Patent Publication Number: US-2023152801-A1

Title: Regression-based line detection for autonomous driving machines

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. Pat. Application No. 16/514,230, filed Jul. 17, 2019, which claims the benefit of U.S. Provisional Application No. 62/699,669, filed on Jul. 17, 2018. Each of which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     The ability to accurately and precisely detect lane lines, lane edges, road boundaries, text, and/or other features in an environment is imperative for autonomous machine applications at all levels of autonomy - e.g., semi-autonomous vehicles to fully-autonomous vehicles. Due to the variation in road marking quality, geographical differences in lane and road marking conventions, as well as road marking obstructions, degradation, and/or occlusion due to wear and tear, weather conditions, lighting conditions, temporary markings (e.g., due to construction or disaster relief), and/or the like, the diversity of lane markings that can be encountered in the environment during driving is very high. 
     Some conventional approaches to lane or line detection have used deep neural network (DNN) processing, where high-resolution images of driving surfaces and associated annotations of lanes and lines are used to train the DNN (e.g., a convolutional neural network (CNN)) to recognize lane lines. These conventional approaches have trained the DNN to generate a segmentation mask showing a general position of lane lines in an image by classifying each pixel of the image as either part of a lane line, or not. However, these conventional approaches suffer from a loss of resolution at the output of the DNN as a result of the incremental down-sampling performed by the DNN during DNN processing through convolutional operations. For example, as a result of down-sampling, individual pixels that corresponded to lane lines at an input resolution of the DNN may become blurred pixel blobs at the output resolution of the DNN. This loss of critical spatial information for inferring the lane lines or edges reduces the precision and accuracy of lane or line detection. 
     In addition, conventional systems that use the DNNs to predict lane or line classes require a separate output channel (e.g., a separate prediction) for each class. As such, the DNNs of these conventional system are required to separately process and generate a prediction for each pixel for each output channel. Using this approach, the run-time of the system is increased, thereby making real-time deployment for lane or line prediction a burdensome task that requires additional compute resources, energy, and processing power. These conventional systems also employ significant post-processing steps that require using the segmentation masks output by the DNN -at the lower resolution where spatial information has been lost - to reconstruct the lanes or lines. However, this approach not only increases processing times at run-time, but also results in less accurate final predictions of lanes and lines by the system. Ultimately, the predictions of the DNNs of these conventional systems impact the ability of the autonomous vehicle to gain an accurate and precise understanding of the driving surface - in real-time - while requiring significant processing, energy, and compute resources. 
     SUMMARY 
     Embodiments of the present disclosure relate to regression-based line detection for autonomous driving machines. Systems and methods are disclosed that preserve rich spatial information through a deep-learning model by providing compressed information at a down-sized spatial resolution or dimension as compared to a spatial resolution or dimension of an input image. As such, embodiments of the present disclosure relate to line detection for autonomous driving machines including, but not limited to, lane lines, road boundaries, text on roads, or signage (e.g., poles, street signs, etc.). 
     In contrast to conventional systems, such as those described above, the system of the present disclosure may train a machine learning model to predict distances - one-dimensional (1D) or two-dimensional (2D) - for each pixel of an input image at an input resolution to a line pixel (or pixel corresponding to any other label class) determined to correspond to a line (or other label class) in the input image. As a result, even though the output resolution of the machine learning model may be less than the input resolution (e.g., two times less, four times less, etc.), the distances may be used to preserve the spatial information of the input resolution in order to precisely recreate the line at the input resolution. As such, by generating predictions at a lower, output resolution using the higher, input resolution for processing, the run-time of the system is decreased, while the preservation of the spatial information maintains the accuracy of predictions. 
     In addition to the location of line pixels, an angle of the line at the location of each line pixel may be computed by the machine learning model to aid the system in understanding the overall geometry of the line - thereby increasing the accuracy of line recreation for use by the system. To further decrease run-time for real-time operation of the system, the machine learning model(s) may be trained to predict label classes using a bit encoding process, thereby removing the constraint of conventional systems that require a prediction for each pixel for each output channel (e.g., for each class). In further contrast to conventional systems - and to reduce the overall post-processing burden of the system - the machine learning models may be trained to predict clusters of line pixels using an embedding algorithm, where each cluster corresponds to an individual line. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present systems and methods for regression-based line detection for autonomous driving machines are described in detail below with reference to the attached drawing figures, wherein: 
         FIG.  1 A  is a data flow diagram illustrating an example process for line predictions using a machine learning model, in accordance with some embodiments of the present disclosure; 
         FIG.  1 B  is an example illustration of using a machine learning model to recreate lines for an image, in accordance with some embodiments of the present disclosure; 
         FIGS.  2 A- 2 B  are example illustrations of voting methods for decoding line predictions, in accordance with some embodiments of the present disclosure; 
         FIG.  2 C  is an example illustration of a method for decoding line angles, in accordance with some embodiments of the present disclosure; 
         FIG.  3    is an example visualization of recreating lines for an image using predictions of a machine learning model, in accordance with some embodiments of the present disclosure; 
         FIG.  4    is a flow diagram showing a method for predicting lines in an image using a machine learning model, in accordance with some embodiments of the present disclosure; 
         FIG.  5    is data flow diagram illustrating an example process for training a machine learning model for line predictions, in accordance with some embodiments of the present disclosure; 
         FIG.  6    is an example visualization of ground truth annotations for training a machine learning model for line predictions, data flow diagram illustrating an example process for line predictions using a machine learning model, in accordance with some embodiments of the present disclosure; 
         FIG.  7 A  is an example illustration of an encoding method for preserving spatial information of an input of a machine learning model, in accordance with some embodiments of the present disclosure; 
         FIG.  7 B  is an example illustration of and encoding method for training a machine learning model to predict line angles, in accordance with some embodiments of the present disclosure; 
         FIG.  8    is example illustration of an encoding method for training a machine learning model to predict line clusters, in accordance with some embodiments of the present disclosure; 
         FIG.  9    is a flow diagram showing a method for training a machine learning model to predict lines in an image, in accordance with some embodiments of the present disclosure; 
         FIG.  10 A  is an illustration of an example autonomous vehicle, in accordance with some embodiments of the present disclosure; 
         FIG.  10 B  is an example of camera locations and fields of view for the example autonomous vehicle of  FIG.  10 A , in accordance with some embodiments of the present disclosure; 
         FIG.  10 C  is a block diagram of an example system architecture for the example autonomous vehicle of  FIG.  10 A , in accordance with some embodiments of the present disclosure; 
         FIG.  10 D  is a system diagram for communication between cloud-based server(s) and the example autonomous vehicle of  FIG.  10 A , in accordance with some embodiments of the present disclosure; and 
         FIG.  11    is a block diagram of an example computing device suitable for use in implementing some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Systems and methods are disclosed related regression-based line detection for autonomous driving machines. Although the present disclosure may be described with respect to an example autonomous vehicle  1000  (alternatively referred to herein as “vehicle  1000 ” or “autonomous vehicle  1000 ,” an example of which is described herein with respect to  FIGS.  10 A- 10 D ), this is not intended to be limiting. For example, the systems and methods described herein may be used by non-autonomous vehicles, semi-autonomous vehicles (e.g., in adaptive driver assistance systems (ADAS)), robots, warehouse vehicles, off-road vehicles, flying vessels, boats, a passenger vehicle, 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 construction vehicle, an underwater craft, a drone, and/or another type of vehicle (e.g., that is unmanned and/or that accommodates one or more passengers). In addition, although the present disclosure may be described with respect to autonomous driving, this is not intended to be limiting. For example, the systems and methods described herein may be used in robotics, aerial systems, boating systems, and/or other technology areas, such as for perception, world model management, path planning, obstacle avoidance, and/or other processes. 
     In contrast to conventional systems, such as those described herein, the system of the present disclosure preserves rich spatial information available at an input resolution of a machine learning model (e.g., a deep neural network (DNN), such as a convolutional neural network (CNN)) while maintaining the advantages of lower resolution processing by the machine learning model. As a result, the accuracy of the predictions of lane lines, road boundaries, text, and/or other features of the environment is preserved even as the machine learning model processes the inputs through convolutional operations to generate compressed outputs. For example, the image data used as an input to the machine learning model may be encoded such that the high-resolution information is not lost during the quantization or down-sampling introduced during machine learning model processing. Encoding may be performed on the ground truth data (e.g., annotated labels corresponding to lane lines, road boundaries, text, and/or other features) in a way that creates enough redundancy to preserve the rich spatial information during the processing inherent in machine learning models - especially in CNNs. Some non-limiting benefits of the system and methods of the present disclosure are increased lane detection range, increased lane edge precision or recall, and the ability to preserve rich spatial information available in high-resolution images while leveraging lower-resolution image processing - thereby reducing the computational burden for in-vehicle inferencing. In addition, in some embodiments, both encoding (e.g., during training) and decoding (e.g., during inference, in deployment) may be GPU accelerated, such as by parallelizing algorithms for encoding and/or decoding through several compute kernels (e.g., CUDA kernels of NVIDIA’s CUDA) to decrease run-time in deployment and processing times during training. 
     The machine learning model of the present disclosure may be trained to predict, in deployment, one dimensional (1D) and/or two-dimensional (2D) distances from each pixel to a closest pixel that belongs to a line, angles along the line, and/or line types (e.g., solid, dashed, road boundary, text, sign, etc.). The 1D and/or 2D distances may be computed to preserve the spatial information of the image data at input resolution. For example, because each pixel may be encoded with a pixel distance that corresponds to a distance to a line pixel (e.g., a pixel determined to correspond to a line in the image, or to another feature type the machine learning model is trained to predict), even when pixel information is lost during processing, the pixel distances may be used to recover the location of the original pixel in the high-resolution image that corresponds to the line. As such, the output resolution of the machine learning model may be less than the input resolution, and a decoding process may be used to reconstruct the line information for the input resolution image from the output of the machine learning model. During decoding, a voting method based on encoded pixel data may be used to reconstruct the line formation for the input resolution image. For example, each of the pixel values from the output may be used to cast votes for pixels in the input resolution image and, where a threshold number of votes are cast for a pixel, the pixel may be determined to belong to a line in the input image. 
     The angles along the lines may be used to reconstruct a shape of the line, where the shape or geometry of the line may be used by an autonomous vehicle for lane keeping, handling in and between lanes, etc. During training, an angle may be calculated and encoded for each pixel corresponding to a line (e.g., using the ground truth line annotations to determine the representative pixels). A 0-360 degree value for the angle may be encoded for each of the pixels, where the angle value is calculated relative to a horizontal line extending along the row of pixels of the pixel for which the angle is being encoded. Instead of encoding the angle itself, in some embodiments, the cosine and sine components of the angle value may be encoded. Pixel-to-pixel variations may be overcome using a smoothing technique. During decoding, the value output by the machine learning model may correspond to the angle for the line at the pixel location. 
     The machine learning model may be trained to detect line types for use by the autonomous vehicle in determining appropriate behaviors within the environment. As non-limiting examples, whenever a dashed line is detected, a vehicle may be able to perform lane changes or passing maneuvers. Likewise, when a solid yellow line is detected, the autonomous control system of vehicle may understand that any maneuver may not cross over the solid yellow line. For different line classes, a different value may be assigned. However, in contrast to conventional systems that may require an output to represent each of N different line classes, the current system may encode a bit value for each different line class. By encoding a bit value, the machine learning model may only need to output log 2 (N) outputs as opposed to an output for each of the N different line classes. The machine learning model may thus output a binary sequence corresponding to a number of bits that the machine learning model is trained to predict (e.g., four bit binary sequence generates four output channels, one for each bit). As a result, the machine learning model training may be quickly scalable to additional classes without requiring the machine learning model to be trained to predict an additional output channel for each class. 
     In some embodiments, to determine which line pixels correspond to a same line, a high-dimensional embedding algorithm may be used by the machine learning model to predict clusters - or to connect the dots - for inferring the full geometry of the line. For example, each line pixel may be mapped to a high-dimensional vector in a way that separates, in space, the high-dimensional vector from other high-dimensional vectors that are not of the same line. This process may be completed for each of the line pixels of the image. The machine learning model may then use the relationship (or proximity) of the high-dimensional vectors to determine clusters, or connectivity, between the line pixels. Pixels that have associated vectors within a first threshold distance (2 x d within ) - where d within  corresponds to a within-cluster variance - may be clustered together, and pixels that have associated vectors greater than a second threshold distance apart (e.g., 4 x d within ) may be determined to be of different clusters. For example, a first vector may be registered as a first cluster (e.g., corresponding to a first line in an image). A second vector may be used to calculate a Euclidean distance between the first vector and the second vector. If the distance is less than the first threshold, the first vector and the second vector may be assigned to the first cluster, and if the distance is greater than the second threshold, the second vector may be registered as a second cluster. In some embodiments, mean-shift clustering may be executed using a kernel radius of d within . 
     During decoding, since each line pixel may be mapped to a pixel coordinate location in the input image, and the line pixels may be mapped to high-dimensional vectors, inverse mapping may be used to determine the line pixels that correspond to the clusters of high-dimensional vectors. To generate a geometric fit of the resulting line in the image, a least squares polyfit process may be executed to produce polynomial coefficients that represent a full line. In some non-limiting examples, third order polyfit (e.g., four coefficients) may be used. 
     Line Predictions Using a Machine Learning Model 
     Now referring to  FIG.  1 A ,  FIG.  1 A  is a data flow diagram illustrating an example process  100  for line predictions using a machine learning model, in accordance with some embodiments of the present disclosure. At a high level, the process  100  may include one or more machine learning models  104  receiving one or more inputs, such as image data  102 , and generating one or more outputs, such as pixel distances  108 , angles  110 , line classes  112 , and/or cluster vectors  114 . The image data  102  may be generated by one or more cameras of an autonomous vehicle (e.g., vehicle  1000 , as described herein at least with respect to  FIGS.  10 A- 10 D ). In some embodiments, the image data  102  may additionally or alternatively include other types of sensor data, such as LIDAR data from one or more LIDAR sensors  1064 , RADAR data from one or more RADAR sensors  1060 , audio data from one or more microphones  1096 , etc. The machine learning model(s)  104  may be trained to generate the outputs  106  that may be used by perception component(s), world model management component(s), planning component(s), control component(s), and/or other components of an autonomous driving software stack. For example, with respect to the vehicle  1000 , lines  122  may be used to inform controller(s)  1136 , ADAS system  1138 , SOC(s)  1104 , and/or other components of the autonomous vehicle  1000  of the environment, to aid the autonomous vehicle  1000  in performing one or more operations (e.g., path planning, mapping, etc.) within the environment. 
     In some embodiments, the image data  102  may include data representative of images of a field of view of one or more cameras of a vehicle, such as stereo camera(s)  1068 , wide-view camera(s)  1070  (e.g., fisheye cameras), infrared camera(s)  1072 , surround camera(s)  1074  (e.g., 360 degree cameras), long-range and/or mid-range camera(s)  1098 , and/or other camera type of the autonomous vehicle  1000  ( FIGS.  10 A- 10 D ). In some examples, the image data  102  may be captured by a single camera with a forward-facing, substantially centered field of view with respect to a horizontal axis (e.g., left to right) of the vehicle  1000 . In a non-limiting embodiment, one or more forward-facing cameras may be used (e.g., a center or near-center mounted camera(s)), such as a wide-view camera  1070 , a surround camera  1074 , a stereo camera  1068 , and/or a long-range or mid-range camera  1098 . The image data captured from this perspective may be useful for perception when navigating - e.g., within a lane, through a lane change, through a turn, through an intersection, etc. - because a forward-facing camera may include a field of view (e.g., the field of view of the forward-facing stereo camera  1068  and/or the wide-view camera  1070  of  FIG.  10 B ) that includes both a current lane of travel of the vehicle  1000 , adjacent lane(s) of travel of the vehicle  1000 , and/or boundaries of the driving surface. In some examples, more than one camera or other sensor (e.g., LIDAR sensor, RADAR sensor, etc.) may be used to incorporate multiple fields of view or sensory fields (e.g., the fields of view of the long-range cameras  1098 , the forward-facing stereo camera  1068 , and/or the forward-facing wide-view camera  1070  of  FIG.  10 B ). 
     In some examples, the image data  102  may be captured in one format (e.g., RCCB, RCCC, RBGC, etc.), and then converted (e.g., during pre-processing of the image data) to another format. In some other examples, the image data  102  may be provided as input to an image data pre-processor (not shown) to generate pre-processed image data. Many types of images or formats may be used as inputs; for example, compressed images such as in Joint Photographic Experts Group (JPEG), Red Green Blue (RGB), or Luminance/Chrominance (YUV) formats, compressed images as frames stemming from a compressed video format (e.g., H.264/Advanced Video Coding (AVC), H.265/High Efficiency Video Coding (HEVC), VP8, VP9, Alliance for Open Media Video 1 (AV1), Versatile Video Coding (VVC), or any other video compression standard), raw images such as originating from Red Clear Blue (RCCB), Red Clear (RCCC) or other type of imaging sensor. In some examples, different formats and/or resolutions could be used for training the machine learning model(s)  104  than for inferencing (e.g., during deployment of the machine learning model(s)  104  in the autonomous vehicle  1000 ). 
     An image data pre-processor may use image data representative of one or more images (or other data representations, such as LIDAR depth maps) and load the sensor data into memory in the form of a multi-dimensional array/matrix (alternatively referred to as tensor, or more specifically an input tensor, in some examples). The array size may be computed and/or represented as W x H x C, where W stands for the image width in pixels, H stands for the height in pixels, and C stands for the number of color channels. Without loss of generality, other types and orderings of input image components are also possible. Additionally, the batch size B may be used as a dimension (e.g., an additional fourth dimension) when batching is used. Batching may be used for training and/or for inference. Thus, the input tensor may represent an array of dimension W x H x C x B. Any ordering of the dimensions may be possible, which may depend on the particular hardware and software used to implement the image data pre-processor. This ordering may be chosen to maximize training and/or inference performance of the machine learning model(s)  104 . 
     In some embodiments, a pre-processing image pipeline may be employed by the image data pre-processor to process a raw image(s) acquired by a sensor(s) (e.g., camera(s)) and included in the image data  102  to produce pre-processed image data which may represent an input image(s) to the input layer(s) (e.g., feature extractor layer(s)  142  of  FIG.  1 C ) of the machine learning model(s)  104 . An example of a suitable pre-processing image pipeline may use a raw RCCB Bayer (e.g., 1-channel) type of image from the sensor and convert that image to a RCB (e.g., 3-channel) planar image stored in Fixed Precision (e.g., 16-bit-per-channel) format. The pre-processing image pipeline may include decompanding, noise reduction, demosaicing, white balancing, histogram computing, and/or adaptive global tone mapping (e.g., in that order, or in an alternative order). 
     Where noise reduction is employed by the image data pre-processor, it may include bilateral denoising in the Bayer domain. Where demosaicing is employed by the image data pre-processor, it may include bilinear interpolation. Where histogram computing is employed by the image data pre-processor, it may involve computing a histogram for the C channel, and may be merged with the decompanding or noise reduction in some examples. Where adaptive global tone mapping is employed by the image data pre-processor, it may include performing an adaptive gamma-log transform. This may include calculating a histogram, getting a mid-tone level, and/or estimating a maximum luminance with the mid-tone level. 
     The machine learning model(s)  104  may use one or more images or other data representations (e.g., LIDAR data, RADAR data, etc.) as represented by the image data  102  as input to generate the output(s)  106 . In a non-limiting example, the machine learning model(s)  104  may take one or more of: an image(s) represented by the image data  102  (e.g., after pre-processing) to generate the pixel distances  108 , the angles  110 , the line classes  112 , and/or the cluster vectors  114  as input. Although examples are described herein with respect to using neural networks and specifically CNNs as the machine learning model(s)  104 , this is not intended to be limiting. For example and without limitation, the machine learning model(s)  104  described herein may 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), Naive Bayes, k-nearest neighbor (Knn), K means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, 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. 
     The outputs of the machine learning model(s)  104  may include the pixel distances  108 , the angles  110 , the line classes  112 , the cluster vectors  114 , and/or other output types. In order to decode the outputs of the machine learning model(s)  104 , in some non-limiting examples, GPU acceleration may be implemented. For example, a parallel processing platform (e.g., NVIDIA’s CUDA) may be implemented to parallelize algorithms through several compute kernels for decoding the outputs - thereby decreasing run-time. 
     The pixel distances  108  may include, for each pixel in an image, a distance in image space to a nearest pixel that is incorporated a line (e.g., a line pixel) as depicted in the image. The pixel distances  108  may include distances in a single dimension (1D) and/or distances in two dimensions (2D). For example, for 1D distances, the pixel distances  108  may include a distance along a row of pixels to a nearest line pixel in a right direction (d_R) and/or a nearest line pixel in a left direction (d_L) (e.g., as illustrated in  FIG.  2 A ). As such, the machine learning model(s)  104  may compute, for each pixel at the output resolution, an output of a distance to a nearest line pixel to the left along the row of pixels and to the right along the row of pixels. As another example, for 2D distances, the pixel distances  108  may include a distance to a nearest line pixel along a row of pixels of the image (d_X) and along a column of line pixels of the image (d_Y) (e.g., as illustrated in  FIG.  2 B ). As such, the machine learning model(s)  104  may compute, for each pixel at the output resolution, an output of a distance to a nearest line pixel along a column of pixels and a distance along a row of pixels, where the distance along the column and the row may be used to determine a magnitude of distance between the pixel and the line pixel. 
     As a result of the processing of the machine learning model(s)  104 , the pixel distances  108  computed by the machine learning model(s)  104  may correspond to the lower spatial resolution output than the spatial resolution of the input image. However, as described herein, an advantage of the present disclosure is the preservation of the spatial information from the higher input resolution of the machine learning model(s)  104  using the pixel distances  108 . For example, the pixel locations from the output of the machine learning model(s)  104  may be converted to a pixel location at the input resolution during decoding  116 . As a non-limiting example, where the output corresponds to a lower relative spatial resolution by a factor of four (e.g., the output corresponds to a resolution that is a quarter of the input resolution), each [y, x] pixel location at the output may be multiplied by four (e.g., [1, 1] at the output may become [4, 4] at the resolution corresponding to the input image). As such, the pixel distances  108  corresponding to the pixel location at the output may be determined to correspond to the pixel location at the resolution of the input - thereby preserving the spatial information from the input resolution. 
     As an example of 1D decoding,  FIG.  2 A  includes an example illustration of a voting method for decoding line predictions, in accordance with some embodiments of the present disclosure. During decoding  116  in a 1D example, the predictions of the pixel distances  108  output by the machine learning model(s)  104  may first be converted back to the pixel locations at the input resolution. For example, table  202  may represent the pixel distances  108  along a row of pixels at the input resolution. As such, because the output may correspond to a resolution that is smaller (e.g., by a factor of four) in the example of  FIG.  2 A , the pixel distances  108  may only be output by the machine learning model(s)  104  for every fourth pixel in the row of pixels (e.g., at 0, 4, 8, 12, 16, etc.) with respect to the input resolution. As such, once the pixel distances  108  to the nearest line pixel to the right (d_R) and to the left (d_L) along the row of pixels have been associated with the proper pixel locations at the input resolution, a voting method may be executed as part of the decoding  116 . For example, each pixel distance  108  may cast a vote for a location of a line pixel, as illustrated in table  204 . The voting may be a combination of left votes (voting_L) and right votes (voting_R), such that a final vote value (vote all) may be computed. As an example, with respect to the left votes, the pixel distance  108  of 0 at pixel 4 in the row from table  202  may cast a vote for pixel 4 at the output resolution of table  204 , the pixel distance  108  of 4 at pixel 8 in the row from table  202  may cast another vote for pixel 4 at the output resolution  204 , and so on. Similarly, for right votes, the pixel distance  108  of 4 at pixel 0 in the row from table  202  may cast a vote for pixel 4 at the output resolution of table  204 , the pixel distance  108  of 5 at pixel 8 in the row from table  202  may cast a vote for pixel 13 at the output resolution of table  204 , and so on. The left votes and the right votes for each pixel in the row of table  204  may then be tallied, or added, to determine the final vote value. In some examples, once a threshold number of votes are computed for a pixel at the output resolution, the pixel may be determined to be a line pixel. The threshold number, in some non-limiting examples, may be 1, 2, 4, 6, or another threshold value. 
     The process of decoding  116  the pixel distances  108  may be repeated for each row of pixels in the image. As such, each of the line pixels in the image at the input resolution may be determined using the voting method of  FIG.  2 A . Although the example of  FIG.  2 A  includes down-sampling by a factor of four, this is not intended to be limiting. In some examples, the machine learning model(s)  104  may not produce output corresponding to lower resolutions than the input resolution, or may produce output corresponding to resolutions that are lower than the input resolution by a factor of two, four, five, six, and/or another factor without departing from the scope of the present disclosure. In addition, although the tables  202  and  204  of  FIG.  2 A  include only 20 pixel locations (e.g., 0-19) along a row of pixels at the input resolution, this is for example purposes only. As such, the number of pixels may correspond to any input resolution width, such as  2048 ,  1920 ,  560 , and/or any other input resolution width. Further, although the examples herein for 1D pixel distances are described with respect to a row of pixels, this is not intended to be limiting. In some examples, in addition to or alternatively from a row of pixels, a column of pixels may be used for determining the 1D pixel distances, and/or encoding may be performed in the up and down directions instead of the left and right directions for at least one pixel. 
     As an example of 2D decoding,  FIG.  2 B  includes an example illustration of a voting method for decoding line predictions, in accordance with some embodiments of the present disclosure. Table  206  may represent an output of the machine learning model(s)  104  with respect to pixel distances  108  for 2D predictions. For example, similar to above, the machine learning model(s)  104  may output predictions corresponding to a lower resolution image space than the input resolution (e.g., the encoded resolution) by a factor of four. As such, the pixel locations at the output resolution (e.g., as represented in the table  206 ) may be converted to pixel locations at the input resolution during decoding  116  (e.g., as represented in table  208 ). The pixel distances  108  in the x (dx) and y (dy) directions may be used to cast votes for pixels at the input resolution. In some examples, the pixel distances  108  may further include a negative x direction (-dx) and a negative y direction (-dy). As such, where the nearest line pixel is along the x axis (e.g., width of image) or y axis (e.g., height of image) in a negative direction, the values for the pixel distances  108  may correspond to the -dx and/or -dy outputs. For example, a pixel location [0, 1] from the table  206  at the output resolution may be converted to a pixel location of (0, 4) at the input resolution during decoding  116 . As such, in the table  208 , the pixel distances of dx = 0 and dy = 1 may be used to cast a vote for pixel [0, 5] as being a line pixel. Similarly, a pixel location [1, 1] from the table  206  at the output resolution may be converted to a pixel location of (4, 4) at the input resolution during decoding  116 . As such, in the table  208 , the pixel distances of dx = 0 and dy = 0 may be used to cast a vote for pixel [4, 4] as being a line pixel (e.g., a line pixel itself, from key  210 ). Similar to the 1D approach described herein, once a threshold number of votes have been cast for a pixel, the pixel may be determined to be a line pixel. Assuming as a non-limiting example the threshold number of votes is one, the pixels [2, 3], [0, 5], [5, 0], and [4, 4] may each receive one vote (based on the table  206 ), and thus may be determined to be line pixels. 
     Using a 2D approach may allow for capturing of arbitrary orientations of a lane edge (e.g., horizontal, vertical, arbitrarily angled, etc.), as well as arbitrary lane marking shapes. In some examples, the pixel distances  108  may be represented in Euclidean coordinates, while in others examples, polar coordinates may be used. In addition, in some 1D and/or 2D embodiments, votes for pixels may be weighted differently. For example, self-votes for a line pixel may be weighted more than votes from other pixels. 
     Another output  106  of the machine learning model(s)  104  may be the angles  110 . The angles  110  may represent the angles of the line at the line pixel (e.g., angles  212  of the line pixels of the table  208  of  FIG.  2 C ). In some embodiments, the machine learning model(s)  104  may output an angle value (e.g., from 0-360 degrees). In such embodiments, decoding  116  the angles  110  may include reading out the binary value of the angle at the output of the machine learning model(s)  104 . In some examples, rather than reading out a value of the angles  110 , cosine and sine components of a 360 degree angle may be determined from the output of the machine learning model(s)  104  (e.g., as illustrated for angle  212 D of  FIG.  2 C ). The cosine and sine components may then be used to determine the angle value at the line pixel. Each pixel at the output resolution may include the angle  110  for the line pixel that it casts a vote for. For example, the pixel at [0, 4] may provide at least one vote or value for the angle  212 D for the pixel at [0, 5]. As another example, the pixel at [4, 4] may provide at least one vote for value for the angle  212 C at the pixel [4, 4] because [4, 4] is a line pixel itself. In some examples, the values of the angles may be different across two or more votes. In such examples, averaging may be used to find an average angle, and/or weighting may be used such that some of the pixels angle values have a greater weight (e.g., line pixels themselves may be weighted more heavily than angles from other pixels). 
     In some examples, the angles  110  may further include a tangent value for the line at the location of the line pixel. In such examples, the machine learning model(s)  104  may be trained to output a tangent value for the line at each line pixel, such that a more accurate representation of the geometry of the line may be determined. In addition, by using the tangent, determining which line pixels belong to the same line may be more effective (e.g., if a first line pixel has a tangent value that represents the line is in a forward and left orientation, and a second line pixel adjacent to -- or within a threshold distance to the first line pixel -- has a tangent value that represents the line is in a backward and left orientation, the first line pixel and the second line pixel likely are not of the same line). 
     The machine learning model(s)  104  may further output the line class  112 . The line class  112  may correspond to a lane line, such as dashed, solid, yellow, white, or a boundary line, such as a line on a boundary of a highway or street. In some examples, the line class  112  may include a pole, a letter, a road marking type (e.g., a turn arrow, a stop indication, etc.), a crosswalk, etc. In other examples, such as where the machine learning model(s)  104  is not used in a vehicle or driving application, any types of lines may be included in the line classes  112  predicted by the machine learning model(s)  104 . As described in more detail herein, the line class  112  may be output by the machine learning model(s)  104  as a bit value, such that the machine learning model(s)  104  does not need to generate an output (e.g., a confidence) for each class type the model  104  is trained to predict. As a result, where conventional approaches may output N predictions for N classes, the machine learning model(s)  104  may output log 2 (N) predictions, which is much more efficient and requires less compute resources. Although the machine learning model(s)  104  may output the line classes  112  as bit values, this is not intended to be limiting, and in other examples the machine learning model(s)  104  output a confidence or prediction for each class type (e.g., where bit encoding is not used to train the machine learning model(s)  104 ). In any example, the line class  112  may be determined by using the value of the output of the machine learning model(s)  104  and determining the line class  112  associated with the value (e.g., if a solid yellow line is the line class  112 , and is associated with a value of 3, when the machine learning model(s)  104  outputs [0 0 1 1] as a bit value that equals 3, the system may know that the line class  112  is a solid yellow line). 
     In some optional embodiments (as indicated by the dashed lines in  FIGS.  1 A and  5   ), the cluster vectors  114  (or clusters of the cluster vectors  114 ) may be output by the machine learning model(s)  104 . For example, the machine learning model(s)  104  may be trained (as described in more detail herein) to predict high-dimensional vectors - or values thereof - for each pixel, or line pixel, such that line pixels associated with similar high-dimensional vectors (or cluster vectors  114 ) may be determined to correspond to a same line in the image. For example, values of d between  and d within , as described in more detail herein, may be used to determine when line pixels correspond to a same line in the image. The process of taking the clusters of the cluster vectors  114  that are output by the machine learning model(s)  104  and determining the line pixels that correspond to the same line may be referred to herein as clustering  118  (e.g., an optional process, as indicated by the dashed lines in  FIG.  1 A ). For example, because each pixel at location (x i , y i ) may be mapped to a high dimensional vector, H (x i , y i ), inverse mapping may be performed to go from the clustered sets of cluster vectors  114  output by the machine learning model(s)  104  to image pixels of an image represented by the image data  102 . As such, pixels that map to vectors of a single vector cluster in the high dimensional space may map to a single line in image space. 
     Geometric fitting  120  may be executed on the output of the clustering  118  (e.g., once the line pixels have been determined to correspond to a same line). The geometric fitting  120  may include a least squares polyfit approach which may produce polynomial coefficients that represent the full line. For example, third order polyfit (e.g., four coefficients) may be used to perform the geometric fitting  120 . However, this is not intended to be limiting, and other geometric method variations may be used without departing from the scope of the present disclosure. 
     As such, in some example, the pixel distances  108  may be used to determine the location of the line pixels in the image, the angles  110  (and/or tangents) may be used to determine an orientation or geometry of the line corresponding to each of the line pixels, the line classes  112  may be used to determine what type of line the line is, and/or the cluster vectors  114  may be used to determine the line pixels that correspond to a same line  122 . This information may be used to determine a layout and identification of the lines  122  in a field(s) of view of one or more cameras (e.g., of an autonomous machine, such as the vehicle  1000 , a camera at a baggage carousel, a camera in a shopping center, etc.). For example, with reference to  FIG.  3   ,  FIG.  3    is an example visualization  302  of recreating lines  122  for an image using predictions of a machine learning model, in accordance with some embodiments of the present disclosure. The visualization  302  may include lines  304  (e.g., lines 304A-304G, and so on) of a road (e.g., lane lines, boundary lines, etc.), where pixels are represented in the visualization  302  with their vectors or geometry to provide an illustration of the location and direction of lines in the image as determined from the output of the machine learning model(s)  104 . For example, each arrow may represent a predicted line angle drawn as a unit vector. In addition, the lines  304  are illustrated with different arrow types to indicate the line classes  112  (e.g., the line  304 A includes dashed arrows, the line  304 B includes solid line arrows, etc.). Although not visually represented, the determination of which of the arrows belong to each of the lines  304  may be made using the cluster vectors  114  and/or the tangent values. 
     Now referring to  FIG.  1 B ,  FIG.  1 B  is an example illustration of using a machine learning model to recreate lines for an image, in accordance with some embodiments of the present disclosure. The machine learning model(s)  104 A of  FIG.  1 B  may be one example of a machine learning model(s)  104  that may be used in the process  100 . However, the machine learning model(s)  104 A of  FIG.  1 B  is not intended to be limiting, and the machine learning model(s)  104  may include additional and/or different machine learning models than the machine learning model(s)  104 A of  FIG.  1 B . The machine learning model(s)  104 A may include or be referred to as a convolutional neural network (CNN) and thus may alternatively be referred to herein as convolutional neural network  104 A, convolutional network  104 A, or CNN  104 A. 
     The CNN  104 A may use the image data  102  (and/or other sensor data types) (with or without any pre-processing) as an input. For example, the CNN  104 A may use the image data  102  - as represented by image  124  - as an input. The image data  102  may represent images generated by one or more cameras (e.g., one or more of the cameras described herein with respect to  FIGS.  10 A- 10 C ). For example, the image data  102  may be representative of a field of view of the camera(s). More specifically, the image data  102  may be representative of individual images generated by the camera(s), and the image data  102  representative of one or more of the individual images may be input into the CNN  104 A at each iteration of the CNN  104 A. In addition to the image data  102 , in some embodiments, sensor data may be input to the CNN  104 A in addition to or alternatively from, the image data  102 . The sensor data may be representative of perspectives of a physical environment (e.g., sensory fields) as observed by one or more sensors - such as a LIDAR sensor(s), a RADAR sensor(s), a microphone(s), a SONAR sensor(s), etc. 
     One or more of the layers of the CNN  104 A may include an input layer. The input layer(s) may hold values associated with the image data  102 , and/or the sensor data. For example, with respect to the image data  102 , the input layer(s) may hold values representative of the raw pixel values of the image(s) as a volume (e.g., a width, W, a height, H, and color channels, C (e.g., RGB), such as 32 x 32 x 3), and/or a batch size, B. 
     One or more layers may include convolutional layers. The image data  102  (and/or the sensor data) may be input into a convolutional layer(s) of the CNN  104 A (e.g., after one or more input layers and/or other layer types). The convolutional layers may compute the output of neurons that are connected to local regions in an input layer (e.g., the input layer), each neuron computing a dot product between their weights and a small region they are connected to in the input volume. A result of a convolutional layer may be another volume, with one of the dimensions based on the number of filters applied (e.g., the width, the height, and the number of filters, such as 32 x 32 x 12, if 12 were the number of filters). 
     One or more of the layers may include a rectified linear unit (ReLU) layer. The ReLU layer(s) may apply an elementwise activation function, such as the max (0, x), thresholding at zero, for example. The resulting volume of a ReLU layer may be the same as the volume of the input of the ReLU layer. 
     One or more of the layers may include a pooling layer. The pooling layer may perform a down-sampling operation along the spatial dimensions (e.g., the height and the width), which may result in a smaller volume than the input of the pooling layer (e.g., 16 x 16 x 12 from the 32 x 32 x 12 input volume). In some examples, the CNN  104 A may not include any pooling layers. In such examples, strided convolution layers may be used in place of pooling layers. 
     One or more of the layers may include a fully connected layer. Each neuron in the fully connected layer(s) may be connected to each of the neurons in the previous volume. The fully connected layer may compute class scores, and the resulting volume may be 1 x 1 x number of classes. 
     Although input layers, convolutional layers, pooling layers, ReLU layers, and fully connected layers are discussed herein with respect to the CNN  104 A, this is not intended to be limiting. For example, additional or alternative layers may be used, such as normalization layers, SoftMax layers, and/or other layer types. 
     Different orders and numbers of the layers of the CNN  104 A may be used depending on the embodiment. As such, the order and number of layers of the CNN  104 A is not limited to any one architecture. In addition, some of the layers may include parameters (e.g., weights and/or biases), such as the layers of the convolutional streams and /or the output layer(s), while others may not, such as the ReLU layers and pooling layers, for example. In some examples, the parameters may be learned during training (e.g., within process  500  of  FIG.  5   ). Further, some of the layers may include additional hyper-parameters (e.g., learning rate, stride, epochs, kernel size, number of filters, type of pooling for pooling layers, etc.), such as the convolutional layers, the output layer(s), and the pooling layers, while other layers may not, such as the ReLU layers. Various activation functions may be used, including but not limited to, ReLU, leaky ReLU, sigmoid, hyperbolic tangent (tanh), exponential linear unit (ELU), etc. The parameters, hyper-parameters, and/or activation functions are not to be limited and may differ depending on the embodiment. 
     The outputs  108  of the CNN  104 A may undergo decoding  116 , (optional) clustering  118 , and/or geometric fitting  120  to generate the lines  122  that may be represented in visualization  126 . The lines  122  may represent the lane lines and/or road boundaries from the image  124 . The lines  122 , and the corresponding information (e.g., the line classes  112 , the angles  110 , etc.), may be used by one or more layers of an autonomous driving software stack (e.g., a perception layer, a world model manager, a planning layer, a control layer, etc.) to aid in controlling or determining controls for the vehicle  1000  through a physical environment (e.g., through the driving surface of the image  124 ). 
     Now referring to  FIG.  4   , each block of method  400 , 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 method  400  may also be embodied as computer-usable instructions stored on computer storage media. The method  400  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, method  400  is described, by way of example, with respect to the process  100  of  FIG.  1   . However, the method  400  may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein. 
       FIG.  4    is a flow diagram showing a method for predicting lines in an image using a machine learning model, in accordance with some embodiments of the present disclosure. The method  400 , at block B 402 , includes receiving image data representative of an image. For example, the image data  102  may be received that is representative of an image captured by an image sensor. In some example, sensor data may be captured and/or received in addition, or alternatively to, the image data  102 . 
     The method  400 , at block B 404 , includes applying the image data at a first spatial resolution to a machine learning model. For example, the image data  102  may be applied to the machine learning model(s)  104  at a first spatial resolution. 
     The method  400 , at block B 406 , includes computing pixel distances for each of a plurality of pixels corresponding to a second spatial resolution less than the first spatial resolution. For example, the pixel distances  108  may be computed, by the machine learning model(s)  104 , for each pixel corresponding to the second spatial resolution (e.g., as a result of the machine learning model(s)  104  having been trained to output lower resolution predictions using higher resolution inputs). The pixel distances  108  may represent distances between pixels at the first spatial resolution and nearest line pixels at the first spatial resolution that correspond to a line in the image. As a result, even though the pixel distances  108  are output to correspond to the second spatial resolution, the values of the pixel distances  108  correspond to values at the first spatial resolution such that the spatial information is preserved through processing by the machine learning model(s)  104 . 
     The method  400 , at block B 408 , includes translating second pixel coordinates of the pixels to first pixel coordinates at the first spatial resolution. For example, the coordinates of the pixels at the output resolution may be converted back to their coordinates at the input resolution. 
     The method  400 , at block B 410 , includes determining locations of line pixels in the image using the pixel distances. For example, locations of line pixels of the lines  122  in the image may be determined using the pixel distances at the coordinates of the input resolution. 
     Training a Machine Learning Model to Predict Lines in an Image 
     Now referring to  FIG.  5   ,  FIG.  5    is data flow diagram illustrating an example process  500  for training a machine learning model for line predictions, in accordance with some embodiments of the present disclosure. Ground truth data  502  may include annotation data, such as labels. The ground truth data  502  may be generate by manual labeling and/or automatic labeling. For example, the labels or other annotation data used for the ground truth data  502  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., labeler, or annotation expert, defines the location of the labels), and/or a combination thereof (e.g., polyline points annotated by human, and rasterizer generates full polygons from the polyline points). In some examples, for each input image, or for each input sensor data representation, there may be corresponding labels or annotations as the ground truth data  502 . 
     As an example, and with respect to  FIG.  6   ,  FIG.  6    is an example visualization  602  of ground truth annotations for training a machine learning model for line predictions, in accordance with some embodiments of the present disclosure. The ground truth data  502  may include labels  604  or annotations  604 , such as those illustrated in the visualization  602  (e.g., labels  604 A- 604 D). For example, various types of labels or annotations (as indicated by key  606 ) may be generated for classes of objects in a scene -- such as road markings, intersections, crosswalks, road boundaries, poles and signs, and/or other objects -- as the ground truth data  502  for association with the image (e.g., represented by training image data  510 ) in the visualization  602 . In further embodiments, different labels and annotations may be generated for sub-classes of objects. For example, road markings may be further distinguished between solid lanes and dashed lanes, single and double lane lines, turn arrows and straight arrows, and/or by color (e.g., white and yellow lane lines); likewise, poles and signs may be further distinguished, in non-limiting examples, between traffic signs, street signs, light poles, etc. The visualization  602  is for example purposes only, and is not intended to be limiting. For example, the process  500  may be used for any application in addition to, or alternatively from, driving applications. 
     Encoding  504  may be executed on the ground truth data  502  to generate encoded ground truth data  506  for training the machine learning model(s)  104  to predict the pixel distances  108 , the angles  110 , the line classes  112 , and/or the cluster vectors  114 . In order to encode the ground truth data  502  to generate the encoded ground truth data  506 , in some non-limiting examples, GPU acceleration may be implemented. For example, a parallel processing platform (e.g., NVIDIA’s CUDA) may be implemented to parallelize algorithms through several compute kernels for generating the encoded ground truth data  506  - thereby decreasing processing time for encoding  504 . 
     The pixel distances  108  may be encoded in 1D or 2D embodiments, as described herein. For example, the ground truth data  502  may be used to determine the pixel distance  108 , from each pixel, to a nearest line pixel (e.g., as determined using the labels or annotations corresponding to lines of the training image data  510 ). For 1D embodiments, for each pixel in each row of pixels of the image, a distance to a nearest line pixel to the left (d_L) and a distance to a nearest line pixel to the right (d_R) along the row of pixels may be encoded from the ground truth data  502 . For example, with respect to  FIG.  7 A ,  FIG.  7 A  is an example illustration of an encoding method for preserving spatial information of an input of a machine learning model, in accordance with some embodiments of the present disclosure. In an image  702 , there may be four lines  704 .  FIG.  7 A  may be an example of determining and encoding the pixel distances  108  for the four lines  704  along a row of pixels  706  (e.g., that may include 19 pixels in width). This process may be repeated for each row of pixels in the image, and  FIG.  7 A  may provide an example of a single row of pixels  706 . At the row of pixels  706 , two of the four lines may cross at locations x = 4 and x = 13. As such, the d_L and d_R values in table  708  may represent the encoded values of the pixel distances  108  at the input resolution for training the machine learning model(s)  104 . By encoding the pixel distances  108  for each pixel in the row of pixels, the locations of the line pixels in the input resolution may be preserved even in image domains with reduced resolution that, in effect are tantamount to down sampling. Similar to the non-limiting examples described herein with respect to the process  100 , the lower resolution with respect to  FIG.  7 A  may include the equivalent of down-sampling by a factor of four. As a result, every fourth value at the input resolution (e.g., in the table  708 ) may be preserved at the output resolution (e.g., in table  710 ). As a result, the pixel distances  108  from the output resolution may be converted back to the input resolution, as described herein at least with respect to  FIG.  2 A , thus preserving the spatial information of the input resolution despite the lower resolution domain of the output. Because the relative resolution may be lower by a factor of four with respect to  FIG.  7 A , every fourth pixel (e.g., pixels 0, 4, 8, 12, and 16) may be referred to as anchor points, as those pixels and their associated pixel distances  108  may always be the values output at the output resolution of the machine learning model(s)  104 . Where a line pixel does not exist in a row to the left or the right of a current pixel, a value of infinite, or null, may be encoded as the value of the pixel distance  108  for training the machine learning model(s)  104 . 
     For 2D embodiments, for each pixel of the image, a distance to a nearest line pixel in an x direction (e.g., along a width of the image) and a y direction (e.g., along a height of the image) may be encoded as the pixel distances  108  from the ground truth data  502 . Similarly to  FIG.  7 A , every fourth pixel of every row of pixels may be preserved at the output resolution. As such, the remaining pixels after down-sampling may be referred to herein as anchor points. 
     The ground truth data  502  may further be used to encode the angles  110  to each of the pixels of the image, where the angles  110  for each pixel correspond to the angle for the nearest line pixel. For example, with respect to  FIG.  7 B ,  FIG.  7 B  includes an example illustration of an encoding method for training a machine learning model to predict line angles, in accordance with some embodiments of the present disclosure. Lines  712 A and  712 B may represent annotations of lines in an image (represented by the training image data  510 ) from the ground truth data  502 . For example, with respect an image of a road, the lines  712 A and  712 B may represent lane lines. Virtual line  714  may be a virtual line used to determine the angles  110  for encoding. For example, pixels in a row of pixels may be scanned to determine an angle, θ, for line pixels with respect to the lines  712 A and/or  712 B. The angles  110  may be angles between 0-360 degrees with respect to the virtual line  714  (e.g., extending horizontally). Per-pixel angle information may be encoded for all line pixels along each row of pixels. Pixel to pixel angle variations may be overcome through angle smoothing techniques, such as those described herein. In some examples, rather than encoding a value between 0-360 degrees, cosine and sine components of the 360 degree angle may be encoded instead. 
     In some embodiments, a tangent may be encoded for each pixel with respect to a tangent of each line pixel. For example, a tangent value of each line pixel may be encoded to each pixel casting a vote for that line pixel (including self-votes). The tangent value may be used to determine a geometry or direction of the line at each line pixel, which may be used to aid in determining which line pixels belong to a same line within the image. 
     The ground truth data  502  may be used to encode the label classes  112  to each of the pixels (e.g., corresponding to the line pixel(s) that the pixel casts a vote for). The label classes  112  may each be denoted by a different value, such as 0 for solid lines, 1 for dashed lines, 2 for road boundary lines, 3 for posts, 4 for signs, 5 for road markings, and so on. As such, the ground truth data  502  may indicate the label class  112  for each line in the image (e.g., prior to generating the annotations, a class type annotator or labeler may be selected or applied). As such, with respect to  FIG.  6   , each of the different label classes  112  may be annotated as ground truth data  502  for training. As described herein, a bit encoding technique may be used to encode the label classes  112 , such that semantic information about N different label classes may be encoded using log 2 (N) output label classes. By using only log 2 (N) output label classes, the machine learning model(s)  104  may be more efficient, thereby reducing run-time as well as decreasing compute resources used. As an example, for an N=16 classification problem, using bit encoding, the label class  112  of 5 (e.g., road marking) may be encoded as a four bit sequence [0101], a label class of 7 may be encoded as a four bit sequence [0111], and so on. Depending on the embodiment and the number of label classes  112 , the number of bits the machine learning model(s)  104  is trained on may change. For example, where there are only two label classes, there may be only one bit, where there are three classes, there may be two bits, where there are sixteen label classes, there may be four bits, and so on. 
     As a result of the processes described herein, precise locations of line pixels may be determined using the machine learning model(s)  104 , in addition to the angles  110  (and/or directions) of the lines, and the label classes  112  to which the line pixels belong. In order to determine the full geometry of the lines  122  (e.g., to connect the dots), a high dimensional embedding algorithm based on clustering may be employed. 
     For example, given a pixel at a coordinates (x i , y i ), the machine learning model(s)  104  may be trained to map this pixel to a high dimensional vector, H (xi, y i ), in a way that this high dimensional vector is separable from other vectors in the high dimensional space. Although the dimensionality of the space may have an arbitrary integer value, D, where D is less than N, and N represents the total number of output channels, in some embodiments, the number of dimensions in the high dimensional space may be selected. For a non-limiting example, the dimensions in the high dimensional space may be selected to be four, such that D = 4. When D = 4, H (xi, y i ) may contain four elements and the mapping between the original pixel (x i , y i ) and the four channel output corresponding to the elements of H (x i , y i ) may be learned directly by the machine learning model(s)  104  (e.g., as the cluster vectors  114 ) through the training data (e.g., the ground truth data  502  and the training image data  510 ). 
     By repeating this process for all the pixels in the image (e.g., for each pixel at each (x i , y i ) location, at the output of the machine learning model(s)  104 , a collection of separable D dimensional vectors may be computed (e.g., as the cluster vectors  114 ). Continuing with the D = 4 example, it may be the case that certain subsets of H (x i , y i ) are sufficiently close to one another to form a cluster of the cluster vectors  114 , while others of the vectors may be sufficiently far apart to not be considered part of the same cluster (and perhaps belong to a different cluster of the cluster vectors  114  instead). As such, the machine learning model(s)  104  may be trained to not only map pixels to D dimensional cluster vectors  114 , but also to determine which of these cluster vectors  114  form clusters with other of the cluster vectors  114  and how many different clusters there are. This may be important, semantically, because a cluster of the cluster vectors  114  in D dimensional space may correspond to a complete line in the real world (e.g., in world space). Similarly, the total number of the clusters of the cluster vectors  114  may correspond to a total number of lines in the environment within a field of view of the camera(s) and/or sensor(s) at any one time. 
     For example, to train the machine learning model(s)  104  to predict the clusters of the cluster vectors  114 , the cluster data may be encoded as the ground truth data  502  with respect to the training image data  510 . In such an example, each line in the training images may be labeled as a cluster (e.g., a label or annotation may be applied with respect to the training image for each line in the training image). The machine learning model(s)  104  may then be trained to treat the high dimensional cluster vectors  114  that are close to each other (e.g. using a thresholding approach) as members of a single cluster, and may be trained to treat the high dimensional cluster vectors  114  that are distanced from each other as members of different clusters. In mathematical terms, and as a non-limiting example, the machine learning model(s)  104  may be trained to minimize the within-cluster variance, d within , and to maximize the between-cluster variance, d between . A prediction of the number of different clusters is also learned by the machine learning model(s)  104  during training, and each cluster may represent a different line edge. With respect to the output of the clusters of the cluster vectors  114 , the machine learning model(s)  104  may be trained to output D channels, as described herein, using one or more loss functions  508 , such as those of equations (1) and (2), below: 
     
       
         
           
             
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      where L within  denotes the within-cluster loss function  508 , L between  denotes the inter-cluster loss function  508 , K is a number of cluster instances in the ground truth data  502 , and c i , i = 1, 2, 3, ... is the cluster ID. In some embodiments, the final loss function  508  may be a linear combination of L within  and L between . The total loss may be compared to a threshold to determine whether the high dimensional cluster vectors  114  belong to the same cluster or different clusters. 
     With reference to  FIG.  8   ,  FIG.  8    is example illustration of an encoding method for training a machine learning model to predict line clusters, in accordance with some embodiments of the present disclosure. As used herein, d between  may correspond to a within-cluster variance. For example, as represented in  FIG.  8   , different cluster vectors  114  may be separable so long as the condition d between  &gt; 4(d within ) is satisfied. When looking at two of the cluster vectors  114 , H (x i , y i ) and H (x j , y j ), for example, if there are no existing clusters, the vector H (x i , y i ) may be registered as a first cluster. Alternatively, if there is an existing cluster, a distance (e.g., a Euclidean distance) between the two cluster vectors  114  may be computed, and the distance value may be compared against 2(d within ). If the value is less than 2(d within ), then H (x i , y i ) may be added to the existing cluster, and if the value is more than 2(d within ), then H (xi, y i ) may be registered as a new cluster. This process may be repeated for each cluster vector  114 . 
     In some embodiments, the high dimensional embedding algorithm using clustering may be executed by performing mean-shift clustering using a kernel radius of d within . For example, from any given cluster vector  114 , H (x i , y i ), the mean-shift operation may be executed until the cluster vector  114  converges. The converged cluster may then be compared to the existing cluster center (or to a center of each existing cluster). If there is no existing cluster, the converged vector may be registered as a cluster. If there is an existing cluster, the distance (e.g., Euclidean distance) between the two vectors may be computed and, if less than 2(d within ), the cluster vector  114  may be registered as belonging to the same cluster. Alternatively, the converted cluster may be registered as a new cluster. This process may be repeated for each of the cluster vectors  114 . In some examples, thresholding may be executed based on hyper-parameter optimization. 
     The loss function(s)  508  may be used to measure loss (e.g., error) in the outputs of the machine learning model(s)  104  with respect to the ground truth data  502  and/or the encoded ground truth data  506  (e.g., error between predictions of the machine learning model(s)  104  as compared to the labels or annotations corresponding to the ground truth data). For example, a gradient descent based loss function, a binary cross entropy loss function, a mean squared error (L2) loss function, an L1 loss function, and/or other loss function types may be used as the loss function(s)  508 . In some embodiments, two or more different loss functions may be used. For example, one or more loss functions may be used for each type of output of the machine learning model(s)  104  where there are two or more outputs, or two or more loss functions may be used for a single output type. Where two or more loss functions are used for a single output type (e.g., the high dimensional embedding algorithm), the loss functions may be weighted with respect to one another to generate a weighted loss function. Backward pass computations may be performed to recursively compute gradients of the loss function with respect to training parameters (e.g., weights, biases, etc.), as indicated in the process  500 . In some examples, weight and biases of the machine learning model(s)  104  may be used to compute these gradients. 
     Now referring to  FIG.  9   , each block of method  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 method  900  may also be embodied as computer-usable instructions stored on computer storage media. The method  900  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, method  900  is described, by way of example, with respect to the process  500  of  FIG.  5   . However, the method  900  may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein. 
       FIG.  9    is a flow diagram showing a method for training a machine learning model to predict lines in an image, in accordance with some embodiments of the present disclosure. The method  900 , at block B 902 , includes receiving image data representative of an image. For example, the training image data  510  may be received, where the training image data  510  may represent an image. 
     The method  900 , at block B 904 , includes receiving annotation data representative of labels and corresponding label classes associated with an image. For example, the ground truth data  502  may be received that corresponds to annotations and/or labels of the lines in the image and the label classes  112 . 
     The method  900 , at block B 906 , includes determining a pixel distance to a nearest line pixel that is associated with a label of the labels. For example, for each pixel in the image, a distance between the pixel and a nearest line pixel may be determined. In some examples, this may include a distance to the left along the row of pixels of the pixel and/or to the right. In other examples, this may include a distance along a row and a distance along a column (e.g., to determine a magnitude) between the pixel and the line pixel. 
     The method  900 , at block B 908 , includes encoding the pixel distance and a label class associated with the label to the pixel to generate ground truth data. For example, the pixel distance  108  and the label class associated with each pixel may be encoded, during encoding  504 , to the pixel as encoded ground truth data  506 . In addition to pixel distances and label classes, Classes, angles, distance information, optional clustering information, and/or other information, as described herein, may be encoded to the pixels. 
     The method  900 , at block B 910 , includes using the first ground truth data and the second ground truth data to train a neural network. For example, the encoded ground truth data  506  may be used to train the machine learning model(s)  104 , where the machine learning model(s)  104  may include a neural network (e.g., a CNN), in some examples. 
     Example Autonomous Vehicle 
       FIG.  10 A  is an illustration of an example autonomous vehicle  1000 , in accordance with some embodiments of the present disclosure. The autonomous vehicle  1000  (alternatively referred to herein as the “vehicle  1000 ”) 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, 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  1000  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  1000  may be capable of conditional automation (Level 3), high automation (Level 4), and/or full automation (Level 5), depending on the embodiment. 
     The vehicle  1000  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  1000  may include a propulsion system  1050 , such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system  1050  may be connected to a drive train of the vehicle  1000 , which may include a transmission, to enable the propulsion of the vehicle  1000 . The propulsion system  1050  may be controlled in response to receiving signals from the throttle/accelerator  1052 . 
     A steering system  1054 , which may include a steering wheel, may be used to steer the vehicle  1000  (e.g., along a desired path or route) when the propulsion system  1050  is operating (e.g., when the vehicle is in motion). The steering system  1054  may receive signals from a steering actuator  1056 . The steering wheel may be optional for full automation (Level 5) functionality. 
     The brake sensor system  1046  may be used to operate the vehicle brakes in response to receiving signals from the brake actuators  1048  and/or brake sensors. 
     Controller(s)  1036 , which may include one or more system on chips (SoCs)  1004  ( FIG.  10 C ) and/or GPU(s), may provide signals (e.g., representative of commands) to one or more components and/or systems of the vehicle  1000 . For example, the controller(s) may send signals to operate the vehicle brakes via one or more brake actuators  1048 , to operate the steering system  1054  via one or more steering actuators  1056 , to operate the propulsion system  1050  via one or more throttle/accelerators  1052 . The controller(s)  1036  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  1000 . The controller(s)  1036  may include a first controller  1036  for autonomous driving functions, a second controller  1036  for functional safety functions, a third controller  1036  for artificial intelligence functionality (e.g., computer vision), a fourth controller  1036  for infotainment functionality, a fifth controller  1036  for redundancy in emergency conditions, and/or other controllers. In some examples, a single controller  1036  may handle two or more of the above functionalities, two or more controllers  1036  may handle a single functionality, and/or any combination thereof. 
     The controller(s)  1036  may provide the signals for controlling one or more components and/or systems of the vehicle  1000  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)  1058  (e.g., Global Positioning System sensor(s)), RADAR sensor(s)  1060 , ultrasonic sensor(s)  1062 , LIDAR sensor(s)  1064 , inertial measurement unit (IMU) sensor(s)  1066  (e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s)  1096 , stereo camera(s)  1068 , wide-view camera(s)  1070  (e.g., fisheye cameras), infrared camera(s)  1072 , surround camera(s)  1074  (e.g., 360 degree cameras), long-range and/or mid-range camera(s)  1098 , speed sensor(s)  1044  (e.g., for measuring the speed of the vehicle  1000 ), vibration sensor(s)  1042 , steering sensor(s)  1040 , brake sensor(s) (e.g., as part of the brake sensor system  1046 ), and/or other sensor types. 
     One or more of the controller(s)  1036  may receive inputs (e.g., represented by input data) from an instrument cluster  1032  of the vehicle  1000  and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display  1034 , an audible annunciator, a loudspeaker, and/or via other components of the vehicle  1000 . The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the HD map  1022  of  FIG.  10 C ), location data (e.g., the vehicle’s  1000  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)  1036 , etc. For example, the HMI display  1034  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 34B in two miles, etc.). 
     The vehicle  1000  further includes a network interface  1024  which may use one or more wireless antenna(s)  1026  and/or modem(s) to communicate over one or more networks. For example, the network interface  1024  may be capable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The wireless antenna(s)  1026  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.  10 B  is an example of camera locations and fields of view for the example autonomous vehicle  1000  of  FIG.  10 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  1000 . 
     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  1000 . 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),  1020  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 clear (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensors (RGGB) color filter array, a monochrome sensor color filter array, and/or another type of color filter array. In some embodiments, clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or an RBGC color filter array, may be used in an effort to increase light sensitivity. 
     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’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  1000  (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  1036  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)  1070  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.  10 B , there may any number of wide-view cameras  1070  on the vehicle  1000 . In addition, long-range camera(s)  1098  (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)  1098  may also be used for object detection and classification, as well as basic object tracking. 
     One or more stereo cameras  1068  may also be included in a front-facing configuration. The stereo camera(s)  1068  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’s environment, including a distance estimate for all the points in the image. An alternative stereo camera(s)  1068  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)  1068  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  1000  (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)  1074  (e.g., four surround cameras  1074  as illustrated in  FIG.  10 B ) may be positioned to on the vehicle  1000 . The surround camera(s)  1074  may include wide-view camera(s)  1070 , fisheye camera(s), 360 degree camera(s), and/or the like. Four example, four fisheye cameras may be positioned on the vehicle’s front, rear, and sides. In an alternative arrangement, the vehicle may use three surround camera(s)  1074  (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  1000  (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)  1098 , stereo camera(s)  1068 ), infrared camera(s)  1072 , etc.), as described herein. 
       FIG.  10 C  is a block diagram of an example system architecture for the example autonomous vehicle  1000  of  FIG.  10 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  1000  in  FIG.  10 C  are illustrated as being connected via bus  1002 . The bus  1002  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  1000  used to aid in control of various features and functionality of the vehicle  1000 , 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  1002  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  1002 , this is not intended to be limiting. For example, there may be any number of busses  1002 , 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  1002  may be used to perform different functions, and/or may be used for redundancy. For example, a first bus  1002  may be used for collision avoidance functionality and a second bus  1002  may be used for actuation control. In any example, each bus  1002  may communicate with any of the components of the vehicle  1000 , and two or more busses  1002  may communicate with the same components. In some examples, each SoC  1004 , each controller  1036 , and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle  1000 ), and may be connected to a common bus, such the CAN bus. 
     The vehicle  1000  may include one or more controller(s)  1036 , such as those described herein with respect to  FIG.  10 A . The controller(s)  1036  may be used for a variety of functions. The controller(s)  1036  may be coupled to any of the various other components and systems of the vehicle  1000 , and may be used for control of the vehicle  1000 , artificial intelligence of the vehicle  1000 , infotainment for the vehicle  1000 , and/or the like. 
     The vehicle  1000  may include a system(s) on a chip (SoC)  1004 . The SoC  1004  may include CPU(s)  1006 , GPU(s)  1008 , processor(s)  1010 , cache(s)  1012 , accelerator(s)  1014 , data store(s)  1016 , and/or other components and features not illustrated. The SoC(s)  1004  may be used to control the vehicle  1000  in a variety of platforms and systems. For example, the SoC(s)  1004  may be combined in a system (e.g., the system of the vehicle  1000 ) with an HD map  1022   which may obtain map refreshes and/or updates via a network interface  1024  from one or more servers (e.g., server(s)  1078  of  FIG.  10 D ). 
     The CPU(s)  1006  may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s)  1006  may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s)  1006  may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s)  1006  may include four dual-core clusters where each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s)  1006  (e.g., the CCPLEX) may be configured to support simultaneous cluster operation enabling any combination of the clusters of the CPU(s)  1006  to be active at any given time. 
     The CPU(s)  1006  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)  1006  may further implement an enhanced algorithm for managing power states, where 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)  1008  may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s)  1008  may be programmable and may be efficient for parallel workloads. The GPU(s)  1008 , in some examples, may use an enhanced tensor instruction set. The GPU(s)  1008  may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96KB 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)  1008  may include at least eight streaming microprocessors. The GPU(s)  1008  may use compute application programming interface(s) (API(s)). In addition, the GPU(s)  1008  may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA’s CUDA). 
     The GPU(s)  1008  may be power-optimized for best performance in automotive and embedded use cases. For example, the GPU(s)  1008  may be fabricated on a Fin field-effect transistor (FinFET). However, this is not intended to be limiting and the GPU(s)  1008  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)  1008  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 HBM 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)  1008  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)  1008  to access the CPU(s)  1006  page tables directly. In such examples, when the GPU(s)  1008  memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s)  1006 . In response, the CPU(s)  1006  may look in its page tables for the virtual-to-physical mapping for the address and transmits the translation back to the GPU(s)  1008 . As such, unified memory technology may allow a single unified virtual address space for memory of both the CPU(s)  1006  and the GPU(s)  1008 , thereby simplifying the GPU(s)  1008  programming and porting of applications to the GPU(s)  1008 . 
     In addition, the GPU(s)  1008  may include an access counter that may keep track of the frequency of access of the GPU(s)  1008  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)  1004  may include any number of cache(s)  1012 , including those described herein. For example, the cache(s)  1012  may include an L3 cache that is available to both the CPU(s)  1006  and the GPU(s)  1008  (e.g., that is connected both the CPU(s)  1006  and the GPU(s)  1008 ). The cache(s)  1012  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)  1004  may include one or more accelerators  1014  (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s)  1004  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., 4MB 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)  1008  and to off-load some of the tasks of the GPU(s)  1008  (e.g., to free up more cycles of the GPU(s)  1008  for performing other tasks). As an example, the accelerator(s)  1014  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)  1014  (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)  1008 , and by using an inference accelerator, for example, a designer may target either the DLA(s) or the GPU(s)  1008  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 GPU(s)  1008  and/or other accelerator(s)  1014 . 
     The accelerator(s)  1014  (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)  1006 . 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)  1014  (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)  1014 . In some examples, the on-chip memory may include at least 4MB 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)  1004  may include a real-time ray-tracing hardware accelerator, such as described in U.S. Pat. Application 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 real0time 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. 
     The accelerator(s)  1014  (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’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  1066  output that correlates with the vehicle  1000  orientation, distance, 3D location estimates of the object obtained from the neural network and/or other sensors (e.g., LIDAR sensor(s)  1064  or RADAR sensor(s)  1060 ), among others. 
     The SoC(s)  1004  may include data store(s)  1016  (e.g., memory). The data store(s)  1016  may be on-chip memory of the SoC(s)  1004 , which may store neural networks to be executed on the GPU and/or the DLA. In some examples, the data store(s)  1016  may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s)  1012  may comprise L2 or L3 cache(s)  1012 . Reference to the data store(s)  1016  may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s)  1014 , as described herein. 
     The SoC(s)  1004  may include one or more processor(s)  1010  (e.g., embedded processors). The processor(s)  1010  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)  1004  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)  1004  thermals and temperature sensors, and/or management of the SoC(s)  1004  power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s)  1004  may use the ring-oscillators to detect temperatures of the CPU(s)  1006 , GPU(s)  1008 , and/or accelerator(s)  1014 . 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)  1004  into a lower power state and/or put the vehicle  1000  into a chauffeur to safe stop mode (e.g., bring the vehicle  1000  to a safe stop). 
     The processor(s)  1010  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 I/O interfaces. In some examples, the audio processing engine is a dedicated processor core with a digital signal processor with dedicated RAM. 
     The processor(s)  1010  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)  1010  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)  1010  may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management. 
     The processor(s)  1010  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)  1010  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)  1070 , surround camera(s)  1074 , 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’s destination, activate or change the vehicle’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)  1008  is not required to continuously render new surfaces. Even when the GPU(s)  1008  is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s)  1008  to improve performance and responsiveness. 
     The SoC(s)  1004  may further include a mobile industry processor interface (MIPI) 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)  1004  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)  1004  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)  1004  may be used to process data from cameras (e.g., connected over Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s)  1064 , RADAR sensor(s)  1060 , etc. that may be connected over Ethernet), data from bus  1002  (e.g., speed of vehicle  1000 , steering wheel position, etc.), data from GNSS sensor(s)  1058  (e.g., connected over Ethernet or CAN bus). The SoC(s)  1004  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)  1006  from routine data management tasks. 
     The SoC(s)  1004  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)  1004  may be faster, more reliable, and even more energy-efficient and space-efficient than conventional systems. For example, the accelerator(s)  1014 , when combined with the CPU(s)  1006 , the GPU(s)  1008 , and the data store(s)  1016 , 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 GPU(s)  1020 ) 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’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’s path-planning software of the presence (or absence) of flashing lights. All three neural networks may run simultaneously, such as within the DLA and/or on the GPU(s)  1008 . 
     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  1000 . 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)  1004  provide for security against theft and/or carjacking. 
     In another example, a CNN for emergency vehicle detection and identification may use data from microphones  1096  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)  1004  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)  1058 . Thus, for example, when operating in Europe the CNN will seek to detect European sirens, and when in the United States the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, 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  1062 , until the emergency vehicle(s) passes. 
     The vehicle may include a CPU(s)  1018  (e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s)  1004  via a high-speed interconnect (e.g., PCIe). The CPU(s)  1018  may include an X86 processor, for example. The CPU(s)  1018  may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADAS sensors and the SoC(s)  1004 , and/or monitoring the status and health of the controller(s)  1036  and/or infotainment SoC  1030 , for example. 
     The vehicle  1000  may include a GPU(s)  1020  (e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s)  1004  via a high-speed interconnect (e.g., NVIDIA’s NVLINK). The GPU(s)  1020  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  1000 . 
     The vehicle  1000  may further include the network interface  1024  which may include one or more wireless antennas  1026  (e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface  1024  may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s)  1078  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  1000  information about vehicles in proximity to the vehicle  1000  (e.g., vehicles in front of, on the side of, and/or behind the vehicle  1000 ). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle  1000 . 
     The network interface  1024  may include a SoC that provides modulation and demodulation functionality and enables the controller(s)  1036  to communicate over wireless networks. The network interface  1024  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 be performed using super-heterodyne processes. In some examples, the radio frequency front end functionality may be provided by a separate chip. The network interface may include wireless functionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or other wireless protocols. 
     The vehicle  1000  may further include data store(s)  1028  which may include off-chip (e.g., off the SoC(s)  1004 ) storage. The data store(s)  1028  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  1000  may further include GNSS sensor(s)  1058 . The GNSS sensor(s)  1058  (e.g., GPS and/or assisted GPS sensors), to assist in mapping, perception, occupancy grid generation, and/or path planning functions. Any number of GNSS sensor(s)  1058  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  1000  may further include RADAR sensor(s)  1060 . The RADAR sensor(s)  1060  may be used by the vehicle  1000  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)  1060  may use the CAN and/or the bus  1002  (e.g., to transmit data generated by the RADAR sensor(s)  1060 ) 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)  1060  may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used. 
     The RADAR sensor(s)  1060  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)  1060  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’s  1000  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’s  1000  lane. 
     Mid-range RADAR systems may include, as an example, a range of up to 1060 m (front) or 80 m (rear), and a field of view of up to 42 degrees (front) or 1050 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  1000  may further include ultrasonic sensor(s)  1062 . The ultrasonic sensor(s)  1062 , which may be positioned at the front, back, and/or the sides of the vehicle  1000 , may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensor(s)  1062  may be used, and different ultrasonic sensor(s)  1062  may be used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s)  1062  may operate at functional safety levels of ASIL B. 
     The vehicle  1000  may include LIDAR sensor(s)  1064 . The LIDAR sensor(s)  1064  may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s)  1064  may be functional safety level ASIL B. In some examples, the vehicle  1000  may include multiple LIDAR sensors  1064  (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch). 
     In some examples, the LIDAR sensor(s)  1064  may be capable of providing a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensor(s)  1064  may have an advertised range of approximately 1000 m, with an accuracy of 2 cm-3 cm, and with support for a 1000 Mbps Ethernet connection, for example. In some examples, one or more non-protruding LIDAR sensors  1064  may be used. In such examples, the LIDAR sensor(s)  1064  may be implemented as a small device that may be embedded into the front, rear, sides, and/or corners of the vehicle  1000 . The LIDAR sensor(s)  1064 , in such examples, may provide up to a 1020-degree horizontal and 35-degree vertical field-of-view, with a 200 m range even for low-reflectivity objects. Front-mounted LIDAR sensor(s)  1064  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  1000 . 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)  1064  may be less susceptible to motion blur, vibration, and/or shock. 
     The vehicle may further include IMU sensor(s)  1066 . The IMU sensor(s)  1066  may be located at a center of the rear axle of the vehicle  1000 , in some examples. The IMU sensor(s)  1066  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 IMU sensor(s)  1066  may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s)  1066  may include accelerometers, gyroscopes, and magnetometers. 
     In some embodiments, the IMU sensor(s)  1066  may be implemented as a miniature, high performance GPS-Aided Inertial Navigation System (GPS/INS) that combines micro-electromechanical 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)  1066  may enable the vehicle  1000  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)  1066 . In some examples, the IMU sensor(s)  1066  and the GNSS sensor(s)  1058  may be combined in a single integrated unit. 
     The vehicle may include microphone(s)  1096  placed in and/or around the vehicle  1000 . The microphone(s)  1096  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)  1068 , wide-view camera(s)  1070 , infrared camera(s)  1072 , surround camera(s)  1074 , long-range and/or mid-range camera(s)  1098 , and/or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle  1000 . The types of cameras used depends on the embodiments and requirements for the vehicle  1000 , and any combination of camera types may be used to provide the necessary coverage around the vehicle  1000 . 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.  10 A  and  FIG.  10 B . 
     The vehicle  1000  may further include vibration sensor(s)  1042 . The vibration sensor(s)  1042  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  1042  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  1000  may include an ADAS system  1038 . The ADAS system  1038  may include a SoC, in some examples. The ADAS system  1038  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)  1060 , LIDAR sensor(s)  1064 , 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  1000  and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle  1000  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  1024  and/or the wireless antenna(s)  1026  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  1000 ), 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  1000 , 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)  1060 , 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)  1060 , coupled to a dedicated processor, DSP, FPGA, and/or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision and, if the driver does not take corrective action, the AEB system 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  1000  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  1000  if the vehicle  1000  starts to exit the lane. 
     BSW systems detects and warn the driver of vehicles in an automobile’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)  1060 , 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  1000  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)  1060 , 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  1000 , the vehicle  1000  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  1036  or a second controller  1036 ). For example, in some embodiments, the ADAS system  1038  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  1038  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’s confidence in the chosen result. If the confidence score exceeds a threshold, the supervisory MCU may follow the primary computer’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’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)  1004 . 
     In other examples, ADAS system  1038  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  1038  may be fed into the primary computer’s perception block and/or the primary computer’s dynamic driving task block. For example, if the ADAS system  1038  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  1000  may further include the infotainment SoC  1030  (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  1030  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  1000 . For example, the infotainment SoC  1030  may radios, disk players, navigation systems, video players, USB and Bluetooth connectivity, carputers, in-car entertainment, Wi-Fi, steering wheel audio controls, hands free voice control, a heads-up display (HUD), an HMI display  1034 , 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  1030  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  1038 , 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  1030  may include GPU functionality. The infotainment SoC  1030  may communicate over the bus  1002  (e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle  1000 . In some examples, the infotainment SoC  1030  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)  1036  (e.g., the primary and/or backup computers of the vehicle  1000 ) fail. In such an example, the infotainment SoC  1030  may put the vehicle  1000  into a chauffeur to safe stop mode, as described herein. 
     The vehicle  1000  may further include an instrument cluster  1032  (e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster  1032  may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster  1032  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  1030  and the instrument cluster  1032 . In other words, the instrument cluster  1032  may be included as part of the infotainment SoC  1030 , or vice versa. 
       FIG.  10 D  is a system diagram for communication between cloud-based server(s) and the example autonomous vehicle  1000  of  FIG.  10 A , in accordance with some embodiments of the present disclosure. The system  1076  may include server(s)  1078 , network(s)  1090 , and vehicles, including the vehicle  1000 . The server(s)  1078  may include a plurality of GPUs  1084 (A)- 1084 (H) (collectively referred to herein as GPUs  1084 ), PCIe switches  1082 (A)- 1082 (H) (collectively referred to herein as PCIe switches  1082 ), and/or CPUs  1080 (A)- 1080 (B) (collectively referred to herein as CPUs  1080 ). The GPUs  1084 , the CPUs  1080 , and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces  1088  developed by NVIDIA and/or PCIe connections  1086 . In some examples, the GPUs  1084  are connected via NVLink and/or NVSwitch SoC and the GPUs  1084  and the PCIe switches  1082  are connected via PCIe interconnects. Although eight GPUs  1084 , two CPUs  1080 , and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s)  1078  may include any number of GPUs  1084 , CPUs  1080 , and/or PCIe switches. For example, the server(s)  1078  may each include eight, sixteen, thirty-two, and/or more GPUs  1084 . 
     The server(s)  1078  may receive, over the network(s)  1090  and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work. The server(s)  1078  may transmit, over the network(s)  1090  and to the vehicles, neural networks  1092 , updated neural networks  1092 , and/or map information  1094 , including information regarding traffic and road conditions. The updates to the map information  1094  may include updates for the HD map  1022 , such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks  1092 , the updated neural networks  1092 , and/or the map information  1094  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)  1078  and/or other servers). 
     The server(s)  1078  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). 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)  1090 , and/or the machine learning models may be used by the server(s)  1078  to remotely monitor the vehicles. 
     In some examples, the server(s)  1078  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)  1078  may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s)  1084 , such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s)  1078  may include deep learning infrastructure that use only CPU-powered datacenters. 
     The deep-learning infrastructure of the server(s)  1078  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  1000 . For example, the deep-learning infrastructure may receive periodic updates from the vehicle  1000 , such as a sequence of images and/or objects that the vehicle  1000  has located in that sequence of images (e.g., via computer vision and/or other machine learning object classification techniques). The deep-learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle  1000  and, if the results do not match and the infrastructure concludes that the AI in the vehicle  1000  is malfunctioning, the server(s)  1078  may transmit a signal to the vehicle  1000  instructing a fail-safe computer of the vehicle  1000  to assume control, notify the passengers, and complete a safe parking maneuver. 
     For inferencing, the server(s)  1078  may include the GPU(s)  1084  and one or more programmable inference accelerators (e.g., NVIDIA’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.  11    is a block diagram of an example computing device  1100  suitable for use in implementing some embodiments of the present disclosure. Computing device  1100  may include a bus  1102  that directly or indirectly couples the following devices: memory  1104 , one or more central processing units (CPUs)  1106 , one or more graphics processing units (GPUs)  1108 , a communication interface  1110 , input/output (I/O) ports  1112 , input/output components  1114 , a power supply  1116 , and one or more presentation components  1118  (e.g., display(s)). 
     Although the various blocks of  FIG.  11    are shown as connected via the bus  1102  with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component  1118 , such as a display device, may be considered an I/O component  1114  (e.g., if the display is a touch screen). As another example, the CPUs  1106  and/or GPUs  1108  may include memory (e.g., the memory  1104  may be representative of a storage device in addition to the memory of the GPUs  1108 , the CPUs  1106 , and/or other components). In other words, the computing device of  FIG.  11    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.  11   . 
     The bus  1102  may represent one or more busses, such as an address bus, a data bus, a control bus, or a combination thereof. The bus  1102  may include one or more bus 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. 
     The memory  1104  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  1100 . 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  1104  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  1100 . As used herein, computer storage media does not comprise signals per se. 
     The communication 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 communication 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)  1106  may be configured to execute the computer-readable instructions to control one or more components of the computing device  1100  to perform one or more of the methods and/or processes described herein. The CPU(s)  1106  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)  1106  may include any type of processor, and may include different types of processors depending on the type of computing device  1100  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  1100 , the processor may be an ARM processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device  1100  may include one or more CPUs  1106  in addition to one or more microprocessors or supplementary co-processors, such as math co-processors. 
     The GPU(s)  1108  may be used by the computing device  1100  to render graphics (e.g., 3D graphics). The GPU(s)  1108  may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s)  1108  may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s)  1106  received via a host interface). The GPU(s)  1108  may include graphics memory, such as display memory, for storing pixel data. The display memory may be included as part of the memory  1104 . The GPU(s)  708  may include two or more GPUs operating in parallel (e.g., via a link). When combined together, each GPU  1108  may generate pixel data for different portions of an output image or for different output images (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 examples where the computing device  1100  does not include the GPU(s)  1108 , the CPU(s)  1106  may be used to render graphics. 
     The communication interface  1110  may include one or more receivers, transmitters, and/or transceivers that enable the computing device  700  to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface  1110  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), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or the Internet. 
     The I/O ports  1112  may enable the computing device  1100  to be logically coupled to other devices including the I/O components  1114 , the presentation component(s)  1118 , and/or other components, some of which may be built in to (e.g., integrated in) the computing device  1100 . Illustrative I/O components  1114  include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components  1114  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  1100 . The computing device  1100  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  1100  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  1100  to render immersive augmented reality or virtual reality. 
     The power supply  1116  may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply  1116  may provide power to the computing device  1100  to enable the components of the computing device  1100  to operate. 
     The presentation component(s)  1118  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)  1118  may receive data from other components (e.g., the GPU(s)  1108 , the CPU(s)  1106 , etc.), and output the data (e.g., as an image, video, sound, etc.). 
     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 specialty 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.