Feature location identification for autonomous systems and applications

Abstract

The invention discloses feature location identification for autonomous systems and applications. In various examples, feature location identification for autonomous and semi-autonomous systems and applications is described. Systems and methods for determining road marker locations within an environment using LiDAR data are disclosed. For example, LiDAR data may be used to generate one or more images representing an environment, such as a top-down image, a bird's eye view (BEV) image, and / or an intensity image. The image may then be processed using one or more machine-learned models configured to determine information related to the road markings, such as boundary shapes (e.g., bounding boxes) indicative of the location of the road markings within the image, classifications (e.g., types of the road markings) related to the road markings, and / or any other information. This information may then be used to perform one or more processes, such as updating a map of the environment and / or navigating the vehicle within the environment.

Description

Background Art

[0001] In order for a vehicle (e.g., an autonomous vehicle, a semi-autonomous vehicle, a robot, etc.) to operate safely in an environment, the vehicle must be able to effectively perform vehicle maneuvers, such as lane keeping, lane changes, lane splitting, turning, stopping and starting at intersections, crosswalks, etc., and / or other vehicle or machine maneuvers. For example, in order for a vehicle to travel through surface streets (e.g., city streets, alleys, neighborhood streets, etc.) and highways (e.g., multi-lane roads), the vehicle needs to navigate between one or more partitions or demarcations of the road (e.g., lanes, intersections, crosswalks, boundaries, etc.), which are typically marked with road markings (e.g., arrows, text, graphics, and / or other types of content or features). Therefore, it is important for the vehicle to be able to detect road markings in the environment so that the vehicle can determine how to navigate based on rules associated with the road markings.

[0002] To detect road markings, a vehicle may use, at least in part, a map corresponding to the environment in which the vehicle is navigating. For example, a map may indicate the locations of important features that the vehicle needs to identify when navigating, such as road surfaces and road markings. Conventional methods of determining the locations of road markings for these maps include processing image data generated using an image sensor of a vehicle that has been navigating the environment. For example, the image data may represent an image depicting road markings within the environment. Thus, the system is able to process the image data, for example by using one or more image processing techniques (e.g., object detection, object recognition, etc.), to detect the locations of road markings within the image. The system may then use the locations of the road markers from the image to determine corresponding locations of the road markers within the map.

[0003] While these systems are able to use image processing to determine the location of road markings in an environment, the accuracy of these systems can still be improved. For example, when using image processing to determine the location of road markings, it can be difficult to obtain very accurate results for the location of road markings, such as results at the centimeter level—for example, because the image presents a two-dimensional (2D) representation of the environment, which then needs to be converted into a three-dimensional (3D) space for localization and navigation. Other factors may also affect the accuracy of these systems, such as distance to road markings, occlusions within the image, perspective distortion, and scene changes. Summary of the invention

[0004] Embodiments of the present disclosure relate to feature location identification for autonomous and semi-autonomous systems and applications. Systems and methods for determining the location of road markings within an environment using LiDAR data and / or other data types (e.g., RADAR data) generated using one or more sensors are disclosed. For example, LiDAR data can be used to generate one or more images representing an environment, such as one or more top-down images, one or more bird's-eye (or referred to as overhead, BEV) images, and / or one or more intensity images. The images can then be processed using one or more machine learning models that are configured to determine information related to road markings, such as a bounding shape (e.g., a bounding box) indicating the location of a road marking within the image, a classification related to the road marking (e.g., a type of road marking), and / or any other information. Subsequently, the information can be used to perform one or more processes, such as updating a map of the environment and / or navigating a vehicle within the environment.

[0005] Compared to the conventional systems described above, in some embodiments, the current systems can more accurately determine the locations of road markings within an environment, e.g., to the centimeter level. This is because the current systems can use LiDAR data processing—more specifically, images generated using attributes associated with points represented by the LiDAR data (e.g., intensity, etc.)—to determine the locations of road markings, which can be more accurate than using image processing. Furthermore, in some embodiments, by using LiDAR data to generate images for determining the locations of road markings, the accuracy of the current systems in determining road markings may not be affected by factors such as distance to the road markings, sensor occlusion, perspective distortion, and / or scene changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The following describes in detail a system and method for feature location recognition for autonomous and semi-autonomous systems and applications with reference to the accompanying drawings, wherein:

[0007] Figure 1 An example data flow diagram illustrating a process for determining information related to road markings according to some embodiments of the present disclosure;

[0008] Figure 2A-2B An example of using LiDAR data to generate an image according to some embodiments of the present disclosure is shown;

[0009] Figure 3 An example of using one or more machine learning models to process image data to determine information related to road markings according to some embodiments of the present disclosure is shown;

[0010] Figure 4shows example outputs of one or more machine learning models trained to determine information related to road markings according to some embodiments of the present disclosure;

[0011] Figure 5 An example of updating a map to include information related to road markings according to some embodiments of the present disclosure is shown;

[0012] Figure 6 is a data flow diagram illustrating a process for training one or more machine learning models to determine information related to road markings according to some embodiments of the present disclosure;

[0013] Figures 7 and 8 A flow chart showing a method for updating a map using LiDAR data to indicate the location of road markings within an environment according to some embodiments of the present disclosure;

[0014] Fig. 9A is an illustration of an example autonomous vehicle according to some embodiments of the present disclosure;

[0015] Fig. 9B According to some embodiments of the present disclosure Fig. 9A Examples of autonomous vehicle camera positions and fields of view;

[0016] Fig. 9C According to some embodiments of the present disclosure Fig. 9A a block diagram of an example system architecture for an example autonomous vehicle;

[0017] Fig.9D is a cloud-based server and Fig. 9A System diagram of an example of communication between autonomous vehicles;

[0018] Fig.10 is a block diagram of an example computing device suitable for implementing some embodiments of the present disclosure; and

[0019] Fig.11 is a block diagram of an example data center suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0020] Systems and methods related to feature location recognition for autonomous and semi-autonomous systems and applications are disclosed. Although the present disclosure may be described with respect to an example autonomous vehicle 900 (referred to herein alternatively as “vehicle 900” or “ego-machine 900”), the examples are referred to herein as Figures 9A-9D), but this is not intended to be limiting. For example, the systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles connected to one or more trailers, spacecraft, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, engineering vehicles, underwater vehicles, drones, and / or other vehicle types. In addition, while the present disclosure may be described with respect to object or feature detection and / or map creation for autonomous machines, this is not intended to be limiting, and the systems and methods described herein may be used for augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and / or any other technology space where object or feature detection and / or map creation may be used.

[0021] For example, (one or more) systems may receive sensor data generated using one or more sensors of one or more vehicles, wherein the sensor data represents at least a portion of an environment. As described herein, the sensor data may include, but is not limited to, image data generated using one or more image sensors (e.g., one or more cameras), LiDAR data generated using one or more LiDAR sensors, RADAR data generated using one or more RADAR sensors, motion data generated using one or more motion sensors (e.g., one or more inertial measurement units (IMU) sensors), and / or any other type of sensor data generated using any other type of sensor. In some examples, the system may then pre-process at least a portion of the sensor data. For example, the system may use the motion data to align points associated with multiple frames of LiDAR data (e.g., multiple rotations of (one or more) LiDAR sensors). Thereafter, the system may use this alignment to generate a point cloud including these points, wherein the density of the points contained in the point cloud increases based on the alignment of the multiple frames. As described in more detail herein, by performing such a process to generate a point cloud, the accuracy associated with determining the location of road markings may be improved.

[0022] The system may then use at least a portion of the data (e.g., the point cloud) to generate one or more images associated with the environment. In some examples, the image may include a top-down (e.g., BEV) image depicting the environment, such as a map. In some examples, such as when a point cloud is used to generate the image(s), the image (e.g., a top-down image) may depict intensities associated with points within the environment. In such examples, because the intensity of the points varies based on one or more factors, such as the composition of the surface associated with the points (e.g., the color of the surface from which light is reflected), the image may indicate the structure of the road marking. For example, if the road marking includes arrows and / or text drawn on the road using a particular color (e.g., white and / or yellow), the image may depict points associated with the road marking as a different color than points associated with other features, such as the road itself (which may be black, gray, etc.).

[0023] The system may then process the image using one or more machine learning models that are trained to determine information associated with an object (e.g., a road sign) depicted by the image. For example, for a road sign depicted by the image, the machine learning model may determine at least the location of the road sign depicted by the image, the location of a boundary shape (e.g., a bounding box) within the image associated with the road sign (e.g., surrounding the road sign), a classification associated with the road sign, and / or any other information. In some examples, the location associated with the road sign may include the location (e.g., coordinates) of a point (e.g., a pixel) that depicts the road sign within the image. In addition, the boundary shape may be associated with vertices (e.g., points (e.g., pixels) of the image) that define the boundary shape structure. In some examples, as described in more detail herein, the boundary shape may include an orientation based at least on a direction of travel associated with a road on which the road sign is located. In addition, the classification may include a road sign such as, but not limited to, a straight arrow, a right arrow, a left arrow, a stop sign, a yield sign, a crosswalk sign, a school zone sign, and / or any other type of road sign.

[0024] The system may then perform one or more processes using the information associated with the road marking. For example, in some examples, the system may use the information to update a map associated with the environment—e.g., by encoding the information into map data. For example, for a road marking, the system may update the map to indicate the location of the road marking and / or the classification associated with the road marking. In such an example, the system may use one or more techniques to update the map using the image. For example, the system may use a boundary shape to determine a portion of the image associated with the road marking, such as by using the locations (e.g., coordinates) of the vertices associated with the boundary shape. Next, the system may determine a portion of the map corresponding to a portion of the image, such as by converting the locations associated with the vertices of the boundary shape to locations on the map. In addition, the system may update the map portion to include information similar to a portion of the image. For example, the system may update (e.g., convert) a point (e.g., pixel) within a map portion to make it similar to a point (e.g., pixel) within a portion of the image. Subsequently, the system may perform a similar process for one or more other road markings identified in the image.

[0025] Additionally, or alternatively, in some examples, for example, if the system is executed on a vehicle that is navigating an environment, the system may use information associated with road markings to determine how to navigate. For example, with respect to road markings, the system may cause the vehicle to navigate according to one or more rules associated with the road markings. For the first example, if the road markings include a stop indicator drawn on the road surface, the system may cause the vehicle to stop at the location indicated by the stop indicator on the road. For the second example, if the road markings include a right arrow again drawn on the road surface, the system may cause the vehicle to turn right while navigating along the road.

[0026] As described herein, a system may process an image using a machine learning model to determine information related to a road marking. Thus, in some examples, a system may use one or more processes for training a machine learning model to determine information. For example, a system may train a machine learning model using training data, such as images depicting road markings located in one or more environments. In some examples, the training data is generated similar to images that are later processed by the machine learning model, such as sensor data (e.g., LiDAR data, etc.) generated using one or more sensors (e.g., one or more LiDAR sensors, etc.) of one or more vehicles. In addition, the system may train the machine learning model using corresponding truth data, such as truth data indicating the location of road markings in the training data, indicating the location of a bounding shape (e.g., a bounding box) associated with the road marking, indicating a classification associated with the road marking, and / or any other information associated with the road marking. Training of a machine learning model is described in more detail herein.

[0027] 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, manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying boats, boats, space shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, engineering vehicles, underwater vehicles, drones, and / or other vehicle types. In addition, the systems and methods described herein may be used for a variety of purposes, such as, but not limited to, machine control, machine motion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and monitoring, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or participant simulation and / or digital twins, data center processing, conversational artificial intelligence (AI), light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation of 3D assets, cloud computing, and / or any other suitable application.

[0028] 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, aviation systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems implementing one or more language models - such as one or more large language models (LLMs), systems including one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least in part in a data center, systems for performing conversational AI operations, systems 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.

[0029] refer to Figure 1 , Figure 1An example data flow diagram of a process 100 for determining information associated with road markings according to some embodiments of the present disclosure is shown. It should be understood that this and other arrangements described herein are set forth by way of example only. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components, or in conjunction with other components and in any suitable combination and location. The various functions described herein as being performed by an entity may be performed by hardware, firmware, and / or software. For example, the various functions may be performed by a processor executing instructions stored in a memory. In some embodiments, the systems, methods, and processes described herein may use Figures 9A-9D Example of autonomous vehicle 900, Fig.10 The example computing device 1000 and / or Fig.11 The example data center 1100 may be performed using components, features, and / or functions similar to the components, features, and / or functions of the example data center 1100.

[0030] The process 100 may include an aggregation component 102 that processes LiDAR data 104 to generate point cloud data 106 representing a 3D point cloud. For example, the LiDAR sensor that generates the LiDAR data 104 may include a given frame rate, such as, but not limited to, 10 frames per second (FPS), 15 FPS, 30 FPS, and / or any other frame rate. Therefore, in order to generate the point cloud data 106, the aggregation component 102 may first use motion data 108 representing vehicle motion generated by one or more motion sensors (e.g., one or more IMU sensors) to align the frames represented by the LiDAR data 104 with each other. Subsequently, the aggregation component 102 may use this alignment to generate the point cloud data 106. By initially aligning multiple frames with each other when generating the point cloud data 106, the 3D point cloud represented by the point cloud data 106 may include a dense distribution of points. As described herein, this can help improve the accuracy and / or precision of determining information related to road markings within the environment.

[0031] In some examples, the LiDAR data 104 and / or the point cloud data 106 may represent an intensity associated with at least a portion of the points. For example, as described herein, a LiDAR sensor used to generate the LiDAR data 104 may measure the intensity of the points as the light returns to the LiDAR sensor. In some examples, the intensity may be represented using a number, such as a number between 0 and 256 (although other ranges may be used in other examples), where the number varies depending on the composition of the surface (e.g., color, texture, material, etc.) on which the light is reflected. For example, a small number may represent low reflectivity, while a large number represents high reflectivity. In some examples, the intensity may depend on other factors, such as the angle of arrival, the range of the points, the moisture content, etc.

[0032] The process 100 may include an image component 110 that processes the point cloud data 106 to generate image data 112. In some examples, the image data 112 may represent one or more images (e.g., one or more maps) that represent an environment associated with the LiDAR data 104 (e.g., surfaces that light reflects off of in the environment). For example, the image data 112 may represent a top-down (BEV) image that represents at least one or more surfaces within the environment. In some examples, the image component 110 may generate the image data 112 using intensities associated with the points, such that the image data 112 represents one or more intensity images, wherein the image represents the environment. As described herein, in such examples, since the intensity of the points may vary based on one or more factors, such as the color of the surface associated with the points (e.g., the color of the surface that light reflects off of), the image may indicate the structure of the road marking. For example, if the road marking includes an arrow drawn on a road surface using a particular color (e.g., white and / or yellow), the image may depict the points associated with the arrow as a different color than points associated with other features (e.g., the road surface itself).

[0033] For example, Figure 2A-2B An example of using LiDAR data (e.g., LiDAR data 104) to generate an image according to some embodiments of the present disclosure is shown. Figure 2AIn the example of FIG. 2 , one or more vehicles may be navigating in environment 202 and generating LiDAR data representative of environment 202. As shown, environment 202 may include at least four different road markings 204(1)-(4) (also referred to as “road markings 204” or “road markings 204”), wherein a first road marking 204(1) includes a right arrow on a first road 206(1) in environment 202, a second road marking 204(2) includes a straight arrow on a second road 206(2) in environment 202, a third road marking 204(3) includes a crosswalk marking on a third road 206(3) in environment 202, and a fourth road marking 204(4) includes a stop indicator on a fourth road 206(4) in environment 202. Although not shown in FIG. 2 , the environment 202 may include at least four different road markings 204(1)-(4) (also referred to as “road markings 204” or “road markings 204”). Figure 2A As shown in the example of FIG. 1 , for clarity, the color of road marking 204 can be different from the color of roads 206 ( 1 )-( 4 ) (also referred to as “road 206 ” in the singular or “roads 206 ” in the plural). For example, road marking 204 can include a lighter color, such as white, while roads 206 include a darker color, such as black.

[0034] Figure 2B An example of an image 208 (e.g., an intensity image) that can be generated using LiDAR data is shown. For example, as described above, road markings 204 can be painted on roads 206(1)-(4) using a particular color (e.g., white) to make road markings 204 more reflective and / or more visible to the driver. Thus, a first point associated with a first road marking 204(1) can be associated with a first intensity 210(1), a second point associated with a second road marking 204(2) can be associated with a second intensity 210(2), a third point associated with a third road marking 204(3) can be associated with a third intensity 210(3), and a fourth point associated with a fourth road marking 204(4) can be associated with a fourth intensity 210(4). Additionally, points associated with other surfaces within environment 202 (e.g., road surface 206) can be associated with other intensities 212. Thus, image 208 can depict points associated with road markings 204 as including a different color than points associated with other surfaces.

[0035] Return to see Figure 1In an example of, process 100 may include one or more machine learning models 114 for processing at least a portion of image data 112 and, based at least on the processing, generating and / or outputting object data 116. As shown, object data 116 may include at least: location data 118 representing one or more locations of one or more road markings depicted by the image; boundary data 120 representing one or more boundary shapes (e.g., one or more bounding boxes) indicating one or more portions of the image associated with the road marking (e.g., depicting the road marking); and classification data 122 representing one or more classifications associated with the road marking. In some examples, the location associated with the road marking may include the location (e.g., coordinates) of a point (e.g., pixel) depicting the road marking within the image. In addition, the boundary shape associated with the road marking may include vertices defining the boundary shape structure, such as points (e.g., pixels) of the image. In some examples, as described in more detail herein, the boundary shape may include an orientation based at least on a direction of travel associated with the road on which the road marking is located. Additionally, the classification associated with the road marking may include the type of road marking, such as, but not limited to, a straight arrow, a right arrow, a left arrow, a stop indicator, a yield indicator, a crosswalk indicator, a school zone indicator, and / or any other type of road marking.

[0036] For example, Figure 3 An example of one or more machine learning models 302 (which may represent and / or include machine learning model 114) processing image data 304 (which may represent and / or include image data 112) to determine information related to road markings in accordance with some embodiments of the present disclosure is shown. The machine learning model(s) 302 may include one or more neural networks that perform one or more processes described herein. In some examples, the neural network(s) may include or be referred to as a convolutional neural network, and thus may be referred to herein alternatively as a convolutional neural network, a convolutional network, or a CNN. In some examples, the neural network may include any type of neural network capable of performing the processes described herein.

[0037] The machine learning model 302 can use image data 304 (with or without preprocessing) as input. The image data 304 can represent one or more images (e.g., one or more maps) that represent the environment associated with the LiDAR data (e.g., surfaces within the environment that reflect light). For example, the image data 304 can represent a top-down (BEV) image that represents at least one or more surfaces within the environment. In some examples, the image data 304 can be generated using intensities associated with points so that the image data 304 represents one or more intensity images representing the environment. In some examples, the image data 304 can be input as a single image, or can be input using batch processing (e.g., mini-batch processing). For example, two or more images can be used as input together (e.g., simultaneously).

[0038] The image data 304 can be input into one or more feature extraction layers 306 of the machine learning model 302. The feature extraction layers 306 can include any number of layers, such as layers 306-306C. One or more layers 306 can include an input layer. The input layer can store values ​​associated with the image data 304. For example, when the image data 304 represents (one or more) images, the input layer can store values ​​representing the raw pixel values ​​of the image as a volume (e.g., width W, height H, and color channels C (e.g., RGB), for example, the volume is 32×32×3) and / or a batch size B (e.g., when batching is used).

[0039] One or more layers 306 may include a convolutional layer. A convolutional layer may compute the output of neurons connected to local regions in an input layer (e.g., the input layer described above), with each neuron computing a dot product between its weights and the small region they are connected to in the input volume. The result of a convolutional layer may be another volume with one dimension based on the number of filters applied (e.g., width, height, and number of filters, if the number of filters is 12, the result is, for example, 32×32×12).

[0040] One or more layers 306 may include a rectified linear unit (ReLU) layer. The (one or more) ReLU layer may apply an element-wise activation function, such as max(0, x), such as thresholding to zero. The resulting volume of the ReLU layer may be the same as the input volume of the ReLU layer.

[0041] One or more layers 306 may include a pooling layer. The pooling layer may perform a downsampling operation along the spatial dimensions (e.g., height and width), which may result in a smaller volume than the input of the pooling layer (e.g., from an input volume of 32×32×12 to 16×16×12). In some examples, the machine learning model 302 may not include any pooling layers. In such examples, other types of convolutional layers may be used instead of pooling layers. In some examples, the feature extraction layer 306 may include alternating convolutional layers and pooling layers.

[0042] One or more layers 306 may include a fully connected layer. Each neuron in the (one or more) fully connected layer may be connected to each neuron in the previous volume. The fully connected layer may calculate the class scores, and the resulting volume may be 1×1×N (where N is the number of classes). In some examples, the feature extraction layer 306 may include a fully connected layer, while in other examples, the fully connected layer of the machine learning model 302 may be a fully connected layer separate from the feature extraction layer 306. In some examples, the feature extraction layer 306 and / or the entire machine learning model 302 may not use a fully connected layer to increase processing time and reduce computing resource requirements. In such examples, if a fully connected layer is not used, the machine learning model 302 may be referred to as a fully convolutional network.

[0043] In some examples, one or more layers 306 can include (one or more) deconvolution layers. However, the use of the term "deconvolution" can be misleading and is not intended to be limiting. For example, a deconvolution layer can alternatively be referred to as a transposed convolution layer or a fractionally strided convolution layer. A deconvolution layer can be used to perform upsampling on the output of a previous layer. For example, a deconvolution layer can be used to upsample to a spatial resolution equal to the spatial resolution of an input image to the machine learning model 302, or to upsample to the input spatial resolution of a next layer.

[0044] Although input layers, convolutional layers, pooling layers, ReLU layers, deconvolutional layers, and fully connected layers are discussed herein with respect to feature extraction layer 306, this is not intended to be limiting. For example, additional or alternative layers 306 may be used in feature extraction layer 306, such as normalization layers, SoftMax layers, and / or other layer types.

[0045] The output of the feature extraction layer 306 can be the input of one or more information layers 308. The information layer 308-C can use one or more layer types described herein with respect to the feature extraction layer 306. As described herein, in some examples, the information layer 308 may not include any fully connected layers to reduce processing speed and reduce computing resource requirements. In such examples, the information layer 308 may be referred to as a fully convolutional layer.

[0046] Depending on the embodiment, different orders and numbers of layers 306 and layers 308 of machine learning model 302 may be used. For example, when two or more cameras or other sensor types are used to generate input, the order and number of layers 306 and 308 may be different. As another example, different ordered and numbered layers may be used depending on the type of sensor used to generate image data 304 or the type of image data 304 (e.g., RGB, YUV, etc.). Therefore, the order and number of layers 306 and 308 of machine learning model 302 are not limited to any one architecture.

[0047] In addition, some of the layers 306 and 308 may include parameters (e.g., weights and / or biases) - such as the feature extraction layer 306 and / or the information layer 308 - while other layers may not include parameters, such as ReLU layers and pooling layers. In some examples, the machine learning model 302 may learn parameters during training. In addition, some of the layers 306 and 308 may include additional hyperparameters (e.g., learning rate, step size, epoch, kernel size, number of filters, pooling type of pooling layer, etc.) - such as convolutional layers, deconvolutional layers, and pooling layers - while other layers may not include additional hyperparameters, such as ReLU layers. Various activation functions may be used, including, but not limited to, ReLU functions, leakyReLU functions, sigmoid functions, hyperbolic tangent (tan h) functions, exponential linear units (ELUs), etc. Parameters, hyperparameters, and / or activation functions are not limited and may vary from embodiment to embodiment.

[0048] In any examples, the output of the machine learning model 302 may include object data 310 (which may represent and / or include the object data 116).

[0049] Figure 4 An example output 402 of a machine learning model 114 (and / or machine learning model 302) trained to determine information associated with road markings according to some embodiments of the present disclosure is shown. As shown, output 402 may include at least boundary shapes 404(1)-(4) associated with road markings 204 (also referred to as "boundary shape 404" in the singular or "boundary shapes 404" in the plural). For example, output 402 may include: a first boundary shape 404(1) indicating a first portion of image 208 associated with first road marking 204(1), a second boundary shape 404(2) indicating a second portion of image 208 associated with second road marking 204(2), a third boundary shape 404(3) indicating a third portion of image 208 associated with third road marking 204(3), and a fourth boundary shape 404(4) indicating a fourth portion of image 208 associated with fourth road marking 204(4). Although Figure 4The example of FIG. 404 shows the boundary shape 404 as including a box (e.g., a rectangle), but in other examples, the boundary shape can include any other type of shape. For example, the boundary shape can include a circle, a triangle, a pentagon, a hexagon, an octagon, and / or any other shape.

[0050] In some examples, boundary shapes 404 may include vertices and / or be defined using vertices. For example, for first boundary shape 404(1) (and / or similarly for other boundary shapes 404(2)-(4), which are not shown for clarity), first boundary shape 404(1) may include vertices 406(1)-(4) (also referred to as "vertex 406" in the singular or "vertices 406" in the plural). In some examples, vertex 406 may be associated with a location within image 208, such as coordinates (e.g., x-coordinates, y-coordinates, etc.) indicating the location of the vertex within image 208. Thus, as described herein, the actual output of machine learning model 114 may include the location of vertex 406 and / or the location of vertex 406 may be used in updating a map associated with environment 202 to include information associated with first road marking 204(1).

[0051] In some examples, such as Figure 4 As shown in the examples of FIG. 4 , boundary shape 404 may include an orientation based at least on a direction of travel associated with road 206 on which road marker 204 is located. For the first example, third boundary shape 404(3) may be oriented based at least on a direction of travel 408(1) associated with third road 206(3) on which third road marker 204(3) is located. For example, as shown, third boundary shape 404(3) is oriented orthogonal to direction of travel 408(1) such that one side of third boundary shape 404(3) is perpendicular to direction of travel 408(1). For the second example, fourth boundary shape 404(4) may be oriented based at least on a direction of travel 408(2) associated with fourth road 206(4) on which fourth road marker 204(4) is located. For example, as shown, fourth boundary shape 404(4) is oriented orthogonal to direction of travel 408(2) such that one side of fourth boundary shape 404(4) is perpendicular to direction of travel 408(2). In some examples, boundary shape 404 can include such an orientation to better define the portion of image 208 associated with road marking 204 , as road marking 204 also tends to be oriented based on the direction of travel of road 206 .

[0052] Return to see Figure 1In an example of, process 100 may include a map component 124 that uses object data 116 to generate and / or update a map associated with an environment, where the map is represented by map data 126. For example, map component 124 may update a map to include information associated with a road marker, such as, but not limited to, a location of the road marker within the environment and / or a classification associated with the road marker. In some examples, map component 124 may update the location of the road marker within the map using a boundary shape associated with the road marker. For example, for a road marker, map component 124 may use the location of the boundary shape within the image (e.g., the location of the vertices, the location of the pixels within the boundary shape, etc.) to determine a corresponding location of the map (e.g., a corresponding pixel). Map component 124 may then convert the corresponding location of the map to be similar to the location of the image. In other words, map component 124 may convert a portion of a map that depicts a portion of an environment that is similar to a boundary shape in the image to be similar to that portion of the image.

[0053] For example, Figure 5 An example visualization of updating a map 502 to include information associated with a road marking 204 is shown in accordance with some embodiments of the present disclosure. As shown, the map 502 may represent at least an environment 202 including a road marking 204 associated with a road 206. Accordingly, the map component 124 may perform one or more processes to determine that a first portion 504(1) of the map 502 corresponds to a first boundary shape 404(1), a second portion 504(2) of the map 502 corresponds to a second boundary shape 404(2), a third portion 504(3) of the map 502 corresponds to a third boundary shape 404(3), and a fourth portion 504(4) of the map 502 corresponds to a fourth boundary shape 404(4). In some examples, the map component 124 uses vertices associated with the boundary shapes 404 to make the above determinations. For example, for the first boundary shape 404(1), the map component 124 may determine that the vertices 406 associated with the boundary shape 404 correspond to points 506(1)-(4) (e.g., pixels) of the map 502. Map component 124 may then determine first portion 504(1) using points 506(1)-(4).

[0054] The map component 124 may then use the correspondence to update the map 502. For example, as shown, the map component 124 may update (e.g., transform) the first portion 504(1) of the map 502 to resemble the portion of the image 208 associated with the first boundary shape 404(1), such that the first portion 504(1) of the map 502 includes a first representation 508(1) of the first road marker 204(1). The map component 124 may also update (e.g., transform) the second portion 504(2) of the map 502 to resemble the portion of the image 208 associated with the second boundary shape 404(2), such that the second portion 504(2) of the map 502 includes a second representation 508(2) of the second road marker 204(2). In addition, the map component 124 may update (e.g., transform) the third portion 504(3) of the map 502 to resemble the portion of the image 208 associated with the third boundary shape 404(3), such that the third portion 504(3) of the map 502 includes a third representation 508(3) of the third road marker 204(3). In addition, the map component 124 may update (e.g., transform) the fourth portion 504(4) of the map 502 to resemble the portion of the image 208 associated with the fourth boundary shape 404(4), such that the fourth portion 504(4) of the map 502 includes a fourth representation 508(4) of the fourth road marker 204(4).

[0055] Return to see Figure 1 In some examples, process 100 may include performing one or more additional and / or alternative processes using object data 116. For example, a vehicle navigating in an environment may use object data 116 to determine one or more navigation operations. For example, the vehicle may use object data 116 to determine at least the location of road markings within the environment and the classification associated with the road markings. The vehicle may then navigate based on the location and / or classification, such as by complying with one or more rules associated with the road markings.

[0056] See now Figure 6 , Figure 6is a data flow diagram illustrating a process 600 for training a machine learning model 114 (and / or similarly, a machine learning model 302) to determine information associated with road markings in accordance with some embodiments of the present disclosure. As shown, the machine learning model 114 may be trained using image data 602 (e.g., training image data). The image data 602 may represent one or more images (e.g., one or more maps) that represent an environment associated with LiDAR data (e.g., surfaces within the environment that reflect light), similar to the image data 112. For example, the image data 602 may represent a top-down (BEV) image that represents at least one or more surfaces within the environment. In some examples, the image data 602 may be generated using intensities associated with points such that the image data 602 represents one or more intensity images representing the environment. For example, the image data 602 may represent a Figure 2B An image of image 208 in the example.

[0057] The machine learning model 114 may be trained using training image data 602 and corresponding ground truth data 604. The ground truth data 604 may include annotations, labels, masks, and the like. For example, in some embodiments, the ground truth data 604 may include at least the location 606 of a road marking within the image, a boundary shape 608 associated with the road marking within the image, and / or a classification 610 associated with the road marking within the image. The ground truth data 604 may be generated in a drawing program (e.g., an annotation program), a computer-aided design (CAD) program, a labeling program, another type of program suitable for generating ground truth data 604, and / or in some examples may be hand-drawn. In any example, the ground truth data 604 may be synthetically generated (e.g., generated from a computer model or rendering), truly generated (e.g., designed and generated from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from the data and then generate labels), manually annotated (e.g., a labeler or annotation expert defines the location of the labels), and / or a combination of the above (e.g., a human identifies the vertices of a polyline and a machine generates polygons using a polygon rasterizer). In some examples, for each input image, there may be corresponding ground truth data 604 .

[0058] The training engine 612 can use one or more loss functions to measure the loss (e.g., error) of the output 614 (which may include object data similar to the object data 116) compared to the true value data 604. Any type of loss function can be used, such as cross entropy loss, mean square error, mean absolute error, mean deviation error, and / or other loss function types. In some examples, different outputs 614 may have different loss functions. For example, the position output may have a first loss function, the boundary shape output may have a second loss function, and / or the classification output may have a third loss function. In such examples, the loss functions can be combined to form a total loss, and the total loss can be used to train the machine learning model 114 (e.g., update the parameters of the machine learning model 114). In any example, a back propagation calculation can be performed to recursively calculate the gradient of the loss function relative to the training parameters. In some examples, the weights and biases of the machine learning model 114 can be used to calculate these gradients.

[0059] Reference now Figure 7 and Figure 8 , each block of methods 700 and 800 described herein includes a computing process that can be performed using any combination of hardware, firmware, and / or software. For example, various functions can be performed by a processor executing instructions stored in a memory. Methods 700 and 800 can also be embodied as computer-usable instructions stored on a computer storage medium. Methods 700 and 800 can be provided by a stand-alone application, a service or a hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, methods 700 and 800 are directed to Figure 1 However, these methods 700 and 800 may additionally or alternatively be performed by any one system or any combination of systems, including but not limited to the systems described herein.

[0060] Figure 7A flowchart of a first method 700 for updating a map using LiDAR data to indicate the location of road markings within an environment according to some embodiments of the present disclosure is shown. At block B702, method 700 may include: generating an image representing at least a portion of the environment based at least on the LiDAR data. For example, image component 110 may process point cloud data 106, wherein point cloud data 106 is generated using LiDAR data 104. As described herein, in some examples, LiDAR data 104 may be generated using one or more LiDAR sensors of one or more machines navigating in the environment. Based at least on this processing, image component 110 may generate image data 112 representing an image. As described herein, in some examples, an image may include a top-down (BEV) image depicting at least the portion of the environment. Additionally or alternatively, in some examples, an image may include an intensity image depicting at least the portion of the environment.

[0061] At block B704, method 700 may include determining, using one or more machine learning models and based at least on the image, a location associated with a road marking within an environment. For example, machine learning model 114 may process image data 112 representing at least an image. Based at least on the processing, machine learning model 114 may output object data 116 associated with the road marking. As described herein, object data 116 may include at least location data 118 representing a location of a road marking within the image, boundary data 120 representing a boundary shape within the image associated with the road marking, and classification data 122 representing a classification associated with the road marking.

[0062] At block B706, method 700 may include causing a map to indicate a location associated with a road marker within the environment. For example, map component 124 may use object data 116 to update a map, where the map is represented by map data 126. As described herein, map component 124 may update the map to indicate at least a location of a road marker within the environment and / or a classification associated with the road marker. For example, map component 124 may use a boundary shape associated with the road marker from an image to convert a corresponding portion of the map to include a representation of the road marker.

[0063] Figure 8A flowchart of a second method 800 for updating a map to indicate the location of road markings within an environment using LiDAR data according to some embodiments of the present disclosure is shown. At block B802, method 800 may include: generating an image representing at least a portion of the environment based at least on the LiDAR data. For example, image component 110 may process point cloud data 106, wherein point cloud data 106 is generated using LiDAR data 104. As described herein, in some examples, LiDAR data 104 may be generated using one or more LiDAR sensors of one or more machines navigating in the environment. Based at least on this processing, image component 110 may generate image data 112 representing an image. As described herein, in some examples, the image may include a top-down (BEV) image depicting at least the portion of the environment. Additionally, or alternatively, in some examples, the image may include an intensity image depicting at least the portion of the environment.

[0064] At block B804, method 800 may include determining, using one or more machine learning models and based at least on the image, that a portion of the image depicts a road marking. For example, machine learning model 114 may process image data 112 representing at least the image. Based at least on the processing, machine learning model 114 may output object data 116 associated with the road marking. As described herein, object data 116 may include at least location data 118 representing a first portion of the image (e.g., a first pixel) depicting a road marking and / or boundary data 120 representing a second portion of the image (e.g., a second pixel) associated with the road marking. In some examples, object data 116 may also include classification data 122 representing a classification associated with the road marking.

[0065] At block B806, method 800 may include determining that a portion of the image corresponds to a portion of a map. For example, map component 124 may use object data 116 to determine that a portion of the image corresponds to a portion of a map, where the map is represented by map data 126. In some examples, map component 124 uses one or more locations (e.g., one or more pixel locations and / or one or more coordinates) associated with a portion of the image to make the determination. For example, map component 124 may determine that one or more pixel locations from the image correspond to one or more pixel locations from the map.

[0066] At block B808, method 800 may include updating a portion of the map to depict road markings. For example, map component 124 may then update a portion of the map to depict road markings. In some examples, updating the map may include converting one or more pixels associated with the portion of the map to be similar to one or more pixels associated with the image.

[0067] Example Autonomous Vehicle

[0068] Fig. 9A is an illustration of an example autonomous vehicle 900 according to some embodiments of the present disclosure. Autonomous vehicle 900 (also referred to herein as “vehicle 900”) 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 vehicle, a robotic vehicle, a drone, an airplane, a vehicle connected to a trailer (e.g., a semi-trailer truck for hauling cargo), and / or other types of vehicles (e.g., unmanned and / or capable of accommodating one or more passengers). Autonomous vehicles are generally described according to the automation levels defined by the National Highway Traffic Safety Administration (NHTSA) of the U.S. Department of Transportation and the Society of Automotive Engineers (SAE) in the “Classification and Definition of Terms Related to Driving Automation Systems for Road Motor Vehicles” (Standard No. J3016-201806 issued on June 15, 2018, Standard No. J3016-201609 issued on September 30, 2016, and previous and future versions of the standard). The vehicle 900 may be capable of having one or more functions according to levels 3-5 of autonomous driving levels. The vehicle 900 may be capable of functioning according to one or more of levels 1-5 of autonomous driving levels. For example, the vehicle 900 may be capable of providing driver assistance (level 1), partial automation (level 2), conditional automation (level 3), high automation (level 4) and / or full automation (level 5), depending on the embodiment. As used herein, the term "autonomy" may include any and / or all types of autonomy of the vehicle 900 or other machines, such as full autonomy, high autonomy, conditional autonomy, partial autonomy, providing assisted autonomy, semi-autonomy, main autonomy or other names.

[0069] The vehicle 900 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. The vehicle 900 may include a propulsion system 950, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. The propulsion system 950 may be connected to a drive train of the vehicle 900, which may include a transmission, to achieve propulsion of the vehicle 900. The propulsion system 950 may be controlled in response to receiving a signal from a throttle / accelerator 952.

[0070] A steering system 954, which may include a steering wheel, may be used to steer the vehicle 900 (e.g., along a desired path or route) when the propulsion system 950 is operating (e.g., when the vehicle is in motion). The steering system 954 may receive signals from a steering actuator 956. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0071] Brake sensor system 946 may be used to operate vehicle brakes in response to receiving signals from brake actuator 948 and / or brake sensors.

[0072] May include one or more system on chip (SoC) 904 ( Fig. 9C ) and / or one or more controllers 936 of one or more GPUs may provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 900. For example, the one or more controllers may send signals to operate vehicle brakes via one or more brake actuators 948, to operate a steering system 954 via one or more steering actuators 956, and to operate a propulsion system 950 via one or more throttles / accelerators 952. The one or more controllers 936 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 900. The one or more controllers 936 may include a first controller 936 for autonomous driving functions, a second controller 936 for functional safety functions, a third controller 936 for artificial intelligence functions (e.g., computer vision), a fourth controller 936 for infotainment functions, a fifth controller 936 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 936 may handle two or more of the above functions, two or more controllers 936 may handle a single function, and / or any combination thereof.

[0073] The one or more controllers 936 may provide signals for controlling one or more components and / or systems of the vehicle 900 in response to sensor data (e.g., sensor input) received from one or more sensors. The sensor data may be received from, for example and without limitation, a global navigation satellite system (GNSS) sensor 958 (e.g., a global positioning system sensor), a RADAR sensor 960, an ultrasonic sensor 962, a LiDAR sensor 964, an inertial measurement unit (IMU) sensor 966 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 996, a stereo camera 968, a wide angle camera 970 (e.g., a fisheye camera), an infrared camera 972, a surround camera 974 (e.g., a 360 degree camera), a long-range and / or mid-range camera 998, a speed sensor 944 (e.g., for measuring the velocity of the vehicle 900), a vibration sensor 942, a steering sensor 940, a brake sensor (e.g., as part of a brake sensor system 946), and / or other sensor types.

[0074] One or more of the controllers 936 may receive inputs (e.g., represented by input data) from the instrument cluster 932 of the vehicle 900 and provide outputs (e.g., represented by output data, display data, etc.) via a human machine interface (HMI) display 934, an audible annunciator, a speaker, and / or via other components of the vehicle 900. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Fig. 9C The HMI display 934 may include information such as a high-definition (HD) map 922 of the vehicle 900 , location data (e.g., the location of the vehicle 900 on the map), directions, locations of other vehicles (e.g., an occupancy grid), information about objects and states of objects as sensed by the controller 936 , and the like. For example, the HMI display 934 may display information about the presence of one or more objects (e.g., street signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that the vehicle has made, is making, or will make (e.g., changing lanes now, leaving 34B in two miles, etc.).

[0075] The vehicle 900 further includes a network interface 924 that can communicate via one or more networks using one or more wireless antennas 926 and / or a modem. For example, the network interface 924 may be capable of communicating via Long Term Evolution (LTE), Wideband Code Division Multiple Access (WCDMA), Universal Mobile Telecommunications System (UMTS), Global System for Mobile Communications (GSM), IMT-CDMA Multi-Carrier (CDMA2000), etc. The one or more wireless antennas 926 may also enable communication between objects (e.g., vehicles, mobile devices, etc.) in the environment using one or more local area networks such as Bluetooth, Low Energy (LE) Bluetooth, Z-wave, ZigBee, etc. and / or one or more low power wide area networks (LPWAN) such as LoRaWAN, SigFox, etc.

[0076] Fig. 9B For use according to some embodiments of the present disclosure Fig. 9A An example of camera positions and fields of view for an example autonomous vehicle 900 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 900.

[0077] The camera type for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 900. 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 transparent (RCCC) color filter array, a red transparent blue (RCCB) color filter array, a red blue green transparent (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensor (RGGB) color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In some embodiments, a clear pixel camera such as a camera with an RCCC, RCCB, and / or RBGC color filter array may be used in an effort to improve light sensitivity.

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

[0079] One or more of the cameras may be mounted in a mounting assembly such as a custom designed (three-dimensional (3D) printed) assembly to cut 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 3D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras may be integrated into the wing mirror. For side view cameras, one or more cameras may also be integrated into the four pillars at each corner of the cab.

[0080] A camera (e.g., a front-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 900 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 936 and / or control SoCs. The front-facing camera can be used to perform many of the same ADAS functions as LiDAR, including emergency braking, pedestrian detection, and collision avoidance. The front-facing camera can also be used for ADAS functions and systems, including lane departure warning ("LDW"), autonomous cruise control ("ACC"), and / or other functions such as traffic sign recognition.

[0081] A variety of cameras may be used in the front-facing configuration, including, for example, a monocular camera platform including a complementary metal oxide semiconductor (CMOS) color imager. Another example may be a wide-angle camera 970, which may be used to sense objects (e.g., pedestrians, intersection traffic, or bicycles) entering the field of view from the periphery. Fig. 9B Only one wide-angle camera is shown in the figure, but there may be any number (including 0) of wide-angle cameras 970 on the vehicle 900. In addition, any number of long-range cameras 998 (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not yet been trained. Long-range cameras 998 may also be used for object detection and classification and basic object tracking.

[0082] Any number of stereo cameras 968 may also be included in the front configuration. In at least one embodiment, one or more stereo cameras 968 may include an integrated control unit including an expandable processing unit that may provide a multi-core microprocessor and programmable logic (FPGA) with an integrated controller area network (CAN) or Ethernet interface on a single chip. Such a unit can be used to generate a 3D map of the vehicle environment, including distance estimates for all points in the image. Alternative stereo cameras 968 may include a compact stereo vision sensor that may include two camera lenses (one on each side) and an image processing chip that can 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 968 may be used.

[0083] A camera having a field of view of a portion of the environment including the sides of the vehicle 900 (e.g., a side view camera) can be used for surround viewing, providing information used to create and update the occupancy grid and generate side impact collision warnings. Fig. 9B The four surround cameras 974 shown in FIG. 9 can be placed on the vehicle 900. The surround cameras 974 can include a wide-angle camera 970, a fisheye camera, a 360-degree camera, and / or the like. For example, the four fisheye cameras can be placed in front, behind, and on the sides of the vehicle. In an alternative arrangement, the vehicle can use three surround cameras 974 (e.g., left, right, and rear), and can utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0084] A camera having a field of view that includes a portion of the environment behind the vehicle 900 (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 cameras as described herein (e.g., long-range and / or mid-range cameras 998, stereo cameras 968, infrared cameras 972, etc.).

[0085] Fig. 9C For use according to some embodiments of the present disclosure Fig. 9A Block diagram of an example system architecture for an example autonomous vehicle 900. It should be understood that this arrangement and other arrangements described herein are set forth merely as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any appropriate combination and location. The various functions described herein as being performed by an entity may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory.

[0086] Fig. 9C Each of the components, features, and systems of the vehicle 900 is illustrated as being connected via a bus 902. The bus 902 may include a controller area network (CAN) data interface (alternatively, referred to herein as a "CAN bus"). The CAN may be a network inside the vehicle 900 that assists in controlling various features and functions of the vehicle 900, such as the actuation of brakes, acceleration, braking, steering, windshield wipers, and the like. The CAN bus may be configured to have tens or even hundreds of nodes, each with its own unique identifier (e.g., a CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compatible.

[0087] Although bus 902 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 902 is represented by a single line, this is not intended to be limiting. For example, there can be any number of buses 902, 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 902 can be used to perform different functions, and / or can be used for redundancy. For example, a first bus 902 can be used for a collision avoidance function, and a second bus 902 can be used for drive control. In any example, each bus 902 can communicate with any component of the vehicle 900, and two or more buses 902 can communicate with the same component. In some examples, each SoC 904, each controller 936, and / or each computer in the vehicle can have access to the same input data (e.g., input from a sensor of the vehicle 900), and can be connected to a common bus such as a CAN bus.

[0088] The vehicle 900 may include one or more controllers 936, such as those described herein. Fig. 9A Those controllers described. Controller 936 can be used for a variety of functions. Controller 936 can be coupled to any other different components and systems of vehicle 900, and can be used for control of vehicle 900, artificial intelligence of vehicle 900, infotainment for vehicle 900, and / or the like.

[0089] The vehicle 900 may include one or more system on chip (SoC) 904. The SoC 904 may include a CPU 906, a GPU 908, a processor 910, a cache 912, an accelerator 914, a data store 916, and / or other components and features not shown. The SoC 904 may be used to control the vehicle 900 in a variety of platforms and systems. For example, one or more SoCs 904 may be combined with an HD map 922 in a system (e.g., a system of the vehicle 900), and the HD map may be downloaded from one or more servers (e.g., a server) via a network interface 924. Fig.9D one or more servers 978) to obtain map refreshes and / or updates.

[0090] The CPU 906 may include a CPU cluster or CPU complex (alternatively, referred to herein as a "CCPLEX"). The CPU 906 may include multiple cores and / or L2 caches. For example, in some embodiments, the CPU 906 may include eight cores in a coherent multiprocessor configuration. In some embodiments, the CPU 906 may include four dual-core clusters, each of which has a dedicated L2 cache (e.g., a 2MB L2 cache). The CPU 906 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, such that any combination of CPU 906 clusters can be active at any given time.

[0091] The CPU 906 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. The CPU 906 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wake-up times are specified, and hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core may support a simplified power state entry sequence in software, with the work being offloaded to the microcode.

[0092] The GPU 908 may include an integrated GPU (alternatively, referred to herein as an "iGPU"). The GPU 908 may be programmable and efficient for parallel workloads. In some examples, the GPU 908 may use an enhanced tensor instruction set. The GPU 908 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB storage capacity). In some embodiments, the GPU 908 may include at least eight streaming microprocessors. The GPU 908 may use a computing application programming interface (API). In addition, the GPU 908 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0093] In the case of automotive and embedded use, GPU 908 can be power optimized to achieve optimal performance. For example, GPU 908 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 908 can be manufactured using other semiconductor manufacturing processes. Each streaming microprocessor can merge several mixed precision processing cores divided into multiple blocks. For example and without limitation, 64 PF32 cores and 32 PF64 cores can be divided into four processing blocks. In such an example, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed precision NVIDIA tensor cores for deep learning matrix arithmetic, L0 instruction cache, thread beam (warp) scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor can include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of calculations and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. A streaming microprocessor may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0094] GPU 908 may 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), may be used in addition to or in lieu of HBM memory.

[0095] The GPU 908 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 may be used to allow the GPU 908 to directly access the CPU 906 page table. In such an example, when the GPU 908 memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU 906. In response, the CPU 906 may look up the virtual-physical mapping for the address in its page table and transmit the translation back to the GPU 908. In this way, unified memory technology may allow a single unified virtual address space to be used for memory of both the CPU 906 and the GPU 908, thereby simplifying GPU 908 programming and porting applications to the GPU 908.

[0096] In addition, GPU 908 may include access counters that can track how often GPU 908 accesses the memory of other processors. The access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0097] SoC 904 may include any number of caches 912, including those described herein. For example, cache 912 may include an L3 cache available to both CPU 906 and GPU 908 (e.g., connected to both CPU 906 and GPU 908). Cache 912 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4MB or more, although smaller cache sizes may also be used.

[0098] The SoC 904 may include an arithmetic logic unit (ALU) that may be used to perform processing for any of a variety of tasks or operations with respect to the vehicle 900 - such as processing a DNN. In addition, the SoC 904 may include a floating point unit (FPU) - or other math coprocessor or digital coprocessor type - for performing mathematical operations within the system. For example, the SoC 104 may include one or more FPUs integrated as execution units within the CPU 906 and / or GPU 908.

[0099] SoC 904 may include one or more accelerators 914 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, SoC 904 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 908 and offload some tasks of GPU 908 (e.g., free up more cycles of GPU 908 for performing other tasks). As an example, accelerator 914 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are sufficiently stable to be easily controlled for acceleration. When used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0100] Accelerator 914 (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.

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

[0102] The DLA can perform any function of the GPU 908, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 908. For example, the designer can focus the processing of CNNs and floating point operations on the DLA, and leave other functions to the GPU 908 and / or other accelerators 914.

[0103] The accelerator 914 (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.

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

[0105] The DMA may enable components of the PVA to access system memory independently of the CPU 906. The DMA may support any number of features used to provide optimizations for the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support addressing in up to six or more dimensions, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

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

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

[0108] The accelerator 914 (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 914. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and not limited to, eight field-configurable memory blocks that can be accessed by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, 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).

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

[0110] In some examples, SoC 904 may include a real-time ray tracing hardware accelerator such as described in U.S. Patent Application No. 16 / 101,232 filed on August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for SONAR system simulations, for general wave propagation simulations, for comparison with LiDAR data for the purpose of positioning and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0111] The accelerator 914 (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-intensive or intensive 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.

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

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

[0114] DLA can be used to run any type of network to enhance control and driving safety, including, for example, a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability, or as providing a relative "weight" of each detection compared to other detections. This confidence value enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positive detections. In an automatic emergency braking (AEB) system, a false positive detection will cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detection should be considered a trigger for AEB. DLA can run a neural network for regressing confidence values. The neural network can take at least some subset of parameters as its input, such as bounding box dimensions, ground plane estimates obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 966 output related to vehicle 900 orientation and distance, 3D position estimates of objects obtained from neural networks and / or other sensors (e.g., LiDAR sensor 964 or RADAR sensor 960), etc.

[0115] SoC 904 may include one or more data stores 916 (e.g., memory). Data store 916 may be on-chip memory of SoC 904 that may store neural networks to be executed on the GPU and / or DLA. In some examples, data store 916 may be large enough to store multiple instances of the neural network for redundancy and safety. Data store 912 may include an L2 or L3 cache 912. References to data store 916 may include references to memory associated with a PVA, DLA, and / or other accelerator 914 as described herein.

[0116] SoC 904 may include one or more processors 910 (e.g., embedded processors). Processor 910 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 904 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 904 thermal and temperature sensor management, and / or SoC 904 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC 904 may use the ring oscillator to detect the temperature of CPU 906, GPU 908, and / or accelerator 914. 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 904 in a lower power state and / or place the vehicle 900 in a driver safety parking mode (e.g., parking the vehicle 900 safely).

[0117] The processor 910 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.

[0118] The processor 910 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.

[0119] The processor 910 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.

[0120] Processor 910 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0121] Processor 910 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.

[0122] The processor 910 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 a video playback application to produce a final image for a player window. The video image compositer may perform lens distortion correction for the wide-angle camera 970, the surround camera 974, and / or for an in-cab surveillance camera sensor. The in-cab surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC, configured to recognize in-cab events and respond accordingly. The in-cab system may perform lip reading to activate mobile phone service and place calls, dictate emails, change vehicle destinations, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other circumstances.

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

[0124] The video image compositor may also be configured to perform stereoscopic rectification on the input stereoscopic footage frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use and the GPU 908 does not need to continuously render new surfaces. Even when the GPU 908 is powered on and active for 3D rendering, the video image compositor may be used to offload the GPU 908 to improve performance and responsiveness.

[0125] The SoC 904 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. The SoC 904 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.

[0126] SoC 904 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management and / or other devices. SoC 904 may be used to process data from cameras (connected via Gigabit multimedia serial link and Ethernet), sensors (e.g., LiDAR sensor 964, RADAR sensor 960, etc., which may be connected via Ethernet), data from bus 902 (e.g., speed of vehicle 900, steering wheel position, etc.), data from GNSS sensor 958 (connected via Ethernet or CAN bus). SoC 904 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines, and which may be used to release CPU 906 from routine data management tasks.

[0127] SoC 904 can be an end-to-end platform with a flexible architecture that spans automation levels 3-5, thereby providing a comprehensive functional safety architecture that utilizes 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 904 can be faster, more reliable, and even more energy efficient and space efficient than conventional systems. For example, when combined with CPU 906, GPU 908, and data storage 916, accelerator 914 can provide a fast and efficient platform for level 3-5 autonomous vehicles.

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

[0129] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a 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 920) 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.

[0130] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks may be running simultaneously. For example, a warning sign consisting of "Caution: Flashing Lights Indicate Icing Conditions" along with electric lights may be interpreted by several neural networks independently or collectively. The sign itself may be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing Lights Indicate Icing Conditions" may be interpreted by a deployed second neural network that informs the vehicle's path planning software (preferably executing on a CPU complex) that icing conditions exist when the flashing lights are detected. The flashing lights may be recognized 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 908.

[0131] In some examples, a CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 900. 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 904 provides security against theft and / or carjacking.

[0132] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 996 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 904 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 958. 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 962, the control program can be used to execute emergency vehicle safety routines to slow the vehicle, drive to the side of the road, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0133] The vehicle may include a CPU 918 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 904 via a high-speed interconnect (e.g., PCIe). The CPU 918 may include, for example, an X86 processor. The CPU 918 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 904, and / or monitoring the status and health of the controller 936 and / or the infotainment SoC 930.

[0134] The vehicle 900 may include a GPU 920 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 904 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 920 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 input from sensors of the vehicle 900 (e.g., sensor data).

[0135] The vehicle 900 may further include a network interface 924, which may include one or more wireless antennas 926 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). The network interface 924 can be used to enable wireless connection with the cloud (e.g., with a server 978 and / or other network devices), with other vehicles, and / or with a computing device (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 900 with information about vehicles approaching the vehicle 900 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 900). This functionality can be part of the collaborative adaptive cruise control functionality of the vehicle 900.

[0136] The network interface 924 may include a SoC that provides modulation and demodulation functions and enables the controller 936 to communicate via a wireless network. The network interface 924 may include a radio frequency front end for up-conversion from baseband to radio frequency and down-conversion from radio frequency to baseband. Frequency conversion may be performed by a known process, and / or may be performed using a super-heterodyne process. In some examples, the radio frequency front end function may be provided by a separate chip. The network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0137] The vehicle 900 may further include data storage 928, which may include off-chip storage (e.g., outside the SoC 904). The data storage 928 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, hard disk, and / or other components and / or devices that can store at least one bit of data.

[0138] The vehicle 900 may further include a GNSS sensor 958. The GNSS sensor 958 (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 958 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0139] The vehicle 900 may further include a RADAR sensor 960. The RADAR sensor 960 may be used by the vehicle 900 for remote vehicle detection even in darkness and / or inclement weather conditions. The RADAR functional safety level may be ASILB. The RADAR sensor 960 may use CAN and / or bus 902 (e.g., to transmit data generated by the RADAR sensor 960) 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 960 may be suitable for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0140] The RADAR sensor 960 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, and the like. In some examples, the long-range RADAR may be used for adaptive cruise control functions. The long-range RADAR system may provide a wide field of view (e.g., within a range of 250m) achieved by two or more independent scans. The RADAR sensor 960 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 900 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 900.

[0141] As an example, a medium-range RADAR system may include a range of up to 960m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 950 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on both ends of a rear bumper. When mounted on both ends of a rear bumper, such a RADAR sensor system may create two beams that continuously monitor the blind spots behind and beside the vehicle.

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

[0143] The vehicle 900 may further include an ultrasonic sensor 962. The ultrasonic sensor 962, which may be placed on the front, rear, and / or sides of the vehicle 900, may be used for parking assistance and / or creating and updating an occupancy grid. A variety of ultrasonic sensors 962 may be used, and different ultrasonic sensors 962 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensor 962 may operate at a functional safety level of ASIL B.

[0144] The vehicle 900 may include a LiDAR sensor 964. The LiDAR sensor 964 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LiDAR sensor 964 may be ASIL B for functional safety level. In some examples, the vehicle 900 may include multiple LiDAR sensors 964 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0145] In some examples, the LiDAR sensor 964 may be able to provide a list of objects and their distances for a 360-degree field of view. Commercially available LiDAR sensors 964 may have, for example, an advertised range of approximately 900m, an accuracy of 2cm-3cm, and support for 900Mbps Ethernet connections. In some examples, one or more non-protruding LiDAR sensors 964 may be used. In such examples, the LiDAR sensor 964 may be implemented as a small device that can be embedded in the front, back, side, and / or corner of the vehicle 900. In such examples, the LiDAR sensor 964 may provide a field of view of up to 120 degrees horizontally and 35 degrees vertically, with a range of 200m, even for low reflectivity objects. The front-mounted LiDAR sensor 964 may be configured for a horizontal field of view between 45 and 135 degrees.

[0146] In some examples, LiDAR technologies such as 3D flash LiDAR may also be used. 3D flash LiDAR uses a flash of laser as an emission source to illuminate the vehicle's surroundings up to about 200m. The flash LiDAR unit includes a receiver that records the laser pulse transmission time and the reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LiDAR can allow highly accurate and distortion-free images of the surrounding environment to be generated with each laser flash. In some examples, four flash LiDAR sensors can be deployed, one on each side of the vehicle 900. Available 3D flash LiDAR systems include solid-state 3D staring array LiDAR cameras (e.g., non-scanning LiDAR devices) with no moving parts other than fans. The flash LiDAR device can use 5 nanosecond Class I (eye-safe) laser pulses per frame, and can capture the reflected laser light in the form of a 3D range point cloud and co-registered intensity data. By using flash LiDAR, and because flash LiDAR is a solid-state device with no moving parts, the LiDAR sensor 964 may be less susceptible to motion blur, vibration, and / or shock.

[0147] The vehicle may further include an IMU sensor 966. In some examples, the IMU sensor 966 may be located at the center of the rear axle of the vehicle 900. The IMU sensor 966 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 966 may include an accelerometer and a gyroscope, and in a nine-axis application, the IMU sensor 966 may include an accelerometer, a gyroscope, and a magnetometer.

[0148] In some embodiments, the IMU sensor 966 can be implemented as a miniature high-performance GPS-assisted inertial navigation system (GPS / INS) that combines a micro-electromechanical system (MEMS) inertial sensor, a high-sensitivity GPS receiver, and an advanced Kalman filter algorithm to provide estimates of position, velocity, and attitude. Thus, in some examples, the IMU sensor 966 can enable the vehicle 900 to estimate heading by directly observing and correlating velocity changes from the GPS to the IMU sensor 966 without the need for input from a magnetic sensor. In some examples, the IMU sensor 966 and the GNSS sensor 958 can be combined into a single integrated unit.

[0149] The vehicle may include a microphone 996 positioned in and / or around the vehicle 900. The microphone 996 may be used for, among other things, emergency vehicle detection and identification.

[0150] The vehicle may further include any number of camera types, including a stereo camera 968, a wide angle camera 970, an infrared camera 972, a surround camera 974, a long-range and / or mid-range camera 998, and / or other camera types. These cameras may be used to capture image data around the entire periphery of the vehicle 900. The type of camera used depends on the embodiment and the requirements of the vehicle 900, and any combination of camera types may be used to provide the necessary coverage around the vehicle 900. In addition, the number of cameras may vary depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or another number of cameras. As an example and not limitation, the cameras may support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may include a 10GbE ... Fig. 9A and Fig. 9B Described in more detail.

[0151] The vehicle 900 may further include a vibration sensor 942. The vibration sensor 942 may measure vibrations of components of the vehicle, such as axles. For example, changes in vibration may indicate changes in the road surface. In another example, when two or more vibration sensors 942 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 freely rotating shaft).

[0152] The vehicle 900 may include an ADAS system 938. In some examples, the ADAS system 938 may include a SoC. The ADAS system 938 may include autonomous / adaptive / automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward collision warning (FCW), automatic emergency braking (AEB), lane departure warning (LDW), lane keeping assist (LKA), blind spot warning (BSW), rear cross traffic warning (RCTW), collision warning system (CWS), lane centering (LC), and / or other features and functions.

[0153] The ACC system may use a RADAR sensor 960, a LiDAR sensor 964, 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 900, 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 900 change lanes when necessary. The lateral ACC is related to other ADAS applications such as LCA and CWS.

[0154] CACC uses information from other vehicles, which can be received indirectly from other vehicles via a wireless link or through a network connection (e.g., through the Internet) via a network interface 924 and / or a wireless antenna 926. 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 900 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 900, CACC can be more reliable, and it is possible to improve the smoothness of traffic flow and reduce road congestion.

[0155] The FCW system is designed to alert the driver to hazards so that the driver can take corrective action. The FCW system uses a front camera and / or RADAR sensor 960 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibrating components. The FCW system can provide warnings in the form of, for example, sound, visual warnings, vibrations and / or rapid brake pulses.

[0156] The AEB system detects an impending 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 960 coupled to a dedicated processor, DSP, FPGA and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes in an effort to prevent or at least mitigate the effects of the predicted collision. The AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0157] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 900 crosses a lane marking. When the driver indicates an intention to leave the lane, by activating a turn signal, the LDW system is not activated. The LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0158] The LKA system is a variation of the LDW system. If the vehicle 900 begins to leave the lane, the LKA system provides steering input or braking to correct the vehicle 900.

[0159] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, auditory and / or tactile alerts to indicate that merging or changing lanes is unsafe. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use a rear-facing camera and / or RADAR sensor 960 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibration components.

[0160] The RCTW system can provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 900 is in reverse. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. The RCTW system can use one or more rear RADAR sensors 960 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0161] Conventional ADAS systems may be prone to false positive results, which may annoy and distract the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether the safety condition really exists and take action accordingly. However, in the autonomous vehicle 900, in the case of conflicting results, the vehicle 900 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 936 or the second controller 936). For example, in some embodiments, the ADAS system 938 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 938 can be provided to the supervisory MCU. If the outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to coordinate the conflict to ensure safe operation.

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

[0163] The supervisory MCU can 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 main computer and the auxiliary computer. Therefore, the neural network in the supervisory MCU can learn when the output of the auxiliary computer can be trusted and when it cannot. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU can learn when the FCW system is identifying a metal object that is not actually dangerous, such as a drainage grid or manhole cover that triggers an alarm. Similarly, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU can learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In an embodiment including 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 904 and / or be included as a component of SoC 904.

[0164] In other examples, the ADAS system 938 may include an auxiliary computer that uses traditional computer vision rules to perform ADAS functions. In this way, the auxiliary computer can use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or error in the software running on the main computer and the non-identical software code running on the auxiliary computer provides the same overall result, the supervisory MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware on the main computer does not cause a substantial error.

[0165] In some examples, the output of the ADAS system 938 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 938 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information when identifying the object. In other examples, the auxiliary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0166] The vehicle 900 may further include an infotainment SoC 930 (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 930 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 media, 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 900. For example, the infotainment SoC 930 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connections, an onboard computer, onboard entertainment, Wi-Fi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 934, 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 930 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 938, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0167] The infotainment SoC 930 may include GPU functionality. The infotainment SoC 930 may communicate with other devices, systems, and / or components of the vehicle 900 via a bus 902 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 930 may be coupled to a supervisory MCU so that in the event of a failure of a main controller 936 (e.g., a main and / or backup computer of the vehicle 900), the GPU of the infotainment system may perform some self-driving functions. In such an example, the infotainment SoC 930 may place the vehicle 900 in a driver safety parking mode as described herein.

[0168] The vehicle 900 may further include an instrument cluster 932 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 932 may include a controller and / or a supercomputer (e.g., a separate controller or a supercomputer). The instrument cluster 932 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, a lighting control, a safety system control, navigation information, and the like. In some examples, information may be displayed and / or shared between the infotainment SoC 930 and the instrument cluster 932. In other words, the instrument cluster 932 may be included as part of the infotainment SoC 930, or vice versa.

[0169] Fig.9D For a cloud-based server and Fig. 9A 900. System 976 may include a server 978, a network 990, and a vehicle including the vehicle 900. The server 978 may include a plurality of GPUs 984 (A) -984 (H) (collectively referred to herein as GPUs 984), PCIe switches 982 (A) -982 (H) (collectively referred to herein as PCIe switches 982), and / or CPUs 980 (A) -980 (B) (collectively referred to herein as CPUs 980). The GPUs 984, CPUs 980, and PCIe switches may be interconnected with high-speed interconnects and / or PCIe connections 986 such as, for example and without limitation, the NVLink interface 988 developed by NVIDIA. In some examples, the GPUs 984 are connected via NVLink and / or NVSwitch SoCs, and the GPUs 984 and PCIe switches 982 are connected via PCIe interconnects. Although eight GPUs 984, two CPUs 980, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the servers 978 may include any number of GPUs 984, CPUs 980, and / or PCIe switches. For example, each of the servers 978 may include eight, sixteen, thirty-two, and / or more GPUs 984.

[0170] Server 978 may receive image data over network 990 and from a vehicle, the image data representing images showing unexpected or changed road conditions, such as recently begun road work. Server 978 may transmit neural network 992, updated neural network 992, and / or map information 994, including information about traffic and road conditions, over network 990 and to the vehicle. Updates to map information 994 may include updates to HD map 922, such as information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, neural network 992, updated neural network 992, and / or map information 994 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 978 and / or other servers).

[0171] Server 978 can be used to train a machine learning model (e.g., a neural network) based on training data. The training data can be generated by the vehicle, and / or can be generated in a simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., when the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other examples, the training data is not labeled and / or preprocessed (e.g., when the neural network does not require supervised learning). Training can be performed according to any one or more categories of machine learning techniques, including but not limited to: categories such as supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including main components and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternate dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations thereof. Once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via network 990), and / or the machine learning model can be used by server 978 to remotely monitor the vehicle.

[0172] In some examples, server 978 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. Server 978 may include a deep learning supercomputer and / or a dedicated AI computer powered by GPU 984, such as DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 978 may include a deep learning infrastructure of a data center powered only by CPUs.

[0173] The deep learning infrastructure of server 978 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 900. For example, the deep learning infrastructure may receive periodic updates from vehicle 900, such as a sequence of images and / or objects located in the sequence of images that vehicle 900 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 900, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 900 has failed, then server 978 may transmit a signal to vehicle 900 instructing the fail-safe computer of vehicle 900 to take control, notify passengers, and complete a safe parking maneuver.

[0174] For reasoning, the server 978 may include a GPU 984 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and reasoning acceleration can make real-time responses possible. In other examples, such as when performance is not so important, CPU, FPGA, and other processor-powered servers can be used for reasoning.

[0175] Example computing device

[0176] Fig.10 1 is a block diagram of an example computing device 1000 suitable for implementing some embodiments of the present disclosure. The computing device 1000 may include an interconnect system 1002 that directly or indirectly couples the following devices: a memory 1004, one or more central processing units (CPUs) 1006, one or more graphics processing units (GPUs) 1008, a communication interface 1010, an input / output (I / O) port 1012, an input / output component 1014, a power supply 1016, one or more presentation components 1018 (e.g., a display), and one or more logic units 1020. In at least one embodiment, one or more computing devices 1000 may include one or more virtual machines (VMs), and / or any of their components may include virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs 1008 may include one or more vGPUs, one or more of the CPUs 1006 may include one or more vCPUs, and / or one or more of the logic units 1020 may include one or more virtual logic units. As such, one or more computing devices 1000 may include discrete components (eg, a full GPU dedicated to computing device 1000 ), virtual components (eg, a portion of a GPU dedicated to computing device 1000 ), or a combination thereof.

[0177] although Fig.10 The various blocks of are shown as being connected via an interconnect system 1002 having lines, but this is not intended to be limiting and is merely for clarity. For example, in some embodiments, a presentation component 1018 such as a display device may be considered an I / O component 1014 (e.g., if the display is a touch screen). As another example, CPU 1006 and / or GPU 1008 may include memory (e.g., memory 1004 may represent a storage device in addition to the memory of GPU 1008, CPU 1006, and / or other components). In other words, Fig.10 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 Fig.10 within the range of computing devices.

[0178] The interconnection system 1002 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 1002 may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and / or another type of bus or link. In some embodiments, there is a direct connection between components. For example, the CPU 1006 may be directly connected to the memory 1004. In addition, the CPU 1006 may be directly connected to the GPU 1008. In the case where there is a direct or point-to-point connection between components, the interconnection system 1002 may include a PCIe link to perform the connection. In these examples, the computing device 1000 does not need to include a PCI bus.

[0179] Memory 1004 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 1000. 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.

[0180] 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 1004 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system). Computer storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by computing device 1000. As used herein, computer storage media does not include the signals themselves.

[0181] Computer storage media may include 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.

[0182] The CPU 1006 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 1000 to perform one or more of the methods and / or processes described herein. Each of the CPUs 1006 may include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of simultaneously processing a large number of software threads. The CPU 1006 may include any type of processor, and may include different types of processors, depending on the type of computing device 1000 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 1000, the processor may be an Advanced RISC Machine (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 1000 may also include one or more CPUs 1006 in addition to one or more microprocessors or supplementary coprocessors such as math coprocessors.

[0183] In addition to or as an alternative to CPU 1006, one or more GPUs 1008 may be configured to execute at least some of the computer-readable instructions to control one or more components of computing device 1000 to perform one or more of the methods and / or processes described herein. One or more GPUs 1008 may be integrated GPUs (e.g., with one or more CPUs 1006) and / or one or more GPUs 1008 may be discrete GPUs. In an embodiment, one or more GPUs 1008 may be coprocessors of one or more CPUs 1006. Computing device 1000 may use GPU 1008 to render graphics (e.g., 3D graphics) or perform general purpose computing. For example, one or more GPUs 1008 may be used for general purpose computing (GPGPU) on GPUs. One or more GPUs 1008 may include hundreds of thousands of cores capable of processing hundreds of thousands of software threads simultaneously. GPU 1008 may generate pixel data for outputting an image in response to a rendering command (e.g., a rendering command from CPU 1006 received via a host interface). GPU 1008 may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. Display memory may be included as part of memory 1004. One or more GPUs 1008 may include two or more GPUs operating in parallel (e.g., via a link). The link may connect the GPUs directly (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU 1008 may generate pixel data or GPGPU data for different parts of the output or different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory or may share memory with other GPUs.

[0184] In addition to or as an alternative to the CPU 1006 and / or the GPU 1008, the logic unit 1020 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 1000 to perform one or more of the methods and / or processes described herein. In an embodiment, the CPU 1006, the GPU 1008, and / or the logic unit 1020 may perform any combination of methods, processes, and / or portions thereof separately or jointly. The one or more logic units 1020 may be a part of and / or integrated into one or more of the CPU 1006 and / or the GPU 1008, and / or the one or more logic units 1020 may be a discrete component or otherwise external to the CPU 1006 and / or the GPU 1008. In an embodiment, the one or more logic units 1020 may be a coprocessor of the one or more CPUs 1006 and / or the one or more GPUs 1008.

[0185] Examples of logic unit 1020 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.

[0186] The communication interface 1010 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 1000 to communicate with other computing devices via an electronic communication network, including wired and / or wireless communications. The communication interface 1010 may include components and functions that enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low power wide area networks (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, the one or more logic units 1020 and / or the communication interface 1010 may include one or more data processing units (DPUs) to transmit data received over a network and / or over the interconnect system 1002 directly to (e.g., memory) one or more GPUs 1008.

[0187] I / O ports 1012 can enable computing device 1000 to be logically coupled to other devices including I / O components 1014, presentation components 1018, and / or other components, some of which can be built into (e.g., integrated into) computing device 1000. Illustrative I / O components 1014 include microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dishes, scanners, printers, wireless devices, and the like. I / O components 1014 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 computing device 1000 (as described in more detail below). Computing device 1000 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 1000 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 1000 to render immersive augmented reality or virtual reality.

[0188] The power supply 1016 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 1016 may provide power to the computing device 1000 to enable the components of the computing device 1000 to operate.

[0189] The presentation component 1018 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 1018 may receive data from other components (e.g., GPU 1008, CPU 1006, DPU, etc.) and output the data (e.g., as an image, video, sound, etc.).

[0190] Sample Data Center

[0191] Fig.11 An example data center 1100 that can be used in at least one embodiment of the present disclosure is shown. The data center 1100 can include a data center infrastructure layer 1110, a framework layer 1120, a software layer 1130, and / or an application layer 1140.

[0192] like Fig.11As shown, the data center infrastructure layer 1110 may include a resource coordinator 1112, grouped computing resources 1114, and node computing resources ("node CRs") 1116(1)-1116(N), where "N" represents any complete positive integer. In at least one embodiment, the node CRs 1116(1)-1116(N) may include, but are not limited to, any number of central processing units ("CPUs") or other processors (including accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memories), storage devices (e.g., solid-state or disk drives), network input / output ("NW I / O") devices, network switches, virtual machines ("VMs"), power modules and / or cooling modules, etc. In some embodiments, one or more of the node CRs 1116(1)-1116(N) may correspond to a server having one or more of the above-mentioned computing resources. Furthermore, in some embodiments, nodes CR1116(1)-11161(N) may include one or more virtual components, such as vGPU, vCPU, etc., and / or one or more of nodes CR1116(1)-1116(N) may correspond to a virtual machine (VM).

[0193] In at least one embodiment, the grouped computing resources 1114 may include a separate grouping of node CR1116 housed in one or more racks (not shown), or many racks housed in a data center at different geographical locations (also not shown). The separate grouping of node CR1116 in the grouped computing resources 1114 may include grouped computing, network, memory or storage resources that can be configured or allocated to support one or more workloads. In at least one embodiment, several node CR1116 including CPU, GPU, DPU and / or other processors can be grouped in one or more racks to provide computing resources to support one or more workloads. One or more racks may also include any number of power modules, cooling modules and / or network switches in any combination.

[0194] Resource coordinator 1122 may configure or otherwise control one or more nodes CR 1116(1)-1116(N) and / or grouped computing resources 1114. In at least one embodiment, resource coordinator 1122 may include a software design infrastructure ("SDI") management entity for data center 1100. Resource coordinator 1122 may include hardware, software, or some combination thereof.

[0195] In at least one embodiment, Fig.11As shown, the framework layer 1120 may include a job scheduler 1133, a configuration manager 1134, a resource manager 1136, and / or a distributed file system 1138. The framework layer 1120 may include a framework that supports software 1132 of the software layer 1130 and / or one or more applications 1142 of the application layer 1140. The software 1132 or the application 1142 may include network-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 1120 may be, but is not limited to, a free and open source software network application framework (such as Apache Spark) that can utilize the distributed file system 1138 for large-scale data processing (e.g., "big data"). TM (hereinafter referred to as "Spark")). In at least one embodiment, the job scheduler 1133 may include a Spark driver to facilitate scheduling workloads supported by different layers of the data center 1100. The configuration manager 1134 may be able to configure different layers, such as the software layer 1130 and the framework layer 1120 (which includes Spark and a distributed file system 1138 for supporting large-scale data processing). The resource manager 1136 may be able to manage clustered or grouped computing resources mapped to the distributed file system 1138 and the job scheduler 1133 or allocated to support the distributed file system 1138 and the job scheduler 1133. In at least one embodiment, the clustered or grouped computing resources may include grouped computing resources 1114 at the data center infrastructure layer 1110. The resource manager 1136 may coordinate with the resource coordinator 1112 to manage these mapped or allocated computing resources.

[0196] In at least one embodiment, the software 1132 included in the software layer 1130 may include software used by at least a portion of the node CRs 1116(1)-1116(N), the grouped computing resources 1114, and / or the distributed file system 1138 of the framework layer 1120. The one or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0197] In at least one embodiment, the applications 1142 included in the application layer 1140 may include one or more types of applications used by at least a portion of the node CRs 1116(1)-1116(N), the grouped computing resources 1114, and / or the distributed file system 1138 of the framework layer 1120. The one or more types of applications may include, but are not limited to, any number of genomic applications, cognitive computing, and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and / or other machine learning applications used in conjunction with one or more embodiments.

[0198] In at least one embodiment, any of configuration manager 1134, resource manager 1136, and resource coordinator 1112 can implement any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. The self-modification actions can save a data center operator of data center 1100 from making potentially poor configuration decisions and potentially avoiding underutilized and / or poorly performing portions of the data center.

[0199] According to one or more embodiments described herein, data center 1100 may include tools, services, software, or other resources to train one or more machine learning models or use one or more machine learning models to predict or infer information. For example, the (one or more) machine learning models may be trained by calculating weight parameters according to a neural network architecture using the software and / or computing resources described above with respect to data center 1100. In at least one embodiment, a trained or deployed machine learning model corresponding to one or more neural networks may be used to infer or predict information using the resources described above with respect to data center 1100 using weight parameters calculated by one or more training techniques, such as, but not limited to, those described herein.

[0200] In at least one embodiment, the data center 1100 may use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, and / or other hardware (or virtual computing resources corresponding thereto) to perform training and / or inference using the above resources. In addition, one or more software and / or hardware resources described above may be configured to allow a user to train or perform services that infer information, such as image recognition, speech recognition, or other artificial intelligence services.

[0201] Example network environment

[0202] A network environment suitable for implementing embodiments of the present disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and / or other device types. The client devices, servers, and / or other device types (e.g., each device) may be configured to communicate with the client device, server, or other device types. Fig.10 The computing device 1000 of the embodiment of the present invention may be implemented on one or more instances of the computing device 1000 - for example, each device may include similar components, features and / or functions of the computing device 1000. 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 1100, and the example of the data center 1100 is described in this article. Fig.11 Describe in more detail.

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

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

[0205] In at least one embodiment, the network environment may include one or more cloud-based network environments, distributed computing environments, combinations thereof, and the like. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more servers, 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").

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

[0207] The client device may include Fig.10 At least some of the components, features, and functionality of the described example computing device 1000. 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 smart phone, a tablet computer, a smart watch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality headset, a global positioning system (GPS) or device, a video player, a camera, a surveillance device or system, a vehicle, a 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 the devices described, or any other suitable device.

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

[0209] As used herein, the 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.

[0210] The subject matter of the present disclosure is described in detail herein to meet statutory requirements. However, the description itself is not intended to limit the scope of the present disclosure. On the contrary, the 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.

[0211] Example paragraph

[0212] A. A method comprising: generating image data representing an image corresponding to at least a portion of an environment based at least on LiDAR data obtained using one or more LiDAR sensors; determining, using one or more machine learning models and based at least on the image data, a location associated with a road marker within the environment; and causing a map to indicate the location associated with the road marker within the environment.

[0213] B. A method according to paragraph A, wherein determining a location associated with a road marking within the environment comprises: determining a boundary shape indicating a portion of the image depicting the road marking using the one or more machine learning models and based at least on the image data; and determining the location associated with the road marking within the environment based at least on the portion of the image.

[0214] C. A method according to paragraph B, wherein causing the map to indicate the location associated with the road marking within the environment includes: determining one or more first locations associated with one or more first points of the boundary shape within the image; determining one or more second locations of one or more second points within the map corresponding to the one or more first locations of the one or more first points within the image; and updating a portion of the map to indicate the location associated with the road marking within the environment based at least on the one or more second locations of the one or more second points.

[0215] D. A method according to paragraph B, wherein the boundary shape includes: an orientation based at least on a driving direction of the road on which the road marking is set.

[0216] E. According to the method described in any of paragraphs AD, the method also includes: using the one or more machine learning models and determining a classification associated with the road marking based at least on the image data; and making the map indicate the classification associated with the road marking.

[0217] F. A method according to any of paragraphs AE, wherein the image includes at least one of: a top-down image corresponding to the at least portion of the environment; or a top-down image indicating one or more intensities associated with one or more points corresponding to the at least portion of the environment.

[0218] G. A method according to any of paragraphs AF, the method further comprising: using the one or more LiDAR sensors to determine a position associated with a machine that generated the LiDAR data, wherein determining the position associated with the road marking within the environment is also based at least on the position associated with the machine.

[0219] H. According to the method described in any of paragraphs AG, the method also includes: generating point cloud data representing points based at least on the LiDAR data and motion data representing the movement of the machine when generating the LiDAR data, wherein the generation of the image data is based at least on the point cloud data.

[0220] I. A system comprising: one or more processing units for: generating image data representing a top-down image corresponding to at least a portion of an environment based at least on sensor data obtained using one or more sensors; determining, using one or more machine learning models and based at least on the image data, locations associated with road markings within the environment; and encoding the locations associated with the road markings into map data associated with a map corresponding to the portion of the environment.

[0221] J. A system according to paragraph I, wherein determining the position associated with the road marking within the environment includes: determining a boundary shape indicative of a portion of the top-down image depicting the road marking using the one or more machine learning models and based at least on the image data; and determining the position of the road marking within the environment based at least on the portion of the top-down image.

[0222] K. A system according to paragraph J, wherein the location is encoded based on at least: determining that a portion of the map corresponds to a portion of the top-down image associated with the boundary shape; and encoding the location associated with the road marking.

[0223] L. A system according to paragraph J, wherein the boundary shape includes: an orientation based at least on a direction of travel of the road on which the road marking is set.

[0224] M. A system according to any of paragraphs IL, wherein the one or more processing units are also used to: determine a classification associated with the road sign using the one or more machine learning models and based at least on the image data; and encode the classification associated with the road sign into the map data.

[0225] N. A system as described in any of paragraphs IM, wherein the top-down image indicates one or more intensities associated with one or more points within the at least a portion of the environment.

[0226] O. A system according to any of paragraphs IN, wherein the sensor data obtained using the one or more sensors includes: LiDAR data obtained using one or more LiDAR sensors; and the generation of the image data representing the top-down image corresponding to the at least a portion of the environment is based at least on a point cloud associated with the LiDAR data.

[0227] P. A system according to any of paragraphs IO, wherein the one or more processing units are also used to: use the one or more sensors to determine a position associated with a machine that generated the sensor data, wherein determining the position associated with the road marking within the environment is also based at least on the position associated with the machine.

[0228] Q. A system according to any of paragraphs IP, wherein the system includes at least one of the following: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation of 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system implementing one or more large language models; a system for performing conversational AI operations; a system for generating synthetic data; a system including one or more virtual machines (VMs); a system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources.

[0229] R. A processor comprising: one or more processing units, the one or more processing units being configured to cause: one or more operations associated with a machine to be performed based at least on a location associated with a road marker encoded in map data corresponding to a map, wherein the location associated with the road marker is determined using one or more machine learning models and based at least on an image indicating one or more intensity values ​​associated with one or more points represented by LiDAR data, the image depicting at least a portion of an environment including the road marker.

[0230] S. A processor according to paragraph R, wherein the position associated with the road marking is also determined at least in part by: using the one or more machine learning models and determining, based at least on the image, a boundary shape indicating a portion of the image that depicts the road marking; and determining, based at least on the portion of the image, the position associated with the road marking within the environment.

[0231] T. A system according to paragraph R or paragraph S, wherein the system includes at least one of the following: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulations; a system for performing collaborative content creation of 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system implementing one or more large language models; a system for performing conversational AI operations; a system for generating synthetic data; a system including one or more virtual machines (VMs); a system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources.

Claims

1. A method comprising: generating image data representing an image corresponding to at least a portion of an environment based at least on LiDAR data obtained using one or more LiDAR sensors; determining, using one or more machine learning models and based at least on the image data, a location associated with a road marking within the environment; as well as A map is caused to indicate the location associated with the road marking within the environment.

2. The method according to claim 1, wherein: Determining a location associated with a road marking within the environment includes: determining, using the one or more machine learning models and based at least on the image data, a boundary shape indicative of a portion of the image depicting the road marking; and The location associated with the road marking within the environment is determined based at least on the portion of the image.

3. The method according to claim 2, wherein: Causing the map to indicate the location associated with the road marking within the environment comprises: determining one or more first locations associated with one or more first points of the boundary shape within the image; determining one or more second positions of one or more second points within the map corresponding to the one or more first positions of the one or more first points within the image; and Based at least on the one or more second positions of the one or more second points, a portion of the map is updated to indicate the position associated with the road marking within the environment.

4. The method according to claim 2, wherein: The boundary shape includes an orientation based at least on a travel direction of the road on which the road marking is set.

5. The method according to claim 1, further comprising: determining, using the one or more machine learning models and based at least on the image data, a classification associated with the road marking; as well as The map is caused to indicate a classification associated with the road marking.

6. The method according to claim 1, wherein: The image includes at least one of the following: a top-down image corresponding to said at least a portion of said environment; or A top-down image is indicative of one or more intensities associated with one or more points corresponding to the at least a portion of the environment.

7. The method according to claim 1, further comprising: determining, using the one or more LiDAR sensors, a location associated with a machine that generated the LiDAR data, Wherein determining the position associated with the road marking within the environment is also based at least on the position associated with the machine.

8. The method according to claim 1, further comprising: generating point cloud data representing points based at least on the LiDAR data and motion data representing motion of the machine when generating the LiDAR data, Wherein, generating the image data is at least based on the point cloud data.

9. A system comprising: One or more processing units for: generating image data representing a top-down image corresponding to at least a portion of the environment based at least on sensor data obtained using one or more sensors; determining, using one or more machine learning models and based at least on the image data, a location associated with a road marking within the environment; as well as The location associated with the road marking is encoded into map data associated with a map corresponding to the portion of the environment.

10. The system according to claim 9, wherein: Determining the position associated with the road marking within the environment comprises: determining, using the one or more machine learning models and based at least on the image data, a boundary shape indicative of a portion of the top-down image depicting the road marking; and The position of the road marking within the environment is determined based on at least the portion of the top-down image.

11. The system according to claim 10, wherein: The position is encoded based on at least: determining that a portion of the map corresponds to a portion of the top-down image associated with the boundary shape; and The location associated with the road marking is encoded.

12. The system according to claim 10, wherein: The boundary shape includes an orientation based at least on a travel direction of the road on which the road marking is set.

13. The system according to claim 9, wherein: The one or more processing units are further configured to: determining, using the one or more machine learning models and based at least on the image data, a classification associated with the road marking; and The classification associated with the road marking is encoded into the map data.

14. The system according to claim 9, wherein: The top-down image indicates one or more intensities associated with one or more points within the at least a portion of the environment.

15. The system according to claim 9, wherein: The sensor data obtained using the one or more sensors includes: LiDAR data obtained using one or more LiDAR sensors; and The generation of the image data representing the top-down image corresponding to the at least a portion of the environment is based at least on a point cloud associated with the LiDAR data.

16. The system of claim 9, wherein: The one or more processing units are further configured to: determining, using the one or more sensors, a location associated with a machine that generated the sensor data, Wherein determining the position associated with the road marking within the environment is also based at least on the position associated with the machine.

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

18. A processor, comprising: One or more processing units configured to cause one or more operations associated with a machine to be performed based at least on a location associated with a road marking encoded in map data corresponding to a map, wherein the location associated with the road marking is determined using one or more machine learning models and based at least on an image indicating one or more intensity values ​​associated with one or more points represented by LiDAR data, the image depicting at least a portion of an environment including the road marking.

19. The processor of claim 18, wherein: The position associated with the road marking is also determined at least in part by: determining, using the one or more machine learning models and based at least on the image, a boundary shape indicative of a portion of the image depicting the road marking; as well as The location associated with the road marking within the environment is determined based at least on the portion of the image.

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

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