IN-CABIN OCCUPANCY DETECTION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

Methods and systems to perform in-cabin occupancy detection in autonomous systems or applications are disclosed. Specifically, in many conventional trucks or other commercial vehicles, to improve a comfort level for drivers, a cabin of the vehicle can be connected to a chassis via a cabin suspension system, which may include damping springs. In some embodiments, sensors can be installed adjacent to or integrated with the damping springs of the cabin suspension system. Signals from the sensors can be used to detect a change in weight of the cabin compared to a baseline value, thereby detecting the presence of a person or other object in the cabin of the vehicle. A safety feature may be implemented in autonomous vehicles that prevents the operation of the vehicle in a fully autonomous driving mode when the cabin is occupied.

BACKGROUND

Autonomous machines (e.g., fully autonomous vehicles, such as L4 automated trucks) may sometimes have the necessity to be operated by human operators from within the cabin for certain tasks (e.g., maintenance, relocation, navigating heavily populated areas, or when there is an issue with the automated driving system). Thus, a steering wheel may be needed in the cabin so that the machine can also be operated by a human. However, during autonomous operation, a human person should be prevented from intervening with the driving while the autonomous driving system is engaged. For example, if the person accidentally grabs the steering wheel, pulls the steering wheel, pushes the brake, and/or performs another action that causes actuation of the machine, undesired driving behavior can occur. In addition, if the occupant is not seated safely within the machine, the occupant may be in an unsafe position when the accidental or intentional actuation occur—especially where the actuation is not in sync with current autonomous actuation of the autonomous driving system. In such situations, it can be unclear whether the autonomous driving system or the human person is in charge of the situation and, in instances where the autonomous driving system is not decoupled from the physical steering and control mechanisms internal to the cabin, the disconnect between the autonomous driving system and the human controlled driving system may result in erratic control of the machine.

To solve this problem, traditional solutions use in-cabin monitoring to identify the presence of a person or other actor within the cabin. For example, in-cabin perception sensors (e.g., cameras, LiDAR, RADAR, ultrasonic, etc.) may be used to detect the presence of a person or other actor in the cabin. However, these methods can sometimes be less reliable or accurate than desired if used as the sole detection method, thus providing false negatives or false positives with respect to the presence of an occupant in the cabin. For example, the cabin of a truck can be relatively large, and a person may be in the back of the cabin that is outside the fields of view of the sensors, may be bent over or crouched down to be out of the fields of view of the sensors, and/or may otherwise not be detected (e.g., where the system is for person detection, and the occupant is an animal, the system may not flag the presence of the animal). Performance of in-cabin perception systems can also be subject to lighting conditions—e.g., glare from the sun, darkness during the evening or in enclosed spaces, etc. Sensors that are less compromised by light—such as LiDAR sensors—may be expensive and/or less capable of being used for classifying detected objects, thus leading to less accurate predictions.

SUMMARY

Embodiments of the present disclosure provide methods and systems for detecting the presence of a person or other actor (e.g., an animal, a robot, etc.) in a cabin of an autonomous machine (e.g., an autonomous truck, aircraft, construction equipment, etc.) using sensor feedback. If it is detected that a person or other actor is present in the cabin, an electronic control unit (ECU) of the autonomous machine can cause the machine to stop (e.g., if already in motion) or to refrain from driving off (e.g., if parked or at a stop). The detection system can be a standalone system, or can be combined with other monitoring systems to improve the safety and reliability of the occupant presence detection system.

In accordance with a first aspect of the disclosure, a method is provided for detecting in-cabin occupancy of an autonomous system or application. The method includes: determining, using one or more sensors, a value of a suspension characteristic of a suspension system of the autonomous machine; and determining whether an occupant is present in a cabin of the autonomous machine based at least on the value of the suspension characteristic.

In an example embodiment of the first aspect, the determining whether the occupant is present in the cabin comprises: computing a difference based at least on comparing the value to a reference value; and determining that the occupant is present in the cabin responsive to determining that the difference exceeds a pre-defined threshold, or determining that the occupant is not present in the cabin responsive to determining that the difference is less than or equal to the pre-defined threshold.

In an example embodiment of the first aspect, measuring, using one or more accelerometers disposed on the cabin, a number of samples of an acceleration of the cabin over a period of time to generate an acceleration pattern during acceleration of the autonomous machine from a first velocity to a second velocity that is greater than the first velocity; and computing another difference based at least on comparing the acceleration pattern to a reference acceleration pattern obtained when the cabin is unoccupied and the autonomous machine is accelerating from the first velocity to the second velocity. The determining whether the occupant is present in the cabin is further based at least on the another difference.

In an example embodiment of the first aspect, the method further comprises determining a distribution inside the cabin based at least on sensor data generated by the one or more sensors; and computing another difference based at least on comparing the distribution to a reference distribution. The determining whether the occupant is present in the cabin is further based at least on the another difference.

In an example embodiment of the first aspect, the method further comprises adjusting the reference value based on a speed of the autonomous machine or a measured wind speed.

In an example embodiment of the first aspect, the method further comprises adjusting the reference value based on a parameter from a radio frequency identifier (RFID) tag detected in the cabin.

In an example embodiment of the first aspect, the suspension system comprises one or more springs disposed between a chassis and the cabin of the autonomous machine. The one or more sensors are configured to measure characteristics of the one or more springs, and a characteristic of each spring comprises at least one of a deflection, a force, or a pressure associated with the spring.

In an example embodiment of the first aspect, the method further comprises at least one of: generating, based at least on sensor data generated by at least one of a motion sensor or a perception sensor, a motion signal, wherein the determining whether the occupant is present in the cabin is further based at least on the motion signal; acquiring, using one or more perception sensors, first sensor data representative of an interior of the cabin, wherein the determining whether the occupant is present in the cabin is further based at least on the first sensor data; or acquiring, using one or more RADAR sensors, second sensor data representative of the interior of the cabin, wherein the determining whether the occupant is present in the cabin is further based at least on the second sensor data.

In an example embodiment of the first aspect, the method further comprises sending a result of the determination of whether the occupant is present in the cabin to a controller over an in-vehicle network. The result is used by the controller to prevent or disengage operation of an autonomous driving mode of the autonomous machine responsive to determining that the occupant is present in the cabin.

In an example embodiment of the first aspect, the determining the value of the suspension characteristic of the suspension system of the autonomous machine comprises processing sensor data of the one or more sensors using an artificial intelligence algorithm to estimate the value.

In an example embodiment of the first aspect, the artificial intelligence algorithm comprises a deep neural network. An input to the deep neural network comprises, for each of the one or more sensors, a vector of samples of the suspension characteristic measured by the sensor over a period of time.

In an example embodiment of the first aspect, the input to the deep neural network further comprises at least one of: a speed of the autonomous machine, an acceleration of the autonomous machine, or a steering position of the autonomous machine over the period of time.

In an example embodiment of the first aspect, the determining the value of the suspension characteristic of the suspension system of the autonomous machine comprises processing sensor data of the one or more sensors in accordance with a mass-spring-damper model.

In accordance with a second aspect of the disclosure, a system is provided for detecting in-cabin occupancy of an autonomous machine. The system includes: one or more sensors installed adjacent one or more suspension components that connect a chassis to a cabin of an autonomous machine, the one or more sensors each configured to generate a signal associated with a corresponding suspension component of the one or more suspension components; and one or more processors. The one or more processors determine, based on signals from the one or more sensors, a value of a suspension characteristic of a suspension system of the autonomous machine; and determine whether an occupant is present in the cabin based at least on the value of the suspension characteristic.

In an example embodiment of the second aspect, the one or more suspension components comprise at least one air spring, and the one or more sensors comprise one or more pressure sensors configured to measure a change in pressure in a corresponding air spring.

In an example embodiment of the second aspect, the determining whether the occupant is present in the cabin comprises: computing a difference based at least on comparing the value to a reference value; and determining that the occupant is present in the cabin responsive to determining that the difference exceeds a pre-defined threshold, or determining that the occupant is not present in the cabin responsive to determining that the difference is less than or equal to the pre-defined threshold.

In an example embodiment of the second aspect, the system further includes one or more accelerometers disposed on the cabin and configured to measure a number of samples of an acceleration of the cabin over a period of time to generate an acceleration pattern during acceleration of the autonomous machine from a first velocity to a second velocity that is greater than the first velocity. The one or more processors further compute another difference based at least on comparing the acceleration pattern to a reference acceleration pattern obtained when the cabin is unoccupied and the autonomous machine is accelerating from the first velocity to the second velocity. The determining whether the occupant is present in the cabin is further based at least on the another difference.

In an example embodiment of the second aspect, the system further includes at least one of: one or more motion sensors or perception sensors configured to detect a motion signal, wherein the determining whether the occupant is present in the cabin is further based at least on the motion signal; one or more perception sensors configured to acquire first sensor data representative of an interior of the cabin, wherein the determining whether the occupant is present in the cabin is further based at least on the first sensor data; or one or more RADAR sensors configured to acquire second sensor data representative of an interior of the cabin, wherein the determining whether the occupant is present in the cabin is further based at least on the second sensor data.

In an example embodiment of the second aspect, the one or more processors are included in a safety module connected to an in-vehicle network, the safety module is configured to send the determination of whether the occupant is present in the cabin to a controller of the autonomous machine via the in-vehicle network. The result is used by the controller to prevent or disengage operation of an autonomous driving mode responsive to determining that the occupant is present in the cabin.

In accordance with a third aspect of the disclosure, a non-transitory computer-readable media is provided storing computer instructions that, when executed by one or more processors, cause the one or more processors to perform the method of the first aspect.

DETAILED DESCRIPTION

Systems and methods are disclosed related to in-cabin occupancy detection for autonomous systems and applications. Although the present disclosure may be described with respect to an example autonomous vehicle100(alternatively referred to herein as “vehicle100” or “ego-vehicle100,” an example of which is described with respect toFIGS.1A-1D), this is not intended to be limiting. For example, the systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. In addition, although the present disclosure may be described with respect to autonomous driving modes of operation of large commercial vehicles (e.g., trucks, semi-trailers, etc.), 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 in-cabin occupancy detection may be used.

Embodiments of the present disclosure provide methods and systems to detect the presence of an occupant (e.g., person) in the cabin of an autonomous vehicle by using sensors in a damping system of the vehicle. Specifically, in many conventional trucks or other commercial vehicles, to improve a comfort level for drivers, a driver's cabin can be connected to a chassis of the vehicle via a damping system, such as damping springs. In some cases, the damping springs can include air springs that can be dynamically adjusted to change the characteristics of the cabin motion. This cabin suspension system is separate and distinct from the chassis suspension system attached to the axles or wheels of the vehicle. In some embodiments, sensors can be installed adjacent to or integrated with the damping springs of the cabin suspension system.

A controller of the vehicle can be configured to detect the presence of a human or other object in the cabin that indicates a driving condition where autonomous mode should not be engaged. For example, presence of a human or an unsecured load in the cabin while an autonomous mode of operation is engaged could lead to a situation where the human attempts to intervene using the accelerator, brake, or steering wheel, or a heavy object falls off a seat and lands on the accelerator or brake. Signals from input controls such as these that directly contradict the commands of the autonomous mode of operation can lead to undesired operating conditions if the signals override the intent of the algorithm for the autonomous mode of operation. Consequently, one solution to prevent the this condition is to prevent engagement of, or the continued operation in, the autonomous mode of operation when someone or something is detected in the cabin.

While some systems exist to detect the presence of a human subject in the cabin, they may prove unreliable. For example, existing systems may use a camera located in the cabin. The camera may have a fixed field of view, and the system may rely on image processing algorithms to recognize whether a human appears in the frame of the camera. However, as may be common in typical use scenarios, a large vehicle may have a large cabin with areas that can be obscured from the camera's field of view. For example, a human may be asleep in a berth behind the driver's seat in some truck cabins. Alternatively, the object recognition algorithm may not be 100% accurate in detecting a human subject in the scene because the pose and clothing worn by different people can vary greatly and/or blend in with a background of the cabin (e.g., when a driver's shirt or hat blend in with a color or pattern of a seat cover or curtain behind the driver). As such, alternative means for detecting the presence of a human or other object in the cabin, in lieu of or in addition to cameras and/or depth sensors, can help to improve the safety of some autonomous vehicles.

As further examples, embodiments of the present disclosure provide a method to detect the presence of a person in a cabin of a machine by using one or more suspension sensors in the damping system of the vehicle. As an example, to improve comfort for drivers, the cabin may be connected to the chassis via a damping system, such as damping springs. The suspension sensors can be part of the damping system, and may, in non-limiting embodiments, be installed adjacent the damping springs (e.g., as illustrated inFIG.2).

The suspension sensors can measure the weight distribution inside the cabin. During operation, the signals received from the suspension sensors (and/or other sensor types capable of measuring weight, mass, or the distribution thereof) can be continuously monitored by a monitoring and damping spring control unit against a baseline value. The baseline value is also referred to as the reference value, and can be the reading of the suspension sensor when the cabin is empty (or at least unoccupied by a person or other animate actor). For example, when an occupant enters the cabin, the overall weight of the cabin increases. Consequently, the reading of the suspension sensor can be increased with respect to the reference value. If the measured value exceeds the reference value by more than a threshold amount, it can be determined that there is a person present in the cabin; otherwise, it can be determined that there is no person present in the cabin. The baseline weight may be set prior to the occupant being inside, but may differ depending on the other items or objects within the cabin (e.g., boxes or other objects that may add weight to the cabin). As such, the baseline value may be recalibrated prior to each driving session, and/or at an interval (e.g., each time the machine stops, each time the machine is to depart, every x amount of time, etc.). Detection results can be sent to the autonomous driving system (e.g., L4 System) and, if it is determined that a person is present in the cabin, the autonomous driving system can stop the machine or refrain from departing. If no person is present in the cabin, the autonomous driving system can continue driving the machine or may depart from a standstill.

In some embodiments, to distinguish the weight of a person from the weight of other objects that might be in the cabin (e.g., 60 Kg of cargo/widgets, a cooler, etc.), those other objects can carry RFIDs. An RFID reader can be installed in the cabin to scan the RFIDs, so that the baseline value can be adjusted accordingly. It may also be determined whether the balance of the cabin is changed, e.g., due to an open door. In other embodiments, an in-cabin camera can be used to determine other objects that may be located in the cabin. An image and/or depth map of the cabin can be processed by an object recognition algorithm and objects that are detected in the cabin can be matched to a corresponding reference weight for the object stored in a lookup table. In addition, the position of the object may be determined based on the image and/or depth map. In yet other embodiments, QR codes may be included on object that can be read via the in-cabin camera, where the weight of the object can be encoded in the QR code. The weights and/or position of the objects can then be used to adjust the baseline value.

To further improve the detection reliability, the system can monitor the acceleration behavior of the cabin while driving off from standstill (e.g., the way the cabin moves forward and backward during acceleration). For example, the detection system can include accelerometers or other inertial measurement unit (IMU) sensors configured to measure acceleration or other IMU values related to the cabin. If the time series pattern does not match the pattern of an empty cabin, the autonomous driving system can be informed. Accordingly, the autonomous driving system can bring the vehicle to a stop. For example, a first reference acceleration pattern can be recorded when the cabin is empty, and a second reference acceleration pattern can be recorded when a person is inside the cabin. If the measured acceleration pattern is different from the first reference pattern, and is more similar to the second reference pattern, it can be determined that there is a person in the cabin. In some embodiments, to rule out effects of wind on the acceleration behavior, real-time weather forecast can be obtained and/or weather instruments can be used to detect current weather conditions.

According to some embodiments, other types of sensors can be included in addition to sensors that measure suspension characteristics. For example, motion sensors can be installed in the cabin to monitor whether there is motion, e.g., caused by a person moving in the cabin. Cameras, LiDAR, and/or RADAR sensors can also be installed in the cabin to monitor the presence of any person. By combining the different types of sensors, the detection system can be more reliable or robust to different motion types and/or actor types.

According to some embodiments, to increase the sensitivity, the monitoring and damping spring control unit can change the resistance of the spring. For example, if air or pneumatic springs are used, the air volume can be decreased, so that the air pressure change can be more sensitive to a weight change.

Additionally, the monitoring and damping spring control unit can provide accuracy information about the occupancy detection. It can also request a retrial of the start from standstill procedure in case of uncertainty.

With reference to the attached figures,FIG.1Ais an illustration of an example autonomous vehicle, 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 vehicle100ofFIGS.1A-1D, example computing device500ofFIG.5, and/or example data center600ofFIG.6.

Disclosed embodiments may be comprised in a variety of different systems such as automotive systems (e.g., a control system for an autonomous or semi-autonomous machine, a perception system for an autonomous or semi-autonomous machine), systems implemented using a robot, aerial systems, medial systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using an edge device, systems incorporating one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations, systems for hosting real-time streaming applications, systems for presenting one or more of virtual reality content, augmented reality content, or mixed reality content, systems for performing light transport simulation, systems for performing collaborative content creation for 3D assets, systems implemented at least partially using cloud computing resources, and/or other types of systems.

The vehicle100may 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 vehicle100may include a propulsion system150, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system150may be connected to a drive train of the vehicle100, which may include a transmission, to enable the propulsion of the vehicle100. The propulsion system150may be controlled in response to receiving signals from the throttle/accelerator152.

A steering system154, which may include a steering wheel, may be used to steer the vehicle100(e.g., along a desired path or route) when the propulsion system150is operating (e.g., when the vehicle is in motion). The steering system154may receive signals from a steering actuator156. The steering wheel may be optional for full automation (Level 5) functionality.

The brake sensor system146may be used to operate the vehicle brakes in response to receiving signals from the brake actuators148and/or brake sensors.

Controller(s)136, which may include one or more system on chips (SoCs)104(FIG.1C) and/or GPU(s), may provide signals (e.g., representative of commands) to one or more components and/or systems of the vehicle100. For example, the controller(s) may send signals to operate the vehicle brakes via one or more brake actuators148, to operate the steering system154via one or more steering actuators156, to operate the propulsion system150via one or more throttle/accelerators152. The controller(s)136may 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 vehicle100. The controller(s)136may include a first controller136for autonomous driving functions, a second controller136for functional safety functions, a third controller136for artificial intelligence functionality (e.g., computer vision), a fourth controller136for infotainment functionality, a fifth controller136for redundancy in emergency conditions, and/or other controllers. In some examples, a single controller136may handle two or more of the above functionalities, two or more controllers136may handle a single functionality, and/or any combination thereof.

The controller(s)136may provide the signals for controlling one or more components and/or systems of the vehicle100in 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)158(e.g., Global Positioning System sensor(s)), RADAR sensor(s)160, ultrasonic sensor(s)162, LIDAR sensor(s)164, inertial measurement unit (IMU) sensor(s)166(e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s)196, stereo camera(s)168, wide-view camera(s)170(e.g., fisheye cameras), infrared camera(s)172, surround camera(s)174(e.g., 360 degree cameras), long-range and/or mid-range camera(s)198, speed sensor(s)144(e.g., for measuring the speed of the vehicle100), vibration sensor(s)142, steering sensor(s)140, brake sensor(s) (e.g., as part of the brake sensor system146), and/or other sensor types.

One or more of the controller(s)136may receive inputs (e.g., represented by input data) from an instrument cluster132of the vehicle100and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display134, an audible annunciator, a loudspeaker, and/or via other components of the vehicle100. The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the High Definition (“HD”) map122ofFIG.1C), location data (e.g., the vehicle's100location, 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)136, etc. For example, the HMI display134may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit34B in two miles, etc.).

The vehicle100further includes a network interface124which may use one or more wireless antenna(s)126and/or modem(s) to communicate over one or more networks. For example, the network interface124may 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)126may 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.1Bis an example of camera locations and fields of view for the example autonomous vehicle100ofFIG.1A, 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 vehicle100.

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)170that 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.1B, there may be any number (including zero) of wide-view cameras170on the vehicle100. In addition, any number of long-range camera(s)198(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)198may also be used for object detection and classification, as well as basic object tracking.

Any number of stereo cameras168may also be included in a front-facing configuration. In at least one embodiment, one or more of stereo camera(s)168may 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)168may 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)168may 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 vehicle100(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)174(e.g., four surround cameras174as illustrated inFIG.1B) may be positioned to on the vehicle100. The surround camera(s)174may include wide-view camera(s)170, 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)174(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 vehicle100(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)198, stereo camera(s)168), infrared camera(s)172, etc.), as described herein.

Each of the components, features, and systems of the vehicle100inFIG.1Care illustrated as being connected via bus102. The bus102may include a Controller Area Network (CAN) data interface (alternatively referred to herein as a “CAN bus”). A CAN may be a network inside the vehicle100used to aid in control of various features and functionality of the vehicle100, 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 bus102is 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 bus102, this is not intended to be limiting. For example, there may be any number of busses102, 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 busses102may be used to perform different functions, and/or may be used for redundancy. For example, a first bus102may be used for collision avoidance functionality and a second bus102may be used for actuation control. In any example, each bus102may communicate with any of the components of the vehicle100, and two or more busses102may communicate with the same components. In some examples, each SoC104, each controller136, and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle100), and may be connected to a common bus, such the CAN bus. The CAN bus, FlexRay, or Ethernet communication interfaces included in one or more vehicle components that are communicatively coupled via the communications interfaces may alternately be referred to as an in-vehicle network.

The vehicle100may include one or more controller(s)136, such as those described herein with respect toFIG.1A. The controller(s)136may be used for a variety of functions. The controller(s)136may be coupled to any of the various other components and systems of the vehicle100, and may be used for control of the vehicle100, artificial intelligence of the vehicle100, infotainment for the vehicle100, and/or the like.

The vehicle100may include a system(s) on a chip (SoC)104. The SoC104may include CPU(s)106, GPU(s)108, processor(s)110, cache(s)112, accelerator(s)114, data store(s)116, and/or other components and features not illustrated. The SoC(s)104may be used to control the vehicle100in a variety of platforms and systems. For example, the SoC(s)104may be combined in a system (e.g., the system of the vehicle100) with an HD map122which may obtain map refreshes and/or updates via a network interface124from one or more servers (e.g., server(s)178ofFIG.1D).

The CPU(s)106may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s)106may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s)106may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s)106may include four dual-core clusters where each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s)106(e.g., the CCPLEX) may be configured to support simultaneous cluster operation enabling any combination of the clusters of the CPU(s)106to be active at any given time.

The GPU(s)108may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s)108may be programmable and may be efficient for parallel workloads. The GPU(s)108, in some examples, may use an enhanced tensor instruction set. The GPU(s)108may 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)108may include at least eight streaming microprocessors. The GPU(s)108may use compute application programming interface(s) (API(s)). In addition, the GPU(s)108may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA's CUDA).

The GPU(s)108may 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)108to access the CPU(s)106page tables directly. In such examples, when the GPU(s)108memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s)106. In response, the CPU(s)106may look in its page tables for the virtual-to-physical mapping for the address and transmits the translation back to the GPU(s)108. As such, unified memory technology may allow a single unified virtual address space for memory of both the CPU(s)106and the GPU(s)108, thereby simplifying the GPU(s)108programming and porting of applications to the GPU(s)108.

In addition, the GPU(s)108may include an access counter that may keep track of the frequency of access of the GPU(s)108to 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)104may include any number of cache(s)112, including those described herein. For example, the cache(s)112may include an L3 cache that is available to both the CPU(s)106and the GPU(s)108(e.g., that is connected both the CPU(s)106and the GPU(s)108). The cache(s)112may 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)104may 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 vehicle100—such as processing DNNs. In addition, the SoC(s)104may 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)106and/or GPU(s)108.

The SoC(s)104may include one or more accelerators114(e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s)104may 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)108and to off-load some of the tasks of the GPU(s)108(e.g., to free up more cycles of the GPU(s)108for performing other tasks). As an example, the accelerator(s)114may 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)108, and by using an inference accelerator, for example, a designer may target either the DLA(s) or the GPU(s)108for 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)108and/or other accelerator(s)114.

The SoC(s)104may include data store(s)116(e.g., memory). The data store(s)116may be on-chip memory of the SoC(s)104, which may store neural networks to be executed on the GPU and/or the DLA. In some examples, the data store(s)116may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s)112may comprise L2 or L3 cache(s)112. Reference to the data store(s)116may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s)114, as described herein.

The SoC(s)104may include one or more processor(s)110(e.g., embedded processors). The processor(s)110may 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)104boot 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)104thermals and temperature sensors, and/or management of the SoC(s)104power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s)104may use the ring-oscillators to detect temperatures of the CPU(s)106, GPU(s)108, and/or accelerator(s)114. 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)104into a lower power state and/or put the vehicle100into a chauffeur to safe stop mode (e.g., bring the vehicle100to a safe stop).

The processor(s)110may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management.

The processor(s)110may 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)108is not required to continuously render new surfaces. Even when the GPU(s)108is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s)108to improve performance and responsiveness.

The SoC(s)104may further include a broad range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and/or other devices. The SoC(s)104may be used to process data from cameras (e.g., connected over Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s)164, RADAR sensor(s)160, etc. that may be connected over Ethernet), data from bus102(e.g., speed of vehicle100, steering wheel position, etc.), data from GNSS sensor(s)158(e.g., connected over Ethernet or CAN bus). The SoC(s)104may 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)106from routine data management tasks.

The SoC(s)104may 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)104may be faster, more reliable, and even more energy-efficient and space-efficient than conventional systems. For example, the accelerator(s)114, when combined with the CPU(s)106, the GPU(s)108, and the data store(s)116, 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 vehicle100. 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)104provide for security against theft and/or carjacking.

The vehicle may include a CPU(s)118(e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s)104via a high-speed interconnect (e.g., PCIe). The CPU(s)118may include an X86 processor, for example. The CPU(s)118may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADAS sensors and the SoC(s)104, and/or monitoring the status and health of the controller(s)136and/or infotainment SoC130, for example.

The vehicle100may include a GPU(s)120(e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s)104via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s)120may 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 vehicle100.

The vehicle100may further include the network interface124which may include one or more wireless antennas126(e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface124may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s)178and/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 vehicle100information about vehicles in proximity to the vehicle100(e.g., vehicles in front of, on the side of, and/or behind the vehicle100). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle100.

The vehicle100may further include data store(s)128which may include off-chip (e.g., off the SoC(s)104) storage. The data store(s)128may 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 vehicle100may further include GNSS sensor(s)158. The GNSS sensor(s)158(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)158may be used, including, for example and without limitation, a GPS using a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle100may further include RADAR sensor(s)160. The RADAR sensor(s)160may be used by the vehicle100for long-range vehicle detection, even in darkness and/or severe weather conditions. RADAR functional safety levels may be ASIL B. The RADAR sensor(s)160may use the CAN and/or the bus102(e.g., to transmit data generated by the RADAR sensor(s)160) 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)160may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The vehicle100may further include ultrasonic sensor(s)162. The ultrasonic sensor(s)162, which may be positioned at the front, back, and/or the sides of the vehicle100, may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensor(s)162may be used, and different ultrasonic sensor(s)162may be used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s)162may operate at functional safety levels of ASIL B.

The vehicle100may include LIDAR sensor(s)164. The LIDAR sensor(s)164may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s)164may be functional safety level ASIL B. In some examples, the vehicle100may include multiple LIDAR sensors164(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)166. The IMU sensor(s)166may be located at a center of the rear axle of the vehicle100, in some examples. The IMU sensor(s)166may 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)166may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s)166may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s)166may 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)166may enable the vehicle100to 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)166. In some examples, the IMU sensor(s)166and the GNSS sensor(s)158may be combined in a single integrated unit.

The vehicle may include microphone(s)196placed in and/or around the vehicle100. The microphone(s)196may 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)168, wide-view camera(s)170, infrared camera(s)172, surround camera(s)174, long-range and/or mid-range camera(s)198, and/or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle100. The types of cameras used depends on the embodiments and requirements for the vehicle100, and any combination of camera types may be used to provide the necessary coverage around the vehicle100. 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.1AandFIG.1B.

The vehicle100may further include vibration sensor(s)142. The vibration sensor(s)142may 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 sensors142are 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 vehicle100may include an ADAS system138. The ADAS system138may include a SoC, in some examples. The ADAS system138may 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)160, LIDAR sensor(s)164, 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 vehicle100and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle100to 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 vehicle100if the vehicle100starts to exit the lane.

RCTW systems may provide visual, audible, and/or tactile notification when an object is detected outside the rear-camera range when the vehicle100is backing up. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. RCTW systems may use one or more rear-facing RADAR sensor(s)160, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component.

The vehicle100may further include the infotainment SoC130(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 SoC130may 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 vehicle100. For example, the infotainment SoC130may include 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 display134, 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 SoC130may 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 system138, 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 SoC130may include GPU functionality. The infotainment SoC130may communicate over the bus102(e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle100. In some examples, the infotainment SoC130may 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)136(e.g., the primary and/or backup computers of the vehicle100) fail. In such an example, the infotainment SoC130may put the vehicle100into a chauffeur to safe stop mode, as described herein.

The vehicle100may further include an instrument cluster132(e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster132may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster132may 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 SoC130and the instrument cluster132. In other words, the instrument cluster132may be included as part of the infotainment SoC130, or vice versa.

FIG.1Dis a system diagram for communication between cloud-based server(s) and the example autonomous vehicle100ofFIG.1A, in accordance with some embodiments of the present disclosure. The system176may include server(s)178, network(s)190, and vehicles, including the vehicle100. The server(s)178may include a plurality of GPUs184(A)-184(H) (collectively referred to herein as GPUs184), PCIe switches182(A)-182(H) (collectively referred to herein as PCIe switches182), and/or CPUs180(A)-180(B) (collectively referred to herein as CPUs180). The GPUs184, the CPUs180, and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces188developed by NVIDIA and/or PCIe connections186. In some examples, the GPUs184are connected via NVLink and/or NVSwitch SoC and the GPUs184and the PCIe switches182are connected via PCIe interconnects. Although eight GPUs184, two CPUs180, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s)178may include any number of GPUs184, CPUs180, and/or PCIe switches. For example, the server(s)178may each include eight, sixteen, thirty-two, and/or more GPUs184.

The server(s)178may receive, over the network(s)190and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work. The server(s)178may transmit, over the network(s)190and to the vehicles, neural networks192, updated neural networks192, and/or map information194, including information regarding traffic and road conditions. The updates to the map information194may include updates for the HD map122, such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks192, the updated neural networks192, and/or the map information194may 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)178and/or other servers).

In some examples, the server(s)178may 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)178may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s)184, such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s)178may include deep learning infrastructure that use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s)178may 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 vehicle100. For example, the deep-learning infrastructure may receive periodic updates from the vehicle100, such as a sequence of images and/or objects that the vehicle100has 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 vehicle100and, if the results do not match and the infrastructure concludes that the AI in the vehicle100is malfunctioning, the server(s)178may transmit a signal to the vehicle100instructing a fail-safe computer of the vehicle100to assume control, notify the passengers, and complete a safe parking maneuver.

For inferencing, the server(s)178may include the GPU(s)184and 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

FIG.2is a schematic illustration of a suspension system that connects a cabin210to the chassis220of a vehicle200, in accordance with some embodiments of the present disclosure. The vehicle200may include similar components to those included in the vehicle100, described above. While the vehicle100may be illustrated, in some embodiments, as a passenger vehicle (e.g., a car, sedan, etc.), the vehicle200is a commercial vehicle such as a truck, and may include additional components that may not be included in typical passenger sedans. For example, the vehicle200may include additional suspension components located between the cabin and chassis of the vehicle. It will be appreciated that the vehicle200includes all of those components included in the vehicle100unless explicitly disclosed otherwise or clearly contradicted by context.

In some example embodiments, the suspension system can include one or more damping springs230(A) and230(B) (collectively referred to as “springs230”). The damping springs230(A) and230(B) can include, for example, air springs, coil springs, leaf springs, coil over springs, lowering springs, and the like. One or more suspension sensors240(A) or240(B) (collectively referred to as “sensors240”) can be installed adjacent the damping springs230. In some embodiments, each damping spring230includes at least one corresponding sensor240.

According to some embodiments, the suspension sensors240can measure the weight distribution inside the cabin210. For example, by measuring a deflection (or position) of each spring230(A) and230(B) compared to an unloaded compression, the feedback from the sensors240(A) and240(B) can be converted into a center of mass of the cabin. The difference between the calculated center of mass and an “unoccupied” center of mass can indicate a weight distribution inside the cabin. During operation, the signals received from the suspension sensors240(A) and240(B) can be continuously monitored by a monitoring and damping spring control unit (e.g., a safety module) against a baseline value. The baseline value is also referred to as the reference value, and can be the reading of the suspension sensor when the cabin210is empty. For example, when a driver enters the cabin210, the overall weight of the cabin210increases. Consequently, the reading of the suspension sensor240increases with respect to the baseline value or reference value. If the measured value exceeds the reference value by more than a threshold amount, it can be determined that there is a person present in the cabin210; otherwise, it can be determined that there is no person present in the cabin210. Detection results can be sent to the autonomous driving system (e.g., L4-System). If it is detected that a person is present in the cabin210, the autonomous driving system can stop the vehicle and exit an autonomous driving mode or prevent an autonomous driving mode from being engaged. If no person is present in the cabin210, the autonomous driving system can continue driving the vehicle or be engaged.

As used herein, comparison of a measured value with a baseline value can refer to a comparison of a single value from a particular sensor or comparison of an aggregate value from a plurality of sensors. For example, in the case with more than one sensor, a measured value can refer to a sum or average value calculated based on the measured values from each of the sensors. Similarly, the baseline value can refer to an expected value of the sum or average value when the cabin is “unoccupied.” In some cases, the sum can be a weighted sum to account for the different positions of the different springs230relative to the center of mass of the cabin210. Any well-known technique for converting multiple sensor measurements into a single aggregate value can be utilized in order to compare with an aggregate baseline value.

FIG.3shows a simplified block diagram of an electronic system300for detecting in-cabin occupancy in autonomous systems or applications, in accordance with some example embodiments of the present disclosure. It will be appreciated that some of the components of the system300may be included in the system shown inFIG.1C. For example, the vehicle controller302and ECUs306may be part of controllers136and/or SoCs104. The bus304may be similar to the bus102. Similarly, components of the system300may be added to the system shown inFIG.1Cfor implementing the techniques described herein in the vehicle100.

In an embodiment, the system300includes a vehicle controller302, an electronic control unit (ECU)306, a system bus304, a safety module310, and one or more sensors312. The vehicle controller302can include one or more processors and a memory storing instructions. The ECU306can be associated with an engine or other system of the vehicle(s)100/200. The vehicle controller302can communicate with the ECU306via the system bus304, which can be a controller area network (CAN) bus or other communication network included in a vehicle. Although not shown explicitly, the system300can also include additional ECUs for other systems in the vehicle. For example, each of the engine, transmission, instrument panel, and/or navigation or entertainment system may be associated with a separate and distinct ECU that communicates over the bus304.

In an embodiment, the vehicle controller302operates as a master controller of the system300and sends instructions to each of the other ECUs that cause the various ECUs to adjust parameters of the vehicle(s)100/200. For example, the vehicle controller302can send instructions to the engine ECU306to adjust a throttle body to accelerate or decelerate the vehicle(s)100/200. The vehicle controller302can also send instructions to a steering mechanism ECU to turn the vehicle(s)100/200left or right.

The vehicle controller302can operate the vehicle(s)100/200in either a manual driving mode or one or more autonomous driving modes. For example, a first autonomous driving mode may control the vehicle's speed without requiring accelerometer or braking inputs from the driver (sometimes referred to as cruise control), while still requiring a driver to steer the vehicle and avoid collisions with other objects or vehicles. A second autonomous driving mode may control most or all aspects of a vehicles trajectory without inputs from a driver. This can be referred to as full autonomous mode. In some cases, full autonomous mode is only available when a driver is not present in the cabin210of the vehicle(s)100/200.

The system300also includes a safety module310and a number of sensors312(1),312(2), . . . ,312(N) connected to the safety module310. The sensors312can include cameras or image sensors, range finders (e.g., LiDAR, depth sensors, etc.), encoders, limit switches, pressure transducers, and the like. The safety module310can accept inputs or signals from the sensors312and generate feedback for the vehicle controller302. In some cases, the safety module310can also be configured to override certain systems in the case of detected hazards. For example, the safety module310may be configured to prevent the acceleration of the vehicle(s)100/200when a sensor312detects an object in the path of the vehicle. In some cases, the safety module310may override the vehicle controller302in order to prevent operation of the vehicle autonomously in an unsafe driving condition.

In some embodiments, the safety module310is incorporated into the vehicle controller302, either as a dedicated hardware module or implemented as one or more software modules that are executed within the context of the main autonomous driving algorithm. In an embodiment, the safety module310can be implemented as a separate and distinct ECU of the vehicle(s)100/200. Although not shown explicitly, in some embodiments, additional sensors may be connected to the vehicle controller302, the ECU306, and/or other components of the system300.

In an embodiment, the sensors312can include the one or more sensors240associated with the cabin suspension system and configured to provide feedback about the weight distribution of the cabin210. The safety module310may receive the signals from the one or more sensors240and determine, based on the signals, whether there is a human or other large object in the cabin210. For example, the safety module310can determine a position or compression of each spring230based on the signals from the sensors240and compare the position or compression of each spring230to a baseline value to determine whether the cabin is occupied. If the difference between the measured value and the baseline value is more than a threshold value, then the safety module310can send a signal to the vehicle controller302that indicates the cabin210is occupied and not safe for full autonomous driving mode. If the vehicle(s)100/200is not moving, then the vehicle controller302can prevent the vehicle(s)100/200from entering the full autonomous driving mode. However, if the vehicle(s)100/200is in motion and operating in the full autonomous driving mode, then the vehicle controller302can be commanded to safely exit the full autonomous driving mode. For example, the vehicle controller302can be commanded to safely move the vehicle(s)100/200to a shoulder of the road and reduce a speed of the vehicle(s)100/200to zero before exiting the full autonomous driving mode. In some cases, the vehicle controller302can also alert any passenger in the cabin that the full autonomous driving mode is disengaged due to cabin occupancy, either through the instrument panel of the vehicle, via audio alert (e.g., through a speaker system in the cabin), through visual signals (e.g., through a navigation or entertainment system, or via one or more light indicators), or any combination of the above.

It will be appreciated that the vehicle(s)100/200may be configured to allow a user to reset the baseline value for each of the one or more sensors240. For example, the cabin210may be loaded with a normal load of gear or other objects that are fully secured within the cabin210. Once the cabin210is fully loaded, the current position of the springs230(and/or any other damping mechanism) may be measured by each of the one or more sensors240, and a new baseline value is established for the sensors240. Thus, the safety mechanism to prevent operation of the full autonomous driving mode can be calibrated to a particular load in the cabin210. This may be helpful when the cabin210can be fitted out with various options, such as different seats, different accessories (e.g., radio equipment), sleeping quarters, and the like as well as allow for certain items (e.g., luggage, gear, cargo, etc.) to be placed in the cabin210without causing the unoccupied weight of the cabin210to exceed the original baseline value.

In some embodiments, additional cargo may be associated with an RFID tag that indicates a weight of the cargo. When the cargo is placed in the cabin, an RFID reader can detect the presence of the cargo and adjust the baseline value based on a parameter read from the RFID tag. Thus, various containers of specified weight can be loaded into the cabin while still allowing for detection of a human in the cabin by simply adjusting the baseline value accordingly.

In some embodiments, the signals from the sensors240can be used to determine a position of a person within the cabin210. For example, some commercial trucks include sleeping compartments behind the driver and/or passenger seats. If the position and number of springs230in the cabin suspension system are sufficient, the feedback from the sensors240may enable a position of a passenger in the cabin210to be determined. For example, a position of a human can be determined based on a detected change in the center of mass of the cabin, caused by someone moving around the vehicle. In some embodiments, if the passenger is determined to be in the sleeping quarters, then the vehicle(s)100/200is capable of being placed into the full autonomous driving mode. However, once the passenger moves out of the sleeping quarters, the vehicle(s)100/200is caused to disengage the full autonomous driving mode. In other embodiments, full autonomous driving mode cannot be engaged at all if any passenger is in the cabin210, even if the passenger is located in the sleeping quarters.

In an embodiment, detection of a person may be improved by monitoring a time series pattern (e.g., an acceleration pattern) during initial acceleration of the vehicle after entering a full autonomous driving mode. For example, when the full autonomous driving mode is engaged, the vehicle may begin a route by accelerating from a first velocity (e.g., 0 miles per hour) up to a second velocity (e.g., 15 miles per hour). Accelerometers included in the vehicle can measure the acceleration of the cabin210during this initial acceleration and compare the time series pattern of acceleration values to a reference time series pattern of acceleration values associated with an unoccupied cabin210. If the time series patterns do not match, then the safety module302can bring the vehicle to a stop and disengage the full autonomous driving mode. For example, a first reference acceleration pattern can be recorded when the cabin is empty, and a second reference acceleration pattern can be recorded when a person is inside the cabin. If the measured acceleration pattern is different from the first reference pattern, and is more similar to the second reference pattern, it can be determined that there is a person in the cabin. Additionally, the safety module302can provide accuracy information about the occupancy detection, and can request a retrial of the start from standstill procedure in case of uncertainty.

In an embodiment, it will be appreciated that the cabin suspension system encompasses components that are separate and distinct from the main suspension system of the vehicle(s)100/200connecting the chassis of the vehicle to the wheels, axles, etc. Thus, the cabin suspension system measures only a deflection and/or weight of the cabin including the passenger compartment, instrument panel, steering components, and/or any other components of the vehicle(s)100/200that are not directly supported by the chassis, but are connected through the cabin suspension system. Nevertheless, in operation, forces from the main vehicle suspension system can be transmitted to the cabin through the cabin suspension system. For example, while the vehicle is in motion, the contour of the road can cause the chassis220to move up towards the cabin210, which can cause the springs230in the cabin suspension system to compress. As the cabin210accelerates upward away from the chassis220, a deflection of the springs230is relaxed. The cabin suspension system can also include dampers, such as shocks, that reduce an oscillation of the cabin210relative to the chassis220caused by forces such as that described above. It will be appreciated that instantaneous loads from the motion of the vehicle and/or the contour of the road may cause a temporary deflection of the cabin210that is consistent with adding weight to the cabin. In some embodiments, the signals of the sensors230may be filtered to remove high frequency components of the signals caused by external disturbances due to road conditions while the vehicle is in motion. For example, by filtering the signal via a moving average or some other type of low pass filter, the signals can reflect the weight of the cabin and any objects in the cabin as opposed to dynamic forces on the cabin caused by up or down motion of the wheels while the vehicle is in motion.

Similarly, in some embodiments, the baseline values for the signals can be adjusted based on a speed of the vehicle or any other form of feedback. For example, vehicles may be designed to be as aerodynamic as possible to reduce the energy required to move from one point to another. However, the shape of any vehicle moving through air can cause aerodynamic forces to be applied to the cabin210and/or chassis220. Some of these forces can be down-forces that cause the springs230to compress more under motion than compared to a baseline position when the vehicle is at rest. In an embodiment, the baseline values can be adjusted based on a speed/velocity of the vehicle in order to account for such downforces. The amount that the baseline value is adjusted can be modeled based on a theoretical load on the cabin under fluid dynamics of the airflow over the cabin, or can be measured and established through experimentation. In yet another embodiment, a wind speed can be measured by one or more sensors attached to the cabin210or chassis220of the vehicle(s)100/200, and the baseline values can be adjusted based on the measured windspeed. In some embodiments, to rule out effects of wind on the acceleration behavior, a real-time weather forecast can be obtained to detect current weather conditions and adjust the baseline value based on local windspeed in the weather forecast.

In an embodiment, the safety module310can capture a signal of each of the sensors240over a period of time. As the vehicle is in motion during the full autonomous driving mode, the sensor data associated with the vehicle is captured. The sensor data can include position data of each spring in the cabin suspension system as well as other parameters related to the vehicle (such as speed, acceleration, throttle position, brake pressure, etc.). Because the spring constant of each spring and the damping coefficient of any shocks, as well as a location in the cabin suspension system of each of the aforementioned components, are known or can be measured for a particular vehicle, a model of the system can be used to calculate an estimate of the weight (e.g., mass) of the cabin within a spring/damper system. While it may be difficult to capture all of the current road conditions that could affect these signals in real-time, the output of the model may be sufficient to give an acceptable level of accuracy for the safety system. Instead of simply comparing a measured value of the sensors240to a baseline value, the model can use the measured values of the sensors240as well as other feedback information to estimate the weight of the cabin based on the model.

In yet another embodiment, the safety module310can incorporate an artificial intelligence (AI) algorithm. The AI algorithm can be used to, e.g., capture a number of samples of the position of each of the springs230for a short duration (e.g., 5 seconds, 30 seconds, 2 minutes, etc.), while simultaneously measuring other parameters associated with the vehicle, such as speed, acceleration, steering position (e.g., an angle of each wheel), location of the vehicle (e.g., GPS position), road grade (e.g., elevation angle of the road), braking input, and the like. Data for each of the parameters discussed above may be captured over a period of time, including multiple samples for each parameter at discrete time steps distributed throughout the period of time. In one embodiment, a location of the vehicle, such as coordinates collected through a GPS sensor, can be used to estimate a characterization of the average road conditions in the area. Road conditions within a particular area can be relatively flat, hilly, or even in poor maintenance such as having lots of potholes. Roads could also be gravel or dirt roads as compared to paved roads. Thus, the expected road conditions in a given area could be used to adjust the expectations of the dynamic motion of the cabin. In other embodiments, road conditions within a particular area can be measured using additional sensors rather than by comparing the location of the vehicle to a database of mapped road conditions, such as by measuring feedback from sensors on the springs of the main suspension system of the vehicle (e.g., the main coil-over or air springs attached to the front wheels of the vehicle) in addition to the springs of the cabin suspension system.

The AI algorithm can process all of these inputs to estimate a weight of the cabin. The AI algorithm may be trained initially by placing objects of various weights in the cabin and driving the vehicle in normal driving conditions while capturing training data. By varying the driving conditions (e.g., location, speed, traffic congestion, etc.) across a range of training data, using the known weight of the objects as the target output of the AI algorithm, enough independent data can be captured for a specific vehicle to determine an estimate of the weight included in the cabin based on the measured parameters from the one or more sensors and/or the vehicle controller302and/or ECU306. For example, the safety module310may be configured to capture a speed of the vehicle by communicating with the vehicle controller302, ECU306, or some other speed sensor312located on the vehicle and communicatively coupled to the safety module310via the bus304. By periodically sampling the vehicle speed, the safety module310can track this parameter and combine a vector of speed samples with one or more vectors of position signal data from each of the sensors240. The AI algorithm can then recognize patterns in the sensor data that correspond to different load conditions in the cabin210, thereby recognizing whether the cabin is occupied or not occupied.

In other embodiments, the safety module310can incorporate a classical algorithm used for time series analysis. For example, a series of data samples related to spring position over a period of time can be analyzed using a Fourier transformation algorithm such as Fast Fourier Transform (FFT). The FFT algorithm can transform time domain data into frequency domain data, which than then be analyzed by additional algorithms to determine whether the cabin is occupied. It will be appreciated that the safety module310can incorporate other types of classical or machine learning algorithms such as, but not limited to, Gaussian (probabilistic) ML algorithms or Kalman Filters (e.g., Interacting Multiple Model Kalman Filter, IMM-KF).

In yet other embodiments, additional sensors can be installed in the cabin to augment the sensors240associated with the springs230. For example, motion sensors can be installed in the cabin to monitor whether there is a motion, e.g., caused by a person moving in the cabin. Cameras and/or depth sensors (e.g., LiDAR) can also be installed in the cabin to monitor the presence of any person. By combining the different types of sensors, the detection system can be more reliable. For example, determining whether the person is present in the cabin can be based on a logical combination of the comparison of the measured value with the baseline value OR whether the motion sensor(s), camera(s), or LiDAR sensor(s) separately detect the presence of the person. Thus, even if the sensors of the cabin suspension system do not detect that a person is in the cabin of the vehicle, the motion sensors, cameras, and/or LiDAR sensors could detect the presence of a person and override the measured value from the sensors of the cabin suspension system. It will be appreciated that it may be safer to have multiple redundant systems that can independently prevent operation of the vehicle in a full autonomous driving mode. In some embodiments, certain safety systems may be overridden to prevent a faulty sensor from keeping the vehicle out of a fully autonomous driving mode, as long as one system is operational.

According to some embodiments, to increase the sensitivity of the measurement, the safety module302can change the resistance of the springs230. For example, if air springs are used in the cabin suspension system, the air volume or pressure in the spring can be decreased, so that the springs can be more sensitive to a weight change in the cabin310.

FIG.4illustrates a flowchart of a method400for detecting in-cabin occupancy for autonomous systems or applications, in accordance with some example embodiments of the present disclosure. Each block of method400, 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. In addition, method400is described, by way of example, with respect to the systems ofFIGS.1A-3. 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. Furthermore, persons of ordinary skill in the art will understand that any system that performs method400is within the scope and spirit of embodiments of the present disclosure.

At402, a value of a suspension characteristic of the cabin suspension system of the autonomous machine is determined. In an embodiment, the value is derived from signals from the one or more sensors associated with springs of the cabin suspension system of a commercial vehicle. Each sensor may be attached to a spring or other component of the cabin suspension system to measure a deflection, position, force, pressure, or the like of the spring. In an embodiment, the springs are air springs and the sensor is a pressure sensor configured to measure a change in pressure in a corresponding air spring. The sensors can be any type of sensor capable of measuring a characteristic of the spring, including position sensors, strain/force sensors, pressure transducers, or the like.

In some embodiments, the value of the suspension characteristic is determined by processing sensor data of the one or more suspension sensors in accordance with a mass-spring-damper model. A value of the mass of the cabin can be determined by solving a system of equations based on the mass-spring-damper model.

In other embodiments, the value of the suspension characteristic is determined by processing sensor data of the one or more suspension sensors using an artificial intelligence algorithm to estimate the value. The artificial intelligence algorithm can be implemented as a deep neural network (e.g., a convolutional neural network (CNN), a recurrent neural network (RNN), or the like). An input to the deep neural network includes, for each of the one or more suspension sensors, a vector of samples of the suspension characteristic measured by the suspension sensor over a period of time. The input can also include vectors of other parameters over time, such as but not limited to, a speed of the autonomous machine, an acceleration of the autonomous machine, or a steering position of the autonomous machine. The deep neural network can be processed by one or more GPU(s)108, CPU(s)106, or any other processors capable of implementing the artificial intelligence algorithm, including remote processors based in the cloud or accessible over a network.

At404, a difference is computed based at least on comparing the value to a reference value. The difference may then be compared to a threshold value. If the difference is less than the threshold value, then, at406, the autonomous machine is allowed to continue operating in a full autonomous driving mode. However, if the difference is greater than (exceeds) the threshold value, then, at408, the autonomous machine is disengaged or prevented from operating in the full autonomous driving mode. It will be appreciated that disengaging the fully autonomous driving mode can include preventing the autonomous machine from entering the fully autonomous driving mode and/or safely bringing the autonomous machine to a stop before exiting the fully autonomous driving mode. It will be appreciated that as used herein, the terms “less than” or “greater than” can be inclusive or exclusive of the compared value (e.g., can refer to “less than or equal to” or “greater than or equal to”, respectively).

In some embodiments, the difference can refer to a result of subtracting the reference value from the value. In other embodiments, the difference can refer to the result of any function that computes a result based on a comparison of the value to the reference value. It will be appreciated that the function does not have to be linear and, in some cases, could be configured to output a binary value (e.g., 0 or 1). In some cases, the values of multiple suspension characteristics can be compared to one or more references values to generate the result. For example, the function can take measured values from multiple springs of the suspension and compare each of the values to a set of reference values to compute a difference that is an aggregate of the comparison of each value to a corresponding reference value.

Alternatively, at404, the value of the suspension characteristic could be analyzed, by one or both of an AI algorithm or classical algorithm, to generate a classification output that indicates a classification of whether the cabin is occupied. If the classification output indicates that the cabin is not occupied, then the autonomous machine is allowed to continue operating in a full autonomous driving mode at406. However, if the classification output indicates that the cabin is occupied, then the autonomous machine is disengaged or prevented from operating in the full autonomous driving mode at408.

FIG.5is a block diagram of an example computing device(s)500suitable for use in implementing some embodiments of the present disclosure. Computing device500may include an interconnect system502that directly or indirectly couples the following devices: memory504, one or more central processing units (CPUs)506, one or more graphics processing units (GPUs)508, a communication interface510, input/output (I/O) ports512, input/output components514, a power supply516, one or more presentation components518(e.g., display(s)), and one or more logic units520. In at least one embodiment, the computing device(s)500may 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 GPUs508may comprise one or more vGPUs, one or more of the CPUs506may comprise one or more vCPUs, and/or one or more of the logic units520may comprise one or more virtual logic units. As such, a computing device(s)500may include discrete components (e.g., a full GPU dedicated to the computing device500), virtual components (e.g., a portion of a GPU dedicated to the computing device500), or a combination thereof.

Although the various blocks ofFIG.5are shown as connected via the interconnect system502with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component518, such as a display device, may be considered an I/O component514(e.g., if the display is a touch screen). As another example, the CPUs506and/or GPUs508may include memory (e.g., the memory504may be representative of a storage device in addition to the memory of the GPUs508, the CPUs506, and/or other components). In other words, the computing device ofFIG.5is 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.5.

The interconnect system502may represent one or more links or busses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system502may 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 CPU506may be directly connected to the memory504. Further, the CPU506may be directly connected to the GPU508. Where there is direct, or point-to-point connection between components, the interconnect system502may include a PCIe link to carry out the connection. In these examples, a PCI bus need not be included in the computing device500.

The CPU(s)506may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device500to perform one or more of the methods and/or processes described herein. The CPU(s)506may 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)506may include any type of processor, and may include different types of processors depending on the type of computing device500implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device500, 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 device500may include one or more CPUs506in addition to one or more microprocessors or supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s)506, the GPU(s)508may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device500to perform one or more of the methods and/or processes described herein. One or more of the GPU(s)508may be an integrated GPU (e.g., with one or more of the CPU(s)506and/or one or more of the GPU(s)508may be a discrete GPU. In embodiments, one or more of the GPU(s)508may be a coprocessor of one or more of the CPU(s)506. The GPU(s)508may be used by the computing device500to render graphics (e.g., 3D graphics) or perform general purpose computations. For example, the GPU(s)508may be used for General-Purpose computing on GPUs (GPGPU). The GPU(s)508may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s)508may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s)506received via a host interface). The GPU(s)508may 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 memory504. The GPU(s)508may 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 GPU508may 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)506and/or the GPU(s)508, the logic unit(s)520may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device500to perform one or more of the methods and/or processes described herein. In embodiments, the CPU(s)506, the GPU(s)508, and/or the logic unit(s)520may discretely or jointly perform any combination of the methods, processes and/or portions thereof. One or more of the logic units520may be part of and/or integrated in one or more of the CPU(s)506and/or the GPU(s)508and/or one or more of the logic units520may be discrete components or otherwise external to the CPU(s)506and/or the GPU(s)508. In embodiments, one or more of the logic units520may be a coprocessor of one or more of the CPU(s)506and/or one or more of the GPU(s)508.

The communication interface510may include one or more receivers, transmitters, and/or transceivers that enable the computing device500to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface510may 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)520and/or communication interface510may include one or more data processing units (DPUs) to transmit data received over a network and/or through interconnect system502directly to (e.g., a memory of) one or more GPU(s)508.

The I/O ports512may enable the computing device500to be logically coupled to other devices including the I/O components514, the presentation component(s)518, and/or other components, some of which may be built in to (e.g., integrated in) the computing device500. Illustrative I/O components514include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components514may 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 device500. The computing device500may 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 device500may 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 device500to render immersive augmented reality or virtual reality.

The power supply516may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply516may provide power to the computing device500to enable the components of the computing device500to operate.

The presentation component(s)518may 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)518may receive data from other components (e.g., the GPU(s)508, the CPU(s)506, DPUs, etc.), and output the data (e.g., as an image, video, sound, etc.).

FIG.6illustrates an example data center600that may be used in at least one embodiments of the present disclosure. The data center600may include a data center infrastructure layer610, a framework layer620, a software layer630, and/or an application layer640.

As shown inFIG.6, the data center infrastructure layer610may include a resource orchestrator612, grouped computing resources614, and node computing resources (“node C.R.s”)616(1)-616(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s616(1)-616(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.s616(1)-616(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.s616(1)-6161(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.s616(1)-616(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources614may include separate groupings of node C.R.s616housed 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.s616within grouped computing resources614may 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.s616including 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 orchestrator612may configure or otherwise control one or more node C.R.s616(1)-616(N) and/or grouped computing resources614. In at least one embodiment, resource orchestrator612may include a software design infrastructure (SDI) management entity for the data center600. The resource orchestrator612may include hardware, software, or some combination thereof.

In at least one embodiment, as shown inFIG.6, framework layer620may include a job scheduler633, a configuration manager634, a resource manager636, and/or a distributed file system638. The framework layer620may include a framework to support software632of software layer630and/or one or more application(s)642of application layer640. The software632or application(s)642may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. The framework layer620may 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 system638for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler633may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center600. The configuration manager634may be capable of configuring different layers such as software layer630and framework layer620including Spark and distributed file system638for supporting large-scale data processing. The resource manager636may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system638and job scheduler633. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource614at data center infrastructure layer610. The resource manager636may coordinate with resource orchestrator612to manage these mapped or allocated computing resources.

In at least one embodiment, software632included in software layer630may include software used by at least portions of node C.R.s616(1)-616(N), grouped computing resources614, and/or distributed file system638of framework layer620. 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)642included in application layer640may include one or more types of applications used by at least portions of node C.R.s616(1)-616(N), grouped computing resources614, and/or distributed file system638of framework layer620. 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 manager634, resource manager636, and resource orchestrator612may 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 center600from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

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)500ofFIG.5—e.g., each device may include similar components, features, and/or functionality of the computing device(s)500. In addition, where backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may be included as part of a data center600, an example of which is described in more detail herein with respect toFIG.6.