figure 5B According to some embodiments of the present disclosure Figure 5A Examples of autonomous vehicle camera positions and fields of view;

[0019] Figure 5C According to some embodiments of the present disclosure Figure 5A a block diagram of an example system architecture for an example autonomous vehicle;

[0020] Figure 5D A cloud-based server and Figure 5A System diagram of an example of communication between autonomous vehicles;

[0021] Figure 6 is a block diagram of an example computing device suitable for implementing some embodiments of the present disclosure; and

[0022] Figure 7 is a block diagram of an example data center suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0023] Systems and methods are disclosed that relate to end-to-end indicator validation for automotive systems and applications. Although the present disclosure may be directed to an example autonomous vehicle or semi-autonomous vehicle or machine 500 (referred to herein interchangeably as “vehicle 500,” “ego vehicle 500,” “machine 500,” or “ego machine 500”), the examples are directed to Figures 5A-5DThe systems and methods described herein may be used with, but are not limited to, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles connected to one or more trailers, aircraft, boats, space shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, airplanes, engineering vehicles, underwater vehicles, drones, and / or other vehicle types. Furthermore, while the present disclosure may be described with respect to end-to-end indicator validation for autonomous systems and applications, this is not limiting, and the systems and methods described herein may be used for augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and / or any other technology space where camera views may be used.

[0024] As described herein, a simple method for detecting errors related to indicators is to perform checksum verification in individual input channels during the pre-composition processing stage of the display pipeline. However, these methods are unable to detect errors in the composition processing stage and the post-composition processing stage that occur before display within the display pipeline. Meanwhile, conventional methods for detecting indicator display errors in the composition processing stage and the post-composition processing stage of the display pipeline involve implementing checksum logic in a host virtual machine that has full access to the display hardware at the back end of the display pipeline. However, this can complicate communication between the guest virtual machine operating the window input channel and the host virtual machine and / or cause problems in synchronizing the host virtual machine with the guest virtual machine, which can cause the host virtual machine to erroneously identify a checksum mismatch when there is no mismatch and / or determine that there is a checksum match when the area of ​​the frame associated with the checksum match does not contain the corresponding indicator.

[0025] In order to improve the detection of indicator display errors in the display pipeline, the disclosed technology allows a guest virtual machine operating a window channel to specify commands that can be used to verify the indicators to be displayed within a frame on the screen. These commands may include (but are not limited to) a full-screen or partial screen surface (image) containing one or more non-overlapping indicators, settings for enabling or disabling the display and / or verification of a single indicator, settings for hardware checksum calculations of the area containing the indicator after compositing multiple window channels into a frame, the position of the indicator within the frame, the area of ​​the frame corresponding to the indicator, a mask for identifying the pixel position of the indicator within the area and / or frame, and / or the expected checksum value for each area. These commands are transmitted in a flip-synchronized manner together with the image and / or pixel values ​​of the corresponding window, thereby allowing the display device driving the screen to access and execute these commands on a per-frame basis.

[0026] For example, commands may be written to a buffer in a memory accessible to the display device. During the period between displaying the previous frame on the screen and displaying the next frame on the screen, the display device may execute commands to verify whether each indicator is to be included in the next frame. The verification process may involve retrieving an expected checksum (e.g., a cyclic redundancy check (CRC)) for each indicator from a location in the memory, calculating a checksum for a portion of the frame corresponding to the indicator, and comparing the calculated checksum to the expected checksum. If the two checksums match, no error is found and the frame can continue to be displayed. If the two checksums are different, an error in the display of the indicator can be detected, and an alarm and / or notification of the error can be output instead of the frame or in addition to the frame. Thus, these commands can allow a given indicator to be verified for a single frame to be displayed on the screen, including (but not limited to) the first frame containing the indicator.

[0027] One technical advantage of the disclosed technology relative to previous methods is the ability to detect errors in indicators to be displayed on the screen after multiple windows and / or input channels containing indicators and / or other visual content are synthesized into a frame. Therefore, the disclosed technology is able to detect indicator errors that occur in the synthesis processing stage and the post-synthesis processing stage immediately before the frame is displayed on the screen. Another technical advantage of the disclosed technology is the synchronous transmission and execution of commands for verifying indicators using pixel values ​​of indicators and / or frames. Therefore, compared with traditional methods, the disclosed technology avoids potential mismatch errors and / or reduces synchronization complexity, which involves large-scale communication between guest virtual machines operating window channels in the synthesis processing stage and the post-synthesis processing stage of the display pipeline and the host virtual machine performing indicator verification. This synchronous transmission and execution of commands for verifying indicators using corresponding frames further allows indicator errors to be detected and handled as early as the first frame containing a given indicator.

[0028] Figure 1 A computing device 100 configured to implement one or more aspects of various embodiments is shown. In at least one embodiment, the computing device 100 includes a desktop computer, a laptop computer, a smart phone, a personal digital assistant (PDA), a tablet computer, a server, one or more virtual machines, an embedded system, a system on a chip, a vehicle computing device, and / or any other type of computing device configured to receive input, process data, and optionally display images, and is suitable for practicing one or more embodiments. The computing device 100 is configured to run a virtual machine 122 and a display pipeline 124 that may reside in a memory 116.

[0029] In one embodiment, the computing device 100 includes, but is not limited to, an interconnect (bus) 112 connecting one or more processors 102, an input / output (I / O) device interface 104 coupled to one or more input / output (I / O) devices 108, a memory 116, a storage device 114, and / or a network interface 106. The processor 102 may include any suitable processor implemented as a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), an artificial intelligence (AI) accelerator, a parallel processing unit (PPU), a data processing unit (DPU), any other type of processing unit, or a combination of different processing units, such as a CPU configured to operate in conjunction with a GPU. In general, the processor 102 may include any technically feasible hardware unit capable of processing data and / or executing software applications. In addition, in the context of the present disclosure, the computing elements shown in the computing device 100 may correspond to a physical computing system (e.g., a system in a data center) and / or may correspond to a virtual computing instance executed in a computing cloud.

[0030] In at least one embodiment, the I / O devices 108 include devices capable of receiving input, such as a keyboard, a mouse, a touch screen, a touchpad, a VR / MR / AR headset, a gesture recognition system, and / or a microphone, and devices capable of providing output, such as a display device, a haptic device, and / or a speaker. In addition, the I / O devices 108 may include devices capable of receiving input and providing output, such as a touch screen, a universal serial bus (USB) port, and the like. The I / O devices 108 may be configured to receive various types of input from an end user (e.g., a designer) of the computing device 100, and also provide various types of output to the end user of the computing device 100, such as a displayed digital image or digital video or text. In some embodiments, one or more I / O devices 108 are configured to couple the computing device 100 to a network 110.

[0031] In one embodiment, the network 110 is any technically feasible communication network that allows data to be exchanged between the computing device 100 and an internal, local, remote, or external entity or device (e.g., a web server or another networked computing device). For example, the network 110 may include a wide area network (WAN), a local area network (LAN), a wireless (e.g., WiFi) network, a cellular network, and / or the Internet, etc.

[0032] In at least one embodiment, storage 114 includes non-volatile memory for applications and data, and may include fixed or removable disk drives, flash memory devices, and CD-ROM, DVD-ROM, Blu-ray, HD-DVD, or other magnetic, optical, or solid-state storage devices. Virtual machines 122 and / or display pipelines 124 may be stored in storage 114 and loaded into memory 116 when executed.

[0033] In one embodiment, the memory 116 includes a random access memory (RAM) module, a flash memory unit, and / or any other type of memory unit or combination thereof. The processor 102, the I / O device interface 104, and the network interface 106 may be configured to read data from and write data to the memory 116. The memory 116 may include various software programs executable by the processor 102 and application data associated with the software programs, including a virtual machine 122 and a display pipeline 124.

[0034] Virtual machine 122 includes functionality to coordinate and control image data processing within input channels. An input channel may correspond to an independent source of image data (e.g., a "window") that may be used to generate a frame for output on a display. The image data may include frames and / or portions of frames captured by a set of cameras. The cameras may capture multiple views of the environment surrounding a vehicle, a robotic system, a surveillance system, or other type of system that includes computing device 100. The image data may also or alternatively include one or more indicators to alert a user to potential hazards associated with nearby objects, weather, road conditions, traffic conditions, malfunctions, failures, and / or other types of safety-related situations encountered by the system.

[0035] More specifically, the virtual machine 122 is configured to execute a control flow that sets up and controls the execution of various components within the input channel's corresponding display pipeline 124. As described in further detail below, these components can execute multiple stages of the display pipeline 124 to convert image data within the input channel into a form that can be output on a display and / or used in other ways by a system including the computing device 100.

[0036] In addition, multiple instances of virtual machine 122 and display pipeline 124 can be executed on one or more instances of computing device 100, distributed system and / or cloud computing system to control the processing of image data from multiple corresponding input channels into output frames including one or more indicators. For example, virtual machine 122 and display pipeline 124 instances can operate to combine multiple image data "windows" of different camera views depicting the environment, indicators and / or other visual content into output frames. The output frames can then be displayed on one or more screens in a vehicle, a computer system and / or another system including one or more instances of computing device 100.

[0037] Figure 2 According to various embodiments Figure 1 1 and 12. A more detailed illustration of the virtual machine 122 and display pipeline 124 is provided herein. As described herein, the virtual machine 122 configures and controls the operation of multiple components in the display pipeline 124 to combine and / or convert image data from one or more image sources 206(1)-206(A) (each of which is referred to herein as an image source 206) into frames 236 that can be displayed on a display device 222. These components include a set of blending engines 210(1)-210(C) (each of which is referred to herein as a blending engine 210), a display device 222, a set of buffers 216(1)-216(X) between the image sources 206 and the blending engines 210 (each of which is referred to herein as a buffer 216), and a set of buffers 220(1)-220(X) between the blending engines 210 and the display device 222 (each of which is referred to herein as a buffer 220). Each of these components is described in further detail below.

[0038] Image source 206 includes one or more image data sources that may be included in frame 236. For example, image source 206 may include one or more cameras that generate a sequence of video frames at a particular rate. The video frames captured by each camera may depict areas in front of, behind, to the side, below, and / or above a vehicle or another system in which display pipeline 124 is deployed. In another example, image source 206 may include two-dimensional (2D) and / or three-dimensional (3D) graphics processing components that combine multiple camera views of the system's surroundings into a single image that represents a two-dimensional (2D) and / or three-dimensional (3D) visualization, a top-down visualization, a multi-viewport visualization, and / or other views of the environment.

[0039] In some embodiments, the image source 206 includes a source of one or more indicators 234(1)-234(N) (where each indicator is individually referred to herein as an indicator 234) to be included in the frame 236. As used herein, the indicator 234 includes an alert of a security-related condition associated with the system in which the display pipeline 124 is deployed. For example, the virtual machine 122, the security MCU, and / or another component of the system may detect a potential hazard associated with a nearby object, weather, road conditions, traffic conditions, a malfunction, a failure, and / or another type of security-related condition. In response to such a potential hazard, the component may transmit a corresponding indicator 234 to the display pipeline 124 for inclusion in the frame 236. The component may also (or alternatively) send one or more indicators 234 for inclusion in a given frame 236 during boot-up of a system in which the display pipeline 124 is deployed (e.g., as part of a "splash screen" displayed during boot-up), during debugging of the system and / or display pipeline 124, and / or during another event not necessarily related to detecting a security-related condition.

[0040] The images, video frames, indicators, and / or other image data generated and / or provided by the image source 206 are stored in the buffer 216 and subsequently read by the one or more blending engines 210. Each blending engine 210 performs additional processing on the image data to generate one or more portions of the frame 236 for display by the display device 222. For example, each blending engine 210 may perform scaling, color adjustment, blending, rasterization, and / or other operations to convert the image data into pixel values ​​suitable for display by the display device 222.

[0041] The pixel values ​​output by the 2D engine 210 are stored in a set of buffers 220 for subsequent consumption by a display device 222. The display device 222 retrieves these pixel values ​​from the buffers 220 and eventually synthesizes the data into a frame 236. For example, the display device 222 may synthesize and / or layer pixel values ​​from multiple buffers 220, multiple instances of the display pipeline 124, and / or multiple input channels corresponding to respective instances of the display pipeline 124 into a single frame 236 for display on the screen. After the frame 236 is generated, the display device 222 displays the frame 236 on the screen. The display device 222 also updates the displayed frame 236 with new pixel values ​​from the buffers 220 according to a configurable frequency. For example, the display device 222 may output frames on the screen at a frequency of approximately 60 Hz, while one or more cameras included in the image source 206 may capture images at a frequency of approximately 30 Hz. Therefore, the display device 222 may display a given frame 236 twice before generating a new frame 236 using new pixel values ​​from the buffers 220.

[0042] In one or more embodiments, the virtual machine 122 includes applications, processes, services, user mode drivers, kernel mode drivers, and / or other components. The components within the virtual machine 122 are configured to be hosted on the same computing device (e.g., Figure 1 The hypervisor may be configured to execute in an isolated manner from underlying hardware on the computing device 100 of the computing device 100 and / or other virtual machines (e.g., additional instances of virtual machine 122, other types of virtual machines, etc.). For example, virtual machine 122 and / or other virtual machines executing on the computing device may access hardware resources on the computing device through a Type I hypervisor running directly on one or more processors in the computing device or a Type II hypervisor running as a software layer on a host operating system executing on the computing device. The hypervisor may also enable or disable individual virtual machines and / or individual instances of virtual machine 122, connect one or more virtual machines and / or instances of virtual machine 122 to a given display pipe 124 and / or display device 222, and / or otherwise configure the arrangement of virtual machines, display pipes, and / or other components associated with displaying output frames within a given system.

[0043] like Figure 2 As shown, the virtual machine 122 coordinates the operation of various components (e.g., image source 206, blending engine 210, display device 222, etc.) in the display pipeline 124 by communicating and / or storing commands 204(1)-204(Y) (where each command is individually referred to herein as a command 204) with these components using one or more command interfaces 202. Each command interface 202 may allow the virtual machine 122 to communicate with a corresponding component (or type of component) in the display pipeline 124. For example, each command interface 202 may include one or more command buffers, registers, and / or other locations to which the virtual machine 122 writes commands 204. These commands 204 may include, but are not limited to, configuring processing settings 242 for the capture of images by a camera, security-related conditions for triggering a display indicator 234 by a security component, processing of image data by the blending engine 210, and / or composition and / or display of image data by the display device 222 in a given frame 236.

[0044] In some embodiments, the value of X, which represents the number of buffers in each set of buffers 216(1)-216(X), 218(1)-218(X), and 220(1)-220(X), is set to the depth of the display pipeline 124 (e.g., the number of stages in the display pipeline 124). As the image source 206 provides new image data, the virtual machine 122 can write new commands 204 to the command interface 202 in a manner that causes the image source 206, the blending engine 210, and / or the display device 222 to cycle through the corresponding buffer sets 216 and 220 (e.g., similar to scrolling buffers). These commands 204 can also cause the image source 206, the blending engine 210, and / or the display device 222 to capture, transmit, and / or process image data in a manner that reflects the latest conditions surrounding the system and / or the latest user input received through a user interface associated with the display pipeline 124.

[0045] For example, the virtual machine 122 may transmit commands 204 to the image sources 206, the blending engine 210, and the display device 222 via the command interface 202 in a manner that is synchronized with the rate at which frames are output on the display device 222 and / or the rate at which frames are captured by a camera included in the image source 206. During this synchronization, the virtual machine 122 may transmit commands 204 that instruct the image sources 206 to switch the buffers 216 into which image data is written, such that changes to the images captured and / or transmitted by each image source 206 are written to different buffers 216, and the newest image data from each image source 206 is written to a given buffer 216 that previously stored the oldest image data from the same image source 206 and / or different image sources 206. The virtual machine 122 may also or alternatively transmit commands 204 that instruct each blending engine 210 to switch from reading older image data from one or more buffers 216 to reading newer image data from one or more other buffers 216 and / or to write the most recent set of pixel values ​​generated from the image data to one or more buffers 220 storing the oldest pixel values. These commands 204 to the blending engines 210 may also instruct each blending engine 210 to generate overlays and / or other pixel values ​​to include in the frame 236 content in a manner that reflects the latest environmental and / or safety-related conditions surrounding the system. The virtual machine 122 may also (or alternatively) transmit commands 204 that instruct the display device 222 to generate and / or display a new frame 236 by switching from reading older pixel values ​​from one or more buffers 220 to reading newer pixel values ​​from one or more other buffers 220.

[0046] Because the display pipeline 124 includes multiple components, buffer groups 216 and 220, and processing stages, an error in any component and / or stage may cause the display device 222 to output frame 236 content that does not include one or more indicators 234 from the image source 206, which may cause safety issues. For example, the failure to display a given indicator 234 and / or the erroneous display of the indicator 234 may prevent an operator of a machine, vehicle, or system that includes the display pipeline 124 from becoming aware of a corresponding safety-related event or condition.

[0047] In one or more embodiments, the virtual machine 122 and the display device 222 operate to detect and manage issues associated with the display of the indicator 234 in a frame to be output by the display device 222. Figure 2 As shown, the virtual machine 122 writes a command 204 including a checksum setting 244 and an indicator setting 246 to one or more command interfaces 202 associated with the display device 222. One or more image sources 206 and / or security components in the display pipeline 124 can use the checksum setting 244 and / or the indicator setting 246 to (i) calculate the expected checksum of the indicator 234 to be included in a given frame 236, and (ii) write the expected checksum to the corresponding buffer and / or memory address. The display device 222 includes a plurality of checkers 232(1)-232(N) (each checker is individually referred to herein as a checker 232) that use the checksum setting 244 and / or the indicator setting 246 to (i) calculate the checksum of the frame 236 region corresponding to the indicator 234, (ii) read the expected checksum of these indicators 234 from the corresponding buffer and / or memory address, and (iii) compare the calculated checksum with the expected checksum. These inspectors 232 may be implemented using dedicated circuits, microcontrollers, and / or other hardware components on the display device 222 .

[0048] As used herein, a checksum refers to a data element derived from another data block that is used to detect errors or alterations to a data block during storage or transmission. A checksum may be calculated using a CRC, a hash function, a parity check, a Fletcher checksum, an Adler checksum, and / or other techniques to convert a data block into a value that can be used to "check" or verify the integrity of the data block.

[0049] In some embodiments, the checksum settings 244 are used to configure the verification of the checksums of the indicators 234 to be included in a given frame 236. For example, the checksum settings 244 may include (but are not limited to) the type of checksum calculated for each indicator 234 in a given frame 236, the number of checksums calculated for a given indicator 234 and / or frame 236, the location (e.g., address, buffer, etc.) of the expected checksum for each indicator 234, rules for generating an alert based on a comparison of a calculated checksum to an expected checksum for the same indicator 234, and / or other settings related to the use of checksums to verify indicators 234.

[0050] The indicator settings 246 define the indicators 234 within a given frame 236 for which checksums are to be verified. For example, the indicator settings 246 may include (but are not limited to) an image, shape, pixel value, border, and / or other representation of each indicator 234; one or more types of indicators 234 to be included in the frame 236 (e.g., indicators 234 corresponding to safety alerts, proximity alerts, weather alerts, road condition alerts, etc.); a flag for enabling and / or disabling a given checker 232 and / or corresponding indicator 234; a location (e.g., a memory address and / or buffer 220) where image data for a given indicator 234 is stored; a total number of indicators 234 for which checksums are to be verified; and / or other settings related to defining the indicators 234 to be displayed and / or verified within the content of the frame 236. As the content in each frame 236 updates to reflect the latest environment and / or conditions surrounding the system, indicator settings 246 may also be updated to specify indicators 234 within each frame 236 that correspond to indications of safety hazards associated with the environment and / or conditions.

[0051] In one or more embodiments, a given indicator 234 includes one or more arbitrarily shaped regions within a given frame 236. These regions may be included in a larger rectangular region (or larger arbitrarily shaped region) from which the checksum of the indicator 234 is calculated. One or more portions of the larger region that are not part of the indicator 234 may also include pixel values ​​that vary with the image data that is composited and / or layered with the indicator 234 to form the frame 236, as described below with respect to Figure 3A discussed in further detail.

[0052] Figure 3A 2. The apparatus includes an indicator (e.g., Figure 2 The output frame (e.g., Figure 2 236). Figure 3AAs shown, the portion includes a rectangular area within the output frame. The area includes a plurality of sub-areas 304(1)-304(4), which form an indicator to be displayed within the rectangular area. More specifically, sub-area 304(1) includes a rounded rectangle representing the outer boundary of the indicator, sub-area 304(2) includes an inverted rectangle disposed within the rounded rectangle, and sub-areas 304(3)-304(4) form an exclamation mark disposed within the inverted rectangle. Pixels within sub-areas 304(1)-304(4) may be opaque to ensure that the indicator is visible to a user to whom the output frame is displayed.

[0053] The portion also includes a plurality of sub-regions 302(1)-302(3) that are not included in the indicator. Sub-region 302(1) corresponds to the area between the rounded rectangle of sub-region 304(1) and the inverted triangle of sub-region 304(2), sub-region 302(2) corresponds to the area between the inverted triangle of sub-region 304(2) and the exclamation mark formed by sub-regions 304(3)-304(4), and sub-region 302(3) corresponds to the area outside the rounded rectangle of sub-region 304(1). Unlike sub-regions 304(1)-304(4) corresponding to the indicator, pixels in sub-regions 302(1)-302(3) may be partially and / or fully transparent to allow the indicator to be overlaid on other image data. For example, sub-regions 302(1)-302(2) may include partially transparent pixels to allow the indicator to be blended with image data (e.g., camera views and / or visualizations) from an underlying window and / or input channel. Sub-region 302(3) may include fully transparent pixels to allow image data other than the pointer to be shown on the screen.

[0054] In one or more embodiments, using Figure 3AThe alpha channel of the RGBA pixel values ​​of the rectangular area can be used to represent the sub-area 304(1)-304(4) corresponding to the indicator and the sub-area 302(1)-302(3) not included in the indicator. For example, the alpha channel can define a mask representing the pixel positions corresponding to the indicator within the larger rectangular area. Within the alpha channel, a value of 1 can be stored in the pixel positions corresponding to the sub-areas 304(1)-304(4) to indicate that the corresponding pixel values ​​will be used to calculate the checksum of the indicator. The alpha channel can store values ​​ranging from 0 to less than 1 in the pixel positions within the sub-areas 302(1)-302(3) to indicate that the corresponding pixel values ​​will be excluded from the checksum of the indicator. An alpha channel value of 0 may indicate that the corresponding pixel position is completely transparent (e.g., allowing image data from a different window and / or input channel to be fully visible at the pixel position). Alpha channel values ​​between 0 and 1 may indicate that the corresponding pixel position is partially transparent (e.g., allowing image data from different windows and / or input channels to blend with the pixel value of the indicator at that pixel position), with lower alpha channel values ​​indicating greater transparency.

[0055] Back to Figure 2 As discussed above, each checker 232 in the display device 222 verifies a corresponding indicator 234 within a given frame 236 that the display device 222 will display on the screen by retrieving one or more commands 204 from one or more command interfaces 202 with the virtual machine 122. Each checker 232 uses the indicator settings 246 specified in the retrieved commands 204 to obtain the pixel value representing the indicator 234. Each checker 232 also uses the checksum settings 244 specified in the retrieved commands 204 to calculate a checksum corresponding to the pixel value of the indicator 234, and compares the calculated checksum with the expected checksum of the indicator 234. Each checker 232 also generates an alert and / or other output based on the comparison results. Reference will be made to the following. Figure 3B The operation of the checker 232 is described in more detail.

[0056] Figure 3B According to various embodiments Figure 2 A more detailed illustration of the inspector 232 in FIG. Figure 3BAs shown, the checker 232 calculates a checksum 306 for a given indicator 234 in an output frame 236 to be displayed on the display device 222. For example, the checker 232 may receive one or more indicator settings 246 and / or checksum settings 244 in the form of one or more commands 204 from the virtual machine 122. The checker 232 may use these settings to determine an indicator mask 336 that identifies pixel locations within the frame 236 (or a region having a frame 236) corresponding to the indicator 234 and / or retrieve pixel values ​​corresponding to the indicator from the frame 236. The checker 232 may also use these settings to calculate a CRC and / or another type of checksum 306 using pixel values ​​at the identified pixel locations.

[0057] As described herein, the indicator mask 336 may be defined in an alpha channel of the frame 236 and / or in an area within the frame 236 that includes the indicator 234. For example, the alpha channel may store a particular pixel value (e.g., 1) at pixel locations corresponding to the indicator 234 and store a different pixel value (e.g., non-1) at pixel locations that do not correspond to the indicator 234. If the indicator mask 336 is defined for the indicator 234, the checker 232 may use the pixel values ​​at these pixel locations to calculate the checksum 306. If the indicator mask 336 is not defined for the indicator 234, the checker 232 may use the pixel values ​​that fall within the boundaries of the rectangular area (or another portion of the frame 236) specified in the command 204 as corresponding to the indicator 234 to calculate the checksum 306.

[0058] The checker 232 performs a comparison 308 of the checksum 306 to an expected checksum 334 for the same indicator 234. For example, the checker 232 may read the expected checksum 334 from a location (e.g., a memory address, a buffer, etc.) specified in one or more commands 204 of the virtual machine 122 and determine whether the calculated checksum 306 matches the expected checksum 334. The expected checksum 334 may be calculated based on the image data of the indicator 234 by a source of image data and / or another component operating on an input channel that includes image data. The expected checksum 334 may also (or alternatively) be extracted from a given checker 232 (e.g., after the checker has calculated the checksum based on the image data of the given indicator 234 provided and / or verified by the developer).

[0059] The checker 232 also generates an alert 312 based on the result of the comparison 308. More specifically, the checker 232 outputs the alert 312 (e.g., to indicate a potential error associated with the display of the indicator 234 on the screen) when the calculated checksum 306 does not match the expected checksum 334. Conversely, the checker 232 may omit the alert 312 (e.g., because no error is detected in the portion of the frame 236 corresponding to the indicator 234) when the calculated checksum 306 matches the expected checksum 334.

[0060] Back to Figure 2 As discussed above, a display controller in a display device 222 combines the results of the checksum comparisons performed by all checkers 232 for a given frame 236 into an action 230 performed for the frame 236. For example, the display controller may discard the frame 236 and / or one or more subsequent frames, blank the screen, output an error associated with the display of an indicator (e.g., as an overlay, audio output, tactile output, etc.), and / or perform another action 230 to prevent a user from operating a system including the display device 222 and / or making decisions using stale, misleading, or less secure than desired image data in response to receiving an alert of one or more checksum mismatches from the checkers 232. Conversely, upon determining that the checkers 232 did not detect a checksum mismatch for an indicator 2343 to be included in the frame 236, the display controller may display the given frame 236 and / or one or more subsequent frames.

[0061] In another example, the display controller may determine an overall "error level" associated with a given frame 236 as the sum and / or other aggregation of the priority and / or importance levels associated with all indicators 234 for which the corresponding checkers 232 detected a checksum mismatch. If the overall error level does not meet a threshold, the display controller may display the frame 236 with or without additional notification of the potential error associated with the display of the indicators 234 in the frame 236. If the overall error level exceeds a threshold, the display controller may discard the frame 236, blacken the screen, and / or perform another action 230 to prevent a user from operating a system including the display device 222 and / or making decisions using stale, misleading, or less secure than desired image data.

[0062] In a third example, the display controller may use a counter to track the number of consecutive frames in which an indicator 234 error is detected. When an error in one or more indicators 234 is detected within a given frame 236, the display controller may increment the counter. When no error in the indicator 234 is found in a given frame 236 and / or a certain number of consecutive frames, the display controller may reset the counter. If the counter does not reach or exceed the threshold, the display controller may display the frame 236 on the screen. If the counter reaches or exceeds the threshold, the display controller may discard the frame 236; blank the screen; output a visual, audio, tactile, and / or other type of notification of a potential error in the indicator 234 on the screen; and / or perform another action 230 to mitigate potential safety issues associated with the accumulation of potential indicator 234 errors in frames displayed on the screen.

[0063] In a fourth example, the display controller may use time series analysis and / or machine learning techniques to determine actions 230 based on alerts and / or other data from the checker 232. These techniques may indicate that when a series of alerts and / or other data for the N most recent frames from the checker 232 is associated with less than a threshold likelihood of a certain type of security issue and / or another outcome, a given frame 236 is to be output on the screen. These techniques may also or alternatively indicate that when the series is associated with at least a threshold likelihood of an outcome, the frame 236 is to be dropped, the screen is to be blanked, an alert is to be output indicating that the screen may include an indicator 234 error, and / or another action 230 related to managing the indicator 234 content in the frame 236 that may be erroneous is to be performed.

[0064] It should be understood that this arrangement and other arrangements described herein are set forth by way of example only. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of the arrangements and elements shown, and certain elements may be omitted entirely. In addition, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and may be implemented in any suitable combination and location. The various functions performed by the entities described herein may be performed by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory. In some embodiments, the systems, methods, and processes described herein may use instructions that are similar to those described herein. Figures 5A-5D Example of autonomous vehicle 500, Figure 6 The example computing device 600 and / or Figure 7 The example data center 700 may be implemented with similar components, features and / or functions.

[0065] Reference now Figure 4, each block of the method 400 described herein includes a computing process that can be performed using any combination of hardware, firmware, and / or software. For example, various functions can be implemented by a processor executing instructions stored in a memory. The methods can also be embodied as computer usable instructions stored on a computer storage medium. The methods can be provided by a stand-alone application, a service or a hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, the method 400 is provided by way of example with respect to Figure 1 -3. However, these methods may additionally or alternatively be performed by any one system or any combination of systems, including but not limited to the systems described herein.

[0066] Figure 4 A flow chart of a method for verifying an output frame associated with one or more indicators according to various embodiments is shown. Figure 4 As shown, method 400 begins at operation 402, in which display device 222 determines one or more settings associated with verifying image data contained in output frame 236. For example, display device 222 may receive one or more settings in the form of commands from virtual machine 122. These commands may be received via one or more command interfaces, command buffers, registers, and / or other sources. These settings may define rectangular areas and / or other portions of output frame 236 that include indicator 234 to be verified, checksum types to be calculated for each portion of output frame 236, the number of checksums to be calculated for output frame 236, the location of expected checksums of indicator 234, rules for generating alarms based on comparison of calculated checksums with expected checksums, and / or other settings related to using checksums to detect and / or manage errors in displaying indicator 234 in output frame by display device 222.

[0067] In operation 404, the display device 222 receives one or more portions of the output frame 236 including image data. For example, the display device 222 may read the image data from one or more buffers 220 included in one or more instances of the display pipeline 124. Each buffer 220 read by the display device 222 and / or each input channel represented by a corresponding instance of the display pipeline 124 may store at least a subset of pixel values ​​to be included in the output frame 236.

[0068] In operation 406, the display device 222 calculates one or more checksums for one or more indicators 234 within the output frame 236 based on the settings. Continuing with the above example, the display device 222 may composite, layer, and / or otherwise combine image data from multiple buffers and / or input channels into the output frame 236. The display device 222 may then use the indicator mask and / or other information in the settings to identify one or more portions of the output frame 236 that correspond to a given indicator and calculate a checksum based on the pixel values ​​in those (one or more) portions.

[0069] In operation 408, the display device 222 generates an alert and / or displays the output frame 236 on the screen based on the comparison of the calculated checksum with one or more expected checksums of the indicator. For example, the display device 222 may include a separate checker 232 for each indicator 234. Each checker 232 may retrieve the expected checksum of the corresponding indicator 234 from a memory address and / or other location specified in the settings, and compare the expected checksum with the calculated checksum of the indicator 234. If a given checker 232 determines that the calculated checksum of the corresponding indicator 234 does not match the expected checksum of the indicator 234, the checker 232 may output an alert for the mismatch. If a given checker 232 determines that the calculated checksum of the corresponding indicator 234 matches the expected checksum of the indicator 234, the checker 232 may ignore the alarm generated for the indicator 234 and / or generate a different alarm to indicate that no checksum mismatch was detected. The display device 222 may then compare the number of alerts received from the checker 232 and / or the number of consecutive frames in which the alerts are received from the checker 232 to a threshold. If the threshold is met or exceeded, a display controller in the display device 222 may generate an error that is output to the screen (e.g., as an overlay, audio output, tactile output, etc.) instead of or in addition to the output frame 236, for discarding the output frame 236, for blanking the screen, and / or for performing another action 230 to prevent a user from operating a system including the display device 222 and / or making decisions using stale, misleading, or less secure than desired image data. If the threshold is not met or exceeded, the display device 222 may cause the output frame 236 to be output to the screen.

[0070] In operation 410, the display device 222 determines whether to continue processing output frames. For example, the display device 222 may continue processing output frames while generating output frames for display on the screen and / or for displaying the indicator 234. While the display device 222 continues processing output frames, the display device 222 repeats operations 402, 404, 406, and 408 for each output frame 236. The display device 222 may continue to process output frames in this manner until output frames including indicators, image data, and / or input channels are no longer output on the display device.

[0071] The systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, airships, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, engineering vehicles, submarines, drones, and / or other vehicle types. In addition, the systems and methods described herein may be used for a variety of purposes, such as, but not limited to, machine control, machine motion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, safety and surveillance, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actor simulation and / or digital twins, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation of 3D assets, cloud computing, and / or any other suitable application.

[0072] The disclosed embodiments include in a variety of different systems, such as automotive systems (e.g., control systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aviation systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems including one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least in part in a data center, systems for performing conversational AI operations, systems implementing one or more large language models (LLMs), one or more visual language models (VLMs), one or more multimodal language models, etc., systems for performing light transport simulations, systems for performing collaborative content creation of 3D assets, systems implemented at least in part using cloud computing resources, and / or other types of systems.

[0073] Example Autonomous Vehicle

[0074] Figure 5Ais an illustration of an example autonomous vehicle 500 according to some embodiments of the present disclosure. Autonomous vehicle 500 (alternatively referred to herein as "vehicle 500") may include, but is not limited to, a passenger vehicle such as a car, a truck, a bus, an emergency vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police car, an ambulance, a boat, an engineering vehicle, a submarine, a robotic vehicle, a drone, an airplane, a vehicle coupled to a trailer (a semi-tractor-trailer truck for hauling cargo), and / or other types of vehicles (e.g., unmanned and / or capable of accommodating one or more passengers). Autonomous vehicles are generally described in terms of automation levels as defined by the National Highway Traffic Safety Administration (NHTSA), a division of the U.S. Department of Transportation, and the Society of Automotive Engineers (SAE), “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201806, issued on June 15, 2018, Standard No. J3016-201609, issued on September 30, 2016, and previous and future versions of the standard). The vehicle 500 may be capable of implementing one or more functions that are consistent with levels 3-5 of autonomous driving levels. The vehicle 500 may be capable of implementing one or more functions that are consistent with levels 3-5 of autonomous driving levels. For example, depending on the embodiment, the vehicle 500 may be capable of driver assistance (level 1), semi-automation (level 2), conditional automation (level 3), high automation (level 4), and / or full automation (level 5). The term “autonomy” as used herein may include any and / or all types of autonomy of a vehicle 500 or other machine, such as fully autonomous, highly autonomous, conditionally autonomous, partially autonomous, providing assistive autonomy, semi-autonomous, primarily autonomous, or other designations.

[0075] The vehicle 500 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. The vehicle 500 may include a propulsion system 550, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. The propulsion system 550 may be connected to a drive train of the vehicle 500, which may include a transmission, to achieve propulsion of the vehicle 500. The propulsion system 550 may be controlled in response to receiving a signal from a throttle / accelerator 552.

[0076] A steering system 554, which may include a steering wheel, may be used to steer vehicle 500 (e.g., along a desired path or route) when propulsion system 550 is operating (e.g., when the vehicle is in motion). Steering system 554 may receive signals from steering actuator 556. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0077] Brake sensor system 546 may be used to operate vehicle brakes in response to receiving signals from brake actuator 548 and / or brake sensors.

[0078] May include one or more system on chip (SoC) 504 ( Figure 5C ) and / or one or more controllers 536 of one or more GPUs can provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 500. For example, the one or more controllers can send signals to operate vehicle brakes via one or more brake actuators 548, to operate the steering system 554 via one or more steering actuators 556, and to operate the propulsion system 550 via one or more throttles / accelerators 552. The one or more controllers 536 may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals and output operating commands (e.g., signals representing commands) to achieve autonomous driving and / or assist a human driver in driving the vehicle 500. The one or more controllers 536 may include a first controller 536 for autonomous driving functions, a second controller 536 for functional safety functions, a third controller 536 for artificial intelligence functions (e.g., computer vision), a fourth controller 536 for infotainment functions, a fifth controller 536 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 536 may handle two or more of the above functions, two or more controllers 536 may handle a single function, and / or any combination thereof.

[0079] The one or more controllers 536 may provide signals for controlling one or more components and / or systems of the vehicle 500 in response to sensor data (e.g., sensor input) received from one or more sensors. The sensor data may be received from, for example and without limitation, a global navigation satellite system sensor ("GNSS") 558 (e.g., a global positioning system sensor), a RADAR sensor 560, an ultrasonic sensor 562, a LIDAR sensor 564, an inertial measurement unit (IMU) sensor 566 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 596, a stereo camera 568, a wide angle camera 570 (e.g., a fisheye camera), an infrared camera 572, a surround camera 574 (e.g., a 360 degree camera), a long-range and / or mid-range camera 598, a speed sensor 544 (e.g., for measuring the velocity of the vehicle 500), a vibration sensor 542, a steering sensor 540, a brake sensor (e.g., as part of a brake sensor system 546), and / or other sensor types.

[0080] One or more of the controllers 536 may receive inputs (e.g., represented by input data) from the instrument cluster 532 of the vehicle 500 and provide outputs (e.g., represented by output data, display data, etc.) via a human machine interface (HMI) display 534, an audible annunciator, a speaker, and / or via other components of the vehicle 500. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Figure 5C The HMI display 534 may include information such as a high-definition ("HD") map 522 of the vehicle 500, location data (e.g., the location of the vehicle 500 on the map), directions, the locations of other vehicles (e.g., an occupancy grid), information about objects and object states as sensed by the controller 536, and the like. For example, the HMI display 534 may display information about the presence of one or more objects (e.g., street signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that the vehicle has made, is making, or will make (e.g., changing lanes now, leaving 34B in two miles, etc.). In another example, the HMI display 534 may display one or more indicators validated using the techniques discussed herein.

[0081] The vehicle 500 further includes a network interface 524 that can communicate via one or more networks using one or more wireless antennas 526 and / or a modem. For example, the network interface 524 may be capable of communicating via Long Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile Communications (“GSM”), IMT-CDMA Multi-Carrier (“CDMA2000”), etc. The one or more wireless antennas 526 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.) using one or more local area networks such as Bluetooth, Bluetooth Low Energy (“LE”), Z-wave, ZigBee, etc. and / or one or more low power wide area networks (LPWANs) such as LoRaWAN, SigFox, etc.

[0082] Figure 5B According to some embodiments of the present disclosure, Figure 5A An example of camera positions and fields of view for an example autonomous vehicle 500 is shown. The cameras and respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or replaceable cameras may be included, and / or the cameras may be located at different locations on the vehicle 500.

[0083] The camera type for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 500. The camera may operate at an automotive safety integrity level (ASIL) B and / or at another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120fps, 240fps, etc., depending on the embodiment. The camera may be capable of using a rolling shutter, a global shutter, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red-white-white-white (RCCC) color filter array, a red-white-white-blue (RCCB) color filter array, a red-blue-green-white (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensor (RGGB) color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In some embodiments, a clear pixel camera such as a camera with an RCCC, RCCB, and / or RBGC color filter array may be used in an effort to improve light sensitivity.

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

[0085] One or more of the cameras may be mounted in a mounting assembly such as a custom designed (three-dimensional "3D" printed) assembly to cut out stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirror) that may interfere with the camera's image data capture capabilities. With respect to the wing mirror mounting assembly, the wing mirror assembly may be custom 3D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras may be integrated into the wing mirror. For side view cameras, one or more cameras may also be integrated into the four pillars at each corner of the cab.

[0086] A camera (e.g., a front-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 500 can be used for surround view to help identify the forward path and obstacles, as well as assist in providing information critical to generating an occupancy grid and / or determining a preferred vehicle path with the help of one or more controllers 536 and / or control SoCs. The front-facing camera can be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. The front-facing camera can also be used for ADAS functions and systems, including lane departure warning ("LDW"), autonomous cruise control ("ACC"), and / or other functions such as traffic sign recognition.

[0087] A variety of cameras may be used in the front-facing configuration, including, for example, a monocular camera platform including a complementary metal oxide semiconductor ("CMOS") color imager. Another example may be a wide-angle camera 570, which may be used to sense objects (e.g., pedestrians, intersection traffic, or bicycles) entering the field of view from the periphery. Figure 5B Only one wide-angle camera is shown in the figure, but there may be any number (including zero) of wide-angle cameras 570 on the vehicle 500. In addition, any number of long-range cameras 598 (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not been trained. Long-range cameras 598 may also be used for object detection and classification and basic object tracking.

[0088] One or more stereo cameras 568 may also be included in the front configuration. In at least one embodiment, one or more of the stereo cameras 568 may include an integrated control unit including an expandable processing unit that may provide a multi-core microprocessor and programmable logic ("FPGA") with an integrated controller area network ("CAN") or Ethernet interface on a single chip. Such a unit may be used to generate a 3D map of the vehicle environment, including distance estimates for all points in the image. An alternative stereo camera 568 may include a compact stereo vision sensor that may include two camera lenses (one on each side) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. Other types of stereo cameras 568 may be used in addition to or alternatively to those described herein.

[0089] A camera (e.g., a side view camera) having a field of view of a portion of the environment including the sides of the vehicle 500 can be used for surround viewing, providing information used to create and update the occupancy grid and generate side impact collision warnings. Figure 5B The four surround cameras 574 shown in FIG. 5 can be placed on the vehicle 500. The surround cameras 574 can include a wide-angle camera 570, a fisheye camera, a 360-degree camera, and / or the like. For example, the four fisheye cameras can be placed in front, behind, and on the sides of the vehicle. In an alternative arrangement, the vehicle can use three surround cameras 574 (e.g., left, right, and rear), and can utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0090] A camera having a field of view that includes a portion of the environment behind the vehicle 500 (e.g., a rear view camera) can be used to assist with parking, surround view, rear collision warning, and creating and updating an occupancy grid. A variety of cameras can be used, including but not limited to cameras that are also suitable as front-facing cameras as described herein (e.g., long-range and / or mid-range cameras 598, stereo cameras 568, infrared cameras 572, etc.).

[0091] Figure 5C According to some embodiments of the present disclosure, Figure 5ABlock diagram of an example system architecture of an example autonomous vehicle 500. It should be understood that this arrangement and other arrangements described herein are set forth merely as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any appropriate combination and location. The various functions described herein as being performed by an entity may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory.

[0092] Figure 5C Each of the components, features, and systems of the vehicle 500 is illustrated as being connected via a bus 502. The bus 502 may include a controller area network (CAN) data interface (alternatively, referred to herein as a "CAN bus"). The CAN may be a network inside the vehicle 500 that assists in controlling various features and functions of the vehicle 500, such as the actuation of brakes, acceleration, braking, steering, windshield wipers, and the like. The CAN bus may be configured to have tens or even hundreds of nodes, each with its own unique identifier (e.g., a CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compatible.

[0093] Although bus 502 is described as a CAN bus here, this is not intended to be limiting. For example, in addition to or alternatively to the CAN bus, FlexRay and / or Ethernet can be used. In addition, although bus 502 is represented by a single line, this is not intended to be limiting. For example, there can be any number of buses 502, 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 502 can be used to perform different functions, and / or can be used for redundancy. For example, a first bus 502 can be used for a collision avoidance function, and a second bus 502 can be used for drive control. In any example, each bus 502 can communicate with any component of the vehicle 500, and two or more buses 502 can communicate with the same component. In some examples, each SoC 504, each controller 536, and / or each computer in the vehicle can have access to the same input data (e.g., input from a sensor of the vehicle 500), and can be connected to a common bus such as a CAN bus.

[0094] The vehicle 500 may include one or more controllers 536, such as those described herein. Figure 5A Those controllers described. Controller 536 can be used for a variety of functions. Controller 536 can be coupled to any other different components and systems of vehicle 500, and can be used for control of vehicle 500, artificial intelligence of vehicle 500, infotainment for vehicle 500, and / or the like.

[0095] The vehicle 500 may include one or more system on chip (SoC) 504. The SoC 504 may include a CPU 506, a GPU 508, a processor 510, a cache 512, an accelerator 514, a data store 516, and / or other components and features not shown. The SoC 504 may be used to control the vehicle 500 in a variety of platforms and systems. For example, one or more SoCs 504 may be combined with an HD map 522 in a system (e.g., a system of the vehicle 500), and the HD map may be downloaded from one or more servers (e.g., a server) via a network interface 524. Figure 5D one or more servers 578) to obtain map refreshes and / or updates.

[0096] CPU 506 may include a CPU cluster or CPU complex (alternatively, referred to herein as "CCPLEX"). CPU 506 may include multiple cores and / or L2 caches. For example, in some embodiments, CPU 506 may include eight cores in a coherent multiprocessor configuration. In some embodiments, CPU 506 may include four dual-core clusters, each with a dedicated L2 cache (e.g., 2MB L2 cache). CPU 506 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, such that any combination of CPU 506 clusters can be active at any given time.

[0097] CPU 506 may implement power management capabilities including one or more of the following features: each hardware block may be automatically clock gated when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to the execution of WFI / WFE instructions; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. CPU 506 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wake-up times are specified, and hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core may support a simplified power state entry sequence in software, with the work being offloaded to the microcode.

[0098] The GPU 508 may include an integrated GPU (alternatively, referred to herein as an "iGPU"). The GPU 508 may be programmable and efficient for parallel workloads. In some examples, the GPU 508 may use an enhanced tensor instruction set. The GPU 508 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB storage capacity). In some embodiments, the GPU 508 may include at least eight streaming microprocessors. The GPU 508 may use a computing application programming interface (API). In addition, the GPU 508 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0099] In the case of automotive and embedded use, GPU 508 can be power optimized to achieve optimal performance. For example, GPU 508 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 508 can be manufactured using other semiconductor manufacturing processes. Each streaming microprocessor can merge several mixed precision processing cores divided into multiple blocks. For example and without limitation, 64 PF32 cores and 32 PF64 cores can be divided into four processing blocks. In such an example, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed precision NVIDIA tensor cores for deep learning matrix arithmetic, L0 instruction cache, thread beam (warp) scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor can include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of calculations and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. A streaming microprocessor may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0100] GPU 508 can include high bandwidth memory (HBM) and / or a 16GB HBM2 memory subsystem that provides a peak memory bandwidth of approximately 900 GB / s in some examples. In some examples, synchronous graphics random access memory (SGRAM), such as fifth generation graphics double data rate synchronous random access memory (GDDR5), can be used in addition to or in lieu of HBM memory.

[0101] The GPU 508 may include unified memory technology that includes access counters to allow memory pages to be more accurately migrated to the processor that accesses them most frequently, thereby improving the efficiency of memory ranges shared between processors. In some examples, address translation service (ATS) support can be used to allow the GPU 508 to directly access the CPU 506 page table. In such an example, when the GPU 508 memory management unit (MMU) experiences a miss, an address translation request can be transmitted to the CPU 506. In response, the CPU 506 can look up the virtual-physical mapping for the address in its page table and transmit the translation back to the GPU 508. In this way, unified memory technology can allow a single unified virtual address space to be used for the memory of both the CPU 506 and the GPU 508, thereby simplifying GPU 508 programming and porting applications to the GPU 508.

[0102] In addition, GPU 508 may include access counters that can track how often GPU 508 accesses the memory of other processors. Access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0103] SoC 504 may include any number of caches 512, including those described herein. For example, cache 512 may include an L3 cache available to both CPU 506 and GPU 508 (e.g., connected to both CPU 506 and GPU 508). Cache 512 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4MB or more, although smaller cache sizes may also be used.

[0104] The SoC 504 may include one or more arithmetic logic units (ALUs) that may be used to perform processing for any of a variety of tasks or operations with respect to the vehicle 500, such as processing a DNN. Additionally, the SoC 504 may include a floating point unit (FPU) or other math coprocessor or digital coprocessor type for performing mathematical operations within the system. For example, the SoC 504 may include one or more FPUs integrated as execution units within the CPU 506 and / or GPU 508.

[0105] SoC 504 may include one or more accelerators 514 (e.g., hardware accelerators, software accelerators, or combinations thereof). For example, SoC 504 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. The large on-chip memory (e.g., 4MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to supplement GPU 508 and offload some tasks of GPU 508 (e.g., free up more cycles of GPU 508 for performing other tasks). As an example, accelerator 514 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are sufficiently stable to be easily controlled for acceleration. When used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0106] The accelerator 514 (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 may be configured to provide an additional 10 trillion operations per second for deep learning applications and reasoning. The TPU may be an accelerator configured to perform image processing functions (e.g., for CNN, RCNN, etc.) and optimized for performing image processing functions. The DLA may be further optimized for a specific set of neural network types and floating point operations and reasoning. The design of the DLA may provide higher performance per millimeter than a general-purpose GPU, and far exceeds the performance of the CPU. The TPU may perform several functions, including a single instance convolution function, support, for example, INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0107] The DLA can quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a wide variety of functions, such as, but not limited to: a CNN for object recognition and detection using data from a camera sensor; a CNN for distance estimation using data from a camera sensor; a CNN for emergency vehicle detection and identification and detection using data from a microphone; a CNN for facial recognition and vehicle owner identification using data from a camera sensor; and / or a CNN for safety and / or security-related events.

[0108] The DLA can perform any function of the GPU 508, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 508. For example, the designer can focus the processing of CNNs and floating point operations on the DLA, and leave other functions to the GPU 508 and / or other accelerators 514.

[0109] The accelerator 514 (e.g., a hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may be alternatively 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 may provide a balance between performance and flexibility. For example, each PVA may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

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

[0111] The DMA may enable components of the PVA to access system memory independently of the CPU 506. The DMA may support any number of features used to provide optimizations for the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support addressing in up to six or more dimensions, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0112] The vector processor can be a programmable processor that can be designed to efficiently and flexibly perform programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA can include a PVA core and two vector processing subsystem partitions. The PVA core can include a processor subsystem, one or more DMA engines (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem can operate as the main processing engine of the PVA, and can include a vector processing unit (VPU), an instruction cache, and / or a vector memory (e.g., VMEM). The VPU core can include a digital signal processor, such as, for example, a single instruction multiple data (SIMD), a very long instruction word (VLIW) digital signal processor. The combination of SIMD and VLIW can enhance throughput and rate.

[0113] Each of the vector processors may include an instruction cache and may be coupled to a dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, multiple vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on sequence images or portions of an image. Among other things, any number of PVAs may be included in a hardware acceleration cluster, and any number of vector processors may be included in each of these PVAs. In addition, the PVA may include additional error correction code (ECC) memory to enhance overall system security.

[0114] The accelerator 514 (e.g., a hardware acceleration cluster) may include an on-chip computer vision network and SRAM to provide high bandwidth, low latency SRAM for the accelerator 514. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and not limited to, eight field-configurable memory blocks, which may be accessed by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides high-speed memory access to the PVA and DLA. The backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using APB).

[0115] The on-chip computer vision network may include an interface that determines that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such an interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface may comply with ISO 26262 or IEC 61508 standards, but other standards and protocols may also be used.

[0116] In some examples, SoC 504 may include a real-time ray tracing hardware accelerator such as described in U.S. Patent Application No. 16 / 101,232 filed on August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) in order to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for SONAR system simulations, for general wave propagation simulations, for comparison with LIDAR data for the purpose of positioning and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0117] The accelerator 514 (e.g., a cluster of hardware accelerators) has a wide range of uses in autonomous driving. The PVA can be a programmable visual accelerator that can be used for key processing levels in ADAS and autonomous vehicles. The capabilities of the PVA are a good match for algorithmic domains that require predictable processing, low power, and low latency. In other words, the PVA performs well on semi-dense or dense rule computations, and even on small data sets that require predictable runtimes with low latency and low power. Therefore, in the context of a platform for autonomous vehicles, the PVA is designed to run classic computer vision algorithms because they are efficient at object detection and integer math.

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

[0119] In some examples, PVA can be used to perform dense optical flow. According to the process raw RADAR data (e.g., using 4D fast Fourier transform) to provide processed RADAR. In other examples, PVA is used for time-of-flight depth processing, which is for example by processing raw time-of-flight data to provide processed time-of-flight data.

[0120] DLA can be used to run any type of network to enhance control and driving safety, including, for example, a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability, or as providing a relative "weight" of each detection compared to other detections. This confidence value enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positive detections. In an automatic emergency braking (AEB) system, a false positive detection will cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detection should be considered a trigger for AEB. DLA can run a neural network for regressing confidence values. The neural network can take at least some subset of parameters as its input, such as bounding box dimensions, ground plane estimates obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 566 output related to vehicle 500 orientation and distance, 3D position estimates of objects obtained from neural networks and / or other sensors (e.g., LIDAR sensor 564 or RADAR sensor 560), etc.

[0121] SoC 504 may include one or more data stores 516 (e.g., memory). Data store 516 may be on-chip memory of SoC 504 that may store neural networks to be executed on the GPU and / or DLA. In some examples, data store 516 may be large enough to store multiple instances of the neural network for redundancy and safety. Data store 512 may include an L2 or L3 cache 512. References to data store 516 may include references to memory associated with PVA, DLA, and / or other accelerators 514 as described herein.

[0122] SoC 504 may include one or more processors 510 (e.g., embedded processors). Processor 510 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management functions and related safety implementations. The boot and power management processor may be part of the SoC 504 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, auxiliary system low power state transitions, SoC 504 thermal and temperature sensor management, and / or SoC 504 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC 504 may use the ring oscillator to detect the temperature of CPU 506, GPU 508, and / or accelerator 514. If it is determined that the temperature exceeds a threshold, the boot and power management processor may enter a temperature fault routine and place SoC 504 in a lower power state and / or place the vehicle 500 in a driver safety parking mode (e.g., parking the vehicle 500 safely).

[0123] Processor 510 may further include a set of embedded processors that may be used as an audio processing engine. The audio processing engine may be an audio subsystem that allows for full hardware support for multi-channel audio through multiple interfaces and a wide range of flexible audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core having a digital signal processor with dedicated RAM.

[0124] The processor 510 may further include an always-on processor engine that may provide the necessary hardware features to support low-power sensor management and wake-up use cases. The always-on processor engine may include a processor core, tightly coupled RAM, supporting peripherals (e.g., timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0125] The processor 510 may further include a safety cluster engine, which includes a dedicated processor subsystem that handles safety management of automotive applications. The safety cluster engine may include two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.), and / or routing logic. In safety mode, the two or more cores may operate in lockstep mode and act as a single core with comparison logic to detect any differences between their operations.

[0126] Processor 510 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0127] Processor 510 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] The processor 510 may include a video image compositer, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required for the video playback application to produce a final image for the player window. The video image compositer may perform lens distortion correction for the wide-angle camera 570, the surround camera 574, and / or for an in-cab surveillance camera sensor. The in-cab surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC, configured to recognize in-cab events and respond accordingly. The in-cab system may perform lip reading to activate mobile phone service and place calls, dictate emails, change vehicle destinations, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other circumstances.

[0129] The video image compositer may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, in the case of motion in the video, the noise reduction appropriately weights the spatial information and reduces the weight of information provided by adjacent frames. In the case where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositer may use information from previous images to reduce noise in the current image.

[0130] The video image compositor may also be configured to perform stereoscopic rectification on the input stereoscopic footage frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use and the GPU 508 does not need to continuously render new surfaces. Even when the GPU 508 is powered on and active for 3D rendering, the video image compositor may be used to offload the GPU 508 to improve performance and responsiveness.

[0131] SoC 504 may further include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from a camera, a high-speed interface, and / or a video input block that may be used for camera and related pixel input functions. SoC 504 may further include an input / output controller that may be controlled by software and may be used to receive I / O signals that are not committed to a specific role.

[0132] SoC 504 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management and / or other devices. SoC 504 may be used to process data from cameras (connected via Gigabit multimedia serial link and Ethernet), sensors (e.g., LIDAR sensor 564, RADAR sensor 560, etc., which may be connected via Ethernet), data from bus 502 (e.g., speed of vehicle 500, steering wheel position, etc.), data from GNSS sensor 558 (connected via Ethernet or CAN bus). SoC 504 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines, and which may be used to free up CPU 506 from routine data management tasks.

[0133] SoC 504 can be an end-to-end platform with a flexible architecture that spans automation levels 3-5, providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS technologies to achieve diversity and redundancy, together with deep learning tools to provide a platform for a flexible and reliable driving software stack. SoC 504 can be faster, more reliable, and even more energy efficient and space efficient than conventional systems. For example, when combined with CPU 506, GPU 508, and data storage 516, accelerator 514 can provide a fast and efficient platform for level 3-5 autonomous vehicles.

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

[0135] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a hardware acceleration cluster, the techniques described herein allow multiple neural networks to be executed simultaneously and / or sequentially, and the results to be combined together to achieve Level 3-5 autonomous driving functions. For example, a CNN executed on a DLA or dGPU (e.g., GPU 520) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which a neural network has not been specifically trained. The DLA may 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 the CPU complex.

[0136] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks may be running simultaneously. For example, a warning sign consisting of "Caution: Flashing Lights Indicate Icing Conditions" along with electric lights may be interpreted by several neural networks independently or collectively. The sign itself may be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing Lights Indicate Icing Conditions" may be interpreted by a deployed second neural network that informs the vehicle's path planning software (preferably executing on a CPU complex) that icing conditions exist when the flashing lights are detected. The flashing lights may be identified by operating a deployed third neural network over multiple frames that informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks may be running simultaneously, for example, within the DLA and / or on the GPU 508.

[0137] In some examples, a CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 500. The always-on sensor processing engine can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's door, and in security mode, disable the vehicle when the owner leaves the vehicle. In this way, the SoC 504 provides security against theft and / or carjacking.

[0138] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 596 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 504 uses CNN to classify environmental and urban sounds and to classify visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing rate of emergency vehicles (e.g., by using the Doppler effect). The CNN can also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by the GNSS sensor 558. Thus, for example, when operating in Europe, the CNN will seek to detect European sirens, and when in the United States, the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, with the assistance of the ultrasonic sensor 562, the control program can be used to execute emergency vehicle safety routines to slow the vehicle, drive to the side of the road, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0139] The vehicle may include a CPU 518 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 504 via a high-speed interconnect (e.g., PCIe). The CPU 518 may include, for example, an X86 processor. The CPU 518 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 504, and / or monitoring the status and health of the controller 536 and / or the infotainment SoC 530.

[0140] The vehicle 500 may include a GPU 520 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 504 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 520 may provide additional artificial intelligence functionality, for example, by executing redundant and / or different neural networks, and may be used to train and / or update the neural network based on inputs (e.g., sensor data) from sensors of the vehicle 500.

[0141] The vehicle 500 may further include a network interface 524, which may include one or more wireless antennas 526 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). The network interface 524 can be used to enable wireless connections with the cloud (e.g., with a server 578 and / or other network devices), with other vehicles, and / or with computing devices (e.g., a passenger's client device) via the Internet. In order to communicate with other vehicles, a direct link can be established between the two vehicles, and / or an indirect link can be established (e.g., across a network and through the Internet). The direct link can be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link can provide the vehicle 500 with information about vehicles approaching the vehicle 500 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 500). This functionality can be part of the collaborative adaptive cruise control functionality of the vehicle 500.

[0142] The network interface 524 may include a SoC that provides modulation and demodulation functions and enables the controller 536 to communicate over a wireless network. The network interface 524 may include a radio frequency front end for up-conversion from baseband to radio frequency and down-conversion from radio frequency to baseband. The frequency conversion may be performed by a known process and / or may be performed using a super-heterodyne process. In some examples, the radio frequency front end function may be provided by a separate chip. The network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0143] The vehicle 500 may further include data storage 528, which may include off-chip storage (e.g., outside the SoC 504). The data storage 528 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, hard disk, and / or other components and / or devices that can store at least one bit of data.

[0144] The vehicle 500 may further include a GNSS sensor 558. The GNSS sensor 558 (e.g., GPS, assisted GPS sensor, differential GPS (DGPS) sensor, etc.) is used to assist in mapping, perception, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 558 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0145] The vehicle 500 may further include a RADAR sensor 560. The RADAR sensor 560 may be used by the vehicle 500 for remote vehicle detection even in darkness and / or inclement weather conditions. The RADAR functional safety level may be ASILB. The RADAR sensor 560 may use CAN and / or bus 502 (e.g., to transmit data generated by the RADAR sensor 560) for control and access to object tracking data, accessing Ethernet in some examples to access raw data. A variety of RADAR sensor types may be used. For example and without limitation, the RADAR sensor 560 may be suitable for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0146] The RADAR sensor 560 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, and the like. In some examples, the long-range RADAR may be used for adaptive cruise control functions. The long-range RADAR system may provide a wide field of view (e.g., within a range of 250m) achieved by two or more independent browsing. The RADAR sensor 560 may help distinguish between static objects and moving objects, and may be used by the ADAS system for emergency braking assistance and forward collision warnings. The long-range RADAR sensor may include a single-station multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In an example with six antennas, the central four antennas may create a focused beam pattern designed to record the surroundings of the vehicle 500 at a higher rate with minimal traffic interference from adjacent lanes. The other two antennas may expand the field of view, making it possible to quickly detect vehicles entering or leaving the lane of the vehicle 500.

[0147] As an example, a medium-range RADAR system may include a range of up to 560m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 550 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on both ends of a rear bumper. When mounted on both ends of a rear bumper, such a RADAR sensor system may create two beams that continuously monitor the blind spots behind and beside the vehicle.

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

[0149] The vehicle 500 may further include ultrasonic sensors 562. The ultrasonic sensors 562, which may be placed on the front, rear, and / or sides of the vehicle 500, may be used for parking assistance and / or creating and updating occupancy grids. A variety of ultrasonic sensors 562 may be used, and different ultrasonic sensors 562 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensors 562 may operate at a functional safety level of ASIL B.

[0150] The vehicle 500 may include a LIDAR sensor 564. The LIDAR sensor 564 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 564 may be ASIL B for functional safety level. In some examples, the vehicle 500 may include multiple LIDAR sensors 564 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0151] In some examples, the LIDAR sensor 564 may be able to provide a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensors 564 may have, for example, an advertised range of approximately 500m, an accuracy of 2cm-3cm, and support for 500Mbps Ethernet connections. In some examples, one or more non-protruding LIDAR sensors 564 may be used. In such examples, the LIDAR sensor 564 may be implemented as a small device that can be embedded in the front, back, side, and / or corner of the vehicle 500. In such examples, the LIDAR sensor 564 may provide a field of view of up to 120 degrees horizontally and 35 degrees vertically, with a range of 200m, even for low reflectivity objects. The front-mounted LIDAR sensor 564 may be configured for a horizontal field of view between 45 and 135 degrees.

[0152] In some examples, LIDAR technologies such as 3D flash LIDAR may also be used. 3D flash LIDAR uses a flash of laser as an emission source to illuminate the vehicle's surroundings up to about 200m. The flash LIDAR unit includes a receiver that records the laser pulse transmission time and the reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LIDAR can allow highly accurate and distortion-free images of the surrounding environment to be generated with each laser flash. In some examples, four flash LIDAR sensors can be deployed, one on each side of the vehicle 500. Available 3D flash LIDAR systems include solid-state 3D staring array LIDAR cameras (e.g., non-browsing LIDAR devices) with no moving parts other than fans. The flash LIDAR device can use 5 nanosecond Class I (eye-safe) laser pulses per frame, and can capture the reflected laser light in the form of a 3D range point cloud and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor 564 may be less susceptible to motion blur, vibration, and / or shock.

[0153] The vehicle may further include an IMU sensor 566. In some examples, the IMU sensor 566 may be located at the center of the rear axle of the vehicle 500. The IMU sensor 566 may include, for example and without limitation, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, for example, in a six-axis application, the IMU sensor 566 may include an accelerometer and a gyroscope, and in a nine-axis application, the IMU sensor 566 may include an accelerometer, a gyroscope, and a magnetometer.

[0154] In some embodiments, the IMU sensor 566 can be implemented as a miniature high-performance GPS-assisted inertial navigation system (GPS / INS) that combines a micro-electromechanical system (MEMS) inertial sensor, a high-sensitivity GPS receiver, and an advanced Kalman filter algorithm to provide estimates of position, velocity, and attitude. Thus, in some examples, the IMU sensor 566 can enable the vehicle 500 to estimate heading by directly observing and correlating velocity changes from the GPS to the IMU sensor 566 without the need for input from a magnetic sensor. In some examples, the IMU sensor 566 and the GNSS sensor 558 can be combined into a single integrated unit.

[0155] The vehicle may include microphones 596 positioned in and / or around the vehicle 500. The microphones 596 may be used for, among other things, emergency vehicle detection and identification.

[0156] The vehicle may further include any number of camera types, including a stereo camera 568, a wide angle camera 570, an infrared camera 572, a surround camera 574, a long-range and / or mid-range camera 598, and / or other camera types. These cameras may be used to capture image data around the entire periphery of the vehicle 500. The type of camera used depends on the embodiment and the requirements of the vehicle 500, and any combination of camera types may be used to provide the necessary coverage around the vehicle 500. In addition, 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 not limitation, the cameras may support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may be described in detail with respect to Figure 5A and Figure 5B Described in more detail.

[0157] The vehicle 500 may further include a vibration sensor 542. The vibration sensor 542 may measure vibrations of components of the vehicle, such as an axle. For example, changes in vibration may indicate changes in the road surface. In another example, when two or more vibration sensors 542 are used, the difference between the vibrations may be used to determine friction or slip of the road surface (e.g., when there is a vibration difference between a powered drive shaft and a free-spinning shaft).

[0158] The vehicle 500 may include an ADAS system 538. In some examples, the ADAS system 538 may include a SoC. The ADAS system 538 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] The ACC system may use a RADAR sensor 560, a LIDAR sensor 564, and / or a camera. The ACC system may include a longitudinal ACC and / or a lateral ACC. The longitudinal ACC monitors and controls the distance to the vehicle immediately in front of the vehicle 500, and automatically adjusts the vehicle speed to maintain a safe distance from the vehicle in front. The lateral ACC performs distance keeping and suggests that the vehicle 500 change lanes when necessary. The lateral ACC is related to other ADAS applications such as LCA and CWS.

[0160] CACC uses information from other vehicles, which can be received from other vehicles indirectly via a wireless link or through a network connection (e.g., through the Internet) via a network interface 524 and / or a wireless antenna 526. A direct link can be provided by a vehicle-to-vehicle (V2V) communication link, while an indirect link can be an infrastructure-to-vehicle (I2V) communication link. Typically, the V2V communication concept provides information about the vehicle immediately ahead (e.g., the vehicle immediately ahead of the vehicle 500 and in the same lane as it), while the I2V communication concept provides information about traffic farther ahead. The CACC system may include either or both of the I2V and V2V information sources. Given information about the vehicle ahead of the vehicle 500, CACC can be more reliable, and it is possible to improve the smoothness of traffic flow and reduce road congestion.

[0161] The FCW system is designed to alert the driver to hazards so that the driver can take corrective action. The FCW system uses a front camera and / or RADAR sensor 560 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibrating components. The FCW system can provide warnings in the form of, for example, sound, visual warnings, vibrations and / or rapid brake pulses.

[0162] The AEB system detects an impending front collision with another vehicle or other object and can automatically apply the brakes if the driver does not take corrective action within specified time or distance parameters. The AEB system can use a front camera and / or RADAR sensor 560 coupled to a dedicated processor, DSP, FPGA and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes in an effort to prevent or at least mitigate the effects of the predicted collision. The AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0163] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 500 crosses a lane marking. When the driver indicates an intention to leave the lane, by activating a turn signal, the LDW system is not activated. The LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0164] The LKA system is a variation of the LDW system. If the vehicle 500 begins to leave the lane, the LKA system provides steering input or braking to correct the vehicle 500.

[0165] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, audible, and / or tactile alerts to indicate that it is unsafe to merge or change lanes. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use a rear-facing camera and / or RADAR sensor 560 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0166] The RCTW system can provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 500 is in reverse. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. The RCTW system can use one or more rear RADAR sensors 560 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0167] Conventional ADAS systems may be prone to false positive results, which may annoy and distract the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether the safety condition actually exists and take action accordingly. However, in the autonomous vehicle 500, in the case of conflicting results, the vehicle 500 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 536 or the second controller 536). For example, in some embodiments, the ADAS system 538 can be a backup and / or auxiliary computer for providing perception information to the backup computer rationality module. The backup computer rationality monitor can run redundant and diverse software on hardware components to detect faults in perception and dynamic driving tasks. The output from the ADAS system 538 can be provided to the supervisory MCU. If the outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to coordinate the conflict to ensure safe operation.

[0168] In some examples, the master computer can be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the direction of the master computer, regardless of whether the auxiliary computer provides conflicting or inconsistent results. In the case where the confidence score does not meet the threshold and the master computer and the auxiliary computer indicate different results (e.g., conflicts), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0169] The supervisory MCU may be configured to run a neural network that is trained and configured to determine conditions under which the auxiliary computer provides a false alarm based on outputs from the primary computer and the auxiliary computer. Thus, the neural network in the supervisory MCU may learn when the output of the auxiliary computer may be trusted and when it may not. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU may learn when the FCW system is identifying a metal object that is not actually dangerous, such as a drain grate or manhole cover that triggers an alarm. Similarly, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In 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 with associated memory. In a preferred embodiment, the supervisory MCU may include and / or be included as a component of the SoC 504.

[0170] In other examples, the ADAS system 538 may include an auxiliary computer that uses traditional computer vision rules to perform ADAS functions. In this way, the auxiliary computer can use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or error in the software running on the main computer and the non-identical software code running on the auxiliary computer provides the same overall result, the supervisory MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware on the main computer does not cause a substantial error.

[0171] In some examples, the output of the ADAS system 538 can be fed to the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, if the ADAS system 538 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information in identifying the object. In other examples, the auxiliary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0172] The vehicle 500 may further include an infotainment SoC 530 (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 530 may include a combination of hardware and software that may be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, 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 distance covered, brake fuel level, oil level, door open / closed, air filter information, etc.) to the vehicle 500. For example, the infotainment SoC 530 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connections, an onboard computer, in-car entertainment, Wi-Fi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 534, a telematics device, a control panel (e.g., for controlling various components, features and / or systems, and / or interacting with them), and / or other components. The infotainment SoC 530 may further be used to provide information (e.g., visual and / or auditory) to a user of the vehicle, such as information from an ADAS system 538, 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. In some embodiments, the infotainment SoC 530 may be used to display indicators indicating respective safety-related conditions around the vehicle. These indicators may be verified using the techniques discussed herein.

[0173] The infotainment SoC 530 may include GPU functionality. The infotainment SoC 530 may communicate with other devices, systems, and / or components of the vehicle 500 via a bus 502 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 530 may be coupled to a supervisory MCU so that in the event of a failure of a main controller 536 (e.g., a main and / or backup computer of the vehicle 500), the GPU of the infotainment system may perform some self-driving functions. In such an example, the infotainment SoC 530 may place the vehicle 500 in a driver safety parking mode as described herein.

[0174] The vehicle 500 may further include an instrument cluster 532 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 532 may include a controller and / or a supercomputer (e.g., a separate controller or a supercomputer). The instrument cluster 532 may include a set of instruments, such as a speedometer, a fuel level, an oil pressure, a tachometer, an odometer, a turn indicator, a shift position indicator, a seat belt warning light, a parking brake warning light, an engine fault light, an airbag (SRS) system information, lighting controls, safety system controls, navigation information, and the like. In some examples, information may be displayed and / or shared between the infotainment SoC 530 and the instrument cluster 532. In other words, the instrument cluster 532 may be included as part of the infotainment SoC 530, or vice versa.

[0175] Figure 5D For a cloud-based server and Figure 5A500. System 576 may include a server 578, a network 590, and a vehicle including the vehicle 500. The server 578 may include a plurality of GPUs 584(A)-584(H) (collectively referred to herein as GPUs 584), PCIe switches 582(A)-582(H) (collectively referred to herein as PCIe switches 582), and / or CPUs 580(A)-580(B) (collectively referred to herein as CPUs 580). The GPUs 584, CPUs 580, and PCIe switches may be interconnected with a high-speed interconnect and / or PCIe connection 586 such as, for example and without limitation, an NVLink interface 588 developed by NVIDIA. In some examples, the GPUs 584 are connected via NVLink and / or NVSwitch SoCs, and the GPUs 584 and PCIe switches 582 are connected via PCIe interconnects. Although eight GPUs 584, two CPUs 580, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the servers 578 may include any number of GPUs 584, CPUs 580, and / or PCIe switches. For example, each of the servers 578 may include eight, sixteen, thirty-two, and / or more GPUs 584.

[0176] Server 578 may receive image data over network 590 and from a vehicle, the image data representing images showing unexpected or changed road conditions, such as recently begun road work. Server 578 may transmit neural network 592, updated neural network 592, and / or map information 594, including information about traffic and road conditions, over network 590 and to the vehicle. Updates to map information 594 may include updates to HD map 522, such as information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, neural network 592, updated neural network 592, and / or map information 594 may have been generated from new training and / or data received from any number of vehicles in the environment and / or based on experience from training performed at a data center (e.g., using server 578 and / or other servers).

[0177] Server 578 can be used to train a machine learning model (e.g., a neural network) based on training data. The training data can be generated by the vehicle, and / or can be generated in a simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., when the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other examples, the training data is not labeled and / or preprocessed (e.g., when the neural network does not require supervised learning). Training can be performed according to any one or more classes of machine learning techniques, including but not limited to the following classes: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including principal component and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternate dictionary learning), rule-based machine learning, anomaly detection, and any variant or combination thereof. Once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via network 590), and / or the machine learning model can be used by server 578 to remotely monitor the vehicle.

[0178] In some examples, server 578 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. Server 578 may include a deep learning supercomputer and / or a dedicated AI computer powered by GPU 584, such as DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 578 may include a deep learning infrastructure of a data center powered only by CPUs.

[0179] The deep learning infrastructure of server 578 may be capable of rapid real-time inference, and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 500. For example, the deep learning infrastructure may receive periodic updates from vehicle 500, such as a sequence of images and / or objects located in the sequence of images that vehicle 500 has located (e.g., via computer vision and / or other machine learning object classification techniques). The deep learning infrastructure may run its own neural network to identify objects and compare them to the objects identified by vehicle 500, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 500 has failed, then server 578 may transmit a signal to vehicle 500 instructing the fail-safe computer of vehicle 500 to take control, notify passengers, and complete a safe parking maneuver.

[0180] For reasoning, the server 578 may include a GPU 584 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT 3). The combination of GPU-powered servers and reasoning acceleration can make real-time responses possible. In other examples, such as when performance is not so important, CPU, FPGA, and other processor-powered servers can be used for reasoning.

[0181] Example computing device

[0182] Figure 6 6 is a block diagram of an example computing device 600 suitable for implementing some embodiments of the present disclosure. The computing device 600 may include an interconnect system 602 that directly or indirectly couples the following devices: memory 604, one or more central processing units (CPUs) 606, one or more graphics processing units (GPUs) 608, communication interfaces 610, input / output (I / O) ports 612, input / output components 614, power supplies 616, one or more presentation components 618 (e.g., displays), and one or more logic units 620. In at least one embodiment, the computing device 600 may include one or more virtual machines (VMs), and / or any of its components may include virtual components (e.g., virtual hardware components). For non-limiting examples, the one or more GPUs 608 may include one or more vGPUs, the one or more CPUs 606 may include one or more vCPUs, and / or the one or more logic units 620 may include one or more virtual logic units. Thus, computing device 600 may include discrete components (eg, a complete GPU dedicated to computing device 600 ), virtual components (eg, a portion of a GPU dedicated to computing device 600 ), or a combination thereof.

[0183] although Figure 6 The various blocks of are shown as being connected via an interconnect system 602 having wires, but this is not intended to be limiting and is merely for clarity. For example, in some embodiments, a presentation component 618 such as a display device may be considered an I / O component 614 (e.g., if the display is a touch screen). As another example, CPU 606 and / or GPU 608 may include memory (e.g., memory 604 may represent a storage device in addition to the memory of GPU 608, CPU 606, and / or other components). In other words, Figure 6The computing devices described herein are illustrative only. No distinction is made between categories such as "workstations," "servers," "laptops," "desktops," "tablets," "client devices," "mobile devices," "handheld devices," "game consoles," "electronic control units (ECUs)," "virtual reality systems," and / or other device or system types, as all are considered Figure 6 within the range of computing devices.

[0184] The interconnection system 602 may represent one or more links or buses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnection system 602 may include one or more links or bus types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and / or another type of bus or link. In some embodiments, there is a direct connection between components. As an example, the CPU 606 may be directly connected to the memory 604. In addition, the CPU 606 may be directly connected to the GPU 608. In the case where there is a direct or point-to-point connection between components, the interconnection system 602 may include a PCIe link to perform the connection. In these examples, the PCI bus does not need to be included in the computing device 600.

[0185] Memory 604 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by computing device 600. Computer-readable media may include volatile and nonvolatile media and removable and non-removable media. By way of example and not limitation, computer-readable media may include computer storage media and communication media.

[0186] Computer storage media may include volatile and nonvolatile media and / or removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 604 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system). Computer storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by computing device 600. As used herein, computer storage media does not include the signals themselves.

[0187] Computer storage media may contain computer readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in such a way that information is encoded into the signal. By way of example and not limitation, computer storage media may include wired media such as a wired network or a direct wired connection, and wireless media such as sound, RF, infrared, and other wireless media. Any combination of the above should also be included within the scope of computer readable media.

[0188] The CPU 606 may be configured to execute at least some of the computer-readable instructions so as to control one or more components of the computing device 600 to perform one or more of the methods and / or processes described herein. Each of the CPUs 606 may include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of simultaneously processing a large number of software threads. The CPU 606 may include any type of processor, and may include different types of processors, depending on the type of computing device 600 implemented (e.g., a processor with fewer cores for mobile devices and a processor with more cores for servers). For example, depending on the type of computing device 600, 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). The computing device 600 may also include one or more CPUs 606 in addition to one or more microprocessors or supplementary coprocessors such as math coprocessors.

[0189] In addition to or in place of the CPU 606, the GPU 608 may also be configured to execute at least some computer-readable instructions to control one or more components of the computing device 600 to perform one or more methods and / or processes described herein. One or more GPUs 608 may be integrated GPUs (e.g., with one or more CPUs 606) and / or one or more GPUs 608 may be discrete GPUs. In an embodiment, one or more GPUs 608 may be coprocessors of one or more CPUs 606. The computing device 600 may use the GPU 608 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, the GPU 608 may be used for general-purpose computing on the GPU (GPGPU). The GPU 608 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. The GPU 608 may generate pixel data for outputting an image in response to a rendering command (e.g., a rendering command from the CPU 606 received via a host interface). The GPU 608 may include a graphics memory such as a display memory for storing pixel data or any other suitable data (e.g., GPGPU data). Display memory may be included as part of memory 604. GPU 608 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 through a switch (e.g., using NVSwitch). When combined together, each GPU 608 may generate pixel data or GPGPU data for different portions of output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory or may share memory with other GPUs.

[0190] In addition to or in lieu of the CPU 606 and / or GPU 608, the logic unit 620 may be configured to execute at least some computer-readable instructions to control one or more components of the computing device 600 to perform one or more methods and / or processes described herein. In an embodiment, the CPU 606, the GPU 608, and / or the logic unit 620 may discretely or jointly perform any combination of methods, processes, and / or portions thereof. The one or more logic units 620 may be a part of and / or integrated into the one or more CPUs 606 and / or the one or more GPUs 608, and / or the one or more logic units 620 may be discrete components of the CPU 606 and / or the GPU 608 or otherwise external thereto. In an embodiment, the one or more logic units 620 may be a processor of the one or more CPUs 606 and / or the one or more GPUs 608.

[0191] Examples of logic unit 620 include one or more processing cores and / or components thereof, such as a data processing unit (DPU), a tensor core (TC), a tensor processing unit (TPU), a pixel vision core (PVC), a vision processing unit (VPU), a graphics processing cluster (GPC), a texture processing cluster (TPC), a streaming multiprocessor (SM), a tree traversal unit (TTU), an artificial intelligence accelerator (AIA), a deep learning accelerator (DLA), an arithmetic logic unit (ALU), an application specific integrated circuit (ASIC), a floating point unit (FPU), an input / output (I / O) element, a peripheral component interconnect (PCI) or a peripheral component interconnect express (PCIe) element, etc.

[0192] The communication interface 610 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 600 to communicate with other computing devices via an electronic communication network, including wired and / or wireless communications. The communication interface 610 may include components and functions that enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low power wide area networks (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, the one or more logic units 620 and / or the communication interface 610 may include one or more data processing units (DPUs) for transmitting data received over the network and / or interconnect system 602 directly to one or more GPUs 608 (e.g., memory of GPUs 608).

[0193] The I / O port 612 can enable the computing device 600 to be logically coupled to other devices including I / O components 614, presentation components 618, and / or other components, some of which can be built into (e.g., integrated into) the computing device 600. Illustrative I / O components 614 include microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dishes, browsers, printers, wireless devices, and the like. The I / O components 614 can provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by the user. In some instances, the input can be transmitted to an appropriate network element for further processing. The NUI can implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition on and adjacent to the screen, air gestures, head and eye tracking, and touch recognition associated with the display of the computing device 600 (as described in more detail below). The computing device 600 may include a depth camera such as a stereo camera system, an infrared camera system, an RGB camera system, touch screen technology, and a combination of these for gesture detection and recognition. Additionally, computing device 600 may include an accelerometer or gyroscope to enable 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 600 to render immersive augmented reality or virtual reality.

[0194] The power supply 616 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 616 may provide power to the computing device 600 to enable the components of the computing device 600 to operate.

[0195] The presentation component 618 may include a display (e.g., a monitor, a touch screen, a television screen, a head-up display (HUD), other display types, or combinations thereof), speakers, and / or other presentation components. The presentation component 618 may receive data from other components (e.g., GPU 608, CPU 606, DPU, etc.) and output the data (e.g., as an image, video, sound, etc.). In some embodiments, the output data includes a visual indicator that is used to alert the driver to potential safety-related conditions around the vehicle or system in which the computing device 600 is located. As described herein, individual indicators within a frame output by the presentation component 618 may be pre-verified by comparing the checksum of the portion of the frame corresponding to the indicator with the expected checksum of the indicator.

[0196] Sample Data Center

[0197] Figure 7 An example data center 700 is shown, which may be used in at least one embodiment of the present disclosure. The data center 700 may include a data center infrastructure layer 710, a framework layer 720, a software layer 730, and an application layer 740.

[0198] like Figure 7 As shown, the data center infrastructure layer 710 may include a resource coordinator 712, grouped computing resources 714, and node computing resources ("node CRs") 716(1)-716(N), where "N" represents any complete positive integer. In at least one embodiment, the node CRs 716(1)-716(N) may include, but are not limited to, any number of central processing units ("CPUs") or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only 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 of the node CRs 716(1)-716(N) may correspond to a server having one or more of the above-mentioned computing resources. In addition, in some embodiments, the node CRs

[0199] 716(1)-716(N) may include one or more virtual components, such as vGPU, vCPU, etc., and / or one or more of the node CRs 716(1)-716(N) may correspond to a virtual machine (VM).

[0200] In at least one embodiment, the computing resources 714 of the grouping may include a separate grouping (not shown) of the node CR716 contained in one or more racks, or many racks (also not shown) contained in the data center of each geographical location. The separate grouping of the node CR716 in the computing resources 714 of the grouping may include computing, network, memory or storage resources that can be configured or assigned to support the grouping of one or more workloads. In at least one embodiment, several node CR716 including CPU, GPU, DPU and / or other processors may be grouped in one or more racks to provide computing resources to support one or more workloads. One or more racks may also include any number of power modules, cooling modules and / or network switches in any combination.

[0201] Resource coordinator 712 may configure or otherwise control one or more nodes CR 716(1)-716(N) and / or grouped computing resources 714. In at least one embodiment, resource coordinator 712 may include a software design infrastructure ("SDI") management entity for data center 700. Resource coordinator 712 may include hardware, software, or some combination thereof.

[0202] In at least one embodiment, Figure 7 As shown, the framework layer 720 may include a job scheduler 733, a configuration manager 734, a resource manager 736, and a distributed file system 738. The framework layer 720 may include a framework that supports the software 732 of the software layer 730 and / or one or more applications 742 of the application layer 740. The software 732 or the application 742 may include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 720 may be, but is not limited to, a free and open source software network application framework, such as Apache Spark, which may utilize the distributed file system 738 for large-scale data processing (e.g., "big data"). TM (hereinafter referred to as "Spark"). In at least one embodiment, the job scheduler 733 may include a Spark driver to facilitate scheduling of workloads supported by various layers of the data center 700. In at least one embodiment, the configuration manager 734 may be able to configure different layers, such as the software layer 730 and the framework layer 720 including Spark and a distributed file system 738 for supporting large-scale data processing. The resource manager 736 can manage cluster or group computing resources mapped to or allocated to support the distributed file system 738 and the job scheduler 733. In at least one embodiment, the cluster or group computing resources may include grouped computing resources 714 at the data center infrastructure layer 710. The resource manager 736 can coordinate with the resource coordinator 712 to manage these mapped or allocated computing resources.

[0203] In at least one embodiment, software 732 included in software layer 730 may include software used by at least a portion of node CRs 716(1)-716(N), grouped computing resources 714, and / or distributed file system 738 of framework layer 720. One or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0204] In at least one embodiment, one or more applications 742 included in the application layer 740 may include one or more types of applications used by at least a portion of the node CRs 716(1)-716(N), the grouped computing resources 714, and / or the distributed file system 738 of the framework layer 720. 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.

[0205] In at least one embodiment, any of the configuration manager 734, resource manager 736, and resource coordinator 712 can implement any number and type of self-modification actions based on any number and type of data acquired in any technically feasible manner. The self-modification actions can relieve the data center operator of the data center 700 from making potentially bad configuration decisions and can avoid underutilized and / or underperforming portions of the data center.

[0206] The data center 700 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 may be trained by calculating weight parameters according to a neural network architecture using the software and computing resources described above with respect to the data center 700. In at least one embodiment, by using weight parameters calculated by one or more training techniques, the resources described above with respect to the data center 700 may be used to infer or predict information using a trained machine learning model corresponding to one or more neural networks, such as but not limited to those described herein.

[0207] In at least one embodiment, the data center 700 can use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, and / or other hardware (or virtual computing resources corresponding thereto) to use the above resources to perform training and / or reasoning. In addition, one or more of the above software and / or hardware resources can be configured as a service to allow users to train or perform information reasoning, such as image recognition, speech recognition, or other artificial intelligence services.

[0208] Example network environment

[0209] A network environment suitable for implementing embodiments of the present disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and / or other device types. The client devices, servers, and / or other device types (e.g., each device) may be configured to communicate with the client device, server, or other device types. Figure 6 The backend device 700 may be implemented on one or more instances of the computing device 600 of the embodiment of the present invention—for example, each device may include similar components, features and / or functions of the computing device 600. In addition, in the case of implementing a backend device (e.g., a server, NAS, etc.), the backend device may be included as part of the data center 700, an example of which is described herein with respect to Figure 7 Describe in more detail.

[0210] The components of the network environment can communicate with each other through the network, which can be wired, wireless, or both. The network can include multiple networks, or a network of multiple networks. For example, the network can include one or more wide area networks (WANs), one or more local area networks (LANs), one or more public networks (such as the Internet and / or the Public Switched Telephone Network (PSTN)), and / or one or more private networks. In the case where the network includes a wireless telecommunications network, components such as base stations, communication towers, or even access points (and other components) can provide wireless connections.

[0211] Compatible network environments may include one or more peer-to-peer network environments (in which case the server may not be included in the network environment), and one or more client-server network environments (in which case one or more servers may be included in the network environment). In a peer-to-peer network environment, the functionality described herein with respect to the server may be implemented on any number of client devices.

[0212] In at least one embodiment, the network environment may include one or more cloud-based network environments, distributed computing environments, combinations thereof, and the like. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more servers, 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 application may include network-based service software or application programs, respectively. In an embodiment, one or more client devices may use network-based service software or application programs (e.g., by accessing the service software and / or application programs 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 a distributed file system that may be used for large-scale data processing (e.g., "big data").

[0213] A cloud-based network environment can provide cloud computing and / or cloud storage that performs any combination of the computing and / or data storage functions (or one or more portions thereof) described herein. Any of these various functions can be distributed across multiple locations from a central or core server (e.g., one or more data centers that can be distributed across states, regions, countries, the world, etc.). If the connection to the user (e.g., client device) is relatively close to an edge server, the core server can assign at least a portion of the functionality to the edge server. A cloud-based network environment can be private (e.g., limited to a single organization), public (e.g., available to many organizations), and / or a combination thereof (e.g., a hybrid cloud environment).

[0214] The client device may include Figure 6 At least some of the components, features, and functionality of the described example computing device 600. By way of example and not limitation, the client device may be embodied as a personal computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality head mounted display, a global positioning system (GPS) or device, a video player, a camera, a surveillance device or system, a vehicle, a vessel, an aircraft, a virtual machine, a drone, a robot, a handheld communication device, a hospital device, a gaming device or system, an entertainment system, an in-vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these described devices, or any other suitable device.

[0215] In summary, the disclosed technology allows a guest virtual machine operating a window channel to specify commands that can be used to verify indicators to be displayed within a frame on the screen. These commands may include (but are not limited to) a full-screen or partial screen surface (image) containing one or more non-overlapping indicators, settings for enabling or disabling the display and / or verification of a single indicator, settings for hardware checksum calculations of the area containing the indicator after combining multiple window channels into a frame, the position of the indicator within the frame, the area of ​​the frame corresponding to the indicator, a mask for identifying the pixel position of the indicator within the area and / or frame, and / or the expected checksum value for each area. These commands are transmitted in a flip-synchronized manner along with the image and / or pixel values ​​of the corresponding window, thereby allowing the display device driving the screen to access and execute these commands on a per-frame basis.

[0216] For example, the command may be written to a buffer in a memory accessible to the display device. During the period between displaying a previous frame on the screen and displaying a next frame on the screen, the display device may execute the command to verify each indicator to be included in the next frame. The verification process may involve retrieving an expected checksum (e.g., a cyclic redundancy check (CRC)) for each indicator from a location in the memory, calculating a checksum for a portion of the frame corresponding to the indicator, and comparing the calculated checksum to the expected checksum. If the two checksums match, no error was found and the frame may continue to be displayed. If the two checksums are different, an error in the indicator display may be detected and an error alert and / or notification may be output instead of or in addition to the frame.

[0217] 1. In some embodiments, a method includes: receiving a set of commands associated with a frame to be displayed on a screen from one or more buffers; determining, based at least on the set of commands, (i) an expected checksum of an indicator to be included in the frame, and (ii) at least a portion of the frame associated with the indicator; calculating a checksum for the at least a portion of the frame; and generating an alarm associated with the indicator based at least on a comparison of the calculated checksum with the expected checksum.

[0218] 2. The method according to clause 1 further includes: calculating a second checksum for at least a second portion of the frame corresponding to a second indicator; and generating a second alarm associated with the second indicator based at least on another comparison of the calculated second checksum with a second expected checksum of the second indicator.

[0219] 3. The method according to any of clauses 1-2, further comprising: preventing the frame or another frame from being displayed on the screen based at least on the alert and the second alert.

[0220] 4. A method according to any of clauses 1-3, wherein said at least part of said frame comprises a region within said frame.

[0221] 5. A method according to any one of clauses 1-4, wherein calculating the checksum comprises: determining a mask associated with at least a portion of the frame; and calculating the checksum based at least on a set of pixel values ​​corresponding to the mask within the frame.

[0222] 6. A method according to any of clauses 1-5, wherein the mask is specified using a set of alpha channel values ​​associated with the at least a portion of the frame.

[0223] 7. The method of any of clauses 1-6, wherein causing the alert to be generated comprises causing an error to be output upon determining that the calculated checksum does not match the expected checksum.

[0224] 8. A method according to any of clauses 1-7, wherein causing the alarm to be generated comprises: incrementing a counter upon determining that the calculated checksum does not match the expected checksum; and causing an error to be output upon determining that the counter reaches or exceeds a threshold.

[0225] 9. The method according to any one of clauses 1 to 8 further comprises: after synthesizing the frame using image data from multiple input channels, performing a comparison of the calculated checksum with the expected checksum.

[0226] 10. A method according to any of clauses 1 to 9, wherein the indicator comprises at least one of: a safety alert, a proximity alert, a weather alert or a road condition alert.

[0227] 11. In some embodiments, a processor includes: one or more circuits, wherein the one or more circuits are used to perform operations including the following: receiving a set of commands associated with a frame to be displayed on a screen from a buffer; determining, based at least on the set of commands, (i) an expected checksum of an indicator to be included in the frame, and (ii) at least a portion of the frame associated with the indicator; calculating a checksum for the at least a portion of the frame; and generating an alarm associated with the indicator based at least on a comparison of the calculated checksum with the expected checksum.

[0228] 12. A processor according to clause 11, wherein the operation further comprises: calculating a second checksum for at least a second portion of the frame corresponding to a second indicator; and generating a second alarm associated with the second indicator based at least on at least another comparison between the calculated second checksum and a second expected checksum of the second indicator.

[0229] 13. A processor according to any of clauses 11-12, wherein the operation further includes: receiving a second set of commands associated with a second frame to be displayed on the screen from the buffer; calculating one or more additional checksums for one or more portions of the second frame corresponding to one or more additional indicators; and causing the second frame to be displayed on the screen based at least on one or more additional comparisons between the one or more additional checksums and one or more additional expected checksums of the one or more additional indicators.

[0230] 14. A processor according to any of clauses 11-13, wherein determining the expected checksum comprises: retrieving the expected checksum from a location specified in the set of commands, wherein the expected checksum is written to the location by a component based on at least a second set of commands associated with the frame.

[0231] 15. A processor according to any one of clauses 11-14, wherein calculating the checksum includes: determining (i) an area within the frame corresponding to the at least part and (ii) a mask within the area corresponding to the indicator based at least on the set of commands; and calculating the checksum based at least on a set of pixel values ​​corresponding to the mask within the area.

[0232] 16. A processor according to any of clauses 11-15, wherein the processor corresponds to a display controller of the screen and the screen is included in a vehicle.

[0233] 17. A processor according to any of clauses 11-16, wherein the set of commands is written to the buffer by a virtual machine that controls a display pipeline including the display controller based on including the indicator in one or more input channels to be combined into the frame.

[0234] 18. A processor according to any of clauses 11-17, wherein the processor is included in at least one of the following: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation of 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing one or more conversational AI operations; a system implementing one or more large language models (LLMs); a system implementing one or more visual language models (VLMs); a system implementing one or more multimodal language models; a system for performing one or more generative AI operations; a system implementing one or more large language models (LLMs); a system for generating synthetic data; a system comprising one or more virtual machines (VMs); a system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources.

[0235] 19. In some embodiments, a system includes: one or more processing units; and one or more memory units storing instructions, which, when executed by the one or more processing units, cause the one or more processing units to perform operations including: receiving a set of commands associated with a frame to be displayed on a screen from a buffer; determining, based at least on the set of commands, (i) an expected checksum of an indicator to be included in the frame, and (ii) at least a portion of the frame associated with the indicator; calculating a checksum for the at least a portion of the frame; and determining whether to generate an alarm associated with the indicator based at least on a comparison of the calculated checksum with the expected checksum.

[0236] 20. A system according to clause 19, wherein the system includes at least one of the following: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation of 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing one or more conversational AI operations; a system implementing one or more large language models (LLMs); a system implementing one or more visual language models (VLMs); a system implementing one or more multimodal language models; a system for performing one or more generative AI operations; a system implementing one or more large language models (LLMs); a system for generating synthetic data; a system comprising one or more virtual machines (VMs); a system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources.

[0237] One technical advantage of the disclosed technology relative to previous methods is the ability to detect errors in indicators to be displayed on the screen after multiple windows and / or input channels containing indicators and / or other visual content are composited into a frame. Therefore, the disclosed technology is able to detect indicator errors that occur in the composition processing stage and the post-composition processing stage immediately before the frame is displayed on the screen. Another technical advantage of the disclosed technology is the synchronous transmission and execution of commands for verifying indicators using pixel values ​​of indicators and / or frames. Therefore, compared to traditional methods involving large-scale communication between a guest virtual machine operating a window channel and a host virtual machine performing indicator verification at the composition processing stage and the post-composition processing stage of the display pipeline, the disclosed technology avoids potential mismatch errors and / or reduces synchronization complexity.

[0238] The present disclosure may be described in the general context of machine-usable instructions or computer codes executed by a computer or other machine such as a personal digital assistant or other handheld device, including computer-executable instructions such as program modules. Typically, program modules including routines, programs, objects, components, data structures, etc. refer to codes that perform specific tasks or implement specific abstract data types. The present disclosure may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communication network.

[0239] As used herein, the description of "and / or" about two or more elements should be interpreted as referring to only one element or a combination of elements. For example, "element A, element B and / or element C" may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B and C. In addition, "at least one of element A or element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, "at least one of element A and element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B.

[0240] The subject matter of the present disclosure is described in detail herein to meet statutory requirements. However, the description itself is not intended to limit the scope of the present disclosure. On the contrary, the inventors have contemplated that the claimed subject matter may also be embodied in other ways to include steps different from the steps described herein in conjunction with other current or future technologies or combinations of similar steps. Moreover, although the terms "step" and / or "block" may be used herein to imply different elements of the method employed, these terms should not be interpreted as implying any particular order among or between the various steps disclosed herein, unless the order of the steps is explicitly described.

Claims

1. A method comprising: receiving a set of commands associated with a frame to be displayed on a screen from one or more buffers; determining, based at least on the set of commands, (i) an expected checksum of an indicator to be included in the frame, and (ii) at least a portion of the frame associated with the indicator; calculating a checksum for the at least a portion of the frame; as well as An alarm associated with the indicator is caused to be generated based at least on a comparison of the calculated checksum with the expected checksum.

2. The method according to claim 1, further comprising: calculating a second checksum for at least a second portion of the frame corresponding to a second indicator; as well as A second alarm associated with the second indicator is caused to be generated based on at least another comparison of the calculated second checksum and a second expected checksum for the second indicator.

3. The method according to claim 2, further comprising: The frame or another frame is prevented from being displayed on the screen based on at least the alert and the second alert.

4. The method according to claim 1, wherein: The at least a portion of the frame includes a region within the frame.

5. The method according to claim 1, wherein: Calculating the checksum includes: determining a mask associated with the at least a portion of the frame; and The checksum is calculated based on at least a set of pixel values ​​corresponding to the mask within the frame.

6. The method according to claim 5, wherein: The mask is specified using a set of alpha channel values ​​associated with the at least a portion of the frame.

7. The method according to claim 1, wherein: Causing generation of the alert includes causing output of an error upon determining that the calculated checksum does not match the expected checksum.

8. The method according to claim 1, wherein: Causing the generating of the alert to include: incrementing a counter upon determining that the calculated checksum does not match the expected checksum; and An error is output when it is determined that the counter reaches or exceeds a threshold.

9. The method according to claim 1, further comprising: After the frame is synthetically generated using image data from a plurality of input channels, a comparison of the calculated checksum with the expected checksum is performed.

10. The method according to claim 1, wherein: The indicator includes at least one of: a safety alert, a proximity alert, a weather alert, or a road condition alert.

11. A processor, comprising: One or more circuits configured to perform operations including: receiving from the buffer a set of commands associated with a frame to be displayed on the screen; determining, based at least on the set of commands, (i) an expected checksum of an indicator to be included in the frame, and (ii) at least a portion of the frame associated with the indicator; calculating a checksum for the at least a portion of the frame; as well as An alarm associated with the indicator is caused to be generated based at least on a comparison of the calculated checksum with the expected checksum.

12. The processor of claim 11, wherein: The operations also include: calculating a second checksum for at least a second portion of the frame corresponding to a second indicator; and A second alarm associated with the second indicator is caused to be generated based on at least one further comparison between the calculated second checksum and a second expected checksum for the second indicator.

13. The processor of claim 11, wherein: The operations also include: receiving from the buffer a second set of commands associated with a second frame to be displayed on the screen; calculating one or more additional checksums for one or more portions of the second frame corresponding to one or more additional indicators; and The second frame is caused to be displayed on the screen based at least on one or more additional comparisons between the one or more additional checksums and one or more additional expected checksums of the one or more additional indicators.

14. The processor of claim 11, wherein: Determining the expected checksum includes retrieving the expected checksum from a location specified in the set of commands, wherein the expected checksum is written to the location by a component based on at least a second set of commands associated with the frame.

15. The processor of claim 11, wherein: Calculating the checksum includes: Based at least on the set of commands, determining (i) a region within the frame corresponding to the at least a portion and (ii) a mask within the region corresponding to the indicator; and The checksum is calculated based on at least a set of pixel values ​​corresponding to the mask within the region.

16. The processor of claim 11, wherein: The processor corresponds to a display controller of the screen, and the screen is included in a vehicle.

17. The processor of claim 16, wherein: The set of commands is written to the buffer by a virtual machine that controls a display pipeline including the display controller based on including the indicator in one or more input channels to be combined into the frame.

18. The processor of claim 11, wherein: The processor is included in at least one of the following: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing one or more simulation operations; A system for performing one or more digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; A system for performing one or more deep learning operations; Systems implemented using edge devices; A system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; Systems implemented using robots; A system for performing one or more conversational AI operations; A system implementing one or more Large Language Models (LLMs); A system implementing one or more visual language models (VLMs); A system implementing one or more multimodal language models; A system for performing one or more generative AI operations; A system implementing one or more Large Language Models (LLMs); Systems for generating synthetic data; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

19. A system comprising: one or more processing units; as well as one or more memory units storing instructions that, when executed by the one or more processing units, cause the one or more processing units to perform operations including: receiving from the buffer a set of commands associated with a frame to be displayed on the screen; determining, based at least on the set of commands, (i) an expected checksum of an indicator to be included in the frame, and (ii) at least a portion of the frame associated with the indicator; calculating a checksum for the at least a portion of the frame; as well as A determination is made whether to generate an alarm associated with the indicator based at least on a comparison of the calculated checksum with the expected checksum.

20. The system of claim 19, wherein: The system comprises at least one of the following: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing one or more simulation operations; A system for performing one or more digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; A system for performing one or more deep learning operations; Systems implemented using edge devices; A system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; Systems implemented using robots; A system for performing one or more conversational AI operations; A system implementing one or more Large Language Models (LLMs); A system implementing one or more visual language models (VLMs); A system implementing one or more multimodal language models; A system for performing one or more generative AI operations; A system implementing one or more Large Language Models (LLMs); Systems for generating synthetic data; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

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