GENERATION OF GROUND TRUTH GAZE DATA FOR TRAINING IN-CABIN MONITORING SYSTEMS AND APPLICATIONS

In various examples, systems and method are provided for generation of ground truth gaze data for training in-cabin monitoring systems and applications. A gaze target projector mounted to a known position inside a cabin may be used to project a gaze target onto an interior surface of the cabin. Because a beam of light may be used to produce the projected gaze target, the projected gaze target may be displayed at a projection point on the surface of the cabin interior, even if the surface at the projection point is curved, small, or an irregular shape. Three-dimensional coordinates of a projected gaze target in the cabin coordinate system may be determined and used to label image data that is captured as a projected gaze target is selectively projected onto an interior surface of the cabin and a test occupant's gaze is directed at the projected gaze target.

BACKGROUND

Autonomous and semi-autonomous vehicles rely on machine learning approaches—such as those using deep neural networks (DNNs)—to analyze images of an interior space (e.g., cabin, cockpit) of a vehicle or other machine. An Occupant Monitoring System (OMS) is an example of a system that may be used within a vehicle cabin to perform real-time assessments of occupant or operator presence, gaze, alertness, and/or other conditions. For example, OMS sensors (such as, but not limited to, RGB sensors, infrared (IR) sensors, depth sensor, cameras, and/or other optical sensors) may be used to track an occupant's or operator's gaze direction, head pose, and/or blinking. This gaze information may be used to determine a level of attentiveness of the occupant or operator (e.g., to detect drowsiness, fatigue, and/or distraction), and/or to take responsive action to prevent harm to the occupant or operator—e.g., by redirecting their attention to a potential hazard, pulling the vehicle over, and/or the like. For example, DNNs may be used to detect that an operator is falling asleep at the wheel, based on the operator's downward gaze toward the floor of the vehicle, and the detection may lead to an adjustment in the speed and direction of the car (e.g., pulling the vehicle over to the side of the road) or an auditory alert to the operator. Occupant monitoring systems often rely on training DNNs with a high volume of training image data that reflects the facial features of different persons to help increase the accuracy of gaze predictions across all persons.

SUMMARY

Embodiments of the present disclosure relate to techniques for generating ground truth gaze data for training in-cabin monitoring systems and applications. During the collection of the gaze training data it may be challenging to collect gaze data corresponding to surfaces that are curved, small, or an irregular shape. As a result of this challenge, gaze data may not be collected—or may not be collected accurately—from certain locations within the cabin of a vehicle. As a result, the robustness of the training data for training a DNN may be limited to the gaze target locations that are easier to implement.

In contrast to conventional systems, such as those described above, the systems and methods presented in this disclosure may use a gaze target projector mounted at a known position inside a cabin to produce (e.g., cause a projection of) a projected gaze target onto an interior surface of the cabin. In some embodiments, the gaze target projector may include a robotic gaze target projector (e.g., such as a gimbal mounted robotic laser and/or laser range finder). In some embodiments, the gaze target projector may include a heads-up display projector. Because a beam of light may be used to produce the projected gaze target, the projected gaze target may be displayed at a projection point on the surface of the cabin interior, even if the surface at the projection point is curved, small, or an irregular shape, as long as there is an unobstructed line of sight between the gaze target projector and the desired projection point. Moreover, the gaze target projector may include a range finding sensor (e.g., a laser range finder, an ultrasonic range finder) to determine a distance from the gaze target projector to the target point where the projected gaze target appears. A representation of the projection point location of the projected gaze target (e.g., in polar coordinates azimuth, elevation, and distance), may be transformed to Cartesian coordinates with respect to the gaze target projector, which in turn may be mapped to the cabin coordinate system. As such, when the gaze target projector is controlled to produce a projected gaze target at a projection point on an interior surface of the cabin, the 3D coordinates of that projected gaze target in the cabin coordinate system may be readily ascertained.

Ground truth gaze data may be generated using a gaze target projector by capturing image data that include, for example, image frames of a test occupant's eyes and gaze direction. The image data is captured as the projected gaze targets are selectively projected onto an interior surface of the cabin and the test driver's gaze is directed at the projected gaze target. The projection of the gaze target should catch the test driver's attention as image frames capture the test occupant's eyes as their gaze is directed at the illumination of the projected gaze target. The captured image frames may be labeled (e.g., tagged) with the 3D coordinates of the projected gaze target to produce ground truth data corresponding to the training image. Additional ground truth gaze data may be generated in the same manner to produce a set of training data by sequentially generating additional projected gaze targets onto various interior surfaces of the cabin while image frames capture the driver's (or other occupant's) eyes and gaze direction.

DETAILED DESCRIPTION

Systems and methods are disclosed related to techniques for generating ground truth gaze data for training in-cabin monitoring systems and applications. Although the present disclosure may be described with respect to generation of ground truth gaze data for training in-cabin monitoring systems of an example autonomous vehicle1200(alternatively referred to herein as “vehicle1200” or “ego-vehicle1200,” an example of which is described with respect toFIGS.12A-12D), this is not intended to be limiting. For example, the systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more advanced driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, trains, underwater craft, remotely operated vehicles such as drones, and/or other vehicle types. In addition, although the present disclosure may be described with respect to generation of ground truth gaze data for training in-cabin monitoring systems and applications, this is not intended to be limiting, and the systems and methods described herein may be used in augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and/or any other technology spaces where ground truth gaze data may be used.

The present disclosure relates to vehicle occupant monitoring technologies. More specifically, the systems and methods presented in this disclosure provide for generating ground truth gaze data for training an occupant monitoring system (OMS) to determine an occupant gaze direction from an image frame representing the occupant, for example, to provide driver and occupant assessment functions (e.g., driver and occupant presence, gaze, alertness, and/or other conditions). An OMS may comprise a driver monitoring system (DMS), a system that monitors non-driver occupants, or a system that monitors driver occupant(s) and/or non-driver occupant(s).

Light emitting diode (LED) panels are an example of one type of existing ground truth data collection technology that may be used to collect training image data to train a DNN for an OMS. The LED panel may include an array of LEDs that may be selectively illuminated to capture the attention of a test occupant, and cause the test occupant to gaze at the illuminated LEDs. Ground truth gaze data may be generated by capturing (e.g., with a calibrated OMS sensor) images of a test occupant's eyes and gaze direction as LED gaze targets are selectively activated. For example, a test operator may illuminate a first gaze target while a calibrated OMS sensor captures images of a test occupant seated in the cabin. The illumination of the first gaze target may catch the test occupant's attention as image frames capture images of the test occupant's eyes as their gaze is directed at the illumination of the gaze target.

However, during the collection of the training data using tools such as LED panels, it may be challenging to arrange and activate target points for a subject to look at—especially where the surface where the tool is placed is curved, small, or an irregular shape. As a result of this challenge, gaze data may not be collected—or may not be collected accurately—from certain locations (e.g., within the cabin of a vehicle). As a result, the robustness of the training data for training a DNN may be limited to the gaze target locations that are easier to implement. The training data may thus include an absence of training data for other gaze target locations that may be common, useful, and/or necessary for accurate gaze detection, but that are challenging to generate accurate or precise data for. Moreover, many LED panels may be needed to capture ground truth gaze data covering the full area of the cabin interior that is within an occupant's field of view.

In contrast to existing gaze data collection technologies, the systems and methods presented in this disclosure may use a gaze target projector mounted to a known position inside a cabin to cause a projection of a gaze target to appear on an interior surface of the cabin. In some embodiments, the gaze target projector may include a robotic gaze target projector (e.g., such as a gimbal mounted robotic laser and/or laser range finder). In some embodiments, the gaze target projector may include a heads-up display projector. A base of the gaze target projector may include one or more fiducial point markers (e.g., AprilTag patterns, ARtag patterns, and/or other patterns) that localize and facilitate determining a 3D position and orientation of the base of the gaze target projector with respect to a 3D in-cabin coordinate system (which may be referred to herein as the cabin coordinate system). By capturing an image frame of the gaze target projector, 2D coordinates of the fiducial point marker(s) may be determined with respect to the image frame, and a pose and 3D coordinates of the gaze target projector may be computed with respect to the cabin coordinate system.

In some embodiments, the gaze target projector may include one or more motors and/or incremental encoders, coupled to a controller. The controller may control a motor to rotate a laser (or other visual projection emitter) to point in the direction of a specified polar coordinate (e.g., azimuth and elevation) with respect to an origin defined by the base of the gaze target projector. The controller may activate the laser to produce a projected gaze target on an interior surface of the cabin at which the laser is pointed. Because a beam of light may be used to produce the projected gaze target, the projected gaze target may be produced at a projection point on the surface of the cabin interior, even if the surface at the projection point is curved, small, or an irregular shape, as long as there is an unobstructed line of sight between the gaze target projector and the desired projection point. Moreover, the gaze target projector may include a range finding sensor (e.g., a laser range finder, an ultrasonic range finder) to determine a distance from the gaze target projector to the target point where the projected gaze target appears. A representation of the projection point location of the projected gaze target (e.g., in polar coordinates azimuth, elevation, and distance), for example as measured in the coordinate system of the gaze target projector, may be transformed to (e.g., Cartesian coordinates) a coordinate system of the gaze target projector, which in turn may be mapped to the cabin coordinate system based on knowing the 3D position and orientation of the base of the gaze target projector in the cabin coordinate system. As such, when the gaze target projector is controlled to produce a projected gaze target at a projection point on an interior surface of the cabin, the 3D coordinates of that projected gaze target in the cabin coordinate system may be readily ascertained.

In some embodiments, ground truth gaze data may be generated using a gaze target projector by capturing (e.g., using a calibrated OMS sensor) image data (e.g., one or more image frames) of a test occupant's eyes and gaze direction. The image data is captured as the projected gaze targets are selectively projected onto an interior surface of the cabin and the test driver's gaze is directed at the projected gaze target. For example, a test operator may control the gaze target projector to produce a gaze target within the cabin, while an OMS sensor (e.g., a driver monitoring system (DMS) camera) captures image data of a test occupant. The OMS sensor may be positioned at a known 3D coordinate and pose with respect to the cabin coordinate system so that features of images captured by the OMS sensor may be translated to the cabin coordinate system. The projection of the gaze target should catch the test driver's attention as image frames capture the test occupant's eyes as their gaze is directed at the illumination of the projected gaze target. The captured image frames may be labeled (e.g., tagged) with the 3D coordinates of the projected gaze target to produce ground truth data corresponding to the training image. Additional ground truth gaze data may be generated in the same manner to produce a set of training data by sequentially generating additional projected gaze targets onto various different interior surfaces of the cabin while image frames capture the driver's (or other occupant's) eyes and gaze direction. In some embodiments, the gaze target projector may be controlled to process through a predetermined sequence of gaze target locations. Additionally or alternatively, the gaze target projector may be controlled to proceed through a random sequence of gaze target locations. The labeled ground truth gaze data may be used to train one or more machine learning models such as, but not limited to a DNN used by an OMS, or for other machine learning applications.

Because using the gaze target projector may include activating a laser within a cabin that is occupied by a test occupant, surfaces upon which the gaze targets are projected (e.g., windows, mirrors, instrument panels and/or dashboards) may comprise an anti-reflective surface treatment that attenuates and/or diffuses reflections. For example, an optical film that scatters light from the interior surface of the cabin (e.g., a film having a matte finish) may be applied to target surfaces (where gaze targets may be projected) such that the gaze target is still observable by the test occupant, but the luminosity of the light reaching the test occupant is below a specified exposure threshold.

In some embodiments, the gaze target projector is used in combination with one or more other ground truth data collection technologies to collect ground truth gaze data (e.g., such as an LED panel). For example, one or more LED panels may be placed on a surface that the gaze target projector is not able to be projected onto, such as a surface occluded by the presence of a test occupant. In some embodiments, illuminated gaze targets of LED panels may be interspersed within the sequence of gaze targets produced by the gaze target projector. Additionally or alternatively, the gaze target projector may be mounted to a repositionable platform in the cabin in order to generate projected gaze targets on different surfaces that otherwise may be occluded. In such embodiments, the 3D coordinates of the base of the gaze target projector may be derived for a repositioned gaze target projector using the fiducial point markers, as previously described, so that the position of projected gaze targets in 3D space (e.g., 3D coordinates) may be mapped to the cabin coordinate system. Additionally or alternatively, multiple gaze target projectors may be used in conjunction with each other in some embodiments. In some embodiments involving hybrid systems, where the ground truth gaze data is collected using a gaze target projector in combination with another ground truth data collection technology, the ground truth gaze data may be labeled to indicate whether a test occupant's direction of gaze as represented in an image was elicited by a gaze target produced from a gaze target projector or from another type of ground truth data collection technology.

As discussed herein, in some embodiments, the gaze target projector may be implemented using a robotic gaze target projector. For example, the gaze target projector may include a laser (or other visual projection emitter) that is mounted to a base via an assembly (e.g., a gimbal assembly) that may permit rotation of the laser with respect to the base in one or more degrees of freedom (e.g., about an azimuth axis and/or an elevation axis). The laser may include a laser range finder in order to measure a distance from the gaze target projector to the surface receiving the projected gaze target produced by the laser. In some embodiments, a separate range finder (e.g., a laser and/or ultrasonic range finder) may be coupled to the laser and aligned with the laser in order to measure the distance from the gaze target projector to the projected gaze target produced by the laser. The gaze target projector may include a motor (e.g., an azimuth motor) to control the position of the laser with respect to the azimuth axis. The gaze target projector may include a motor (e.g., an elevation motor) to control the position of the laser with respect to the elevation axis. In some embodiments, the azimuth and/or elevation motors may be individually controlled using a respective motor encoder that provides a closed loop feedback signal by tracking the position (and/or speed) of the motor shaft of the respective motor. The respective motor encoder may additionally, or alternatively, be used to define a zero or home position of the motor shaft(s), which may be used as a reference position for defining an origin (e.g., in polar coordinates) in the coordinate system of the gaze target projector. Based on feedback from the respective motor encoder, the controller of the gaze target projector may select a coordinate and rotate the laser to an angular position to point in the direction (e.g., azimuth, elevation) of the selected coordinate. In some embodiments, the controller may be implemented using one or more state machines to compute and/or track the position of gaze targets based on the angular position of the laser and range to the projected gaze target. The controller may be an integrated component of the gaze target projector, and/or coupled to the motors, motor encoders, lasers and/or range finder of the gaze target projector via a wired or wireless link. In some embodiments, a correspondence between the polar coordinate system of the gaze target projector and the 3D cabin coordinate system may be at least in part determined based on construction details of the gaze target projector. For example, the correspondence may be computed using known offsets (e.g., a predetermined Euclidean distance) between the coordinates of the one or more fiducial point markers on the base of the gaze target projector and the azimuth axis and/or an elevation axis about which the laser is rotated. In some embodiments, the controller may comprise a coordinate conversion algorithm to map motor shaft positions as determined using the feedback from motor encoders, and the known offsets from the fiducial point markers on the base of the gaze target projector, to determine the 3D coordinate(s) of a projected gaze target.

In some embodiments involving a hybrid implementation where the ground truth gaze data is collected using a gaze target projector in combination with another ground truth data collection technology (e.g., an LED panel), the controller may include, for example one or more state machines to compute and/or track the position of gaze targets produced by the other ground truth data collection technology. In some embodiments, the test operator inputs a test sequence profile that is used by the controller to automatically select and activate gaze targets (e.g., via a gaze target projector and/or other ground truth data collection technology) as images of the test occupant's gaze are captured.

In some embodiments, one or more functions of the controller may be implemented using one or more processors and/or on a cloud computing platform. The various functions of the controller may be executed at least in part on one or more graphics processing units that may operate in conjunction with software executed on a central processing unit coupled to a memory. The graphics processing units may be programmed, for example, to execute kernels to implement one or more functions for detecting fiducial point markers from captured images of the gaze target projector and/or computing gaze direction of occupants capture in the images.

As previously mentioned, in some embodiments the gaze target projector may include a heads-up display projector. For example, a heads-up display projector may be positioned within the cabin in a location where it can project and display patterns directly on surfaces like a windshield, windows, and/or other surfaces. The heads-up display projector may include one or more projection elements that when activated cause a projection of a gaze target on a surface of the cabin interior, even if the surface of the projection point is curved, small, or of irregular shape, as long as there is an unobstructed line of sight between the heads-up display projector and the desired projection point. In some embodiments, a base of the heads-up display projector includes one or more fiducial point markers that localize and facilitate determining a 3D position and orientation of the base of the heads-up display projector with respect to the cabin coordinate system. The 3D coordinates of projected gaze targets (in the cabin coordinate system) may be precomputed based on placement of the heads-up display projector at a designated location and known distance (e.g., a known throw distance) from the projection elements to the surface on which the gaze target is projected, and construction details of the heads-up display projector (e.g., known offset(s) between the projection element(s) producing the projected gaze target and the one or more fiducial point markers on the base of the heads-up display projector). In some such embodiments, the controller may determine the 3D coordinates of a projected gaze target based on which projection element is activated to produce the resulting projected gaze target, and label images of the test occupant's gaze based on the 3D coordinates of the projected gaze target. In various embodiments, one or more heads-up display projectors may be used in conjunction with one or more robotic gaze target projectors, and/or other ground truth data collection technology (e.g., an LED panel).

In some embodiments, the heads-up display projector may be an integrated component of a vehicle or machine, such as a projector used to display instrumentation readings or augmented reality images on a windshield, window, and/or other surface. As such, in some embodiments, the OMS may automatically initiate a runtime OMS sensor calibration while the vehicle remains in service using the one or more heads-up display projectors to project gaze targets. Initiation of the runtime OMS sensor calibration may be triggered periodically (e.g., based on time and/or mileage driven), and/or triggered based on other factors. For example, in some embodiments, an OMS sensor used to observe occupant gaze may be mounted within a driver adjustable component (e.g., a steering wheel column). After adjustment of the component, the OMS system may initiate a recalibration where the heads-up display projector controls one or more projection elements to project a series of sequential gaze targets on a surface of the cabin interior, while the OMS sensor captures one or more images of an occupant's gaze while observing the projected gaze targets. The OMS may adjust one or more calibration parameters based on observed gaze directions from the images, and the 3D coordinates of the projected gaze targets.

While embodiments presented in this disclosure may be implemented in the context of vehicle occupant monitoring systems (including driver monitoring systems) for vehicles such as, but not limited to, non-autonomous vehicles, semi-autonomous vehicles, piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, aircraft, spacecraft, boats, shuttles, emergency response vehicles, construction vehicles, underwater craft, drones, and/or other vehicle types, other embodiments other embodiments may include determining extrinsic calibration parameters for sensors that capture image frames of other interior spaces, such as rooms, warehouses, gymnasiums, containers, and/or studios.

With reference toFIG.1,FIG.1is an example data flow diagram illustrating the interconnection of components and flow of information or data for a ground truth gaze data collection system110, which may be used for training components of an ego-machine (such as autonomous vehicle1200discussed below with respect toFIG.12A), in accordance with some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. In some embodiments, the systems, methods, and processes described herein may be executed using similar components, features, and/or functionality to those of example autonomous vehicle1200ofFIGS.12A-12D, example computing device1300ofFIG.13, and/or example data center1400ofFIG.14.

As shown inFIG.1, the process100may include a ground truth training data collection system110that generates ground truth gaze data122by capturing (e.g., using a calibrated OMS sensor101) sensor data102(e.g., image data comprising one or more image frames) of a test driver's eyes and gaze direction. The sensor data102may be captured by the ground truth training data collection system110as a gaze target projector130is controlled to selectively project a gaze target onto an interior surface of the cabin (e.g., of autonomous vehicle1200) and the test occupant's gaze is directed at the projected gaze targets. The sensor data102may be labeled (e.g., tagged) with the 3D coordinates of the projected gaze target to produce ground truth data122corresponding to a training image. Additional ground truth gaze data122may be generated in the same manner to produce a gaze training dataset140by controlling the gaze target projector130to sequentially generate additional projected gaze targets onto various interior surfaces of the cabin while sensor data102capture the driver's (or other occupant's) eye(s) and gaze direction. The labeled ground truth gaze data122included in the gaze training dataset140may be used to train and/or adjust one or more parameters of one or more machine learning models such as, but not limited to, a DNN used by an OMS, or for other machine learning applications.

The sensor data102may include, without limitation, sensor data102from any type and number of optical sensor(s) (e.g., RGB sensor(s), Infrared (IR) sensor(s), depth sensor(s), camera(s), and/or other optical sensor(s)) such as but not limited to those described herein with respect to the vehicle1200and/or other vehicles or objects-such as robotic devices, VR systems, AR systems, mixed reality systems, etc., in some examples. As a non-limiting example, and with reference toFIGS.12A-12C, the sensor data102may include the data generated by, without limitation, RADAR sensor(s)1260, ultrasonic sensor(s)1262, LIDAR sensor(s)1264, stereo camera(s)1268, wide-view camera(s)1270(e.g., fisheye cameras), infrared camera(s)1272, surround camera(s)1274(e.g., 360 degree Cameras), long-range and/or mid-range camera(s)1298, in-cabin cameras, in-cabin heat, pressure or touch sensors, in-cabin motion sensors, and/or other sensor types.

In some embodiments, the sensor data102may correspond to sensor data comprising 2D image frames generated using one or more in-cabin OMS sensors101, such as one or more in-cabin cameras, in-cabin near-infrared (NIR) sensors, in-cabin microphones, and/or the like. The sensor data102may correspond to sensors with a sensory field or field of view internal to the vehicle1200(e.g., cameras with the occupant(s), such as the driver, in its field of view). In some embodiments, the sensor data102may also correspond to sensor data generated using one or more external sensors of the vehicle1200, such as one or more cameras, RADAR sensor(s)1260, ultrasonic sensor(s)1262, LIDAR sensor(s)1264, and/or the like. As such, sensor data102may also correspond to sensors with a sensory field or field of view at least partially external to the vehicle1200(e.g., cameras, LiDAR sensors, etc. with sensory fields including the environment exterior to the vehicle1200).

As illustrated inFIG.1, in some embodiments, the ground truth training data collection system110may include a gaze target selection controller112, a gaze target controller116, a gaze target coordinate mapping function118and a ground truth image data labeling function120. The selection of gaze targets may be performed by the gaze target selection controller112. In some embodiments, a test operator (e.g., via a human machine interface105) may input to the gaze target selection controller112a selection of one or more gaze targets for the gaze target projector to sequentially project while the sensor data102is collected. In some embodiments, the gaze target selection controller112may receive a predetermined selection of gaze targets from gaze target selection sequence data114for the gaze target projector to sequentially project while the sensor data102is collected. To generate a selected projected gaze target, the gaze target selection controller112may output a target selection signal113to the gaze target controller116. For example, the target selection signal113may include a set of rotation coordinates (e.g., an azimuth and elevation) indicating a direction where the gaze target projector130should point to product the projected gaze target. Based on the target selection signal113, the gaze target controller116generates one or more projector control signals115to control the gaze target projector130to rotate to the designated rotation coordinates. When the gaze target controller116determines that the gaze target projector130reaches the designated rotation coordinates (e.g., based on feedback131from the gaze target projector130), the gaze target controller116may control the gaze target projector130to activate a visual projection emitter (e.g., a laser) to produce the projected gaze targets onto the interior surfaces of the cabin, and activate a range finding sensor to measure a distance (which may be referred to herein as projection depth data) from the gaze target projector130to the target point where the projected gaze target appears. The rotation coordinates may be provided (as shown at117) by the gaze target controller116to the gaze target coordinate mapping function118, and the projection depth data may be provided (as shown at132) by the gaze target projector130to the gaze target coordinate mapping function118. The set of rotation coordinates117together with projection depth data132may represent 3D coordinates of a position of the projected gaze target with respect to the 3D polar coordinate system of the gaze target projector130. As further discussed below, the gaze target coordinate mapping function118may convert the 3D coordinates of a position of the projected gaze target with respect to the 3D polar coordinate system into a 3D coordinate (e.g., a Cartesian coordinate) of the cabin coordinate system, and outputs the 3D coordinate119in the cabin coordinate system to the ground truth image data labeling function120. The sensor data102capturing the test occupant's eyes and gaze direction while the projected gaze target is illuminated may be labeled (e.g., tagged) by the ground truth image data labeling function120with the 3D coordinates119of the projected gaze target to produce ground truth data122. In some embodiments, the gaze target projector may be controlled to process through a predetermined sequence of gaze target locations. Additionally or alternatively, the gaze target projector may be controlled to proceed through a random sequence of gaze target locations. The labeled ground truth gaze data122may be used to train one or more machine learning models such as, but not limited to a DNN used by an OMS, or for other machine learning applications.

Referring now toFIG.2,FIG.2illustrates a robotic gaze target projector130, which may be used to implement the gaze target projector130, in accordance with some embodiments of the present disclosure. Gaze target projector200may comprise a mounting arm212rotatably coupled to a base210and further rotatably coupled to a projector member230. In some embodiments, the base210and mounting arm212form a set of gimbals for pivoting the projector member230with respect to a set of orthogonal pivot axes (e.g., an elevation axis and an azimuth axis). The base210and mounting arm212may be coupled via a first motor222(e.g., an azimuth motor) to control the rotational position (e.g., the rotational orientation) of the projector member230with respect to the azimuth axis. In some embodiments, an azimuth motor encoder220tracks the position (and/or speed) of a motor shaft of the azimuth motor220to provide closed loop feedback signal to the gaze target controller116for controlling and/or monitoring the rotation of the projector member230with respect to the azimuth axis. Similarly, the projector member230and mounting arm212may be coupled via a second motor224(e.g., an elevation motor) to control the rotational position of the projector member230with respect to the elevation axis. In some embodiments, an elevation motor encoder226tracks the position (and/or speed) of a motor shaft of the elevation motor224to provide closed loop feedback signal to the gaze target controller116for controlling and/or monitoring the rotation of the projector member230with respect to the elevation axis. In some embodiments, the azimuth motor encoder220may define an azimuth origin244(e.g., azimuth coordinate of zero degrees) for positioning the projector member230based on monitoring the motor shaft of the azimuth motor220. Similarly, the elevation motor encoder226may define an elevation origin242(e.g., elevation coordinate of zero degrees) for positioning the projector member230based on monitoring the motor shaft of the elevation motor220.

As shown inFIG.2, the projector member230may include a visual projection emitter234(e.g., a laser and/or LED device) which when activated generates the projected gaze target on the cabin surface. The projector member230may include a range finding sensor236(e.g., a laser range finder, an ultrasonic range finder) to determine a distance from the gaze target projector to the target point where the projected gaze target appears. The visual projection emitter234and range finding sensor236may be separate devices or at least partially integrated together as a visual projection emitter/range finding sensor232(e.g., such as a laser range finder that uses a visible laser).

In some embodiments, the base210of the gaze target projector200may include one or more fiducial point markers205(e.g., AprilTag patterns, ARtag patterns, and/or other patterns) that localize and facilitate determining a 3D position and orientation of the base of the gaze target projector220(e.g., the pose of gaze target projector200) with respect to the cabin coordinate system. As explained in greater detail below with respect toFIG.5, by capturing an image frame of the gaze target projector200and the one or more fiducial point markers205, a projector pose transform may be computed and used by the target coordinate mapping function to translate the set of rotation coordinates117and projection depth data132from 3D coordinates of the gaze target projector130to 3D coordinates119of the cabin coordinate system for to the ground truth image data labeling function120.

Referring now toFIG.3,FIG.3further illustrates the gaze target controller116interacting in conjunction with a gaze target projector130(e.g., such as a robotic gaze target projector200), in accordance with some embodiments of this disclosure. In some embodiments, the gaze target controller116may be an integrated component of the gaze target projector130. In some embodiments, the gaze target controller116may be a separate component from the gaze target projector130, and/or coupled to the gaze target projector130by one or more wired or wireless links.

As shown inFIG.3, the gaze target controller116may include at least one projector state machine310. The projector state machine310may receive the target selection signal113from the gaze target selection controller112(e.g., a set of rotation coordinates (e.g., an azimuth and elevation) indicating a direction where the gaze target projector130should point to product the projected gaze target). In some embodiments, based on the target selection signal113, the projector state machine310controls an elevation driver314(coupled to the elevation motor224) to control the rotation of the projector member230with respect to the elevation axis and position the projector member230as indicated by the target selection signal113. An elevation decoder312of the gaze target controller116may receive the measurement of motor shaft rotation generated by the elevation encoder226in response to activation of the elevation motor224and decode the measurement into an elevation coordinate (e.g., with respect to elevation origin242) that is used by the projector state machine310to track the position of a projected gaze target. In some embodiments, based on the target selection signal113, the projector state machine310controls an azimuth driver318(coupled to the azimuth motor222) to control the rotation of the projector member230with respect to the azimuth axis and position the projector member230as indicated by the target selection signal113. An azimuth decoder316of the gaze target controller116may receive the measurement of motor shaft rotation generated by the azimuth encoder220in response to activation of the azimuth motor222and decode the measurement into an azimuth coordinate (e.g., with respect to azimuth origin244) that is used by the projector state machine310to track the position of a projected gaze target. When the feedback from the azimuth decoder and elevation encoder226indicates that the projector member230has reached an orientation corresponding to the target selection signal113, the projector state machine310may activate the visual projection emitter234to produce the protected gaze target. In some embodiments, with the visual projection emitter234activated, the projector state machine310may determine a distance from the gaze target projector to the target point where the projected gaze target appears using the range finding sensor236. Projected rotation coordinates117(e.g., as tracked using feedback from the encoders220and226) and protection depth data (e.g., based on measurements using the range finding sensor236) may be output by the projector state machine310to the target coordinate mapping function118.

Referring now toFIG.4,FIG.4further illustrates a coordinate mapping function118, in accordance with some embodiments of this disclosure. In some embodiments, the coordinate mapping function118inputs the rotation coordinates117(e.g., comprising polar coordinates azimuth and elevation coordinates) and projection depth data132(comprising a distance) and performs a polar to Cartesian transform410to map those polar coordinates into a set of 3D Cartesian coordinates412with respect to a 3D coordinate system of the gaze target projector. That is, 3D Cartesian coordinates412may comprise a set of x, y, z, Cartesian coordinates representing a position of the protected gaze target with respect to an origin defined by the location of the gaze target projector130(e.g., which may be defined using the fiducial point markers205). Using a projector pose transform420, the coordinate mapping function118may convert the 3D Cartesian coordinates412into the 3D coordinates119of the cabin coordinate system. As discussed below with respect toFIG.5the projector pose transform420may account for the extrinsic parameters may refer to factors that describe the physical orientation of the gaze target projector130, such as rotation and translation (also referred to as roll and tilt) with respect to the cabin coordinate system.

Referring now toFIG.5,FIG.5illustrates a projector calibrator500which may be used to compute the projector pose transform420based on sensor data102capturing an image frame of the gaze target projector120and the one or more fiducial point markers205. A rotation-translation transform corresponding to gaze target projector130may be computed by a projector calibrator500that may comprise, for example, a fiducial point detector and identifier504, a fiducial point 2D coordinate determination function506, a fiducial point 3D coordinate determination function508, and a transform computation function516. Input to the calibrator500may include one or more of, but not limited to, sensor data102, sensor intrinsic parameters510(e.g., focal length parameters fxand fy, sensor principal point parameters u0and v0and/or an optical distortion coefficient γ of sensor101), vehicle geometry512, and projector geometry514.

In some embodiments, the projector calibrator500may be functionally integrated as a component of the occupant monitoring system of a vehicle1200and/or of the ground truth training data collection system110. The ground truth training data collection system110may, for example, use the rotation-translation transform as the projector pose transform420. The fiducial point detector and identifier504may analyze the sensor data102to detect the presence of the one or more fiducial points205on the base210(or other location) of the gaze target projector130. The fiducial point detector and identifier504may execute one or more machine learning algorithms, deep neural networks, computer vison algorithms, image processing algorithms, mathematical algorithms, and/or other technologies, to determine whether images of one or more fiducial points are represented by or correspond to the sensor data102and/or which portion of the sensor data102(or a representation thereof) includes the one or more fiducial points. For example, the fiducial point detector and identifier504and/or other components of the protector calibrator500, may be implemented using any type of machine learning model or algorithm, such as a machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor (k-NN), 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.), areas of interest detection algorithms, computer vision algorithms, and/or other types of algorithms or machine learning models.

For a set of one or more of the fiducial points205detected by the fiducial point detector and identifier504, the fiducial point 2D coordinate determination function506determines a 2D coordinate within the image space of an image frame of sensor data102. 2D coordinates (e.g., u, v) may be established for a fiducial point205based on the location of the fiducial point205with respect to the image space of the sensor data102. For the set of one or more of the fiducial points205detected by the fiducial point detector and identifier504, the projector calibrator500uses the fiducial point 3D coordinate determination function508to determine a 3D coordinate with respect to the cabin coordinate system. For example, the fiducial point 3D coordinate determination function508may reference vehicle geometry512to lookup a known 3D coordinate corresponding to the placement of the gaze target projector130. The vehicle geometry512may include information regarding the structure of the interior of the vehicle or machine interior, for example from Computer Assisted Drawing (CAD) models, or similar models or specifications, used for manufacturing the vehicle or other machine type (e.g., aircraft, water-based vehicle, robot, drone, construction equipment, warehouse vehicle, etc.). For instance, the vehicle geometry512may include a three-dimensional coordinate system that is mapped to the interior of the vehicle or machine and may include information such as the 3D coordinates, size, and/or orientation of one or more designate positions where the gaze target projector130may be placed. In some embodiments, a correspondence between the polar coordinate system of the gaze target projector130and the 3D cabin coordinate system may be at least in part defined based on construction details of the gaze target projector130represented in projector geometry514. The projector geometry514may describe the relationship between the geometry of the gaze target projector and one or more fiducial points located on the gaze target projector. For example, the projector geometry514may define a correspondence between the position of the one or more fiducial points205in the 3D cabin coordinate system and the azimuth axis and/or an elevation axis about which the projector member230is rotated. The correspondence may be based on known offsets (e.g., a Euclidean distance) between the coordinates of the one or more fiducial point markers205on the base210of the gaze target projector130and the azimuth axis and/or an elevation axis. In some embodiments, the controller may comprise a coordinate conversion algorithm to map motor shaft positions as determined using the feedback from motor encoders, and the known offsets from the fiducial point markers on the base of the gaze target projector, to determine the 3D coordinate(s) of a projected gaze target.

The fiducial point 3D coordinate determination function508may thus further compute coordinates of origin point for the azimuth axis and/or an elevation axis with respect to the 3D cabin coordinate system. The projector calibrator500may apply transform computation516, which comprises a pose computation algorithm to compute a rotation-translation transform for projector member230. For example, transform computation516may compute a rotation-translation transform as a rotation-translation matrix comprising rotation vector (R) and translation vector (T) that may be used for the projector pose transform420. In some embodiments, the pose computation algorithm may include one or more computer vision algorithms such as an algorithm based on the OpenCV (open source computer vision library), Eigen library, bundle adjustment optimization, RANSAC optimization, or other algorithm. Further information on computing rotation-translation transforms using 2D image frames is provided by U.S. patent application Ser. No. 17/935,473, titled “MULTI-MODAL SENSOR CALIBRATION FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS” filed, Sep. 26, 2022, and U.S. patent application Ser. No. 17/935,465, titled “SENSOR CALIBRATION USING FIDUCIAL MARKERS FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS” filed Sep. 26, 2022, each of which are incorporated herein in their entirety.

Referring now toFIG.6,FIG.6at600illustrates an cabin interior610where a plurality of example projected gaze targets620are illuminated onto various positions of cabin surfaces. Because a beam of light may be used to produce the projected gaze target, the projected gaze targets620may be produced at projection points on various surfaces of the cabin interior, even if the surface at the projection point is curved, small, or an irregular shape, as long as there is an unobstructed line of sight between the gaze target projector130and the desired projection point620.

For example, a test operator of the ground truth training data collection system110may sequentially illuminate projected gaze targets620while sensor101captures images of a test occupant705seated in the driver's seat such as image frame700shown inFIG.7. The illumination of a projected gaze target620will catch the test occupant's attention as image frames capture the test occupant's eyes710as their gaze is directed at the projected gaze target620. Because the 3D coordinates of the individual projected gaze target620with respect to the cabin coordinate system are known from the 3D coordinates119, the image frame700may be labeled by the ground truth image data labeling function120with the 3D coordinates of the first gaze target in the cabin coordinate system, to produce an image sample of ground truth gaze data122. Additional ground truth gaze data122may be generated in the same manner to produce the gaze training dataset140by sequentially generating additional projected gaze targets620onto various different interior surfaces of the cabin while the OMS sensor101captures the driver's (or other occupant's) eyes and gaze direction.

Additionally or alternatively, the gaze target projector130may be mounted to a repositionable platform (e.g., within the cabin) in order to generate projected gaze targets620on different surfaces that otherwise may be occluded by elements within the cabin. In such embodiments, the 3D coordinates of the base of the gaze target projector130may be derived for a repositioned gaze target projector using the fiducial point markers205, as previously described, so that the 3D coordinates of projected gaze targets620may be mapped to the cabin coordinate system. Additionally or alternatively, multiple gaze target projectors130may be used in conjunction with each other in some embodiments to produce the gaze training dataset140.

Now referring toFIG.8,FIG.8is a diagram of another example at800of a gaze target projector which may be used as the gaze target projector130in conjunction with the ground truth training data collection system110ofFIG.1. In this example, the gaze target projector800may comprise a heads-up display projector that may be used to project projected gaze targets620onto the surfaces of the cabin interior (e.g., windshield, windows, and/or other surfaces).

The gaze target projector800may include a base that comprises one or more projection elements812. When a projection element812is activated, light emitted by that projection element812produces a projected gaze target830on a surface840of the cabin interior. Similar to the gaze target projector200, the projected gaze targets830generated by the gaze target projector800may be produced at projection points even where the surface840is curved, small, or of irregular shape, as long as there is an unobstructed line of sight between the gaze target projector800and the desired projection point. In some embodiments, the base810of the gaze target projector800may include one or more fiducial point markers805that localize and facilitate determining a 3D position and orientation of the base810of the gaze target projector800with respect to the cabin coordinate system (e.g., such as a processed described above using the fiducial point markers205). The 3D coordinates of projected gaze targets830(e.g., in the cabin coordinate system) produced by the gaze target projector800may be precomputed based on placement of the gaze target projector800at a designated location on a surface820within the cabin, and known distance (e.g., a known throw distance) from the activated projection element812to the surface840on which the gaze target830is projected, and construction details of the gaze target projector800(e.g., known offset(s) between the projection element812producing the projected gaze targets830and the one or more fiducial point markers805on the base810of the heads-up display projector). In some such embodiments, the target coordinate mapping function118(or other element of the ground truth training data collection system110) may determine the 3D coordinates of a projected gaze target830based on which projection element812is activated to produce the resulting projected gaze target830, and label images of the test occupant's gaze based on the 3D coordinates of the projected gaze target830. The sensor data102may be captured by the ground truth training data collection system110as the gaze target projector800is controlled to selectively project a gaze target830onto the interior surface840, and the test occupant's gaze is directed at the projected gaze targets. The sensor data102may be labeled (e.g., tagged) with the 3D coordinates of the projected gaze target830to produce ground truth data122corresponding to a training image. Additional ground truth gaze data122may be generated in the same manner to produce a gaze training dataset140by controlling the gaze target projector130to sequentially generate additional projected gaze targets onto various different interior surfaces of the cabin while sensor data102capture the driver's (or other occupant's) eyes and gaze direction.

In some embodiments, the gaze target projector800as shown inFIG.8may be used together with a gaze target projector200as shown inFIG.2to implement the ground truth training data collection system110ofFIG.1. In such embodiments, the gaze training dataset140may include images of ground truth gaze data122collected using both a gaze target projector200(e.g., a robotic gaze target projector) and gaze target projector800(e.g., a head-up gaze target projector). In some embodiments, an image of ground truth gaze data122may be further labeled to indicate the type of gaze target projector130(e.g., a gaze target projector200verses a heads-up gaze target projector) that was used to generate that image sample of ground truth gaze data122).

In some embodiments, the gaze target projector800may be an integrated component of a vehicle or machine, such as a heads-up projector used to display instrumentation readings or augmented reality images on a windshield, window, and/or other surface. In some embodiments, an OMS may initiate (e.g., either automatically and/or in response to a user command) a runtime OMS sensor calibration using a gaze target projector800using a built-in heads-up display projector to project a sequence of gaze target830to collect ground truth gaze data122as described above.

Now referring toFIG.9,FIG.9is a flow diagram showing a method800for generating ground truth gaze data (such as ground truth gaze data122), in accordance with some embodiments of the present disclosure. It should be understood that the features and elements described herein with respect to the method900ofFIG.8may be used in conjunction with, in combination with, or substituted for elements of, any of the other embodiments discussed herein and vice versa. Further, it should be understood that the functions, structures, and other descriptions of elements for embodiments described inFIG.9may apply to like or similarly named or described elements across any of the figures and/or embodiments described herein and vice versa.

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 method may also be embodied as computer-usable instructions stored on computer storage media. The method may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, method900is described, by way of example, with respect to the ground truth training data collection system110ofFIG.1. However, this method may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein.

The method900, at block B902, includes controlling a gaze target projector to cause a projection of a gaze target to appear on a surface of an interior space. Gaze targets generated by a gaze target projector are produced by directing a beam of light at selected projection point on a surface within an interior space (e.g., cabin, cockpit) of a vehicle or other machine. Such projected gaze targets (as illustrated inFIG.6) may be produced at projection points on various surfaces of the cabin interior, even if the surface at the projection point is curved, small, or an irregular shape, as long as there is an unobstructed line of sight between the gaze target projector and the desired projection point. The gaze target projector may comprise a robotic gaze target projector (e.g., such as illustrated in inFIG.2) where the gaze target projector is rotated on one or more axes to direct a visual projection emitter (e.g., a laser and/or LED device) to aim at the desired projection point and activate to produce the projected gaze target. The gaze target projector may comprise a heads-up display projector (e.g., such as illustrated inFIG.8) that includes a based that comprises one or more projection elements. When a projection element is activated, light emitted by that projection element produces a projected gaze target on a surface of the cabin interior. In some embodiments, a gaze target projector may be controlled to produce the projected gaze target at the desired projection point by a gaze target controller (e.g., such as illustrated inFIG.8). For example, the gaze target controller may control the rotation of a robotic gaze target projector and/or selectively activate projection elements of a heads-up display projector to produce the projected gaze target at the desired projection point.

The method900, at block B904, includes determining a position in a three-dimensional (3D) space corresponding to a location of the projection of the gaze target. For example, for an embodiment using a robotic gaze target projector (e.g., such as gaze target projector200), 3D coordinates of a projected gaze target may initially be established in terms of polar coordinates (altitude, elevation, depth) with respect to the gaze target projector, then transformed Cartesian coordinates with respect to the gaze target projector, which in turn may be mapped to the cabin coordinate system based on knowing the 3D position and orientation of the base of the gaze target projector in the cabin coordinate system. For example, in some embodiments, the coordinate mapping function118inputs the rotation coordinates117(e.g., comprising polar coordinates azimuth and elevation coordinates) and projection depth data132(comprising a distance) and performs a polar to Cartesian transform to map those polar coordinates into a set of 3D Cartesian coordinates412with respect to a 3D coordinate system of the gaze target projector. Using a projector pose transform420, the coordinate mapping function118may convert the 3D Cartesian coordinates412into the 3D coordinates119of the cabin coordinate system. For an embodiment using a heads-up display gaze target projector (e.g., such as gaze target projector800), 3D coordinates of projected gaze targets830produced by the gaze target projector800may be precomputed based on placement of the gaze target projector800at a designated location on a surface820within the cabin, and a known distance from the activated projection element812to the surface840. Since there may be a one-to-one correspondence between a projected gaze target and the projection element812producing that projected gaze target, in some embodiments, 3D coordinates (in the cabin coordinate system) of a projected gaze target may be associated with its corresponding projection element812in a memory of the ground truth training data collection system110for reference. When that projection element812of the gaze target projector800is selected for illumination, the target coordinate mapping function118may determine a position in the 3D space (e.g., 3D coordinates) for the projected gaze target by recalling the set of 3D coordinates from memory associated with the projection element812.

The method900, at block B906, includes generating one or more ground truth data images comprising an image of an occupant of the interior space and a label based on the position in the 3D space. For example, sensor data102capturing the test occupant's eyes and gaze direction while the projected gaze target is illuminated may be labeled (e.g., tagged) by the ground truth image data labeling function120with the 3D coordinates of the projected gaze target (in the cabin coordinate system) to produce ground truth data122. Additional ground truth gaze data122may be generated in the same manner to produce the gaze training dataset140by sequentially generating additional projected gaze targets620onto various different interior surfaces of the cabin while the OMS sensor101captures the driver's (or other occupant's) eyes and gaze direction. In some embodiments, an image of ground truth gaze data122may be further labeled to indicate the type of gaze target projector130(e.g., a gaze target projector200verses a heads-up gaze target projector) was used to generate that image sample of ground truth gaze data122).

As previously discussed, LED panels are an example of one type of existing ground truth data collection technology that may be used to collect training image data to train a DNN for an OMS, though using LED panels to provide gaze targets for producing ground truth training data where the tool needs to be placed on surfaces that are curved, small, or irregular shape in shape. That said, in some embodiments, one or more LED panels may be used together with the ground truth training data collection system110. For example, turning toFIG.10,FIG.10is an illustration of an example ground truth data collection tool1000that comprises a gaze target illuminating panel (such as an LED panel) that may be used with the ground truth training data collection system110(e.g., in combination with a gaze target projector130) to produce gaze training dataset140.

In the embodiment shown inFIG.10, the ground truth data collection tool1000may comprise and one or more driver gaze targets1020. In some embodiments, the gaze targets1020comprise illuminating devices, such as light emitting diodes (LEDs) for example, that may be selectively illuminated (e.g., as selected by the gaze target selection controller112) to capture the attention of a test occupant and cause the test driver to gaze at the illuminated gaze target1020. In some implementations, a plurality of such ground truth data collection tools1000may be positioned within the internal space of the cabin at various designated gaze region locations.

In some embodiments, the ground truth data collection tool1000may comprise one or more fiducial point markers1010and 3D coordinates (in the cabin coordinate system) for each of the gaze targets1020may be determined based on known offsets of a gaze target1020from a fiducial point marker1010. For example, the ground truth data collection tool1000may be placed at a designated gaze region having known 3D coordinates in the cabin coordinate system (e.g., based on vehicle geometry512) so that the 3D coordinates of the fiducial point markers1010are also known. Using images of the fiducial point markers1010, a pose computation algorithm such as discussed above (e.g., the OpenCV algorithm solvePnP) may be used to compute a rotation-translation matrix that defines a rotation-translation transform describing the pose of the ground truth data collection tool1000with respect to the cabin coordinate system. A horizontal offset1030and vertical offset1032between the fiducial point markers1010and the one or more gaze targets1020are known constants from the construction of the ground truth data collection tool1000so that the 3D coordinates with respect to the cabin coordinate system for each of the driver gaze targets1020are therefore readily computed, for example as a function of their offset from one of fiducial point markers1010and the established pose of the ground truth data collection tool1000. In some embodiments sensor data102may be captured by the ground truth training data collection system110as the ground truth data collection tool1000is controlled to selectively illuminate gaze targets1020, and the test occupant's gaze is directed at the projected gaze targets. The sensor data102may be labeled (e.g., tagged) with the 3D coordinates of the illuminate gaze targets1020to produce ground truth data122corresponding to a training image. Additional ground truth gaze data122may be generated in the same manner to produce a gaze training dataset140by controlling the ground truth data collection tool1000to illuminate other gaze targets1020while sensor data102capture the driver's (or other occupant's) eyes and gaze direction.

In some embodiments, a ground truth data collection tool1000may be used together with a gaze target projector130(e.g., such as the gaze target projector200as shown inFIG.2and/or he gaze target projector800as shown inFIG.8) to implement the ground truth training data collection system110ofFIG.1. In such embodiments, the gaze training dataset140may include images of ground truth gaze data122collected using, a gaze target projector200(e.g., a robotic gaze target projector), a gaze target projector800(e.g., a head-up gaze target projector) and or a ground truth data collection tool1000(e.g., an LED panel). In some embodiments, an image of ground truth gaze data122may be further labeled to indicate the type of gaze target projector130(e.g., a gaze target projector200verses a heads-up gaze target projector) and/or ground truth data collection tool1000that was used to generate that image sample of ground truth gaze data122).

FIG.11illustrates a hybrid implementation of a gaze target controller116(e.g., such as shown inFIGS.1and3) where the ground truth gaze data is collected using a gaze target projector in combination with a ground truth data collection tool1000. As shown inFIG.11, this implementation of the gaze target controller116may include an illuminating panel state machine1120that controls illumination of gaze targets1020of round truth data collection tool1000(e.g., based on control signals115from the gaze target selection controller112). In some embodiments, the gaze target controller116may include a hybrid state machine1110that communicates with the gaze target selection controller112. Based on the control signals115from the gaze target selection controller112, the hybrid state machine1110may determine whether a gaze target location of a desired gaze target is located on a ground truth data collection tool1000installed within the cabin, or otherwise is to be generated as a projected gaze target by the gaze target projector. When the hybrid state machine1110determine that the gaze target location corresponds to a ground truth data collection tool1000, the hybrid state machine1110may control the illuminating panel state machine1120to illuminate the corresponding gaze targets1020as discussed above, and may output 3D coordinates1130for the illuminated gaze targets1020to the target coordinate mapping function118, which in turn outputs 3D coordinates119in the cabin coordinate system to the ground truth image data labeling function120. When the hybrid state machine1110determine that the gaze target location does not corresponds to a ground truth data collection tool1000, the hybrid state machine1110may control the projector state machine310to illuminate a corresponding projected gaze target620as discussed above, and may output 3D coordinates117for the projected gaze target620to the target coordinate mapping function118, which in turn outputs 3D coordinates119in the cabin coordinate system to the ground truth image data labeling function120.

Example Autonomous Vehicle

The vehicle1200may 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 vehicle1200may include a propulsion system1250, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system1250may be connected to a drive train of the vehicle1200, which may include a transmission, to enable the propulsion of the vehicle1200. The propulsion system1250may be controlled in response to receiving signals from the throttle/accelerator1252.

A steering system1254, which may include a steering wheel, may be used to steer the vehicle1200(e.g., along a desired path or route) when the propulsion system1250is operating (e.g., when the vehicle is in motion). The steering system1254may receive signals from a steering actuator1256. The steering wheel may be optional for full automation (Level 5) functionality.

The brake sensor system1246may be used to operate the vehicle brakes in response to receiving signals from the brake actuators1248and/or brake sensors.

Controller(s)1236, which may include one or more system on chips (SoCs)1204(FIG.12C) and/or GPU(s), may provide signals (e.g., representative of commands) to one or more components and/or systems of the vehicle1200. For example, the controller(s) may send signals to operate the vehicle brakes via one or more brake actuators1248, to operate the steering system1254via one or more steering actuators1256, to operate the propulsion system1250via one or more throttle/accelerators1252. The controller(s)1236may 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 vehicle1200. The controller(s)1236may include a first controller1236for autonomous driving functions, a second controller1236for functional safety functions, a third controller1236for artificial intelligence functionality (e.g., computer vision), a fourth controller1236for infotainment functionality, a fifth controller1236for redundancy in emergency conditions, and/or other controllers. In some examples, a single controller1236may handle two or more of the above functionalities, two or more controllers1236may handle a single functionality, and/or any combination thereof.

The controller(s)1236may provide the signals for controlling one or more components and/or systems of the vehicle1200in 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 (“GNSS”) sensor(s)1258(e.g., Global Positioning System sensor(s)), RADAR sensor(s)1260, ultrasonic sensor(s)1262, LIDAR sensor(s)1264, inertial measurement unit (IMU) sensor(s)1266(e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s)1296, stereo camera(s)1268, wide-view camera(s)1270(e.g., fisheye cameras), infrared camera(s)1272, surround camera(s)1274(e.g., 360 degree Cameras), long-range and/or mid-range camera(s)1298, speed sensor(s)1244(e.g., for measuring the speed of the vehicle1200), vibration sensor(s)1242, steering sensor(s)1240, brake sensor(s) (e.g., as part of the brake sensor system1246), and/or other sensor types.

One or more of the controller(s)1236may receive inputs (e.g., represented by input data) from an instrument cluster1232of the vehicle1200and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display1234, an audible annunciator, a loudspeaker, and/or via other components of the vehicle1200. The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the High Definition (“HD”) map1222ofFIG.12C), location data (e.g., the vehicle's1200location, 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)1236, etc. For example, the HMI display1234may 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.). In some embodiments, one or more components of the ground truth training data collection system110may be implemented at least in part by one or more of the controller(s)1236. In some embodiments, the human machine interface105may be implemented using HMI display1234.

The vehicle1200further includes a network interface1224which may use one or more wireless antenna(s)1226and/or modem(s) to communicate over one or more networks. For example, the network interface1224may be capable of communication over Long-Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile communication (“GSM”), IMT-CDMA Multi-Carrier (“CDMA2000”), etc. The wireless antenna(s)1226may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.), using local area network(s), such as Bluetooth, Bluetooth Low Energy (“LE”), Z-Wave, ZigBee, etc., and/or low power wide-area network(s) (“LPWANs”), such as LoRaWAN, SigFox, etc.

FIG.12Bis an example of camera locations and fields of view for the example autonomous vehicle1200ofFIG.12A, 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 vehicle1200. For example, one or more cameras and/or other sensors to implement OMS sensor101may be positioned to observe the position and/or movements of occupants within the cabin of vehicle1200.

A variety of cameras may be used in a front-facing configuration, including, for example, a monocular camera platform that includes a complementary metal oxide semiconductor (“CMOS”) color imager. Another example may be a wide-view camera(s)1270that 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.12B, there may be any number (including zero) of wide-view cameras1270on the vehicle1200. In addition, any number of long-range camera(s)1298(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)1298may also be used for object detection and classification, as well as basic object tracking.

Any number of stereo cameras1268may also be included in a front-facing configuration. In at least one embodiment, one or more of stereo camera(s)1268may 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 Controller Area Network (“CAN”) or Ethernet interface on a single chip. Such a unit may be used to generate a 3D map of the vehicle's environment, including a distance estimate for all the points in the image. An alternative stereo camera(s)1268may 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)1268may 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 vehicle1200(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)1274(e.g., four surround cameras1274as illustrated inFIG.12B) may be positioned to on the vehicle1200. The surround camera(s)1274may include wide-view camera(s)1270, 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)1274(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 vehicle1200(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)1298, stereo camera(s)1268), infrared camera(s)1272, etc.), as described herein.

Cameras with a field of view that include portions of the interior environment within the cabin of the vehicle1200(e.g., such as one or more OMS sensors101) may be used for an occupant monitoring system (OMS) such as, but not limited to, a driver monitoring system (DMS). For example, OMS sensors (e.g., such as one or more OMS sensor101) may be used (e.g., by controller(s)1236) to track an occupant's and/or driver's gaze direction, head pose, and/or blinking. This gaze information may be used to determine a level of attentiveness of the occupant or driver (e.g., to detect drowsiness, fatigue, and/or distraction), and/or to take responsive action to prevent harm to the occupant or operator. In some embodiments, data from OMS sensors may be used to enable gaze controlled operations by driver and/or non-driver occupants such as, but not limited to, adjusting cabin temperature and/or airflow, opening and closing windows, controlling cabin lighting, controlling entertainment systems, adjusting mirrors and/or adjusting seat positions, and/or other operations. In some embodiments, an OMS may be used for applications such as determining when objects and/or occupants have been left behind in a vehicle cabin (e.g., by detecting occupant presence after the driver exits the vehicle).

Each of the components, features, and systems of the vehicle1200inFIG.12Care illustrated as being connected via bus1202. The bus1202may include a Controller Area Network (CAN) data interface (alternatively referred to herein as a “CAN bus”). A CAN may be a network inside the vehicle1200used to aid in control of various features and functionality of the vehicle1200, 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 bus1202is 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 bus1202, this is not intended to be limiting. For example, there may be any number of busses1202, 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 busses1202may be used to perform different functions, and/or may be used for redundancy. For example, a first bus1202may be used for collision avoidance functionality and a second bus1202may be used for actuation control. In any example, each bus1202may communicate with any of the components of the vehicle1200, and two or more busses1202may communicate with the same components. In some examples, each SoC1204, each controller1236, and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle1200), and may be connected to a common bus, such the CAN bus.

The vehicle1200may include one or more controller(s)1236, such as those described herein with respect toFIG.12A. The controller(s)1236may be used for a variety of functions. The controller(s)1236may be coupled to any of the various other components and systems of the vehicle1200, and may be used for control of the vehicle1200, artificial intelligence of the vehicle1200, infotainment for the vehicle1200, and/or the like.

The vehicle1200may include a system(s) on a chip (SoC)1204. The SoC1204may include CPU(s)1206, GPU(s)1208, processor(s)1210, cache(s)1212, accelerator(s)1214, data store(s)1216, and/or other components and features not illustrated. The SoC(s)1204may be used to control the vehicle1200in a variety of platforms and systems. For example, the SoC(s)1204may be combined in a system (e.g., the system of the vehicle1200) with an HD map1222which may obtain map refreshes and/or updates via a network interface1224from one or more servers (e.g., server(s)1278ofFIG.12D).

The CPU(s)1206may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s)1206may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s)1206may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s)1206may include four dual-core clusters where each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s)1206(e.g., the CCPLEX) may be configured to support simultaneous cluster operation enabling any combination of the clusters of the CPU(s)1206to be active at any given time.

The GPU(s)1208may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s)1208may be programmable and may be efficient for parallel workloads. The GPU(s)1208, in some examples, may use an enhanced tensor instruction set. The GPU(s)1208may 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)1208may include at least eight streaming microprocessors. The GPU(s)1208may use compute application programming interface(s) (API(s)). In addition, the GPU(s)1208may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA's CUDA).

The GPU(s)1208may 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)1208to access the CPU(s)1206page tables directly. In such examples, when the GPU(s)1208memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s)1206. In response, the CPU(s)1206may look in its page tables for the virtual-to-physical mapping for the address and transmits the translation back to the GPU(s)1208. As such, unified memory technology may allow a single unified virtual address space for memory of both the CPU(s)1206and the GPU(s)1208, thereby simplifying the GPU(s)1208programming and porting of applications to the GPU(s)1208.

In addition, the GPU(s)1208may include an access counter that may keep track of the frequency of access of the GPU(s)1208to 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)1204may include any number of cache(s)1212, including those described herein. For example, the cache(s)1212may include an L3 cache that is available to both the CPU(s)1206and the GPU(s)1208(e.g., that is connected both the CPU(s)1206and the GPU(s)1208). The cache(s)1212may 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)1204may include an arithmetic logic unit(s) (ALU(s)) which may be leveraged in performing processing with respect to any of the variety of tasks or operations of the vehicle1200—such as processing DNNs. In addition, the SoC(s)1204may include a floating point unit(s) (FPU(s))—or other math coprocessor or numeric coprocessor types—for performing mathematical operations within the system. For example, the SoC(s)104may include one or more FPUs integrated as execution units within a CPU(s)1206and/or GPU(s)1208.

The SoC(s)1204may include one or more accelerators1214(e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s)1204may 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)1208and to off-load some of the tasks of the GPU(s)1208(e.g., to free up more cycles of the GPU(s)1208for performing other tasks). As an example, the accelerator(s)1214may 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)1208, and by using an inference accelerator, for example, a designer may target either the DLA(s) or the GPU(s)1208for 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)1208and/or other accelerator(s)1214.

The DLA may be used to run any type of network to enhance control and driving safety, including for example, a neural network that outputs a measure of confidence for each object detection. Such a confidence value may be interpreted as a probability, or as providing a relative “weight” of each detection compared to other detections. This confidence value enables the system to make further decisions regarding which detections should be considered as true positive detections rather than false positive detections. For example, the system may set a threshold value for the confidence and consider only the detections exceeding the threshold value as true positive detections. In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detections should be considered as triggers for AEB. The DLA may run a neural network for regressing the confidence value. The neural network may take as its input at least some subset of parameters, such as bounding box dimensions, ground plane estimate obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor1266output that correlates with the vehicle1200orientation, distance, 3D location estimates of the object obtained from the neural network and/or other sensors (e.g., LIDAR sensor(s)1264or RADAR sensor(s)1260), among others.

The SoC(s)1204may include data store(s)1216(e.g., memory). The data store(s)1216may be on-chip memory of the SoC(s)1204, which may store neural networks to be executed on the GPU and/or the DLA. In some examples, the data store(s)1216may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s)1212may comprise L2 or L3 cache(s)1212. Reference to the data store(s)1216may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s)1214, as described herein.

The SoC(s)1204may include one or more processor(s)1210(e.g., embedded processors). The processor(s)1210may 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)1204boot 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)1204thermals and temperature sensors, and/or management of the SoC(s)1204power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s)1204may use the ring-oscillators to detect temperatures of the CPU(s)1206, GPU(s)1208, and/or accelerator(s)1214. 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)1204into a lower power state and/or put the vehicle1200into a chauffeur to safe stop mode (e.g., bring the vehicle1200to a safe stop).

The processor(s)1210may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management.

The processor(s)1210may 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)1208is not required to continuously render new surfaces. Even when the GPU(s)1208is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s)1208to improve performance and responsiveness.

The SoC(s)1204may further include a broad range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and/or other devices. The SoC(s)1204may be used to process data from cameras (e.g., connected over Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s)1264, RADAR sensor(s)1260, etc. that may be connected over Ethernet), data from bus1202(e.g., speed of vehicle1200, steering wheel position, etc.), data from GNSS sensor(s)1258(e.g., connected over Ethernet or CAN bus). The SoC(s)1204may 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)1206from routine data management tasks.

The SoC(s)1204may 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)1204may be faster, more reliable, and even more energy-efficient and space-efficient than conventional systems. For example, the accelerator(s)1214, when combined with the CPU(s)1206, the GPU(s)1208, and the data store(s)1216, 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 vehicle1200. 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)1204provide for security against theft and/or carjacking.

The vehicle may include a CPU(s)1218(e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s)1204via a high-speed interconnect (e.g., PCIe). The CPU(s)1218may include an X86 processor, for example. The CPU(s)1218may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADAS sensors and the SoC(s)1204, and/or monitoring the status and health of the controller(s)1236and/or infotainment SoC1230, for example.

The vehicle1200may include a GPU(s)1220(e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s)1204via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s)1220may 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 vehicle1200.

The vehicle1200may further include the network interface1224which may include one or more wireless antennas1226(e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface1224may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s)1278and/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 vehicle1200information about vehicles in proximity to the vehicle1200(e.g., vehicles in front of, on the side of, and/or behind the vehicle1200). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle1200.

The vehicle1200may further include data store(s)1228which may include off-chip (e.g., off the SoC(s)1204) storage. The data store(s)1228may 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 vehicle1200may further include GNSS sensor(s)1258. The GNSS sensor(s)1258(e.g., GPS, assisted GPS sensors, differential GPS (DGPS) sensors, etc.), to assist in mapping, perception, occupancy grid generation, and/or path planning functions. Any number of GNSS sensor(s)1258may be used, including, for example and without limitation, a GPS using a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle1200may further include RADAR sensor(s)1260. The RADAR sensor(s)1260may be used by the vehicle1200for long-range vehicle detection, even in darkness and/or severe weather conditions. RADAR functional safety levels may be ASIL B. The RADAR sensor(s)1260may use the CAN and/or the bus1202(e.g., to transmit data generated by the RADAR sensor(s)1260) 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)1260may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The vehicle1200may further include ultrasonic sensor(s)1262. The ultrasonic sensor(s)1262, which may be positioned at the front, back, and/or the sides of the vehicle1200, may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensor(s)1262may be used, and different ultrasonic sensor(s)1262may be used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s)1262may operate at functional safety levels of ASIL B.

The vehicle1200may include LIDAR sensor(s)1264. The LIDAR sensor(s)1264may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s)1264may be functional safety level ASIL B. In some examples, the vehicle1200may include multiple LIDAR sensors1264(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)1266. The IMU sensor(s)1266may be located at a center of the rear axle of the vehicle1200, in some examples. The IMU sensor(s)1266may 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)1266may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s)1266may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s)1266may 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)1266may enable the vehicle1200to 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)1266. In some examples, the IMU sensor(s)1266and the GNSS sensor(s)1258may be combined in a single integrated unit.

The vehicle may include microphone(s)1296placed in and/or around the vehicle1200. The microphone(s)1296may 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)1268, wide-view camera(s)1270, infrared camera(s)1272, surround camera(s)1274, long-range and/or mid-range camera(s)1298, and/or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle1200. The types of cameras used depends on the embodiments and requirements for the vehicle1200, and any combination of camera types may be used to provide the necessary coverage around the vehicle1200. 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.12AandFIG.12B.

The vehicle1200may further include vibration sensor(s)1242. The vibration sensor(s)1242may 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 sensors1242are 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 vehicle1200may include an ADAS system1238. The ADAS system1238may include a SoC, in some examples. The ADAS system1238may 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)1260, LIDAR sensor(s)1264, 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 vehicle1200and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle1200to 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 vehicle1200if the vehicle1200starts to exit the lane.

Conventional ADAS systems may be prone to false positive results which may be annoying and distracting to a driver, but typically are not catastrophic, because the ADAS systems alert the driver and allow the driver to decide whether a safety condition truly exists and act accordingly. However, in an autonomous vehicle1200, the vehicle1200itself must, in the case of conflicting results, decide whether to heed the result from a primary computer or a secondary computer (e.g., a first controller1236or a second controller1236). For example, in some embodiments, the ADAS system1238may be a backup and/or secondary computer for providing perception information to a backup computer rationality module. The backup computer rationality monitor may run a redundant diverse software on hardware components to detect faults in perception and dynamic driving tasks. Outputs from the ADAS system1238may be provided to a supervisory MCU. If outputs from the primary computer and the secondary computer conflict, the supervisory MCU must determine how to reconcile the conflict to ensure safe operation.

The vehicle1200may further include the infotainment SoC1230(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 SoC1230may 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 vehicle1200. For example, the infotainment SoC1230may 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 display1234, 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 SoC1230may 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 system1238, 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 SoC1230may include GPU functionality. The infotainment SoC1230may communicate over the bus1202(e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle1200. In some examples, the infotainment SoC1230may 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)1236(e.g., the primary and/or backup computers of the vehicle1200) fail. In such an example, the infotainment SoC1230may put the vehicle1200into a chauffeur to safe stop mode, as described herein.

The vehicle1200may further include an instrument cluster1232(e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster1232may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster1232may 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 SoC1230and the instrument cluster1232. In other words, the instrument cluster1232may be included as part of the infotainment SoC1230, or vice versa.

FIG.12Dis a system diagram for communication between cloud-based server(s) and the example autonomous vehicle1200ofFIG.12A, in accordance with some embodiments of the present disclosure. The system1276may include server(s)1278, network(s)1290, and vehicles, including the vehicle1200. The server(s)1278may include a plurality of GPUs1284(A)-1284(H) (collectively referred to herein as GPUs1284), PCIe switches1282(A)-1282(H) (collectively referred to herein as PCIe switches1282), and/or CPUs1280(A)-1280(B) (collectively referred to herein as CPUs1280). The GPUs1284, the CPUs1280, and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces1288developed by NVIDIA and/or PCIe connections1286. In some examples, the GPUs1284are connected via NVLink and/or NVSwitch SoC and the GPUs1284and the PCIe switches1282are connected via PCIe interconnects. Although eight GPUs1284, two CPUs1280, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s)1278may include any number of GPUs1284, CPUs1280, and/or PCIe switches. For example, the server(s)1278may each include eight, sixteen, thirty-two, and/or more GPUs1284.

The server(s)1278may receive, over the network(s)1290and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work. The server(s)1278may transmit, over the network(s)1290and to the vehicles, neural networks1292, updated neural networks1292, and/or map information1294, including information regarding traffic and road conditions. The updates to the map information1294may include updates for the HD map1222, such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks1292, the updated neural networks1292, and/or the map information1294may 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)1278and/or other servers).

In some examples, the server(s)1278may 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)1278may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s)1284, such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s)1278may include deep learning infrastructure that use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s)1278may 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 vehicle1200. For example, the deep-learning infrastructure may receive periodic updates from the vehicle1200, such as a sequence of images and/or objects that the vehicle1200has 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 vehicle1200and, if the results do not match and the infrastructure concludes that the AI in the vehicle1200is malfunctioning, the server(s)1278may transmit a signal to the vehicle1200instructing a fail-safe computer of the vehicle1200to assume control, notify the passengers, and complete a safe parking maneuver.

For inferencing, the server(s)1278may include the GPU(s)1284and 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.13is a block diagram of an example computing device(s)1300suitable for use in implementing some embodiments of the present disclosure, such as but not limited to one or more components of the ground truth training data collection system110. Computing device1300may include an interconnect system1302that directly or indirectly couples the following devices: memory1304, one or more central processing units (CPUs)1306, one or more graphics processing units (GPUs)1308, a communication interface1310, input/output (I/O) ports1312, input/output components1314, a power supply1316, one or more presentation components1318(e.g., display(s)), and one or more logic units1320. In at least one embodiment, the computing device(s)1300may comprise one or more virtual machines (VMs), and/or any of the components thereof may comprise virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs1308may comprise one or more vGPUs, one or more of the CPUs1306may comprise one or more vCPUs, and/or one or more of the logic units1320may comprise one or more virtual logic units. As such, a computing device(s)1300may include discrete components (e.g., a full GPU dedicated to the computing device1300), virtual components (e.g., a portion of a GPU dedicated to the computing device1300), or a combination thereof.

Although the various blocks ofFIG.13are shown as connected via the interconnect system1302with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component1318, such as a display device, may be considered an I/O component1314(e.g., if the display is a touch screen). As another example, the CPUs1306and/or GPUs1308may include memory (e.g., the memory1304may be representative of a storage device in addition to the memory of the GPUs1308, the CPUs1306, and/or other components). In other words, the computing device ofFIG.13is 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.13.

The interconnect system1302may represent one or more links or busses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system1302may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standards association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and/or another type of bus or link. In some embodiments, there are direct connections between components. As an example, the CPU1306may be directly connected to the memory1304. Further, the CPU1306may be directly connected to the GPU1308. Where there is direct, or point-to-point connection between components, the interconnect system1302may include a PCIe link to carry out the connection. In these examples, a PCI bus need not be included in the computing device1300.

The CPU(s)1306may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device1300to perform one or more of the methods and/or processes described herein. For example, the CPU(s)1306may execute code to implement one or more components of the ground truth training data collection system110describe herein. The CPU(s)1306may 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)1306may include any type of processor, and may include different types of processors depending on the type of computing device1300implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device1300, the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device1300may include one or more CPUs1306in addition to one or more microprocessors or supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s)1306, the GPU(s)1308may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device1300to perform one or more of the methods and/or processes described herein. For example, the GPU(s)1308may execute code to implement one or more components of the ground truth training data collection system110describe herein. One or more of the GPU(s)1308may be an integrated GPU (e.g., with one or more of the CPU(s)1306and/or one or more of the GPU(s)1308may be a discrete GPU. In embodiments, one or more of the GPU(s)1308may be a coprocessor of one or more of the CPU(s)1306. The GPU(s)1308may be used by the computing device1300to render graphics (e.g., 3D graphics) or perform general purpose computations. For example, the GPU(s)1308may be used for General-Purpose computing on GPUs (GPGPU). The GPU(s)1308may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s)1308may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s)1306received via a host interface). The GPU(s)1308may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory1304. The GPU(s)1308may include two or more GPUs operating in parallel (e.g., via a link). The link may directly connect the GPUs (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU1308may generate pixel data or GPGPU data for different portions of an output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s)1306and/or the GPU(s)1308, the logic unit(s)1320may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device1300to perform one or more of the methods and/or processes described herein. In embodiments, the CPU(s)1306, the GPU(s)1308, and/or the logic unit(s)1320may discretely or jointly perform any combination of the methods, processes and/or portions thereof. One or more of the logic units1320may be part of and/or integrated in one or more of the CPU(s)1306and/or the GPU(s)1308and/or one or more of the logic units1320may be discrete components or otherwise external to the CPU(s)1306and/or the GPU(s)1308. In embodiments, one or more of the logic units1320may be a coprocessor of one or more of the CPU(s)1306and/or one or more of the GPU(s)1308.

The communication interface1310may include one or more receivers, transmitters, and/or transceivers that enable the computing device1300to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface1310may include components and functionality to enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or the Internet. In one or more embodiments, logic unit(s)1320and/or communication interface1310may include one or more data processing units (DPUs) to transmit data received over a network and/or through interconnect system1302directly to (e.g., a memory of) one or more GPU(s)1308.

The I/O ports1312may enable the computing device1300to be logically coupled to other devices including the I/O components1314, the presentation component(s)1318, and/or other components, some of which may be built in to (e.g., integrated in) the computing device1300. Illustrative I/O components1314include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components1314may 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 device1300. The computing device1300may 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 device1300may 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 device1300to render immersive augmented reality or virtual reality.

The power supply1316may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply1316may provide power to the computing device1300to enable the components of the computing device1300to operate.

The presentation component(s)1318may 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)1318may receive data from other components (e.g., the GPU(s)1308, the CPU(s)1306, DPUs, etc.), and output the data (e.g., as an image, video, sound, etc.). For example, in some embodiments, a gaze target projector may be implemented using one or more of presentation component(s)1318.

Example Data Center

FIG.14illustrates an example data center1400that may be used in at least one embodiments of the present disclosure. The data center1400may include a data center infrastructure layer1410, a framework layer1420, a software layer1430, and/or an application layer1440.

As shown inFIG.14, the data center infrastructure layer1410may include a resource orchestrator1412, grouped computing resources1414, and node computing resources (“node C.R.s”)1416(1)-1416(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s1416(1)-1416(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (NW I/O) devices, network switches, virtual machines (VMs), power modules, and/or cooling modules, etc. In some embodiments, one or more node C.R.s from among node C.R.s1416(1)-1416(N) may correspond to a server having one or more of the above-mentioned computing resources. In addition, in some embodiments, the node C.R.s1416(1)-14161(N) may include one or more virtual components, such as vGPUs, vCPUs, and/or the like, and/or one or more of the node C.R.s1416(1)-1416(N) may correspond to a virtual machine (VM). In some embodiments, one or more components of the ground truth training data collection system may be implemented by one or more of the node C.R.s1416(1)-1416(N).

In at least one embodiment, grouped computing resources1414may include separate groupings of node C.R.s1416housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s1416within grouped computing resources1414may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s1416including CPUs, GPUs, DPUs, and/or other processors may be grouped within one or more racks to provide compute resources to support one or more workloads. The one or more racks may also include any number of power modules, cooling modules, and/or network switches, in any combination.

The resource orchestrator1412may configure or otherwise control one or more node C.R.s1416(1)-1416(N) and/or grouped computing resources1414. In at least one embodiment, resource orchestrator1412may include a software design infrastructure (SDI) management entity for the data center1400. The resource orchestrator1412may include hardware, software, or some combination thereof.

In at least one embodiment, as shown inFIG.14, framework layer1420may include a job scheduler1433, a configuration manager1434, a resource manager1436, and/or a distributed file system1438. The framework layer1420may include a framework to support software1432of software layer1430and/or one or more application(s)1442of application layer1440. The software1432or application(s)1442may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. The framework layer1420may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system1438for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler1433may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center1400. The configuration manager1434may be capable of configuring different layers such as software layer1430and framework layer1420including Spark and distributed file system1438for supporting large-scale data processing. The resource manager1436may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system1438and job scheduler1433. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource1414at data center infrastructure layer1410. The resource manager1436may coordinate with resource orchestrator1412to manage these mapped or allocated computing resources.

In at least one embodiment, software1432included in software layer1430may include software used by at least portions of node C.R.s1416(1)-1416(N), grouped computing resources1414, and/or distributed file system1438of framework layer1420. One or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.

In at least one embodiment, application(s)1442included in application layer1440may include one or more types of applications used by at least portions of node C.R.s1416(1)-1416(N), grouped computing resources1414, and/or distributed file system1438of framework layer1420. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and/or other machine learning applications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager1434, resource manager1436, and resource orchestrator1412may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. Self-modifying actions may relieve a data center operator of data center1400from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

Example Network Environments

Network environments suitable for use in implementing embodiments of the disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and/or other device types. The client devices, servers, and/or other device types (e.g., each device) may be implemented on one or more instances of the computing device(s)1300ofFIG.13—e.g., each device may include similar components, features, and/or functionality of the computing device(s)1300. In addition, where backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may be included as part of a data center1400, an example of which is described in more detail herein with respect toFIG.14.