Regression-based line detection for autonomous driving machines

In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.

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.

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 vehicle1000(alternatively referred to herein as “vehicle1000” or “autonomous vehicle1000,” an example of which is described herein with respect toFIGS.10A-10D), 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 log2(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×dwithin)—where dwithincorresponds 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×dwithin) 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 dwithin.

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 toFIG.1A,FIG.1Ais a data flow diagram illustrating an example process100for line predictions using a machine learning model, in accordance with some embodiments of the present disclosure. At a high level, the process100may include one or more machine learning models104receiving one or more inputs, such as image data102, and generating one or more outputs, such as pixel distances108, angles110, line classes112, and/or cluster vectors114. The image data102may be generated by one or more cameras of an autonomous vehicle (e.g., vehicle1000, as described herein at least with respect toFIGS.10A-10D). In some embodiments, the image data102may additionally or alternatively include other types of sensor data, such as LIDAR data from one or more LIDAR sensors1064, RADAR data from one or more RADAR sensors1060, audio data from one or more microphones1096, etc. The machine learning model(s)104may be trained to generate the outputs106that 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 vehicle1000, lines122may be used to inform controller(s)1136, ADAS system1138, SOC(s)1104, and/or other components of the autonomous vehicle1000of the environment, to aid the autonomous vehicle1000in performing one or more operations (e.g., path planning, mapping, etc.) within the environment.

In some embodiments, the image data102may 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 vehicle1000(FIGS.10A-10D). In some examples, the image data102may 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 vehicle1000. 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 camera1070, a surround camera1074, a stereo camera1068, and/or a long-range or mid-range camera1098. 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 camera1068and/or the wide-view camera1070ofFIG.10B) that includes both a current lane of travel of the vehicle1000, adjacent lane(s) of travel of the vehicle1000, 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 cameras1098, the forward-facing stereo camera1068, and/or the forward-facing wide-view camera1070ofFIG.10B).

In some examples, the image data102may 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 data102may 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)104than for inferencing (e.g., during deployment of the machine learning model(s)104in the autonomous vehicle1000).

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×H×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×H×C×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 data102to produce pre-processed image data which may represent an input image(s) to the input layer(s) (e.g., feature extractor layer(s)142ofFIG.1C) 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)104may use one or more images or other data representations (e.g., LIDAR data, RADAR data, etc.) as represented by the image data102as input to generate the output(s)106. In a non-limiting example, the machine learning model(s)104may take one or more of: an image(s) represented by the image data102(e.g., after pre-processing) to generate the pixel distances108, the angles110, the line classes112, and/or the cluster vectors114as 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)104described 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), Naïve 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)104may include the pixel distances108, the angles110, the line classes112, the cluster vectors114, 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 distances108may 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 distances108may include distances in a single dimension (1D) and/or distances in two dimensions (2D). For example, for 1D distances, the pixel distances108may 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 inFIG.2A). As such, the machine learning model(s)104may 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 distances108may 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 inFIG.2B). As such, the machine learning model(s)104may 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 distances108computed by the machine learning model(s)104may 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)104using the pixel distances108. For example, the pixel locations from the output of the machine learning model(s)104may be converted to a pixel location at the input resolution during decoding116. 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 distances108corresponding 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.2Aincludes an example illustration of a voting method for decoding line predictions, in accordance with some embodiments of the present disclosure. During decoding116in a 1D example, the predictions of the pixel distances108output by the machine learning model(s)104may first be converted back to the pixel locations at the input resolution. For example, table202may represent the pixel distances108along 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 ofFIG.2A, the pixel distances108may only be output by the machine learning model(s)104for 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 distances108to 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 decoding116. For example, each pixel distance108may cast a vote for a location of a line pixel, as illustrated in table204. 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 distance108of 0 at pixel4in the row from table202may cast a vote for pixel4at the output resolution of table204, the pixel distance108of 4 at pixel8in the row from table202may cast another vote for pixel4at the output resolution204, and so on. Similarly, for right votes, the pixel distance108of 4 at pixel0in the row from table202may cast a vote for pixel4at the output resolution of table204, the pixel distance108of 5 at pixel8in the row from table202may cast a vote for pixel13at the output resolution of table204, and so on. The left votes and the right votes for each pixel in the row of table204may 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 decoding116the pixel distances108may 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 ofFIG.2A. Although the example ofFIG.2Aincludes down-sampling by a factor of four, this is not intended to be limiting. In some examples, the machine learning model(s)104may 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 tables202and204ofFIG.2Ainclude 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.2Bincludes an example illustration of a voting method for decoding line predictions, in accordance with some embodiments of the present disclosure. Table206may represent an output of the machine learning model(s)104with respect to pixel distances108for 2D predictions. For example, similar to above, the machine learning model(s)104may 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 table206) may be converted to pixel locations at the input resolution during decoding116(e.g., as represented in table208). The pixel distances108in the x (dx) and y (dy) directions may be used to cast votes for pixels at the input resolution. In some examples, the pixel distances108may 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 distances108may correspond to the −dx and/or −dy outputs. For example, a pixel location [0, 1] from the table206at the output resolution may be converted to a pixel location of (0, 4) at the input resolution during decoding116. As such, in the table208, 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 table206at the output resolution may be converted to a pixel location of (4, 4) at the input resolution during decoding116. As such, in the table208, 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 key210). 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 table206), 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 distances108may 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 output106of the machine learning model(s)104may be the angles110. The angles110may represent the angles of the line at the line pixel (e.g., angles212of the line pixels of the table208ofFIG.2C). In some embodiments, the machine learning model(s)104may output an angle value (e.g., from 0-360 degrees). In such embodiments, decoding116the angles110may 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 angles110, 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 angle212D ofFIG.2C). 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 angle110for 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 angle212D for the pixel at [0, 5]. As another example, the pixel at [4, 4] may provide at least one vote for value for the angle212C 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 angles110may further include a tangent value for the line at the location of the line pixel. In such examples, the machine learning model(s)104may 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)104may further output the line class112. The line class112may 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 class112may 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)104is not used in a vehicle or driving application, any types of lines may be included in the line classes112predicted by the machine learning model(s)104. As described in more detail herein, the line class112may be output by the machine learning model(s)104as a bit value, such that the machine learning model(s)104does not need to generate an output (e.g., a confidence) for each class type the model104is trained to predict. As a result, where conventional approaches may output N predictions for N classes, the machine learning model(s)104may output log2(N) predictions, which is much more efficient and requires less compute resources. Although the machine learning model(s)104may output the line classes112as bit values, this is not intended to be limiting, and in other examples the machine learning model(s)104output 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 class112may be determined by using the value of the output of the machine learning model(s)104and determining the line class112associated with the value (e.g., if a solid yellow line is the line class112, and is associated with a value of 3, when the machine learning model(s)104outputs [0 0 1 1] as a bit value that equals 3, the system may know that the line class112is a solid yellow line).

In some optional embodiments (as indicated by the dashed lines inFIGS.1A and5), the cluster vectors114(or clusters of the cluster vectors114) may be output by the machine learning model(s)104. For example, the machine learning model(s)104may 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 vectors114) may be determined to correspond to a same line in the image. For example, values of dbetweenand dwithin, 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 vectors114that are output by the machine learning model(s)104and determining the line pixels that correspond to the same line may be referred to herein as clustering118(e.g., an optional process, as indicated by the dashed lines inFIG.1A). For example, because each pixel at location (xi, yi) may be mapped to a high dimensional vector, H (xi, yi), inverse mapping may be performed to go from the clustered sets of cluster vectors114output by the machine learning model(s)104to image pixels of an image represented by the image data102. 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 fitting120may be executed on the output of the clustering118(e.g., once the line pixels have been determined to correspond to a same line). The geometric fitting120may 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 fitting120. 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 distances108may be used to determine the location of the line pixels in the image, the angles110(and/or tangents) may be used to determine an orientation or geometry of the line corresponding to each of the line pixels, the line classes112may be used to determine what type of line the line is, and/or the cluster vectors114may be used to determine the line pixels that correspond to a same line122. This information may be used to determine a layout and identification of the lines122in a field(s) of view of one or more cameras (e.g., of an autonomous machine, such as the vehicle1000, a camera at a baggage carousel, a camera in a shopping center, etc.). For example, with reference toFIG.3,FIG.3is an example visualization302of recreating lines122for an image using predictions of a machine learning model, in accordance with some embodiments of the present disclosure. The visualization302may include lines304(e.g., lines304A-304G, and so on) of a road (e.g., lane lines, boundary lines, etc.), where pixels are represented in the visualization302with 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 lines304are illustrated with different arrow types to indicate the line classes112(e.g., the line304A includes dashed arrows, the line304B includes solid line arrows, etc.). Although not visually represented, the determination of which of the arrows belong to each of the lines304may be made using the cluster vectors114and/or the tangent values.

Now referring toFIG.1B,FIG.1Bis 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)104A ofFIG.1Bmay be one example of a machine learning model(s)104that may be used in the process100. However, the machine learning model(s)104A ofFIG.1Bis not intended to be limiting, and the machine learning model(s)104may include additional and/or different machine learning models than the machine learning model(s)104A ofFIG.1B. The machine learning model(s)104A may include or be referred to as a convolutional neural network (CNN) and thus may alternatively be referred to herein as convolutional neural network104A, convolutional network104A, or CNN104A.

The CNN104A may use the image data102(and/or other sensor data types) (with or without any pre-processing) as an input. For example, the CNN104A may use the image data102—as represented by image124—as an input. The image data102may represent images generated by one or more cameras (e.g., one or more of the cameras described herein with respect toFIGS.10A-10C). For example, the image data102may be representative of a field of view of the camera(s). More specifically, the image data102may be representative of individual images generated by the camera(s), and the image data102representative of one or more of the individual images may be input into the CNN104A at each iteration of the CNN104A. In addition to the image data102, in some embodiments, sensor data may be input to the CNN104A in addition to or alternatively from, the image data102. 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 CNN104A may include an input layer. The input layer(s) may hold values associated with the image data102, and/or the sensor data. For example, with respect to the image data102, 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 32×32×3), and/or a batch size, B.

One or more layers may include convolutional layers. The image data102(and/or the sensor data) may be input into a convolutional layer(s) of the CNN104A (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 32×32×12, 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., 16×16×12 from the 32×32×12 input volume). In some examples, the CNN104A 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 1×1×number of classes.

Although input layers, convolutional layers, pooling layers, ReLU layers, and fully connected layers are discussed herein with respect to the CNN104A, 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 CNN104A may be used depending on the embodiment. As such, the order and number of layers of the CNN104A 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 process500ofFIG.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 outputs108of the CNN104A may undergo decoding116, (optional) clustering118, and/or geometric fitting120to generate the lines122that may be represented in visualization126. The lines122may represent the lane lines and/or road boundaries from the image124. The lines122, and the corresponding information (e.g., the line classes112, the angles110, 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 vehicle1000through a physical environment (e.g., through the driving surface of the image124).

FIG.4is 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 method400, at block B402, includes receiving image data representative of an image. For example, the image data102may 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 data102.

The method400, at block B404, includes applying the image data at a first spatial resolution to a machine learning model. For example, the image data102may be applied to the machine learning model(s)104at a first spatial resolution.

The method400, at block B406, 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 distances108may 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)104having been trained to output lower resolution predictions using higher resolution inputs). The pixel distances108may 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 distances108are output to correspond to the second spatial resolution, the values of the pixel distances108correspond to values at the first spatial resolution such that the spatial information is preserved through processing by the machine learning model(s)104.

The method400, at block B408, 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 method400, at block b410, includes determining locations of line pixels in the image using the pixel distances. For example, locations of line pixels of the lines122in 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 toFIG.5,FIG.5is data flow diagram illustrating an example process500for training a machine learning model for line predictions, in accordance with some embodiments of the present disclosure. Ground truth data502may include annotation data, such as labels. The ground truth data502may be generate by manual labeling and/or automatic labeling. For example, the labels or other annotation data used for the ground truth data502may 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 data502.

As an example, and with respect toFIG.6,FIG.6is an example visualization602of ground truth annotations for training a machine learning model for line predictions, in accordance with some embodiments of the present disclosure. The ground truth data502may include labels604or annotations604, such as those illustrated in the visualization602(e.g., labels604A-604D). For example, various types of labels or annotations (as indicated by key606) 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 data502for association with the image (e.g., represented by training image data510) in the visualization602. 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 visualization602is for example purposes only, and is not intended to be limiting. For example, the process500may be used for any application in addition to, or alternatively from, driving applications.

Encoding504may be executed on the ground truth data502to generate encoded ground truth data506for training the machine learning model(s)104to predict the pixel distances108, the angles110, the line classes112, and/or the cluster vectors114. In order to encode the ground truth data502to generate the encoded ground truth data506, 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 data506—thereby decreasing processing time for encoding504.

The pixel distances108may be encoded in 1D or 2D embodiments, as described herein. For example, the ground truth data502may be used to determine the pixel distance108, from each pixel, to a nearest line pixel (e.g., as determined using the labels or annotations corresponding to lines of the training image data510). 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 data502. For example, with respect toFIG.7A,FIG.7Ais 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 image702, there may be four lines704.FIG.7Amay be an example of determining and encoding the pixel distances108for the four lines704along a row of pixels706(e.g., that may include 19 pixels in width). This process may be repeated for each row of pixels in the image, andFIG.7Amay provide an example of a single row of pixels706. At the row of pixels706, two of the four lines may cross at locations x=4 and x=13. As such, the d_L and d_R values in table708may represent the encoded values of the pixel distances108at the input resolution for training the machine learning model(s)104. By encoding the pixel distances108for 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 process100, the lower resolution with respect toFIG.7Amay 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 table708) may be preserved at the output resolution (e.g., in table710). As a result, the pixel distances108from the output resolution may be converted back to the input resolution, as described herein at least with respect toFIG.2A, 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 toFIG.7A, every fourth pixel (e.g., pixels0,4,8,12, and16) may be referred to as anchor points, as those pixels and their associated pixel distances108may 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 distance108for 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 distances108from the ground truth data502. Similarly toFIG.7A, 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 data502may further be used to encode the angles110to each of the pixels of the image, where the angles110for each pixel correspond to the angle for the nearest line pixel. For example, with respect toFIG.7B,FIG.7Bincludes 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. Lines712A and712B may represent annotations of lines in an image (represented by the training image data510) from the ground truth data502. For example, with respect an image of a road, the lines712A and712B may represent lane lines. Virtual line714may be a virtual line used to determine the angles110for encoding. For example, pixels in a row of pixels may be scanned to determine an angle, θ, for line pixels with respect to the lines712A and/or712B. The angles110may be angles between 0-360 degrees with respect to the virtual line714(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 data502may be used to encode the label classes112to each of the pixels (e.g., corresponding to the line pixel(s) that the pixel casts a vote for). The label classes112may 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 data502may indicate the label class112for 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 toFIG.6, each of the different label classes112may be annotated as ground truth data502for training. As described herein, a bit encoding technique may be used to encode the label classes112, such that semantic information about N different label classes may be encoded using log2(N) output label classes. By using only log2(N) output label classes, the machine learning model(s)104may 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 class112of 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 classes112, the number of bits the machine learning model(s)104is 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 angles110(and/or directions) of the lines, and the label classes112to which the line pixels belong. In order to determine the full geometry of the lines122(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 (xi, yi), the machine learning model(s)104may be trained to map this pixel to a high dimensional vector, H (xi, yi), 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, yi) may contain four elements and the mapping between the original pixel (xi, yi) and the four channel output corresponding to the elements of H (xi, yi) may be learned directly by the machine learning model(s)104(e.g., as the cluster vectors114) through the training data (e.g., the ground truth data502and the training image data510).

By repeating this process for all the pixels in the image (e.g., for each pixel at each (xi, yi) 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 vectors114). Continuing with the D=4 example, it may be the case that certain subsets of H (xi, yi) are sufficiently close to one another to form a cluster of the cluster vectors114, 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 vectors114instead). As such, the machine learning model(s)104may be trained to not only map pixels to D dimensional cluster vectors114, but also to determine which of these cluster vectors114form clusters with other of the cluster vectors114and how many different clusters there are. This may be important, semantically, because a cluster of the cluster vectors114in 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 vectors114may 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)104to predict the clusters of the cluster vectors114, the cluster data may be encoded as the ground truth data502with respect to the training image data510. 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)104may then be trained to treat the high dimensional cluster vectors114that 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 vectors114that are distanced from each other as members of different clusters. In mathematical terms, and as a non-limiting example, the machine learning model(s)104may be trained to minimize the within-cluster variance, dwithin, and to maximize the between-cluster variance, dbetween. A prediction of the number of different clusters is also learned by the machine learning model(s)104during training, and each cluster may represent a different line edge. With respect to the output of the clusters of the cluster vectors114, the machine learning model(s)104may be trained to output D channels, as described herein, using one or more loss functions508, such as those of equations (1) and (2), below:

With reference toFIG.8,FIG.8is 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, dbetweenmay correspond to a within-cluster variance. For example, as represented inFIG.8, different cluster vectors114may be separable so long as the condition dbetween>4(dwithin) is satisfied. When looking at two of the cluster vectors114, H (xi, yi) and H (xj, yj), for example, if there are no existing clusters, the vector H (xi, yi) 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 vectors114may be computed, and the distance value may be compared against 2(dwithin). If the value is less than 2(dwithin), then H (xi, yi) may be added to the existing cluster, and if the value is more than 2(dwithin), then H (xi, yi) may be registered as a new cluster. This process may be repeated for each cluster vector114.

In some embodiments, the high dimensional embedding algorithm using clustering may be executed by performing mean-shift clustering using a kernel radius of dwithin. For example, from any given cluster vector114, H (xi, yi), the mean-shift operation may be executed until the cluster vector114converges. 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(dwithin), the cluster vector114may 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 vectors114. In some examples, thresholding may be executed based on hyper-parameter optimization.

The loss function(s)508may be used to measure loss (e.g., error) in the outputs of the machine learning model(s)104with respect to the ground truth data502and/or the encoded ground truth data506(e.g., error between predictions of the machine learning model(s)104as 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)104where 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 process500. In some examples, weight and biases of the machine learning model(s)104may be used to compute these gradients.

Now referring toFIG.9, each block of method900, 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 method900may also be embodied as computer-usable instructions stored on computer storage media. The method900may 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, method900is described, by way of example, with respect to the process500ofFIG.5. However, the method900may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein.

FIG.9is 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 method900, at block B902, includes receiving image data representative of an image. For example, the training image data510may be received, where the training image data510may represent an image.

The method900, at block B904, includes receiving annotation data representative of labels and corresponding label classes associated with an image. For example, the ground truth data502may be received that corresponds to annotations and/or labels of the lines in the image and the label classes112.

The method900, at block B906, 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 method900, at block B908, 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 distance108and the label class associated with each pixel may be encoded, during encoding504, to the pixel as encoded ground truth data506. 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 method900, at block B910, includes using the first ground truth data and the second ground truth data to train a neural network. For example, the encoded ground truth data506may be used to train the machine learning model(s)104, where the machine learning model(s)104may include a neural network (e.g., a CNN), in some examples.

Example Autonomous Vehicle

The vehicle1000may 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 vehicle1000may include a propulsion system1050, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system1050may be connected to a drive train of the vehicle1000, which may include a transmission, to enable the propulsion of the vehicle1000. The propulsion system1050may be controlled in response to receiving signals from the throttle/accelerator1052.

A steering system1054, which may include a steering wheel, may be used to steer the vehicle1000(e.g., along a desired path or route) when the propulsion system1050is operating (e.g., when the vehicle is in motion). The steering system1054may receive signals from a steering actuator1056. The steering wheel may be optional for full automation (Level 5) functionality.

The brake sensor system1046may be used to operate the vehicle brakes in response to receiving signals from the brake actuators1048and/or brake sensors.

Controller(s)1036, which may include one or more system on chips (SoCs)1004(FIG.10C) and/or GPU(s), may provide signals (e.g., representative of commands) to one or more components and/or systems of the vehicle1000. For example, the controller(s) may send signals to operate the vehicle brakes via one or more brake actuators1048, to operate the steering system1054via one or more steering actuators1056, to operate the propulsion system1050via one or more throttle/accelerators1052. The controller(s)1036may 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 vehicle1000. The controller(s)1036may include a first controller1036for autonomous driving functions, a second controller1036for functional safety functions, a third controller1036for artificial intelligence functionality (e.g., computer vision), a fourth controller1036for infotainment functionality, a fifth controller1036for redundancy in emergency conditions, and/or other controllers. In some examples, a single controller1036may handle two or more of the above functionalities, two or more controllers1036may handle a single functionality, and/or any combination thereof.

The controller(s)1036may provide the signals for controlling one or more components and/or systems of the vehicle1000in 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 vehicle1000), vibration sensor(s)1042, steering sensor(s)1040, brake sensor(s) (e.g., as part of the brake sensor system1046), and/or other sensor types.

One or more of the controller(s)1036may receive inputs (e.g., represented by input data) from an instrument cluster1032of the vehicle1000and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display1034, an audible annunciator, a loudspeaker, and/or via other components of the vehicle1000. The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the HD map1022ofFIG.10C), location data (e.g., the vehicle's1000location, 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 display1034may 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 exit34B in two miles, etc.).

The vehicle1000further includes a network interface1024which may use one or more wireless antenna(s)1026and/or modem(s) to communicate over one or more networks. For example, the network interface1024may be capable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The wireless antenna(s)1026may 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.10Bis an example of camera locations and fields of view for the example autonomous vehicle1000ofFIG.10A, 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 vehicle1000.

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)1070that 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 inFIG.10B, there may any number of wide-view cameras1070on the vehicle1000. 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)1098may also be used for object detection and classification, as well as basic object tracking.

One or more stereo cameras1068may also be included in a front-facing configuration. The stereo camera(s)1068may 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)1068may 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)1068may 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 vehicle1000(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 cameras1074as illustrated inFIG.10B) may be positioned to on the vehicle1000. The surround camera(s)1074may 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 vehicle1000(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.

Each of the components, features, and systems of the vehicle1000inFIG.10Care illustrated as being connected via bus1002. The bus1002may include a Controller Area Network (CAN) data interface (alternatively referred to herein as a “CAN bus”). A CAN may be a network inside the vehicle1000used to aid in control of various features and functionality of the vehicle1000, 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 bus1002is 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 bus1002, this is not intended to be limiting. For example, there may be any number of busses1002, 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 busses1002may be used to perform different functions, and/or may be used for redundancy. For example, a first bus1002may be used for collision avoidance functionality and a second bus1002may be used for actuation control. In any example, each bus1002may communicate with any of the components of the vehicle1000, and two or more busses1002may communicate with the same components. In some examples, each SoC1004, each controller1036, and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle1000), and may be connected to a common bus, such the CAN bus.

The vehicle1000may include one or more controller(s)1036, such as those described herein with respect toFIG.10A. The controller(s)1036may be used for a variety of functions. The controller(s)1036may be coupled to any of the various other components and systems of the vehicle1000, and may be used for control of the vehicle1000, artificial intelligence of the vehicle1000, infotainment for the vehicle1000, and/or the like.

The vehicle1000may include a system(s) on a chip (SoC)1004. The SoC1004may 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)1004may be used to control the vehicle1000in a variety of platforms and systems. For example, the SoC(s)1004may be combined in a system (e.g., the system of the vehicle1000) with an HD map1022which may obtain map refreshes and/or updates via a network interface1024from one or more servers (e.g., server(s)1078ofFIG.10D).

The CPU(s)1006may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s)1006may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s)1006may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s)1006may 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)1006to be active at any given time.

The GPU(s)1008may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s)1008may 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)1008may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB storage capacity), and two or more of the streaming microprocessors may share an L2 cache (e.g., an L2 cache with a 512 KB storage capacity). In some embodiments, the GPU(s)1008may include at least eight streaming microprocessors. The GPU(s)1008may use compute application programming interface(s) (API(s)). In addition, the GPU(s)1008may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA's CUDA).

The GPU(s)1008may 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)1008to access the CPU(s)1006page tables directly. In such examples, when the GPU(s)1008memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s)1006. In response, the CPU(s)1006may 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)1006and the GPU(s)1008, thereby simplifying the GPU(s)1008programming and porting of applications to the GPU(s)1008.

In addition, the GPU(s)1008may include an access counter that may keep track of the frequency of access of the GPU(s)1008to 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)1004may include any number of cache(s)1012, including those described herein. For example, the cache(s)1012may include an L3 cache that is available to both the CPU(s)1006and the GPU(s)1008(e.g., that is connected both the CPU(s)1006and the GPU(s)1008). The cache(s)1012may 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)1004may include one or more accelerators1014(e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s)1004may include a hardware acceleration cluster that may include optimized hardware accelerators and/or large on-chip memory. The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to complement the GPU(s)1008and to off-load some of the tasks of the GPU(s)1008(e.g., to free up more cycles of the GPU(s)1008for performing other tasks). As an example, the accelerator(s)1014may 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 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)1008for 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)1008and/or other accelerator(s)1014.

The SoC(s)1004may include data store(s)1016(e.g., memory). The data store(s)1016may 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)1016may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s)1012may comprise L2 or L3 cache(s)1012. Reference to the data store(s)1016may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s)1014, as described herein.

The SoC(s)1004may include one or more processor(s)1010(e.g., embedded processors). The processor(s)1010may 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)1004boot 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)1004thermals and temperature sensors, and/or management of the SoC(s)1004power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s)1004may 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)1004into a lower power state and/or put the vehicle1000into a chauffeur to safe stop mode (e.g., bring the vehicle1000to a safe stop).

The processor(s)1010may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management.

The processor(s)1010may 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 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)1008is not required to continuously render new surfaces. Even when the GPU(s)1008is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s)1008to improve performance and responsiveness.

The SoC(s)1004may 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)1004may 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)1004may further include a broad range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and/or other devices. The SoC(s)1004may 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 bus1002(e.g., speed of vehicle1000, steering wheel position, etc.), data from GNSS sensor(s)1058(e.g., connected over Ethernet or CAN bus). The SoC(s)1004may 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)1006from routine data management tasks.

The SoC(s)1004may 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)1004may 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.

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 vehicle1000. 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)1004provide for security against theft and/or carjacking.

The vehicle may include a CPU(s)1018(e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s)1004via a high-speed interconnect (e.g., PCIe). The CPU(s)1018may include an X86 processor, for example. The CPU(s)1018may 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)1036and/or infotainment SoC1030, for example.

The vehicle1000may include a GPU(s)1020(e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s)1004via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s)1020may 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 vehicle1000.

The vehicle1000may further include the network interface1024which may include one or more wireless antennas1026(e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface1024may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s)1078and/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 vehicle1000information about vehicles in proximity to the vehicle1000(e.g., vehicles in front of, on the side of, and/or behind the vehicle1000). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle1000.

The network interface1024may include a SoC that provides modulation and demodulation functionality and enables the controller(s)1036to communicate over wireless networks. The network interface1024may 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 vehicle1000may further include data store(s)1028which may include off-chip (e.g., off the SoC(s)1004) storage. The data store(s)1028may 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 vehicle1000may 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)1058may be used, including, for example and without limitation, a GPS using a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle1000may further include RADAR sensor(s)1060. The RADAR sensor(s)1060may be used by the vehicle1000for long-range vehicle detection, even in darkness and/or severe weather conditions. RADAR functional safety levels may be ASIL B. The RADAR sensor(s)1060may use the CAN and/or the bus1002(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)1060may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The vehicle1000may 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 vehicle1000, may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensor(s)1062may be used, and different ultrasonic sensor(s)1062may be used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s)1062may operate at functional safety levels of ASIL B.

The vehicle1000may include LIDAR sensor(s)1064. The LIDAR sensor(s)1064may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s)1064may be functional safety level ASIL B. In some examples, the vehicle1000may include multiple LIDAR sensors1064(e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

The vehicle may further include IMU sensor(s)1066. The IMU sensor(s)1066may be located at a center of the rear axle of the vehicle1000, in some examples. The IMU sensor(s)1066may 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)1066may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s)1066may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s)1066may be implemented as a miniature, high performance GPS-Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. As such, in some examples, the IMU sensor(s)1066may enable the vehicle1000to 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)1066and the GNSS sensor(s)1058may be combined in a single integrated unit.

The vehicle may include microphone(s)1096placed in and/or around the vehicle1000. The microphone(s)1096may 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 vehicle1000. The types of cameras used depends on the embodiments and requirements for the vehicle1000, and any combination of camera types may be used to provide the necessary coverage around the vehicle1000. 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 toFIG.10AandFIG.10B.

The vehicle1000may further include vibration sensor(s)1042. The vibration sensor(s)1042may 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 sensors1042are 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 vehicle1000may include an ADAS system1038. The ADAS system1038may include a SoC, in some examples. The ADAS system1038may 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 vehicle1000and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle1000to change lanes when necessary. Lateral ACC is related to other ADAS applications such as LCA and CWS.

LKA systems are a variation of LDW systems. LKA systems provide steering input or braking to correct the vehicle1000if the vehicle1000starts to exit the lane.

The vehicle1000may further include the infotainment SoC1030(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 SoC1030may 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 vehicle1000. For example, the infotainment SoC1030may 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 display1034, 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 SoC1030may 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 system1038, 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 SoC1030may include GPU functionality. The infotainment SoC1030may communicate over the bus1002(e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle1000. In some examples, the infotainment SoC1030may 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 vehicle1000) fail. In such an example, the infotainment SoC1030may put the vehicle1000into a chauffeur to safe stop mode, as described herein.

The vehicle1000may further include an instrument cluster1032(e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster1032may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster1032may 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 SoC1030and the instrument cluster1032. In other words, the instrument cluster1032may be included as part of the infotainment SoC1030, or vice versa.

FIG.10Dis a system diagram for communication between cloud-based server(s) and the example autonomous vehicle1000ofFIG.10A, in accordance with some embodiments of the present disclosure. The system1076may include server(s)1078, network(s)1090, and vehicles, including the vehicle1000. The server(s)1078may include a plurality of GPUs1084(A)-1084(H) (collectively referred to herein as GPUs1084), PCIe switches1082(A)-1082(H) (collectively referred to herein as PCIe switches1082), and/or CPUs1080(A)-1080(B) (collectively referred to herein as CPUs1080). The GPUs1084, the CPUs1080, and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces1088developed by NVIDIA and/or PCIe connections1086. In some examples, the GPUs1084are connected via NVLink and/or NVSwitch SoC and the GPUs1084and the PCIe switches1082are connected via PCIe interconnects. Although eight GPUs1084, two CPUs1080, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s)1078may include any number of GPUs1084, CPUs1080, and/or PCIe switches. For example, the server(s)1078may each include eight, sixteen, thirty-two, and/or more GPUs1084.

The server(s)1078may receive, over the network(s)1090and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work. The server(s)1078may transmit, over the network(s)1090and to the vehicles, neural networks1092, updated neural networks1092, and/or map information1094, including information regarding traffic and road conditions. The updates to the map information1094may include updates for the HD map1022, such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks1092, the updated neural networks1092, and/or the map information1094may 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)1078and/or other servers).

The server(s)1078may 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)1078to remotely monitor the vehicles.

In some examples, the server(s)1078may 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)1078may 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)1078may include deep learning infrastructure that use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s)1078may 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 vehicle1000. For example, the deep-learning infrastructure may receive periodic updates from the vehicle1000, such as a sequence of images and/or objects that the vehicle1000has 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 vehicle1000and, if the results do not match and the infrastructure concludes that the AI in the vehicle1000is malfunctioning, the server(s)1078may transmit a signal to the vehicle1000instructing a fail-safe computer of the vehicle1000to assume control, notify the passengers, and complete a safe parking maneuver.

For inferencing, the server(s)1078may include the GPU(s)1084and 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.11is a block diagram of an example computing device1100suitable for use in implementing some embodiments of the present disclosure. Computing device1100may include a bus1102that directly or indirectly couples the following devices: memory1104, one or more central processing units (CPUs)1106, one or more graphics processing units (GPUs)1108, a communication interface1110, input/output (I/O) ports1112, input/output components1114, a power supply1116, and one or more presentation components1118(e.g., display(s)).

Although the various blocks ofFIG.11are shown as connected via the bus1102with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component1118, such as a display device, may be considered an I/O component1114(e.g., if the display is a touch screen). As another example, the CPUs1106and/or GPUs1108may include memory (e.g., the memory1104may be representative of a storage device in addition to the memory of the GPUs1108, the CPUs1106, and/or other components). In other words, the computing device ofFIG.11is 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 ofFIG.11.

The bus1102may represent one or more busses, such as an address bus, a data bus, a control bus, or a combination thereof. The bus1102may 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 CPU(s)1106may be configured to execute the computer-readable instructions to control one or more components of the computing device1100to perform one or more of the methods and/or processes described herein. The CPU(s)1106may 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)1106may include any type of processor, and may include different types of processors depending on the type of computing device1100implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device1100, 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 device1100may include one or more CPUs1106in addition to one or more microprocessors or supplementary co-processors, such as math co-processors.

The GPU(s)1108may be used by the computing device1100to render graphics (e.g., 3D graphics). The GPU(s)1108may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s)1108may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s)1106received via a host interface). The GPU(s)1108may include graphics memory, such as display memory, for storing pixel data. The display memory may be included as part of the memory1104. The GPU(s)708may include two or more GPUs operating in parallel (e.g., via a link). When combined together, each GPU1108may 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 device1100does not include the GPU(s)1108, the CPU(s)1106may be used to render graphics.

The communication interface1110may include one or more receivers, transmitters, and/or transceivers that enable the computing device700to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface1110may 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 ports1112may enable the computing device1100to be logically coupled to other devices including the I/O components1114, the presentation component(s)1118, and/or other components, some of which may be built in to (e.g., integrated in) the computing device1100. Illustrative I/O components1114include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components1114may 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 device1100. The computing device1100may 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 device1100may 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 device1100to render immersive augmented reality or virtual reality.

The power supply1116may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply1116may provide power to the computing device1100to enable the components of the computing device1100to operate.

The presentation component(s)1118may 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)1118may 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.).