Scene lighting detection for autonomous systems and applications

Abstract

The disclosure relates to scene lighting detection for autonomous systems and applications, in particular to determining a first lighting level corresponding to an area based at least on a first lighting detection obtained using a first lighting detector corresponding to a machine. A second illumination level corresponding to the area may be determined based at least on a second illumination detection obtained using a second illumination detector corresponding to the machine. A scene lighting state of the area may be determined based at least on the first lighting level and the second lighting level. One or more lights of the machine may be controlled based at least on the scene lighting state.

Description

Background Art

[0001] The illumination of a scene may 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 bright daytime, where there is a lot of natural light illuminating the scene. A "low natural" illumination state may correspond to dark daytime, dawn, or dusk, where the scene is illuminated by natural light, but at a soft or low 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 with almost no illumination from any light source. Different illumination states may affect the visibility of machine operation. For example, depending on the amount of illumination, a machine may enable or disable an illumination beam (e.g., a headlight). In addition, different levels of illumination beams may be employed depending on the illumination states. For example, a high illumination beam may be enabled for a dark area.

[0002] Furthermore, in some cases, the machine may travel through different areas corresponding to different lighting conditions. For example, a vehicle may travel through a dark area, then through an artificially lit area, and then back to a dark area. In some cases, this transition may occur quite rapidly and / or quite periodically. For example, a vehicle may travel in an artificially lit area, then may temporarily transition to a dark area, and then transition back to an artificially lit area.

[0003] Such transitions may make it difficult for the system to make decisions about when to switch between different types of lighting beams. Additionally or alternatively, such transitions may cause some conventional systems to quickly transition between different levels of lighting beams, which may be distracting to a machine operator cycling through different lighting settings and / or other machine operators, other machines, pedestrians, etc. who may be near the machine or in the light field. Summary of the invention

[0004] According to one or more embodiments of the present disclosure, one or more (e.g., multiple) lighting sensors may be used to determine the scene lighting state of an area where a machine may be located and / or traveling. For example, in some embodiments, a first lighting sensor may be configured to obtain first sensor data corresponding to a near-field range around the machine and / or in a direction of travel. In addition or alternatively, a second lighting sensor may be configured to obtain second sensor data corresponding to a far-field range around the machine and / or in a direction of travel. In these or other embodiments, the first sensor data and the second sensor data may be used to determine the scene lighting state of an area where the machine is located and / or an area in front of the machine in a direction of travel of the machine.

[0005] One or more embodiments of the present disclosure may help to more accurately determine not only the lighting state of an area where a machine may currently be located, but also the lighting state of an area where a machine may be located within a relatively short amount of time. Additionally or alternatively, one or more embodiments of the present disclosure may better help determine whether transitions between lighting states are relatively short, which may allow for better decisions to be made regarding deciding a lighting state applicable to a machine and / or changing that lighting state. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The present system and method for determining lighting conditions for autonomous and semi-autonomous systems and applications is described in detail below with reference to the accompanying drawings, wherein:

[0007] Figure 1A An example system configured to determine a lighting state of a scene according to one or more embodiments of the present disclosure is shown;

[0008] Figure 1B-1E shows example lighting levels according to one or more embodiments of the present disclosure;

[0009] Figure 2 is a flow chart of an example method of determining a lighting state of a scene according to one or more embodiments of the present disclosure;

[0010] Figure 3 is a flow chart illustrating an example method of determining scene lighting status according to one or more embodiments of the present disclosure;

[0011] Figure 4A is an illustration of an example autonomous vehicle according to one or more embodiments of the present disclosure;

[0012] Figure 4B According to one or more embodiments of the present disclosure Figure 4A Examples of autonomous vehicle camera positions and fields of view;

[0013] Figure 4C According to one or more embodiments of the present disclosure Figure 4A a block diagram of an example system architecture for an example autonomous vehicle;

[0014] Figure 4D A cloud-based server and a Figure 4A System diagram of an example of communication between autonomous vehicles;

[0015] Figure 5 is a block diagram of an example computing device suitable for implementing one or more embodiments of the present disclosure; and

[0016] Figure 6is a block diagram of an example data center suitable for implementing one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Systems and methods are described that relate to detecting lighting conditions of areas corresponding to machine locations. For example, one or more embodiments may involve detecting different lighting levels for different areas and determining lighting conditions for the areas based on the lighting levels. For example, a lighting detection system ("detection system") may detect lighting levels for areas corresponding to machine locations using one or more lighting detectors. In these and other embodiments, the detection system may determine the lighting conditions for the areas. Additionally or alternatively, the lighting detection system may identify changes in lighting conditions as a vehicle or other machine type travels through different areas.

[0018] One or more embodiments disclosed herein may involve detecting lighting conditions that may be associated with one or more autonomous machines, which may include any applicable machine or system capable of performing one or more autonomous or semi-autonomous operations. Example autonomous machines may include, but are not limited to, vehicles (land, sea, space, and / or air), robots, robotic platforms, and the like. For example, an autonomous machine computing application may include one or more applications that may be performed by an autonomous vehicle or a semi-autonomous vehicle, such as, for example, Figure 4A-4D An example autonomous vehicle 400 (or, alternatively, referred to herein as "vehicle 400" or "self-machine 400") is depicted. In this disclosure, references to an "autonomous vehicle" or "semi-autonomous vehicle" may include any vehicle that can be configured to perform one or more autonomous or semi-autonomous navigation or driving operations. Thus, such vehicles may also include vehicles that require an operator or that an operator may also perform such operations.

[0019] The systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, driverless and driverless robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, airships, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, engineering vehicles, underwater vehicles, drones, and / or other vehicle types. Further, the systems and methods described herein may be used for a variety of purposes, by way of example and not limitation, for machine control, machine motion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, safety and surveillance, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actor simulation and / or digital twins, data center processing, conversational AI (e.g., by utilizing one or more language models (e.g., 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 any other suitable application.

[0020] The disclosed embodiments may be included in a variety of different systems, such as automotive systems (e.g., control systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aerial systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems including one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least in part in a data center, systems for performing conversational AI operations (e.g., systems implementing one or more language models (such as a large language model (LLM))), systems for hosting live streaming applications, systems for presenting one or more of virtual reality content, augmented reality content, or mixed reality content, systems for performing light transport simulations, systems for performing collaborative content creation of 3D assets, systems implemented at least in part using cloud computing resources, and / or other types of systems.

[0021] Embodiments of the present disclosure may help improve the accuracy of detecting lighting conditions and determining scene-illumination states of areas where machines may be located or traveling. For example, in one or more embodiments of the present disclosure, a detection system may include one or more illumination sensors that may improve the accuracy of the detection system. For example, a detection system may include a first illumination sensor ("first sensor") and a second illumination sensor ("second sensor"). For example, a detection system may include a wide-angle camera and / or a telescopic camera. In some embodiments, the detection system may determine the illumination state based on first sensor data detected using a first sensor. For example, in some embodiments, the first illumination state may provide sufficient confidence for the detection system to determine the scene illumination state. In contrast, in some cases, due to an impending change in the illumination state or slight illumination in the area, the first illumination state may not provide sufficient confidence for the detection system to make a determination. For example, the machine may be moving away from or toward a light source, and the area may not be adequately illuminated nor dark. In these and other cases, the detection system may use second sensor data detected by a second sensor. For example, the second sensor data may provide illumination information for an area further ahead of the machine. For example, the second lighting state can indicate that the machine is heading toward an illuminated area or a dark area. For example, the second sensor can indicate to the detection system that there is a light source in the area in front of the machine. In these cases, the detection system can determine that the scene lighting state is illuminated based on the first lighting state and the second lighting state.

[0022] Embodiments of the present disclosure may help improve the accuracy of scene lighting determination compared to some traditional methods. For example, some traditional lighting determination methods may include the use of maps. Maps may include information about the locations of light sources in a particular area. In some cases, such information may be used to determine whether a particular area is illuminated. For example, when an indication is given that a light source is present at a particular location, it may be assumed that the area around the particular location is illuminated. However, such techniques may be limited by the accuracy of the map and / or whether the light source actually provides light at a particular time instance. In addition, such techniques may require regular updates to the map and require a larger coverage area.

[0023] In addition, other techniques may include metering ambient light using photoelectric sensors. For example, a photoelectric sensor may be located on a machine to detect ambient light in an area where the machine is traveling. For example, the scene lighting state of the area may be determined based on the amount of light detected by the photoelectric sensor. However, the use of a single photoelectric sensor may be limited due to the range of a single photoelectric sensor. For example, a photoelectric sensor may be able to determine the lighting state of an area close to the machine based on the amount of ambient light. However, when the machine travels through areas with different lighting states, the photoelectric sensor may not be able to effectively determine the lighting state. For example, the machine may be in an area between illuminated and dark. For example, the machine may be traveling away from a light source, such as a lamp post. The photoelectric sensor may still detect some ambient light from the lamp post, so the lighting detected by the photoelectric sensor itself may not provide enough information about whether the machine is approaching a dark area until the machine actually reaches the dark area.

[0024] The embodiments of the present disclosure will be explained with reference to the accompanying drawings. It should be understood that the accompanying drawings are diagrammatic and schematic representations of such example embodiments and are not restrictive and are not necessarily drawn to scale. In the drawings, unless otherwise specified, features with the same number represent the same structure and function.

[0025] about Figure 1A , Figure 1A An example system 100 configured to determine the lighting state of a scene or area according to some embodiments of the present disclosure is shown. In some embodiments, the system 100 can be configured to determine the lighting state of a scene or area in a machine (e.g., Figures 4A-4D Additionally or alternatively, the machine may be traveling in the area or located in the area, such that the system 100 may be used by the machine to determine the lighting state corresponding to the area where the machine may be located or traveling.

[0026] As detailed herein, in general, the system 100 may include a first illumination sensor ("first sensor") 102 and a second illumination sensor ("second sensor") 106. Although described as using two sensors, 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 obtain first sensor data ("first data") 104 corresponding to the area, and the second sensor 106 may be used to obtain second sensor data ("second data") 108 corresponding to the area. Although described as having two sensor data, 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.

[0027] The system 100 may also include an illumination module 110, which may be configured to obtain the first data 104 and the second data 108. As discussed in further detail herein, the illumination module 110 may be configured to determine a scene-illumination state 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 obtain the first data 104, and may determine a first illumination state 114 corresponding to the area based on the first data 104. The second detector 116 may obtain the second data 108, and may determine a second illumination state 118 corresponding to the area based on the second data 108. In these or other embodiments, the scene-illumination estimator 120 may obtain the first illumination state 114 and / or the second illumination state 118, and determine the scene-illumination state 130 based on the first illumination state 114 and / or the second illumination state 118.

[0028] 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 address (DMA) systems and / or vector processing units (VPUs)), and / or other processor types. 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 corresponding module may include operations that the corresponding module may instruct the 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 with respect to Figures 4A-4D , Figure 5 and / or Figure 6 A computing device is described in more detail.

[0029] 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 that represents an amount of light in an area corresponding to the first sensor 102.

[0030] For example, in some embodiments, the first sensor 102 may include a camera configured to capture an image (which may be stored as image data). In these or other embodiments, the first data 104 may accordingly include an image of an area captured by the first sensor 102 and image data corresponding thereto. For example, the image (and / or corresponding image data) may indicate a certain level of exposure (light exposure) associated with the image and corresponding to the amount of light present in the area when the image was captured. In the present disclosure, references to an "image" may also refer to image data corresponding thereto.

[0031] In other 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 the two terminals of the variable resistor may decrease as the variable resistor detects more light in the area. Therefore, 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.

[0032] In some embodiments, the first sensor 102 can be configured to obtain first data 104 about an area relatively close to the machine. For example, in some embodiments, the first sensor 102 can include a wide-angle camera. Additionally or alternatively, as indicated herein, the first sensor 102 can include a photoelectric sensor. In some embodiments, the first data 104 can include a raw signal obtained using the first sensor 102.

[0033] Second sensor 106 may include any suitable system, device, or apparatus that may be used to obtain second data 108. Second data 108 may include any type of data that represents an amount of light in an area corresponding to second sensor 106. In some embodiments, second data 108 may include the same type of data as first data 104. In other embodiments, second data 108 may include a different type of data than first data 104.

[0034] For example, in some embodiments, the second sensor 106 may include a camera configured to capture images (which may be stored as image data). In these and other embodiments, the second data 108 may accordingly include an image of the area captured by the second sensor 106. For example, the image may indicate a certain level of exposure associated with the image and corresponding to the amount of light present in the area when the image was captured.

[0035] In some embodiments, the second sensor 106 can be configured to obtain second data 108 about an area relatively farther from the machine. For example, in some embodiments, the second sensor 106 can include a telescopic camera.

[0036] The first detector 112 may be configured to obtain 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 state 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 state 114 may include a first illumination level. For example, the illumination level may be a numerical representation of the first illumination state 114. For example, the first illumination level may represent the 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 state 114 may include a value along a sliding illumination scale that may be represented by a digital or analog representation.

[0037] In these and other embodiments, the first detector 112 can be configured to determine the amount of exposure present in the first data 104. For example, in some embodiments, the first detector 112 can be configured to determine the amount of exposure in the first data 104 by converting the image to a certain color format (e.g., RGB, YUV, etc.) and calculating the luminance of the image based on the color format.

[0038] For example, the image can be converted to an RGB color format, and the brightness can be calculated based on the color values ​​of red, green, and blue in the image. In these embodiments, the brightness can be represented as a value within a typical brightness range of the RGB color format (e.g., 0 to 255). For example, the minimum brightness (e.g., black) can be represented as 0, and the maximum brightness (e.g., white) can be represented as 255. In some embodiments, the brightness can be analyzed to determine the probability that the area depicted in the image is illuminated. For example, a higher brightness can indicate that the probability that the area is illuminated is higher, while a lower brightness can indicate that the probability that the area is illuminated is lower. In these embodiments, the typical brightness range of the RGB color format can be converted to a first illumination level (e.g., 0 to 1). In these or other cases, 0 can indicate that the area is completely determined to be dark, and 1 can indicate that the area is completely determined to be illuminated. In these embodiments, when the possibility that the area is illuminated is greater than the possibility of darkness, the probability generated from the brightness can be higher (e.g., closer to 1).

[0039] The first detector 112 may include any suitable system, device, or apparatus that can be used to determine the first lighting state 114. For example, in some embodiments, the first detector 112 can be configured to communicate with a machine learning model configured to determine the first lighting state 114. In other embodiments, the first detector 112 may include a machine learning model. In the present disclosure, references to "machine learning models" may include references to any suitable algorithms, computer systems, neural networks, deep learning models, and / or other models that can be configured to analyze one or more characteristics corresponding to input data. By way of example and not limitation, machine learning models may include supervised models, unsupervised models, and / or semi-supervised models.

[0040] In some embodiments, the first lighting state 114 may include a first discrete lighting state. For example, the first discrete lighting state may include a representation of the first lighting state 114 as a particular state (e.g., illuminated, dark, temporary). In these and other embodiments, the first discrete lighting state may be determined based at least on the first lighting level. For example, the first detector 112 may be configured to determine the first discrete lighting state based at least on a set of threshold ranges corresponding to the first lighting level. For example, the first lighting level may be a number on the first lighting level that covers a certain range of values ​​(e.g., 0 to 1) indicating the amount of lighting present in the area.

[0041] Figure 1B An example first illumination scale 140 according to one or more embodiments of the present disclosure is shown. The first illumination scale 140 may include a set of threshold ranges. In some embodiments, the set of threshold ranges may include at least a first threshold range 150 and a second threshold range 160. For example, 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. A first illumination level (determined as a number between 0 and 1) may be placed on the first illumination scale 140.

[0042] Back to About Figure 1B The first illumination level 140 is described Figure 1AIn some embodiments, the first detector 112 can determine the first discrete lighting state as a dark state in response to the first lighting level satisfying the first threshold range 150. For example, in some embodiments, the first threshold range 150 can be satisfied by the first lighting level falling within the first threshold range 150 on the first lighting level 140. Additionally or alternatively, the first detector 112 can determine the first discrete lighting state as a lit state in response to the first lighting level satisfying the second threshold range 160. For example, in some embodiments, the second threshold range 160 can be satisfied by the first lighting level falling within the second threshold range 160 on the first lighting level 140.

[0043] In some embodiments, the first illumination level 140 may include a transitory range 170. The transitory range 170 may be a range between the first threshold range 150 and the second threshold range 160 on the first illumination level 140. For example, the transitory range 170 may be located between the first point 155 and the second point 165, such as Figure 1B In these cases, the first detector 112 may determine that it is in a transient state.

[0044] In some embodiments, the temporary state may indicate that the area corresponding to the first sensor 102 may not be deterministically dark or illuminated. For example, the machine may be traveling toward or away from a light source, and the area may be slightly illuminated so that it is not dark enough or not illuminated enough for the first detector 112 to determine whether the area is dark or illuminated. In these and other embodiments, the first detector 112 may retrieve a previous lighting state. For example, the previous lighting state may be an illuminated state, indicating that the machine may be traveling from an illuminated area toward a dark area. In these embodiments, a temporary position may be determined to reflect how close the determined distance is to being deterministically in a dark state. For example, the temporary position may be determined based on how far the first lighting level is from the first threshold range 150 within the first lighting level 140. For example, the temporary position may be represented as a number between a range (e.g., 0 to 1). In these cases, 0 may indicate that the lighting level may be closest to the first threshold range 150, while 1 may indicate that the first lighting level may be farthest from the first threshold range 150 and therefore may be closest to the second threshold range 160.

[0045] In some embodiments, the previous lighting state may be retrieved as a dark state, indicating that the machine may be traveling from a dark area to an illuminated area. In these embodiments, the temporary position may be determined based on how far the first lighting level is from the second threshold range 160, in a manner similar to how the temporary position is determined if the previous lighting state is an illuminated state.

[0046] In some embodiments, the scene illumination estimator 120 may obtain a first discrete illumination state. In response to the first discrete illumination state being a dark state or a lit state, the scene illumination estimator 120 may be configured to obtain a confirmation from the second detector.

[0047] In these and other embodiments, the second detector 116 can be configured to obtain the second data 108 from the second sensor 106 and determine a second lighting state 118. In some embodiments, the second lighting state 118 can include a second lighting level indication. In some embodiments, the second lighting level indication can include a value of the degree of lighting along the slide that can be represented by a digital or analog representation.

[0048] For example, the second detector 116 may be configured to quantify the second data 108 as an illumination level. For example, the second illumination level may include a probability associated with an amount of illumination. For example, in some embodiments, the second sensor 106 may be or include a telescopic camera configured to capture an image of an area further away from the machine than the area corresponding to the first sensor 102. Thus, the second data 108 may be an image of the area obtained by 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 an amount of exposure in the second data 108 by converting the image to a certain color format (e.g., RGB, YUV, etc.) and calculating the brightness of the image based on the color format.

[0049] For example, the image may be converted to an RGB color format, and the brightness may be determined based on the color values ​​of red, green, and blue in the image. In these embodiments, the brightness may be represented as a value within a typical brightness range (e.g., 0 to 255) of the RGB color format. For example, the minimum brightness (e.g., black) may be represented as 0, and the maximum brightness (e.g., white) may be represented as 255. In some embodiments, the brightness may be converted to represent the probability that the region depicted in the image is illuminated. For example, a higher brightness may indicate that the region is illuminated with a higher probability, while a lower brightness may indicate that the region is illuminated with a lower probability. In these embodiments, the typical brightness range of the RGB color format may be converted to a second illumination level (e.g., 0 to 1). In these cases, 0 may indicate that the region is completely determined to be dark, while 1 may indicate that the region is completely determined to be illuminated. In these embodiments, when the region is more likely to be illuminated than dark, the probability generated from the brightness may be higher (e.g., closer to 1). In other embodiments, the second illumination level may cover any suitable range of values. For example, the second illumination level may cover a wider range (e.g., 0-255) or a narrower range (e.g., 0-0.5). In some embodiments, the second illumination level may include a different range of values ​​than the first illumination level. In other embodiments, the second illumination level may include a similar or identical range of values ​​as the first illumination level.

[0050] The second detector 116 may include any suitable system, device, or apparatus that may be used to determine the second lighting state 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 lighting state 118. In other embodiments, the second detector 116 may include a machine learning model.

[0051] In these and other embodiments, the scene lighting estimator 120 may be configured to apply a confirmatory lighting level to the second lighting state 118 to confirm the first lighting state 114. For example, in response to the first lighting state 114 being dark, the confirmatory lighting level may be applied to the second lighting state (e.g., the second lighting level) 118 to confirm the first lighting state 114. For example, the confirmed lighting level may include a numerical range (e.g., 0 to 1) that may correspond to the second lighting state 118. In some cases, the confirmed lighting level may also include a confirmed darkness threshold. In response to the second lighting state 118 falling within the confirmed darkness threshold (e.g., falling between 0 and the confirmed darkness threshold), the scene lighting estimator 120 may confirm the first lighting state 114 and determine that the scene lighting state 130 is a dark state. In some cases, the first lighting state 114 may be determined to be an illuminated state. In these cases, the confirmed lighting level may include a confirmed illuminated threshold. In response to second lighting state 118 falling within the confirmed lit threshold (eg, falling between the confirmed lit threshold and 1), scene lighting estimator 120 may confirm first lighting state 114 and determine scene lighting state 130 to be a lit state.

[0052] In some embodiments, the first lighting state 114 may be determined to be a transient state. In these and other embodiments, in response to the first lighting state 114 being determined to be a transient state, the scene lighting estimator 120 may be configured to determine the scene lighting state 130 based on the first lighting level, the second lighting state 118, and the previous lighting state. For example, in response to the first lighting state 114 being a transient state and the previous lighting state being determined to be an illuminated state, the scene lighting estimator 120 may seek to gain additional confidence from the second lighting state 118 to make a determination that the machine is actually traveling from an illuminated area to a dark area.

[0053] For example, in some embodiments, the second lighting state 118 can be compared to the second lighting level. Figure 1C An example second illumination level 180a is shown, which is divided into at least a dark range 185 and an illuminated range 190. For example, the second illumination level 180a may range from 0 to 1, while the dark range 185 may range from 0 to a split point 195, and the illuminated range 190 may range from the split point 195 to 1. In some embodiments, the split point 195 may equally divide the second illumination level 180a between the dark range 185 and the illuminated range 190, such as Figure 1CIn some embodiments, scene lighting estimator 120 may determine that the lighting state is a dark state in response to second lighting state 118 falling within dark range 185. In these or other embodiments, scene lighting estimator 120 may determine that second lighting state 118 is an illuminated state in response to second lighting state 118 falling within illuminated range 190.

[0054] In some embodiments, the size of the dark range 185 and the illuminated range 190 may vary based on the temporary position determined with respect to the first illumination level 140. For example, the temporary position may reflect the confidence that the scene illumination estimator 120 may use from the second illumination state 118. For example, in some cases, the temporary position may be closer to the first point 155 than the second point 165, reflecting that the first illumination level is relatively close to the first threshold range 150. In these cases, the strength of the evidence provided by the second illumination state 118 for determining that the illumination state is a dark state may be relaxed. For example, the dark range 185 and the illuminated range 190 may be adjusted so that the dark range 185 covers a larger range than the illuminated range 190 to make it easier for the second illumination state 118 to fall within the dark range. Figure 1D Another example second illumination level 180b is shown to illustrate this situation, where the dark range 185 is wider than the illuminated range 190. For example, the segmentation point 195 may be closer to 1 than 0, dividing the second illumination level 180b into the dark range 185 being wider than the illuminated range 190.

[0055] Similarly, as the temporary position moves further away from the first threshold range 150 and toward the second threshold range 160, the size of the dark range may decrease, while the size of the illuminated range may increase. Figure 1E Another example second illumination level 180c is shown to illustrate this situation, where the dark range 185 is narrower than the illuminated range 190. For example, the division point 195 may be closer to 1 than 0, dividing the second illumination level 180c into the dark range 185 being narrower than the illuminated range 190.

[0056] In some embodiments, the previous lighting state may be determined to be a dark state. In these embodiments, the scene lighting estimator 120 may be configured to confirm that the machine is traveling from a dark area to an illuminated area. For example, the scene lighting estimator 120 may seek additional confidence from the lighting state 118 to determine it as an illuminated state. In these and other embodiments, the second lighting state 118 may be compared with a second lighting level 180 having a dark range 185 and an illuminated range 190. The ranges of the dark range 185 and the illuminated range 190 may be determined according to the temporary position in a manner similar to the example above where the previous lighting state is an illuminated state. In these embodiments, the scene lighting estimator 120 may determine the scene lighting state 130 as a dark state in response to the second lighting state 118 falling within the dark range 185, and may determine the scene lighting state as an illuminated state in response to the second lighting state 118 falling within the illuminated range 190.

[0057] Figure 2 is a flowchart of an example method for determining the lighting state of a scene according to one or more embodiments of the present disclosure. One or more operations of method 200 may be performed by any suitable system, device, or apparatus, such as the system described in the present disclosure. Figure 1A The system 100 and / or lighting module 110 of Figures 4A-4D Autonomous vehicle systems described, Figure 5 Computing devices described and / or related Figure 6 Describe the data system.

[0058] Method 200 may include one or more blocks. Although shown as discrete blocks, operations associated with one or more blocks of method 200 may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation.

[0059] 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 lighting sensor corresponding to the machine. In some embodiments, the first lighting 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 representing an amount of light in an area corresponding to the first lighting sensor.

[0060] For example, the first illumination sensor may include a camera configured to capture images of a scene toward which the machine is traveling. Additionally or alternatively, the first sensor data may include images captured by the camera and / or 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. Figure 1A The first illumination sensor 102 may be an example of a first illumination sensor.

[0061] 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 of the amount of light or illumination that may be present in the area as indicated by the first sensor data. In some embodiments, the determination of the first illumination level may include determining Figure 1A and Figure 1B One or more operations corresponding to determining a first illumination level are described.

[0062] At block 206, a determination may be made as to whether the first illumination level satisfies the first threshold range. In some embodiments, the first illumination level may satisfy the first threshold range by falling within the first threshold range on the first illumination level. For example, in some embodiments, the first illumination level may include at least a 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 level. In some embodiments, Figure 1B The first illumination level 140 , the first threshold range 150 , and the second threshold range 160 may be examples of a first illumination level, a first threshold range, and a second threshold range.

[0063] In response to the first illumination level satisfying the first threshold range, method 200 may proceed to block 208. At block 208, it may be determined that the illumination state of the scene is a dark state. In some embodiments, this determination may be made without using any illumination detection obtained using the second illumination detector. In some embodiments, determining the illumination state may be as described in the present disclosure, for example, with respect to Figure 1A and Figure 1B Further discussion and explanation will be given.

[0064] In some embodiments, the first illumination level may not satisfy the first threshold range. For example, the first illumination level may not fall within the first threshold range. In these embodiments, method 200 may proceed to block 210. At block 210, it may be determined whether the first illumination level satisfies the second threshold range. In some embodiments, the second threshold range may be satisfied by the first illumination level falling within the second threshold range.

[0065] In response to the first illumination level satisfying the second threshold range, method 200 may proceed to block 212. At block 212, it may be determined that the illumination state of the scene is an illuminated state. In some embodiments, this determination may be made without using any sensor data obtained using the second illumination sensor. Additionally or alternatively, sensor data obtained using the second illumination sensor may be used to confirm the determination made using the first illumination level. In some embodiments, the determination of the illumination state may be as described in the present disclosure, for example, with respect to Figure 1A and Figure 1B Further discussion and explanation will be given.

[0066] Method 200 may be modified, added, or omitted 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 simultaneously. In addition, the operations and actions outlined are provided only as examples, and some operations and actions may be optional, combined into fewer operations and actions, or expanded into additional operations and actions without departing from the essence of the described embodiments.

[0067] Figure 3 is a flow chart illustrating a method 300 for determining scene lighting conditions according to one or more embodiments of the present disclosure. In some embodiments, one or more operations of the method 300 may be related to Figure 1A One or more operations of method 300 may be performed by any suitable system, device, or apparatus, such as the scene lighting estimator 120 of the present disclosure. Figure 1A The system 100 and / or lighting module 110 of Figures 4A-4D Autonomous vehicle systems described, Figure 5 Computing devices described and / or related Figure 6 Describe the data system.

[0068] Method 300 may include one or more blocks. Although shown as discrete blocks, operations associated with one or more blocks of method 300 may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the specific implementation. In some embodiments, method 300 may be iterated and / or executed at a certain rate. For example, method 300 may be executed 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.

[0069] In some embodiments, method 300 may include block 301. At block 302, a first lighting state may be obtained. For example, the first lighting state may include a discrete representation of a lighting state of an area corresponding to a first lighting sensor. In these and other embodiments, the first lighting state may be one of dark, illuminated, and temporary. In some embodiments, obtaining the first lighting state may include determining the lighting state of the area corresponding to the first lighting sensor. Figure 1A One or more operations corresponding to determining first lighting state 114 are described.

[0070] At block 304 , it may be determined whether the first lighting state is dark or illuminated. In response to whether the first lighting state is dark or illuminated, the method 300 may proceed to block 306 .

[0071] At block 306, the first lighting state can be confirmed based on the second lighting state. For example, the second lighting state can be obtained from a second detector, which can determine the second lighting state based on at least second sensor data obtained using a second lighting sensor. In these and other embodiments, the second lighting state can be represented in an analog representation. For example, the second lighting state can represent the amount of light in the second sensor data in digital form as a number within a range (e.g., 0 to 1).

[0072] In some embodiments, the scene lighting state may be confirmed in response to the second lighting state satisfying the second detection threshold. For example, a confirmed lighting level may be applied to the second lighting state. The confirmed lighting level may be a numerical range that may correspond to a range (e.g., 0 to 1). In these and other embodiments, the confirmed lighting level may include a confirmed darkness threshold or a confirmed illuminated threshold. For example, in response to the first lighting state being dark, the confirmed darkness threshold may be applied to the second lighting state, and in response to the first lighting state being illuminated, the confirmed illuminated threshold may be applied to the second lighting state. In these and other embodiments, in response to the second lighting state satisfying the second detection threshold, the scene lighting state may be determined to be the first lighting state.

[0073] In some embodiments, the first lighting state may not be confirmed. For example, the second lighting state may not meet the second detection threshold. In these and other embodiments, the scene lighting state may remain unchanged from the previous lighting state. In these cases, the previous lighting state may refer to the scene lighting state determined during the previous iteration of method 300.

[0074] In some embodiments, method 300 may not include block 306. For example, in response to the first lighting state being determined to be dark or lit at block 304, the scene lighting state may correspond to the first lighting state being determined to be dark or lit without confirming the scene lighting state based on the second lighting state.

[0075] In some embodiments, returning to block 304, the first lighting state may not be dark or illuminated. For example, the first lighting state may be a temporary state. In these cases, method 300 may proceed to block 314. At block 314, a second lighting state may be obtained. In some embodiments, obtaining the second lighting state may include one or more operations described in block 306.

[0076] At block 316, a temporary location may be determined. The temporary location may provide an indication of how far the first illumination level is from the first threshold range or the second threshold range. In some embodiments, the determination of the temporary location may include determining Figure 1A and Figure 1B The description corresponds to one or more operations for determining a temporary location.

[0077] At block 318, the first range and the second range may be determined based at least on the temporary position. In some embodiments, the first range and the second range may divide the second illumination level based on the temporary position. In some embodiments, the first range may be a dark range and the second range may be an illuminated range. In some embodiments, the determination of the first range and the second range may include determining the second illumination level. Figures 1A-1E The description corresponds to one or more operations for determining the dark range 185 and the illuminated range 190 .

[0078] At block 320, it may be determined whether the second illumination level falls within the first range. In response to the second illumination level falling within the first range, method 300 may proceed to block 322. At block 322, it may be determined that the scene illumination state is a dark state. In some embodiments, determining the illumination state may be as described in the present disclosure, for example, with respect to Figures 1A-1E Further discussion and explanation will be given.

[0079] 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, method 300 may proceed to block 324. At block 324, the scene illumination state may be determined to be an illuminated state. In some embodiments, determining the illumination state may be as described in the present disclosure, for example, with respect to Figures 1A-1E Further discussion and explanation.

[0080] Method 300 may be modified, added, or omitted 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 simultaneously. Furthermore, the operations and actions outlined are provided only as examples, and some operations and actions may be optional, combined into fewer operations and actions, or expanded into additional operations and actions without departing from the essence of the described embodiments.

[0081] 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 lighting state. For example, if the scene lighting state is determined to be a dark state, one or more lighting components (e.g., headlights) of the machine may be turned on. In other cases, if the lighting state is determined to be an illuminated state, one or more lighting components of the machine may be turned off.

[0082] Example Autonomous Vehicle

[0083] Figure 4A 4 is a diagram of an example autonomous vehicle 400 according to some embodiments of the present disclosure. Autonomous vehicle 400 (alternatively, referred to herein as "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 truck, a police car, an ambulance, a boat, an engineering vehicle, an underwater vessel, a drone, and / or other types of vehicles (e.g., unmanned and / or capable of accommodating one or more passengers). Autonomous vehicles are generally described according to the 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. J3016-201506, issued on June 15, 2018, Standard No. J3016-201609, issued on September 30, 2016, and previous and future versions of the standard). The vehicle 400 is capable of implementing functions that comply with one or more of levels 3-5 of autonomous driving levels. The vehicle 400 is capable of implementing functions that comply with one or more of levels 1-5 of autonomous driving levels. For example, depending on the embodiment, the vehicle 400 is capable of driver assistance (level 1), partial automation (level 2), conditional automation (level 3), high automation (level 4) and / or full automation (level 5). The term "autonomy" as used herein may include any and / or all types of autonomy of 400 or other machines, such as full autonomy, high autonomy, conditional autonomy, partial autonomy, providing assisted autonomy, semi-autonomy, main autonomy or other names.

[0084] Vehicle 400 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. Vehicle 400 may include a propulsion system 450, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another propulsion system type. Propulsion system 450 may be connected to a drive train of vehicle 400, which may include a transmission, to achieve propulsion of vehicle 400. Propulsion system 450 may be controlled in response to receiving a signal from throttle / accelerator 452.

[0085] A steering system 454, which may include a steering wheel, may be used to steer vehicle 400 (e.g., along a desired path or route) when propulsion system 450 is operating (e.g., when the vehicle is in motion). Steering system 454 may receive signals from steering actuator 456. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0086] Brake sensor system 446 may be used to operate vehicle brakes in response to receiving signals from brake actuator 448 and / or brake sensors.

[0087] May include one or more CPUs, system on chip (SoC) 404 ( Figure 4C ) and / or one or more controllers 436 of one or more GPUs may provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 400. For example, the one or more controllers may send signals to operate vehicle brakes via one or more brake actuators 448, to operate a steering system 454 via one or more steering actuators 456, and / or to operate a propulsion system 450 via one or more throttles / accelerators 452. The one or more controllers 436 may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals and output operating commands (e.g., signals representing commands) to enable autonomous driving and / or assist a human driver in driving the vehicle 400. The one or more controllers 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 functions (e.g., computer vision), a fourth controller 436 for infotainment functions, a fifth controller 436 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 436 may handle two or more of the above functions, two or more controllers 436 may handle a single function, and / or any combination thereof.

[0088] The one or more controllers 436 may provide signals for controlling one or more components and / or systems of the vehicle 400 in response to sensor data (e.g., sensor input) received from one or more sensors. The sensor data may be received from, for example and without limitation, a global navigation satellite system sensor 458 (e.g., a global positioning system sensor), a RADAR sensor 460, an ultrasonic sensor 462, a LIDAR sensor 464, an inertial measurement unit (IMU) sensor 466 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 796, a stereo camera 468, a wide angle camera 470 (e.g., a fisheye camera), an infrared camera 472, a surround camera 474 (e.g., a 360 degree camera), a long-range and / or mid-range camera 498, a speed sensor 444 (e.g., for measuring the velocity of the vehicle 400), a vibration sensor 442, a steering sensor 440, a brake sensor (e.g., as part of a brake sensor system 446), and / or other sensor types.

[0089] One or more of the controllers 436 may receive inputs (e.g., represented by input data) from the 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. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Figure 4C The HMI display 434 may include information such as the HD map 422 of the vehicle 400, location data (e.g., the location of the vehicle 400 on the map), directions, locations of other vehicles (e.g., occupancy grid), information about objects and states of objects as sensed by the controller 436, etc. For example, the HMI display 434 may display information about the presence of one or more objects (e.g., street signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that the vehicle has made, is making, or will make (e.g., changing lanes now, leaving 34B in two miles, etc.).

[0090] The vehicle 400 further includes a network interface 424 that can communicate via one or more networks using one or more wireless antennas 426 and / or a modem. For example, the network interface 424 may be capable of communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The one or more wireless antennas 426 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.) using one or more local area networks such as Bluetooth, Bluetooth LE, Z-wave, ZigBee, etc. and / or one or more low power wide area networks (LPWANs) such as LoRaWAN, SigFox, etc.

[0091] Figure 4BFor use according to some embodiments of the present disclosure Figure 4A An example of camera positions and fields of view for an example autonomous vehicle 400 is shown. The cameras and respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or replaceable cameras may be included, and / or the cameras may be located at different locations on the vehicle 400.

[0092] The camera type for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 400. The camera may operate at an automotive safety integrity level (ASIL) B and / or at another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120fps, 240fps, etc., depending on the embodiment. The camera may be capable of using a rolling shutter, a global shutter, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red-white-white-white (RCCC) color filter array, a red-white-white-blue (RCCB) color filter array, a red-blue-green-white (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensor (RGGB) color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In some embodiments, a clear pixel camera such as a camera with an RCCC, RCCB, and / or RBGC color filter array may be used in an effort to improve light sensitivity.

[0093] In some examples, one or more of the cameras can be used to perform advanced driver assistance system (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a multi-function monocular camera can be installed to provide functions including lane departure warning, traffic sign assistance, and intelligent headlight control. One or more of the cameras (e.g., all of the cameras) can simultaneously record and provide image data (e.g., video).

[0094] One or more of the cameras may be mounted in a mounting assembly such as a custom designed (3-D printed) assembly to cut off stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirror) that may interfere with the camera's image data capture capabilities. With respect to the wing mirror mounting assembly, the wing mirror assembly may be custom 3-D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras may be integrated into the wing mirror. For side view cameras, one or more cameras may also be integrated into the four pillars at each corner of the cab.

[0095] A camera (e.g., a front-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 400 can be used for surround view to help identify the forward path and obstacles, as well as assist in providing information critical to generating an occupancy grid and / or determining a preferred vehicle path with the help of one or more controllers 436 and / or control SoCs. The front-facing camera can be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. The front-facing camera can also be used for ADAS functions and systems, including lane departure warning (LDW), autonomous cruise control (ACC), and / or other functions such as traffic sign recognition.

[0096] A variety of cameras may be used in the front-facing configuration, including, for example, a monocular camera platform including a CMOS (complementary metal oxide semiconductor) color imager. Another example may be a wide-angle camera 470, which may be used to sense objects (e.g., pedestrians, intersection traffic, or bicycles) entering the field of view from the periphery. Figure 4B Only one wide-angle camera is shown in the figure, but there may be any number of wide-angle cameras 470 on the vehicle 400. In addition, long-range 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 been trained. Long-range cameras 498 may also be used for object detection and classification and basic object tracking.

[0097] One or more stereo cameras 468 may also be included in the front configuration. The stereo camera 468 may include an integrated control unit including an expandable processing unit that may provide a multi-core microprocessor and programmable logic (FPGA) with an integrated CAN or Ethernet interface on a single chip. Such a unit may be used to generate a 3-D map of the vehicle environment, including distance estimates for all points in the image. An alternative stereo camera 468 may include a compact stereo vision sensor that may include two camera lenses (one on each side) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. In addition to those described herein or alternatively, other types of stereo cameras 468 may be used.

[0098] A camera (e.g., a side view camera) having a field of view of a portion of the environment including the sides of the vehicle 400 can be used for surround viewing, providing information used to create and update the occupancy grid and generate side impact collision warnings. Figure 4BThe four surround cameras 474 shown in FIG. 4 can be placed on the vehicle 400. The surround cameras 474 can include wide-angle cameras 470, fisheye cameras, 360-degree cameras, and / or the like. For example, four fisheye cameras can be placed in front, back, and sides of the vehicle. In an alternative arrangement, the vehicle can use three surround cameras 474 (e.g., left, right, and back), and can utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0099] A camera having a field of view that includes a portion of the environment behind the vehicle 400 (e.g., a rear view camera) can be used to assist with parking, surround view, rear collision warning, and creating and updating an occupancy grid. A variety of cameras can be used, including but not limited to cameras that are also suitable as front-facing cameras as described herein (e.g., long-range and / or mid-range cameras 498, stereo cameras 468, infrared cameras 472, etc.).

[0100] Figure 4C For use according to some embodiments of the present disclosure Figure 4A Block diagram of an example system architecture of an example autonomous vehicle 400. It should be understood that this arrangement and other arrangements described herein are set forth merely as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any appropriate combination and location. The various functions described herein as being performed by an entity may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory.

[0101] Figure 4C Each of the components, features, and systems of vehicle 400 is 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"). CAN may be a network inside vehicle 400 that assists in controlling various features and functions of vehicle 400, such as actuation of brakes, acceleration, braking, steering, windshield wipers, and the like. The CAN bus may be configured to have tens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compliant.

[0102] Although bus 402 is described as a CAN bus here, this is not intended to be limiting. For example, in addition to or alternatively to the CAN bus, FlexRay and / or Ethernet can be used. In addition, although bus 402 is represented by a single line, this is not intended to be limiting. For example, there can 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 different protocols. In some examples, two or more buses 402 can be used to perform different functions, and / or can be used for redundancy. For example, a first bus 402 can be used for a collision avoidance function, and a second bus 402 can be used for drive control. In any example, each bus 402 can communicate with any component of the vehicle 400, and two or more buses 402 can communicate with the same component. In some examples, each SoC 404, each controller 436, and / or each computer in the vehicle can have access to the same input data (e.g., input from a sensor of the vehicle 400), and can be connected to a common bus such as a CAN bus.

[0103] The vehicle 400 may include one or more controllers 436, such as those described herein. Figure 4A Those controllers described. Controller 436 can be used for a variety of functions. Controller 436 can be coupled to any other different components and systems of vehicle 400, and can be used for control of vehicle 400, artificial intelligence of vehicle 400, infotainment for vehicle 400, and / or the like.

[0104] The vehicle 400 may include one or more system on chip (SoC) 404. The SoC 404 may include a CPU 406, a GPU 408, a processor 410, a cache 412, an accelerator 414, a data store 416, and / or other components and features not shown. The SoC 404 may be used to control the vehicle 400 in a variety of platforms and systems. For example, one or more SoCs 404 may be combined with an HD map 422 in a system (e.g., a system of the vehicle 400), and the HD map may be downloaded from one or more servers (e.g., a server) via a network interface 424. Figure 4D one or more servers 478) to obtain map refreshes and / or updates.

[0105] CPU 406 may include a CPU cluster or CPU complex (alternatively, referred to herein as "CCPLEX"). CPU 406 may include multiple cores and / or L2 caches. For example, in some embodiments, CPU 406 may include eight cores in a coherent multiprocessor configuration. In some embodiments, CPU 406 may include four dual-core clusters, each of which has a dedicated L2 cache (e.g., 2MB L2 cache). CPU 406 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, so that any combination of clusters of CPU 406 can be active at any given time.

[0106] CPU 406 may implement power management capabilities including one or more of the following features: each hardware block may be automatically clock gated when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to the execution of WFI / WFE instructions; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. CPU 406 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wake-up times are specified, and hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core may support a simplified power state entry sequence in software, with the work being offloaded to the microcode.

[0107] GPU 408 may include an integrated GPU (alternatively, referred to herein as an "iGPU"). GPU 408 may be programmable and efficient for parallel workloads. In some examples, GPU 408 may use an enhanced tensor instruction set. GPU 408 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB storage capacity). In some embodiments, GPU 408 may include at least eight streaming microprocessors. GPU 408 may use a computing application programming interface (API). In addition, GPU 408 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0108] In the case of automotive and embedded use, GPU 408 can be power optimized to achieve optimal performance. For example, GPU 408 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 408 can be manufactured using other semiconductor manufacturing processes. Each streaming microprocessor can merge several mixed precision processing cores divided into multiple blocks. For example and without limitation, 64 PF32 cores and 32 PF64 cores can be divided into four processing blocks. In such an example, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed precision NVIDIA tensor cores for deep learning matrix arithmetic, L0 instruction cache, thread beam (warp) scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor can include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of calculations and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. A streaming microprocessor may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0109] GPU 408 can include high bandwidth memory (HBM) and / or a 16GB HBM2 memory subsystem that provides a peak memory bandwidth of approximately 900 GB / s in some examples. In some examples, synchronous graphics random access memory (SGRAM), such as fifth generation graphics double data rate synchronous random access memory (GDDR5), can be used in addition to or in lieu of HBM memory.

[0110] The GPU 408 may include unified memory technology that includes access counters to allow memory pages to be more accurately migrated to the processor that accesses them most frequently, thereby improving the efficiency of memory ranges shared between processors. In some examples, address translation service (ATS) support can be used to allow the GPU 408 to directly access the CPU 406 page table. In such an example, when the GPU 408 memory management unit (MMU) experiences a miss, an address translation request can be transmitted to the CPU 406. In response, the CPU 406 can look up the virtual-physical mapping for the address in its page table and transmit the translation back to the GPU 408. In this way, the unified memory technology can allow a single unified virtual address space to be used for the memory of both the CPU 406 and the GPU 408, thereby simplifying GPU 408 programming and porting applications to the GPU 408.

[0111] In addition, GPU 408 may include access counters that can track how often GPU 408 accesses the memory of other processors. Access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0112] SoC 404 may include any number of caches 412, including those described herein. For example, cache 412 may include an L3 cache available to both CPU 406 and GPU 408 (e.g., connected to both CPU 406 and GPU 408). Cache 412 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4MB or more, although smaller cache sizes may also be used.

[0113] SoC 404 may include one or more arithmetic logic units (ALUs) that may be used to perform processing for any of a variety of tasks or operations with respect to vehicle 400, such as processing a DNN. Additionally, SoC 404 may include a floating point unit (FPU) or other math coprocessor or digital coprocessor type for performing mathematical operations within the system. For example, SoC 404 may include one or more FPUs integrated as execution units within CPU 406 and / or GPU 408.

[0114] SoC 404 may include one or more accelerators 414 (e.g., hardware accelerators, software accelerators, or combinations thereof). For example, SoC 404 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. The large on-chip memory (e.g., 4MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to supplement GPU 408 and offload some tasks of GPU 408 (e.g., free up more cycles of GPU 408 for performing other tasks). As an example, accelerator 414 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are sufficiently stable to be easily controlled for acceleration. When used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0115] Accelerator 414 (e.g., hardware acceleration cluster) may include a deep learning accelerator (DLA). DLA may include one or more tensor processing units (TPUs) that may be configured to provide an additional 10 trillion operations per second for deep learning applications and reasoning. TPU may be an accelerator configured to perform image processing functions (e.g., for CNN, RCNN, etc.) and optimized for performing image processing functions. DLA may be further optimized for a specific set of neural network types and floating point operations and reasoning. The design of DLA may provide higher performance per millimeter than a general-purpose GPU, and far exceeds the performance of a CPU. TPU may perform several functions, including a single instance convolution function, support, for example, INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0116] The DLA can quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a wide variety of functions, such as, but not limited to: a CNN for object recognition and detection using data from a camera sensor; a CNN for distance estimation using data from a camera sensor; a CNN for emergency vehicle detection and identification and detection using data from a microphone; a CNN for facial recognition and vehicle owner identification using data from a camera sensor; and / or a CNN for safety and / or security-related events.

[0117] The DLA can perform any function of the GPU 408, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 408. For example, the designer can focus the processing of CNNs and floating point operations on the DLA, and leave other functions to the GPU 408 and / or other accelerators 414.

[0118] The accelerator 414 (e.g., a hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may be alternatively referred to herein as a computer vision accelerator. The PVA may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (ADAS), autonomous driving, and / or augmented reality (AR) and / or virtual reality (VR) applications. The PVA may provide a balance between performance and flexibility. For example, each PVA may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0119] The RISC core can interact with an image sensor (e.g., an image sensor of any camera described herein), an image signal processor, and / or the like. Each of these RISC cores can include any amount of memory. Depending on the embodiment, the RISC core can use any of a number of protocols. In some examples, the RISC core can execute a real-time operating system (RTOS). The RISC core can be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and / or storage devices. For example, the RISC core can include an instruction cache and / or a tightly coupled RAM.

[0120] The DMA may enable components of the PVA to access system memory independently of the CPU 406. The DMA may support any number of features used to provide optimizations for the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support addressing in up to six or more dimensions, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0121] The vector processor can be a programmable processor that can be designed to efficiently and flexibly perform programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA can include a PVA core and two vector processing subsystem partitions. The PVA core can include a processor subsystem, one or more DMA engines (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem can operate as the main processing engine of the PVA, and can include a vector processing unit (VPU), an instruction cache, and / or a vector memory (e.g., VMEM). The VPU core can include a digital signal processor, such as, for example, a single instruction multiple data (SIMD), a very long instruction word (VLIW) digital signal processor. The combination of SIMD and VLIW can enhance throughput and rate.

[0122] Each of the vector processors may include an instruction cache and may be coupled to a dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, multiple vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on sequence images or portions of an image. Among other things, any number of PVAs may be included in a hardware acceleration cluster, and any number of vector processors may be included in each of these PVAs. In addition, the PVA may include additional error correction code (ECC) memory to enhance overall system security.

[0123] The accelerator 414 (e.g., a hardware acceleration cluster) may include an on-chip computer vision network and SRAM to provide high bandwidth, low latency SRAM for the accelerator 414. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and not limited to, eight field-configurable memory blocks, which may be accessed by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, a configuration circuit system, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides high-speed memory access to the PVA and DLA. The backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using APB).

[0124] The on-chip computer vision network may include an interface that determines that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such an interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface may comply with ISO 26262 or IEC 61508 standards, but other standards and protocols may also be used.

[0125] In some examples, SoC 404 may include a real-time ray tracing hardware accelerator such as described in U.S. Patent Application No. 16 / 101,232 filed on August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) in order to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for SONAR system simulations, for general wave propagation simulations, for comparison with LIDAR data for the purpose of positioning and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0126] The accelerator 414 (e.g., a hardware accelerator cluster) has a wide range of uses in autonomous driving. The PVA can be a programmable visual accelerator that can be used in key processing stages in ADAS and autonomous vehicles. The capabilities of the PVA are a good match for algorithmic domains that require predictable processing, low power, and low latency. In other words, the PVA performs well on semi-dense or dense rule computations, and even on small data sets that require predictable runtimes with low latency and low power. Therefore, in the context of a platform for autonomous vehicles, the PVA is designed to run classic computer vision algorithms because they are efficient at object detection and integer math.

[0127] For example, according to one embodiment of the technology, PVA is used to perform computer stereo vision. In some examples, semi-global matching based algorithms can be used, but this is not intended to be limiting. Many applications for Level 3-5 autonomous driving require instant motion estimation / stereo matching (e.g., structure from motion, pedestrian recognition, lane detection, etc.). PVA can perform computer stereo vision functions on inputs from two monocular cameras.

[0128] In some examples, PVA can be used to perform dense optical flow. According to processing raw RADAR data (e.g., using 4D fast Fourier transform) to provide processed RADAR. In other examples, PVA is used for time-of-flight depth processing, which is for example by processing raw time-of-flight data to provide processed time-of-flight data.

[0129] DLA can be used to run any type of network to enhance control and driving safety, including, for example, a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability, or as providing a relative "weight" of each detection compared to other detections. This confidence value enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positive detections. In an automatic emergency braking (AEB) system, a false positive detection will cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detection should be considered a trigger for AEB. DLA can run a neural network for regressing confidence values. The neural network can take at least some subset of parameters as its input, such as bounding box dimensions, ground plane estimates obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 466 output related to vehicle 400 orientation and distance, 3D position estimates of objects obtained from neural networks and / or other sensors (e.g., LIDAR sensor 464 or RADAR sensor 460), etc.

[0130] SoC 404 may include one or more data stores 416 (e.g., memory). Data store 416 may be on-chip memory of SoC 404 that may store neural networks to be executed on the GPU and / or DLA. In some examples, data store 416 may be large enough to store multiple instances of a neural network for redundancy and safety. Data store 412 may include an L2 or L3 cache 412. References to data store 416 may include references to memory associated with a PVA, DLA, and / or other accelerator 414 as described herein.

[0131] SoC 404 may include one or more processors 410 (e.g., embedded processors). Processor 410 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management functions and related safety implementations. The boot and power management processor may be part of the SoC 404 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, auxiliary system low power state transitions, SoC 404 thermal and temperature sensor management, and / or SoC 404 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC404 may use the ring oscillator to detect the temperature of CPU 406, GPU 408, and / or accelerator 414. If it is determined that the temperature exceeds a threshold, the boot and power management processor may enter a temperature fault routine and place SoC 404 in a lower power state and / or place vehicle 400 in a driver safety parking mode (e.g., parking vehicle 400 safely).

[0132] Processor 410 may further include a set of embedded processors that may be used as an audio processing engine. The audio processing engine may be an audio subsystem that allows for full hardware support for multi-channel audio through multiple interfaces and a wide range of flexible audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core having a digital signal processor with dedicated RAM.

[0133] The processor 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.

[0134] The processor 410 may further include a safety cluster engine, which includes a dedicated processor subsystem that handles safety management of automotive applications. The safety cluster engine may include two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.), and / or routing logic. In safety mode, the two or more cores may operate in lockstep mode and act as a single core with comparison logic to detect any differences between their operations.

[0135] Processor 410 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0136] Processor 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.

[0137] The processor 410 may include a video image compositer, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required for the video playback application to produce a final image for the player window. The video image compositer may perform lens distortion correction for the wide-angle camera 470, the surround camera 474, and / or for an in-cab surveillance camera sensor. The in-cab surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC, configured to recognize in-cab events and respond accordingly. The in-cab system may perform lip reading to activate mobile phone service and place a call, dictate an email, change a vehicle destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other circumstances.

[0138] The video image compositer may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, in the case of motion in the video, the noise reduction appropriately weights the spatial information and reduces the weight of information provided by adjacent frames. In the case where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositer may use information from previous images to reduce noise in the current image.

[0139] The video image compositor may also be configured to perform stereoscopic rectification on the input stereoscopic footage frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use and the GPU 408 does not need to continuously render new surfaces. Even when the GPU 408 is powered on and active for 3D rendering, the video image compositor may be used to offload the GPU 408 to improve performance and responsiveness.

[0140] SoC 404 may further include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from a camera, a high-speed interface, and / or a video input block that may be used for camera and related pixel input functions. SoC 404 may further include an input / output controller that may be controlled by software and may be used to receive I / O signals that are not committed to a specific role.

[0141] SoC 404 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management and / or other devices. SoC 404 may be used to process data from cameras (connected via Gigabit multimedia serial link and Ethernet), sensors (e.g., LIDAR sensor 464, RADAR sensor 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 458 (connected via Ethernet or CAN bus). SoC 404 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines, and which may be used to free up CPU 406 from routine data management tasks.

[0142] SoC 404 can be an end-to-end platform with a flexible architecture that spans automation levels 3-5, providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS technologies to achieve diversity and redundancy, together with deep learning tools to provide a platform for a flexible and reliable driving software stack. SoC 404 can be faster, more reliable, and even more energy efficient and space efficient than conventional systems. For example, when combined with CPU 406, GPU 408, and data storage 416, accelerator 414 can provide a fast and efficient platform for level 3-5 autonomous vehicles.

[0143] The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms can be executed on CPUs, which can be configured using high-level programming languages ​​such as the C programming language to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs often fail to meet the performance requirements of many computer vision applications, such as those related to, for example, execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, which is a requirement for in-vehicle ADAS applications and a requirement for practical Level 3-5 autonomous vehicles.

[0144] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a hardware acceleration cluster, the techniques described herein allow multiple neural networks to be executed simultaneously and / or sequentially, and the results to be combined together to achieve Level 3-5 autonomous driving functions. For example, a CNN executed on a DLA or dGPU (e.g., GPU 420) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which a neural network has not been specifically trained. The DLA may further include a neural network capable of recognizing, interpreting, and providing semantic understanding of the signs, and passing that semantic understanding to a path planning module running on the CPU complex.

[0145] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks may be running simultaneously. For example, a warning sign consisting of "Caution: Flashing Lights Indicate Icing Conditions" along with electric lights may be interpreted by several neural networks independently or collectively. The sign itself may be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing Lights Indicate Icing Conditions" may be interpreted by a deployed second neural network that informs the vehicle's path planning software (preferably executing on a CPU complex) that icing conditions exist when the flashing lights are detected. The flashing lights may be identified by operating a deployed third neural network over multiple frames that informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks may be running simultaneously, for example, within the DLA and / or on the GPU 408.

[0146] In some examples, a CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 400. The always-on sensor processing engine can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's door, and in security mode, disable the vehicle when the owner leaves the vehicle. In this way, the SoC 404 provides security against theft and / or carjacking.

[0147] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 796 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 404 uses CNN to classify environmental and urban sounds and to classify visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing rate of emergency vehicles (e.g., by using the Doppler effect). The CNN can also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by the GNSS sensor 458. Thus, for example, when operating in Europe, the CNN will seek to detect European sirens, and when in the United States, the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, with the assistance of the ultrasonic sensor 462, the control program can be used to execute emergency vehicle safety routines to slow the vehicle, drive to the side of the road, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0148] The vehicle may include a CPU 418 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 404 via a high-speed interconnect (e.g., PCIe). The CPU 418 may include, for example, an X86 processor. The CPU 418 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 404, and / or monitoring the status and health of the controller 436 and / or the infotainment SoC 430.

[0149] The vehicle 400 may include a GPU 420 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 404 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 420 may provide additional artificial intelligence functionality, for example, by executing redundant and / or different neural networks, and may be used to train and / or update the neural network based on inputs (e.g., sensor data) from sensors of the vehicle 400.

[0150] The vehicle 400 may further include a network interface 424, which may include one or more wireless antennas 426 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). The network interface 424 can be used to enable wireless connections with the cloud (e.g., with a server 478 and / or other network devices), with other vehicles, and / or with computing devices (e.g., a passenger's client device) via the Internet. In order to communicate with other vehicles, a direct link can be established between the two vehicles, and / or an indirect link can be established (e.g., across a network and through the Internet). The direct link can be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link can provide the vehicle 400 with information about vehicles approaching the vehicle 400 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 400). This functionality can be part of the cooperative adaptive cruise control functionality of the vehicle 400.

[0151] The network interface 424 may include a SoC that provides modulation and demodulation functions and enables the controller 436 to communicate over a wireless network. The network interface 424 may include a radio frequency front end for up-conversion from baseband to radio frequency and down-conversion from radio frequency to baseband. The frequency conversion may be performed by a known process and / or may be performed using a super-heterodyne process. In some examples, the radio frequency front end function may be provided by a separate chip. The network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0152] The vehicle 400 may further include data storage 428, which may include off-chip storage (e.g., outside the SoC 404). The data storage 428 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, hard disk, and / or other components and / or devices that can store at least one bit of data.

[0153] The vehicle 400 may further include a GNSS sensor 458. The GNSS sensor 458 (e.g., GPS, assisted GPS sensor, differential GPS (DGPS) sensor, etc.) is used to assist in mapping, perception, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 458 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0154] The vehicle 400 may further include a RADAR sensor 460. The RADAR sensor 460 may be used by the vehicle 400 for remote vehicle detection even in darkness and / or inclement weather conditions. The RADAR functional safety level may be ASIL B. The RADAR sensor 460 may use CAN and / or bus 402 (e.g., to transmit data generated by the RADAR sensor 460) for control and access to object tracking data, accessing Ethernet in some examples to access raw data. A variety of RADAR sensor types may be used. For example and without limitation, the RADAR sensor 460 may be suitable for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0155] The RADAR sensor 460 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, and the like. In some examples, the long-range RADAR may be used for adaptive cruise control functions. The long-range RADAR system may provide a wide field of view (e.g., within 250m) achieved through two or more independent browsing. The RADAR sensor 460 may help distinguish between static objects and moving objects, and may be used by the ADAS system for emergency braking assistance and forward collision warnings. The long-range RADAR sensor may include a single-station multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In an example with six antennas, the central four antennas may create a focused beam pattern designed to record the surroundings of the vehicle 400 at a higher rate with minimal traffic interference from adjacent lanes. The other two antennas may expand the field of view, making it possible to quickly detect vehicles entering or leaving the lane of the vehicle 400.

[0156] As an example, a medium-range RADAR system may include a range of up to 160m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 150 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on both ends of a rear bumper. When mounted on both ends of a rear bumper, such a RADAR sensor system may create two beams that continuously monitor the blind spots behind and beside the vehicle.

[0157] Short-range RADAR systems can be used in ADAS systems for blind spot detection and / or lane change assistance.

[0158] The vehicle 400 may further include ultrasonic sensors 462. The ultrasonic sensors 462, which may be placed on the front, rear, and / or sides of the vehicle 400, may be used for parking assistance and / or creating and updating occupancy grids. A variety 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 sensors 462 may operate at a functional safety level of ASIL B.

[0159] The vehicle 400 may include a LIDAR sensor 464. The LIDAR sensor 464 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 464 may be ASIL B for functional safety level. 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).

[0160] In some examples, LIDAR sensor 464 may be able to provide a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensor 464 may have, for example, an advertised range of approximately 100m, an accuracy of 2cm-3cm, and support for 100Mbps Ethernet connections. In some examples, one or more non-protruding LIDAR sensors 464 may be used. In such examples, LIDAR sensor 464 may be implemented as a small device that can be embedded in the front, back, side, and / or corner of vehicle 400. In such examples, LIDAR sensor 464 may provide a field of view of up to 120 degrees horizontally and 35 degrees vertically, with a range of 200m, even for low reflectivity objects. Front-mounted LIDAR sensor 464 may be configured for a horizontal field of view between 45 and 135 degrees.

[0161] In some examples, LIDAR technologies such as 3D flash LIDAR may also be used. 3D flash LIDAR uses flashes of laser as an emission source to illuminate the vehicle's surroundings up to about 200m. The flash LIDAR unit includes a receiver that records the laser pulse transmission time and reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LIDAR can allow highly accurate and distortion-free images of the surrounding environment to be generated with each laser flash. In some examples, four flash LIDAR sensors can be deployed, one on each side of the vehicle 400. Available 3D flash LIDAR systems include solid-state 3D staring array LIDAR cameras (e.g., non-browsing LIDAR devices) with no moving parts other than fans. The flash LIDAR device can use 5 nanosecond Class I (eye-safe) laser pulses per frame, and can capture the reflected laser light in the form of a 3D range point cloud and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, LIDAR sensor 464 may be less susceptible to motion blur, vibration, and / or shock.

[0162] The vehicle may further include an IMU sensor 466. In some examples, the IMU sensor 466 may be located at the center of the rear axle of the vehicle 400. The IMU sensor 466 may include, for example and without limitation, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, for example, in a six-axis application, the IMU sensor 466 may include an accelerometer and a gyroscope, and in a nine-axis application, the IMU sensor 466 may include an accelerometer, a gyroscope, and a magnetometer.

[0163] In some embodiments, IMU sensor 466 can be implemented as a miniature high-performance GPS-assisted inertial navigation system (GPS / INS) that combines micro-electromechanical system (MEMS) inertial sensors, high-sensitivity GPS receivers, and advanced Kalman filter algorithms to provide estimates of position, velocity, and attitude. Thus, in some examples, IMU sensor 466 can enable vehicle 400 to estimate heading by directly observing and correlating velocity changes from GPS to IMU sensor 466 without input from a magnetic sensor. In some examples, IMU sensor 466 and GNSS sensor 458 can be combined into a single integrated unit.

[0164] The vehicle may include microphones 496 positioned in and / or around the vehicle 400. The microphones 496 may be used for, among other things, emergency vehicle detection and identification.

[0165] The vehicle may further include any number of camera types, including a stereo camera 468, a wide angle camera 470, an infrared camera 472, a surround camera 474, a long-range and / or mid-range camera 498, and / or other camera types. These cameras can be used to capture image data around the entire periphery of the vehicle 400. The type of camera used depends on the embodiment and the requirements of the vehicle 400, and any combination of camera types can be used to provide the necessary coverage around the vehicle 400. In addition, the number of cameras can vary depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or another number of cameras. As an example and not limitation, the cameras can support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may be capable of supporting Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Figure 4A and Figure 4B Described in more detail.

[0166] The vehicle 400 may further include a vibration sensor 442. The vibration sensor 442 may measure vibrations of components of the vehicle, such as an axle. For example, changes in vibration may indicate changes in the road surface. In another example, when two or more vibration sensors 442 are used, the difference between the vibrations may be used to determine friction or slip of the road surface (e.g., when there is a vibration difference between a powered drive shaft and a free-spinning shaft).

[0167] The vehicle 400 may include an ADAS system 438. In some examples, the ADAS system 438 may include a SoC. The ADAS system 438 may include autonomous / adaptive / automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward collision warning (FCW), automatic emergency braking (AEB), lane departure warning (LDW), lane keeping assist (LKA), blind spot warning (BSW), rear cross traffic warning (RCTW), collision warning system (CWS), lane centering (LC), and / or other features and functions.

[0168] The ACC system may use a RADAR sensor 460, a LIDAR sensor 464, and / or a camera. The ACC system may include a longitudinal ACC and / or a lateral ACC. The longitudinal ACC monitors and controls the distance to the vehicle immediately in front of the vehicle 400, and automatically adjusts the vehicle speed to maintain a safe distance from the vehicle in front. The lateral ACC performs distance keeping and suggests that the vehicle 400 change lanes when necessary. The lateral ACC is related to other ADAS applications such as LCA and CWS.

[0169] CACC uses information from other vehicles, which can be received indirectly from other vehicles via a wireless link or through a network connection (e.g., through the Internet) via a network interface 424 and / or a wireless antenna 426. A direct link can be provided by a vehicle-to-vehicle (V2V) communication link, while an indirect link can be an infrastructure-to-vehicle (I2V) communication link. Typically, the V2V communication concept provides information about the vehicle immediately ahead (e.g., the vehicle immediately ahead of the vehicle 400 and in the same lane as it), while the I2V communication concept provides information about traffic farther ahead. The CACC system may include either or both of the I2V and V2V information sources. Given information about the vehicle ahead of the vehicle 400, CACC can be more reliable, and it is possible to improve the smoothness of traffic flow and reduce road congestion.

[0170] The FCW system is designed to alert the driver to hazards so that the driver can take corrective action. The FCW system uses a front camera and / or RADAR sensor 460 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibrating components. The FCW system can provide warnings in the form of, for example, sound, visual warnings, vibrations and / or rapid brake pulses.

[0171] The AEB system detects an impending front collision with another vehicle or other object and can automatically apply the brakes if the driver does not take corrective action within specified time or distance parameters. The AEB system can use a front camera and / or RADAR sensor 460 coupled to a dedicated processor, DSP, FPGA and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes in an effort to prevent or at least mitigate the effects of the predicted collision. The AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0172] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 400 crosses a lane marking. When the driver indicates an intention to leave the lane, by activating a turn signal, the LDW system is not activated. The LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0173] The LKA system is a variation of the LDW system. If the vehicle 400 begins to leave the lane, the LKA system provides steering input or braking to correct the vehicle 400.

[0174] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, audible and / or tactile alerts to indicate that it is unsafe to merge or change lanes. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use one or more rear-facing cameras and / or one or more RADAR sensors.

[0175] The RCTW system may provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 400 is in reverse. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. The RCTW system may use one or more rear RADAR sensors 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0176] Conventional ADAS systems may be prone to false positive results, which may annoy and distract the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether the safety condition really exists and take action accordingly. However, in the autonomous vehicle 400, in the case of conflicting results, the vehicle 400 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 436 or the second controller 436). For example, in some embodiments, the ADAS system 438 can be a backup and / or auxiliary computer for providing perception information to the backup computer rationality module. The backup computer rationality monitor can run redundant and diverse software on hardware components to detect faults in perception and dynamic driving tasks. The output from the ADAS system 438 can be provided to the supervisory MCU. If the outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to coordinate the conflict to ensure safe operation.

[0177] In some examples, the master computer can be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the direction of the master computer, regardless of whether the auxiliary computer provides conflicting or inconsistent results. In the case where the confidence score does not meet the threshold and the master computer and the auxiliary computer indicate different results (e.g., conflicts), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0178] The supervisory MCU may be configured to run a neural network that is trained and configured to determine conditions under which the auxiliary computer provides a false alarm based on outputs from the primary computer and the auxiliary computer. Thus, the neural network in the supervisory MCU may learn when the output of the auxiliary computer may be trusted and when it may not. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU may learn when the FCW system is identifying a metal object that is not actually dangerous, such as a drain grate or manhole cover that triggers an alarm. Similarly, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In an embodiment that includes a neural network running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or GPU suitable for running the neural network with associated memory. In a preferred embodiment, the supervisory MCU may include a component of SoC 404 and / or be included as a component of SoC 404.

[0179] In other examples, the ADAS system 438 may include an auxiliary computer that uses traditional computer vision rules to perform ADAS functions. In this way, the auxiliary computer can use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or error in the software running on the main computer and the non-identical software code running on the auxiliary computer provides the same overall result, the supervisory MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware on the main computer does not cause a substantial error.

[0180] In some examples, the output of the ADAS system 438 can be fed to the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, if the ADAS system 438 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information in identifying the object. In other examples, the auxiliary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0181] The vehicle 400 may further include an infotainment SoC 430 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as an SoC, the infotainment system may not be an SoC and may include two or more discrete components. The infotainment SoC 430 may include a combination of hardware and software that may be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and / or information services (e.g., navigation system, rear parking assistance, radio data system, vehicle-related information such as fuel level, total distance covered, brake fuel level, oil level, door open / closed, air filter information, etc.) to the vehicle 400. For example, the infotainment SoC 430 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connectivity, an onboard computer, onboard entertainment, Wi-Fi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 434, a telematics device, a control panel (e.g., for controlling and / or interacting with various components, features, and / or systems), and / or other components. The infotainment SoC 430 may further be used to provide information (e.g., visual and / or auditory) to a user of the vehicle, such as information from an ADAS system 438, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0182] 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 a bus 402 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 430 may be coupled to a supervisory MCU so that in the event of a failure of a master controller 436 (e.g., a main and / or backup computer of the vehicle 400), the GPU of the infotainment system may perform some self-driving functions. In such an example, the infotainment SoC 430 may place the vehicle 400 in a driver-safe parking mode as described herein.

[0183] 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 separate controller or a supercomputer). The instrument cluster 432 may include a set of instruments, such as a speedometer, a fuel level, an oil pressure, a tachometer, an odometer, a turn indicator, a shift position indicator, a seat belt warning light, a parking brake warning light, an engine fault light, an airbag (SRS) system information, lighting controls, safety system controls, navigation information, 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.

[0184] Figure 4D For a cloud-based server and Figure 4A 4. System diagram of communication between example autonomous vehicles 400. System 476 may include server 478, network 490, and vehicles including vehicle 400. Server 478 may include multiple GPUs 484 (A)-484 (H) (collectively referred to as GPU 484 here), PCIe switches 482 (A)-482 (H) (collectively referred to as PCIe switches 482 here), and / or CPUs 480 (A)-480 (B) (collectively referred to as CPU 480 here). GPU 484, CPU 480, and PCIe switches may be interconnected with high-speed interconnects and / or PCIe connections 486 such as, for example and without limitation, NVLink interface 488 developed by NVIDIA. In some examples, GPU 484 is connected via NVLink and / or NVSwitch SoC, and GPU 484 and PCIe switch 482 are connected via PCIe interconnects. Although eight GPUs 484, two CPUs 480, and two PCIe switches are shown, 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, each of the servers 478 may include eight, sixteen, thirty-two, and / or more GPUs 484.

[0185] Server 478 may receive image data over network 490 and from a vehicle, the image data representing images showing unexpected or changed road conditions, such as recently begun road work. Server 478 may transmit neural network 492, updated neural network 492, and / or map information 494, including information about traffic and road conditions, over network 490 and to the vehicle. Updates to map information 494 may include updates to HD map 422, such as information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, neural network 492, updated neural network 492, and / or map information 494 may have been generated from new training and / or data received from any number of vehicles in the environment and / or based on experience from training performed at a data center (e.g., using server 478 and / or other servers).

[0186] Server 478 can be used to train a machine learning model (e.g., a neural network) based on training data. The training data can be generated by the vehicle, and / or can be generated in a simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., when the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other examples, the training data is not labeled and / or preprocessed (e.g., when the neural network does not require supervised learning). Training can be performed according to any one or more classes of machine learning techniques, including but not limited to the following classes: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including principal component and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternate dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations thereof. Once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via network 490), and / or the machine learning model can be used by server 478 to remotely monitor the vehicle.

[0187] In some examples, server 478 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. Server 478 may include a deep learning supercomputer and / or a dedicated AI computer powered by GPU 484, such as DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 478 may include a deep learning infrastructure of a data center powered only by CPUs.

[0188] The deep learning infrastructure of server 478 may be capable of rapid real-time inference, and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 400. For example, the deep learning infrastructure may receive periodic updates from vehicle 400, such as a sequence of images and / or objects located in the sequence of images that vehicle 400 has located (e.g., via computer vision and / or other machine learning object classification techniques). The deep learning infrastructure may run its own neural network to identify objects and compare them to the objects identified by vehicle 400, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 400 has failed, server 478 may transmit a signal to vehicle 400 instructing the fail-safe computer of vehicle 400 to take control, notify passengers, and complete a safe parking maneuver.

[0189] For reasoning, the server 478 may include a GPU 484 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT 3). The combination of GPU-powered servers and reasoning acceleration can make real-time responses possible. In other examples, such as when performance is not so important, CPU, FPGA, and other processor-powered servers can be used for reasoning.

[0190] Example computing device

[0191] Figure 5 5 is a block diagram of an example computing device 500 suitable for implementing some embodiments of the present disclosure. The 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 communication interface 510, an input / output (I / O) port 512, an input / output component 514, a power supply 516, one or more presentation components 518 (e.g., a display), and one or more logic units 520. In at least one embodiment, the computing device 500 may include one or more virtual machines (VMs), and / or any of its components may include virtual components (e.g., virtual hardware components). For non-limiting examples, the one or more GPUs 508 may include one or more vGPUs, the one or more CPUs 506 may include one or more vCPUs, and / or the one or more logic units 520 may include one or more virtual logic units. Thus, computing device 500 may include discrete components (eg, a complete GPU dedicated to computing device 500 ), virtual components (eg, a portion of a GPU dedicated to computing device 500 ), or a combination thereof.

[0192] although Figure 5The various blocks of are shown as being connected via an interconnect system 502 having wires, but this is not intended to be limiting and is merely for clarity. For example, in some embodiments, a presentation component 518 such as a display device may be considered an I / O component 514 (e.g., if the display is a touch screen). As another example, CPU 506 and / or GPU 508 may include memory (e.g., memory 504 may represent a storage device in addition to the memory of GPU 508, CPU 506, and / or other components). In other words, Figure 5 The computing devices described herein are illustrative only. No distinction is made between categories such as "workstations," "servers," "laptops," "desktops," "tablets," "client devices," "mobile devices," "handheld devices," "game consoles," "electronic control units (ECUs)," "virtual reality systems," and / or other device or system types, as all are considered Figure 5 within the range of computing devices.

[0193] The interconnection 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 interconnection system 502 may include one or more links or bus types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and / or another type of bus or link. In some embodiments, there is a direct connection between components. As an example, the CPU 506 may be directly connected to the memory 504. In addition, the CPU 506 may be directly connected to the GPU 508. In the case where there is a direct or point-to-point connection between components, the interconnection system 502 may include a PCIe link to perform the connection. In these examples, the computing device 500 does not need to include a PCI bus.

[0194] Memory 504 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by computing device 500. Computer-readable media may include volatile and nonvolatile media and removable and non-removable media. By way of example and not limitation, computer-readable media may include computer storage media and communication media.

[0195] Computer storage media may include volatile and nonvolatile media and / or removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 504 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system). Computer storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by computing device 500. As used herein, computer storage media does not include the signals themselves.

[0196] Computer storage media may contain computer readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in such a way that information is encoded into the signal. By way of example and not limitation, computer storage media may include wired media such as a wired network or a direct wired connection, and wireless media such as sound, RF, infrared, and other wireless media. Any combination of the above should also be included within the scope of computer readable media.

[0197] The CPU 506 may be configured to execute at least some of the computer-readable instructions so as to control one or more components of the computing device 500 to perform one or more of the methods and / or processes described herein. Each of the CPUs 506 may include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of processing a large number of software threads simultaneously. The CPU 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., a processor with fewer cores for mobile devices and a processor with more cores for servers). For example, depending on the type of computing device 500, the processor may be an Advanced RISC Mechanism (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 500 may also include one or more CPUs 506 in addition to one or more microprocessors or supplementary coprocessors such as math coprocessors.

[0198] In addition to or in place of the CPU 506, the GPU 508 may also be configured to execute at least some computer-readable instructions to control one or more components of the computing device 500 to perform one or more methods and / or processes described herein. One or more GPUs 508 may be integrated GPUs (e.g., with one or more CPUs 506) and / or one or more GPUs 508 may be discrete GPUs. In an embodiment, one or more GPUs 508 may be coprocessors of one or more CPUs 506. The computing device 500 may use the GPU 508 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, the GPU 508 may be used for general-purpose computing on a GPU (GPGPU). The GPU 508 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. The GPU 508 may generate pixel data for outputting an image in response to a rendering command (e.g., a rendering command from the CPU 506 received via a host interface). The GPU 508 may include a graphics memory such as a display memory for storing pixel data or any other suitable data (e.g., GPGPU data). Display memory may be included as part of memory 504. GPU 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 NVLINK) or through a switch (e.g., using NVSwitch). When combined together, each GPU 508 may generate pixel data or GPGPU data for different portions of output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory or may share memory with other GPUs.

[0199] In addition to or in lieu of the CPU 506 and / or the GPU 508, the logic unit 520 may be configured to execute at least some computer-readable instructions to control one or more components of the computing device 500 to perform one or more methods and / or processes described herein. In an embodiment, the CPU 506, the GPU 508, and / or the logic unit 520 may discretely or jointly perform any combination of methods, processes, and / or portions thereof. The one or more logic units 520 may be a part of and / or integrated into the one or more CPUs 506 and / or the one or more GPUs 508 and / or the one or more logic units 520 may be a discrete component of the CPU 506 and / or the GPU 508 or otherwise external thereto. In an embodiment, the one or more logic units 520 may be a processor of the one or more CPUs 506 and / or the one or more GPUs 508.

[0200] Examples of logic unit 520 include one or more processing cores and / or components thereof, such as a data processing unit (DPU), a tensor core (TC), a tensor processing unit (TPU), a pixel vision core (PVC), a vision processing unit (VPU), a graphics processing cluster (GPC), a texture processing cluster (TPC), a streaming multiprocessor (SM), a tree traversal unit (TTU), an artificial intelligence accelerator (AIA), a deep learning accelerator (DLA), an arithmetic logic unit (ALU), an application specific integrated circuit (ASIC), a floating point unit (FPU), an input / output (I / O) element, a peripheral component interconnect (PCI) or a peripheral component interconnect express (PCIe) element, etc.

[0201] 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 via an electronic communication network, including wired and / or wireless communications. The communication interface 510 may include components and functions that enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low power wide area networks (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, the logic unit 520 and / or the communication interface 510 may include one or more data processing units (DPUs) to transmit data received over a network and / or over the interconnect system 502 directly to one or more GPUs 508 (e.g., memory in a GPU 508).

[0202] The I / O port 512 can enable the computing device 500 to be logically coupled to other devices including I / O components 514, presentation components 518, and / or other components, some of which can be built into (e.g., integrated into) the computing device 500. Illustrative I / O components 514 include microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dishes, browsers, printers, wireless devices, and the like. The I / O components 514 can provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, the input can be transmitted to an appropriate network element for further processing. The NUI can implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition on and adjacent to the screen, air gestures, head and eye tracking, and touch recognition associated with the display of the computing device 500 (as described in more detail in this disclosure). The computing device 500 may include a depth camera such as a stereo camera system, an infrared camera system, an RGB camera system, touch screen technology, and combinations of these for gesture detection and recognition. Additionally, computing device 500 may include an accelerometer or gyroscope to enable motion detection (e.g., as part of an inertial measurement unit (IMU)). In some examples, the output of the accelerometer or gyroscope may be used by computing device 500 to render immersive augmented reality or virtual reality.

[0203] The power supply 516 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 516 may provide power to the computing device 500 to enable the components of the computing device 500 to operate.

[0204] The presentation component 518 may include a display (e.g., a monitor, a touch screen, a television screen, a head-up display (HUD), other display types, or a combination thereof), a speaker, and / or other presentation components. The presentation component 518 may receive data from other components (e.g., GPU 508, CPU 506, DPU, etc.) and output the data (e.g., as an image, video, sound, etc.).

[0205] Sample Data Center

[0206] Figure 6 An example data center 600 is shown, which may be used in at least one embodiment of the present disclosure. The data center 600 may include a data center infrastructure layer 610, a framework layer 620, a software layer 630, and an application layer 640.

[0207] like Figure 6As shown, the data center infrastructure layer 610 may include a resource coordinator 612, grouped computing resources 614, and node computing resources ("node CRs") 616(1)-616(N), where "N" represents any complete positive integer. In at least one embodiment, the node CRs 616(1)-616(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state drives or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules and cooling modules, etc. In some embodiments, one or more of the node CRs 616(1)-616(N) may correspond to a server having one or more of the above computing resources. Furthermore, in some embodiments, node CRs 616 ( 1 )- 616 (N) may include one or more virtual components, such as vGPUs, vCPUs, etc., and / or one or more of node CRs 616 ( 1 )- 616 (N) may correspond to virtual machines (VMs).

[0208] In at least one embodiment, the computing resources 614 of the grouping may include a separate grouping (not shown) of the node CR616 contained in one or more racks, or many racks (also not shown) contained in the data center of each geographical location. The separate grouping of the node CR616 in the computing resources 614 of the grouping may include computing, network, memory or storage resources that can be configured or assigned to support the grouping of one or more workloads. In at least one embodiment, several node CR616 including CPU, GPU, DPU and / or other processors may be grouped in one or more racks to provide computing resources to support one or more workloads. One or more racks may also include any number of power modules, cooling modules and / or network switches in any combination.

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

[0210] In at least one embodiment, Figure 6As shown, the framework layer 620 may include a job scheduler 633, a configuration manager 634, a resource manager 636, and a distributed file system 638. The framework layer 620 may include a framework that supports software 632 of the software layer 630 and / or one or more applications 642 of the application layer 640. The software 632 or the application 642 may 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, but is not limited to, a free and open source software network application framework, such as Apache Spark, which may utilize the distributed file system 638 for large-scale data processing (e.g., "big data"). TM (hereinafter referred to as "Spark"). In at least one embodiment, the job scheduler 633 may include a Spark driver to facilitate scheduling of workloads supported by various layers of the data center 600. In at least one embodiment, the configuration manager 634 may be able to configure different layers, such as the software layer 630 and the framework layer 620 including Spark and a distributed file system 638 for supporting large-scale data processing. The resource manager 636 can manage cluster or group computing resources mapped to or allocated to support the distributed file system 638 and the job scheduler 633. In at least one embodiment, the cluster or group computing resources may include grouped computing resources 614 at the data center infrastructure layer 610. The resource manager 636 can coordinate with the resource coordinator 612 to manage these mapped or allocated computing resources.

[0211] In at least one embodiment, software 632 included in software layer 630 may include software used by at least a portion of node CRs 616(1)-616(N), grouped computing resources 614, and / or distributed file system 638 of framework layer 620. One or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0212] In at least one embodiment, one or more applications 642 included in the application layer 640 may include one or more types of applications used by at least a portion of the node CRs 616(1)-616(N), the grouped computing resources 614, and / or the distributed file system 638 of the framework layer 620. The one or more types of applications may include, but are not limited to, any number of genomics applications, cognitive computing, and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and / or other machine learning applications used in conjunction with one or more embodiments.

[0213] In at least one embodiment, any of configuration manager 634, resource manager 636, and resource coordinator 612 can implement any number and type of self-modification actions based on any number and type of data acquired in any technically feasible manner. The self-modification actions can relieve the data center operator of data center 600 from making potentially bad configuration decisions and can avoid underutilized and / or underperforming portions of the data center.

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

[0215] In at least one embodiment, the data center 600 can use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, and / or other hardware (or virtual computing resources corresponding thereto) to use the above resources to perform training and / or reasoning. In addition, one or more software and / or hardware resources described in the present disclosure can be configured as a service to allow users to train or perform information reasoning, such as image recognition, speech recognition, or other artificial intelligence services.

[0216] Example network environment

[0217] A network environment suitable for implementing embodiments of the present disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and / or other device types. The client devices, servers, and / or other device types (e.g., each device) may be configured to communicate with the client device, server, or other device types. Figure 5 The backend device 600 may be implemented on one or more instances of the computing device 500 of the present invention—for example, each device may include similar components, features, and / or functions of the computing device 500. In addition, in the case of implementing a backend device (e.g., a server, NAS, etc.), the backend device may be included as part of the data center 600, examples of which are described herein with respect to Figure 6 Describe in more detail.

[0218] The components of the network environment can communicate with each other through the network, which can be wired, wireless, or both. The network can include multiple networks, or a network of multiple networks. For example, the network can include one or more wide area networks (WANs), one or more local area networks (LANs), one or more public networks (such as the Internet and / or the Public Switched Telephone Network (PSTN)), and / or one or more private networks. In the case where the network includes a wireless telecommunications network, components such as base stations, communication towers, or even access points (and other components) can provide wireless connections.

[0219] Compatible network environments may include one or more peer-to-peer network environments (in which case the server may not be included in the network environment), and one or more client-server network environments (in which case one or more servers may be included in the network environment). In a peer-to-peer network environment, the functionality described herein with respect to the server may be implemented on any number of client devices.

[0220] In at least one embodiment, the network environment may include one or more cloud-based network environments, distributed computing environments, combinations thereof, and the like. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more servers, which may include one or more core network servers and / or edge servers. The framework layer may include a framework for supporting software at the software layer and / or one or more applications at the application layer. The software or application may include network-based service software or application programs, respectively. In an embodiment, one or more client devices may use network-based service software or application programs (e.g., by accessing the service software and / or application programs via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a type of free and open source software network application framework, such as a distributed file system that may be used for large-scale data processing (e.g., "big data").

[0221] A cloud-based network environment can provide cloud computing and / or cloud storage that performs any combination of the computing and / or data storage functions (or one or more portions thereof) described herein. Any of these various functions can be distributed across multiple locations from a central or core server (e.g., one or more data centers that can be distributed across states, regions, countries, the world, etc.). If the connection to the user (e.g., client device) is relatively close to an edge server, the core server can assign at least a portion of the functionality to the edge server. A cloud-based network environment can be private (e.g., limited to a single organization), public (e.g., available to many organizations), and / or a combination thereof (e.g., a hybrid cloud environment).

[0222] The client device may include Figure 5 At least some of the components, features, and functionality of the described example computing device 500. By way of example and not limitation, the client device may be embodied as a personal computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality head mounted display, a global positioning system (GPS) or device, a video player, a camera, a surveillance device or system, a vehicle, a vessel, an aircraft, a virtual machine, a drone, a robot, a handheld communication device, a hospital device, a gaming device or system, an entertainment system, an in-vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these described devices, or any other suitable device.

[0223] The present disclosure may be described in the general context of machine-usable instructions or computer codes executed by a computer or other machine such as a personal digital assistant or other handheld device, including computer-executable instructions such as program modules. Typically, program modules including routines, programs, objects, components, data structures, etc. refer to codes that perform specific tasks or implement specific abstract data types. The present disclosure may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communication network.

[0224] As used herein, the description of "and / or" about two or more elements should be interpreted as referring to only one element or a combination of elements. For example, "element A, element B and / or element C" may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B and C. In addition, "at least one of element A or element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, "at least one of element A and element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. In addition, the use of the term "based on" should not be interpreted as "based only on" or "based only on". On the contrary, the first element "based on" the second element includes an instance in which 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 in detail herein to meet statutory requirements. However, the description itself is not intended to limit the scope of the present disclosure. On the contrary, the inventors have contemplated that the claimed subject matter may also be embodied in other ways to include steps different from the steps described herein in conjunction with other current or future technologies or combinations of similar steps. Moreover, although the terms "step" and / or "block" may be used herein to imply different elements of the method employed, these terms should not be interpreted as implying any particular order among or between the various steps disclosed herein, unless the order of the steps is explicitly described.

Claims

1. A method comprising: determining a first lighting state corresponding to the area based on at least first sensor data obtained using a first lighting sensor corresponding to the machine; determining a second lighting state corresponding to the area based at least on second sensor data obtained using a second lighting sensor corresponding to the machine; determining a scene lighting state of the area based at least on the first lighting state and the second lighting state; as well as One or more lighting components of the machine are controlled based at least on the scene lighting state. 2 . The method of claim 1 , wherein the determination of the scene lighting state is based at least on a relationship between the first lighting state and a set of threshold ranges corresponding to the first lighting state.

3. The method of claim 1, wherein the determination of the scene lighting state is based at least on the second lighting state in response to the first lighting state being between a set of threshold ranges corresponding to the first lighting state.

4. The method of claim 3, wherein the determination of the scene lighting state is based at least on a relationship between the second lighting state and a second set of threshold ranges corresponding to the second lighting state.

5. The method of claim 1, wherein determining one or more of the first lighting state, the second lighting state, or the scene lighting state is performed using one or more neural networks. The method of claim 1 , wherein the second illumination sensor is used to detect illumination levels at a different distance than the first illumination sensor.

7. The method of claim 1, wherein the first illumination sensor comprises a wide-angle camera and the second illumination sensor comprises a telescopic camera.

8. The method of claim 2, wherein the set of threshold ranges corresponding to the first lighting state includes a dark state range and a lit state range.

9. A system comprising: One or more processors configured to cause execution of operations, the operations comprising: determining a first lighting state corresponding to the area based on at least first sensor data obtained using a first lighting sensor corresponding to the machine; determining a second lighting state corresponding to the area based at least on second sensor data obtained using a second lighting sensor corresponding to the machine; determining a scene lighting state for the area based at least on the first lighting state and the second lighting state; and One or more lighting components of the machine are controlled based at least on the scene lighting state.

10. The system of claim 9, wherein the determination of the scene lighting state is based at least on a relationship between the first lighting state and a set of threshold ranges corresponding to the first lighting state.

11. The system of claim 9, wherein the determination of the scene lighting state is based at least on the second lighting state in response to the first lighting state being between a set of threshold ranges corresponding to the first lighting state.

12. The system of claim 11, wherein the determination of the scene lighting state is based at least on a relationship between the second lighting state and a second set of threshold ranges corresponding to the second lighting state.

13. The system of claim 9, wherein determining one or more of the first lighting state, the second lighting state, or the scene lighting state is performed using one or more neural networks.

14. The system of claim 9, wherein the second illumination sensor is used to detect illumination levels at a greater distance than the first illumination sensor.

15. The system of claim 9, wherein the first illumination sensor comprises a wide angle camera and the second illumination sensor comprises a telescopic camera.

16. The system of claim 10, wherein the set of threshold ranges corresponding to the first lighting state includes a dark state range and a lit state range.

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

18. A system comprising one or more processing units, wherein the one or more processing units are used to control one or more lighting components of a machine based at least on a lighting state of an environment determined using two or more lighting sensors of the machine, wherein at least a first lighting sensor of the two or more lighting sensors is of a different type than at least a second lighting sensor of the two or more lighting sensors.

19. The system of claim 18, wherein the second illumination sensor is configured to detect illumination levels at a different distance from the first illumination sensor.

20. The system of claim 19, wherein the first illumination sensor comprises a wide angle camera and the second illumination sensor comprises a telescopic camera.

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