Patent Publication Number: US-2023135234-A1

Title: Using neural networks for 3d surface structure estimation based on real-world data for autonomous systems and applications

Description:
BACKGROUND 
     Designing a system to drive a vehicle autonomously, safely, and comfortably without supervision is tremendously difficult. An autonomous vehicle should at least be capable of performing as a functional equivalent of an attentive driver who draws upon a perception and action system that has an incredible ability to identify and react to moving and static obstacles in a complex environment—to navigate along the path of the vehicle through the surrounding three-dimensional (3D) environment. Thus, the ability to detect parts of an environment is often critical for autonomous driving perception systems. This capability has become increasingly important, as the operational environment for the autonomous vehicle has begun to expand from highway environments to semi-urban and urban settings characterized by complex scenes with complex shapes. 
     One important component of the 3D environment is the 3D road surface. Knowledge of the 3D road surface enables autonomous vehicles to provide a comfortable and safe driving experience. For example, an autonomous vehicle may adapt the vehicle&#39;s suspension system to match the current road surface (e.g., by compensating for bumps in the road). In another example, an autonomous vehicle may navigate to avoid protuberances (e.g., dips, holes) in the road. In yet another example, an autonomous vehicle may apply an early acceleration or deceleration based on an approaching surface slope in the road. Any of these functions may serve to enhance safety, improve the longevity of the vehicle, improve energy-efficiency, and/or provide a smooth driving experience. 
     One way to estimate the structure of the road surface is with 3D reconstruction. Existing approaches for 3D road surface reconstruction rely on either LiDAR sensors or cameras. Conventional techniques that use LiDAR sensors emit a laser pulse and detect the reflected signal from the road surface to reconstruct 3D points on the road. However, LiDAR sensors are expensive, have limited range, and their accuracy may not suffice for certain applications in autonomous driving. Conventional techniques that use cameras rely on multi-view geometry to reconstruct 3D entities. However, conventional reconstruction techniques with cameras cannot efficiently compute dense measurements, and conventional post-processing techniques such as interpolation or plane fitting are often insufficient to provide accurate enough models of the complex road surfaces that exist in the real world. As such, there is a need for improved 3D road surface reconstruction techniques for autonomous driving applications. 
     SUMMARY 
     Embodiments of the present disclosure relate to 3D surface estimation. In some embodiments, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the representation may be sparse, one or more densification techniques may be applied to generate a dense representation of the 3D surface structure, which may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle. 
     In an example embodiment, one or more cameras may be affixed to or otherwise disposed on a vehicle or other object and used to capture image(s) of a 3D environment as the vehicle or object navigates (e.g., along a road) through the 3D environment, and any suitable 3D structure estimation technique may be applied to generate a representation of a 3D surface structure of interest, such a 3D road surface. The representation of the 3D surface structure may be densified using, for example, a Markov random field and/or a deep neural network (DNN). In an example densification technique using a Markov random field, sparse and dense projection images (e.g., height maps) may be modeled with an undirected graph, and Maximum a Posterior (MAP) inference may be used to estimate the most likely dense values given the sparse values. In an example densification technique using a DNN, a sparse projection image may be fed into a DNN to predict a corresponding dense projection image. Training data for such a DNN may be generated in various ways and used to train the DNN to predict a dense representation of 3D surface structure, given a sparse representation. Example techniques for generating training data. include 1) rendering frames of virtual sensor data, segmentation masks, and depth maps; 2) parametric mathematical modeling of a 3D road surface; 3) collecting and annotating real sensor data from a single LiDAR sensor; and/or 4) collecting and annotating real sensor data accumulated from multiple LiDAR sensors. 
     As such, the techniques described herein may be used to observe and reconstruct a 3D surface such as a 3D road surface, and a representation of the 3D surface structure (and/or corresponding confidence values) may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle. For example, an autonomous vehicle may adapt the vehicle&#39;s suspension system to match the current road surface (e.g., by compensating for bumps in the road). In another example, an autonomous vehicle may navigate to avoid protuberances (e.g., dips, holes) in the road. In yet another example, an autonomous vehicle may apply an early acceleration or deceleration based on an approaching surface slope in the road. Any of these functions may serve to enhance safety, improve the longevity of the vehicle, improve energy-efficiency, and/or provide a smooth driving experience. 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present systems and methods for 3D surface estimation are described in detail below with reference to the attached drawing figures, wherein: 
         FIG.  1    is a data flow diagram illustrating an example 3D surface reconstruction pipeline, in accordance with some embodiments of the present disclosure; 
         FIG.  2    is a diagram illustrating an example 3D structure estimator, in accordance with some embodiments of the present disclosure; 
         FIG.  3    is a diagram illustrating an example detection densifier, in accordance with some embodiments of the present disclosure; 
         FIG.  4    is a diagram illustrating an example undirected graph that models the relationship between sparse and dense height maps, in accordance with some embodiments of the present disclosure; 
         FIG.  5    is data flow diagram illustrating an example deep learning model surface estimator, in accordance with some embodiments of the present disclosure; 
         FIG.  6    is data flow diagram illustrating an example deep learning model surface estimator that includes a deep learning model(s) with multiple inputs heads, in accordance with some embodiments of the present disclosure; 
         FIG.  7    is a flow diagram showing a method for generating a representation of a three-dimensional (3D) surface structure during a capture session, in accordance with some embodiments of the present disclosure; 
         FIG.  8    is a flow diagram showing a method for generating a densified representation of a 3D surface structure based at least on a Markov random field, in accordance with some embodiments of the present disclosure; 
         FIG.  9    is a flow diagram showing a method for controlling a vehicle based at least in part on a 3D road surface structure estimated using one or more neural networks, in accordance with some embodiments of the present disclosure; 
         FIG.  10    is a data flow diagram illustrating an example training data generation pipeline using a simulated environment, in accordance with some embodiments of the present disclosure; 
         FIG.  11    is an illustration of an example parametric mathematical model of a desired. surface, in accordance with some embodiments of the present disclosure; 
         FIG.  12    is a data flow diagram illustrating an example ground truth generation pipeline using collected real-world data, in accordance with some embodiments of the present disclosure; 
         FIG.  13 A  is an illustration of LiDAR data from an example LiDAR scan, and  FIG.  13 B  is an illustration of LiDAR data accumulated from multiple LiDAR scans, in accordance with some embodiments of the present disclosure; 
         FIG.  14    is a flow diagram showing a method for training one or more neural networks (NNs) to generate a densified representation of the 3D surface structure using simulated image data, in accordance with some embodiments of the present disclosure; 
         FIG.  15    is a flow diagram showing a method for generating incomplete and ground truth representations of a synthetic 3D road surface for a training dataset, in accordance with some embodiments of the present disclosure; 
         FIG.  16    is a flow diagram showing a method for training one or more neural networks (NNs) to generate a densified representation of the 3D surface structure using image data and LiDAR, data captured during a capture session, in accordance with some embodiments of the present disclosure; 
         FIG.  17 A  is an illustration of an example autonomous vehicle, in accordance with some embodiments of the present disclosure; 
         FIG.  17 B  is an example of camera locations and fields of view for the example autonomous vehicle of  FIG.  17 A , in accordance with some embodiments of the present disclosure; 
         FIG.  17 C  is a block diagram of an example system architecture for the example autonomous vehicle of  FIG.  17 A , in accordance with some embodiments of the present disclosure; 
         FIG.  17 D  is a system diagram for communication between cloud-based servers(s) and the example autonomous vehicle of  FIG.  17 A , in accordance with some embodiments of the present disclosure; 
         FIG.  18    is a block diagram of an example computing device suitable for use in implementing some embodiments of the present disclosure; and 
         FIG.  19    is a block diagram of an example data center suitable for use in implementing some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Systems and methods relating to three-dimensional (3D) surface estimation are disclosed. For example, the present disclosure describes systems and methods of reconstructing a 3D surface structure of a road or other component of an environment, for use by autonomous vehicles, semi-autonomous vehicles, robots, and/or other object types. Although the present disclosure may be described with respect to an example autonomous vehicle  1700  (alternatively referred to herein as “vehicle  1700 ” or “ego-vehicle  1700 ,” an example of which is described with respect to  FIGS.  17 A- 17 D ), this is not intended to be limiting. For example, the systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. In addition, although some embodiments may be described with respect to 3D surface structure estimation for autonomous driving, this is not intended to be limiting, and the systems and methods described herein may be used in augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and/or any other technology spaces where 3D surface structure estimation may be used. 
     At a high level, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the representation may be sparse, one or more densification techniques may be applied to generate a dense representation of the 3D surface structure, which may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle. 
     In an example technique, one or snore cameras may be affixed to or otherwise disposed on a vehicle or other object and used to capture image(s) of a 3D environment as the vehicle or object navigates (e.g., along a road) through the 3D environment, and the image(s) may be used to estimate the 3D surface structure of the road. Any suitable 3D structure estimation technique may be applied. For example, Structure from Motion (SFM) may be performed to estimate the 3D structure from sequences of images, and/or stereo vision may be applied to estimate 3D structure from images captured by multiple cameras and/or from multiple perspectives. Generally, 3D structure estimation may generate a representation of detected points in the 3D environment, such as a 3D point cloud. In some embodiments, outliers are removed using a statistical or clustering technique. In some cases, free space estimation may be applied to the captured image(s) to detect the road or other drivable space, and a segmentation mask or other representation of the detected road or drivable space may be used to select 3D points on the road surface e.g., points outside the road surface may be filtered out). The result may be a representation of the 3D surface structure of the road, such as a 3D point cloud. 
     Due to limitations of accuracy and computational power of the 3D structure estimation techniques, the representation of the 3D surface structure of the road may be sparse. As such, in some embodiments, the representation of the 3D surface structure may be densified using, for non-limiting examples, a Markov random field and/or a deep neural nets network (DNN) some cases, the 3D surface structure may be represented as a 2D height map. For example, a sparse 3D point cloud may be projected to form a projection image (e.g., a top-down projection image) representing sparse detections, and the projection image (e.g., 2D height map) may be densified to fill in missing values. 
     In an example densification technique using a Markov random field, the sparse and dense projection images may be modeled with an undirected graph, and Maximum a Posterior (MAP) inference may be used to estimate the most likely dense values given the sparse values. For example, each pixel in the dense projection image (e.g., the dense 2D height map) may be modeled with a corresponding node having edges that connect to neighboring nodes (e.g., one edge for each neighboring pixel). Each pixel of the sparse projection image (e.g., the sparse 2D height map) may be considered a noisy observation of the dense projection image and modeled as a node having an edge that connects to a corresponding node (pixel) from the dense projection image. For example, assume the graph has nodes that form two layers of a grid, where the bottom layer corresponds to ground truth (the dense projection image), and the top layer corresponds to a noisy observation (the sparse projection image). Assuming each node in the graph corresponds to a random variable, the Markov random field for the graph may model or otherwise represent a joint probability distribution of the random variables corresponding to the nodes in the graph. Knowing the joint probability distribution and a set of observed values (from the sparse projection image), values for the dense projection image (e.g., a height es ate for each pixel of the dense 2D height map) may be estimated using any known MAP inference algorithm, such as Iterative Conditional Mode, Gaussian Belief Propagation, or others. Thus, a Markov random field may be used to densify the representation of the 3D surface structure. 
     In some embodiments, a deep neural network (DNN) may be used to predict values for a dense representation of the 3D surface structure. For example, a sparse 3D point cloud may be projected to form a sparse projection image (e.g., a top-down projection image) representing sparse detections, and the sparse projection image may be fed into a DNN, such as convolutional neural network (CNN), to predict a dense projection image. In some embodiments, the DNN may include a common trunk (or stream of layers) connected to one or more heads (or at least partially discrete streams of layers) that predict different outputs. For example, a regression head may regress a particular type of information about the 3D surface structure, such as a height value for each pixel. In some embodiments, a confidence head may predict a confidence map with values representing the confidence of a corresponding regressed value predicted by the regression head. As such and as explained in more detail below, the DNN may be trained to predict a dense representation of the 3D surface structure (such as a dense 2D height map) and/or a corresponding confidence map. 
     In some embodiments, a sparse projection image may be normalized before being input into the DNN. For example, a 2D height map may store height values that include a bias corresponding to the height of the camera that captured the images from which the 2D height map was derived. As such, in some embodiments, the mean height of the height values in the 2D height map may be calculated and subtracted from all the height values to remove the bias, which may make it easier for the DNN to learn. in embodiments where a bias is removed from the DNN input, the bias may be reintroduced (e.g., added) to a predicted output of the DNN (e.g., to values predicted by the regression head). 
     In some embodiments, the DNN may include multiple inputs heads (or at least partially discrete streams of layers) for separate inputs. For example, the DNN may include a first input head that accepts a sparse projection image and a second input head that accepts an RGB image, such as a perspective view image from which the sparse projection image was generated. In this way, the DNN may learn from two different views of the underlying dense road profile (e.g., top-down and perspective, 3D point cloud space and 2D image space, etc.). In such an example, the multiple input heads may be connected to a common trunk that fuses the multiple input heads. As such, the DNN may be used to perform multi-modal learning by fusing information from different sources for better prediction. 
     In some embodiments, the DNN may include one or more recurrent layers (e.g., Gated Recurrent Units, Long Short Term Memory) to leverage temporal information. Including one or more recurrent layers may allow the DNN to leverage information from previous time slices, resulting in better predictions and more stable densification results over time. 
     Training data for the DNN may be generated in various ways and used to train the DNN to predict a dense representation of 3D surface structure, given a sparse representation. Generally, real-world data and/or virtual data may be collected and used to derive training data (e.g., sparse input data and/or ground truth representations of 3D surface structure). The type of training data may depend on the implementation of the DNN. For example, input training data may include sparse representations of 3D surface structure (e.g., sparse height maps) and/or image data from some other perspective (e.g., images of a perspective view), Ground truth training data may include dense representations of 3D surface structure (e.g., dense height maps) and/or segmentation masks (e.g., identifying a desired surface such as a road or other drivable space). 
     Example techniques for generating training data include 1) rendering frames of virtual sensor data, segmentation masks, and depth maps; 2) parametric mathematical modeling of a 3D road surface; 3) collecting and annotating real sensor data from a single LiDAR sensor; and/or 4) collecting and annotating real sensor data accumulated from multiple LiDAR sensors. 
     In an example technique for generating training data, a simulation may be performed to render frames of virtual sensor data (e.g., images) representing realistic driving scenarios and to generate corresponding segmentation masks (e.g., ground truth segmentation masks identifying a desired surface such as a road or other driveable space) and depth maps. For any given rendered frame, a 3D surface structure (e.g., 3D road surface) may be estimated from the frame, as described herein, and the resulting sparse values may be projected to form a sparse projection image (e.g., a 2D height map), which may be used as input training data. 
     To generate a corresponding ground truth dense projection image, for any given frame rendered from the perspective of a virtual sensor, a 3D point cloud or other representation of 3D structure may be generated by unprojecting range values from the corresponding depth map into the 3D environment using the location and orientation of the virtual sensor. The segmentation mask may be used to select 3D points on the road surface (e.g., points outside the road surface may be filtered out). Additionally or alternatively, the segmentation mask may be used to select points from the depth map that are on the road surface, and the selected points may be unprojected into the 3D environment to generate the 3D points on the road surface. in some cases, the resulting representation of the 3D road surface may still be sparse. As such, in some embodiments, missing values may be interpolated using a triangulation algorithm. For example, Delaunay triangulation may be performed in 2D (e.g., by projecting the 3D points to form a projection image and performing Delaunay triangulation in the projection image) or in 3D (by computing a surface mesh of triangles surrounding the 3D point cloud), and points may be sampled from the triangles to generate a desired number of points for a ground truth dense projection image. For example, a ground truth 2D height map may be sampled from triangles generated by performing 2D Delaunay triangulation in a projected height map, or by projecting 3D points sampled from a surface mesh generated by performing 3D Delaunay triangulation. As such, the dense projection image and/or segmentation mask may be used as ground truth, paired with the input sparse projection image, and included in a training dataset. 
     In another example technique for generating training data, synthetic training data may be generated using parametric mathematical modeling of a 3D road surface. In an example embodiment, a synthetic 3D road surface may be generated by first sampling longitudinal values (e.g., from 0 to 300 m), then computing lateral values as a second order polynomial of the longitudinal values, using values for polynomial constants sampled to simulate changes in road direction (e.g., left curve, right turn, etc.). The height of the synthetic 3D road surface may be computed as a linear combination of Fourier bases, using different sampled values for the number of bases, weight for a particular basis, and frequency for a particular basis to simulate changes in surface height. These steps generate a longitudinal 3D curve, which may be expanded to a 3D surface by drawing a lateral 3D curve through each point on the longitudinal 3D curve using sampled values for the angle between the lateral 3D curve and the ground plane, to simulate changes in lateral surface slope. Each lateral 3D curve may be sampled to generate a dense 3D point cloud, which may be projected to form a synthetic ground truth projection image (e.g., a ground truth 2D height map). 
     To generate a corresponding sparse projection image for the input training data, a known pattern may be applied to the ground truth projection image to cancel out a subset of pixel values (e.g., setting those pixel values to zero) to simulate unobserved values. For example, frames of real-world data may be collected, a 3D surface structure (e.g., of a 3D road surface) may be estimated from each frame (as described herein), the estimated 3D structure (e.g., a 3D point cloud) may be projected to form a projection image (e.g., a sparse 2D height map), and a corresponding binary map that represents which pixels of projection image are present or observed may be generated. A plurality of binary maps may be generated from real-world data, and one of the binary maps may be randomly chosen and multiplied by a ground truth projection image to generate a corresponding synthetic sparse projection image. As such, a sparse projection image may be generated for each ground truth projection image, and the pairs of synthetic sparse and ground truth projection images may be included in a training dataset. 
     Additionally or alternatively, training data may be generated from real-world data. For example, one or more vehicles may collect sensor data from an equipped sensor, such as one or more cameras and LiDAR. sensors, while navigating through a real-world (e.g., physical) environment. To generate ground truth training data, collected LiDAR data (e.g., LiDAR point clouds) may be smoothed, outliers may be removed, and the LiDAR data may be temporally and/or spatially aligned with corresponding frames of image data. In some embodiments, to densify the collected LiDAR data, missing values may be interpolated using Delaunay triangulation, and/or LiDAR data that is triggered and/or captured from the same time slice by multiple LiDAR sensors may be accumulated in order to densify the collected data. The LiDAR data may be labeled to identify 3D points on a surface of interest (e.g., a 3D road surface), and a representation of the identified 3D points (e.g., a 3D point cloud, a projection image)may be designated as ground truth training data. In some embodiments, a corresponding frame of image data may be classified to generate a ground truth segmentation mask identifying the desired surface. 
     To generate corresponding input training data, a 3D surface structure (e.g., 3D road surface) may be estimated from a frame of image data (as described herein), and a representation of the estimated 3D structure (e.g., a sparse 3D point cloud, a sparse projection image) may be designated as input training data. As such, a corresponding sparse projection image, frame of image data, dense projection image, and/or segmentation mask may be grouped together and included in a training dataset. 
     During training, any suitable loss function may be used to compare predicted output(s) with ground truth to update the DNN. In an example embodiment where the DNN includes a regression head that predicts a height map, a loss function may compare predicted and ground truth height maps and multiply by a ground truth segmentation mask indicating the surface to be densified, effectively cancelling out updates to the DNN based on predictions that occur outside the region to be densified. In this example, the DNN may learn to predict heights maps using ground truth height maps and segmentation masks. In another embodiment where the DNN includes a regression head that predicts a height map and a confidence head that predicts a confidence map corresponding to the height map, a loss function may compare predicted and ground truth heights and compensate based on predicted confidence values. In this example, the DNN may learn to predict both height and confidence maps from ground truth height maps. As such, the DNN may learn how to perform densification by learning a mapping between sparse and dense representations of 3D structure. 
     As such, the techniques described herein may be used to observe and reconstruct a 3D surface such as a 3D road surface, and a representation of the 3D surface structure (and/or corresponding confidence values) may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle. Generally, the techniques described herein may generate a more accurate representation of road surfaces than prior reconstruction techniques. Furthermore, the present techniques may be used to generate a representation of road surfaces with sufficient accuracy and range for certain autonomous driving applications, unlike prior based reconstruction techniques. As such, the representation of road surfaces generated using the present techniques may enable improved navigation, safety, and comfort in autonomous driving. For example, an autonomous vehicle may be better equipped to adapt the vehicle&#39;s suspension system to match the current road surface (e.g., by compensating for bumps in the road), to navigate the vehicle to avoid protuberances (e.g., dips, holes) in the road, and/or to apply an early acceleration or deceleration based on an approaching surface slope in the road. Any of these functions may serve to enhance safety, improve the longevity of the vehicle, improve energy-efficiency, and/or provide a smooth driving experience. 
     Example 3D Surface Reconstruction Pipeline 
     With reference to  FIG.  1   ,  FIG.  1    is a data flow diagram illustrating an example 3D surface reconstruction pipeline  100  for a 3D surface reconstruction system, in accordance with some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead 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. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. 
     At a high level, the pipeline  100  may estimate and generate a representation of an observed 3D surface structure, such as that of a 3D road surface or other environmental part, based on image data  102  of a three-dimensional (3D) environment. The image data  102  may be captured by one or more cameras  101  of an ego-object or ego-actor (e.g., autonomous vehicle  1700  of  FIGS.  17 A- 17 D , also referred to as the vehicle  1700 ) as the ego-object or ego-actor navigates through the 3D environment. A 3D structure estimator  105  may process the image data  102  to generate a representation of a 3D surface structure of interest (e.g., sparse detection data  110 ), which may comprise a 3D point cloud. Since the estimated 3D surface structure may be sparse, a detection densifier  115  may densify the sparse detection data  110  to generate a denser representation of the 3D surface structure e.g., dense detection data  120 ), which may comprise a two-dimensional (2D) top-down height map and/or a 3D point cloud. The dense detection data  120  may represent the observed 3D surface structure, such as a 3D road surface or other environmental part. As such, the dense detection data  120  or other representation of the observed 3D surface structure may be provided to, and used by, control component(s) of the ego-object or ego-actor software stack  122  and/or components of the autonomous vehicle  1700  of  FIGS.  17 A- 17 D  such as controller(s)  1736 , ADAS system  1738 , and/or SOC(s)  1704 ) to aid the ego-object or ego-actor in performing one or more operations within the 3D environment, such as path planning, obstacle or protuberance avoidance, adapting a suspension system of the ego-object or ego-actor to match the current road surface, applying an early acceleration or deceleration based on an approaching surface slope, mapping, and/or others. 
     Generally, 3D surface reconstruction may be performed using image data  102  from any number and any type of camera e.g., the camera(s)  101 ), such as those described below with respect to the autonomous vehicle  1700  of  FIGS.  17 A- 17 D . For example, the camera(s)  101  may include one or more cameras of an ego-object or ego-actor, such as stereo camera(s)  1768 , wide-view camera(s)  1770  (e.g., fisheye cameras), infrared camera(s)  1772 , surround camera(s)  1774  (e.g., 360 degree cameras), and/or long-range and/or mid-range camera(s)  1798  of the autonomous vehicle  1700  of  FIGS.  17 A- 17 D —and the cameras(s)  101  may be used to generate the image data  102  of the 3D environment around the ego-object or ego-actor. In embodiments where multiple cameras are used, the multiple cameras may view a common region of the 3D environment with an overlapping portion of their respective fields of view such that the image data  102  (e.g., images) from different cameras represents the common region. 
     The 3D structure estimator  105  estimates the 3D structure of a particular surface (e.g., sparse detection data  110 ) from the image data  102  using Structure from Motion (SfM), stereo vision, and/or some other 3D surface structure estimation technique. SfM and stereo vision are ranging techniques that estimate 3D structure from multiple images. StM estimates 3D structure front sequences of images (e.g., captured by the same camera  101 ), while stereo vision estimates 3D structure from multiple images captured at substantially the same time from different perspectives (e.g., by different cameras  101 ). in some embodiments, image de-warping and/or distortion correction may be applied to the image data  102  prior to estimating 3D structure. A segmentation mask or other classification data may be used (e.g., by overlaying the cation data on the image data  102 ) to select points from the estimated 3D structure that are on a desired surface, such as road surface. As such, the 3D structure estimator  105  may generate a representation of the 3D structure of a desired surface (e.g., sparse detection data  110 ), which may include a 3D point cloud (e.g., in 3D world coordinates). 
       FIG.  2    is a diagram illustrating an example implementation of the 3D structure estimator  105 , in accordance with some embodiments of the present disclosure. In  FIG.  2   , the 3D structure estimator  105  includes Structure from Motion estimator  210 , stereo vision estimator  230 , outlier remover  220 , and road surface point selector  240 . 
     The Structure from Motion estimator  210  may perform any known SfM technique to estimate 3D structure from the image data  102 . For example, the Structure from Motion estimator  210  may reconstruct 3D positions of features represented in the image data  102  from feature trajectories detected over time. In some embodiments, the Structure from Motion estimator  210  may perform direct estimation of 3D positions without intermediate estimation of feature trajectories. Generally, any known SfM technique may be applied, including incremental SfM, global SfM, out-of-core SfM, and/or others. As such, the Structure from Motion estimator  210  may generate a representation of the 3D structure of features represented in the image data  102 . 
     The stereo vision estimator  230  may estimate 3D structure by applying stereo vision (or a stereo algorithm) to image data  102  representing different perspectives. For example, the stereo vision estimator  230  may project image data  102  from multiple cameras (e.g., the camera(s)  101 ) into a common image space or plane and compare the projected image data using any suitable metric to generate a disparity map, which may represent differences in depth (e.g., in image coordinates, which may be inversely proportional to depth). The disparity map may be projected into a 3D point cloud using the known position and orientations of the multiple cameras. As such, the stereo vision estimator  23 (s) may generate a representation of the 3D structure of features represented in the image data  102 . 
     In some embodiments, outlier remover  220  may evaluate the estimated 3D structure and remove outliers. For example, in some embodiments in which the estimated 3D structure takes the form of a 3D point cloud, the 3D point cloud may be projected to form a projection image, such as a top-down projection image, to produce columns of points (e.g., 0.1 meter×0.1 meter beams). Then for each column, any suitable statistical or clustering technique may be applied to identify a representative point for the column. By way of non-limiting example, a median or mean value of the points in a column may be identified as a representative point for the column. Taking the top-down projection image as an example, the median or mean height of the points in each column may be identified as the height of a representative point for the column. In some embodiments, some other clustering technique may be applied to group points from a 3D point cloud and identify representative points (e.g., cluster centers or means). As such, outlier remover  220  may update the estimated 3D structure with the identified points, and/or otherwise detect and remove outliers. 
     Generally, the estimated 3D structure may include 3D points of parts of the 3D environment and objects in the 3D environment that are represented in the image data  102 . As such, road surface point selector  240  may identify points that belong to a particular surface of interest, such as a 3D road surface or other environment part. For example, a segmentation mask or other classification data may be generated or otherwise obtained, and the road surface point selector  240  may use the segmentation mask or other classification data to select the points. 
     More specifically, in some embodiments, object detection, free space estimation, and/or image segmentation may be applied (e.g., by the 3D estimator  105  or some other component) to classify, segment, and/or predict regions (e.g., pixels) of the image data  102  that are part of a desired class. For example, one more deep learning models (e.g., a convolutional neural network) may be trained to predict one or more segmentation masks and/or confidence maps representing pixels that belong to a drivable road surface or other navigable space, other environmental parts (e.g., sidewalks, buildings), animate objects, and/or other classes. In some embodiments, an individual image (e.g., an RBG image) captured by a single camera may be segmented and/or classified. In some cases, a composite image (e g., an RBG image) may be generated by stitching together images captured by multiple cameras, and the composite image may be segmented and/or classified. As such, a segmentation mask or other classification data delineating or representing the road or drivable space (or some other desired surface) may be obtained and/or generated (e.g., from the predicted masks or confidence maps). 
     As such, the road surface point selector  240  may use the segmentation mask or other classification data to elect points from the estimated 3D structure that belong to the class represented by the segmentation mask or other classification data. Any suitable selection technique may be applied. In some embodiments, 3D points from the estimated 3D structure may be back-projected into the segmentation mask (e.g., using the known location and orientation of the camera  101  that captured the image data  102  from which the segmentation mask was generated), and projected points that land inside the predicted region may be selected (and/or projected points that land outside the predicted region may be removed). As such, road surface point selector  240  may generate or otherwise identify the points of the estimated 3D surface structure that belong to a desired surface, such as the 3D road surface. In embodiments that perform outlier removal, the outlier remover  220  and the road surface point selector  240  may be invoked in any order. The resulting representation of the estimated 3D surface structure (e.g., sparse detection data  110 ) may take any form, such as a 3D point cloud. 
     Although certain embodiments are described in which 3D surface reconstruction uses the image data  102  captured by camera(s)  101 , in some embodiments, other sensor data may be additionally or alternatively be used. By way of non-limiting example, one or more LiDAR sensors or RADAR sensors may be used to capture sparse detection data  110  (e.g., a LiDAR or RADAR point cloud). 
     Returning now to  FIG.  1   , since the estimated 3D surface structure may be sparse, the detection densifier  115  may densify the sparse detection data  110  to generate a denser representation of the 3D surface structure (e.g., the dense detection data  120 ). Generally, the sparse detection data  110  may take any suitable form, such as a sparse 3D point cloud. The sparse detection data  110  may be projected to form a projection image, s as two-dimensional (2D) top-down height map o ∈ N m×n  with missing values. The notation N m×n  represents a projection image (e.g., an overhead image) with spatial dimensions m×n(e.g., in pixels) and with a desired ground sampling distance, where each pixel in the projection image may store a floating point value (e.g., a height value). This sparse 2D height map may be considered a partial noisy observation of the 3D surface structure. In this example, the dense detection data  120  may take the form of, or otherwise represent, a 2D top-down height map g  531   N m×n , and the detection densifier  115  may densify the sparse detection data  110  by inferring g, given o. In some embodiments, the detection densifier  115  may perform this inference using one or more machine learning models, such as a Markov random field and/or one or more deep learning models (e.g., one or more deep neural networks (DNNs)). The resulting representation of the 3D surface structure (e.g., dense detection data  120 ) may take any suitable form, such as 2D height map and/or a 3D point cloud. 
       FIG.  3    is a diagram illustrating an example implementation of the detection densifier  115 , in accordance with some embodiments of the present disclosure. In  FIG.  3   , the detection densifier  115  includes Markov random field surface estimator  310  and deep learning model surface estimator  320 . 
     In some embodiments, the Markov random field surface estimator  310  may densify the sparse detection data  110  to generate a denser representation of the 3D surface structure (e.g., the dense detection data  120 ). For example, the Markov random field surface estimator  310  may densify a sparse 2D top-down height map o (or other sparse projection image), by inferring a dense 2D top-down height map g (or other dense projection image), given o. More specifically, the relationship between g and o may be modeled with a probabilistic model such as a Markov random field, and the Markov random field surface estimator  310  may perform Maximum a Posterior (MAP) inference to estimate the most likely g given the probabilistic model and a set of observed values o. In some embodiments, a Markov random field (e.g., an undirected graph) may be used as the probabilistic model for its ability to model spatial dependencies, such as those that exist in certain 3D surface structures, such as 3D road surfaces, where local regions of surface are often smooth. As such, in some embodiments, the relationship between a sparse height map o and a dense height map g may be modeled with an undirected graph. 
       FIG.  4    is a diagram illustrating an example undirected graph  400  that models the relationship between a sparse height map o and a dense height map g, in accordance with some embodiments of the present disclosure. For example, each pixel in the dense height map g may be modeled with a corresponding node (e.g., g 1 , g 2 , g 3 , g 4 , in  FIG.  4   ) having edges that connect to neighboring nodes (e.g., one edge for each neighboring pixel). In some embodiments, nodes corresponding to interior pixels in g may have four edges (e.g., connecting to horizontally and vertically adjacent nodes), eight edges (e.g., connecting to horizontally, vertically, and diagonally adjacent nodes), or otherwise.  FIG.  4    illustrates a portion of an undirected graph corresponding to interior pixels in g where each corresponding node g 1 , g 2 , g 3 , g 4  has four edges. Nodes corresponding to edge pixels in g may have three edges (e.g., connecting to horizontally and vertically adjacent nodes), five edges (e.g., connecting to horizontally, vertically, and diagonally adjacent nodes), or otherwise. Nodes corresponding to corner pixels in g may have two edges (e.g., connecting to horizontally and vertically adjacent nodes), three edges (e.g., connecting to horizontally, vertically, and diagonally adjacent nodes), or otherwise. 
     Furthermore, each pixel in the sparse height map o may be considered a noisy observation of a corresponding pixel in the dense height map g. Thus, each pixel in the sparse height map o may be modeled with a node having an edge that connects to a corresponding node (representing the corresponding pixel) from the dense height map g.  FIG.  4    illustrates a portion of an undirected graph with nodes o 1 , o 2 , o 3 , o 4  (representing pixels in the sparse height map o) connected to nodes g 1 , g 2 , g 3 , g 4  (representing pixels in the dense height map g). 
     Said in another way, in some embodiments, a desired surface to be modeled may be viewed from a desired perspective (e.g., top-down) and divided into a 2D grid, and an undirected graph may be formed with a 3D grid having two layers of nodes, each layer having a node for each cell or intersection point in the 2D grid. The bottom layer may correspond to ground truth (e.g., the dense height map g), and the top layer may correspond to a noisy observation (the sparse height map o). Note that the layer corresponding to a noisy observation may include a node for each cell or intersection point in the 2D grid, even though the noisy observation may be sparse. As such, some nodes corresponding to pixels in the sparse height map o may not have corresponding observations. 
     Having modeled the relationship between a sparse height map o and a dense height map g with a Markov random field (e.g., an undirected graph), each node in the model may be considered a random variable such that the joint probability distribution of all the random variables may be written as: 
     
       
         
           
             
               
                 
                   
                     P 
                     ⁡ 
                     ( 
                     
                       g 
                       , 
                       o 
                     
                     ) 
                   
                   = 
                   
                     
                       
                         
                           
                             
                               
                                 
                                   1 
                                   Z 
                                 
                                 
                                   ∏ 
                                   
                                     i 
                                     , 
                                     j 
                                   
                                 
                               
                             
                             
                               
                                 
                                   ψ 
                                   ⁡ 
                                   ( 
                                   
                                     
                                       g 
                                       i 
                                     
                                     . 
                                     
                                       g 
                                       j 
                                     
                                   
                                   ) 
                                 
                                    
                                 
                                   ∏ 
                                   i 
                                 
                               
                             
                           
                         
                       
                       
                         
                           
                             ϕ 
                             ⁡ 
                             ( 
                             
                               
                                 g 
                                 i 
                               
                               , 
                               
                                 o 
                                 i 
                               
                             
                             ) 
                           
                           , 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Eq 
                     . 
                         
                     1 
                   
                   ) 
                 
               
             
           
         
       
     
     where ψ(g i , g j ) is a pairwise potential term representing the height relationship between neighboring pixels in g, ϕ(g i , o i ) is a unary potential term indicating the relationship between true height g i  and the observed noisy height o i , and Z is a normalizing constant that ensures that the component distributions sum to one, 
     In some embodiments, to represent special dependencies between neighboring pixels in g, the pairwise potential term may take the following form: 
       ψ( g   i   , g   j )=exp(− w   ij ( g   i   −g   j ) 2 ),   (Eq. 2)
 
     where w ij  specifies the weight between nodes (g i , g j ) corresponding to neighboring pixels, as discussed in more detail below. To represent a contribution from observed pixels, o may be assumed to be a noisy version of g: 
         o   i   =g   i +noise, if pixel i is observed.   (Eq. 3)
 
     As such, in some embodiments, the unary potential term may be given as: 
       ϕ( g   i   , o   i )=exp(− c   i ( g   i   −o   i ) 2 ),   (Eq. 4)
 
     where c i  specifies a weight for pixel i, and c i  may be set to 0 if pixel i is not observed. Generally, any suitable weights may be selected for w ij  and c i , for example, to place more emphasis on the pairwise potential term (e.g., to emphasize relationships between neighboring pixels) or unary potential term (e.g., if there is relatively more confidence in the observed values). In some embodiments, a common weight may be selected for all pairs w ij , a common weight may be selected for each c i  corresponding to an observed pixel, a hyperparameter may be selected for each weight to form a desired ratio between w ij  and c i , and/or otherwise. 
     With the joint probability distribution P (g, o) and a set of observed values of the sparse height map o (or other sparse detection data  110 ), the Markov random field surface estimator  310  may predict a value (e.g., a height estimate) for each pixel in the dense height map g (or other dense detection data  120 ) using any known MAP inference algorithm, such as Iterative Conditional Mode, Gaussian Belief Propagation, or others, Generally, the Markov random field surface estimator  310  may estimate a dense representation g of a 3D surface structure from a sparse representation o (e.g., a noisy and partial observation) of the 3D surface structure. The result may be a representation of the 3D surface structure of the road, such as a 2D height map, which may be transformed into a 3D point cloud (e.g., in 3D world coordinates). In operation, the Markov random field surface estimator  310  may repetitively operate on successive instances of the sparse detection data  110  (e.g., derived from sensor data captured during successive time slices separated by some designated internal) to predict successive instances of the dense detection data  120  (e.g., successive representations of corresponding portions of the 3D surface structure of the road), for example, as the vehicle  1700  of  FIGS.  17 A- 17 D  moves through the 3D environment. 
     Additionally or alternatively, the deep learning model surface estimator  320  may densify the representation of the 3D surface structure. For example, the deep learning model surface estimator  320  may densify the sparse detection data  110  (e.g., a sparse 2D top-down height map o) by interring values of the dense detection data  120  (e.g., a dense 2D top-down height map g) from the sparse detection data  110  using one or more deep learning models. As such, the deep learning model surface estimator  320  may learn the relationship between the sparse detection data  110  (e.g., a representation of sparse and noisy observations, such as a projection image of a 3D point cloud) and the dense detection data  120  (e.g., a denser representation of 3D surface structure, such as projection image of a dense 3D point cloud). 
       FIG.  5    is a data flow diagram illustrating an example implementation of the deep learning model surface estimator  320 , in accordance with some embodiments of the present disclosure. At a high level, the deep learning model surface estimator  320  may include a pre-processor  510 , one or more deep learning model(s)  535  configured to predict values of the dense detection data  120 , and a post-processor  575 . The pre-processor  510  may encode the sparse detection data  110  into input data  530  that the deep learning model(s)  535  support, and the input data  530  may be fed into the deep learning model(s)  535  to predict regression data  570  and/or confidence data  580 . In some embodiments, the repression data  570  and/or the confidence data  580  predicted by the deep learning model(s)  535  to may be used as the dense detection data  120 . In some embodiments, the pre-processor  510  may include a normalizer  520  that removes a bias from the input data  530 , in which case, the post-processor  575  may reintroduce the bias into the regression data  570  predicted by the deep learning model(s)  535  to generate at least a portion of the dense detection data  120 . 
     In some embodiments, the pre-processor  510  includes an encoder  515  that encodes the sparse detection data  110  into a representation that the deep learning model(s)  535  support. By way of non-limiting example, in some embodiments where the sparse detection data  110  includes a sparse 3D point cloud, the encoder  515  may project the sparse 3D point cloud to form a sparse projection image (e.g., a top-down height map). In some cases (e.g., without the normalizer  520 ), the resulting sparse projection image may be used as the input data  530  and fed into the deep learning model(s)  535  to predict the regression data  570  (e.g., a dense projection image such as a top-down height map) and/or the confidence data  580 . In some cases, the regression data  570  and/or the confidence data  580  predicted by the deep learning model(s)  535  to may be used as the dense detection data  120 . 
     In some cases, the sparse detection data  110  and/or encoded sparse detection data (e.g., a sparse projection image) may include a bias. As such, in some embodiments, the pre-processor  510  may include a normalizer  520  that removes the bias or otherwise normalizes the sparse detection data  110  and/or the encoded sparse detection data. For example, in some embodiments where the sparse detection data  110  includes a sparse 3D point cloud, and the encoder  515  projects the sparse 3D point cloud to form a 2D height map, the 2D height map may store height values that include a bias corresponding to the height of the camera that captured the images from which the 2D height map was derived. As such, in some embodiments, the normalizer  520  calculates the mean height of the height values (e.g., of a desired surface) in the 2D height map and subtracts the mean height from all the height values (e.g., of the desired surface) to remove the bias. The resulting 2D height map (or other normalized, encoded sparse detection data) may be used as the input data  530  and fed into the deep learning model(s)  535  to predict the regression data  570  (e.g., a dense 2D height map) and/or the confidence data  580 , and the post-processor  575  may reintroduce the bias to the predicted output (e.g., by adding the bias to some or all predicted values of the regression data  570 ). 
     Turning now to the deep learning model(s)  535 , in some embodiments, the deep learning model(s)  535  may be implemented using a DNN, such as a convolutional neural network (CNN). Although certain embodiments are described with the deep learning model(s)  535  being implemented using neural network(s) and specifically CNN(s), this is not intended to be limiting. For example, and without limitation, the deep learning model(s)  535  may additionally or alternatively include any type of machine learning model, such as a machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), K means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, Markov random fields, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and/or other types of machine learning models. 
     In some embodiments, the deep learning model(s)  535  may include a common trunk (or stream of layers) with one or more heads (or at least partially discrete streams of layers) for predicting different outputs based on the input data  530 . For example, the deep learning model(s)  535  may include, without limitation, a feature extractor (e.g., a DNN, an encoder/decoder, etc.) including convolutional layers, pooling layers, and/or other layer types, where the output of the feature extractor is provided as input to each of a plurality of heads that predict different outputs. The different heads may receive parallel inputs, in some examples, and thus may produce different outputs from similar input data. In the example of  FIG.  5   , the deep learning model(s)  535  is illustrated with an example architecture that extracts features from the input data  106  and executes regression on the extracted features. More specifically, the deep learning model(s)  535  may include an encoder/decoder  540 , a regression head  545 , and/or a confidence head  550 . 
     The encoder/decoder  540  may be implemented using encoder and decoder components with skip connections (e.g., similar to ResNet, Feature Pyramid Network, U-Net, etc.). For example, the encoder/decoder  540  may accept the input data  530  (e.g., a projection image, a 2D height map) and apply various convolutions, pooling, and/or other types of operations to extract features into some latent space. In  FIG.  5   , the encoder/decoder  540  is illustrated with an example implementation involving (from left to right) an encoding (contracting) path and a decoding (expansive) path, Along the contracting path, each resolution may include any number of layers (e.g., convolutions, dilated convolutions, inception blocks, etc.) and a downampling operation (e.g., max pooling). Along the expansive path, each resolution may include any number of layers (e.g., deconvolutions, upsampling followed by convolution(s), and/or other types of operations). In the expansive path, each resolution of a feature map may be upsampled and concatenated (e.g., in the depth dimension) with feature maps of the same resolution from the contracting path. In this example, corresponding resolutions of the contracting and expansive paths may be connected with skip connections, which may be used to add or concatenate feature maps from corresponding resolutions. As such, the encoder/decoder  540  may extract features into some latent space, and a representation of the extracted features may be input into the regression head  545  and/or the confidence head  550 . 
     The regression head  545  may include any number of layers (e.g., convolutions, pooling, classifiers such as softmax, and/or other types of operations, etc.) that predict a particular type of information about the 3D surface structure of interest (e.g., a height value for each pixel) from the output of the encoder/decoder  540 . In some embodiments, the regression data  570  predicted by the regression head  545  may take the form of a 2D height map with each pixel storing a floating-point number that regresses the height of the portion of the 3D surface represented by the pixel. 
     The confidence head  550  may include any number of layers (e.g., convolutions, pooling, classifiers such as softmax, and/or other types of operations, etc.) that predict the confidence data  580  for the regression data  570 , from the output of the encoder/decoder  540 . For example, in some embodiments where the regression data  570  takes the form of a 2D height map ∈°N m×n , the confidence data  580  may take the form of a corresponding confidence map ∈ N m×n with pixels that store a floating-point number that regresses a representation of the confidence of a corresponding predicted value in the 2D height map. 
     As such,  FIG.  5    illustrates an embodiment of the deep learning model(s)  535  that predicts regression data  570  (e.g., a 2D height map) and confidence data  580 . However, any number of variations may be implemented. For example, in some embodiments, the deep learning model(s)  535  may be implemented with a single output channel corresponding to the regression head  545 . In another example, in some embodiments, the deep learning model(s)  535  may include one or more recurrent layers (e.g., Gated Recurrent Units, Long Short Term Memory) to leverage temporal information. In some cases, including one or more recurrent layers may allow the deep learning model(s)  535  to leverage information from previous time slices, resulting in better predictions and more stable densification results over time. In yet another example, the deep learning model(s)  535  may be implemented with multiple inputs heads that accept different inputs, such as an input image (e.g., an RGB image) with a perspective view and a projection image with another view (e.g., a top-down height map).  FIG.  6    illustrates such an example. 
     More specifically,  FIG.  6    is a data flow diagram illustrating an example implementation of the deep learning model surface estimator  320  of  FIG.  3    with a deep learning model(s) that includes multiple input heads. Generally, the implementations of the deep learning model surface estimator  320  illustrated in  FIGS.  5  and  6    have similar components, except the implementation illustrated in  FIG.  6    extends the deep learning model(s)  535  to include an image encoder  610 . As such, whereas the implementation of the deep learning model(s)  535  illustrated in  FIG.  5    includes a single input head (e.g., the encoder portion of the encoder/decoder  540 ) that accepts the input data  530  (e.g., a projection of a 3D point cloud), the implementation of the deep learning model(s)  535  illustrated in  FIG.  6    additionally accepts the image data  102  (e.g., an RBG frame) into a second input head (e.g., the image encoder  610 ). Generally, the image encoder  610  (and/or any other input head) may include any number of layers (e.g., convolutions, pooling, and/or other types of operations) to extract features into some latent space, and the extracted features may be combined (e.g., concatenated) with extracted features from the encoder portion of the encoder/decoder  540  (and/or extracted features from other input heads). As such, in some embodiments such as the one illustrated in  FIG.  6   , the deep learning model(s)  535  may learn from two different views of an observed surface structure (e.g., top-down and perspective, 3D point cloud space and 2D image space, etc.). 
     As such and returning to  FIG.  3   , the deep learning model surface estimator  320  of  FIG.  3    may be implemented using a variety of architectures for a constituent deep learning model (e.g., the deep learning model(s)  535  of  FIG.  5  or  6   ) and/or some other machine learning model(s) to predict the dense detection data  120  from the sparse detection data  110 . The result may be a representation of the 3D surface structure of the road, such as a 2D height map, which may be transformed into a 3D point cloud (e.g., in 3D world coordinates). In operation, the deep learning model surface estimator  320  may repetitively operate on successive instances of the sparse detection data  110  (e.g., derived from sensor data captured during successive time slices separated by some designated internal) to predict successive instances of the dense detection data  120  (e.g., successive representations of corresponding portions of the 3D surface structure of the road), for example, as the vehicle  1700  of  FIGS.  17 A- 17 D  moves through the 3D environment. 
     Returning to  FIG.  1   , once the 3D structure of the detected surface been determined, positional values that are not already in 3D world coordinates may be converted to 3D world coordinates, associated with a corresponding class label identifying the detected surface (e.g., a road), and/or may be provided for use by the vehicle  1700  of  FIGS.  17 A- 17 D  in performing one or more operations. For example, the dense detection data  120  (e.g., a 3D point cloud, a projection image, corresponding labels) may be used by control component(s) of the vehicle  1700 , such as an autonomous driving software stack  122  executing on one or more components of the vehicle  1700  of  FIGS.  17 A- 17 D  (e.g., the SoC(s)  1704 , the CPU(s)  1718 , the GPU(s)  1720 , etc.). For example, the vehicle  1700  may use this information (e.g., instances of obstacles) to navigate, plan, or otherwise perform one or more operations (e.g., obstacle or protuberance avoidance, lane keeping, lane changing, merging, splitting, adapting a suspension system of the ego-object or ego-actor to match the current road surface, applying an early acceleration or deceleration based on an approaching surface slope, mapping, etc.) within the environment. 
     In some embodiments, the dense detection data  120  may be used by one or more layers of the autonomous driving software stack  122  (alternatively referred to herein as “drive stack  122 ”). The drive stack  122  may include a sensor manager (not shown), perception component(s) (e.g., corresponding to a perception layer of the drive stack  122 ), a world model manager  126 , planning component(s)  128  (e.g., corresponding to a planning layer of the drive stack  122 ), control component(s)  130  (e.g., corresponding to a control layer of the drive stack  122 ), obstacle avoidance component(s)  132  (e.g., corresponding to an obstacle, or collision avoidance layer of the drive stack  122 ), actuation component(s)  134  (e.g., corresponding to an actuation layer of the drive stack  122 ), and/or other components corresponding to additional and/or alternative layers of the drive stack  122 . The process  100  may, in some examples, be executed by the perception component(s) which may feed up the layers of the drive stack  122  to the world model manager, as described in more detail herein. 
     The sensor manager may manage and/or abstract sensor data from the sensors of the vehicle  1700 . For example, and with reference to  FIG.  17 C , the sensor data may be generated (e.g., perpetually, at intervals, based on certain conditions) by RADAR sensor(s)  1760 . The sensor manager may receive the sensor data from the sensors in different formats (e.g., sensors of the same type may output sensor data in different formats), and may be configured to convert the different formats to a uniform format (e.g., for each sensor of the same type). As a result, other components, features, and/or functionality of the autonomous vehicle  1700  may use the uniform format, thereby simplifying processing of the sensor data. In some examples, the sensor manager may use a uniform format to apply control back to the sensors of the vehicle  1700 , such as to set frame rates or to perform gain control. The sensor manager may also update sensor packets or communications corresponding to the sensor data with timestamps to help inform processing of the sensor data by various components, features, and functionality of an autonomous vehicle control system. 
     A world model manager  126  may be used to generate, update, and/or define a world model. The world model manager  126  may use information generated by and received from the perception component(s) of the drive stack  122  (e.g., the locations of detected obstacles). The perception component(s) may include an obstacle perceiver, a path perceiver, a wait perceiver, a map perceiver, and/or other perception component(s). For example, the world model may be defined, at least in part, based on affordances for obstacles, paths, and wait conditions that can be perceived in real-time or near real-time by the obstacle perceiver, the path perceiver, the wait perceiver, and/or the map perceiver. The world model manager  126  may continually update the world model based on newly generated and/or received inputs (e.g., data) from the obstacle perceiver, the path perceiver, the wait perceiver, the map perceiver, and/or other components of the autonomous vehicle control system. 
     The world model may be used to help inform planning component(s)  128 , control component(s)  130 , obstacle avoidance component(s)  132 , and/or actuation component(s)  134  of the drive stack  122 . The obstacle perceiver may perform obstacle perception that may be based on where the vehicle  1700  is allowed to drive or is capable of driving (e.g., based on the location of the drivable or other navigable paths defined by avoiding detected obstacles in the environment and/or detected protuberances in the road surface), and how fast the vehicle  1700  can drive without colliding with an obstacle (e.g., an object, such as a structure, entity, vehicle, etc.) that is sensed by the sensors of the vehicle  1700 . 
     The path perceiver may perform path perception, such as by perceiving nominal paths that are available in a particular situation. In some examples, the path perceiver may further take into account lane changes for path perception. A lane graph may represent the path or paths available to the vehicle  1700 , and may be as simple as a single path on a highway on-ramp. In some examples, the lane graph may include paths to a desired lane and/or may indicate available changes down the highway (or other road type), or may include nearby lanes, lane changes, forks, turns, cloverleaf interchanges, merges, and/or other information. In some embodiments, the path perceiver may take into account the dense detection data  120 . For example, the path perceiver may evaluate a reconstructed 3D road surface to identify protuberances and include paths that avoid the protuberances. 
     The wait perceiver may be responsible to determining constraints on the vehicle  1700  as a result of rules, conventions, and/or practical considerations. For example, the rules, conventions, and/or practical considerations may be in relation to a 3D road surface, traffic lights, multi-way stops, yields, merges, toll booths, gates, police or other emergency personnel, road workers, stopped buses or other vehicles, one-way bridge arbitrations, ferry entrances, etc. Thus, the wait perceiver may be leveraged to identify potential obstacles and implement one or more controls (e.g., slowing down, coming to a stop, etc.) that may not have been possible relying solely on the obstacle perceiver. In some embodiments, the wait perceiver may take into account the dense detection data  120 . For example, the wait perceiver may evaluate a reconstructed 3D road surface to identify an approaching surface slope and determine to apply and/or apply an early acceleration or deceleration to accommodate the approaching surface slope. Additionally or alternatively, the wait perceiver may evaluate a reconstructed 3D road surface to identify a portion of an approaching road surface and determine to adapt and/or adapt a suspension system of the vehicle  1700  such that, once the vehicle  1700  reaches a corresponding portion of the road, the suspension system matches the identified road surface. 
     The map perceiver may include a mechanism by which behaviors are discerned, and in some examples, to determine specific examples of what conventions are applied at a particular locale. For example, the map perceiver may determine, from data representing prior drives or trips, that at a certain intersection there are no U-turns between certain hours, that an electronic sign showing directionality of lanes changes depending on the time of day, that two traffic lights in close proximity (e.g., barely offset from one another) are associated with different roads, that in Rhode Island, the first car waiting to make a left turn at traffic light breaks the law by turning before oncoming traffic when the light turns green, and/or other information. The map perceiver may inform the vehicle  1700  of static or stationary infrastructure objects and obstacles. The map perceiver may also generate information for the wait perceiver and/or the path perceiver, for example, such as to determine which light at an intersection has to be green for the vehicle  1700  to take a particular path. 
     In some examples, information from the map perceiver may be sent, transmitted, and/or provided to server(s) to a map manager of server(s)  1778  of  FIG.  17 D ), and information from the servers) may be sent, transmitted, and/or provided to the map perceiver and/or a localization manager of the vehicle  1700 . The map manager may include a cloud mapping application that is remotely located from the vehicle  1700  and accessible by the vehicle  1700  over one or more network(s). For example, the map perceiver and/or the localization manager of the vehicle  1700  may communicate with the map manager and/or one or more other components or features of the server(s) to inform the map perceiver and/or the localization manager of past and present drives or trips of the vehicle  1700 , as well as past and present drives or trips of other vehicles. The map manager may provide mapping outputs (e.g., map data) that may be localized by the localization manager based on a particular location of the vehicle  1700 , and the localized mapping outputs may be used by the world model manager  126  to generate and/or update the world model. 
     The planning component(s)  128  may include a route planner, a lane planner, a behavior planner, and a behavior selector, among other components, features, and/or functionality. The route planner may use the information from the map perceiver, the map manager, and/or the localization manger, among other information, to generate a planned path that may consist of GNSS waypoints (e.g., GPS waypoints), 3D world coordinates (e.g., Cartesian, polar, etc.) that indicate coordinates relative to an origin point on the vehicle  1700 , etc. The waypoints may be representative of a specific distance into the future for the vehicle  1700 , such as a number of city blocks, a number of kilometers, a number of feet, a number of inches, a number of miles, etc., that may be used as a target for the lane planner. 
     The lane planner may use the lane graph (e.g., the lane graph from the path perceiver), object poses within the lane graph (e.g., according to the localization manager), and/or a target point and direction at the distance into the future from the route planner as inputs. The target point and direction may be mapped to the best matching drivable point and direction in the lane graph (e.g., based on GNSS and/or compass direction). A graph search algorithm may then be executed on the lane graph from a current edge in the lane graph to find the shortest path to the target point. 
     The behavior planner may determine the feasibility of basic behaviors of the vehicle  1700 , such as staying in the lane or changing lanes left or right, so that the feasible behaviors may be matched up with the most desired behaviors output from the lane planner. For example, if the desired behavior is determined to not be safe and/or available, a default behavior may be selected instead (e.g., default behavior may be to stay in lane when desired behavior or changing lanes is not safe). 
     The control component(s)  130  may follow a trajectory or path (lateral and longitudinal) that has been received from the behavior selector (e.g., based on the dense detection data  120 ) of the planning component(s)  128  as closely as possible and within the capabilities of the vehicle  1700 . The control component(s)  130  may use tight feedback to handle unplanned events or behaviors that are not modeled and/or anything that causes discrepancies from the ideal (e.g., unexpected delay). In some examples, the control component(s)  130  may use a forward prediction model that takes control as an input variable, and produces predictions that may be compared with the desired state (e.g., compared with the desired lateral and longitudinal path requested by the planning component(s)  128 ). The control(s) that minimize discrepancy may be determined. 
     Although the planning component(s)  128  and the control component(s)  130  are illustrated separately, this is not intended to be limiting. For example, in some embodiments, the delineation between the planning component(s)  128  and the control component(s)  130  may not be precisely defined. As such, at least some of the components, features, and/or functionality attributed to the planning component(s)  128  may be associated with the control component(s)  130 , and vice versa. This may also hold true for any of the separately illustrated components of the drive stack  122 . 
     The obstacle avoidance component(s)  132  may aid the autonomous vehicle  1700  in avoiding collisions with objects (e.g., moving and stationary objects). The obstacle avoidance component(s)  132  may include a computational mechanism at a “primal level” of obstacle avoidance, and may act as a “survival brain” or “reptile brain” for the vehicle  1700 . In some examples, the obstacle avoidance component(s)  132  may be used independently of components, features, and/or functionality of the vehicle  1700  that is required to obey traffic rules and drive courteously. In such examples, the obstacle avoidance component(s) may ignore traffic laws, rules of the road, and courteous driving norms in order to ensure that collisions do not occur between the vehicle  1700  and any objects. As such, the obstacle avoidance layer may be a separate layer from the rules of the road layer, and the obstacle avoidance layer may ensure that the vehicle  1700  is only performing safe actions from an obstacle avoidance standpoint. The rules of the road layer, on the other hand, may ensure that vehicle obeys traffic laws and conventions, and observes lawful and conventional right of way (as described herein). 
     In some examples, the drivable or other navigable paths and/or the dense detection data  120  may be used by the obstacle avoidance component(s)  132  in determining controls or actions to take. For example, the drivable paths may provide an indication to the obstacle avoidance component(s)  132  of where the vehicle  1700  may maneuver without striking any objects, protuberances, structures, and/or the like, or at least where no static structures may exist. 
     In non-limiting embodiments, the obstacle avoidance component(s)  132  may be implemented as a separate, discrete feature of the vehicle  1700 . For example, the obstacle avoidance component(s)  132  may operate separately (e.g., in parallel with, prior to, and/or after) the planning layer, the control layer, the actuation layer, and/or other layers of the drive stack  122 . 
     As such, the vehicle  1700  may use this information (e.g., as the edges, or rails of the paths) to navigate, plan, or otherwise perform one or more operations (e.g., lane keeping, lane changing, merging, splitting, etc.) within the environment. 
     Now referring to  FIGS.  7 - 9   , each block of methods  700 - 900 , described herein, comprises a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media. The methods may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, the methods  700 - 900  are described, by way of example, with respect, to the surface reconstruction pipeline  100  of  FIG.  1   . However, these methods may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein. 
       FIG.  7    is a flow diagram showing a method  700  for generating a representation of a three-dimensional (3D) surface structure during a capture session, in accordance with some embodiments of the present disclosure. The method  700 , at block B 702 , includes generating, based at least in part on image data generated during a capture session using one or more cameras of an ego-object in an environment, a first representation of a three-dimensional (3D) surface structure of a component of the environment. For example, with respect to  FIG.  1   , one or more cameras  101  of an ego-object may be used to capture the image data  102  as the ego-object navigates through the environment, and the 3D structure estimator  105  may process the image data  102  to estimate the 3D structure of a particular component of the environment, such as a 3D road surface or other environmental part. Any suitable 3D structure estimation technique may be used, such as Structure from Motion (SfM), stereo vision, and/or some other 3D surface structure estimation technique. In some embodiments, a segmentation mask or other classification data may be used to select points from the estimated 3D structure that are on the component of the environment of interest. The resulting representation of the 3D structure may include a 3D point cloud, a projection image, or some other representation. 
     The method  700 , at block B 704 , includes generating a second representation of the 3D surface structure based at least in part on densifying the first representation of the 3D surface structure. For example, with respect to  FIG.  1   , the detection densifier  115  may densify the sparse detection data  110  to generate a denser representation of the 3D surface structure (e.g., dense detection data  120 ). Generally, the sparse detection data  110  may take any suitable form, such as a sparse 3D point cloud or a projection image of the sparse 3D point cloud (e.g., a 2D top-down height map). In some embodiments, the detection densifier  115  may densify the sparse detection data  110  using one or more machine learning models, such as a Markov random field (e.g., via the Markov random field surface estimator  310  of  FIG.  3   ) and/or one or more deep neural networks (DNNs) (e.g., via the deep learning model surface estimator  320  of  FIG.  3   ). The resulting representation of the 3D surface structure (dense detection data  120 ) may take any suitable form, such as 2D height map and/or a 3D point cloud. 
     The method  700 . at block B 706 , includes providing the second representation of the 3D surface structure to a control component of the ego-Object during the capture session. For example, the dense detection data  120  of  FIG.  1    or other representation of the 3D surface structure may be provided to, and used by, control component(s) of the ego-object (e.g., software stack  122   FIG.  1   , components of the autonomous vehicle  1700  of  FIGS.  17 A- 17 D  such as controller(s)  1736 , ADAS system  1738 , and/or SOC(s)  1704 ) to aid the ego-object in performing one or more operations within the environment, such as path planning, obstacle or protuberance avoidance, adapting a suspension system of the ego-object or ego-actor to match the current road surface, applying an early acceleration or deceleration based on an approaching surface slope, mapping, and/or others. 
       FIG.  8    is a flow diagram showing a method  800  for generating a densified representation of a 3D surface structure based at least on a Markov random field, in accordance with some embodiments of the present disclosure. The method  800 , at block B 802 , includes generating, using image data from one or more cameras of an ego-object in an environment, a first representation of a three-dimensional (3D) surface structure of a component of the environment. For example, with respect to  FIG.  1   , one or more cameras  101  of an ego-object may be used to capture the image data  102  as the ego-object navigates through the environment, and the 3D structure estimator  105  may process the mage data  102  to estimate the 3D structure of a particular surface of interest. 
     The method  800 , at block B 804 , includes generating a densified representation of the 3D surface structure based at least on a Markov random field that models a relationship between the first representation and the densified representation. For example, the Markov random field surface estimator  310  of  FIG.  3    may perform Maximum a Posterior (MAP) inference to estimate the most likely densified representation (e.g., the dense detection data  120 ), given the Markov random field and the first representation e.g, the sparse detection data  110 ). 
     The method  800 , at block B 806 , includes providing the densified representation of the 3D surface structure to a control component of the ego-object. For example, the dense detection data  120  of  FIG.  1    or other representation of the 3D surface structure may be provided to, and used by, control component(s) of the ego-object (e.g., software stack  122   FIG.  1   , components of the autonomous vehicle  1700  of  FIGS.  17 A- 17 D  such as controller(s)  1736 , ADAS system  1738 , and/or SOC(s)  1704 ) to aid the ego-object in performing one or more operations within the environment. 
       FIG.  9    is a flow diagram showing a method  900  for controlling a vehicle based at least in part on a 3D road surface structure estimated using one or more neural networks, in accordance with some embodiments of the present disclosure. The method  900 , at block B 902 , includes receiving image data generated using one or more cameras of a vehicle during operation of the vehicle in an environment. For example, with respect to  FIG.  1   , one or more cameras  101  of a vehicle may be used to capture the image data  102  as the vehicle navigates through the environment. 
     The method  900 , at block B 904 , includes virtually reconstructing a road surface in the environment, during the operation of the vehicle in the environment, based at least in part on blocks B 906  and B 908 . The method  900 , at block B 906 , includes generating, using the image data, a first estimated 3D surface structure of the road surface. For example, with respect to  FIG.  1   , the 3D structure estimator  105  may process the image data  102  to estimate the 3D structure of a particular surface of interest. The method  900 , at block B 908 , includes generating a densified estimated 3D surface structure of the road surface based at least in part on applying the first estimated 3D surface structure to one or more neural networks (NNs). For example, the deep learning model surface estimator  320  of  FIG.  3  or  5    may densify the sparse detection data  110  (e.g., a sparse 2D top-down height map) by inferring values of the dense detection data  120  (e.g., a dense 2D top-down height map) from the sparse detection data  110  using one or more NNs, such as one or more DNNs. By way of non-limiting example, in some embodiments where the sparse detection data  110  includes a sparse 3D point cloud, the encoder  515  of  FIG.  5    may project the sparse 3D point cloud to form a sparse projection image (e.g., a top-down height map), and the sparse projection image may be fed into the deep learning model(s)  535  to predict the regression data  570  (e.g., a dense projection image such as a top-down height map) and/or the confidence data  580 . 
     The method  900 , at block B 910 , includes controlling the vehicle based at least in par on data representing the densified estimated 3D surface structure. For example, the dense detection data  120  of  FIG.  1    or other representation of the densified estimated 3D surface structure may be provided to, and used by, control component(s) of the ego-object (e.g., software stack  122 .  FIG.  1   , components of the autonomous vehicle  1700  of  FIGS.  17 A- 17 D  such as controller(s)  1736 , ADAS system  1738 , and/or SOC(s)  1704 ) to aid the ego-object in performing one or more operations within the environment. 
     Generating Training Data and Training Deep Learning Model(s) A 3D Surface Reconstruction System 
     In order to support training a deep learning model for a 3D surface reconstruction system (e.g., the deep learning model(s)  535  of  FIG.  5  or  6   ), a training dataset (e.g., comprising sparse input data and/or ground truth representations of 3D surface structure) may be generated, compiled, and/or selected in a variety of ways. Generally, the type of training data may depend on the architecture of the deep learning model to be trained. For example, certain implementations may call for input training data comprising sparse representations of 3D surface structure (e.g., sparse height maps) and/or image data from some other perspective (e.g., images of a perspective view), and ground truth training data comprising dense representations of 3D surface structure (e.g., dense height maps) and/or segmentation masks (e.g., identifying a desired surface such as a road or other drivable space). In some embodiments, real-world data and/or virtual data may be collected and used to derive training data. By way of non-limiting example, training data may be generated by rendering frames of virtual sensor data, segmentation masks, and depth maps; parametric mathematical modeling of a 3D road surface; collecting and annotating real sensor data from a single LiDAR sensor; and/or collecting and annotating real sensor data accumulated from multiple LiDAR sensors. 
     Generating Training Data from a Simulated Environment. In some embodiments, training data may be generated by rendering or generating frames of virtual sensor data, segmentation masks, and/or depth maps representing a simulated environment. For example, a simulation may be run to simulate a virtual world or environment (e.g., a simulated environment), and a virtual vehicle or other object may be simulated within the simulated environment. The virtual vehicle or object may include any number of sensors (e.g., virtual or simulated sensors), and virtual sensor data may be simulated for the sensors. As such, frames of virtual sensor data (e.g., virtual image data corresponding to a fields) of view of virtual camera(s) of a virtual vehicle), and corresponding segmentation masks and depth maps, may be generated based on the simulated environment. The virtual sensor data may be used to generate (or used as) input training data, and the segmentation masks and/or depth maps may be used to generate (or used as) ground truth training data. 
       FIG.  10    is a data flow diagram illustrating an example training data generation pipeline  1000  using a simulated environment, in accordance with some embodiments of the present disclosure. The training data generation pipeline  1000  includes a simulator component  1010 , which may generate a simulated environment, and frame(s)  1020  of virtual sensor data, segmentation mask(s)  1030 , and/or depth map(s)  1040  representing the simulated environment. A 3D structure estimator  1050  (e.g., which may correspond to the 3D structure estimator  105  of  FIG.  1   ) may generate a sparse representation of a 3D structure of a surface of interest (e.g., a sparse point cloud, a projection image) from the frame(s)  1020  (e.g., a rendered image), and the sparse representation of 3D structure and/or the frame(s)  1020  (e.g., a rendered image) may be used as input training data  1080 . To generate ground truth training data  1090 , a 3D point cloud generator  1060  may unproject range values from the depth map(s)  1040  into 3D world coordinates using the known position and orientation of the virtual camera relative to which the range values of the depth map(s)  1040  were generated, and the 3D point cloud generator  1060  may use the segmentation mask(s)  1030  to filter out 3D points on the surface of interest (e.g., a road surface). Since the resulting 3D point cloud may be sparse, a post-processor  1070  may be used to interpolate missing values and generate a dense representation of the 3D structure of the surface of interest (e.g., a point cloud, a projection image), and the dense representation of 3D structure and/or the segmentation mask(s)  1030  may be used as the ground truth training data  1090 . 
     The simulator component  1010  may comprise a simulation system that simulates a virtual world or environment (e.g., a simulated environment). For example, the simulation system may generate a global simulation that generates a simulated environment that may include artificial intelligence (AI) vehicles or other objects (e.g., pedestrians, animals, etc), hardware-in-the-loop (HIL) vehicles or other objects, software-in-the-loop (SIL) vehicles or other objects, and/or person-in-the-loop (PIL) vehicles or other objects. The simulated environment may be generated using rasterization, ray-tracing, using DNNs such as generative adversarial networks (GANs), another rendering technique, and/or a combination thereof. The simulated environment may include features of a driving environment, such as roads, bridges, tunnels, street signs, stop lights, crosswalks, buildings, trees and foliage, the sun, the moon, reflections, shadows, etc., in an effort to simulate a real-world environment. The global simulation may be maintained within an engine (e.g., a game engine), or other software-development environment, that may include a rendering engine (e.g., for 2D and/or 3D graphics), a physics engine (e.g., for collision detection, collision response, etc.), sound, scripting, animation, AI, networking, streaming, memory management, threading, localization support, scene graphs, cinematics, and/or other features. An example simulation system and an example global simulation are described in U.S. Non-Provisional patent application Ser. No. 16/818,551, filed on Mar. 13, 2020 and entitled “Sensor Simulation and Learning Sensor Models with Generative Machine Learning Methods,” the contents of which are herein incorporated by reference in their entirety. 
     In some embodiments, the simulator component  1010  may generate frame(s)  1020  of virtual sensor data (e.g., image data), segmentation mask(s)  1030 , and/or depth map(s)  1040  representing the simulated environment. For example, the simulator component  1010  may render images of the simulated environment from the perspective of a virtual camera disposed on a virtual vehicle or other object in the simulated environment. In some embodiments, the simulator component  1010  may use known coordinates of a simulated surface of interest (e.g., a road surface) in the simulated environment to generate segmentation mask(s)  1030  and/or depth map(s)  1040  (e.g., per-pixel depth map(s)) corresponding to the frame(s)  1020  of virtual sensor data. The frame(s)  1020  of virtual sensor data, segmentation mask(s)  1030 , and/or depth map(s)  1040  (collectively, simulated or virtual data) may be grouped together, and the simulator component  1010  may generate simulated or virtual data representing successive time slices in the simulated environment, for example, as the virtual vehicle or other object navigates through the simulated environment. As such, the simulator component  1010  may generate frame(s)  1020  of virtual sensor data, segmentation mask(s)  1030 , and/or depth map(s)  1040  representing realistic (e.g., driving) scenarios. 
     For any given frame(s)  102 ( 1 , the 3D structure estimator  105 (s) may estimate a 3D surface structure of a surface of interest (e.g., a road surface) from the frame(s)  1020 . For example, 3D structure may be estimated using the techniques described herein with respect to the 3D structure estimator  105  of  FIG.  1    (e.g., using Structure from Motion, stereo vision, outlier removal, and/or surface point selection). In some embodiments, the 3D structure estimator  1050  may use the segmentation mask(s)  1030  to select points from an estimated 3D structure that belong to a class represented by the segmentation mask (e.g., points that belong to a surface of interest, such as a 3D road surface). In some embodiments, the resulting points may be projected to form a projection image (e.g., a 2D height map). The result may be a sparse representation of the 3D structure of the surface of interest (e.g., a sparse point cloud, a sparse projection image). The sparse representation of 3D structure and/or the frame(s)  1020  (e.g., a rendered image) may be designated as input training data  1080  and included in a training dataset. 
     In some embodiments, to generate corresponding ground truth training data  1090 , the 3D point cloud generator  1060  may generate a 3D point cloud or other representation of 3D structure using the depth map(s)  1040 . For example, the 3D point cloud generator  1060  may generate 3D points by unprojecting range values from the depth map(s)  1040  into 3D world coordinates of the simulated environment using the location and orientation of the virtual camera relative to which the range values of the depth map(s)  1040  were generated, and the 3D point cloud. generator  1060  may select 3D points on the surface of interest using the segmentation mask(s)  1030  (e.g., by selecting 3D points that project onto a portion of the segmentation mask(s)  1030  that represents the surface of interest). Additionally or alternatively, the 3D point cloud generator  1060  may use the segmentation mask(s)  1030  to select range values from the depth map(s)  1040  for points that are on the surface of interest (e.g., by overlaying the segmentation mask(s)  1030  on the depth map(s)  1040 ), and the 3D point cloud generator  1060  may unproject the selected range values into the simulated environment to generate the 3D points on the surface of interest. 
     Since the resulting 3D points (e.g., a 3D point cloud) may be sparse, the post-processor  1070  may be used to interpolate missing values using a triangulation algotithm. For example, the post-processor  1070  may perform Delaunay triangulation in 2D and/or in 3D. In an example embodiment involving 2D triangulation, the post-processor  1070  may project the 3D points on the surface of interest to form a projection image (e.g., a 2D height map) and perform Delaunay triangulation in the projection image to generate triangles, and the post-processor  1070  may sample points from the triangles to generate a desired number of points for a ground truth dense projection image (e.g., ground truth 2D height map). In an example embodiment involving 3D triangulation, the post-processor  1070  may perform 3D Delaunay triangulation to compute a surface mesh of triangles surrounding the 3D points on the surface of interest, and sample 3D points from the triangles of the surface mesh to generate a desired number of points for a ground truth dense projection image (e.g., ground truth 2D height map). For example, the post-processor  1070  may sample 3D points from the surface mesh and project the sampled 3D points to form a ground truth projection image (e.g., ground truth 2D height map). Pixels in a ground truth projection image that do not represent sampled points may be set to zero. As such, a dense projection image or other representation of the 3D points on the surface of interest and/or the segmentation mask(s)  1030  may be designated as the ground truth training data  1090 , paired with corresponding input training data  1090 , and included in a training dataset. 
     Generating; Synthetic Training Data using Parametric Modeling. In another example technique for generating training data, synthetic training data may be generated using parametric mathematical modeling of a desired surface, such as 3D road surface. For example, a variety of synthetic 3D road surfaces may be generated by modeling a 3D road surface with varied parameters to simulate changes in road direction and lateral surface slope. By way of non-limiting example, a synthetic 3D surface may be created by modeling a 3D curve on the synthetic 3D surface and expanding the 3D curve to a 3D surface. The resulting synthetic 3D surface (or its component curves) may be sampled, and sampled points may be projected to form a synthetic ground truth projection image (e.g., a 2D height map). To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3D structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image with unobserved values. As such, synthetic sparse input projection images and dense ground truth projection images may be generated and included in a training dataset. 
       FIG.  11    is an illustration of an example parametric mathematical model of a desired surface, in accordance with some embodiments of the present disclosure. In the example illustrated in  FIG.  11   , a 3D surface is modeled with longitudinal curve ι and lateral curves q i . In an example embodiment in which the 3D surface being modeled is a 3D road surface, parameters of parametric equations that define the longitudinal curve ι and the lateral curves q i  may be varied to simulate different types of 3D road surfaces. 
     By way of non-limiting example, a 3D curve on a synthetic 3D road surface may be generated by sampling longitudinal, lateral, and height values for the 3D curve. For example, a desired set of longitudinal values [x 0 , . . . , x n ] for a synthetic 3D curve on a synthetic 3D surface may be initially sampled or otherwise chosen. For an example road surface, the longitudinal values may represent a desired perception range for a deep learning model surface estimator, such as 0 to 300 m. In some embodiments, lateral values for the synthetic 3D curve may be computed as a second order polynomial of the longitudinal values x: y=a x 2 +bx+c. In embodiments involving synthetic 3D road surfaces, multiple synthetic 3D curves may be generated by sampling different values for polynomial constants a, b, and/or c to simulate different changes in road direction (e.g., curves, turns, etc.) for different synthetic 3D curves. In some embodiments, height values for the synthetic 3D curve may be computed as a linear combination of Fourier bases: 
         z =Σ k=1   k   c[k ]*cos( f[k]*x )  Eq. (5)
 
     where K is the number of Fourier bases, c is a weight for a particular basis k, and f is the frequency for a particular basis k. In embodiments involving synthetic 3D road surfaces, different height values may be calculated for different synthetic 3D curves using different sampled values for the number of bases K, weight c for a particular basis k, and/or frequency f for a particular basis k to simulate different changes in surface height for different synthetic 3D curves. The result may be a longitudinal 3D curve represented by curve ι in the example illustrated in  FIG.  11   . 
     In some embodiments, the longitudinal 3D curve may be expanded to a 3D surface. For example, a longitudinal 3D curve may include any number of points {x j , y j , z j } for j in [1, . . . ,n], and any given point on the longitudinal 3D curve (e.g., each point) may be expanded into a corresponding lateral 3D curve, represented by curves q j  in the example illustrated in  FIG.  11   . For example, a parameter α may be defined to denote the angle between a synthetic 3D surface e.g., the synthetic 3D road surface) and a surface (e.g., the ground plane, z=0), and different values of α may be sampled to simulate different lateral surface slopes at different points on the longitudinal 3D curve and/or for different synthetic 3D curves. For a particular 3D point p j ={x j , y j , z j } on the longitudinal 3D curve ι the 3D point may be expanded into a lateral 3D curve q j  that passes through p j , perpendicular to the curve ι at p j , and having angle α relative to surface z = 0 . Any type of lateral 3D curve may be used (e.g., linear, polynomial, etc.), and any given lateral 3D curve q j  may be sampled in times to expand a corresponding 3D point p ji ={x j , y j , z j } on a longitudinal 3D curve ι into a set of 3D points {(x ij , y ij , z ij }, i=[1, . . . , m], where different values of m may be sampled to simulate different road widths at different points on the longitudinal 3D curve and/or for different synthetic 3D surfaces. The process may be repeated for any given point on a longitudinal 3D curve (e.g., each point) to generate a dense 3D point cloud, which may be projected to form a ground truth projection image (e.g., a ground truth 2D height map). 
     To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved by 3D estimation may be generated and applied to the ground truth projection image to cancel out a subset of pixel values (e.g., setting those pixel values to zero) to simulate unobserved values. For example, suppose a ground truth height map is of size H×W. In this example, a pattern represented by N binary maps of size H×W may be generated by performing 3D estimation on real-world data. For example, one or more vehicles (e.g., vehicle  1700  of  FIGS.  17 A-D ) may collect frames of sensor data (e.g., image data) from one or more sensors (e.g., cameras) of the vehicle(s) in real-world (e.g., physical) environments, as explained in more detail below. A 3D surface structure of a desired surface (e.g., 3D road surface) may be estimated from each frame of sensor data (as described herein), and the resulting representation of 3D structure (e.g., a sparse 3D point cloud) may be projected to form a sparse projection image (e.g., a sparse 2D height lap), which may include both observed and unobserved values. For each of N sparse projection images of size H×W, a corresponding binary map of size H×W may be generated to represent which pixels are observed and unobserved. For example, pixels of a binary map corresponding to observed values may be set to 1, and pixels corresponding to unobserved values may be to 0. As such, an N×H×W pattern of binary maps may be generated to represent which pixels may remain unobserved by 3D estimation. 
     For each synthetic ground truth projection image, one of the N binary maps may be randomly sampled and applied to the synthetic ground truth projection image (e.g., using element-wise multiplication) to generate a corresponding synthetic sparse projection image. As such, pairs of synthetic input and ground truth projection images may be generated and added to a training dataset. 
     Generating Training Data from Real-World Sensor Data. In some embodiments, training data may be generated by collecting and annotating real-world sensor data. For example, one or more vehicles may collect frames of sensor data (e.g., image data and LiDAR data) from one or more sensors (e.g., camera(s) and LiDAR sensor(s)) of the vehicle(s) in real-world (e.g., physical) environments. In some embodiments, LIDAR data may be smoothed, subject. to outlier removal, subject to triangulation to interpolate missing values, accumulated from multiple LiDAR sensors, temporally and/or spatially aligned with corresponding frames of image data, and annotated to identify 3D points on a surface of interest (e.g., a 3D road surface). A representation of the identified 3D points (e.g., a 3D point cloud, a projection image) may be designated as ground truth training data. In some embodiments, object detection, free space estimation, and/or image segmentation may be applied to frames of image data to generate corresponding segmentation masks, which may be designated as ground truth training data. Corresponding frames of image data may be subject to 3D estimation, and the resulting sparse representation of the surface of interest (e.g., a 3D point cloud, a projection image) may be designated as input training data. For example, a corresponding sparse projection image, camera frame, dense projection image, and/or segmentation mask may be grouped together and included in a training dataset. 
       FIG.  12    is a data flow diagram illustrating an example ground truth generation pipeline  1200  using collected real-world data, in accordance with some embodiments of the present disclosure. The example ground truth generation pipeline  1200  includes a recording engine  1205 , a 3D structure estimator  1220 , a free space estimator 1225 , a pre-processor  1240 , an aligner  1250 , and an annotation component  1260 . 
     In some embodiments, one or more data collection vehicles (e.g., vehicle  1700  of  FIGS.  17 A-D ) may be equipped with one or more camera(s) and LiDAR sensor(s), and a recording engine  1205  associated with each data collection vehicle may record sensor data while the vehicle travels through real-world (e.g., physical) environments. Generally, a data capture vehicle may be equipped with any number and type of sensor (including, but not limited to, the sensors illustrated in  FIGS.  17 A- 17 C ). For example, a number of camera(s) (e.g., stereo camera(s)  1768 , wide-view cameras) 1770  (e.g., fisheye cameras), infrared camera(s)  1772 . surround camera.(s)  1774  (e.g., 360 degree cameras), and/or long-range and/or mid-range camera(s)  1798 ), LIDAR sensors  1764 , and/or other sensor types may be positioned on the vehicle such that there is overlap between fields of view of the cameras and fields of view or sensory fields of the sensors. The spatial layout of the sensors may be calibrated, in some embodiments, through self-calibration algorithms, and the synchronization of the sensors may be controlled to exhibit time alignment of sensor captures. As such, the recording engine  1205  may capture frame(s)  1210  of image data from one or more cameras and/or LiDAR data  1215  from one or more LiDAR sensors. 
     In some embodiments, the LiDAR data  1215  may be used to generate ground truth training data. In the example illustrated in  FIG.  12   , the pre-processor  1240  performs one or more processing operations on the LiDAR data  1215  prior to labeling. For example, in some embodiments, the pre-processor  1240  may perform temporal smoothing, which may include a state estimator such as a Kalman filter. The temporal smoothing may be applied in 3D world space relative to the data capture vehicle, in 3D world space relative to some fixed origin in world space, or in a birds-eye view in 2D world space. In some embodiments, the pre-processor  1240  may perform outlier removal on the LiDAR data  1215  (e.g., similar to the technique described herein with respect to outlier remover  220  of  FIG.  2   ). In some cases, the resulting LiDAR may still be sparse, As such, in some embodiments, the pre-processor  1240  may interpolate missing values using a triangulation algorithm (e.g., as described herein with respect to the post-processor  1070  of  FIG.  10   ). Additionally or alternatively, the pre-processor  1240  may accumulate LiDAR data from multiple LidAR sensors to densify the resulting LiDAR data. By way of illustration, FIG.  13 A shows an example of LiDAR data collected from a single LiDAR scan, and  FIG.  13 B  shows an example of LiDAR data accumulated from multiple LiDAR scans (e.g., from multiple LiDAR sensors). These are just a few examples, and other types of pre-processing operations may additionally or alternatively be performed. 
     In some embodiments, the aligner  1250  may temporally align the LiDAR data with corresponding frame(s)  1210  of image data. Generally, sensor data may be obtained from different sensors at different frequencies for various reasons, such as differences in delay lines, differences in sampling; frequencies (e.g., cameras running at 30 fps vs. LiDAR running at 10 fps), different trigger times, and other reasons. In order to facilitate grouping and/or presenting sensor data of similar world states (e.g., sensor data captured at substantially the same time), temporal alignment may be performed to synchronize the sensor data from the different sensors. For example, a particular sensor may be used as a reference sensor, and other sensors may be referred to as child sensors. For a given frame of sensor data from the reference sensor (a reference frame), an offset such as a time delta may be identified between the reference frame and the temporally closest frame of sensor data from each child sensor. The offset for each child sensor may be recorded and/or applied to the capture times or some other index for the sensor data from the child sensor. Thus, determining and/or applying per-sensor offsets may serve to temporally align the different types of sensor data (e.g., by aligning their indices). Example techniques for aligning sensor data from different types of sensors are described in U.S. Non-Provisional patent application Ser. No. 17/187,350, filed on Apr. 26, 2021 and entitled “Ground Truth Data Generation for Deep Neural Network Perception in Autonomous Driving Applications,” the contents of which are herein incorporated by reference in their entirety. 
     Additionally or alternatively, aligner  1250  may spatially align the LiDAR data with corresponding frame(s)  1210  of image data to match different types of sensor data that represent the same object or other portion of the environment. For example, LIDAR data points may be correlated with pixels in the image space using relative orientation, location, fields-of-view, and the like between the LiDAR sensor that captured the LiDAR data point and the camera that generated the image data. Techniques for correlating sensor data from different sensors are described in U.S. Provisional Patent Application No. 62/514,404, filed on Mar. 15, 2019 and entitled “Sequential Neural Network-Based Temporal Information Prediction in Autonomous Driving Applications,” and U.S. Non-Provisional patent application Ser. No.  16 / 514 , 404 , filed on Jul. 17, 2019 and entitled “Temporal Information Prediction in Autonomous Machine Applications,” the contents of each of which are herein incorporated by reference in their entirety. 
     In some embodiments, the LiDAR data may be annotated to identify points on a 3D surface of interest (e.g., a 3D road surface). Generally, annotations may be synthetically produced (e.g., generated from computer models or renderings), real produced (e.g., designed and produced from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from data and then generate labels), human annotated (e.g., a labeler, or annotation expert, inputting the annotations), and/or a combination thereof (e.g., a human identifies vertices of polylines, a machine generates polygons using polygon rasterizer). 
     In some embodiments, the annotation component  1260  may include a software tool (also called a labeling tool) such as a web tool. A sequence of annotation scenes (e.g., sets of aligned LiDAR data and image data captured at approximately the time) may be generated, a corresponding labeling task(s) may be encoded into the labeling tool, and annotations may be generated using the software tool. In some embodiments, the labeling tool may present the aligned LiDAR data and image data in an annotation scene to a human labeler (e.g., side-by-side), and/or information may be projected across the different types of sensor data to provide useful contextual information, such as correspondences among the different types of sensor data. The labeling tool may accept inputs specifying ground truth annotations identifying points on a surface of interest (e.g., 3D points, boundaries, enclosed regions, class labels), and the labeling tool may associate the annotations with the sensor data. An example labeling tool is described in U.S. Non-Provisional patent application Ser. No. 17/187,350, filed on Apr. 26, 2021 and entitled “Ground Truth Data Generation for Deep Neural Network Perception in Autonomous Driving Applications.” As such, the annotation component  1260  may accept inputs identifying points on a surface of interest (e.g., a 3D point cloud, a projection image), and generate a representation of the identified points matching the view, size, and dimensionality of the output(s) of the deep learning model(s) to be trained may be designated as ground truth training data  1295 . 
     In some embodiments, ground truth segmentation mask(s) r ray be generated from the frame(s)  1210  of image data. For example, a free space estimator  1225  may perform free space estimation and/or image segmentation on the captured image(s) to classify, segment, and/or predict regions (e.g., pixels) of the image data that are part of a desired class (e.g., a road surface). For example, one more machine learning models (e.g., a convolutional neural network) may be trained to predict one or more segmentation mask(s)  1230  and/or confidence maps representing pixels that belong to a drivable road surface or other navigable space, other environmental parts (e.g., sidewalks, buildings), animate objects, and/or other classes. As such, the segmentation mask(s)  1230  or other representation of a detected surface may be designated as ground truth training data  1295 . 
     To generate corresponding input training data, for any given frame(s)  1210  of image data, the 3D structure estimator  1220  may estimate a 3D surface structure of a surface of interest (e.g., a road surface) from the frame(s)  1210  (e.g., as described above with respect to the 3D structure estimator  105  of  FIG.  1    and/or the 3D structure estimator  1050  of  FIG.  10   ). In some embodiments, the 3D structure estimator  1220  may use the segmentation mask(s)  1230  to select points from an estimated 3D structure that belong to a class represented by the segmentation mask (e.g., points that belong to a surface of interest, such as a 3D road surface). In some embodiments, the resulting points may be projected to form a projection image (e.g., a 2D height map). The result may be a sparse representation of the 3D structure of the surface of interest (e.g., a sparse point cloud, a sparse projection image). The sparse representation of 3D structure and/or the frame(s)  1210  of image data may be designated as input training data  1290 . 
     As such, the input training data  1290  may be paired with corresponding ground truth training data  1295  (e.g., a dense projection image or other representation of the 3D points on the surface of interest and/or the segmentation masks)  1230 ) and included in a training dataset. 
     Training. In some embodiments, a training dataset for a deep learning model(s) for a 3D surface reconstruction system (e.g., the deep learning model(s)  535  of  FIG.  5  or  6   ) may be generated, compiled, and/or selected based on the inputs and outputs of the deep learning model(s) to be trained. For example, certain implementations may call for input training comprising sparse representations of 3D surface structure (e.g., sparse height maps) and/or image data from some other perspective (e.g., images of a perspective view), and ground truth training data comprising dense representations of 3D surface structure dense height maps) and/or segmentation masks (e.g., identifying a desired surface such as a road or other drivable space). As such, a training dataset having input and ground truth training data matching the view, size, and dimensionality of the input(s) and output(s) of a desired deep learning model(s) may be obtained using techniques described herein, and the deep learning model(s) may be trained using the selected training dataset. In embodiments where the deep learning model(s) includes one or more recurrent layers (e.g., Gated Recurrent Units, Long Short Term Memory), the input training data may include multiple frames e.g., from consecutive time slices) as a single sample. 
     Generally, any suitable loss function may be used to update the deep learning model(s) during training. For example, one or more loss functions may be used (e.g., a regression loss function such as L1 or L2 loss may be used for regression tasks) to compare the accuracy of the output(s) of the deep learning model(s) to ground truth, and the parameters of the deep learning model(s) may be updated (e.g., using backward passes, backpropagation, forward passes, etc.) until the accuracy reaches an optimal or acceptable level. In some embodiments in which the deep learning models) includes multiple heads, the multiple heads may be co-trained together on the same dataset, with a common trunk. In this manner, the different heads (tasks) may help each other to learn. 
     In an example embodiment where the deep learning model(s) includes a regression head that predicts a height map, the deep learning models) may learn to predict heights maps using ground truth height maps and ground truth segmentation masks. For example, a regression loss function such as L1 or L2 loss may be used to compare a predicted height map with a ground truth height map, and the result may be multiplied by a ground truth segmentation mask indicating the surface to be densified, effectively cancelling out updates to the deep learning model(s) based on predictions that occur outside the region to be densified. 
     In another embodiment where the deep learning model(s) includes a regression head that predicts a height map and a confidence head that predicts a confidence map corresponding to the height map, the deep learning model(s) may learn to predict both height and confidence maps from ground truth height maps. For example, a loss function that compares predicted and ground truth height, and compensates based on a predicted confidence value, may be used. An example of such a loss function may be given by: 
     
       
         
           
             
               
                 
                   L 
                   = 
                   
                     
                       
                         
                            
                           
                             
                               y 
                               ′ 
                             
                             - 
                             y 
                           
                            
                         
                         2 
                       
                       
                         2 
                         ⋆ 
                         
                           c 
                           2 
                         
                       
                     
                     + 
                     
                       
                         1 
                         2 
                       
                       ⋆ 
                       
                         log 
                         ⁢ 
                            
                         
                           c 
                           2 
                         
                       
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                       
                   
                     ( 
                     6 
                     ) 
                   
                 
               
             
           
         
       
     
     where y is a predicted height, y′ is a corresponding ground truth height, and c is a predicted confidence value corresponding to the predicted height. In this example, if the predicted height is substantially wrong (and ∥y′-y∥ is therefore large), minimizing this loss function encourages a large value of c. As such, in this example, a large value of c may indicate a low confidence. The log term in the example loss given by equation 6 prevents c from becoming infinitely large. As such, a loss function such as this may be used to train a deep learning model(s) to predict both a height map and a confidence map, without the need for a ground truth confidence map. As such, the deep learning model(s) may be trained to perform densification by learning a mapping between sparse and dense representations of 3D structure. 
     Now referring to  FIGS.  14 - 16   , each block of methods  1400 - 1600 , described herein, comprises a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media. The methods may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, although the methods  1400 - 1600  may be described, by way of example, with respect to an example system, these methods may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein. 
       FIG.  14    is a flow diagram showing a method  1400  for training one or more neural networks (NNs) to generate a densified representation of the 3D surface structure using simulated image data, in accordance with some embodiments of the present disclosure. The method  1400 . at block  131402 , includes accessing simulated image data and corresponding classification data and range data. For example, the simulator component  1010  of  FIG.  10    may run a simulation to simulate a virtual world or environment (e.g., a simulated environment), and a virtual vehicle or other object may be simulated within the simulated environment. The virtual vehicle or object may include any number of sensors (e.g., virtual or simulated sensors), and virtual sensor data may be simulated for the sensors. As such, frames of virtual sensor data (e.g., virtual image data corresponding to a field(s) of view of virtual camera(s) of a virtual vehicle), and corresponding segmentation masks and depth maps, may be generated based on the simulated environment. 
     The method  1400 , at block B 1404 , includes generating, based at least in part on the simulated image data, a first representation of a three-dimensional (3D) surface structure of a road represented by the simulated image data. For example, the 3D structure estimator  1050  of  FIG.  10    may generate a sparse representation (e.g., a sparse point cloud, a projection image) of a 3D surface structure of a road depicted in a rendered image (e.g., the frame(s)  1020 ) by performing 3D structure estimation on the rendered image. 
     The method  1400 , at block B 1406 , includes generating, based at least on the range data and the classification data, a second representation of the 3D surface structure of the road. For example, the 3D point cloud generator  1060  of  FIG.  10    may unproject range values from the depth map(s)  1040  into 3D world coordinates using the known position and orientation of the virtual camera relative to which the range values of the depth map(s)  1040  were generated, the 3D point cloud generator  1060  may use the segmentation mask(s)  1030  to filter out 3D points on the surface of interest (e.g., a road surface), and the post-processor  1070  may be used to fill in missing values. 
     The method  1400 , at block B 1408 , includes training one or more neural networks (NNs) to generate a densified representation of the 3D surface structure using the first representation of the 3D surface structure as input training data and using the second representation of the 3D surface structure as ground truth training data. 
       FIG.  15    is a flow diagram showing a method  1500  for generating incomplete and ground truth representations of a synthetic 3D road surface for a training dataset, in accordance with some embodiments of the present disclosure. The method  1500 , at block B 1502 , includes generating a representation of a longitudinal three-dimensional (3D) curve representing a synthetic road. For example, with respect to  FIG.  11   , a representation of longitudinal curve ι may be generated with longitudinal values representing a desired perception range for a deep learning model surface estimator, lateral values computed as a second order polynomial of the longitudinal values, and height values computed as a linear combination of Fourier bases. 
     The method  1500 , at block B 1504 , includes, for each point of one or more points on the longitudinal 3D curve, expanding the point into a lateral 3D curve through the point. For example, with respect to  FIG.  11   , any given point (e.g., each point) on the longitudinal 3D curve ι may be expanded into a corresponding lateral 3D curve, represented by curves q j . In some embodiments, a parameter a may be defined to denote the angle between a synthetic 3D surface (e.g., the synthetic 3D road surface) and the surface z=0 (e.g., the ground plane), and different values of a may be sampled to simulate different lateral surface slopes at different points on the longitudinal 3D curve and/or for different synthetic 3D curves. As such, any given point (e.g., each point) on the longitudinal 3D curve ι, the point may be expanded into a lateral 3D curve q j  that passes through p j , perpendicular to the curve ι at p j , and having angle α relative to surface z=0. 
     The method  1500 , at block B 1506 , includes generating a ground truth representation of a synthetic 3D road surface of the synthetic road based at least on the lateral 3D curve for two or more points on the longitudinal 3D curve. For example, with respect to  FIG.  11   , any given lateral 3D curve q j  may be sampled in times to expand a corresponding 3D point on the longitudinal 3D curve ι into a set of 3D points to simulate different road widths at different points on the longitudinal 3D curve. The process may be repeated for any given point (e.g., each point) of the longitudinal 3D curve ι to generate a dense 3D point cloud, which may be projected to form a ground truth projection image (e.g., a ground truth 2D height map). 
     The method  1500 , at block B 1508 , includes generating an incomplete representation of the synthetic 3D road surface based at least on the ground truth representation of the synthetic 3D road surface. For example, a pattern represented by N binary maps of size H×W may be generated by performing 3D estimation on real-world data and encoding a representation of which pixels are observed and unobserved upon performing 3D estimation from captured images. As such, one of the N binary maps may be randomly sampled and applied to the ground truth representation of the synthetic 3D road surface (e.g., using element-wise multiplication to generate a corresponding incomplete representation of the synthetic 3D road surface. 
     At block B 1510 , the incomplete representation and the ground truth representation are included in a training dataset. 
       FIG.  16    is a flow diagram showing a method  1600  for training one or more neural networks (NNs) to generate a densified representation of the 3D surface structure using image data and LiDAR data captured during a capture session, in accordance with some embodiments of the present disclosure. The method  1600 , at block B 1602 , includes accessing image data and LiDAR data captured during a capture session in an environment. For example, a data collection vehicle may be equipped with one or more cameras) and LiDAR sensor(s), and the recording engine  1205  of  FIG.  12    may record sensor data while the vehicle travels through real-world (e.g., physical) environments. 
     The method  1600 , at block B 1604 , includes generating, based at least on the image data, an incomplete representation of a three-dimensional (3D) surface structure of road in the environment. For example, for any given frame(s)  1210  of image data, the 3D structure estimator  1220  of  FIG.  12    may estimate a 3D surface structure of a road from the frame(s)  1210  (e.g., as described above with respect to the 3D structure estimator  105  of  FIG.  1    and/or the 3D structure estimator  1050  of  FIG.  10   ). In some embodiments, the 3D structure estimator  1220  may use the segmentation mask(s)  1230  to select points from an estimated 3D structure that belong to a class represented by the segmentation mask (e.g., points that belong to a 3D road surface). In some embodiments, the resulting points may be projected to form a projection image (e.g., a 2D height map). 
     The method  1600 , at block  1606 , includes generating, based at least on labeling of the LiDAR data, a second representation of the 3D surface structure of the road. For example, with respect to  FIG.  12   , the pre-processor  1240  may perform one or more processing operations on the LiDAR data  1215  prior to labeling, such as temporal smoothing, outlier removal, triangulation, and/or accumulation from multiple LiDAR sensors. In some embodiments, the aligner  1250  may temporally and/or spatially align the LiDAR data with corresponding frame(s)  1210  of image data. In an example embodiment, the annotation component  1260  may present aligned LiDAR data and image data in an annotation scene to a human labeler, accept inputs specifying ground truth annotations identifying points on a surface of interest, and generate a representation of the identified points matching the view, size, and dimensionality of the output(s) of the deep learning model(s) to be trained. 
     The method  1600 , at block B 1608 , includes training one or more neural networks (NNs) to generate a densified representation of the 3D surface structure using the incomplete representation of the 3D surface structure as input training data and using the second representation of the 3D surface structure as ground truth training data. 
     Example Autonomous Vehicle 
       FIG.  17 A  is an illustration of an example autonomous vehicle  1700 , in accordance with some embodiments of the present disclosure. The autonomous vehicle  1700  (alternatively referred to herein as the “vehicle  1700 ”) may include, without limitation, a passenger vehicle, such as a car, a truck, a bus, a first responder vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police vehicle, an ambulance, a boat, a construction vehicle, an underwater craft, a drone, a vehicle coupled to a trailer, and/or another type of vehicle (e.g., that is unmanned and/or that accommodates one or more passengers). Autonomous vehicles are generally described in terms of automation levels, defined by the National Highway Traffic Safety Administration (NHTSA), a division of the US Department of Transportation, and the Society of Automotive Engineers (SAE) “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201806, published on Jun. 15, 2018, Standard No. J3016-201609, published on Sep. 30, 2016, and previous and future versions of this standard). The vehicle  1700  may be capable of functionality in accordance with one or more of Level 3-Level 5 of the autonomous driving levels. For example, the vehicle  1700  may be capable of conditional automation (Level 3), high automation (Level 4), and/or full automation (Level 5), depending on the embodiment. 
     The vehicle  1700  may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle  1700  may include a propulsion system  1750 , such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system  1750  may be connected to a drive train of the vehicle  1700 , which may include a transmission, to enable the propulsion of the vehicle  1700 . The propulsion system  1750  may be controlled in response to receiving signals from the throttle/accelerator  1752 . 
     A steering system  1754 , which may include a steering wheel, may be used to steer the vehicle  1700  (e.g., along a desired path or route) when the propulsion system  1750  is operating (e.g., when the vehicle is in motion). The steering system  1754  may receive signals from a steering actuator  1756 . The steering wheel may be optional for full automation (Level 5) functionality. 
     The brake sensor system  1746  may be used to operate the vehicle brakes in response to receiving signals from the brake actuators  1748  and/or brake sensors. 
     Controller(s)  1736 , which may include one or more system on chips (SoCs)  1704  ( FIG.  17 C ) and/or GPU(s), may provide signals (e.g., representative of commands) to one or more components and/or systems of the vehicle  1700 . For example, the controllers) may send signals to operate the vehicle brakes via one or more brake actuators  1748 , to operate the steering system  1754  via one or more steering actuators  1756 , to operate the propulsion system  1750  via one or more throttle/accelerators  1752 . The controller(s)  1736  may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals, and output operation commands (e.g., signals representing commands) to enable autonomous driving and/or to assist a human driver in driving the vehicle  1700 . The controller(s)  1736  may include a first controller  1736  for autonomous driving functions, a second controller  1736  for functional safety functions, a third controller  1736  for artificial intelligence functionality (e.g., computer vision), a fourth controller  1736  for infotainment functionality, a fifth controller  1736  for redundancy in emergency conditions, and/or other controllers. in some examples, a single controller  1736  may handle two or more of the above functionalities, two or more controllers  1736  may handle a single functionality, and/or any combination thereof. 
     The controller(s)  1736  may provide the signals for controlling one or more components and/or systems of the vehicle  1700  in response to sensor data received from one or more sensors (e.g., sensor inputs). The sensor data may be received from, for example and without limitation, global navigation satellite systems sensor(s)  1758  (e.g., Global Positioning System sensor(s)), RADAR sensor(s)  1760 , ultrasonic sensor(s)  1762 , LIDAR sensor(s)  1764 , inertial measurement unit (MU) sensor(s)  1766  (e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s)  1796 , stereo camera(s)  1768 , wide-view cameras)  1770  (e.g., fisheye cameras), infrared camera(s)  1772 , surround camera(s)  1774  (e.g., 360 degree cameras), long-range and/or mid-range camera(s)  1798 , speed sensor(s)  1744  (e.g., for measuring the speed of the vehicle  1700 ), vibration sensor(s)  1742 , steering sensor(s)  1740 , brake sensor(s) (e.g., as part of the brake sensor system  1746 ), and/or other sensor types. 
     One or more of the controller(s)  1736  may receive inputs (e.g., represented by input data) from an instrument cluster  1732  of the vehicle  1700  and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display  1734 , an audible annunciator, a loudspeaker, and/or via other components of the vehicle  1700 . The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the HD map  1722  of  FIG.  17 C ), location data (e.g., the vehicle&#39;s  1700  location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid), information about objects and status of objects as perceived by the controller(s)  1736 , etc. For example, the HMI display  1734  may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit  34 B in two miles, etc.). 
     The vehicle  1700  further includes a network interface  1724  which may use one or more wireless antenna(s)  1726  and/or modem(s) to communicate over one or more networks. For example, the network interface  1724  may be capable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The wireless antenna(s)  1726  may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.), using local area network(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc., and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox, etc. 
       FIG.  17 B  is an example of camera locations and fields of view for the example autonomous vehicle  1700  of  FIG.  17 A , in accordance with some embodiments of the present disclosure. The cameras and respective fields of view are one example embodiment and are not intended to be limiting. For example, additional and/or alternative cameras may be included and/or the cameras may be located at different locations on the vehicle  1700 . 
     The camera types for the cameras may include, but are not limited to, digital cameras that may be adapted for use with the components and/or systems of the vehicle  1700 . The camera(s) may operate at automotive safety integrity level (ASIL) B and/or at another ASIL. The camera types may be capable of any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc., depending on the embodiment. The cameras may be capable of using rolling shutters, global shutters, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red clear clear clear (RCCC) color filter array, a red clear clear blue (RCCB) color filter array, a red blue green (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensors (RG-GB) color filter array, a monochrome sensor color filter array, and/or another type of color filter array. In some embodiments, clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or an RBGC color filter array, may be used in an effort to increase light sensitivity. 
     In some examples, one or more of the camera(s) may be used to perform advanced driver assistance systems (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a Multi-Function Mono Camera may be installed to provide functions including lane departure warning, traffic sign assist and intelligent headlamp control. One or more of the camera(s) (e.g., all of the cameras) may record and provide image data (e.g., video) simultaneously. 
     One or more of the cameras may be mounted in a mounting assembly, such as a custom designed (3-D printed) assembly, in order to cut out stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirrors) which may interfere with the camera&#39;s image data capture abilities. With reference to wing-mirror mounting assemblies, the wing-mirror assemblies may be custom 3-D printed so that the camera mounting plate matches the shape of the wing-mirror. In some examples, the camera(s) may be integrated into the wing-mirror. For side-view cameras, the camera(s) may also be integrated within the four pillars at each corner of the cabin. 
     Cameras with a field of view that include portions of the environment in front of the vehicle  1700  (e.g., front-facing cameras) may be used for surround view, to help identify forward facing paths and obstacles, as well aid in, with the help of one or more controllers  1736  and/or control SoCs, providing information critical to generating an occupancy grid and/or determining the preferred vehicle paths. Front-facing cameras may be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. Front-facing cameras may also be used for ADAS functions and systems including Lane Departure Warnings (LDW), Autonomous Cruise Control (ACC), and/or other functions such as traffic sign recognition. 
     A variety of cameras may be used in a front-facing configuration, including, for example, a monocular camera platform that includes a CMOS (complementary metal oxide semiconductor) color imager. Another example may be a wide-view camera(s)  1770  that may be used to perceive objects coming into view from the periphery (e.g., pedestrians, crossing traffic or bicycles). Although only one wide-view camera is illustrated in  FIG.  1713   , there may any number of wide-view cameras  1770  on the vehicle  1700 . In addition, long-range camera(s)  1798  (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not yet been trained. The long-range camera(s)  1798  may also be used for object detection and classification, as well as basic object tracking. 
     One or more stereo cameras  1768  may also be included in a front-facing configuration. The stereo camera(s)  1768  may include an integrated control unit comprising a scalable processing unit, which may provide a programmable logic (FPGA) and a multi-core micro-processor with an integrated CAN or Ethernet interface on a single chip. Such a unit may be used to generate a 3-D map of the vehicle&#39;s environment, including a distance estimate for all the points in the image. An alternative stereo camera(s)  1768  may include a compact stereo vision sensor(s) that may include two camera lenses (one each on the left and right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate the autonomous emergency braking and lane departure warning functions. Other types of stereo camera(s)  1768  may be used in addition to, or alternatively from, those described herein. 
     Cameras with a field of view that include portions of the environment to the side of the vehicle  1700  (e.g., side-view cameras) may be used for surround view, providing information used to create and update the occupancy grid, as well as to generate side impact collision warnings. For example, surround camera(s)  1774  (e.g., four surround cameras  1774  as illustrated in  FIG.  17 B ) may be positioned to on the vehicle  1700 . The surround camera(s)  1774  may include wide-view camera(s)  1770 , fisheye camera(s), 360 degree camera(s), and/or the like. Four example, four fisheye cameras may be positioned on the vehicle&#39;s front, rear, and sides. In an alternative arrangement, the vehicle may use three surround camera(s)  1774  (e.g., left, right, and rear), and may leverage one or more other camera(s) (e.g., a forward-facing camera) as a fourth surround view camera. 
     Cameras with a field of view that include portions of the environment to the rear of the vehicle  1700  (e.g., rear-view cameras) may be used for park assistance, surround view, rear collision warnings, and creating and updating the occupancy grid. A wide variety of cameras may be used including, but not limited to, cameras that are also suitable as a front-facing camera(s) (e.g., long-range and/or mid-range camera(s)  1798 , stereo camera(s)  1768 ), infrared camera(s)  1772 , etc.), as described herein. 
       FIG.  17 C  is a block diagram of an example system architecture for the example autonomous vehicle  1700  of  FIG.  17 A , in accordance with some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead 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. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. 
     Each of the components, features, and systems of the vehicle  1700  in  FIG.  17 C  are illustrated as being connected via bus  1702 . The bus  1702  may include a Controller Area Network (CAN) data interface (alternatively referred to herein as a “CAN bus”). A CAN may be a network inside the vehicle  1700  used to aid in control of various features and functionality of the vehicle  1700 , such as actuation of brakes, acceleration, braking, steering, windshield wipers, etc. A CAN bus may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., a CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine revolutions per minute (RPMs), button positions, and/or other vehicle status indicators. The CAN bus may be ASIL B compliant. 
     Although the bus  1702  is described herein as being a CAN bus, this is not intended to be limiting. For example, in addition to, or alternatively from, the CAN bus, FlexRay and/or Ethernet may be used. Additionally, although a single line is used to represent the bus  1702 , this is not intended to be limiting. For example, there may be any number of busses  1702 , which may include one or more CAN busses, one or more FlexRay busses, one or more Ethernet busses, and/or one or more other types of busses using a different protocol. In some examples, two or more busses  1702 . may be used to perform different functions, and/or may be used for redundancy. For example, a first bus  1702  may be used for collision avoidance functionality and a second bus  1702  may be used for actuation control. In any example, each bus  1702  may communicate with any of the components of the vehicle  1700 , and two or more busses  1702  may communicate with the same components. In some examples, each SoC  1704 , each controller  1736 , and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle  1700 ), and may be connected to a common bus, such the CAN bus. 
     The vehicle  1700  may include one or more controller(s)  1736 , such as those described herein with respect to  FIG.  17 A . The controller(s)  1736  may be used for a variety of functions. The controller(s)  1736  may be coupled to any of the various other components and systems of the vehicle  1700 , and may be used for control of the vehicle  1700 , artificial intelligence of the vehicle  1700 , infotainment for the vehicle  1700 , and/or the like. 
     The vehicle  1700  may include a system(s) on a chip (SoC)  1704 . The SoC  1704  may include CPU(s)  1706 , GPU(s)  1708 , processor(s)  1710 , cache(s)  1712 , accelerator(s)  1714 , data store(s)  1716 , and/or other components and features not illustrated. The SoC(s)  1704  may be used to control the vehicle  1700  in a variety of platforms and systems. For example, the SoC(s)  1704  may be combined in a system (e.g., the system of the vehicle  1700 ) with an HD map  1722  which may obtain map refreshes and/or updates via a network interface  1724  from one or more servers (e.g., server(s)  1778  of  FIG.  17 D ). 
     The CPU(s)  1706  may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s)  1706  may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s)  1706  may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s)  1706  may include four dual-core clusters where each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s)  1706  (e.g., the CCPLEX) may be configured to support simultaneous cluster operation enabling any combination of the clusters of the CPU(s)  1706  to be active at any given time. 
     The CPU(s)  1706  may implement power management capabilities that include one or more of the following features: individual hardware blocks may be clock-gated automatically when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to 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(s)  1706  may further implement an enhanced algorithm for managing power states, when allowed power states and expected wakeup times are specified, and the hardware/microcode determines the best power state to enter for the core, cluster, and CCPLEX. The processing cores may support simplified power state entry sequences in software with the work offloaded to microcode. 
     The GPU(s)  1708  may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s)  1708  may be programmable and may be efficient for parallel workloads. The GPU(s)  1708 , in some examples, may use an enhanced tensor instruction set. The GPU(s)  1708  may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB storage capacity), and two or more of the streaming microprocessors may share an L2 cache (e.g., an L2 cache with a 512 KB storage capacity). In some embodiments, the GPU(s)  1708  may include at least eight streaming microprocessors. The GPU(s)  1708  may use compute application programming interface(s) (API(s)). In addition, the GPU(s)  1708  may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA&#39;s CUDA). 
     The GPU(s)  1708  may be power-optimized for best performance in automotive and embedded use cases. For example, the GPU(s)  1708  may be fabricated on a Fin field-effect transistor (FinFET). However, this is not intended to be limiting and the GPU(s)  1708  may be fabricated using other semiconductor manufacturing processes. Each streaming microprocessor may incorporate a number of mixed-precision processing cores partitioned into multiple blocks. For example, and without limitation, 64 PF32 cores and 32 PF64 cores may be partitioned into four processing blocks. In such an example, each processing block may be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs for deep learning matrix arithmetic, an L0 instruction cache, a warp scheduler, a dispatch unit, and/or a 64 KB register file. In addition, the streaming microprocessors may include independent parallel integer and floating-point data paths to provide for efficient execution of workloads with a mix of computation and addressing calculations. The streaming microprocessors may include independent thread scheduling capability to enable finer-grain synchronization and cooperation between parallel threads. The streaming microprocessors may include a combined L1 data cache and shared memory unit in order to improve performance while simplifying programming. 
     The GPU(s)  1708  may include a high bandwidth memory (HBM) and/or a 16 GB HBM2 memory subsystem to provide, in some examples, about 900 GB/second peak memory bandwidth. In some examples, in addition to, or alternatively from, the FIBM memory, a synchronous graphics random-access memory (SGRAM) may be used, such as a graphics double data rate type five synchronous random-access memory (GDDR5). 
     The GPU(s)  1708  may include unified memory technology including access counters to allow for more accurate migration of memory pages to the processor that accesses them most frequently, thereby improving efficiency for memory ranges shared between processors. In some examples, address translation services (ATS) support may be used to allow the GPU(s)  1708  to access the CPU(s)  1706  page tables directly. In such examples, when the GPU(s)  1708  memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s)  1706 . In response, the CPU(s)  1706  may look in its page tables for the virtual-to-physical mapping for the address and transmits the translation back to the CiPU(s)  1708 . As such, unified memory technology may allow a single unified virtual address space for memory of both the CPU(s)  1706  and the GPU(s)  1708 , thereby simplifying the GPU(s)  1708  programming and porting of applications to the GPU(s)  1708 . 
     In addition, the GPU(s)  1708  may include an access counter that may keep track of the frequency of access of the GPU(s)  1708  to memory of other processors. The access counter may help ensure that memory pages are moved to the physical memory of the processor that is accessing the pages most frequently. 
     The SoC(s)  1704  may include any number of caches)  1712 , including those described herein. For example, the cache(s)  1712  may include an L3 cache that is available to both the CPU(s)  1706  and the GPU(s)  1708  (e.g., that is connected both the CPU(s)  1706  and the GPU(s)  1708 ). The cache(s)  1712  may include a write-back cache that may keep track of states of lines, such as by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache may include 4 MB or more, depending on the embodiment, although smaller cache sizes may be used. 
     The SoC(s)  1704  may include an arithmetic logic unit(s) (ALU(s)) which may be leveraged in performing processing with respect to any of the variety of tasks or operations of the vehicle  1700 —such as processing DNNs. In addition, the SoC(s)  1704  may include a floating point unit(s) (FPU(s))—or other math coprocessor or numeric coprocessor types for performing mathematical operations within the system. For example, the SoC(s)  104  may include one or more FPUs integrated as execution units within a CPU(s)  1706  and/or GPU(s)  1708 . 
     The SoC(s)  1704  may include one or more accelerators  1714  (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s)  1704  may include a hardware acceleration cluster that may include optimized hardware accelerators and/or large on-chip memory. The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to complement the GPU(s)  1708  and to off-load some of the tasks of the GPU(s)  1708  (e.g., to free up more cycles of the GPU(s)  1708  for performing other tasks). As an example, the accelerator(s)  1714  may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are stable enough to be amenable to acceleration. The term “CNN,” as used herein, may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and Fast RCNNs (e.g., as used for object detection). 
     The accelerator(s)  1714  (e.g., the hardware acceleration cluster) may include a deep learning accelerator(s) (DLA). The DLA(s) may include one or more Tensor processing units (TPUs) that may be configured to provide an additional ten trillion operations per second for deep learning applications and inferencing. The TPUs may be accelerators configured to, and optimized for, performing image processing functions (e.g., for CNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specific set of neural network types and floating point operations, as well as inferencing. The design of the DLA(s) may provide more performance per millimeter than a general-purpose GPU, and vastly exceeds the performance of a CPU. The TPU(s) may perform several functions, including a single-instance convolution function, supporting, for example, INT8, INT16, and FP16 data types for both features and weights, as well as post-processor functions. 
     The DLA(s) may quickly and efficiently execute neural networks, especially CNNs, on processed or unprocessed data for any of a variety of functions, including, for example and without limitation: a CNN for object identification and detection using data from camera sensors; a CNN for distance estimation using data from camera sensors; a CNN for emergency vehicle detection and identification and detection using data from microphones; a CNN for facial recognition and vehicle owner identification using data from camera sensors; and/or a CNN for security and/or safety related events. 
     The DLA(s) may perform any function of the GPU(s)  1708 , and by using an inference accelerator, for example, a designer may target either the DLA(s) or the GPU(s)  1708  for any function. For example, the designer may focus processing of CNNs and floating point operations on the DLA(s) and leave other functions to the GP 11 (s)  1708  and/or other accelerator(s)  1714 . 
     The accelerator(s)  1714  (e.g., the hardware acceleration cluster) may include a programmable vision accelerator(s) (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA(s) may be designed and configured to accelerate computer vision algorithms for the advanced driver assistance systems (ADAS), autonomous driving, and/or augmented reality (AR) and/or virtual reality (VR) applications. The PVA(s) may provide a balance between performance and flexibility. For example, each PVA(s) 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. 
     The RISC cores may interact with image sensors (e.g., the image sensors of any of the cameras described herein), image signal processor(s), and/or the like. Each of the RISC cores may include any amount of memory. The RISC cores may use any of a number of protocols, depending on the embodiment. In some examples, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and/or memory devices. For example, the RISC cores may include an instruction cache and/or a tightly coupled RAM. 
     The DMA may enable components of the PVA(s) to access the system memory independently of the CPU(s)  1706 . The DMA may support any number of features used to provide optimization to the PVA including, but not limited to, supporting multi-dimensional addressing and/or circular addressing. In some examples, the DMA may support up to six or more dimensions of addressing, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and/or depth stepping. 
     The vector processors may be programmable processors that may be designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMA engines), and/or other peripherals. The vector processing subsystem may operate as the primary processing engine of the PVA, and may include a vector processing unit (VPU), an instruction cache, and/or vector memory (e.g., VMEM). A VPU core may include a digital signal processor such as, for example, a single instruction, multiple data (SIMD), very long instruction word (VLIW) digital signal processor. The combination of the SIMD and VLIW may enhance throughput and speed. 
     Each of the vector processors may include an instruction cache and may be coupled to 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 that are included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, the plurality of vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may simultaneously execute different computer vision algorithms, on the same image, or even execute different algorithms on sequential images or portions of an image. Among other things, any number of PVAs may be included in the hardware acceleration cluster and any number of vector processors may be included in each of the PVAs. In addition, the PVA(s) may include additional error correcting code (ECC) memory, to enhance overall system safety. 
     The accelerator(s)  1714  (e.g., the hardware acceleration cluster) may include a computer vision network on-chip and SRAM, for providing a high-bandwidth, low latency SRAM for the accelerator(s)  1714 . In some examples, the on-chip memory may include at least 4 MB SRAM, consisting of, for example and without limitation, eight field-configurable memory blocks, that may be accessible by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides the PVA and DLA with high-speed access to memory. The backbone may include a computer vision network on-chip that interconnects the PVA and the DLA to the memory (e.g., using the APB). 
     The computer vision network on-chip may include an interface that determines, before transmission of any control signal/address/data, that both the PVA and the DLA provide ready and valid signals. Such an interface may provide for separate phases and separate channels for transmitting control signals/addresses/data, as well as burst-type communications for continuous data transfer. This type of interface may comply with ISO 26262 or IEC 61508 standards, although other standards and protocols may be used. 
     In some examples, the SoC(s)  1704  may include a real-time ray-tracing hardware accelerator, such as described in U.S. patent application Ser. No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracing hardware accelerator may be used to quickly and efficiently determine the positions and extents of objects (e.g., within a world model), to generate real-time visualization simulations, for RADAR signal interpretation, for sound propagation synthesis and/or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison to LIDAR data for purposes of localization and/or other functions, and/or for other uses. In some embodiments, one or more tree traversal units (TTUs) may be used for executing one or more ray-tracing related operations. 
     The accelerator(s)  1714  (e.g., the hardware accelerator cluster) have a wide array of uses for autonomous driving. The PVA may be a programmable vision accelerator that may be used for key processing stages in ADAS and autonomous vehicles. The PVA&#39;s capabilities are a good match for algorithmic domains needing predictable processing, at low power and low latency. In other words, the PVA performs well on semi-dense or dense regular computation, even on small data sets, which need predictable run-times with low latency and low power. Thus, in the context of platforms for autonomous vehicles, the PVAs are designed to run classic computer vision algorithms, as they are efficient at object detection and operating on integer math. 
     For example, according to one embodiment of the technology, the PVA is used to perform computer stereo vision. A semi-global matching-based algorithm may be used in some examples, although this is not intended to be limiting. Many applications for Level 3-5 autonomous driving require motion estimation/stereo matching on-the-fly (e.g., structure from motion, pedestrian recognition, lane detection, etc.). The PVA may perform computer stereo vision function on inputs from two monocular cameras. 
     In some examples, the PVA may be used to perform dense optical flow. According to process raw RADAR data (e.g., using a 4D Fast Fourier Transform) to provide Processed RADAR. In other examples, the PVA is used for time of flight depth processing, by processing raw time of flight data to provide processed time of flight data, for example. 
     The DLA may be used to run any type of network to enhance control and driving safety, including for example, a neural network that outputs a measure of confidence for each object detection. Such a confidence value may 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 regarding which detections should be considered as true positive detections rather than false positive detections. For example, the system may set a threshold value for the confidence and consider only the detections exceeding the threshold value as true positive detections. In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detections should be considered as triggers for AEB. The DLA may run a neural network for regressing the confidence value. The neural network may take as its input at least some subset of parameters, such as bounding box dimensions, ground plane estimate obtained (e.g. from another subsystem), inertial measurement unit (IMU) sensor  1766  output that correlates with the vehicle  1700  orientation, distance, 3D location estimates of the object obtained from the neural network and/or other sensors (e.g., LIDAR sensor(s)  1764  or RADAR sensor(s)  1760 ), among others. 
     The SoC(s)  1704  may include data store(s)  1716  (e.g., memory). The data store(s)  1716  may be on-chip memory of the SoC(s)  1704 , which may store neural networks to be executed on the GPU and/or the In some examples, the data store(s)  1716  may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s)  1712  may comprise L2 or L 3  cache(s)  1712 . Reference to the data store(s)  1716  may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s)  1714 , as described herein. 
     The SoC(s)  1704  may include one or more processor(s)  1710  (e.g., embedded processors). The processor(s)  1710  may include a boot and power management processor that may be a dedicated processor and subsystem to handle boot power and management functions and related security enforcement. The boot and power management processor may be a part of the SoC(s)  1704  boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, assistance in system low power state transitions, management of SoC(s)  1704  thermals and temperature sensors, and/or management of the SoC(s)  1704  power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s)  1704  may use the ring-oscillators to detect temperatures of the CPU(s)  1706 , GPU(s)  1708 , and/or accelerators)  1714 . If temperatures are determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine and put the SoC(s)  1704  into a lower power state and/or put the vehicle  1700  into a chauffeur o safe stop mode (e.g., bring the vehicle  1700  to a safe stop). 
     The processor(s)  1710  may further include a set of embedded processors that may serve as an audio processing engine. The audio processing engine may be an audio subsystem that enables full hardware support for multi-channel audio over multiple interfaces, and a broad and flexible range of audio  110  interfaces. In some examples, the audio processing engine is a dedicated processor core with a digital signal processor with dedicated RAM. 
     The processor(s)  1710  may further include an always on processor engine that may provide necessary hardware features to support low power sensor management and wake use cases. The always on processor engine may include a processor core, a tightly coupled RAM, supporting peripherals (e.g., timers and interrupt controllers), various I/O controller peripherals, and routing logic. 
     The processor(s)  1710  may further include a safety cluster engine that includes a dedicated processor subsystem to handle safety management for automotive applications. The safety cluster engine may include two or more processor cores, a tightly coupled RAM, support peripherals (e.g., timers, an interrupt controller, etc.), and/or routing logic. In a safety mode, the two or more cores may operate in a lockstep mode and function as a single core with comparison logic to detect any differences between their operations. 
     The processor(s)  1710  may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management. 
     The processor(s)  1710  may further include a high-dynamic range signal processor that may include an image signal processor that is a hardware engine that is part of the camera processing pipeline. 
     The processor(s)  1710  may include a video image compositor that may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions needed by a video playback application to produce the final image for the player window. The video image compositor may perform lens distortion correction on wide-view camera(s)  1770 , surround camera(s)  1774 , and/or on in-cabin monitoring camera sensors. In-cabin monitoring camera sensor is preferably monitored by a neural network running on another instance of the Advanced SoC, configured to identify in cabin events and respond accordingly. An in-cabin system may perform lip reading to activate cellular service and place a phone call, dictate emails, change the vehicle&#39;s destination, activate or change the vehicle&#39;s infotainment system and settings, or provide voice-activated web surfing. Certain functions are available to the driver only when the vehicle is operating in an autonomous mode, and are disabled otherwise. 
     The video image compositor may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, where motion occurs in a video, the noise reduction weights spatial information appropriately, decreasing the weight of information provided by adjacent frames. Where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositor may use information from the previous image to reduce noise in the current image. 
     The video image compositor may also be configured to perform stereo rectification on input stereo lens frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use, and the GPU(s)  1708  is not required to continuously render new surfaces. Even when the GPU(s)  1708  is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s)  1708  to improve performance and responsiveness. 
     The SoC(s)  1704  may further include a mobile industry processor interface (WM) camera serial interface for receiving video and input from cameras, a high-speed interface, and/or a video input block that may be used for camera and related pixel input functions. The SoC(s)  1704  may further include an input/output controller(s) that may be controlled by software and may be used for receiving I/O signals that are uncommitted to a specific role. 
     The SoC(s)  1704  may further include a broad range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and/or other devices. The SoC(s)  1704  may be used to process data from cameras connected over Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s)  1764 , RADAR sensor(s)  1760 , etc. that may be connected over Ethernet), data from bus  1702  (e.g., speed of vehicle  1700 , steering wheel position, etc.), data from GNSS sensor(s)  1758  (e.g., connected over Ethernet or CAN bus). The SoC(s)  1704  may further include dedicated high-performance mass storage controllers that may include their own DMA engines, and that may be used to free the CPU(s)  1706  from routine data management tasks. 
     The SoC(s)  1704  may be an end-to-end platform with a flexible architecture that spans automation levels 3-5, thereby providing a comprehensive functional safety architecture that leverages and makes efficient use of computer vision and ADAS techniques for diversity and redundancy, provides a platform for a flexible, reliable driving software stack, along with deep learning tools. The SoC(s)  1704  may be faster, more reliable, and even more energy-efficient and space-efficient than conventional systems. For example, the accelerators)  1714 , when combined with the CPU(s)  1706 , the CPU(s)  1708 , and the data store(s)  1716 , may provide for a fast, efficient platform for level 3-5 autonomous vehicles. 
     The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms may be executed on CPUs, which may be configured using high-level programming language, such as the C programming language, to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs are oftentimes unable to meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption, for example. In particular, many CPUs are unable to execute complex object detection algorithms in real-time, which is a requirement of in-vehicle ADAS applications, and a requirement for practical Level 3-5 autonomous vehicles. 
     In contrast to conventional systems, by providing a CPU complex, GPU complex, and a hardware acceleration cluster, the technology described herein allows for multiple neural networks to be performed simultaneously and/or sequentially, and for the results to be combined together to enable Level 3-5 autonomous driving functionality. For example, a CNN executing on the DLA or dGPU (e.g., the (IPU(s)  1720 ) may include a text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which the neural network has not been specifically trained. The DLA may further include a neural network that is able to identify, interpret, and provides semantic understanding of the sign, and to pass that semantic understanding to the path planning modules running on the CPU Complex. 
     As another example, multiple neural networks may be run simultaneously, as is required for Level 3, 4, or 5 driving. For example, a warning sign consisting of “Caution: flashing lights indicate icy conditions,” along with an electric light, may be independently or collectively interpreted by several neural networks. The sign itself may be identified as a traffic sign by a first deployed neural network (e.g., a neural network that has been trained), the text “Flashing lights indicate icy conditions” may be interpreted by a second deployed neural network, which informs the vehicle&#39;s path planning software (preferably executing on the CPU Complex) that when flashing lights are detected, icy conditions exist. The flashing light may be identified by operating a third deployed neural network over multiple frames, informing the vehicle&#39;s path-planning software of the presence (or absence) of flashing lights. All three neural networks may run simultaneously, such as within the and/or on the GPU(s)  1708 . 
     In some examples, a CNN for facial recognition and vehicle owner identification may use data from camera sensors to identify the presence of an authorized driver and/or owner of the vehicle  1700 . The always on sensor processing engine may be used to unlock the vehicle when the owner approaches the driver door and turn on the lights, and, in security mode, to disable the vehicle when the owner leaves the vehicle. In this way, the SoC(s)  1704  provide for security against theft and/or carjacking. 
     In another example, a CNN for emergency vehicle detection and identification may use data from microphones  1796  to detect and identify emergency vehicle sirens. In contrast to conventional systems, that use general classifiers to detect sirens and manually extract features, the SoC(s)  1704  use the CNN for classifying environmental and urban sounds, as well as classifying visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing speed of the emergency vehicle (e.g., by using the Doppler Effect). The CNN may also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by GNSS sensor(s)  1758 . Thus, for example, when operating in mope 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, a control program may be used to execute an emergency vehicle safety routine, slowing the vehicle, pulling over to the side of the road, parking the vehicle, and/or idling the vehicle, with the assistance of ultrasonic sensors  1762 , until the emergency vehicle(s) passes. 
     The vehicle may include a CPU(s)  1718  (e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s)  1704  via a high-speed interconnect (e.g., PCIe). The CPU(s)  1718  may include an X86 processor, for example. The CPU(s)  1718  may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADA S sensors and the SoC(s)  1704 , and/or monitoring the status and health of the controller(s)  1736  and/or infotainment SoC  1730 , for example. 
     The vehicle  1700  may include a GPU(s)  1720  (e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s)  1704  via a high-speed interconnect (e.g., NVIDIA&#39;s NVLINK). The GPU(s)  1720  may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks based on input (e.g., sensor data) from sensors of the vehicle  1700 . 
     The vehicle  170 (s) may further include the network interface  1724  which may include one or more wireless antennas  1726  (e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface  1724  may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s)  1778  and/or other network devices), with other vehicles, and/or with computing devices (e.g., client devices of passengers). To communicate with other vehicles, a direct link may be established between the two vehicles and/or an indirect link may be established (e.g., across networks and over the Internet). Direct links may be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link may provide the vehicle  1700  information about vehicles in proximity to the vehicle  1700  (e.g., vehicles in front of, on the side of, and/or behind the vehicle  1700 ). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle  1700 . 
     The network interface  1724  may include a SoC that provides modulation and demodulation functionality and enables the controller(s)  1736  to communicate over wireless networks. The network interface  1724  may include: a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband. The frequency conversions may be performed through well-known processes, and/or may he performed using super-heterodyne processes. In some examples, the radio frequency front end functionality may be provided by a separate chip. The network interface may include wireless functionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, WI-FI, Z-Wave, ZigBee, LoRaWAN, and/or other wireless protocols. 
     The vehicle  1700  may further include data store(s)  1728  which may include off-chip (e.g., off the SoC(s)  1704 ) storage. The data store(s)  1728  may include one or more storage elements including RAM, SRAM, DRAM, VRAM, Flash, hard disks, and/or other components and/or devices that may store at least one bit of data. 
     The vehicle  1700  may further include GNSS sensor(s)  1758 . The GNSS sensor(s)  1758  (e.g., GPS, assisted GPS sensors, differential GPS (DGPS) sensors, etc.), to assist in mapping, perception, occupancy grid generation, and/or path planning functions. Any number of GNSS sensor(s)  1758  may be used, including, for example and without limitation, a GPS using a USB connector with an Ethernet to Serial (RS-232) bridge. 
     The vehicle  1700  may further include RADAR sensor(s)  1760 . The RADAR sensor(s)  1760  may be used by the vehicle  1700  for long-range vehicle detection, even in darkness and/or severe weather conditions. RADAR functional safety levels may be ASIL B. The RADAR sensor(s)  1760  may use the CAN and/or the bus  1702  (e.g., to transmit data generated by the RADAR sensor(s)  1760 ) for control and to access object tracking data, with access to Ethernet to access raw data in some examples. A wide variety of RADAR sensor types may be used. For example, and without limitation, the RADAR sensor(s)  1760  may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used. 
     The RADAR sensor(s)  1760  may include different configurations, such as long range with narrow field of view, short range with wide field of view, short range side coverage, etc. In some examples, long-range RADAR may be used for adaptive cruise control functionality. The long-range RADAR systems may provide a broad field of view realized by two or more independent scans, such as within a 250 m range. The RADAR sensor(s)  1760  may help in distinguishing between static and moving objects, and may be used by ADAS systems for emergency brake assist and forward collision warning, Long-range RADAR sensors may include monostatic multimodal RADAR with multiple (e.g., six or more) fixed RADAR antennae and a high-speed CAN and FlexRay interface. In an example with six antennae, the central four antennae may create a focused beam pattern, designed to record the vehicle&#39;s  1700  surroundings at higher speeds with minimal interference from traffic in adjacent lanes. The other two antennae may expand the field of view, making it possible to quickly detect vehicles entering or leaving the vehicle&#39;s  1700  lane. 
     Mid-range RADAR systems may include, as an example, a range of up to 1760 m (front) or 80 m (rear), and a field of view of up to 42 degrees (front) or 1750 degrees (rear). Short-range RADAR systems may include, without limitation, RADAR sensors designed to be installed at both ends of the rear bumper. When installed at both ends of the rear bumper, such a RADAR sensor systems may create two beams that constantly monitor the blind spot in the rear and next to the vehicle. 
     Short-range RADAR systems may be used in an ADAS system for blind spot detection and/or lane change assist. 
     The vehicle  1700  may further include ultrasonic sensor(s)  1762 . The ultrasonic sensor(s)  1762 , which may be positioned at the front, back, and/or the sides of the vehicle  1700 , may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensors)  1762  may be used, and different ultrasonic sensor(s)  1762  may be used for different ranges of detection (e.g., 2.5 m, 4 m). Thee ultrasonic sensor(s)  1762  may operate at functional safety levels of ASIL B. 
     The vehicle  1700  may include LIDAR sensor(s)  1764 . The LIDAR sensor(s)  1764  may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s)  1764  may be functional safety level ASIL B. In some examples, the vehicle  1700  may include multiple LIDAR sensors  1764  (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch). 
     In some examples, the LID AR sensor(s)  1764  may be capable of providing a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensor(s)  1764  may have an advertised range of approximately 1700 m, with an accuracy of 2 cm-3 cm, and with support for a 1700 Mbps Ethernet connection, for example. In some examples, one or more non-protruding LIDAR. sensors  1764  may be used. In such examples, the LIDAR sensor(s)  1764  may be implemented as a small device that may be embedded into the front, rear, sides, and/or corners of the vehicle  1700 . The LIDAR sensor(s)  1764 , in such examples, may provide up to a 120-degree horizontal and 35-degree vertical field-of-view, with a 200 m range even for low-reflectivity objects. Front-mounted LIDAR sensor(s)  1764  may be configured for a horizontal field of view between 45 degrees and 135 degrees. 
     In some examples, LIDAR technologies, such as 3D flash LIDAR, may also be used. 3D Flash LIDAR uses a flash of a laser as a transmission source, to illuminate vehicle surroundings up to approximately 200 m. A flash LIDAR unit includes a receptor, which records the laser pulse transit time and the reflected light on each pixel, which in turn corresponds to the range from the vehicle to the objects. Flash LIDAR may allow for highly accurate and distortion-free images of the surroundings to be generated with every laser flash. In some examples, four flash LIDAR sensors may be deployed, one at each side of the vehicle  1700  Available 3D flash LIDAR systems include a solid-state 3D staring array LIDAR camera with no moving parts other than a fan (e.g., a non-scanning LIDAR device). The flash LIDAR device may use a 5 nanosecond class I (eye-safe) laser pulse per frame and may capture the reflected laser light in the form of 3D range point clouds and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)  1764  may be less susceptible to motion blur, vibration, and/or shock. 
     The vehicle may further include IMU sensor(s)  1766 . The IMU sensor(s)  1766  may be located at a center of the rear axle of the vehicle  1700 , in some examples. The IMU sensor(s)  1766  may include, for example and without limitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), a magnetic compass(es), and/or other sensor types. In some examples, such as in six-axis applications, the IMlU sensor(s)  1766  may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s)  1766  may include accelerometers, gyroscopes, and magnetometers. 
     In some embodiments, the IMlU sensor(s)  1766  may be implemented as a miniature, high performance GPS-Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. As such, in some examples, the IMU sensor(s)  1766  may enable the vehicle  1700  to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s)  1766 . In some examples, the IMU sensor(s)  1766  and the GNSS sensor(s)  1758  may be combined in a single integrated unit. 
     The vehicle may include microphone(s)  1796  placed in and/or around the vehicle  1700 . The microphone(s)  1796  may be used for emergency vehicle detection and identification, among other things. 
     The vehicle may further include any number of camera types, including stereo camera(s)  1768 , wide-view camera(s)  1770 , infrared cameras)  1772 , surround camera(s)  1774 , long-range and/or mid-range camera(s)  1798 , and/or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle  1700 . The types of cameras used depends on the embodiments and requirements for the vehicle  1700 , and any combination of camera types may be used to provide the necessary coverage around the vehicle  1700 . In addition, the number of cameras may differ depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and/or another number of cameras. The cameras may support, as an example and without limitation, Gigabit Multimedia Serial Link (GMSL) and/or Gigabit Ethernet. Each of the camera(s) is described with more detail herein with respect to  FIG.  17 A  and  FIG.  17 B . 
     The vehicle  1700  may further include vibration sensor(s)  1742 . The vibration sensor(s)  1742  may measure vibrations of components of the vehicle, such as the axle(s). For example, changes in vibrations may indicate a change in road surfaces. In another example, when two or more vibration sensors  1742  are used, the differences between the vibrations may be used to determine friction or slippage of the road surface (e.g., when the difference in vibration is between a power-driven axle and a freely rotating axle). 
     The vehicle  1700  may include an ADAS system  1738 . The ADAS system  1738  may include a SoC, in some examples. The ADAS system  1738  may include autonomous/adaptive/automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward crash warning (FCW), automatic emergency braking (AEB), lane departure warnings (LDW), lane keep assist (LKA), blind spot warning (BSW), rear cross-traffic warning (RCTW), collision warning systems (CWS), lane centering (LC), and/or other features and functionality. 
     The ACC systems may use RADAR sensor(s)  1760 , LIDAR sensor(s)  1764 , and/or a camera(s). The ACC systems may include longitudinal ACC and/or lateral ACC. Longitudinal ACC monitors and controls the distance to the vehicle immediately ahead of the vehicle  1700  and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle  1700  to change lanes when necessary. Lateral ACC is related to other ADAS applications such as LCA and CWS. 
     CACC uses information from other vehicles that may be received via the network interface  1724  and/or the wireless antenna(s)  1726  from other vehicles via a wireless link, or indirectly, over a network connection (e.g., over the Internet). Direct links may be provided by a vehicle-to-vehicle (V2V) communication link, while indirect links may be infrastructure-to-vehicle (I2V) communication link. In general, the V2V communication concept provides information about the immediately preceding vehicles (e.g., vehicles immediately ahead of and in the same lane as the vehicle  1700 ), while the I2V communication concept provides information about traffic further ahead. CACC systems may include either or both I2V and V2V information sources. Given the information of the vehicles ahead of the vehicle  1700 , CACC may be more reliable and it has potential to improve traffic flow smoothness and reduce congestion on the road. 
     FCW systems are designed to alert the driver to a hazard, so that the driver may take corrective action. FCW systems use a front-facing camera and/or RADAR sensor(s)  1760 , 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. FCW systems may provide a warning, such as in the form of a sound, visual warning, vibration and/or a quick brake pulse. 
     AEB systems detect an impending forward collision with another vehicle or other object, and may automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use front-facing camera(s) and/or RADAR sensor(s)  1760 , 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 no take corrective action, the AEB system may automatically apply the brakes in an effort to prevent, or at least mitigate, the impact of the predicted collision. AEB systems, may include techniques such as dynamic brake support and/or crash imminent braking. 
     LDW systems provide visual, audible, and/or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle  1700  crosses lane markings. A LDW system does not activate when the driver indicates an intentional lane departure, by activating a turn signal. LDW systems may use front-side facing cameras, 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. 
     LKA systems are a variation of LDW systems. LKA systems provide steering input or braking to correct the vehicle  1700  if the vehicle  1700  starts to exit the lane. 
     BSW systems detects and warn the driver of vehicles in an automobile&#39;s blind spot. BSW systems may provide a visual, audible, and/or tactile alert to indicate that merging or changing lanes is unsafe. The system may provide an additional warning when the driver uses a turn signal. BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)  1760 , 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. 
     RCTW systems may provide visual, audible, and/or tactile notification when an object is detected outside the rear-camera range when the vehicle  1700  is backing up. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. RCTW systems may use one or more rear-facing RADAR sensor(s)  1760 , 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. 
     Conventional ADAS systems may be prone to false positive results which may be annoying and distracting to a driver, but typically are not catastrophic, because the ADAS systems alert the driver and allow the driver to decide whether a safety condition truly exists and act accordingly. However, in an autonomous vehicle  1700 , the vehicle  1700  itself must, in the case of conflicting results, decide whether to heed the result from a primary computer or a secondary computer (e.g., a first controller  1736  or a second controller  1736 ). For example, in some embodiments, the ADAS system  1738  may be a backup and/or secondary computer for providing perception information to a backup computer rationality module. The backup computer rationality monitor may run a redundant diverse software on hardware components to detect faults in perception and dynamic driving tasks. Outputs from the ADAS system  1738  may be provided to a supervisory MCU. If outputs from the primary computer and the secondary computer conflict, the supervisory MCU must determine how to reconcile the conflict to ensure safe operation. 
     In some examples, the primary computer may be configured to provide the supervisory MCU with a confidence score, indicating the primary computer&#39;s confidence in the chosen result. If the confidence score exceeds a threshold, the supervisory MCU may follow the primary computer&#39;s direction, regardless of whether the secondary computer provides a conflicting or inconsistent result. Where the confidence score does not meet the threshold, and where the primary and secondary computer indicate different results (e.g., the conflict), the supervisory MCU may arbitrate between the computers to determine the appropriate outcome. 
     The supervisory MCU may be configured to run a neural network(s) that is trained and configured to determine, based on outputs from the primary computer and the secondary computer, conditions under which the secondary computer provides false alarms. Thus, the neural network(s) in the supervisory MCU may learn when the secondary computer&#39;s output may be trusted, and when it cannot. For example, when the secondary computer is a RADAR-based FCW system, a neural network(s) in the supervisory MCU may learn when the FCW system is identifying metallic objects that are not, in fact, hazards, such as a drainage grate or manhole cover that triggers an alarm. Similarly, when the secondary computer is a camera-based LDW system, a neural network in the supervisory MCU may learn to override the LDW when bicyclists or pedestrians are present and a lane departure is, in fact, the safest maneuver. In embodiments that include a neural network(s) running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or GPU suitable for running the neural network(s) with associated memory. In preferred embodiments, the supervisory MCU may comprise and/or be included as a component of the SoC(s)  1704 . 
     In other examples, ADAS system  1738  may include a secondary computer that performs ADAS functionality using traditional rules of computer vision. As such, the secondary computer may use classic computer vision rules (if-then), and the presence of a neural network(s) in the supervisory MCU may improve reliability, safety and performance. For example, the diverse implementation and intentional non-identity makes the overall system more fault-tolerant, especially to faults caused by software (or software-hardware interface) functionality. For example, if there is a software bug or error in the software running on the primary computer, and the non-identical software code running on the secondary computer provides the same overall result, the supervisory MCU may have greater confidence that the overall result is correct, and the bug in software or hardware on primary computer is not causing material error. 
     In some examples, the output of the ADAS system  1738  may be fed into the primary computer&#39;s perception block and/or the primary computer&#39;s dynamic driving task block. For example, if the ADAS system  1738  indicates a forward crash warning due to an object immediately ahead, the perception block may use this information when identifying objects. In other examples, the secondary computer may have its own neural network which is trained and thus reduces the risk of false positives, as described herein. 
     The vehicle  1700  may further include the infotainment SoC  1730  (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as a SoC, the infotainment system may not be a SoC, and may include two or more discrete components. The infotainment SoC  1730  may include a combination of hardware and software that may be used to provide audio (e.g., music, a personal digital assistant, navigational instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and/or information services (e.g., navigation systems, rear-parking assistance, a radio data system, vehicle related information such as fuel level, total distance covered, brake fuel level, oil level, door open/close, air filter information, etc.) to the vehicle  1700 . For example, the infotainment SoC  1730  may radios, disk players, navigation systems, video players, USB and Bluetooth connectivity, carputers, in-car entertainment, Wi-Fi, steeling wheel audio controls, hands free voice control, a heads-up display (HUD), an HMI display  1734 , 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  1730  may further be used to provide information (e.g., visual and/or audible) to a user(s) of the vehicle, such as information from the ADAS system  1738 , 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. 
     The infotainment SoC  1730  may include GPU functionality. The infotainment SoC  1730  may communicate over the bus  1702  (e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle  1700 . In some examples, the infotainment SoC  1730  may be coupled to a supervisory MCU such that the GPU of the infotainment system may perform some self-driving functions in the event that the primary controller(s)  1736  (e.g., the primary and/or backup computers of the vehicle  1700 ) fail. In such an example, the infotainment SoC  1730  may put the vehicle  1700  into a chauffeur to safe stop mode, as described herein. 
     The vehicle  1700  may further include an instrument cluster  1732  (e.g., a digital dash, an electronic instrument duster, a digital instrument panel, etc.). The instrument cluster  1732  may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster  1732  may include a set of instrumentation such as a speedometer, fuel level, oil pressure, tachometer, odometer, turn indicators, gearshift position indicator, seat belt warning light(s), parking-brake warning light(s), engine-malfunction light(s), airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and/or shared among the infotainment SoC  1730  and the instrument cluster  1732 . In other words, the instrument cluster  1732  may be included as part of the infotainment SoC  1730 , or vice versa. 
       FIG.  17 D  is a system diagram for communication between cloud-based server(s) and the example autonomous vehicle  1700  of  FIG.  17 A , in accordance with some embodiments of the present disclosure. The system  1776  may include server(s)  1778 , network(s)  1790 , and vehicles, including the vehicle  1700 . The server(s)  1778  may include a plurality of GPUs  1784 (A)- 1784 (H) (collectively referred to herein as GPUs  1784 ), PCIe switches  1782 (A)- 1782 (H) (collectively referred to herein as PCIe switches  1782 ), and/or CPUs  1780 (A)- 1780 (B) (collectively referred to herein as CPUs  1780 ). The GPUs  1784 , the CPUs  1780 , and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces  1788  developed by NVIDIA and/or PCIe connections  1786 . In some examples, the GPUs  1784  are connected via NVLink and/or NVSwitch SoC and the GPUs  1784  and the PCIe switches  1782  are connected via PCIe interconnects. Although eight GPUs  1784 , two CPUs  1780 , and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s)  1778  may include any number of GPUs  1784 , CPUs  1780 , and/or PCIe switches. For example, the server(s)  1778  may each include eight, sixteen, thirty-two, and/or more GPUs  1784 . 
     The server(s)  1778  may receive, over the network(s)  1790  and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work. The server(s)  1778  may transmit, over the network(s)  1790  and to the vehicles, neural networks  1792 , updated neural networks  1792 , and/or map information  1794 , including information regarding traffic and road conditions. The updates to the map information  1794  may include updates for the HD map  1722 , such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks  1792 , the updated neural networks  1792 , and/or the map information  1794  may have resulted from new training and/or experiences represented in data received from any number of vehicles in the environment, and/or based on training performed at a datacenter (e.g., using the server(s)  1778  and/or other servers). 
     The server(s)  1778  may be used to train machine learning models (e.g., neural networks) based on training data. The training data may be generated by the vehicles, and/or may be generated in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., where the neural network benefits from supervised learning) and/or undergoes other pre-processing, while in other examples the training data is not tagged and/or pre-processed (e.g., where the neural network does not require supervised learning). Training may be executed according to any one or more classes of machine learning techniques, including, without limitation, classes such as: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analyses), multi-linear subspace learning, manifold learning, representation learning (including spare dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations therefor. Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s)  1790 , and/or the machine learning models may be used by the server(s)  1778  to remotely monitor the vehicles. 
     In some examples, the server(s)  1778  may receive data from the vehicles and apply the data to up-to-date real-time neural networks for real-time intelligent inferencing. The server(s)  1778  may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s)  1784 , such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s)  1778  may include deep learning infrastructure that use only CPU-powered datacenters. 
     The deep-learning infrastructure of the server(s)  1778  may be capable of fast, real-time inferencing, and may use that capability to evaluate and verify the health of the processors, software, and/or associated hardware in the vehicle  1700 . For example, the deep-learning infrastructure may receive periodic updates from the vehicle  1700 , such as a sequence of images and/or objects that the vehicle  1700  has located in that sequence of images (e.g., via computer vision and/or other machine learning object classification techniques). Tire deep-learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle  1700  and, if the results do not match and the infrastructure concludes that the AI in the vehicle  1700  is malfunctioning, the server(s)  1778  may transmit a signal to the vehicle  1700  instructing a fail-safe computer of the vehicle  1700  to assume control, notify the passengers, and complete a safe parking maneuver. 
     For inferencing, the server(s)  1778  may include the GPU(s)  1784  and one or more programmable inference accelerators (e.g., NVIDIA&#39;s TensorRT). The combination of GPU-powered servers and inference acceleration may make real-time responsiveness possible. In other examples, such as where performance is less critical, servers powered by CPUs, FPGAs, and other processors may be used for inferencing. 
     Example Computing Device 
       FIG.  18    is a block diagram of an example computing device(s)  1800  suitable for use in implementing some embodiments of the present disclosure. Computing device  1800  may include an interconnect system  1802  that directly or indirectly couples the following devices: memory  1804 , one or more central processing units (CPUs)  1806 , one or more graphics processing units (GPUs)  1808 , a communication interface  1810 , input/output (I/O) ports  1812 , input/output components  1814 , a power supply  1816 , one or more presentation components  1818  (e.g., display(s)), and one or more logic units  1820 . In at least one embodiment, the computing device(s)  1800  may comprise one or more virtual machines (VMs), and/or any of the components thereof may comprise virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs  1808  may comprise one or more vGPUs, one or more of the CPUs  1806  may comprise one or more vCPUs, and/or one or more of the logic units  1820  may comprise one or more virtual logic units. As such, a computing device(s)  1800  may include discrete components (e.g., a full GPU dedicated to the computing device  1800 ), virtual components (e.g., a portion of a GPU dedicated to the computing device  1800 ), or a combination thereof. 
     Although the various blocks of  FIG.  18    are shown as connected via the interconnect system  1802  with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component  1818 , such as a display device, may be considered an I/O component  1814  (e.g., if the display is a touch screen). As another example, the CPUs  1806  and/or GPUs  1808  may include memory (e.g., the memory  1804  may be representative of a storage device in addition to the memory of the GPUs  1808 , the CPUs  1806 , and/or other components). In other words, the computing device of  FIG.  18    is merely illustrative. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “hand-held device,” “game console,” “electronic control unit (ECU),” “virtual reality system,” and/or other device or system types, as all are contemplated within the scope of the computing device of  FIG.  18   . 
     The interconnect system  1802  may represent one or more links or busses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system  1802  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 standards 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 are direct connections between components. As an example, the CPU  1806  may be directly connected to the memory  1804 . Further, the CPU  1806  may be directly connected to the GPU  1808 . Where there is direct, or point-to-point connection between components, the interconnect system  1802  may include a PCIe link to carry out the connection. In these examples, a PCI bus need not be included in the computing device  1800 . 
     The memory  1804  may include any of a variety of computer-readable media. The computer-readable media may be any available media that may be accessed by the computing device  1800 . The computer-readable media may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer-storage media and communication media. 
     The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types. For example, the memory  1804  may store computer-readable instructions (e.g., that represent a program(s) and/or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device  1800 . As used herein, computer storage media does not comprise signals per se. 
     The computer storage media may embody computer-readable instructions, data structures, program modules, and/or other data types in a modulated data signal such as a carrier wave or other transport mechanism and includes 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 manner as to encode information in the signal. By way of example, and not limitation, the computer storage media may include wired media. such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media. 
     The CPU(s)  1806  may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device  1800  to perform one or more of the methods and/or processes described herein. The CPU(s)  1806  may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) that are capable of handling a multitude of software threads simultaneously. The CPU(s)  1806  may include any type of processor, and may include different types of processors depending on the type of computing device  1800  implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device  1800 , the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device  1800  may include one or more CPUs  1806  in addition to one or more microprocessors or supplementary co-processors, such as math co-processors. 
     In addition to or alternatively from the CPU(s)  1806 , the GPU(s)  1808  may be configured to execute at least some of the computer-readable instructions o control one or more components of the computing device  1800  to perform one or more of the methods and/or processes described herein. One or more of the GPU(s)  1808  may be an integrated GPU (e.g., with one or more of the CPU(s)  1806  and/or one or more of the GPU(s)  1808  may be a discrete GPU. In embodiments, one or more of the GPU(s)  1808  may be a coprocessor of one or more of the CPU(s)  1806 . The GPU(s)  1808  may be used by the computing device  1800  to render graphics (e.g., 3D graphics) or perform general purpose computations. For example, the GPU(s)  1808  may be used for General-Purpose computing on GPUs (GPGPU). The GPU(s)  1808  may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s)  1808  may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s)  1806  received via a host interface). The GPU(s)  1808  may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory  1804 . The GPU(s)  1808  may include two or more GPUs operating in parallel (e.g., via a link). The link may directly connect the GPUs (e.g., using NVLINK) or may connect the CPUs through a switch (e.g., using NVSwitch). When combined together, each GPU  1808  may generate pixel data or GPGPU data for different portions of an output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs. 
     In addition to or alternatively from the CPU(s)  1806  and/or the GPU(s)  1808 , the logic unit(s)  1820  may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device  1800  to perform one or more of the methods and/or processes described herein. In embodiments, the CPU(s)  1806 , the GPU(s)  1808 , and/or the logic unit(s)  1820  may discretely or jointly perform any combination of the methods, processes and/or portions thereof. One or more of the logic units  1820  may be part of and/or integrated in one or more of the CPU(s)  1806  and/or the GPU(s)  1808  and/or one or more of the logic units  1820  may be discrete components or otherwise external to the CPU(s)  1806  and/or the GPU(s)  1808 . In embodiments, one or more of the logic units  1820  may be a coprocessor of one or more of the CPU(s)  1806  and/or one or more of the GPU(s)  1808 . 
     Examples of the logic unit(s)  1820  include one or more processing cores and/or components thereof, such as Tensor Cores (TCs), Tensor Processing Units(TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), Artificial Intelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits (ASICs), Floating Point Units (FPUs), input/output (I/O) elements, peripheral component interconnect (PCI) or petipheral component interconnect express (PCIe) elements, and/or the like. 
     The communication interface  1810  may include one or more receivers, transmitters, and/or transceivers that enable the computing device  1800  to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface  1810  may include components and functionality to 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 LoRaWAN, SigFox, etc.), and/or the Internet. 
     The I/O ports  1812  may enable the computing device  1800  to be logically coupled to other devices including the I/O components  1814 , the presentation component(s)  1818 , and/or other components, some of which may be built in to (e.g., integrated in) the computing device  1800 . Illustrative I/O components  1814  include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components  1814  may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, inputs may be transmitted to an appropriate network element for further processing. An NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with a display of the computing device  1800 . The computing device  1800  may be include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these, for gesture detection and recognition. Additionally, the computing device  1800  may include accelerometers or gyroscopes (e.g., as part of an inertia measurement unit (IMU)) that enable detection of motion. In some examples, the output of the accelerometers or gyroscopes may be used by the computing device  1800  to render immersive augmented reality or virtual reality. 
     The power supply  1816  may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply  1816  may provide power to the computing device  1800  to enable the components of the computing device  1800  to operate. 
     The presentation component(s)  1818  may include a display (e.g., a monitor, a touch screen, a television screen, a heads-up-display (HUD), other display types, or a combination thereof), speakers, and/or other presentation components. The presentation component(s)  1818  may receive data from other components (e.g., the GPU(s)  1808 , the CPU(s)  1806 , etc.), and output the data (e.g., as an image, video, sound, etc.). 
     Example Data Center 
       FIG.  19    illustrates an example data center  1900  that may be used in at least one embodiments of the present disclosure. The data center  1900  may include a data center infrastructure layer  1910 , a framework layer  1920 , a software layer  1930 , and/or an application layer  1940 . 
     As shown in  FIG.  19   , the data center infrastructure layer  1910  may include a resource orchestrator  1912 , grouped computing resources  1914 , and node computing resources (“node C.R.s”)  1916 ( 1 )- 1916 (N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s  1916 ( 1 )- 1916 (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 (CPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (“NW I/O”) devices, network switches, virtual machines (“VMs”), power modules, and/or cooling modules, etc. In some embodiments, one or more node C.R.s from among node C.R.s  1916 ( 1 )- 1916 (N) may correspond to a server having one or more of the above-mentioned computing resources. In addition, in some embodiments, the node C.R.s  1916 ( 1 )- 19161 (N) may include one or more virtual components, such as vGPUs, vCPUs, and/or the like, and/or one or more of the node C.R.s  1916 ( 1 )- 1916 (N) may correspond to a virtual machine (VM). 
     In at least one embodiment, grouped computing resources  1914  may include separate groupings of node C.R.s  1916  housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s  1916  within grouped computing resources  1914  may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s  1916  including CPUs, GPUs, and/or other processors may be grouped within one or more racks to provide compute resources to support one or more workloads. The one or more racks may also include any number of power modules, cooling modules, and/or network switches, in any combination. 
     The resource orchestrator  1922  may configure or otherwise control one or more node C.R.s  1916 ( 1 )- 1916 (N) and/or grouped computing resources  1914 . In at least one embodiment, resource orchestrator  1922  may include a software design infrastructure (“SDI”) management entity for the data center  1900 . The resource orchestrator  1922  may include hardware, software, or some combination thereof. 
     In at least one embodiment, as shown in  FIG.  19   , framework layer  192 (s) may include a job scheduler  1932 , a configuration manager  1934 , a resource manager  1936 , and/or a distributed file system  1938 . The framework layer  1920  may include a framework to support software  1932  of software layer  1930  and/or one or more application(s)  1942  of application layer  1940 . The software  1932  or application(s)  1942  may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. The framework layer  1920  may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system  1938  for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler  1932  may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center  1900 . The configuration manager  1934  may be capable of configuring different layers such as software layer  1930  and framework layer  1920  including Spark and distributed file system  1938  for supporting large-scale data processing. The resource manager  1936  may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system  1938  and job scheduler  1932 . In at least one embodiment, clustered or grouped computing resources may include grouped computing resource  1914  at data center infrastructure layer  1910 . The resource manager  1036  may coordinate with resource orchestrator  1912  to manage these mapped or allocated computing resources. 
     In at least one embodiment, software  1932  included in software layer  1930  may include software used by at least portions of node C.R.s  1916 ( 1 )- 1916 (N), grouped computing resources  1914 , and/or distributed file system  1938  of framework layer  1920 . One or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software. 
     In at least one embodiment, application(s)  1942  included in application layer  1940  may include one or more types of applications used by at least portions of node C.R.s  1916 ( 1 )- 1916 (N), grouped computing resources  1914 , and/or distributed file system  1938  of framework layer  1920 . One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing 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. 
     In t least one embodiment, any of configuration manager  1934 , resource manager  1936 , and resource orchestrator  1912  may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. Self-modifying actions may relieve a data center operator of data center  1900  from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center. 
     The data center  1900  may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, a machine learning model(s) may be trained by calculating weight parameters according to a neural network architecture using software and/or computing resources described above with respect to the data center  1900 . In at least one embodiment, trained or deployed machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to the data center  1900  by using weight parameters calculated through one or more training techniques, such as but not limited to those described herein. 
     In at least one embodiment, the data center  1900  may use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, and/or other hardware (or virtual compute resources corresponding thereto) to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described above may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services. 
     Example Network Environments 
     Network environments suitable for use in implementing embodiments of the disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and/or other device types. The client devices, servers, and/or other device types (e.g., each device) may be implemented on one or more instances of the computing device(s)  1800  of  FIG.  18   —e.g., each device may include similar components, features, and/or functionality of the computing device(s)  1800 . In addition, where backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may be included as part of a data center  1900 , an example of which is described in more detail herein with respect to  FIG.  19   . 
     Components of a network environment may communicate with each other via a network(s), which may be wired, wireless, or both. The network may include multiple networks, or a network of networks. By way of example, the network may 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. Where the network includes a wireless telecommunications network, components such as a base station, a communications tower, or even access points (as well as other components) may provide wireless connectivity. 
     Compatible network environments may include one or more peer-to-peer network environments—in which case a server may not be included in a network environment—and one or more client-server network environments—in which case one or more servers may be included in a network environment. In peer-to-peer network environments, functionality described herein with respect to a server(s) may be implemented on any number of client devices. 
     In at least one embodiment, a network environment may include one or more cloud-based network environments, a distributed computing environment, a combination thereof, etc. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more of servers, which may include one or more core network servers and/or edge servers. A framework layer may include a framework to support software of a software layer and/or one or more application(s) of an application layer. The software or application(s) may respectively include web-based service software or applications. In embodiments, one or more of the client devices may use the web-based service software or applications (e.g., by accessing the service software and/or applications 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 web application framework such as that may use a distributed file system for large-scale data processing (e.g., “big data”). 
     A cloud-based network environment may provide cloud computing and/or cloud storage that carries out any combination of computing and/or data storage functions described herein (or one or more portions thereof). Any of these various functions may be distributed over multiple locations from central or core servers (e.g., of one or more data centers that may be distributed across a state, a region, a country, the globe, etc.). If a connection to a user (e.g., a client device) is relatively close to an edge server(s), a core server(s) may designate at least a portion of the functionality to the edge server(s). A cloud-based network environment may be private (e.g., limited to a single organization), may be public (e.g., available to many organizations), and/or a combination thereof (e.g., a hybrid cloud environment). 
     The client device(s) may include at least some of the components, features, and functionality of the example computing device(s)  1800  described herein with respect to  FIG.  18   . By way of example and not limitation, a client device may be embodied as a Personal Computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a Personal Digital Assistant (PDA), an MP3 player, a virtual reality headset, a Global Positioning System (GPS) or device, a video player, a video camera, a surveillance device or system, a vehicle, a boat, a flying vessel, a virtual machine, a drone, a robot, a handheld communications device, a hospital device, a gaming device or system, an entertainment system, a vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these delineated devices, or any other suitable device. 
     The disclosure may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc., refer to code that perform particular tasks or implement particular abstract data types. The disclosure may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specially computing devices, etc. The disclosure may also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network. 
     As used herein, a recitation of “and/or” with respect to two or more elements should be interpreted to mean only one element, or a combination of elements. For example, “element A, element B, and/or element C” 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. 
     The subject matter of the present disclosure is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and/or “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described.