SCENE ILLUMINATION DETECTION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

Abstract

The present disclosure relates to determining a first illumination level, corresponding to an area, based on at least one first illumination detection obtained using a first illumination detector, corresponding to a machine. A second illumination level, corresponding to the area, can be determined based on at least one second illumination detection obtained using a second illumination detector, corresponding to the machine. Based at least on the first and second illumination levels, a scene illumination status of the area can be determined. Based at least on the scene illumination status, one or more lights of the machine can be controlled.

Description

BACKGROUND

[0001] The illumination of a scene can correspond to different states depending on the amount and / or type of light illuminating the scene. For example, a "bright natural" illumination state may correspond to a bright day, where plenty of natural light illuminates the scene. A "dim natural" illumination state may correspond to dark days, dawn, or dusk, where the scene is illuminated by natural light, but at a subdued or dim level. An "artificial" illumination state may correspond to illumination by artificial light in an otherwise dark area (e.g., at night, in a tunnel, etc.). A "dark" illumination state may correspond to a dark area, including little to no illumination from any source. The different illumination states may impair the visibility conditions for machine operation.For example, a machine can activate or deactivate illumination lights (e.g., headlights) depending on the amount of illumination required. Furthermore, different levels of illumination lights can be used depending on different illumination statuses. For example, a high beam illumination can be activated for dark areas.

[0002] Furthermore, in some cases, a machine may travel through different areas corresponding to different illumination states. For example, a vehicle may travel through a dark area, then an artificially lit area, then back into a dark area. In some cases, such transitions may occur quite quickly and / or fairly regularly. For example, a vehicle may travel in an artificially lit area, then briefly transition into a dark area, and then back into an artificially lit area.

[0003] Such transitions can make it difficult for systems to make decisions about when to switch between different types of illumination lights. Additionally or alternatively, such transitions can cause traditional systems to rapidly switch between different levels of illumination lights, distracting machine operators switching between different illumination settings and / or other machine operators, other machines, pedestrians, etc., who are near or within the light field of the machines. OVERVIEW

[0004] The invention is defined by the claims. To illustrate the invention, aspects and embodiments are described herein, which may or may not be within the scope of the claims.

[0005] The present disclosure relates to determining a first illumination level corresponding to an area based on at least one first illumination detection obtained using a first illumination detector corresponding to a machine. A second illumination level corresponding to the area may be determined based on at least one second illumination detection obtained using a second illumination detector corresponding to the machine. A scene illumination status of the area may be determined based at least on the first illumination level and the second illumination level. One or more lights of the machine may be controlled based at least on the scene illumination status.

[0006] According to one or more embodiments of the present disclosure, one or more (e.g., multiple) illumination sensors may be used to determine a scene illumination status of an area in which a machine may be located and / or traveling. For example, in some embodiments, a first illumination sensor may be configured to obtain first sensor data corresponding to a near-field area around and / or in a direction of travel of the machine. Additionally or alternatively, a second illumination sensor may be configured to obtain second sensor data corresponding to a far-field area around and / or in a direction of travel of the machine. In these or other embodiments, the first sensor data and the second sensor data may be used to determine a scene illumination status of the area in which the machine is located and / or an area in front of the machine in the direction of travel of the machine.

[0007] One or more embodiments of the present disclosure may help more accurately determine illumination states not only for areas where a machine may currently be located, but also for areas where the machine may be located in a relatively short period of time. Additionally or alternatively, one or more embodiments of the present disclosure may better assist in determining whether transitions between illumination states are relatively short, which may allow for better decision-making regarding deciding between and / or changing the illumination states that may be applied to the machine.

[0008] The disclosure extends to any novel aspects or features described and / or illustrated herein.

[0009] Further features of the disclosure are characterized by the independent and dependent claims.

[0010] Any feature in one aspect of the disclosure may be applied to other aspects of the disclosure in any suitable combination. In particular, method aspects may be applied to device or system aspects, and vice versa.

[0011] Furthermore, features implemented in hardware may also be implemented in software, and vice versa. Any reference to software and hardware features in this description should be construed accordingly.

[0012] Any system or device feature described herein may also be provided as a method feature, and vice versa. Functionally described system and / or device aspects (including means-plus-function features) may alternatively be expressed in terms of their corresponding structure, such as a suitably programmed processor and associated memory.

[0013] It should also be understood that certain combinations of the various features described and defined in any aspects of the disclosure may be implemented and / or provided and / or used independently of one another.

[0014] This disclosure also provides computer programs and computer program products comprising software code adapted, when executed on a computing device, to perform any of the methods described herein and to embody device and system features, including any or all substeps of a method.

[0015] The disclosure also provides a computer or computing system (including networked or distributed systems) having an operating system that supports a computer program for performing any of the methods described herein and / or embodying any of the device or system features described herein.

[0016] The disclosure also provides a computer-readable medium having stored thereon one or more of the aforementioned computer programs.

[0017] The disclosure also provides a signal carrying one or more of the aforementioned computer programs.

[0018] The disclosure extends to methods and / or devices and / or systems as described herein with reference to the accompanying drawings.

[0019] Aspects and embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present systems and methods for determining illumination status for autonomous and semi-autonomous systems and applications are described in detail below with reference to the accompanying figures, wherein: Fig. 1A-1E illustrate an example system configured to determine an illumination status of a scene and illumination scales, in accordance with one or more embodiments of the present disclosure; Fig. 2 is a flow diagram of an exemplary method for determining an illumination status of a scene, in accordance with one or more embodiments of the present disclosure; Fig. 3 is a flowchart illustrating an example method for determining a scene illumination status according to one or more embodiments of the present disclosure; Fig. 4A is an illustration of an exemplary autonomous vehicle according to one or more embodiments of the present disclosure; Fig. 4B shows an example of camera locations and fields of view for the exemplary autonomous vehicle of Fig. 4A is in accordance with one or more embodiments of the present disclosure; Fig. 4C is a block diagram of an exemplary system architecture for the exemplary autonomous vehicle of Fig. 4A is in accordance with one or more embodiments of the present disclosure; Fig. 4D is a system diagram according to one or more embodiments of the present disclosure for communication between one or more cloud-based servers and the exemplary autonomous vehicle of Fig. 4A is; Fig. 5 is a block diagram of an exemplary computing device suitable for use in implementing one or more embodiments of the present disclosure; and Fig. 6 is a block diagram of an exemplary data center suitable for use in implementing one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0021] Systems and methods are described related to detecting illumination statuses of areas corresponding to a location of a machine. For example, one or more embodiments may relate to detecting different levels of illumination of different regions and determining the illumination of the regions based on the levels of illumination. For example, an illumination detection system ("detection system") may determine the illumination level of a region corresponding to the location of the machine using one or more illumination detectors. In these and other embodiments, the detection system may determine an illumination status of the region. Additionally or alternatively, the illumination detection system may identify changes in the illumination status as a vehicle or other type of machine travels through different regions.

[0022] One or more of the embodiments disclosed herein may relate to detecting illumination states that may be associated with one or more ego machines, which may include any applicable machine or system capable of performing one or more autonomous or semi-autonomous operations. Example ego machines may include, but are not limited to, vehicles (land, sea, space, and / or air), robots, robotic platforms, etc. As an example, the ego machine computing applications may include one or more applications that may be executed by an autonomous vehicle or semi-autonomous vehicle, such as an example autonomous vehicle 400 (alternatively referred to herein as "vehicle 400" or "ego machines 400") that may be used with respect to Fig. 4A-4D. In the present disclosure, reference to "autonomous vehicle" or "semi-autonomous vehicle" may include any vehicle configured to perform one or more autonomous or semi-autonomous navigation or driving operations. As such, such may include vehicles where a driver is required or where a driver is also capable of performing such operations.

[0023] The systems and methods described herein may be used by, among others, 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, guided and unguided robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vehicles, boats, shuttle vehicles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater vehicles, drones, and / or other types of vehicles.Furthermore, the systems and methods described herein may be used for a variety of purposes, including, but not limited to, machine control, machine locomotion, machine propulsion, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actuator simulation and / or digital twinning, data center processing, conversational AI (such as by employing one or more language models, such as one or more large language models (LLMs)), light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing, and / or other suitable applications.

[0024] Disclosed embodiments may be included in a number of different systems, such as automotive systems (e.g., a control system for an autonomous or semi-autonomous machine, a perception system for an autonomous or semi-autonomous machine), systems implemented using a robot, antenna systems, media systems, boat systems, intelligent area surveillance systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twinning operations, systems implemented using an edge device, systems incorporating one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations (e.g.,Systems that implement one or more language models (LLMs), systems for hosting real-time streaming applications, systems for presenting one or more of virtual reality content, augmented reality content, or mixed reality content, systems for performing light transport simulation, systems for performing collaborative content creation for 3D assets, systems that are implemented at least in part using cloud computing resources, and / or other types of systems.

[0025] Embodiments of the present disclosure may help improve the accuracy of detecting illumination status and determining a scene illumination status of a region in which a machine may be located or traveling. For example, in one or more embodiments of the present disclosure, the detection system may include one or more illumination sensors, which may improve the accuracy of the detection system. For example, the detection system may include a first illumination sensor ("first sensor") and a second illumination sensor ("second sensor"). For example, the detection system may include a wide-angle camera and / or a telescopic camera. In some embodiments, the detection system may make a determination of the illumination status based on first sensor data detected using the first sensor.For example, in some embodiments, the first illumination status may provide enough confidence for the detection system to make the determination of the scene illumination status. Conversely, in some cases, the first illumination status may not provide enough confidence for the detection system to make the determination due to an impending change in illumination status or low illumination in the region. For example, the machine may be traveling away from or toward a light source, and the region may be insufficiently illuminated or dark. In these and other cases, the detection system may use second sensor data detected by the second sensor. For example, the second sensor data may provide illumination information of a region further forward of the machine.For example, a second illumination status may indicate that the machine is traveling to a well-lit region or a dark region. For example, the second sensor may indicate to the detection system that a light source is present in the region in front of the machine. In these cases, the detection system may determine the scene illumination status as well-lit based on the first illumination status and the second illumination status.

[0026] Embodiments of the present disclosure may help improve accuracy over some traditional approaches to scene illumination determination. For example, some traditional approaches to illumination determination may involve the use of maps. The maps may include information about locations of light sources in a particular region. In some cases, such information may be used to determine whether a particular area is illuminated. For example, if a light source is indicated to be present at a particular location, the area around the particular location may be assumed to be illuminated. However, such a technique may depend on the accuracy of the map and / or whether the light sources are actually providing light at a particular time.Furthermore, such techniques may require maps to be updated regularly and a large coverage area.

[0027] Furthermore, other techniques may include the use of photoelectric sensors that sense ambient light. For example, a photoelectric sensor may be located on a machine to detect ambient light in the area where the machine is traveling. For example, the scene illumination status of the area can be determined based on the amount of light the photoelectric sensor detects. However, the use of a single photoelectric sensor may be limited by its range. For example, the photoelectric sensor may determine the illumination status of a region near the machine based on the amount of ambient light. However, the photoelectric sensor may not be as effective in determining the illumination status when the machine is traveling through regions with different illumination statuses. For example, the machine may be in a region that is between lit and dark.For example, the machine may be moving away from a light source, such as a light pole. The photoelectric sensor may still detect some ambient light from the light pole, and thus, the illumination detected by the photoelectric sensor alone will not provide sufficient information about whether the machine is approaching a dark region until the machine actually reaches the dark region.

[0028] The embodiments of the present disclosure are explained with reference to the accompanying figures. It should be understood that the figures are diagrammatic and schematic representations of such exemplary embodiments and are not limiting, nor are they necessarily drawn to scale. Throughout the figures, features with like reference numerals indicate like structures and functions unless otherwise described.

[0029] In relation to Fig. 1A, illustrated Fig. 1A illustrates an exemplary system 100, according to some embodiments of the present disclosure, configured to determine an illumination status of a scene or area. In some embodiments, the system 100 may be implemented on a machine (e.g., the vehicle 400 of Fig. 4A-4D). Additionally or alternatively, the machine may be traveling or located in the area, so that the system 100 may be used by the machine to determine the illumination status according to the area in which the machine is located or traveling.

[0030] As generally detailed herein, the system 100 may include a first illumination sensor (“first sensor”) 102 and a second illumination sensor (“second sensor”) 106. Although the use of two sensors is described herein, this is not intended to be limiting, and any number of sensors (e.g., 1, 2, 3, 4, 5, etc.) may be used without departing from the scope of the present disclosure. The first sensor 102 may be used to determine first sensor data (“first data”) 104 corresponding to the area, and the second sensor 106 may be used to determine second sensor data (“second data”) 108 corresponding to the area. Although two sensor data are described herein, this is not intended to be limiting, and any number of sensor data (e.g., 1, 2, 3, 4, 5, etc.) may be used without departing from the scope of the present disclosure.

[0031] The system 100 may additionally include an illumination module 110, which may be configured to receive the first data 104 and the second data 108. As discussed in more detail herein, the illumination module 110 may be configured to determine a scene illumination status 130 based on one or more of the first data 104 or the second data 108. In some embodiments, the illumination module 110 may include a first illumination detector ("first detector") 112, a second illumination detector ("second detector") 116, and a scene illumination estimator 120. The first detector 112 may receive the first data 104 and may determine a first illumination status 114 corresponding to the area and based on the first data 104. The second detector 116 may receive the second data 108 and may determine a second illumination status 118 corresponding to the area and based on the second data 108.In these or other embodiments, the scene illumination estimator 120 may receive the first illumination status 114 and / or the second illumination status 118, and determine the scene illumination status 130 based on the first illumination status 114 and / or the second illumination status 118.

[0032] In some embodiments, one or more of the modules described herein may be implemented using hardware, including one or more processors, central processing units (CPUs), graphics processing units (GPUs), data processing units (DPUs), parallel processing units (PPUs), microprocessors (e.g., for performing or controlling the performance of one or more operations), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), accelerators (e.g., deep learning accelerators (DLAs)), programmable vision accelerators (including one or more direct memory access (DMA) systems and / or vector processing units (VPUs)), and / or other types of processors.In some other cases, one or more of these modules may be implemented using a combination of hardware and software. In the present disclosure, operations described as being performed by a respective module may include operations that the respective module directs a corresponding computing system to perform. In these or other embodiments, one or more of these modules may be implemented by one or more computing devices, such as those described with respect to [the disclosure]. Fig. 4A-4D, 5 and / or 6 described in more detail.

[0033] The first sensor 102 may include any suitable system, device, or apparatus that may be used to obtain the first data 104. The first data 104 may include any type of data representative of an amount of light in an area corresponding to the first sensor 102.

[0034] For example, in some embodiments, the first sensor 102 may include a camera configured to capture images that may be stored as image data. Accordingly, in these or other embodiments, the first data 104 may include an image of the area captured by the first sensor 102 and the image data corresponding thereto. For example, the image (and / or the corresponding image data) may indicate a certain level of light exposure associated with the image and corresponding to an amount of light present in the area at the time the image is captured. In the present disclosure, reference to an "image" may also refer to the image data corresponding thereto.

[0035] In some embodiments, the first sensor 102 may include a variable resistor configured to detect ambient light in the area. In these or other embodiments, the resistance between two terminals of the variable resistor may decrease as the variable resistor detects more light in the area. Accordingly, the first data 104 may include the resistance measured by the first sensor 102. For example, the resistance may indicate a certain level of ambient light associated with the area.

[0036] In some embodiments, the first sensor 102 may be configured to obtain the first data 104 relating to the area in relatively close proximity to the machine. For example, in some embodiments, the first sensor 102 may include a wide-angle camera. Additionally or alternatively, as stated herein, the first sensor 102 may include a photoelectric sensor. In some embodiments, the first data 104 may include a Euro signal obtained using the first sensor 102.

[0037] The second sensor 106 may include any suitable system, device, or apparatus that may be used to obtain the second data 108. The second data 108 may include any type of data representative of an amount of light in an area corresponding to the second sensor 106. In some embodiments, the second data 108 may include the same type of data as the first data 104. In other embodiments, the second data 108 may include a type of data that is different from the first data 104.

[0038] For example, in some embodiments, the second sensor 106 may include a camera configured to capture images that may be stored as image data. Accordingly, in these and other embodiments, the second data 108 may include an image of the area captured by the second sensor 106. For example, the image may indicate a certain level of light exposure associated with the image and correspond to an amount of light present in the area at the time the image is captured.

[0039] In some embodiments, the second sensor 106 may be configured to obtain the second data 108 relative to the area farther away from the machine. For example, in some embodiments, the second sensor 106 may include a telescopic camera.

[0040] The first detector 112 may be configured to receive the first data 104 from the first sensor 102. In these or other embodiments, the first detector 112 may be configured to determine the first illumination status 114 based on the first data 104. For example, in some embodiments, the first sensor 102 may be a wide-angle camera and the first data 104 may be an image generated by the first sensor 102. In some embodiments, the first illumination status 114 may include a first illumination level. For example, the illumination level may be a numerical representation of the first illumination status 114. For example, the first illumination level may represent an amount of light in the area corresponding to the first sensor 102.In these and other embodiments, the first illumination level included in the first illumination status 114 may include a value along a sliding illumination scale, which may be represented by a digital or analog representation.

[0041] In these and other embodiments, the first detector 112 may be configured to determine an amount of light exposure present in the first data 104. For example, in some embodiments, the first detector 112 may be configured to determine the amount of light exposure in the first data 104 by converting the image to a certain color format (e.g., RGB, YUV, etc.) and calculating a luminance of the image based on the color format.

[0042] For example, the image may be converted to an RGB color format, and the luminance may be calculated based on color values ​​of red, green, and blue in the image. In these embodiments, the luminance may be represented as a value within a typical luminance range for the RGB color format (e.g., 0 to 255). For example, the lowest luminance (e.g., black) may be represented as 0, and the strongest luminance (e.g., white) may be represented as 255. In some embodiments, the luminance may be analyzed to determine a probability that the area illustrated in the image is illuminated. For example, a higher luminance may indicate a higher probability that the area is illuminated, while a lower luminance may indicate a lower probability that the area is illuminated. In these embodiments, the typical luminance range for the RGB color format may be converted to a first illumination scale (e.g., 0 to 255).B. 0 to 1). In these or other cases, 0 may indicate that the area is dark with full certainty, and 1 may indicate that the area is lit with full certainty. In these embodiments, the probability generated from the luminance may be higher (e.g., closer to 1) if the area is lit rather than dark.

[0043] The first detector 112 may include any suitable system, device, or apparatus for determining the first illumination status 114. For example, in some embodiments, the first detector 112 may be configured to communicate with a machine learning model configured to determine the first illumination status 114. In other embodiments, the first detector 112 may include the machine learning model. In the present disclosure, reference to a "machine learning model" may include reference to any suitable algorithms, computing systems, neural networks, deep learning models, and / or other models that may be configured to analyze one or more characteristics corresponding to the input data.By way of example and not limitation, the machine learning model may include: a supervised model, an unsupervised model, and / or a semi-supervised model.

[0044] In some embodiments, the first illumination status 114 may include a first discrete illumination status. For example, the first discrete illumination status may include a representation of the first illumination status 114 as a specific status (e.g., lit, dark, transient). In these and other embodiments, the first discrete illumination status may be determined based at least on the first illumination level. For example, the first detector 112 may be configured to determine the first discrete illumination status based at least on a set of thresholds corresponding to the first illumination level. For example, the first illumination level may be a number on the first illumination scale that covers a certain range of values ​​(e.g., 0 to 1), thereby indicating an amount of illumination present in the range.

[0045] Fig. 1B illustrates an example first illumination scale 140 according to one or more embodiments of the present disclosure. The first illumination scale 140 may include a set of threshold ranges. In some embodiments, the first set of threshold ranges may include at least a first threshold range 150 and a second threshold range 160. For example, in cases where the first illumination scale 140 ranges from 0 to 1, the first threshold range 150 may be a range from 0 to a first point 155 on the first illumination scale 140, and the second threshold range 160 may be a range from a second point 165 on the first illumination scale 140 to 1. In some embodiments, the second point 165 may be greater than the first point 155. The first illumination level, determined as a number between 0 and 1, may be placed on the first illumination scale 140.

[0046] Back to Fig. 1A, which in relation to the first illumination scale 140 of Fig. 1B, in some embodiments, the first detector 112 may determine the first discrete illumination status as the dark status in response to the first illumination level meeting the first threshold range 150. For example, in some embodiments, the first threshold range 150 may be met by the first illumination level falling within the first threshold range 150 on the first illumination scale 140. Additionally or alternatively, the first detector 112 may determine the first discrete illumination status as the illuminated status in response to the first illumination level meeting the second threshold range 160. For example, in some embodiments, the second threshold range 160 may be met by the first illumination level falling within the second threshold range 160 on the first illumination scale 140.

[0047] In some embodiments, the first illumination scale 140 may include a transitory region 170. The transitory region 170 may be a region between the first threshold region 150 and the second threshold region 160 on the first illumination scale 140. For example, the transitory region 170 may be between the first point 155 and the second point 165, as shown in Fig. 1B. In these cases, the first detector 112 may make a determination corresponding to the transitory state.

[0048] In some embodiments, the transitory status may indicate that the area corresponding to the first sensor 102 does not decisively correspond to dark or illuminated. For example, the machine may be traveling toward or away from a light source, and the area may be slightly illuminated, which is not dark enough or illuminated enough for the first detector 112 to determine the area as dark or illuminated. In these and other embodiments, the first detector 112 may retrieve a previous illumination status. For example, the previous illumination status may be the illuminated status, indicating that the machine is traveling from a illuminated region to a dark region. In these embodiments, a transitory position may be determined to reflect how closely the determination decisively corresponds to the dark status.For example, the transitory position may be determined according to how far the first illumination level is from the first threshold range 150 within the first illumination scale 140. For example, the transitory position may be represented by a number within a range (e.g., 0 to 1). In these cases, 0 may indicate that the illumination level is closest to the first threshold range 150, and 1 may indicate that the first illumination level is farthest from the first threshold range 150 and, accordingly, closest to the second threshold range 160.

[0049] In some embodiments, the previous illumination state may be retrieved as a dark state, indicating that the machine is traveling from a dark region to a lit region. In these embodiments, the transitory position may be determined according to how far the first illumination level is from the second threshold 160, in a similar manner to how the transitory position is determined in cases with the previous illumination state as a lit state.

[0050] In some embodiments, the scene illumination estimator 120 may determine the first discrete illumination state. In response to the first discrete illumination state being one of the dark state and the illuminated state, the scene illumination estimator 120 may be configured to receive confirmation from the second detector.

[0051] In these and other embodiments, the second detector 116 may be configured to receive the second data 108 from the second sensor 106 and determine the second illumination status 118. In some embodiments, the second illumination status 118 may include a second illumination level indication. The second illumination level indication, in some embodiments, may include a value along a sliding illumination scale, which may be represented by a digital or analog representation.

[0052] For example, the second detector 116 may be configured to quantify the second data 108 into an exposure level. For example, the second exposure level may include a probability related to an amount of exposure. For example, in some embodiments, the second sensor 106 may be or include a telescopic camera configured to capture an image of an area farther from the machine than the area corresponding to the first sensor 102. Accordingly, the second data 108 may be the image of the area obtained from the second sensor 106. The second detector 116 may be configured to determine an amount of exposure present in the second data 108. For example, in some embodiments, the second detector 116 may be configured to determine the amount of exposure in the second data 108 by converting the image into some color format (e.g., RGB, YUV, etc.).) and a luminance of the image is calculated based on the color format.

[0053] For example, the image may be converted to an RGB color format, and the luminance may be determined based on color values ​​for red, green, and blue in the image. In these embodiments, the luminance may be represented as a value within a typical luminance range for the RGB color format (e.g., 0 to 255). For example, the lowest luminance (e.g., black) may be represented as 0, and the highest luminance (e.g., white) may be represented as 255. In some embodiments, the luminance may be converted to represent a probability that the area illustrated in the image is illuminated. For example, a higher luminance may indicate a higher probability that the area is illuminated, while a lower luminance may indicate a lower probability that the area is illuminated.In these embodiments, the typical luminance range for the RGB color format may be converted to a second illumination scale (e.g., 0 to 1). In these cases, 0 may indicate that the area is dark with full certainty, and 1 may indicate that the area is lit with full certainty. In these embodiments, the probability generated from the luminance may be higher (e.g., closer to 1) when the area is lit rather than dark. In other embodiments, the second illumination scale may cover any suitable range of values. For example, the second illumination scale may cover a wider range (e.g., 0-255) or a narrower range (e.g., 0-0.5). In some embodiments, the second illumination scale may include a different range of values ​​than the first illumination scale.In other embodiments, the second illumination scale may include a similar or the same range of values ​​as the first illumination scale.

[0054] The second detector 116 may include a suitable system, device, or apparatus for determining the second illumination status 118. For example, in some embodiments, the second detector 116 may be configured to communicate with a machine learning model configured to determine the second illumination status 118. In other embodiments, the second detector 116 may include the machine learning model.

[0055] In these and other embodiments, the scene illumination estimator 120 may be configured to apply a confirmatory illumination scale to the second illumination state 118 to confirm the first illumination state 114. For example, in response to the first illumination state 114 being dark, the confirmatory illumination scale may be applied to the second illumination state (e.g., the second illumination level) 118 to confirm the first illumination state 114. For example, the confirmatory illumination scale may include a range of numbers (e.g., 0 to 1) that may correspond to the second illumination state 118. In some cases, the confirmatory illumination scale may further include a confirmatory dark threshold. In response to the second illumination state 118 falling within the confirmatory dark threshold (e.g.,falls between 0 and the confirming dark threshold), the scene illumination estimator 120 may confirm the first illumination state 114 and determine the scene illumination state 130 as the dark state. In some cases, the first illumination state 114 may be determined as the illuminated state. In these cases, the confirming illumination scale may include a confirming illuminated threshold. In response to the second illumination state 118 falling within the confirming illuminated threshold (e.g., falling between the confirming illuminated threshold and 1), the scene illumination estimator 120 may confirm the first illumination state 114 and determine the scene illumination state 130 as the illuminated state.

[0056] In some embodiments, the first illumination state 114 may be determined as the transitory state. In these and other embodiments, in response to the first illumination state 114 being determined as the transitory state, the scene illumination estimator 120 may be configured to determine the scene illumination state 130 based on the first illumination level, the second illumination state 118, and the previous illumination state. For example, in response to the first illumination state 114 being the transitory state and the previous illumination state being determined as the illuminated state, the scene illumination estimator 120 may attempt to obtain additional confidence from the second illumination state 118 to make a determination that the machine is actually traveling from a lit region to a dark region.

[0057] For example, in some embodiments, the second illumination status 118 may be compared to the second illumination scale. For example, Fig. 1C an exemplary second illumination scale 180a divided into at least one dark area 185 and one illuminated area 190. For example, the second illumination scale 180a may range from 0 to 1, and the dark area 185 may range from 0 to a division point 195, and the illuminated area 190 may range from the division point 195 to 1. In some embodiments, the division point 195 may divide the second illumination scale 180a evenly between the dark area 185 and the illuminated area 190, as in Fig. 1C. In some embodiments, the scene illumination estimator 120 may make a determination of the illumination state as the dark state in response to the second illumination state 118 falling within the dark region 185. In these or other embodiments, the scene illumination estimator 120 may make the determination as the illuminated state in response to the second illumination state 118 falling within the illuminated region 190.

[0058] In some embodiments, the dark area 185 and the illuminated area 190 may vary in size according to the transitory position determined relative to the first illumination scale 140. For example, the transitory position may reflect a degree of confidence that the scene illumination estimator 120 may use from the second illumination state 118. For example, in some situations, the transitory position may be closer to the first point 155 than to the second point 165, thereby reflecting that the first illumination level is relatively close to the first threshold range 150. In these cases, the strength of evidence provided by the second illumination state 118 used to determine that the illumination state is the dark state may be relaxed.For example, the dark region 185 and the illuminated region 190 may be adjusted such that the dark region 185 covers a larger area than the illuminated region 190 to make it easier for the second illumination state 118 to fall within the dark region. Fig. 1D illustrates another exemplary second illumination scale 180b with the dark area 185 wider than the illuminated area 190 to illustrate such a scenario. For example, the division point 195 may be closer to 1 than 0, thereby dividing the second illumination scale 180b to allow the dark area 185 to be wider than the illuminated area 190.

[0059] Similarly, as the transitory position moves further away from the first threshold region 150 and toward the second threshold region 160, the dark region may decrease in size and the illuminated region may increase in size. For example, Fig. 1E shows another exemplary second illumination scale 180c with the dark area 185 narrower than the illuminated area 190 to illustrate such a scenario. For example, the division point 195 may be closer to 0 than 1, thereby dividing the second illumination scale 180c such that the dark area 185 is narrower than the illuminated area 190.

[0060] In some embodiments, the previous illumination state may be determined from the dark state. In these embodiments, the scene illumination estimator 120 may be configured to confirm that the machine is traveling from a dark region to a lit region. For example, the scene illumination estimator 120 may seek additional confidence from the lit state 118 to make the determination as the lit state. In these and other embodiments, the second illumination state 118 may be compared to the second illumination scale 180 with the dark area 185 and the lit area 190. The areas of the dark area 185 and the lit area 190 may be determined according to the transitory position in a manner similar to an example above with the previous illumination state as the lit state.In these embodiments, the scene illumination estimator 120 may determine the scene illumination status 130 as the dark status in response to the second illumination status 118 falling within the dark region 185 and determine the scene illumination status as the lit status in response to the second illumination status 118 falling within the lit region 190.

[0061] Fig. 2 is a flow diagram of an exemplary method according to one or more embodiments of the present disclosure for determining an illumination status of a scene; One or more operations of the method 200 may be performed by a suitable system, device, or apparatus, such as the system 100 and / or the illumination module 110 in Fig. 1, the autonomous vehicle system(s) that are / are capable of Fig. 4A-4D, the computing device(s) described in relation to Fig. 5 and / or the data system(s) used in relation to Fig. 6 is / are described in the present disclosure.

[0062] The method 200 may include one or more blocks. Although illustrated with discrete blocks, the operations associated with one or more of the blocks of the method 200 may be divided into additional blocks, combined into fewer blocks, or eliminated depending on the particular implementation.

[0063] In some embodiments, method 200 may include block 202. At block 202, first sensor data may be obtained. In some embodiments, the first sensor data may be obtained using a first illumination sensor corresponding to a machine. In some embodiments, the first illumination sensor may include any suitable system, device, or apparatus that may be used to obtain the first sensor data. The first sensor data may include any type of data representative of an amount of light in an area corresponding to the first illumination sensor.

[0064] For example, the first illumination sensor may include a camera configured to capture an image of a scene toward which the machine is traveling. Additionally or alternatively, the first sensor data may include the image captured by the camera and / or the corresponding image data. Additionally or alternatively, the first illumination sensor may be configured to detect illumination levels relatively close to the machine. For example, the first illumination sensor may include a wide-angle camera and / or a photoelectric sensor. The first illumination sensor 102 of Fig. 1 A can be an example of the first illumination sensor.

[0065] At block 204, a first illumination level corresponding to the area may be determined based at least on the first sensor data. The first illumination level may provide an indication regarding the amount of light or illumination, as indicated by the first sensor data, that may be present in the area. In some embodiments, determining the first illumination level may include one or more operations related to Fig. 1A and Fig. 1B corresponding to the determination of a first illumination level.

[0066] At block 206, it may be determined whether the first illumination level meets a first threshold range. In some embodiments, the first illumination level may meet the first threshold range by falling within the first threshold range on a first illumination scale. For example, in some embodiments, the first illumination scale may include at least the first threshold range and a second threshold range. The first threshold range and the second threshold range may each cover a different range of numbers within the first illumination scale. In some embodiments, the first illumination scale 140, the first threshold range 150, and the second threshold range 160 may be Fig. 1B Examples of the first illumination scale, the first threshold range and the second threshold range.

[0067] In response to the first illumination level meeting the first threshold range, the method 200 may proceed to block 208. At block 208, a determination may be made that the illumination status of the scene is a dark status. In some embodiments, the determination may be made without using any illumination detections obtained using a second illumination detector. In some embodiments, determining the illumination status may be as further discussed and illustrated in the present disclosure, such as as described with respect to Fig. 1A and Fig. 1B.

[0068] In some embodiments, the first illumination level may not meet the first threshold range. For example, the first illumination level may not fall within the first threshold range. In these embodiments, the method 200 may proceed to block 210. At block 210, it may be determined whether the first illumination level meets the second threshold range. In some embodiments, the second threshold range may be met by the first illumination level falling within the second threshold range.

[0069] In response to the first illumination level meeting the second threshold range, the method 200 may proceed to block 212. At block 212, a determination of the illumination status of the scene as a lit status may be made. In some embodiments, the determination may be made without using any sensor data obtained using the second illumination sensor. Additionally or alternatively, the sensor data obtained using the second illumination sensor may be used to confirm the determination made using the first illumination level. In some embodiments, determining the illumination status may be as further discussed and illustrated in the present disclosure, such as as described with respect to Fig. 1A and Fig. 1B.

[0070] Modifications, additions, or omissions may be made to method 200 without departing from the scope of the present disclosure. For example, the operations of method 200 may be implemented in different orders. Additionally or alternatively, two or more operations may be performed at the same time. Furthermore, the operations and actions set forth are provided only as examples, and some of the operations and actions may be optional, combined into fewer operations and actions, or expanded into additional operations and actions without departing from the spirit of the described embodiments.

[0071] Fig. 3 is a flowchart illustrating a method 300 for determining a scene illumination status according to one or more embodiments of the present disclosure. In some embodiments, one or more of the operations of the method 300 may be performed with respect to the scene illumination estimator 120 of Fig. 1A. One or more operations of the method 300 may be performed by a suitable system, device, or apparatus, such as the system 100 and / or the illumination module 110 of Fig. 1A, the autonomous vehicle system(s) that are / are Fig. 4A-4D, the computing device(s) described in relation to Fig. 5 and / or the data system(s) used in relation to Fig. 6 is / are described in the present disclosure.

[0072] The method 300 may include one or more blocks. Although illustrated with discrete blocks, the operations associated with one or more of the blocks of the method 300 may be divided into additional blocks, combined into fewer blocks, or eliminated depending on the particular implementation. In some embodiments, the method 300 may be iterated and / or performed at a certain rate. For example, the method 300 may be performed once per second, twice per second, three times per second, five times per second, ten times per second, twenty times per second, fifty times per second, etc.

[0073] In some embodiments, method 300 may include block 301. At block 302, a first illumination status may be obtained. For example, the first illumination status may include a discrete representation of an illumination status of an area corresponding to a first illumination sensor. In these and other embodiments, the first illumination status may be one of dark, illuminated, and transient. In some embodiments, obtaining the first illumination status may include one or more operations related to Fig. 1A corresponding to the determination of the first illumination status 114.

[0074] At block 304, it may be determined whether the first illumination state is either dark or illuminated. In response to the first illumination state being dark or illuminated, method 300 may continue to block 306.

[0075] At block 306, the first illumination status may be confirmed based on a second illumination status. The second illumination status may be obtained, for example, from a second detector that may determine the second illumination status based at least on second sensor data obtained using a second illumination sensor. In these and other embodiments, the second illumination status may be represented in an analog representation. The second illumination status may, for example, numerically represent the amount of light in the second sensor data as a number within a certain range (e.g., 0 to 1).

[0076] In some embodiments, the scene illumination status may be confirmed in response to the second illumination status meeting a second detection threshold. For example, a confirmatory illumination scale may be applied to the second illumination status. The confirmatory illumination scale may be a range of numbers corresponding to the certain range (e.g., 0 to 1). In these and other embodiments, the confirmatory illumination scale may include a confirmatory dark threshold or a confirmatory illuminated threshold. For example, in response to the first illumination status being dark, the confirmatory dark threshold may be applied to the second illumination status, and in response to the first illumination status being illuminated, the confirmatory illuminated threshold may be applied to the second illumination status.In these and other embodiments, in response to the second illumination status meeting the second detection threshold, the scene illumination status may be determined to be the first illumination status.

[0077] In some embodiments, confirming the first illumination status may fail. For example, the second illumination status may not meet the second detection threshold. In these and other embodiments, the scene illumination status may remain unchanged from a previous illumination status. In these cases, the previous illumination status will refer to the scene illumination status determined during a previous iteration of method 300.

[0078] In some embodiments, method 300 may not include block 306. For example, in response to the first illumination status being determined as either dark or lit at block 304, the scene illumination status may be determined as dark or lit according to the first illumination status, without confirming the scene illumination status based on the second illumination status.

[0079] In some embodiments, the first illumination state may not be dark or illuminated upon returning to block 304. For example, the first illumination state may be the transitory state. In these cases, the method 300 may proceed to block 314. At block 314, the second illumination state may be obtained. In some embodiments, obtaining the second illumination state may include one or more operations described in block 306.

[0080] At block 316, a transitory situation may be determined. The transitory position may provide an indication of how far a first illumination level may be from a first threshold range or a second threshold range. In some embodiments, determining the transitory position may include one or more operations related to Fig. 1A and Fig. 1B can be described according to the determination of a transitory position.

[0081] At block 318, a first region and a second region may be determined based at least on the transitory position. In some embodiments, the first region and the second region may subdivide a second illumination scale based on the transitory position. In some embodiments, the first region may be a dark region and the second region may be a lit region. In some embodiments, determining the first region and the second region may include one or more steps related to Fig. 1A-1E, which correspond to determining the dark area 185 and the illuminated area 190.

[0082] At block 320, it may be determined whether a second illumination level falls within the first range. In response to the second illumination level falling within the first range, the method 300 may proceed to block 322. At block 322, a determination may be made that the scene illumination status is a dark status. In some embodiments, determining the illumination status may be as further discussed and illustrated in the present disclosure, such as as with respect to Fig. 1A-1E.

[0083] In some embodiments, the second illumination level may not fall within the first range. For example, in some embodiments, the second illumination level may fall within the second range. In these embodiments, the method 300 may continue to block 324. At block 324, a determination may be made that the scene illumination status is a lit status. In some embodiments, determining the illumination status may be as further discussed and illustrated in the present disclosure, such as as with respect to Fig. 1A-1E.

[0084] Modifications, additions, or omissions may be made to method 300 without departing from the scope of the present disclosure. For example, the operations of method 300 may be implemented in different orders. Additionally or alternatively, two or more operations may be performed at the same time. Furthermore, the operations and actions set forth are provided only as examples, and some of the operations and actions may be optional, combined into fewer operations and actions, or expanded into additional operations and actions without departing from the spirit of the described embodiments.

[0085] For example, in some embodiments, method 300 may include controlling and / or operating one or more lighting components of the machine based at least on the scene illumination status. For example, in cases where the scene illumination status is determined to be the dark status, the one or more lighting components (e.g., headlights) of the machine may be turned on. In other cases where the illumination status is determined to be the illuminated status, the one or more lighting components may be turned off. EXEMPLARY AUTONOMOUS VEHICLE

[0086] Fig. 4A is an illustration of an exemplary autonomous vehicle 400 according to some embodiments of the present disclosure. The autonomous vehicle 400 (alternatively referred to herein as the "vehicle 400") 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 engine, a police vehicle, an ambulance, a boat, a construction vehicle, an underwater vehicle, a drone, or another type of vehicle (e.g., that is, for example, unmanned and / or that accommodates one or more passengers).Autonomous vehicles are generally described in terms of automation levels defined by the National Highway Traffic Safety Administration (NHTSA), a division of the U.S. Department of Transportation, and the Society of Automotive Engineers (SAE) "Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles" (Standard No. J3016-201806, published June 15, 2018, Standard No. J3016-201609, published September 30, 2016, and prior and future versions of this standard). The vehicle 400 may be capable of functionality consistent with one or more of Level 3 - Level 5 of the autonomous driving levels. The vehicle 400 may be capable of functionality consistent with one or more of Level 1 - Level 5 of the autonomous driving levels.For example, depending on the embodiment, the vehicle 400 may be capable of driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5). The term "autonomous" as used herein may include any and / or all types of autonomy for the vehicle 400 or other machine, such as fully autonomous, highly autonomous, conditionally autonomous, partially autonomous, providing assistive autonomy, semi-autonomous, primarily autonomous, or another designation.

[0087] The vehicle 400 may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle 400 may include a propulsion system 450, such as an internal combustion engine, a hybrid-electric powerplant, an all-electric motor, and / or another type of propulsion system. The propulsion system 450 may be connected to a drivetrain of the vehicle 400, which may include a transmission to enable propulsion of the vehicle 400. The propulsion system 450 may be controlled in response to receiving signals from the throttle / accelerator 452.

[0088] A steering system 454, which may include a steering wheel, may be used to steer the vehicle 400 (e.g., along a desired path or route) when the propulsion system 450 is operating (e.g., when the vehicle is moving). The steering system 454 may receive signals from a steering actuator 456. The steering wheel may be optional for full automation functionality (Level 5).

[0089] The brake sensor system 446 may be used to operate the vehicle brakes in response to receiving signals from the brake actuators 448 and / or brake sensors.

[0090] One or more controllers 436 that control one or more CPU(s), system-on-chips (“SoCs”) 404 ( Fig. 4C) and / or one or more GPU(s) may provide signals (e.g., representative of commands) to one or more components and / or one or more systems of the vehicle 400. For example, the controller(s) may send signals to operate the vehicle brakes via one or more brake actuators 448, to operate the steering system 454 via one or more steering actuators 456, and / or to operate the propulsion system 450 via throttle / accelerator 452. The controller(s) 436 may include one or more (e.g., integrated) onboard computing devices (e.g., supercomputers) that process sensor signals and issue operational commands (e.g., signals representing commands) to enable autonomous driving and / or to assist a human driver in driving the vehicle 400.The controller(s) 436 may include a first controller 436 for autonomous driving functions, a second controller 436 for functional safety functions, a third controller 436 for artificial intelligence functionality (e.g., computer vision), a fourth controller 436 for infotainment functionality, a fifth controller 436 for emergency redundancy, and / or other controllers. In some embodiments, a single controller 436 may handle two or more of the above functionalities, two or more controllers 436 may handle a single functionality, and / or any combination thereof.

[0091] The controller(s) 436 may provide signals to control one or more components and / or one or more systems of the vehicle 400 in response to sensor data received from one or more sensors (e.g., sensor inputs). The sensor data may be received, for example, from, among others: global navigation satellite system sensors 458 (e.g., global positioning system sensors), RADAR sensors 460, ultrasonic sensors 462, LIDAR sensors 464, inertial measurement unit (IMU) sensors 466 (e.g., accelerometers, gyroscopes, magnetic compasses, magnetometers, etc.), microphones 496, stereo cameras 468, wide-angle cameras 470 (e.g., fisheye cameras), infrared cameras 472, environmental cameras 474 (e.g., 360-degree cameras), long-range and / or medium-range cameras (498), speed sensors 444 (e.g.,for measuring the speed of the vehicle 400), vibration sensors 442, steering sensors 440, brake sensors 446 (e.g., as part of the brake sensor system 446), and / or other sensor types.

[0092] One or more of the controllers 436 may receive inputs (e.g., represented by input data) from an instrument cluster 432 of the vehicle 400 and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display 434, an audible annunciator, a speaker, and / or via other components of the vehicle 400. The outputs may include information such as vehicle speed, pace, time, map data (e.g., the HD map 422 of Fig. 4C), location data (e.g., the location data of the vehicle 400, as on a map), direction, location of other vehicles (e.g., an occupancy grid), information about objects and status of objects as perceived by the controller(s) 436, etc. The HMI display 434 may, for example, display information about the presence of one or more objects (e.g., a road sign, caution sign, traffic signal timing, etc.) and / or information about maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, exit 34B in two miles, etc.).

[0093] The vehicle 400 may further include a network interface 424 that may utilize one or more wireless antennas 426 and / or one or more modems to communicate over one or more networks. For example, the network interface 424 may be capable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The wireless antenna(s) 426 may also enable communication between surrounding objects (e.g., vehicles, mobile devices, etc.) using local area network(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc., and / or low-power wide area network(s) (LPWANs), such as LoRaWAN, SigFox, etc.

[0094] Fig. 4B shows an example of camera locations and fields of view for the example autonomous vehicle 400 of Fig. 4A according to some embodiments of the present disclosure. The cameras and the respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or alternative cameras may be included and / or the cameras may be located at different locations on the vehicle 400.

[0095] The camera types may include, but are not limited to, digital cameras that may be adapted for use with the components and / or systems of the vehicle 400. The camera(s) may be operated at Automotive Safety Integrity Level (ASIL) B and / or another ASIL. The camera types may be capable of any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc., depending on the embodiment. The cameras may be capable of using rolling shutters, global shutters, another type of shutter, or a combination thereof.In some embodiments, the color filter array may include a red clear clear (RCCC) color filter array, a red clear clear blue (RCCB) color filter array, a red blue green clear (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, clear-pixel cameras, such as cameras with an RCCC, an RCCB, and / or an RBGC color filter array, may be used in an effort to increase light sensitivity.

[0096] In some examples, one or more of the cameras may be used to perform advanced driver assistance system (ADAS) functions (e.g., as part of a redundant or fail-safe system). For example, a multi-function mono camera may be installed to provide features such as lane departure warning, traffic sign assistance, and intelligent headlight control. One or more of the cameras (e.g., all of the cameras) may simultaneously record and provide image data (e.g., video).

[0097] One or more of the cameras may be mounted in a mounting arrangement, such as a custom (3D-printed) arrangement, to eliminate stray light and reflections from inside the car (e.g., reflections in the windshield mirrors from the dashboard), which can impair the camera's image data collection capabilities. Regarding side-view mirror mounting arrangements, the side-view mirror assemblies may be custom 3D-printed so that the camera mounting plate conforms to the shape of the side-view mirror. In some examples, the camera(s) may be integrated into the side-view mirror. For side-view cameras, the camera(s) may also be integrated within four pillars at each corner of the cabin.

[0098] Cameras with a field of view that includes portions of the environment in front of the vehicle 400 (e.g., forward-facing cameras) can be used for surround view to identify ahead routes and obstacles, and with the assistance of one or more controllers 436 and / or control SoCs, to assist in providing information critical for generating an occupancy grid and / or determining preferred vehicle routes. Forward-facing cameras can be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. Forward-facing cameras can also be used for ADAS functions and systems, including lane departure warnings (LDW), autonomous cruise control (ACC), and / or other functions such as traffic sign recognition.

[0099] Various cameras may be used in a forward-facing configuration, for example, including a monocular camera platform that includes a CMOS (Complementary Metal Oxide Semiconductor) color imager. Another example may be a wide-angle camera(s) 470 that may be used to perceive objects that enter the field of view from the periphery (e.g., pedestrians, crossing traffic, or bicycles). Although in Fig. 4B illustrates only one wide-angle camera, there may be any number of wide-angle cameras 470 on the vehicle 400. Furthermore, one or more remote cameras 498 (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 yet been trained. The remote camera(s) 498 may also be used for object detection and classification, as well as simple object tracking.

[0100] One or more stereo cameras 468 may also be included in a forward-facing configuration. The stereo camera(s) 468 may include an integrated control unit comprising a scalable processing unit that may provide programmable logic (FPGA) and a multi-core microprocessor with an integrated CAN or Ethernet interface on a single chip. Such a unit may be used to generate a 3D map of the vehicle's surroundings, including a distance estimate for all points in the image. An alternative stereo camera(s) 468 may include a compact stereo vision sensor(s) that may include two camera lenses (one each on the left and right) and an image processing chip, with which the distance from the vehicle to the target object can be measured and the generated information (e.g.,Metadata) may be used to activate the autonomous emergency braking and lane departure warning functions. Other types of stereo camera(s) 468 may be used in addition to or alternatively to those described herein.

[0101] Cameras with a field of view that includes portions of the environment on the side of the vehicle 400 (e.g., side view cameras) may be used for surround view, providing information used to generate and update the occupancy grid and generate side impact collision warnings. For example, surround camera(s) 474 (e.g., four surround cameras 474 as in Fig. 4B) may be positioned on the vehicle 400. The surround view camera(s) 474 may include one or more wide-angle cameras 470, fisheye cameras, 360-degree cameras, and / or the like. For example, four fisheye cameras may be positioned at the front, rear, and sides of the vehicle. In an alternative arrangement, the vehicle may utilize three surround view cameras 474 (e.g., left, right, and rear) and may utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround view camera.

[0102] Cameras with a field of view that includes portions of the environment at the rear of the vehicle 400 (e.g., rear view cameras) may be used for parking assistance, surround view, rear collision warnings, and generating and updating the occupancy grid. A wide range of cameras may be used, including, but not limited to, cameras that are also suitable as forward-facing cameras (e.g., long-range and / or medium-range cameras 498, stereo cameras 468, infrared cameras 472, etc.), as described herein.

[0103] Fig. 4C is a block diagram of an example system architecture for the example autonomous vehicle 400 of Fig. 4A according to some embodiments of the present disclosure. It should be understood that these and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., engines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or in place of those shown, and other elements may be omitted entirely. Furthermore, many of the elements described herein are functional entities that may be implemented as discrete or distributed components, or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by entities may be performed by hardware, firmware, and / or software. For example, various functions may be performed by a processor executing instructions stored in memory.

[0104] The components, features and systems of the vehicle 400 in Fig. 4C are each illustrated as being connected via bus 402. Bus 402 may include a Controller Area Network (CAN) data interface (alternatively referred to herein as a CAN bus). A CAN may be a network within vehicle 400 used to support the control of various features and functionality of vehicle 400, such as brake application, acceleration, braking, steering, windshield wipers, etc. A CAN bus may be configured to have dozens 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 revolutions per minute (RPMs), button positions, and / or other vehicle status indicators. The CAN bus may be ASIL-B compliant.

[0105] Although bus 402 is described herein as being a CAN bus, this is not intended to be limiting. For example, in addition to or alternatively to the CAN bus, FlexRay and / or Ethernet may be used. Furthermore, although a single line is used to represent bus 402, this is not intended to be limiting. For example, there may be any number of buses 402, 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 a different protocol. In some examples, two or more buses 402 may be used to perform different functions and / or used for redundancy. For example, a first bus 402 may be used for collision avoidance functionality, and a second bus 402 may be used for actuation control.In any example, bus 402 may communicate with any of the components of vehicle 400, and two or more buses 402 may communicate with the same components. In some examples, each SoC 404, each controller 436, and / or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of vehicle 400) and may be connected to a common bus, such as the CAN bus.

[0106] The vehicle 400 may include one or more controllers 436, such as those described herein with respect to Fig. 4A. The controller(s) 436 may be used for a variety of functions. The controller(s) 436 may be coupled to any of the various other components and systems of the vehicle 400 and may be used for control of the vehicle 400, artificial intelligence of the vehicle 400, infotainment for the vehicle 400, and / or the like.

[0107] The vehicle 400 includes one or more systems on a chip (SoC) 404. The SoC 404 may include one or more CPU(s) 406, GPU(s) 408, one or more processor(s) 410, cache(s) 412, accelerators 414, data storage 416, and / or other unillustrated components and features. The SoC(s) 404 may be used to control the vehicle 400 in a variety of platforms and systems. For example, the SoC(s) 404 may be combined in a system (e.g., the system of the vehicle 400) with an HD map 422, whereby map refreshes and / or updates may be received via a network interface 424 from one or more servers (e.g., the server(s) 478 of Fig. 4D) can be obtained.

[0108] The CPU(s) 406 may include a CPU cluster or a CPU complex (alternatively referred to herein as a "CCPLEX"). The CPU(s) 406 may include multiple cores and / or L2 caches. For example, in some embodiments, the CPU(s) 406 may include an eight-core coherent multiprocessor configuration. In some embodiments, the CPU(s) 406 may include four dual-core clusters, each cluster having a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s) 406 (e.g., the CCPLEX) may be configured to support concurrent cluster operation, allowing a combination of clusters of the CPU(s) 406 to be active at any given time.

[0109] The CPU(s) 406 may implement power management capabilities that include one or more of the following features: individual hardware blocks may be automatically clock-gated when idle to conserve dynamic power; each core clock may be decoupled 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 if all cores are clock-gated or power-gated; and / or each core cluster may be independently power-gated if all cores are power-gated.The CPU(s) 406 may further implement an enhanced performance state management algorithm, where the allowable performance states and expected wake-up times are specified, and the hardware / microcode for the core, cluster, and CCPLEX determines the best performance state to enter. The processing cores may support simplified performance state entry sequences in software, offloading the work to the microcode.

[0110] The GPU(s) 408 may include an integrated GPU (alternatively referred to herein as an "iGPU"). The GPU(s) 408 may be programmable and efficient for parallel workloads. The GPU(s) 408 may, in some examples, utilize an enhanced tensor instruction set. The GPU(s) 408 may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB of storage capacity) and two or more of the streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512 KB of storage capacity). In some embodiments, the GPU(s) 408 may include at least eight streaming microprocessors. The GPU(s) 408 may utilize compute application programming interface(s) (API(s)). In addition, the GPU(s) 408 may utilize one or more parallel computing platforms and / or programming models (e.g.,NVIDIA's CUDA).

[0111] The GPU(s) 408 may be performance-optimized to achieve the best performance in automotive and embedded use cases. For example, the GPU(s) 408 may be fabricated on a fin field-effect transistor (FinFET). However, this is not intended to be limiting, and the GPU(s) 408 may be fabricated using other semiconductor manufacturing processes. Each streaming microprocessor may include a number of mixed-precision processing cores divided into multiple blocks. For example, 64 PF32 cores and 32 PF64 cores may be divided into four processing blocks, among others. In such an example, each processing block may be assigned 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two NVIDIA TENSOR cores for mixed-precision deep learning matrix arithmetic, an L0 instruction cache, a warp scheduler, a dispatch unit, and / or a 64 KB register file.Additionally, streaming microprocessors can include independent parallel integer and floating-point datapaths to enable efficient execution of workloads with a mix of arithmetic and addressing calculations. Streaming microprocessors can include independent thread scheduling capabilities to enable finer-grained synchronization and collaboration between parallel threads. Streaming microprocessors can include a combined L1 data cache and shared memory unit to improve performance while simplifying programming.

[0112] The GPU(s) 408 may include high-bandwidth memory (HBM) and / or a 16 GB HBM2 memory subsystem to provide, in some examples, a peak memory bandwidth of approximately 900 GB / second. In some examples, synchronous graphics random-access memory (SGRAM), such as double data rate type five (GDDR5), may be used in addition to or as an alternative to the HBM memory.

[0113] The GPU(s) 408 may include unified memory technology, including access counters, to allow more accurate migration of memory pages to the processor that accesses them most frequently, thereby improving efficiency for memory regions shared between processors. In some examples, address translation services (ATS) support may be used to allow the GPU(s) 408 to directly access the page tables of the CPU(s) 406. In such examples, an address translation request may be communicated to the CPU(s) 406 when the memory management unit (MMU) of the GPU(s) 408 experiences a miss. In response, the CPU(s) 406 may look up the virtual-to-physical mapping for the address in their page tables and transmit the translation back to the GPU(s) 408.As such, unified memory technology may allow a single, unified virtual address space for the memory of both the CPU(s) 406 and the GPU(s) 408, thereby simplifying the programming of the GPU(s) 408 and the porting of applications to the GPU(s) 408.

[0114] Additionally, GPU(s) 408 may include an access counter that can track the frequency of GPU(s) 408 accessing the memory of other processors. The access counter can help ensure that memory pages are moved to the physical memory of the processor that accesses the pages most frequently.

[0115] The SoC(s) 404 may include any number of cache(s) 412, including those described herein. For example, the cache(s) 412 may include an L3 cache available to both the CPU(s) 406 and the GPU(s) 408 (e.g., connected to both the CPU(s) 406 and the GPU(s) 408). The cache(s) 412 may include a write-back cache that can track line states, e.g., using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache may include 4 MB or more, although smaller cache sizes may be used.

[0116] The SoC(s) 404 may include one or more arithmetic logic units (ALU(s)) that may be utilized in performing processing related to any of the vehicle 400's array of tasks or operations, such as processing DNNs. Additionally, the SoC(s) 404 may include one or more floating-point units (FPU(s))—or other math coprocessor or numeric coprocessor types—for performing mathematical operations within the system. For example, the SoC(s) 104 may include one or more FPUs integrated as execution units within one or more CPU(s) 406 and / or GPU(s) 408.

[0117] The SoC(s) 404 may include one or more accelerators 414 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s) 404 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or a large on-chip memory. The large on-chip memory (e.g., 4 MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other computations. The hardware acceleration cluster may be used to supplement the GPU(s) 408 and offload some of the tasks from the GPU(s) 408 (e.g., to free up more cycles of the GPU(s) 408 to perform other tasks). As an example, the accelerator(s) 414 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are stable enough to be amenable to acceleration. The term "CNN" as used herein can include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., as used for object detection).

[0118] The accelerator(s) 414 (e.g., the hardware acceleration cluster) may include one or more deep learning accelerators (DLAs). The DLA(s) may include one or more tensor processing units (TPUs), which may be configured to provide an additional ten trillion operations per second for deep learning applications and inferencing. The TPUs may be accelerators configured and optimized to perform image processing functions (e.g., for CNNs, RCNNs, etc.). The DLA(s) may be further optimized for a specific set of neural network types and floating-point operations, as well as for inferencing. The design of the DLA(s) may provide more performance per millimeter than a general-purpose GPU, far exceeding the performance of a CPU.The TPU(s) can perform several functions, including a single-instance convolution function that supports, for example, INT8, INT16, and FP16 data types for both features and weights, as well as post-processor functions.

[0119] The DLA(s) can quickly and efficiently execute neural networks, in particular CNNs, on processed or unprocessed data for any number of functions, including, for example, but not limited to: a CNN for object identification and detection using data from camera sensors; a CNN for estimating distances using data from camera sensors; a CNN for emergency vehicle detection and identification using data from microphones; a CNN for facial recognition and vehicle owner identification using data from camera sensors; and / or a CNN for protection-relevant events,

[0120] The DLA(s) can perform any function of the GPU(s) 408, and using an inference accelerator, for example, a designer can use either the DLA(s) or the GPU(s) 408 for any function. For example, a designer can focus the processing of CNNs and floating-point operations on the DLA(s) and leave other functions to the GPU(s) 408 and / or another accelerator(s) 414.

[0121] The accelerator(s) 414 (e.g., the hardware acceleration cluster) may include one or more programmable vision accelerators (PVAs), which may alternatively be referred to herein as computer vision accelerators. The PVA(s) may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (ADAS), autonomous driving, augmented reality (AR), and / or virtual reality (VR) applications. The PVA(s) may provide a balance between performance and flexibility. For example, each PVA may include, among other things, any number of reduced instruction set computing (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0122] The RISC cores may interact with image sensors (e.g., the image sensors of any of the cameras described herein), image signal processor(s), and / or the like. Each of the RISC cores may include any memory location. The RISC cores may use any of a number of protocols, depending on the embodiment. In some examples, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented using one or more integrated circuit devices, application-specific integrated circuits (ASICs), and / or memory devices. The RISC cores may include, for example, an instruction cache and / or tightly coupled RAM.

[0123] The DMA may enable components of the PVA(s) to access system memory independently of the CPU(s) 406. The DMA may support any number of features provided to optimize the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support up to six or more addressing dimensions, which may include block width, block height, block depth, horizontal block gradation, vertical block gradation, and / or depth gradation.

[0124] The vector processors may be programmable processors that can be designed to efficiently and flexibly execute the programming of computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, a DMA engine(s) (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem may operate as the primary processing engine of the PVA and may include a vector processing unit (VPU), an instruction cache, and / or a vector memory (e.g., VMEM). A VPU core may include a digital signal processor, such as a single-instruction-multiple-data (SIMD), very-long-instruction-word (VLIW) digital signal processor. The combination of SIMD and VLIW can improve throughput and speed.

[0125] Each of the vector processors may include an instruction cache and be coupled to dedicated memory. As a result, in some examples, each of the vector processors may be configured to operate independently of the other vector processors. In other examples, vector processors included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, the plurality of vector processors included in a single PVA may execute the same computer vision algorithm, but in different regions of an image. In other examples, the vector processors included in a particular PVA may concurrently execute different computer vision algorithms on the same image or even execute different algorithms on sequential images or portions of an image.Among other things, any number of PVAs can be included in a hardware acceleration cluster, and any number of vector processors can be included in each of the PVAs. Furthermore, the PVA(s) can include additional ECC (Error Correction Code) memory to improve overall system security.

[0126] The accelerator(s) 414 (e.g., the hardware acceleration cluster) may include a computer vision network on a chip and SRAM to provide high-bandwidth, low-latency SRAM to the accelerator(s) 414. In some examples, the on-chip memory may include at least 4 MB of SRAM, consisting, for example, of, among other things, eight field-configurable memory blocks accessible 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 memory through a backbone that provides high-speed memory access to the PVA and DLA.The backbone may include a computer vision network on a chip that connects the PVA and DLA to the memory (e.g., using the APB).

[0127] The computer vision network can include an on-chip interface that determines that both the PVA and the DLA are providing ready and valid signals before transmitting control signals / addresses / data. Such an interface can provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst communications for continuous data transfer. This type of interface can conform to ISO 26262 or IEC 61508 standards, although other standards and protocols can also be used.

[0128] In some examples, the SoC(s) 404 may include a real-time ray tracing hardware accelerator, as described in U.S. Patent Application No. 16 / 101,232, filed August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the positions and extents of objects (e.g., within a world model), generate real-time visualization simulations, interpret radar signals, synthesize and / or analyze sound propagation, simulate sonar systems, perform general wave propagation simulation, compare to lidar data for localization 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 operations related to ray tracing.

[0129] The accelerator(s) 414 (e.g., the hardware accelerator cluster) has / have a broad field of application for autonomous driving. The PVA can be a programmable vision accelerator that can be used for critical processing stages in ADAS and autonomous vehicles. The capabilities of the PVA are well suited for algorithmic areas that require predictable processing with low power and low latency. In other words, the PVA performs well on semi-dense or dense regular computations, even on small datasets that require predictable runtimes with low latency and low power. In the context of autonomous vehicle platforms, the PVAs are therefore designed to execute classical computer vision algorithms because they are efficient at object detection and operate with integer mathematics.

[0130] For example, according to one embodiment of the technology, the PVA is used to perform computer stereo vision. A semi-global matching-based algorithm may be used in some examples, although this is not intended to be limiting. Many Level 3-5 autonomous driving applications require on-the-fly motion estimation / stereo matching (e.g., structure from motion, pedestrian detection, lane detection, etc.). The PVA can perform computer stereo vision functions with inputs from two monocular cameras.

[0131] In some examples, the PVA can be used to perform dense optical flow, such as processing raw radar data (e.g., using 4D Fast Fourier Transform) for processed radar. In other examples, the PVA is used for deep time-of-flight processing, such as processing the raw time-of-flight data to provide processed time-of-flight data.

[0132] The DLA can be used to power any type of network to improve control and driving safety, 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 allows the system to make further decisions about which detections should be considered true positives rather than false positives. For example, the system can set a confidence threshold and consider only those detections that exceed the threshold as true positives.In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the directions with the highest confidence should be considered as triggers for AEB. The DLA can run a neural network to regress the confidence value. The neural network may use as input at least a subset of parameters, such as, but not limited to, the bounding box dimensions, the ground plane estimate obtained (e.g., from another subsystem), the output of an inertial measurement unit (IMU) sensor 466 correlated with the orientation of the vehicle 400, the distance, 3D location estimates of the object obtained from the neural network and / or other sensors (e.g., LIDAR sensor(s) 464 or RADAR sensor(s) 460).

[0133] The SoC(s) 404 may include one or more data stores 416 (e.g., memory). The data stores 416 may be on-chip memory of the SoC(s) 404 that may store neural networks to be executed on the GPU and / or DLA. In some examples, the data stores 416 may be large enough to store multiple instances of neural networks for redundancy and security. The data stores 416 may include L2 or L3 cache(s) 412. Reference to the data stores 416 may include reference to the memory associated with the PVA, DLA, and / or other accelerator(s) 414, as described herein.

[0134] The SoC(s) 404 may include one or more processor(s) 410 (e.g., embedded processors). The processor(s) 410 may include a boot and power management processor, which may be a dedicated processor and subsystem that handles boot power and management functions and associated security enforcement. The boot and power management processor may be part of the boot sequence of SoC(s) 404 and provide runtime power management services. The boot power and management processor may provide clock and voltage programming, low-power system state transition support, management of the thermals and temperature sensors of the SoC(s) 404, and / or management of the power states of the SoC(s) 404.Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to temperature, and the SoC(s) 404 may use ring oscillators to detect temperatures of the CPU(s) 406, GPU(s) 408, and / or accelerator(s) 414. If temperatures are determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine and place the SoC(s) 404 into a lower power consumption state and / or place the vehicle 400 into a chauffeur-to-safe-stop mode (e.g., bring the vehicle 400 to a safe stop).

[0135] Processor(s) 410 may further include a set of embedded processors that may serve as an audio processing engine. The audio processing engine may be an audio subsystem that enables full hardware support for multi-channel audio across multiple interfaces and a wide and flexible range of audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core with a digital signal processor with dedicated RAM.

[0136] The processor(s) 410 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.

[0137] The processor(s) 410 may further include a security cluster engine, which includes a dedicated processor subsystem for handling security management for automotive applications. The security cluster engine may include two or more processor cores, tightly coupled RAM memory, supporting peripherals (e.g., timers, an interrupt controller, etc.), and / or routing logic. In a security mode, the two or more cores may operate in a lockstep mode, functioning as a single core with comparison logic for detecting differences between their operations.

[0138] The processor(s) 410 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0139] The processor(s) 410 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.

[0140] The processor(s) 410 may include a video image compositor, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required by a video playback application to produce the final image for the playback window. The video image compositor may perform lens distortion correction on the wide-angle camera(s) 470, the surround camera(s) 474, and / or the in-cabin surveillance camera sensors. An in-cabin surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC (Advanced SoC) and configured to identify in-cabin events and respond accordingly.An in-cab system can perform lip reading to activate cellular service and make a phone call, dictate emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web browsing. Certain functions are available to the driver only when the vehicle is operating in autonomous mode and are disabled otherwise.

[0141] The video image compositor may incorporate enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, when motion occurs in a video, the noise reduction may weight spatial information accordingly, reducing the weight of information provided by neighboring frames. When an image or part of an image does not include motion, the temporal noise reduction performed by the video image compositor may use information from the previous frame to reduce noise in the current frame.

[0142] The video compositor may also be configured to perform stereo rectification on the input stereo lens frames. The video compositor may further be used for composing user interfaces when the operating system desktop is in use and the GPU(s) 408 are not required to continuously render new surfaces. The video compositor may be used to offload the GPU(s) 408, even when the GPU(s) 408 is / are turned on and actively performing 3D rendering, to improve performance and responsiveness.

[0143] The SoC(s) 404 may further include a Mobile Industry Processor Interface (MIPI) camera serial interface for receiving video and inputs from cameras, a high-speed interface, and / or a video input block that may be used for camera and associated pixel input functions. The SoC(s) 404 may further include one or more input / output controllers that may be controlled by software and used to receive I / O signals that are not dedicated to a specific role.

[0144] The SoC(s) 404 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and / or other devices. The SoC(s) 404 may be used to process data from cameras (e.g., connected via Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s) 464, RADAR sensor(s) 460, etc., which may be connected via Ethernet), data from bus 402 (e.g., speed of vehicle 400, steering wheel position, etc.), data from GNSS sensor(s) 458 (e.g., connected via an Ethernet bus or a CAN bus), etc. The SoC(s) 404 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines and which may be used to offload routine data management tasks from the CPU(s) 406.

[0145] The SoC(s) 404 may be an end-to-end platform with a flexible architecture that encompasses automation levels 3-5, thus providing a comprehensive functional safety architecture that leverages and efficiently deploys computer vision and ADAS techniques for diversity and redundancy, providing a platform for a flexible, reliable propulsion software stack, along with deep learning tools. The SoC(s) 404 may be faster, more reliable, and even more energy and space efficient than conventional systems. For example, the accelerator(s) 414, in combination with the CPU(s) 406, GPU(s) 408, and data storage(s) 416, may provide a fast, efficient platform for Level 3-5 autonomous vehicles.

[0146] 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 a high-level programming language, such as the C programming language, to execute a wide range of processing algorithms on a wide range of visual data. However, CPUs are often unable to meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, which is a requirement in in-vehicle ADAS applications and a requirement for practical Level 3-5 autonomous vehicles.

[0147] In contrast to conventional systems, the technology described herein, by providing a CPU complex, GPU complex, and a hardware acceleration cluster, allows multiple neural networks to be executed simultaneously and / or sequentially and the results to be combined to enable Level 3-5 autonomous driving functionality. For example, a CNN executing on the DLA or the dGPU (e.g., GPU(s) 420) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which the neural network has not been specifically trained. The DLA may further include a neural network capable of identifying, interpreting, and providing a semantic understanding of a sign, and passing this semantic understanding to the route planning modules running on the CPU complex.

[0148] As another example, multiple networks can operate simultaneously, as required when driving at Level 3, 4, or 5. For example, a warning sign consisting of "Caution: Flashing lights indicate black ice" along with an electric light can be interpreted independently or jointly by multiple neural networks. The sign itself can be identified as a traffic sign by a first deployed neural network (e.g., a trained neural network), and the text "Flashing lights indicate black ice" can be interpreted by a second deployed neural network, which informs the vehicle's route planning software (preferably running on the CPU complex) that black ice is present upon detecting flashing lights.The flashing light can be identified by operating a third deployed neural network over multiple frames, thereby informing the vehicle's route planning software of the presence (or absence) of the flashing light. All three neural networks can be operated simultaneously, such as within the DLA and / or on the GPU(s) 408.

[0149] In some examples, a CNN for facial recognition and vehicle owner identification may use data from camera sensors to identify the presence of an authorized driver and / or vehicle owner of vehicle 400. The always-on sensor processing engine may be used to unlock the vehicle when the owner approaches the driver's door and turns on the lights, and, in security mode, to disable the vehicle when the owner exits the vehicle. In this way, the SoC(s) 404 provides security against theft and / or forced car hijacking.

[0150] In another example, a CNN for emergency vehicle detection and identification may use data from microphones 496 to detect and identify emergency vehicle sirens. Unlike conventional systems that use general classifiers to detect sirens and manually extract features, the SoC(s) 404 may use the CNN to classify ambient and urban sounds, as well as to classify visual data. In a preferred embodiment, the CNN, running on the DLA, is trained to identify the relative approach speed of the emergency vehicle (e.g., 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(s) 458. For example, when operating in Europe, the CNN will attempt to detect European sirens, and in the United States, the CNN will attempt to identify only North American sirens. A control program, with the assistance of ultrasonic sensors 462, can be used, once an emergency vehicle is detected, to execute a safety routine for the emergency vehicle, decelerate the vehicle, pull over to the side of the road, park the vehicle, and / or keep the vehicle idling until the emergency vehicle(s) have passed.

[0151] The vehicle may include one or more CPU(s) 418 (e.g., discrete CPU(s) or dCPU(s)) that may be coupled to the SoC(s) 404 via a high-speed connection (e.g., PCIe). The CPU(s) 418 may include, for example, an x86 processor. The CPU(s) 418 may be used to perform any of a number of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC(s) 404 and / or monitoring the status and health of the controller(s) 436 and / or infotainment SoC 430.

[0152] The vehicle 400 may include one or more GPU(s) 420 (e.g., discrete GPU(s) or dGPU(s)) that may be coupled to the SoC(s) 404 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s) 420 may provide additional artificial intelligence functionality, such as by executing redundant and / or distinct neural networks, and may be used to train and / or update neural networks based on inputs (e.g., sensor data) from sensors of the vehicle 400.

[0153] The vehicle 400 may further include the network interface 424, which may include one or more wireless antennas 426 (e.g., one or more wireless antennas for various communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface 424 may be used to enable wireless connectivity over the internet to the cloud (e.g., to the server(s) 478 and / or other network devices), to other vehicles, and / or computing devices (e.g., passenger client devices). To communicate with other vehicles, a direct connection between the two vehicles and / or an indirect connection (e.g., via networks and over the internet) may be established. Direct connections may be provided using a vehicle-to-vehicle communication link.The vehicle-to-vehicle communication link may provide information to the vehicle 400 about vehicles in the vicinity of the vehicle 400 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 400). The functionality may be part of a cooperative adaptive cruise control functionality of the vehicle 400.

[0154] The network interface 424 may include an SoC that provides modulation and demodulation functionality and enables the controller(s) 436 to communicate over wireless networks. The network interface 424 may include a radio frequency front-end for upconverting from baseband to radio frequency and for downconverting from radio frequency to baseband. The frequency conversions may be performed by known processes and / or may be performed using super-heterodyne processes. In some examples, the radio frequency front-end functionality may be provided by a separate chip. The network interface may include wireless functionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0155] The vehicle 400 may further include one or more data stores 428, which may include off-chip (e.g., off-SoC(s) 404) storage. The data stores 428 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash, hard drives, and / or other components and / or devices capable of storing at least one bit of data.

[0156] The vehicle 400 may further include one or more GNSS sensors 458. The GNSS sensor(s) 458 (e.g., GPS, assisted GPS sensors, differential GPS sensors (DGPS), etc.) are used to support mapping, sensing, occupancy grid generation, and / or route planning functions. Any number of GNSS sensors 458 may be used, including, for example, but not limited to, a GPS using a USB port with an Ethernet-to-serial (e.g., RS-232) bridge.

[0157] The vehicle 400 may further include one or more RADAR sensors 460. The RADAR sensor(s) 460 may be used by the vehicle 400 for long-range vehicle detection, even in darkness and / or adverse weather conditions. The RADAR functional safety levels may be ASIL B. In some examples, the RADAR sensor(s) 460 may use CAN and / or bus 402 (e.g., to transmit data generated by the RADAR sensor(s) 460) for control and access to object tracking data, with access to Ethernet to access raw data. A wide range of RADAR sensor types may be used. For example, the RADAR sensor(s) 460 may be suitable for front, rear, and side RADAR use, among others. In some examples, a pulse Doppler radar sensor(s) is / are used.

[0158] The RADAR sensor(s) 460 may include various configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, etc. In some examples, long-range RADAR may be used for adaptive cruise control functionality. Long-range RADAR systems may provide a wide field of view realized through two or more independent scans, e.g., within a range of 250 m. The RADAR sensor(s) 460 may assist in distinguishing between static and moving objects and may be used by ADAS systems for emergency braking and forward collision warning. Long-range radar sensors may include monostatic multimodal radar with multiple (e.g., six or more) fixed radar antennas and a high-speed CAN and FlexRay interface.In a six-antenna example, the central four antennas can create a focused beam pattern designed to capture the surroundings of vehicle 400 at higher speeds with minimal interference from traffic in adjacent lanes. The other two antennas can expand the field of view so that vehicles entering or exiting the lane of vehicle 400 can be quickly detected.

[0159] Mid-range radar systems, for example, can include a range of up to 160 m (front) or 80 m (rear) and a field of view of up to 42 degrees (front) or 150 degrees (rear). Short-range radar systems can include, among other features, radar sensors designed for installation at both ends of the rear bumper. When such radar sensor systems are installed at both ends of the rear bumper, they can create two beams that constantly monitor the blind spot in a direction toward the rear and near the vehicle.

[0160] Short-range radar systems can be used in ADAS systems to detect blind spots and / or assist with lane changes.

[0161] The vehicle 400 may further include one or more ultrasonic sensors 462. The ultrasonic sensor(s) 462 may be positioned at the front, rear, and / or sides of the vehicle 400, may be used for parking assistance and / or for generating and updating an occupancy grid. A wide range of ultrasonic sensors 462 may be used, and different ultrasonic sensors 462 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensor(s) 462 may operate to ASIL B functional safety levels.

[0162] The vehicle 400 may include one or more LIDAR sensors 464. The LIDAR sensor(s) 464 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor(s) 464 may operate to functional safety level ASIL B. In some examples, the vehicle 400 may include multiple LIDAR sensors 464 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0163] In some examples, the LIDAR sensor(s) 464 may provide a list of objects and their distances for a 360-degree field of view. For example, a commercially available LIDAR sensor(s) 464 may have a stated range of approximately 100 m with an accuracy of 2 cm to 3 cm and support for a 100 Mbps Ethernet connection. In some examples, one or more non-protruding LIDAR sensors 464 may be used. In some examples, the LIDAR sensor(s) 464 may be implemented as a small device that may be embedded in the front, rear, sides, and / or corners of the vehicle 400. The LIDAR sensor(s) 464 days / can provide a horizontal field of view of up to 120 degrees and a vertical field of view of up to 35 degrees, with a range of 200 m, even for objects with low reflectivity.One or more front-mounted LIDAR sensors 464 may be configured to have a horizontal field of view between 45 degrees and 135 degrees.

[0164] In some examples, LIDAR technologies, such as 3D flash LIDAR, may also be used. 3D flash LIDAR uses a laser flash as a transmission source to illuminate the vehicle's surroundings up to approximately 200 m. A flash LIDAR unit includes a receptor that records the laser pulse time of flight and reflected light at each pixel, which in turn corresponds to the vehicle's range to the objects. With flash LIDAR, highly accurate and distortion-free images of the surroundings can be generated with each laser flash. In some examples, four flash LIDAR sensors may be deployed, one on each side of the vehicle. Available 3D flash LIDAR systems include a solid-state, rigid-array 3D LIDAR camera with no moving parts other than a fan (e.g., a non-scanning LIDAR device).The flash lidar device can use a 5-nanosecond Class I (eye-safe) laser pulse per frame and collect reflected laser light as 3D range point clouds 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(s) 464 can be less susceptible to motion blur, vibration, and / or shock.

[0165] The vehicle may further include one or more IMU sensor(s) 466. The IMU sensor(s) 466 may, in some examples, be located at a center of the rear axle of the vehicle 400. The IMU sensor(s) 466 may, for example, include one or more accelerometers, one or more magnetometers, one or more gyroscopes, one or more magnetic compasses, and / or other sensor types, among others. In some examples, such as in six-axis applications, the IMU sensor(s) 466 may include, among others, accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s) 466 may include accelerometers, gyroscopes, and magnetometers.

[0166] In some embodiments, the IMU sensor(s) 466 may be implemented as a miniature, high-performance GPS-aided inertial navigation system (GPS / INS) that combines micro-electromechanical systems (MEMS)-based inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filter algorithms to provide estimates of position, velocity, and attitude. As such, in some examples, the IMU sensor(s) 466 may enable the vehicle 400 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating velocity changes from a GPS to the IMU sensor(s) 466. In some examples, the IMU sensor(s) 466 and the GNSS sensor(s) 458 may be combined into a single integrated unit.

[0167] The vehicle may include one or more microphones 496 located in and / or around the vehicle 400. The microphone(s) 496 may be used for, among other things, emergency vehicle detection and identification.

[0168] The vehicle may further include any number of camera types, including one or more stereo cameras 468, wide-angle cameras 470, infrared cameras 472, surround cameras 474, long-range and / or medium-range cameras 498, and / or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle 400. The types of cameras used depend on the embodiments and requirements for the vehicle 400, and any combination of camera types may be used to provide the necessary coverage around the vehicle 400. Furthermore, the number of cameras may vary depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or a different number of cameras.The cameras, for example, may support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet communication, among other features. Each of the cameras is described in more detail herein with respect to . Fig. 4A and Fig. 4B.

[0169] The vehicle 400 may further include one or more vibration sensors 442. The vibration sensor(s) 442 may measure vibrations from components of the vehicle, such as the axle(s). For example, changes in vibration may indicate a change in the road surfaces. In another example, if two or more vibration sensors 442 are used, the differences between the vibrations may be used to determine friction or slipperiness of the road surface (e.g., if the difference in vibration is between a driven axle and a freely rotating axle).

[0170] The vehicle 400 may include an ADAS system 438. The ADAS system 438 may include an SoC in some examples. The ADAS system 438 may include systems for autonomous / adaptive / automatic and distance control (ACC), cooperative adaptive and distance control (CACC), forward crash warning (FCW), automatic emergency braking (AEB), lane departure warnings (LDW), lane keep assist (LKA), blind spot warning (BSW), rear cross-traffic warning (RCTW), collision warning systems (CWS), lane centering (LC), and / or other features and functions.

[0171] The ACC systems may use one or more RADAR sensors 460, LIDAR sensors 464, and / or one or more cameras. The ACC systems may include longitudinal ACC and / or lateral ACC systems. Longitudinal ACC monitors and controls the distance to the vehicle immediately ahead of the vehicle 400 and automatically adjusts the vehicle's speed to maintain a safe distance from forward vehicles. Lateral ACC maintains the distance and advises the vehicle 400 to change lanes if necessary. Lateral ACC is related to other ADAS applications, such as LCA and CWS.

[0172] CACC utilizes information from other vehicles, which may be received via the network interface 424 and / or the wireless antenna(s) 426 from other vehicles over a wireless connection or indirectly via a network connection (e.g., over the Internet). Direct connections may be provided through a vehicle-to-vehicle (V2V) communication link, while indirect connections may be an infrastructure-to-vehicle (I2V) communication link. Generally, the V2V communication concept provides information about immediately ahead vehicles (e.g., vehicles immediately in front of and in the same lane as vehicle 400), while the I2V communication concept provides information about traffic further away. CACC systems may include one or both of the I2V and V2V information sources.Thanks to the information about the vehicles in front of vehicle 400, the CACC can be more reliable and it has the potential to improve traffic flow and reduce congestion on the road.

[0173] FCW systems are designed to alert the driver to a hazard so the driver can take corrective action. FCW systems utilize a forward-facing camera and / or radar sensors 460 coupled with a dedicated processor, DSP, FPGA, and / or ASIC, which is / are electrically coupled to driver feedback, such as a display, speaker, and / or vibrating component. FCW systems can provide a warning, for example, in the form of a sound, a visual warning, a vibration, and / or a rapid braking pulse.

[0174] AEB systems detect an impending forward collision with another vehicle or object and can automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems utilize one or more forward-facing cameras and / or radar sensors 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC. When the AEB system detects a hazard, it will typically first alert the driver to take corrective action to avoid the collision. If the driver does not take corrective action, the AEB system will automatically apply the brakes in an effort to avoid or at least mitigate the impact of the predicted collision. AEB systems may include techniques such as dynamic brake support and / or imminent collision braking.

[0175] LDW systems provide visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle crosses lane markings. An LDW system will not activate if the driver indicates an intended lane departure, such as by activating a turn signal. LDW systems may utilize forward-facing cameras coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which is electrically coupled to a driver feedback component, such as a display, speaker, and / or vibration component.

[0176] LKA systems are a variation of LDW systems. LKA systems provide steering input or braking to correct the vehicle 400 if the vehicle 400 begins to depart from the lane. BSW systems detect vehicles in an automobile's blind spot and warn the driver. BSW systems may provide a visual, audible, and / or tactile alert to indicate that merging or changing lanes is unsafe. The system may provide an additional warning if the driver uses a turn signal. BSW systems may utilize rear-facing camera(s) and / or radar sensor(s).

[0177] RCTW systems may provide visual, audible, and / or tactile notification when an object outside the range of the rearview camera is detected while reversing the vehicle 400. Some RCTW systems include AEB to ensure the vehicle brakes are applied to avoid a collision. RCTW systems may utilize one or more rear-facing RADAR sensors 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC electrically coupled to driver feedback, such as a display, speaker, and / or vibrating component.

[0178] Conventional ADAS systems can be prone to false positives, which can be annoying and distracting for a driver, but are typically not catastrophic because the ADAS systems warn the driver and allow the driver to decide whether a safety condition actually exists and act accordingly. However, in an autonomous vehicle 400, in the case of conflicting results, the vehicle 400 itself must decide whether the result should be considered by a primary computer or a secondary computer (e.g., a first controller 436 or a second controller 436). For example, in some embodiments, the ADAS system 438 may be a backup and / or secondary computer that provides perception information to a rationality module of the backup computer. The rationality monitor of the backup computer may execute redundant, diverse software on hardware components to detect perception errors and dynamic driving tasks.Outputs from the ADAS system 438 can be provided to a higher-level MCU. If the outputs from the primary and secondary computers conflict, the higher-level MCU must decide how to resolve the conflict to ensure safe operation.

[0179] In some examples, the primary computer may be configured to provide the parent MCU with a confidence value indicating the primary computer's confidence in the selected outcome. If the confidence value exceeds a threshold, the parent MCU may follow the primary computer's guidance, regardless of whether the secondary computer provides a conflicting or inconsistent outcome. If the confidence value does not meet a threshold and the primary and secondary computers indicate different outcomes (e.g., a conflict), the parent MCU may arbitrate between the computers to determine the appropriate outcome.

[0180] The master MCU can be configured to run a neural network(s) trained and configured to determine, based on outputs from the primary and secondary computers, conditions under which the secondary computer will generate false alarms. Thus, the neural network(s) in the master MCU can learn when the secondary computer's output can and cannot be trusted. For example, if the secondary computer is a radar-based FCW system, the neural network(s) in the master MCU can learn when the FCW system identifies metallic objects that are not actually hazards, such as a drainage grate or manhole cover, that trigger an alarm.Similarly, if the secondary computer is a camera-based LDW system, a neural network in the parent MCU can learn to override the LDW when cyclists or pedestrians are present and lane departure is actually the safest maneuver. In embodiments including neural network(s) operating on the parent MCU, the parent MCU can include at least one of a DLA or a GPU suitable for operating the neural network(s) with associated memory. In preferred embodiments, the parent MCU can comprise and / or be included as a component of the SoC(s) 404.

[0181] In other examples, ADAS system 438 may include a secondary processor that performs ADAS functionality using traditional computer vision rules. As such, the secondary processor may use classic computer vision rules (if-then), and the presence of one or more neural networks in the parent MCU may improve reliability, safety, and performance. For example, the diverse implementation and intentional non-identity makes the overall system more fault-tolerant, particularly toward errors caused by software (or software-hardware interface) functionality.For example, if there is a software bug or error in the software running on the primary computer, and the non-identical software code running on the secondary computer provides the same overall result, the parent MCU may have greater confidence that the overall result is correct and the bug in the software or hardware on the primary computer does not cause a significant error.

[0182] In some examples, the output of the ADAS system 438 may be fed into the perception block of the primary computer and / or the dynamic driving task block of the primary computer. For example, if the ADAS system 438 displays a forward collision warning due to an object immediately ahead, the perception block may use this information in identifying objects. In other examples, the secondary computer may have its own neural network trained, thus reducing the risk of false positives, as described herein.

[0183] The vehicle 400 may further include the infotainment SoC 430 (e.g., an in-vehicle infotainment (IVI) system). 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 430 may include a combination of hardware and software that can be used to provide the vehicle 400 with audio (e.g., music, a personal digital assistant, navigation directions, news, radio, etc.), video (e.g., television, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and / or information services (e.g., navigation systems, reverse parking assistance, a radio data system, vehicle-related information such as fuel level, total distance traveled, brake fluid level, oil level, door open / closed, air filter information, etc.).For example, the infotainment SoC 430 may include radios, record players, navigation systems, video players, USB and Bluetooth connectivity, car computers, in-car entertainment, Wi-Fi, steering wheel audio controls, hands-free voice commands, a heads-up display (HUD), HMI display 434, a telematics device, a control panel (e.g., for controlling and / or interacting with various components, functions, and / or systems), and / or other components. The infotainment SoC 430 may further be used to provide information (e.g., visual and / or audible) to the vehicle's user(s), such as information from the ADAS system 438, autonomous driving information such as planned vehicle maneuvers, trajectories, information about the surrounding environment (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0184] The infotainment SoC 430 may include GPU functionality. The infotainment SoC 430 may communicate with other devices, systems, and / or components of the vehicle 400 via the bus 402 (e.g., CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 430 may be coupled to a higher-level MCU so that the infotainment system's GPU may perform some self-controlling functions in the event of a failure of the primary controller(s) 436 (e.g., primary and / or backup computer of the vehicle 400). In such an example, the infotainment SoC 430 may place the vehicle 400 into a chauffeur-to-safe stop mode, as described herein.

[0185] The vehicle 400 may further include an instrument cluster 432 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 432 may include a controller and / or a supercomputer (e.g., a discrete controller or a discrete supercomputer). The instrument cluster 432 may include a set of instruments such as a speedometer, fuel level, oil pressure, tachometer, odometer, turn signal indicator, gear shift indicator, seat belt warning light(s), parking brake warning light(s), engine malfunction light(s), airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and / or shared between the infotainment SoC 430 and the instrument cluster 432.In other words, the instrument cluster 432 may be included as part of the infotainment SoC 430 or vice versa.

[0186] Fig. 4D is a system diagram according to some embodiments of the present disclosure for communication between one or more cloud-based servers and the exemplary autonomous vehicle 400 of Fig. 4A. The system 476 may include one or more servers 478, one or more networks 490, and vehicles, including the vehicle 400. The server(s) 478 may include a plurality of GPUs 484(A)-484(H) (collectively referred to herein as GPUs 484), PCIe switches 482(A)-482(H) (collectively referred to herein as PCIe switches 482), and / or CPUs 480(A)-480(B) (collectively referred to herein as CPUs 480). The GPUs 484, CPUs 480, and PCIe switches may be interconnected using high-speed interconnects, such as, but not limited to, the NVIDIA-developed NVLink interfaces 488 and / or PCIe interconnects 486. In some examples, the GPUs 484 are connected via an NVLink and / or NVSwitch SoC, and the GPUs 484 and PCIe switches 482 are connected via PCIe connections. Although eight GPUs 484, two CPUs 480, and two PCIe switches are illustrated, this is not intended to be limiting.Depending on the embodiment, each of the servers 478 may include any number of GPUs 484, CPUs 480, and / or PCIe switches. For example, the server(s) 478 may include eight, sixteen, thirty-two, and / or more GPUs 484, respectively.

[0187] The server(s) 478 may receive, via the network(s) 490 and from the vehicles, image data representing images depicting unexpected or changed road conditions, such as recently initiated road construction. The server(s) 478 may transmit, via the network(s) 490 and to the vehicles, neural networks 492, updated neural networks 492, and / or map information 494, including information regarding traffic and road conditions. The updates to the map information 494 may include updates to the HD map 422, such as information regarding construction, potholes, detours, flooding, and / or other obstacles.In some examples, the neural networks 492, the updated neural networks 492, and / or the map information 494 may result from new training and / or new experience represented in data received from any number of vehicles in the area and / or based on training performed at a data center (e.g., using the server(s) 478 and / or other servers).

[0188] The server(s) 478 may be used to train machine learning models (e.g., neural networks) based on training data. The training data may be generated by the vehicles and / or in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., if the neural network benefits from supervised learning) and / or undergoes preprocessing, while in other examples, the training data is untagged and / or preprocessed (e.g., if the neural network does not require supervised learning).Training may be performed according to one or more classes of machine learning techniques, including, but not limited to, supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including sparse dictionary learning), rule-based machine learning, anomaly detection, and any other variations or combinations thereof. After training the machine learning models, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles via the network(s) 490) and / or the machine learning models may be used by the server(s) 478 to remotely monitor the vehicles.

[0189] In other examples, the server(s) 478 may receive data from the vehicles and apply the data to real-time neural networks for intelligent real-time inference. The server(s) 478 may include deep learning supercomputers and / or dedicated AI machines powered by GPU(s) 484, such as the DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s) 478 may also include deep learning infrastructure using only CPU-powered data centers.

[0190] The deep learning infrastructure of the server(s) 478 may be capable of fast, real-time inferencing and use this capability to evaluate and verify the state of the processors, software, and / or associated hardware in the vehicle 400. For example, the deep learning infrastructure may receive periodic updates from the vehicle 400, such as a sequence of images and / or objects that the vehicle 400 has located in that sequence of images (e.g., through computer vision and / or other machine learning object classification techniques).The deep learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle 400, and if the results do not match and the infrastructure concludes that the AI ​​in the vehicle 400 is faulty, the server(s) 478 may send a signal to the vehicle 400 and instruct a fail-safe computer of the vehicle 400 to take over control, notify the passengers, and perform a safe parking maneuver.

[0191] The server(s) 478 may include GPU(s) 484 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and inference acceleration may enable real-time responsiveness. In other examples, such as when performance is less critical, servers powered by CPUs, FPGAs, and other processors may be used for inference purposes. EXAMPLE CALCULATION DEVICE

[0192] Fig. 5 is a block diagram of exemplary computing device(s) 500 suitable for use in implementing some embodiments of the present disclosure. Computing device 500 may include an interconnect system 502 that directly or indirectly couples the following devices: memory 504, one or more central processing units (CPUs) 506, one or more graphics processing units (GPUs) 508, a communications interface 510, input / output (I / O) ports 512, input / output components 514, a power supply 516, one or more presentation components 518 (e.g., display(s)), and one or more logic units 520. In at least one embodiment, computing device(s) 500 may include one or more virtual machines (VMs), and / or any of the components thereof may include virtual components (e.g., virtual hardware components).For non-limiting examples, one or more of the GPUs 508 may include one or more vGPUs, one or more of the CPUs 506 may include one or more vCPUs, and / or one or more of the logic units 520 may include one or more virtual logic units. As such, a computing device(s) 500 may include discrete components (e.g., an entire GPU dedicated to the computing device 500), virtual components (e.g., a portion of a GPU dedicated to the computing device 500), or a combination thereof.

[0193] Although the different blocks of Fig. 5 are shown as being connected via the interconnect system 502 with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component 518, a display device, may be considered an I / O component 514 (e.g., if the display is a touchscreen). As another example, the CPUs 506 and / or GPUs 508 may include memory (e.g., memory 504 may be representative of a storage device, in addition to the memory of the GPUs 508, the CPUs 506, and / or other components). In other words, the computing device of Fig. 5 is merely illustrative. No distinction is made between categories such as "workstation", "server", "laptop", "desktop", "tablet", "client device", "mobile device", "handheld device", "game console", "electronic control unit (ECU)", "virtual reality system" and / or other device or system types, since all within the scope of the computing device Fig. 5 should be considered.

[0194] The interconnect system 502 may represent one or more links or buses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system 502 may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCI) bus, and / or another type of bus or link. In some embodiments, there are direct connections between components. As one example, the CPU 506 may be directly connected to the memory 504. Furthermore, the CPU 506 may be directly connected to the GPU 508. For a direct or point-to-point connection between components, the interconnect system 502 may include a PCIe link to implement the connection.In these examples, the computing device 500 need not include a PCI bus.

[0195] The storage 504 may include any of a variety of computer-readable media. The computer-readable media may be any available media accessible by the computing device 500. The computer-readable media may include both volatile and non-volatile media, and both removable and non-removable media. By way of example and not limitation, the computer-readable media may include computer storage media and communication media.

[0196] The computer storage media may include both volatile and non-volatile 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 504 may store computer-readable instructions (e.g., representing a program(s) and / or a program element(s), such as an operating system).Computer storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by the computing device 500. As used herein, computer storage media per se does not include signals.

[0197] The computer storage media may embody computer-readable instructions, data structures, program modules, and / or other data types in a modulated data signal, such as a carrier wave or other transport mechanism, and include information delivery media. The term "modulated data signal" may refer to a signal for which one or more of its characteristics are adjusted or altered in a manner to encode information in the signal. By way of example and not limitation, the computer storage media may include wired media, such as a wired network or a direct-wired connection, and wireless media, such as acoustic, RF, infrared, and other wireless media. Combinations of any of the foregoing should also be included within the scope of computer-readable media.

[0198] The CPU(s) 506 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and / or processes described herein. The CPU(s) 506 may include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of handling a plurality of software threads concurrently. The CPU(s) 506 may include any type of processor and may include different types of processors depending on the type of computing device 500 implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers).For example, depending on the type of computing device 500, the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). Computing device 500 may include one or more CPUs 506 in addition to one or more microprocessors or supplemental coprocessors, such as math coprocessors.

[0199] In addition to or alternatively to the CPU(s) 506, the GPU(s) 508 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and / or one or more of the processes described herein. One or more of the GPU(s) 508 may be an integrated GPU (e.g., within one or more of the CPU(s) 506) and / or one or more of the GPU(s) 508 may be a discrete GPU. In embodiments, one or more of the GPU(s) 508 may be a coprocessor of one or more of the CPU(s) 506. The GPU(s) 508 may be used by the computing device 500 to render graphics (e.g., 3D graphics) or to perform general-purpose computations. For example, the GPU(s) 508 can be used for general-purpose computing on GPUs (GPGPU).The GPU(s) 508 may include hundreds or thousands of cores capable of handling hundreds or thousands of software threads simultaneously. The GPU(s) 508 may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s) 506 received via a host interface). The GPU(s) 508 may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory 504. The GPU(s) 508 may include two or more GPUs operating in parallel (e.g., via a link). The link may connect the GPUs directly (e.g., using an NVLINK) or may connect the GPUs via a switch (e.g., using an NVSwitch).When combined, each GPU can generate 508 pixel data or GPGPU data for different dividers, one output, or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU can include its own memory or share memory with other GPUs.

[0200] In addition to or alternatively to the CPU(s) 506 and / or the GPU(s) 508, the logic unit(s) 520 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and / or processes described herein. In embodiments, the CPU(s) 506, the GPU(s) 508, and / or the logic unit(s) 520 may discretely or jointly perform any combination of the methods, processes, and / or portions thereof. One or more of the logic units 520 may be part of and / or integrated with one or more of the CPU(s) 506 and / or the GPU(s) 508, and one or more of the logic units 520 may be discrete components or otherwise external to the CPU(s) 506 and / or the GPU(s) 508.In embodiments, one or more of the logic units 520 may be a coprocessor of one or more of the CPU(s) 506 and / or one of the plurality of the GPU(s) 508.

[0201] Examples of the logic unit(s) 520 include: one or more processing cores and / or components thereof, such as data processing units (DPUs), tensor cores (TCs), tensor processing units (TPUs), pixel visual cores (PVCs), vision processing units (VPUs), graphics processing clusters (GPCs), texture processing clusters (TPCs), streaming multiprocessors (SMs), tree traversal units (TTUs), artificial intelligence accelerators (AIAs), deep learning accelerators (DLAs), arithmetic logic units (ALUs), application-specific integrated circuits (ASICs), floating-point units (FPUs), input / output (I / O) elements, Peripheral Component Interconnect (PCI) or Peripheral Component Interconnect Express (PCIe) elements and / or the like.

[0202] The communication interface 510 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 500 to communicate with other computing devices over an electronic communication network, including wired and / or wireless communication. The communication interface 510 may include components and functionality to enable communication over any 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 logic unit(s) 520 and / or communication interface 510 may include one or more data processing units (DPUs) to transfer data received over a network and / or via the interconnect system 502 directly to (e.g., a memory of) one or more GPU(s) 508.

[0203] The I / O ports 512 may enable the computing device 500 to be logically coupled to other devices, including the I / O components 514, the presentation component(s) 518, and / or other components, some of which may be incorporated (e.g., integrated) into the computing device 500. Illustrative I / O components 514 include a microphone, a mouse, a keyboard, a joystick, a game pad, a game controller, a satellite table, a scanner, a printer, a wireless device, etc. The I / O components 514 may provide a natural user interface (NUI) that processes air gestures, speech, or other physical inputs generated by a user. In some cases, inputs may be transmitted to an appropriate network element for further processing.A NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on-screen and adjacent to the screen, air gestures, head and eye tracking, and touch detection (as described in more detail in the present disclosure) associated with a display of the computing device 500. The computing device 500 may include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these for gesture detection and recognition. In addition, the computing device 500 may include accelerometers or gyroscopes (e.g., as part of an inertia measurement unit (IMU)) that enable motion detection.In some examples, the output of the accelerometers or gyroscopes from the computing device 500 may be used to render immersive augmented reality or virtual reality.

[0204] Power supply 516 may include a hardwired power supply, a battery power supply, or a combination thereof. Power supply 516 may provide power to computing device 500 to enable the components of computing device 500 to operate.

[0205] The presentation component(s) 518 may include a display (e.g., a monitor, a touchscreen, a television screen, a heads-up display (HUD), other display types, or a combination thereof), speakers, and / or other presentation components. The presentation component(s) 518 may receive data from other components (e.g., the GPU(s) 508, the CPU(s) 506, DPUs, etc.) and output the data (e.g., as an image, video, audio, etc.). EXEMPLARY DATA CENTER

[0206] Fig. Figure 6 illustrates an exemplary data center 600 that may be used in at least one embodiment of the present disclosure. Data center 600 may include a data center infrastructure layer 610, a framework layer 620, a software layer 630, and / or an application layer 640.

[0207] As in Fig. 6, the data center infrastructure layer 610 may include a resource orchestrator 612, clustered compute resources 614, and node compute resources (“node CRs”) 616(1)-616(N), where “N” represents an integer positive number. In at least one embodiment, Node CRs 616(1)-616(N) may include, among other things, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memories), storage devices (e.g., solid-state or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules and / or cooling modules, etc. In some embodiments, one or more Node CRs may bes 616(1)-616(N) correspond to a server having one or more of the computing resources mentioned above. Furthermore, in some embodiments, the node CRs 616(1)-616(N) may include one or more virtual components, such as vGPUs, vCPUs, and / or the like, and one or more of the node CRs 616(1)-616(N) may correspond to a virtual machine (VM).

[0208] In at least one embodiment, grouped computing resources 614 may include separate groupings of node CRs 616 housed in one or more racks (not shown) or multiple racks housed in data centers at different geographical locations (also not shown). Separate groupings of node CRs 616 within grouped computing resources 614 may include grouped computing, networking, memory, or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, multiple node CRs 616, including CPUs, GPUs, DPUs, and / or other processors, may be grouped in one or more racks to provide computing resources to support one or more workloads.The one or more racks may also include any number of power modules, cooling modules, and / or network switches in any combination.

[0209] The resource orchestrator 612 may configure or otherwise control one or more node CRs 616(1)-616(N) and / or grouped computing resources 614. In at least one embodiment, the resource orchestrator 612 may include a software design infrastructure (SDI) management entity for the data center 600. The resource orchestrator 612 may include hardware, software, or a combination thereof.

[0210] In at least one embodiment, as in Fig. 6, a framework layer 620 may include a job scheduler 632, a configuration manager 634, a resource manager 636, and / or a distributed file system 638. The framework layer 620 may include a framework to support software 632 of the software layer 630 and / or one or more applications 642 of the application layer 640. The software 632 or application(s) 642 may each include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 620 may be, among other things, a type of free and open-source software web application framework, such as Apache Spark™ (hereinafter "Spark"), which may utilize the distributed file system 638 for large-scale data processing (e.g., "big data").In at least one embodiment, job scheduler 632 may include a Spark driver to facilitate scheduling workloads supported by various layers of data center 600. Configuration manager 634 may be capable of configuring various layers, such as software layer 630 and framework layer 620, including Spark and distributed file system 638, to support large-scale computing. Resource manager 636 may be capable of managing clustered or grouped compute resources allocated or allocated to support distributed file system 638 and job scheduler 632. In at least one embodiment, the clustered or grouped compute resources may include a clustered compute resource 614 at data center infrastructure layer 610.The resource manager 636 may coordinate with the resource orchestrator 612 to manage these allocated or assigned computing resources.

[0211] In at least one embodiment, software 632 included in software layer 630 may include software used by at least portions of node CRs 616(1)-616(N), clustered computing resources 614, and / or distributed file system 638 of framework layer 620. One or more types of software may include, among others, internet website search software, email virus scanning software, database software, and streaming video content software.

[0212] In at least one embodiment, application(s) 642 included in application layer 640 may include one or more types of applications used by at least portions of node CRs 616(1)-616(N), clustered computing resources 614, and / or distributed file system 638 of framework layer 620. One or more types of applications may include, but are not limited to, any number of genomics applications, cognitive computing applications, 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 connection with one or more embodiments.

[0213] In at least one embodiment, any of the configuration manager 634, resource manager 636, and resource orchestrator 612 may implement any number and type of self-modifying operations based on any amount and type of data collected in any technically feasible manner. Self-modifying operations may offload a data center operator of the data center 600 from potentially making poor configuration decisions and potentially avoiding underutilized and / or poorly performing portions of a data center.

[0214] Data center 600 may include tools, services, software, or other resources to train one or more machine learning models or to predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, machine learning model(s) may be trained by calculating weight parameters according to a neural network architecture using software and / or computational resources as described herein with respect to data center 600.In at least one embodiment, trained or deployed machine learning models corresponding to one or more neural networks may be used to derive and predict information using the resources described in the present disclosure relating to the data center 600 by using weight parameters calculated via one or more training techniques, such as, but not limited to, those described herein.

[0215] In at least one embodiment, the data center 600 may utilize CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, and / or other hardware (or corresponding virtual computing resources) to perform training and / or inference using the resources described above. Furthermore, one or more software and / or hardware resources described in the present disclosure may be configured as a service to allow users to train or perform inference on information, such as image recognition, speech recognition, or other artificial intelligence services. EXAMPLE NETWORK ENVIRONMENTS

[0216] Network environments suitable for use in implementing embodiments of the 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 located at one or more instances of the computing device(s) 500 of Fig. 5 - e.g., each device may include similar components, features, and / or functionality of the computing device(s) 500. Furthermore, if backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may be implemented as part of a data center 600, an example of which is described in more detail herein with reference to Fig. 6 described.

[0217] Components of a network environment can communicate with each other over a network(s), which can be wired, wireless, or both. The network can include multiple networks or a network of networks. As an example, the network can include one or more wide area networks (WANs), one or more local area networks (LANs), one or more public networks such as the Internet, and / or a public switched telephone network (PSTN), and / or one or more private networks. If the network includes a wireless telecommunications network, components such as a base station, a communications tower, or even access points (as well as other components) can provide wireless connectivity.

[0218] Compatible network environments may include one or more peer-to-peer network environments—in which case, no server may be included in a network environment—and one or more client-server network environments—in which case, one or more servers may be included in a network environment. In peer-to-peer network environments, functionality described herein with respect to one or more servers may be implemented on any number of client devices.

[0219] In at least one embodiment, a network environment may include one or more cloud-based network environments, a distributed computing environment, a combination thereof, etc. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more servers, which may include one or more core network servers and / or edge servers. A framework layer may include a framework to support software of a software layer and / or one or more applications of an application layer. The software or application(s) may each include web-based service software or applications. In embodiments, one or more of the client devices may use the web-based service software or applications (e.g.,by accessing the service software and / or applications through one or more application programming interfaces (APIs). The framework layer may, among other things, be a type of free and open-source software web application framework that may utilize a distributed file system for large-scale data processing (e.g., "big data").

[0220] A cloud-based network environment may provide cloud computing and / or cloud storage, performing any combination of computing and / or data storage functions as described herein (or one or more portions thereof). Any of these various functions may be distributed across multiple locations of central or core servers (e.g., one or more data centers that may be distributed across a state, region, country, the world, etc.). If a connection to a user (e.g., a client device) is relatively close to an edge server(s), a core server(s) may designate at least some of the functionality to that edge server(s). A cloud-based network environment may 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).

[0221] The client device(s) may include at least some of the components, features, and functionality of the devices described herein with reference to Fig.5. By way of example and not limitation, a client device may be embodied as a personal computer (PC), laptop computer, mobile device, smartphone, tablet computer, smart watch, wearable computer, personal digital assistant (PDA), MP3 player, virtual reality headset, global positioning system (GPS) or global positioning device, video player, video camera, surveillance device or system, vehicle, boat, flying vehicle, virtual machine, drone, robot, handheld communication device, hospital device, gaming device or system, entertainment system, vehicle computing system, embedded system controller, remote control device, consumer electronics device, workstation, edge device, any combination of these described devices, or any other suitable device.

[0222] The disclosure of this application also includes the following numbered clauses: Clause 1. A procedure which includes: Determining a first illumination status corresponding to an area based on at least first sensor data obtained using a first illumination sensor corresponding to a machine; Determining a second illumination status corresponding to the area based on at least second sensor data obtained using a second illumination sensor corresponding to the machine; Determining a scene illumination status of the area based at least on the first illumination status and the second illumination status; and Controlling one or more lighting components of the machine based at least on the illumination status. Clause 2. The method of clause 1, wherein determining the scene illumination status is based at least on a relationship between the first illumination status and a set of threshold ranges corresponding to the first illumination status. Clause 3. The method of clause 1 or clause 2, wherein determining the scene illumination status is based at least on the second illumination status in response to the first illumination status being between a set of threshold ranges corresponding to the first illumination status. Clause 4. The method of clause 3, wherein determining the scene illumination status is based at least on a relationship between the second illumination status and a second set of threshold ranges corresponding to the second illumination status. Clause 5. The method of any preceding clause, wherein determining the one or more of the first illumination status, the second illumination status, or the scene illumination status is performed using one or more neural networks. Clause 6. The method of any preceding clause, wherein the second illumination sensor is for detecting illumination levels at a different distance than the first illumination sensor. Clause 7. The method of any preceding clause, wherein the first illumination sensor includes a wide-angle camera and the second illumination sensor includes a telescopic camera. Clause 8. The method of Clause 2, wherein the set of threshold regions includes, corresponding to the first illumination state, a dark status region and a lit status region. Clause 9. A system that includes: one or more processors for performing operations including: Determining a first illumination status corresponding to an area based on at least first sensor data obtained using a first illumination sensor corresponding to a machine; Determining a second illumination status corresponding to the area based on at least second sensor data obtained using a second illumination sensor corresponding to the machine; Determining a scene illumination status of the area based on the first illumination status and the second illumination status; and Controlling one or more lighting components of the machine based on the illumination status. Clause 10. The system of Clause 9, wherein determining the scene illumination status is based at least on a relationship between the first illumination status and a set of threshold ranges corresponding to the first illumination status. Clause 11. The system of Clause 9 or Clause 10, wherein determining the scene illumination status is based at least on the second illumination status in response to the first illumination status being between a set of threshold ranges corresponding to the first illumination status. Clause 12. The system of Clause 11, wherein determining the scene illumination status is based at least on a relationship between the second illumination status and a second set of threshold ranges corresponding to the second illumination status. Clause 13. The system of any of clauses 9-12, wherein determining the one or more of the first illumination status, the second illumination status, or the scene illumination status is performed using one or more neural networks. Clause 14. A system according to any one of clauses 9-13, wherein the second illumination sensor is for detecting illumination levels at a greater distance than the first illumination sensor. Clause 15. A system according to any one of clauses 9-14, wherein the first illumination sensor includes a wide-angle camera and the second illumination sensor includes a telescopic camera. Clause 16. A system as defined in Clause 10, wherein the set of threshold areas includes, corresponding to the first illumination state, a dark status area and a lit status area. Clause 17. A system as defined in any of Clauses 9-16, where the system consists of 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 simulation operations; a system for carrying out digital twinning operations; a system for performing a light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for presenting at least one of augmented reality content, virtual reality content, or mixed reality content; a system for hosting one or more real-time streaming applications; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system that implements one or more large language models (LLMs); a system for generating synthetic data; a system that includes one or more virtual machines (VMs); a system that is implemented at least partially in a data center; or a system implemented at least in part using cloud computing resources. Clause 18. A system comprising one or more processing units for causing control of one or more lighting components of a machine based at least on an illumination status of an environment determined using two or more illumination sensors of the machine, wherein at least a first illumination sensor of the two or more illumination sensors is of a different type than at least a second illumination sensor of the two or more illumination sensors. Clause 19. The system of Clause 18, wherein the second illumination sensor is configured to detect an illumination level at a different distance from the second illumination sensor. Clause 20. A system according to Clause 19, wherein the first illumination sensor includes a wide-angle camera and the second illumination sensor includes a telescopic camera.

[0223] The disclosure may generally be described in the context of computer code or machine-usable instructions, including computer-executable instructions, such as program modules, executed by a computer or other machine, such as a personal data assistant or handheld device. Generally, program modules, including routines, programs, objects, components, data structures, etc., refer to code that performs particular tasks or implements particular abstract data types. The disclosure may be practiced in various system configurations, including handheld devices, consumer electronics, general-purpose computers, other special-purpose computing devices, etc. The disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices linked via a communications network.

[0224] As used herein, a reference to "and / or" with reference to two or more elements should be interpreted to mean only one element or a combination of elements. For example, "Element A, Element B, and / or Element C" can 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. Furthermore, "at least one of Element A or Element B" can 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" can 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. Additionally, the use of the term "based on" should not be interpreted to mean "only based on" or "based only on."Rather, a first element “based on” a second element includes cases where the first element is based on the second element, but may also be based on one or more additional elements.

[0225] The subject matter of the present disclosure is described herein with specificity to meet legal requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter may be embodied in other ways to include different steps or combinations of steps similar to those described herein, in conjunction with other current or future technologies. Furthermore, although the terms "step" and / or "block" may be used herein to refer to different elements of employed methods, these terms should not be interpreted to imply any particular ordering of or between different steps disclosed herein, unless the order of individual steps is explicitly described.

[0226] It is to be understood that aspects and embodiments described above are merely exemplary and that modifications in detail may be made within the scope of the claims.

[0227] Each device, method, and feature disclosed in the description and (if applicable) in the claims and drawings may be provided independently or in any suitable combination.

[0228] Reference signs appearing in the claims are for illustrative purposes only and are not intended to have a limiting effect on the scope of the claims. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 16 / 101,232

[0128] Cited non-patent literature

[0000] , and Society of Automotive Engineers (SAE) “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201806, published on June 15, 2018, Standard No. J3016-201609, published on September 30, 2016

[0086]

Claims

[1] A process comprising: Determining a first illumination status corresponding to an area based on at least first sensor data obtained using a first illumination sensor corresponding to a machine; Determining a second illumination status corresponding to the area based on at least second sensor data obtained using a second illumination sensor corresponding to the machine; Determining a scene illumination status of the area based at least on the first illumination status and the second illumination status; and Controlling one or more lighting components of the machine based at least on the illumination status. [2] The method of claim 1, wherein determining the scene illumination status is based at least on a relationship between the first illumination status and a set of threshold ranges corresponding to the first illumination status. [3] The method of claim 1 or claim 2, wherein determining the scene illumination status is based at least on the second illumination status in response to the first illumination status being between a set of threshold ranges corresponding to the first illumination status. [4] The method of claim 3, wherein determining the scene illumination status is based at least on a relationship between the second illumination status and a second set of threshold ranges corresponding to the second illumination status. [5] A method according to any preceding claim, wherein determining the one or more of the first illumination status, the second illumination status, or the scene illumination status is performed using one or more neural networks. [6] A method according to any preceding claim, wherein the second illumination sensor is for detecting illumination levels at a different distance than the first illumination sensor. [7] A method according to any preceding claim, wherein the first illumination sensor includes a wide-angle camera and the second illumination sensor includes a telescopic camera. [8] The method of claim 2, wherein the set of threshold regions corresponding to the first illumination state includes a dark status region and a lit status region. [9] A system comprising: one or more processors for performing operations including: Determining a first illumination status corresponding to an area based on at least first sensor data obtained using a first illumination sensor corresponding to a machine; Determining a second illumination status corresponding to the area based on at least second sensor data obtained using a second illumination sensor corresponding to the machine; Determining a scene illumination status of the area based on the first illumination status and the second illumination status; and Controlling one or more lighting components of the machine based on the illumination status. [10] The system of claim 9, wherein determining the scene illumination status is based at least on a relationship between the first illumination status and a set of threshold ranges corresponding to the first illumination status. [11] The system of claim 9 or claim 10, wherein determining the scene illumination status is based at least on the second illumination status in response to the first illumination status being between a set of threshold ranges corresponding to the first illumination status. [12] The system of claim 11, wherein determining the scene illumination status is based at least on a relationship between the second illumination status and a second set of threshold ranges corresponding to the second illumination status. [13] The system of any of claims 9-12, wherein determining the one or more of the first illumination status, the second illumination status, or the scene illumination status is performed using one or more neural networks. [14] The system of any of claims 9-13, wherein the second illumination sensor is for detecting illumination levels at a greater distance than the first illumination sensor. [15] The system of any of claims 9-14, wherein the first illumination sensor includes a wide-angle camera and the second illumination sensor includes a telescopic camera. [16] The system of claim 10, wherein the set of threshold regions corresponding to the first illumination state includes a dark status region and a lit status region. [17] A system according to any one of claims 9-16, wherein the system consists of 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 simulation operations; a system for carrying out digital twinning operations; a system for performing a light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for presenting at least one of augmented reality content, virtual reality content, or mixed reality content; a system for hosting one or more real-time streaming applications; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system that implements one or more large language models (LLMs); a system for generating synthetic data; a system that includes one or more virtual machines (VMs); a system that is implemented at least partially in a data center; or a system implemented at least in part using cloud computing resources. [18] A system comprising one or more processing units for causing the control of one or more lighting components of a machine based at least on an illumination status of an environment determined using two or more illumination sensors of the machine, wherein at least a first illumination sensor of the two or more illumination sensors is of a different type than at least a second illumination sensor of the two or more illumination sensors. [19] The system of claim 18, wherein the second illumination sensor is configured to detect an illumination level at a different distance from the second illumination sensor. [20] The system of claim 19, wherein the first illumination sensor includes a wide-angle camera and the second illumination sensor includes a telescopic camera.

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