Machine learning-based seat belt detection and usage recognition using fiducial markings

Abstract

To provide systems and methods for machine learning based seatbelt position detection and classification.SOLUTION: A number of fiducial markers are placed on a vehicle seatbelt. A camera or other sensor is placed within the vehicle to capture images or other data relating to positions of the fiducial markers when the seatbelt is in use. One or more models such as machine learning models may then determine spatial positions of the fiducial markers from the captured image information, and determine a worn state of the seatbelt. Specifically, the system may determine whether the seatbelt is being worn in one or more improper states, such as not being worn or being worn in an unsafe or dangerous manner, and if so, the system may alert the vehicle to take a corrective action. In this manner, the system provides constant and real-time monitoring of seatbelts to improve seatbelt usage and safety.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to machine learning-based seat belt detection and usage recognition using fiducial markings. [Background technology]

[0002] FIELD OF THE DISCLOSURE Embodiments of the present disclosure relate generally to machine learning systems, and more particularly to machine learning-based seat belt position detection. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] U.S. Patent Application No. 16 / 101,232 Summary of the Invention [Problem to be solved by the invention]

[0004] Seat belts play an important role in traffic safety. Seat belt use is estimated by some to have reduced the number of serious injuries and fatalities related to vehicle crashes by approximately half, thereby saving tens of thousands of lives in the United States alone. Despite the safety benefits and legal mandate, seat belt use is not widespread. Whether inadvertently or intentionally, many drivers and passengers do not wear seat belts. In other instances, drivers and passengers may wear seat belts but do so improperly. Improper seat belt use not only reduces the safety benefits of seat belts, it can actually result in the wearer being injured by the seat belt.

[0005] Accordingly, systems and methods are described herein for a machine learning-based system that detects the position of a wearer's seat belt and determines whether the seat belt is properly fastened. More specifically, the system may determine one of a number of specific seat belt conditions, including, for example, whether the seat belt is properly fastened, incorrectly fastened under the wearer's shoulder, not fastened, or improperly fastened behind the wearer's body. These and many other conditions may be detected.

[0006] In some embodiments of the present disclosure, the system uses a seat belt with several fiducial markers thereon, and sensors such as cameras mounted to capture images or other fiducial marker position and orientation information. The system may also include an illumination source, if necessary, to enhance the visibility of the fiducial markers. To that end, the sensor may be any type of sensor suitable for determining fiducial marker position and / or orientation information, such as a visible light sensor or camera, one or more infrared or near-infrared sensors, or the like. Thus, the illumination source, if any, may be a visible light wavelength illumination source, an infrared or near-infrared light source, or the like.

[0007] To determine seat belt fastening status, a camera or other sensor can be positioned, for example, behind the driver's seat or on the dashboard, and pointed toward the driver's face to view the seat belt of the passenger of interest. The sensor can then capture images of the fiducial markers. One or more models, such as machine learning models, can then determine the location of the fiducial markers from the captured image information and determine the seat belt fastening status. As described above, several different fastening statuses can be determined. If the seat belt is improperly fastened, for example, not fastened or fastened in an unsafe or dangerous manner, the system can alert the vehicle to take corrective action, for example, alerting the driver or other passengers via an audible or visual alarm, braking the vehicle, shutting off the ignition or otherwise turning off one or more vehicle systems, engaging an autopilot system, or the like. Any such vehicle action is contemplated. [Means for solving the problem]

[0008] Additionally, various machine learning models are contemplated. As an example, one machine learning model can take sensor data (e.g., image data) as input and generate fiducial marker locations as output, while another machine learning model can take this location information as input and generate classifications of the fiducial marker locations. These classifications may correspond to the aforementioned seat belt status. In this manner, systems according to embodiments of the present disclosure can automatically determine a passenger's seat belt status from images of the passenger and his or her seat belt and provide warnings regarding improper seat belt use. The warnings may be intended for various audiences, including the passenger, the vehicle, or a remote server that logs seat belt use.

[0009] Embodiments of the present disclosure also contemplate determining other conditions and information from sensor data. For example, the size or position of various vehicle components, such as passenger seats, child seats or boosters, and vehicle compartment contents, may be determined. The vehicle may then, for example, adapt airbags or direct air vents accordingly, or alert to an improperly positioned seat. As another example, because seat belt contours outline the passenger's body, the passenger's size may be determined from the seat belt contour, i.e., the position of various fiducial markers. This can indirectly inform the system of the passenger's weight, which can then inform the vehicle, for example, whether or how an airbag should be deployed in the event of a crash. As a further example, the position or posture of a passenger may be determined by the position of the fiducial markers, as the positions of the fiducial markers adapt to passengers moving around in their seats. This may, for example, inform the vehicle about passengers who shift position in their seats to the point where their seat belts are positioned improperly or dangerously on their bodies, passengers who unbuckle their seat belts while the vehicle is still moving, drivers who are looking back and therefore not looking at the road, and the like. The vehicle can then take appropriate action, such as, for example, warning passengers to return to proper position in their seats, warning the driver to pay attention to the road, and the like.

[0010] In some embodiments, it may be desirable for the systems of the present disclosure to be able to effectively deal with occlusion of at least some of the fiducial markers, since instances of occlusion often exist in typical use. For example, a passenger's hair may hang over a seat belt, thereby preventing a sensor from recognizing the fiducial markers, loose clothing or hand gestures may similarly obstruct the fiducial markers, and the like. Accordingly, machine learning models of embodiments of the present disclosure may be trained using images in which at least some of the fiducial markers are partially or fully occluded. That is, a portion of the training set of images input to machine learning models of embodiments of the present disclosure may be images in which at least some of the fiducial markers are partially or fully occluded. In this way, machine learning models of embodiments of the present disclosure are trained to deal with fiducial marker occlusion, thereby providing more reliable classification results that remain accurate in a variety of real-world situations.

[0011] The foregoing and other objects and advantages of the present disclosure will become apparent when considered in light of the following detailed description taken in conjunction with the accompanying drawings in which like reference characters refer to like parts and in which: [Brief explanation of the drawings]

[0012] [Figure 1A] FIG. 1 conceptually illustrates a system for seat belt position detection according to an embodiment of the present disclosure. [Figure 1B] FIG. 1 conceptually illustrates the detection of one improper seat belt configuration according to an embodiment of the present disclosure. [Figure 1C] FIG. 10 conceptually illustrates detection of another improper seat belt configuration according to an embodiment of the present disclosure. [Figure 1D] 10A-10C conceptually illustrate further improper seat belt configuration detection according to an embodiment of the present disclosure. [Figure 2A] 1 is a block diagram representation of a seat belt position classification process according to an embodiment of the present disclosure. [Figure 2B]10 is a graph illustrating detected fiducial marker positions according to an embodiment of the present disclosure. [Figure 2C] 2C is a graph of the position points of FIG. 2B to illustrate seat belt position determination according to an embodiment of the present disclosure. [Figure 3] 1 is a block diagram representation of a seat belt position detection system according to an embodiment of the present disclosure. [Figure 4A] 1 is an illustration of an exemplary autonomous vehicle, according to some embodiments of the present disclosure. [Figure 4B] 4B is an illustration of camera positions and fields of view for the example autonomous vehicle of FIG. 4A, in accordance with some embodiments of the present disclosure. [Figure 4C] FIG. 4B is a block diagram of an example system architecture of the example autonomous vehicle of FIG. 4A, in accordance with some embodiments of the present disclosure. [Figure 4D] FIG. 4B is a system diagram of communication between a cloud-based server and the example autonomous vehicle of FIG. 4A, according to some embodiments of the present disclosure. [Figure 5] FIG. 1 is a block diagram of an exemplary computing device suitable for use in implementing some embodiments of the present disclosure. [Figure 6] 1 is a flow diagram illustrating process steps for determining seat belt position according to an embodiment of the present disclosure. [Figure 7] FIG. 1 conceptually illustrates detection and correction of improper seat belt configurations according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] In one embodiment, the present disclosure relates to a machine learning system and a method for machine learning-based seat belt position detection and classification. Several fiducial markers are placed on a vehicle seat belt. A camera or other sensor is placed in the vehicle to capture images or other data related to the positions of the fiducial markers when the seat belt is in use. One or more models, such as machine learning models, can then determine the spatial positions of the fiducial markers from the captured image information and determine the seat belt's fastening status. Specifically, the system can determine whether the seat belt is being fastened in one or more improper states, e.g., not fastened or fastened in an unsafe or dangerous manner, and if so, the system can provide a warning so that, for example, the vehicle or passenger can take corrective action. In this manner, the system performs regular and real-time monitoring of the seat belt to improve seat belt usage and safety.

[0014] 1A conceptually illustrates a system for seat belt position detection according to an embodiment of the present disclosure. Here, the vehicle seat belt position detection system 10 includes a seat belt 20 and a lap belt 30, with several fiducial markers 40 etched or otherwise positioned on the seat belt 20. In this embodiment, the fiducial markers 40 are positioned such that they are both above and below the shoulders of the driver 50 when the seat belt 20 is properly fastened. In this manner, the positions of the fiducial markers 40 outline the upper body of the driver 50, thereby conveying, to some extent, their position, as well as positional information of portions of the driver's 50 upper body.

[0015] The vehicle of system 10 includes a seat having a backrest portion 60 and a seat rest portion 70, and a buckle 80 for backing seat belt 20 and lap belt 30. Seats 60, 70 help maintain driver 50 in a comfortable and correct position for driving. Seat belt 20 extends through pulley 120 and into a retraction mechanism (not shown) that helps keep seat belt 20 taut. The retraction mechanism may be a known retraction mechanism that mechanically applies and maintains tension to seat belt 20, or may be a retraction mechanism that applies tension to seat belt 20 in response to a determined position of fiducial marker 40, as described further below. Pulley 120 is secured to anchor 130, which is connected to column 140 or another portion of the vehicle.

[0016] Sensor 100, which may be a visible light camera or any other sensor suitable for detecting the position of fiducial marker 40, is positioned on or within dashboard 90 to capture images of seat belt 20 and fiducial marker 40. Optional illumination source 110 may also be positioned on or within dashboard 90 to illuminate fiducial marker 40 and then facilitate image or other position data capture by sensor 100. As previously mentioned, sensor 100 may be any sensor capable of capturing sufficient information to determine position information for fiducial marker 40, such as a visible light sensor or camera, or a sensor that detects any other wavelength of light. For example, sensor 100 may be an infrared or near-infrared sensor, and illumination source 110 may be configured to emit light at a corresponding wavelength.

[0017] Fiducial markers 40 may be any markings capable of conveying position and, optionally, orientation information. To that end, they may be any shape with different, yet regular, spatial orientations or appearances that change with position. Examples may include, but are not limited to, letters of the alphabet or any other complex markings, such as AprilTags or the like.

[0018] The system 10 can be used to detect any seat belt 20 position, including those indicative of improper seat belt 20 use. FIGS. 1B-1D conceptually illustrate exemplary seat belt 20 positions that the system 10 can detect and flag as improper use. In FIG. 1B, the driver 50 is not fastening his or her seat belt 20. That is, FIG. 1B illustrates an instance in which the seat belt 20 is not being used. This may occur, for example, when the driver 50 forgets to fasten his or her seat belt 20, or when the seat belt 20 is not being used intentionally, perhaps using a known seat belt simulator 120 that engages the buckle 80 and prevents any vehicle alarms. Here, the seat belt 20 is hanging down rather than across the driver 50, thereby causing the fiducial markers 40 to characteristically appear in a generally vertical configuration extending along the left side of the driver 50 and downward from the pulley 120.

[0019] 1C shows another example in which a driver 50 is using a seat belt 20, but the seat belt 20 is improperly stretched under his or her left arm instead of over his or her left shoulder. In this example, the seat belt 20 extends across the driver 50, and the fiducial markers 40 are positioned lower than they would be if the seat belt 20 were properly worn higher up on the driver's 50's shoulder. Furthermore, because some of the fiducial markers 40 are blocked from the view of the sensor 100 by the driver's 50's left shoulder and upper arm, the markers 40 appear to the sensor 100 as a lower, angled band of markers 40 and a discontinuous upper, more vertically oriented band of markers 40.

[0020] 1D shows a further example where the driver 50 is using the seat belt 20, but the seat belt 20 is improperly stretched behind the back of the driver 50 rather than along the front of the driver 50. In this example, the lower fiducial marker 40 is blocked from detection by the body of the driver 50. Thus, the marker 40 appears to the sensor 100 simply as a single, short, angled strip of marker 40 stretching over the shoulder of the driver 50.

[0021] 2A is a block diagram illustrating the operation of system 10. Sensor 100 captures images or other information from which the positions of fiducial markers 40 can be derived and transmits this information to fiducial position determination module 210, a computer-executable module that executes instructions to determine the spatial locations of fiducial markers 40 appearing in the images or other information from sensor 100. The fiducial positions are then input to pattern recognition module 220, a computer-executable module that executes instructions to classify the spatial locations of fiducial markers 40 into predetermined seat belt 20 use configurations. That is, pattern recognition module 220 outputs a seat belt 20 configuration when seat belt 20 is being worn by driver 50. As previously mentioned, the particular seat belt use configuration may be any configuration. In one embodiment, configurations in which the pattern recognition module 220 may classify seat belt usage may include: 1) a properly worn seat belt ( FIG. 1A ), i.e., shoulder straps extending over the shoulders of the driver 50 and extending diagonally downward across the wearer's torso to the buckle 80; 2) an off- ( FIG. 1B ) or not-worn seat belt (including the use of known seat belt warning stoppers and the like); 3) a properly buckled seat belt that extends under the shoulder and left arm of the driver 50 rather than properly over the wearer's shoulder ( FIG. 1C ); and 4) a properly buckled seat belt that extends behind the driver 50 rather than properly extending across the front of the driver 50 ( FIG. 1D ). These four cases may be referred to herein as “case on,” “case off,” “case under,” and “case back,” respectively.

[0022] The reference position determination module 210 may determine the spatial location of the fiducial marker 40 from the input sensor 100 data in any manner. In some exemplary embodiments, the module 210 may use known computer vision-based detection processes to detect objects such as the fiducial marker 40 without using a neural network, such as edge detection methods, feature retrieval methods, probabilistic face models, graph matching, histogram of oriented gradients (HOG) fed into a classifier such as a support vector machine, HaarCascade classifier, and the like. The spatial location of the detected fiducial marker 40 within the image may then be determined or estimated in any manner, such as via tabulated locations corresponding to each pixel location and determined according to an estimate of the distance between the sensor 100 and a location on the model or simulated driver. In some other exemplary embodiments, the module 210 may use neural network-based object recognition methods, such as those using deep neural network (DNN) object recognition and location determination schemes, as well as others. For example, a DNN can be trained to recognize fiducial markers 40 and their locations using a training set of labeled images of fiducial markers and their spatial location information. Training of such a DNN can be performed in a known manner.

[0023] The pattern recognition module 220 may classify the spatial location information of the various fiducial markers 40 into the aforementioned seat belt position cases in any manner. In some exemplary embodiments, the module 220 may use one or more machine-learning-based classification methods. FIG. 2B is a graph illustrating detected fiducial marker positions determined through experimentation. As shown, the seat belt fiducial markers 40 typically fall within well-defined spatial regions depending on the use case occurring. For example, in case ON, when a seat belt 20 is properly fastened, its fiducial marker 40 generally lies along a diagonal band extending from the upper left of the driver 50 to his or her lower right, as indicated by the diagonal cluster of points extending from the center to the lower left of the graph in FIG. 2B. Similarly, in case OFF, the seat belt 20 is not fastened and therefore hangs to the left of the driver 50, as indicated by the downward-extending band of points extending from the center to the lower right of FIG. 2B.

[0024] In this example, the fiducial markers 40 are located within well-defined clusters based on their usage. It can be observed that seat belt use cases can be determined according to any method of classifying defined clusters of points in space. For example, since each cluster corresponds to a particular case, a k-nearest neighbor model can be trained to determine whether a fiducial marker 40 point belongs to one of the clusters shown in FIG. 2B . Alternatively, any classification-based model can be used to determine the spatial region corresponding to each of the four cases in FIG. 2B , and then the input fiducial marker 40 positions can be classified by the pattern recognition module 220 according to which spatial region they fall within. Similarly, any regression-based model can be used to characterize the points of each cluster corresponding to each particular case, and then the input fiducial marker 40 positions can be classified according to which characterized cluster they are closest to.

[0025] As a further alternative, fiducial marker 40 locations can be classified according to a characteristic distribution or range of their location points. FIG. 2C shows one such example. Here, the same fiducial marker 40 location points shown in FIG. 2B are represented differently, with the x-axis of FIG. 2C representing the location where the tag was detected in the x-direction of the input image, and the y-axis representing the number of fiducial markers 40 found at that x-location. As shown, each seat belt use case has a characteristic distribution of its fiducial marker 40 locations when depicted in this manner. For example, case "ON" has a distinctive distribution, with clusters of marker 40 locations roughly falling within the ranges of 620-820 and 1250-1600. Similarly, case "OFF" has clusters of marker 40 locations roughly falling within the range of 1000-1180, as well as a more diffuse distribution falling within the range of 1400-1780. Thus, marker 40 locations can be classified according to their distribution when depicted as shown in FIG. 2C. Thus, for example, sensor 100 may capture an image of seat belt 20 and fiducial marker 40, and fiducial position determination module 210 may determine the position of fiducial marker 40 captured in the image. Pattern recognition module 220 may then determine which distribution in FIG. 2C is most similar to the corresponding distribution of marker 40 positions in the input image and classify the image, i.e., the current state of seat belt 20, accordingly. Similarity may be measured in any manner. For example, the mean or median of each distribution, or any other characteristic value thereof, may be determined and compared to the mean / median or other characteristic value of each distribution shown in FIG. 2C. The closest comparison may then be selected as the classification, or corresponding case, for that input image.

[0026] 3 is a block diagram representation of one exemplary seat belt position detection system according to an embodiment of the present disclosure. Here, a computing device 300, which may be any electronic computing device including processing circuitry capable of implementing the seat belt position detection operations of an embodiment of the present disclosure, is in electronic communication with both a camera 310 and a seat belt responsive system 320. During operation, the camera 310 captures and transmits an image of a subject to the computing device 300, which then implements modules 210 and 220 of FIG. 2A while determining a corresponding seat belt use case (e.g., case on, case off, case under, or case back) from the image of the camera 310. The computing device 300 transmits the case to the seat belt responsive system 320, which takes action or performs one or more operations in response.

[0027] The seat belt responsive system 320 may be any system capable of performing one or more actions based on the seat belt use cases it receives from the computing device 300. Any configuration of the camera 310, computing device 300, and gaze-assisted system 320 is contemplated. As one example, the seat belt responsive system 320 may be an autonomous vehicle capable of determining and reacting to the seat belt use status of a driver or another passenger. In this example, the camera 310 and computing device 300 may be located within the vehicle, while the seat belt responsive system 320 may represent the vehicle itself. The camera 310 may correspond to the camera 441 in FIGS. 4A and 4C described below and may be located anywhere within the vehicle that allows it a view of the driver or passenger. Thus, the camera 310 may capture images of the driver and his or her seat belt and transmit them to the computing device 300, which calculates the spatial location of the visible fiducial marker 40 and determines the corresponding seat belt use status of the driver. The use status may then be transmitted to another software module, which determines, for example, what action the vehicle can take in response. For example, the vehicle may determine that the driver's seat belt is not fastened (case off) or is improperly fastened (case under or case back) and may initiate any type of action in response. Such action may include any type of alert issued to the driver (e.g., a visual or audible alert, an alert on a head-up display, or the like), autopilot initiation, braking or turning action, ignition shutoff, or any other action. Computing device 300 may correspond to computing device 500 of FIG. 5 , described below, and may be located within the vehicle of seat belt responsive system 320 as a local processor, or may be a remote processor that receives images from camera 310 and wirelessly transmits seat belt use cases or status to the vehicle of seat belt responsive system 320.

[0028] 4A is a diagram of an example autonomous vehicle 400 according to some embodiments of the present disclosure. Autonomous vehicle 400 (alternatively referred to herein as “vehicle 400”) may include, but is not limited to, a passenger vehicle, such as a car, a truck, a bus, a first responder vehicle, a shuttle, an electric or moped, a motorcycle, a fire engine, a police vehicle, an ambulance, a boat, a construction vehicle, a submarine, a drone, and / or another type of vehicle (e.g., unmanned and / or carrying one or more passengers). Autonomous vehicles are generally described in terms of levels of automation as defined by the National Highway Traffic Safety Administration (NHTSA), a division of the U.S. Department of Transportation, and the Society of Automotive Engineers (SAE) "Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles" (Standard No. J3016-201806, published June 15, 2018; Standard No. J3016-201609, published September 30, 2016; and previous and future versions of this standard). Mobile vehicle 400 may be capable of functionality according to one or more of levels 3 through 5 of autonomous driving. For example, mobile vehicle 400 may be capable of conditional automation (Level 3), highly automated (Level 4), and / or fully automated (Level 5), depending on the embodiment.

[0029] The mobile vehicle 400 may include components such as a chassis, body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the mobile vehicle. The mobile vehicle 400 may include a propulsion system 450, such as an internal combustion engine, a hybrid power plant, a fully electric engine, and / or another propulsion system type. The propulsion system 450 may be connected to a drive train of the mobile vehicle 400, which may include a transmission, to enable propulsion of the mobile vehicle 400. The propulsion system 450 may be controlled in response to receiving a signal from a throttle / accelerator 452.

[0030] A steering system 454, which may include a steering wheel, may be used to steer the vehicle 400 (e.g., along a desired course or route) when the propulsion system 450 is operating (e.g., when the vehicle is moving). The steering system 454 may receive signals from a steering actuator 456. A steering wheel may be optional for fully automated (Level 5) functionality.

[0031] Brake sensor system 446 may be used to operate vehicle brakes in response to receiving signals from brake actuators 448 and / or brake sensors.

[0032] A controller 436, which may include one or more CPUs, system on chip (SoC) 404 (FIG. 4C), and / or GPUs, can provide signals (e.g., representations of commands) to one or more components and / or systems of the vehicle 400. For example, the controller can send signals to operate vehicle brakes via one or more brake actuators 448, to operate a steering system 454 via one or more steering actuators 456, and / or to operate a propulsion system 450 via one or more throttle / accelerators 452. The controller 436 may include one or more on-board (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals and output operational commands (e.g., signals representing commands) to enable rhythmic driving and / or assist a driver in operating the vehicle 400. The controllers 436 may include a first controller 436 for autonomous driving functions, a second controller 436 for functional safety functions, a third controller 436 for artificial intelligence functions (e.g., computer vision), a fourth controller 436 for infotainment functions, a fifth controller 436 for redundancy in emergency situations, and / or other controllers. In some instances, a single controller 436 may handle two or more of the foregoing functions, and two or more controllers 436 may handle a single function and / or any combination thereof.

[0033] Controller 436 may provide signals to control one or more components and / or systems of vehicle 400 in response to sensor data (e.g., sensor inputs) received from one or more sensors. Sensor data may be received from, for example, and without limitation, global navigation satellite system sensors 458 (e.g., global positioning system sensors), RADAR sensors 460, ultrasonic sensors 462, LIDAR sensors 464, inertial measurement unit (IMU) sensors 466 (e.g., accelerometers, gyroscopes, magnetic compasses, magnetometers, etc.), microphones 496, stereo cameras 468, wide-view cameras 470 (e.g., fisheye cameras), infrared cameras 472, surround cameras 474 (e.g., 360-degree cameras), long-range and / or medium-range cameras 498, speed sensors 444 (e.g., for measuring the speed of the moving vehicle 400), vibration sensors 442, steering sensors 440, brake sensors 446 (e.g., as part of a brake sensor system 446), and / or other sensor types.

[0034] One or more of the controllers 436 may receive input (e.g., represented by input data) from the instrument cluster 432 of the vehicle 400 and provide output (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display 434, an audible annunciator, a loudspeaker, and / or other components of the vehicle 400. The output may include information such as vehicle velocity, speed, time, map data (e.g., HD map 422 of FIG. 4C ), position data (e.g., the location of the vehicle 400 on a map, etc.), direction, the locations of other vehicles (e.g., an occupancy grid), information about objects and object situations as perceived by the controller 436, etc. For example, the HMI display 434 may display information regarding the presence of one or more objects (e.g., road signs, warning signs, traffic light changes, etc.) and / or a driving maneuver that the moving vehicle has performed, is performing, or will perform (e.g., changing lanes now, taking exit 34B in 3.22 km (2 miles), etc.).

[0035] The mobile vehicle 400 further includes a network interface 424 capable of communicating over one or more networks using one or more wireless antennas 426 and / or a modem. For example, the network interface 424 may be capable of communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The wireless antenna 426 may also enable communication between objects in the environment (e.g., mobile vehicles, mobile devices, etc.) using local area networks such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc., and / or low power wide-area networks (LPWANs) such as LoRaWAN, SigFox, etc.

[0036] 4B is an illustration of camera positions and fields of view of the exemplary autonomous vehicle 400 of FIG. 4A, according to some embodiments of the present disclosure. The cameras and their respective fields of view are one illustrative example 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 positions on the vehicle 400.

[0037] The camera type may include, but is not limited to, a digital camera adapted for use with components and / or systems of the vehicle 400. The camera may be capable of operating at Automotive Safety Integrity Level (ASIL) B and / or another ASIL. The camera type may be capable of any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc., depending on the embodiment. The camera may be capable of using a rolling shutter, a global shutter, another type of shutter, or a combination thereof. In some instances, the color filter array may include a red clear clear clear (RCCC) color filter array, a red clear clear blue (RCCB) color filter array, a red blue green clear (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensor (RGGB) color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In some embodiments, clear pixel cameras, such as cameras with RCCC, RCCB, and / or RBGC color filter arrays, may be used in an effort to increase light sensitivity.

[0038] In some instances, one or more of the cameras may be used to perform advanced driver assistance system (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a multi-function mono camera may be installed to provide functions including lane departure warning, traffic sign assist, and intelligent headlamp control. One or more of the cameras (e.g., all cameras) may simultaneously record and provide image data (e.g., video).

[0039] One or more of the cameras may be mounted in a mounting part, such as a custom-designed (e.g., 3D printed) part, to filter out stray light and reflections from within the vehicle (e.g., reflections from the dashboard reflected in the windshield mirror) that may interfere with the camera's image data capture ability. Referring to a side mirror mounting part, the side mirror part may be custom 3D printed so that the camera mounting plate fits the shape of the side mirror. In some instances, the camera may be integrated into the side mirror. For side view cameras, the camera may also be integrated into four posts at each corner of the cabin.

[0040] A camera (e.g., a forward-facing camera) with a field of view that includes a portion of the environment in front of the vehicle 400 may be used for surround view to aid in identifying a forward path and obstacles and, with the assistance of one or more controllers 436 and / or control SoCs, to provide information essential for generating an occupancy grid and / or determining a preferred vehicle path. Forward-facing cameras may be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. Forward-facing cameras may also be used for ADAS functions and systems, including other functions such as lane departure warning (LDW), autonomous cruise control (ACC), and / or traffic sign recognition.

[0041] Various cameras can be used in a forward-facing configuration, including, for example, a monocular camera platform including a complementary metal oxide semiconductor (CMOS) color imager. Another example can be a wide-view camera 470 that can be used to understand objects that come into view from the periphery (e.g., pedestrians, crossing traffic, or bicycles). While only one wide-view camera is shown in FIG. 4B, any number of wide-view cameras 470 can be present in the vehicle 400. Additionally, a long-range camera 498 (e.g., a long-view stereo camera pair) can be used for depth-based object detection, particularly for objects for which a neural network has not yet been trained. The long-range camera 498 can also be used for object detection and classification, as well as basic object tracking.

[0042] One or more stereo cameras 468 may also be included in the forward-facing configuration. The stereo camera 468 may include an integrated control unit with an extensible processing unit, which may provide programmable logic (e.g., FPGA) and a multi-core microprocessor with a CAN or Ethernet interface integrated on a single chip. Such a unit may be used to generate a 3D map of the vehicle's environment, including distance estimates for all points in the image. An alternative stereo camera 468 may include a compact stereo vision sensor, which may include two camera lenses (one on the left and one on the right) and an image processing chip that can measure the distance from the vehicle to objects of interest and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. Other types of stereo cameras 468 may be used in addition to or instead of those described herein.

[0043] Cameras having a field of view that includes portions of the environment to the sides of the mobile vehicle 400 (e.g., side-view cameras) may be used for surround view, providing information used to create and update the occupancy grid and generate side-impact collision warnings. For example, surround cameras 474 (e.g., four surround cameras 474 as shown in FIG. 4B ) may be positioned around the mobile vehicle 400. The surround cameras 474 may include wide-view cameras 470, fisheye cameras, 360-degree cameras, and / or the like. For example, four fisheye cameras may be positioned at the front, rear, and sides of the mobile vehicle. In an alternative arrangement, the mobile vehicle may use three surround cameras 474 (e.g., left, right, and rear) and utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround view camera.

[0044] A camera having a field of view that includes the portion of the environment behind the moving vehicle 400 (e.g., a rearview camera) may be used for parking assistance, surround view, rear collision warning, and creating and updating an occupancy grid. As described herein, a wide variety of cameras may be used, including, but not limited to, cameras that are also suitable as forward-facing cameras (e.g., long-range and / or medium-range camera 498, stereo camera 468, infrared camera 472, etc.).

[0045] A camera having a field of view that includes portions of the interior or cabin of vehicle 400 may be used to monitor one or more conditions of the driver, passengers, or objects within the cabin. Any type of camera may be used, including, but not limited to, cabin camera 441, which may be any of the types described herein and may be located anywhere on or within vehicle 400 and provide a view of the cabin or interior of vehicle 400. For example, cabin camera 441 may be located in or on some portion of vehicle 400's dashboard, rearview mirror, sideview mirror, seat, or door and may be oriented to capture images of any driver, passenger, or any other object or portion of vehicle 400.

[0046] FIG. 4C is a block diagram of an example system architecture for the example autonomous vehicle 400 of FIG. 4A , in accordance with some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are merely illustrative. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, groupings of functions, etc.) may be used in addition to or instead of those shown, and some elements may be omitted altogether. Furthermore, many of the elements described herein are functional entities that may be implemented as separate 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 implemented by hardware, firmware, and / or software. For example, various functions may be implemented by a processor executing instructions stored in a memory.

[0047] Each of the components, features, and systems of the mobile vehicle 400 in FIG. 4C is shown connected via a bus 402. The bus 402 may include a controller area network (CAN) data interface (alternatively referred to as a "CAN bus"). The CAN may be a network within the mobile vehicle 400 used to help control various features and functions of the mobile vehicle 400, such as braking, acceleration, braking, steering, windshield wiper operation, etc. The CAN bus may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). The CAN bus may be read to determine steering angle, ground speed, engine revolutions per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compliant.

[0048] Although the bus 402 is described herein as being a CAN bus, this is not intended to be limiting. For example, FlexRay and / or Ethernet may be used in addition to or as an alternative to a CAN bus. Additionally, although a single line is used to represent the bus 402, this is not intended to be limiting. There may be any number of buses 402, which may include, for example, one or more CAN buses, one or more FlexRay buses, one or more Ethernet buses, and / or one or more other types of buses using different protocols. In some instances, two or more buses 402 may be used to perform different functions and / or for redundancy. For example, a first bus 402 may be used for collision avoidance functions, and a second bus 402 may be used for actuation control. In any instance, each bus 402 may communicate with any of the components of the vehicle 400, and two or more buses 402 may communicate with the same component. In some instances, each SoC 404, each controller 436, and / or each computer in the vehicle may have access to the same input data (e.g., input from sensors in the vehicle 400) and may be connected to a common bus, such as a CAN bus.

[0049] Mobile vehicle 400 may include one or more controllers 436, such as those described herein with respect to FIG. 4A. Controller 436 may be used for a variety of functions. Controller 436 may be coupled to any of a variety of other components and systems of mobile vehicle 400 and may be used for control of mobile vehicle 400, artificial intelligence of mobile vehicle 400, infotainment for mobile vehicle 400, and / or the like.

[0050] The vehicle 400 may include a system-on-chip (SoC) 404. The SoC 404 may include a CPU 406, a GPU 408, a processor 410, a cache 412, an accelerator 414, a data store 416, and / or other components and features not shown. The SoC 404 may be used to control the vehicle 400 in a variety of platforms and systems. For example, the SoC 404 may be coupled in a system (e.g., the system of the vehicle 400) with an HD map 422 that can obtain map refreshes and / or updates via a network interface 424 from one or more servers (e.g., server 478 of FIG. 4D ).

[0051] CPU 406 may include a CPU cluster or CPU complex (alternatively referred to as a "CCPLEX"). CPU 406 may include multiple cores and / or L2 caches. For example, in some embodiments, CPU 406 may include eight cores in a coherent multiprocessor configuration. In some embodiments, CPU 406 may include four dual-core clusters, each with its own dedicated L2 cache (e.g., a 2M L2 cache). CPU 406 (e.g., a CCPLEX) may be configured to support simultaneous cluster operation, allowing any combination of clusters of CPUs 406 to be active at any given time.

[0052] The CPU 406 may implement power management capabilities including one or more of the following features: individual hardware blocks may be automatically clock gated when idle to conserve dynamic power; each core clock may be gated when the core is not actively executing instructions by executing a WFI / WFE instruction; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. The CPU 406 may further implement an enhanced algorithm for managing power states, where allowable power states and expected wake-up times are specified and hardware / microcode determines the best power state for entering the cores, clusters, and CCPLEX. The processing cores may support simplified power state entry sequences in software with work offloaded to microcode.

[0053] GPU 408 may include an integrated GPU (alternatively referred to herein as an "iGPU"). GPU 408 may be programmable and efficient for parallel workloads. In some instances, GPU 408 may use an enhanced tensor instruction set. GPU 408 may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB of storage capacity) and two or more of the streaming microprocessors may share a cache (e.g., an L2 cache with 512 KB of storage capacity). In some embodiments, GPU 408 may include at least eight streaming microprocessors. GPU 408 may use a computer-based application programming interface (API). Additionally, GPU 408 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0054] The GPU 408 may be power-optimized for best performance in automotive and embedded use cases. For example, the GPU 408 may be fabricated on FinFET (Fin field-effect transistor) chips. However, this is not intended to be limiting, and the GPU 408 may be fabricated using other semiconductor fabrication processes. Each streaming microprocessor may incorporate several mixed-precision processing cores partitioned into multiple blocks. For example, and without limitation, 64 PF32 cores and 32 PF64 cores may be partitioned into four processing blocks. In such an example, each processing block may be assigned 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA tensor cores for deep learning matrix operations, an L0 instruction cache, a warp scheduler, a dispatch unit, and / or a 64KB register file. Additionally, the streaming microprocessor may include independent parallel integer and floating-point data paths to provide efficient execution of workloads with a mix of computational and addressing operations. Streaming microprocessors may include independent thread scheduling capabilities to allow finer-grained synchronization and coordination among concurrent threads. Streaming microprocessors may include a combined L1 data cache and shared memory unit to improve performance while simplifying programming.

[0055] The GPU 408 may, in some instances, include high bandwidth memory (HBM) and / or a 16GB HBM2 memory subsystem to provide up to 900GB / s of peak memory bandwidth. In some instances, synchronous graphics random-access memory (SGRAM), such as graphics double data rate type five synchronous random-access memory (GDDR5), may be used in addition to or in place of the HBM memory.

[0056] The GPU 408 may include unified memory technology, including access counters, to enable more accurate movement of memory pages to the processors that access them most frequently, thereby improving the efficiency of storage areas shared between processors. In some instances, address translation service (ATS) support may be used to enable the GPU 408 to directly access the CPU 406 page tables. In such instances, when the GPU 408 memory management unit (MMU) experiences a miss, an address translation request may be sent to the CPU 406. In response, the CPU 406 may consult its page table for a virtual-to-real mapping of addresses and send the translation back to the GPU 408. As such, unified memory technology may enable a single unified virtual address space for both CPU 406 and GPU 408 memory, thereby simplifying GPU 408 programming and porting of applications to the GPU 408.

[0057] Additionally, GPU 408 may include access counters that can record the frequency of GPU 408's accesses to the memory of other processors. The access counters can help ensure that memory pages are moved to the physical memory of the processors that are accessing the pages most frequently.

[0058] The SoC 404 may include any number of caches 412, including those described herein. For example, the cache 412 may include an L3 cache available to both the CPU 406 and the GPU 408 (e.g., connected to both the CPU 406 and the GPU 408). The cache 412 may include a write-back cache that can record line state, 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 implementation, although smaller cache sizes may also be used.

[0059] The SoC 404 may include an arithmetic logic unit (ALU) that may be utilized in performing processing for any of various tasks or operations (e.g., processing DNNs) of the vehicle 400. Additionally, the SoC 404 may include a floating point unit (FPU) (or other math co-processor or math co-processor type) for performing mathematical operations within the system. For example, the SoC 104 may include one or more FPUs integrated as execution units within the CPU 406 and / or the GPU 408.

[0060] The SoC 404 may include one or more accelerators 414 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC 404 may include a hardware acceleration cluster, which 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 operations. The hardware acceleration cluster may be used to complement the GPU 408 and to offload some of the GPU 408's tasks (e.g., to free up more cycles for the GPU 408 to perform other tasks). As an example, the accelerator 414 may be used for target workloads that are sufficiently stable to be suitable for acceleration (e.g., perception, convolutional neural networks (CNNs), etc.). As used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and Faster RCNNs (e.g., as used for object detection).

[0061] The accelerator 414 (e.g., a hardware acceleration cluster) may include a deep learning accelerator (DLA). The DLA may include one or more tensor processing units (TPUs) that can be configured to provide an additional 10 trillion operations per second for deep learning applications and inference. The TPU may be an accelerator configured and optimized to perform image processing functions (e.g., CNN, RCNN, etc.). The DLA may also be optimized for a specific set of neural network types and floating-point operations, as well as inference. The DLA design can provide more performance per millimeter than a general-purpose GPU, significantly exceeding the performance of a CPU. The TPU can perform several functions, including, for example, single-instance convolution functions and post-processor functions, supporting INT8, INT16, and FP16 data types for both features and weights.

[0062] The DLA can quickly and efficiently run neural networks, particularly CNNs, on processed or unprocessed data for any of a variety of functions, including, but not limited to: CNNs for object identification and detection using data from camera sensors, CNNs for distance estimation using data from camera sensors, CNNs for emergency vehicle detection and identification using data from microphones, CNNs for face recognition and moving vehicle owner identification using data from camera sensors, and / or CNNs for security and / or safety related events.

[0063] The DLA can perform any function of the GPU 408, and by using an inference accelerator, for example, a designer can target either the DLA or the GPU 408 for any function. For example, a designer can focus on processing CNNs and floating-point operations on the DLA, and offload other functions to the GPU 408 and / or other accelerators 414.

[0064] The accelerator 414 (e.g., a hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (ADAS), autonomous driving, and / or augmented reality (AR) and / or virtual reality (VR) applications. The PVA may provide a balance between performance and flexibility. For example, each PVA may include, but is not limited to, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0065] The RISC cores may interact with an image sensor (e.g., an image sensor in any of the cameras described herein), an image signal processor, and / or the like. Each RISC core may include any amount of memory. The RISC cores may use any of several protocols, depending on the embodiment. In some instances, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and / or memory devices. For example, the RISC cores may include an instruction cache and / or tightly coupled RAM.

[0066] The DMA may enable components of the PVA to access system memory independent of the CPU 406. The DMA may support any number of features used to provide optimizations to the PVA, including, but not limited to, supporting multi-dimensional addressing and / or circular addressing. In some instances, the DMA may support up to six or more dimensions of addressing, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0067] A vector processor may be a programmable processor that can be designed to efficiently and flexibly execute computer vision algorithm programming and provide signal processing capabilities. In some instances, a PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, a DMA engine (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem may act as the PVA's primary processing engine and may include a vector processing unit (VPU), an instruction cache, and / or a vector memory (e.g., VMEM). The VPU core may include a digital signal processor, such as a single instruction, multiple data (SIMD), or very long instruction word (VLIW) digital signal processor. The combination of SIMD and VLIW can increase throughput and speed.

[0068] Each vector processor may include an instruction cache and may be coupled to dedicated memory. As a result, in some instances, each vector processor may be configured to execute independently of other vector processors. In other instances, the vector processors included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, multiple vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other instances, the vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on sequential images or portions of an image. In particular, any number of PVAs may be included in a hardware-accelerated cluster, and any number of vector processors may be included in each PVA. Additionally, the PVA may include additional error correcting code (ECC) memory to enhance overall system security.

[0069] The accelerator 414 (e.g., a hardware acceleration cluster) may include a computer vision network-on-chip and SRAM to provide high-bandwidth, low-latency SRAM for the accelerator 414. In some instances, the on-chip memory may include, for example, and without limitation, at least 4 MB of SRAM consisting of eight field-configurable memory blocks that may be accessible by both the PVA and DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA can access the memory through a backbone that provides the PVA and DLA with high-speed access to the memory. The backbone may include a computer vision network-on-chip that interconnects the PVA and DLA to the memory (e.g., using the APB).

[0070] The computer vision network-on-chip may include an interface that determines, prior to the transmission of any control signals, addresses, or data, that both the PVA and DLA provide ready and valid signals. Such an interface may provide separate phases and separate channels for transmitting control signals, addresses, and data, as well as burst-type communication for continuous data transfer. This type of interface may conform to the ISO 26262 or IEC 61508 standards, although other standards and protocols may also be used.

[0071] In some instances, SoC 404 may include a real-time ray tracing hardware accelerator, such as that described in U.S. patent application Ser. No. 16 / 101,232, filed Aug. 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the location and scale of objects (e.g., within a world model) to generate real-time visualization simulations for RADAR signal interpretation, for acoustic propagation synthesis and / or analysis, for SONAR system simulation, for general wave propagation simulation, for comparison to LIDAR data for localization and / or other functions, and / or other uses. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing-related operations.

[0072] The accelerator 414 (e.g., a hardware accelerator cluster) has diverse applications for autonomous driving. The PVA may be a programmable vision accelerator that can be used for critical processing stages in ADAS and autonomous vehicles. The capabilities of the PVA make it well suited to algorithmic domains that require predictable processing at low power and low latency. In other words, the PVA works well for semi-dense or dense regular computations on small data sets that require predictable execution times with low latency and low power. Therefore, because the PVA is efficient at object detection and integer computation, in the context of a platform for autonomous vehicles, the PVA is designed to run classic computer vision algorithms.

[0073] For example, according to one embodiment of the present technology, PVA is used to perform computer stereo vision. A semi-global matching-based algorithm may be used in some instances, but this is not intended to be limiting. Many applications for Level 3-5 autonomous driving require motion estimation / stereo matching on the fly (e.g., structure from motion, pedestrian recognition, lane detection, etc.). PVA can perform computer stereo vision functions with input from two monocular cameras.

[0074] In some instances, PVAs may be used to perform dense optical flow. For example, PVAs may be used to process raw RADAR data (e.g., using a 4D Fast Fourier Transform) to provide a processed RADAR signal before firing the next RADAR pulse. In other instances, PVAs are used for time of flight depth processing, for example, by processing raw time of flight data to provide processed time of flight data.

[0075] DLA can be used to implement any type of network to enhance control and driving safety, including, for example, a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability or as providing the relative "weight" of each detection compared to other detections. This confidence value allows the system to make further decisions regarding which detections should be considered true positives rather than false positives. For example, the system can set a confidence threshold and consider only detections above the threshold as true positives. In an automatic emergency braking (AEB) system, a false positive detection would cause a moving vehicle to automatically apply emergency braking, which is clearly undesirable. Therefore, only the most confident detections should be considered to trigger AEB. DLA can implement a neural network that regresses the confidence value. The neural network may receive as its input at least some subset of parameters, such as bounding box dimensions, a ground plane estimate obtained (e.g., from another subsystem), an inertial measurement unit (IMU) sensor 466 output that correlates with the vehicle 400 orientation, range, and 3D position estimate of the object obtained from the neural network and / or other sensors (e.g., a LIDAR sensor 464 or a RADAR sensor 460), and others.

[0076] The SoC 404 may include a data store 416 (e.g., memory). The data store 416 may be on-chip memory of the SoC 404 and may store neural networks to be executed by the GPU and / or DLA. In some instances, the data store 416 may have a capacity large enough to store multiple instances of the neural network for redundancy and safety. The data store 416 may comprise an L2 or L3 cache 412. References to the data store 416 may include references to memory associated with the GPU, DLA, and / or other accelerators 414, as described herein.

[0077] The SoC 404 may include one or more processors 410 (e.g., embedded processors). The processors 410 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management capabilities and related security enforcement. The boot and power management processor may be part of the SoC 404 boot sequence and may provide run-time power management services. The boot power and management processor may provide clock and voltage programming, assist with system low-power state transitions, manage the SoC 404 thermal and temperature sensors, and / or manage the SoC 404 power state. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to temperature, and the SoC 404 may use the ring oscillator to detect the temperature of the CPU 406, GPU 408, and / or accelerator 414. If the temperature is determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine, place the SoC 404 in a lower power state, and / or place the vehicle 400 in a Chauffeur safe shutdown mode (e.g., bring the vehicle 400 to a safe shutdown).

[0078] The processor 410 may further include a set of embedded processors that can perform the functions of an audio processing engine. The audio processing engine may be an audio subsystem that allows full hardware support for multi-channel audio through multiple interfaces and a wide and flexible range of audio I / O interfaces. In some instances, the audio processing engine is a dedicated processor core that includes a digital signal processor with dedicated RAM.

[0079] The processor 410 may further include an always-on processor engine that can provide the necessary hardware features to support low-power sensor management and wake use cases. The always-on processor engine may include a processor core, tightly coupled RAM, support peripherals (e.g., timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0080] The processor 410 may further include a safety cluster engine that includes a processor subsystem dedicated to handling safety management for automotive applications. The safety cluster engine may include two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.), and / or routing logic. In safety mode, the two or more cores may operate in lockstep mode and function as a single core with comparison logic to detect any differences between their operations.

[0081] The processor 410 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0082] The processor 410 may further include a high dynamic range signal processor, which may include an image signal processor, which is a hardware engine that is part of the camera processing pipeline.

[0083] The processor 410 may include a video image compositor, which may be a processing block (e.g., implemented in a microprocessor) that implements video post-processing functions required by the video playback application to produce the final image for the player window. The video image compositor may perform lens distortion correction on the wide-view camera 470, the surround camera 474, and / or the in-cabin surveillance camera sensor. The in-cabin surveillance camera sensor is preferably monitored by a neural network running on a separate instance of the advanced SoC, configured to identify in-cabin events and respond appropriately. The in-cabin system may perform lip reading to activate cellular service and make phone calls, dictate emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain features are available to the driver only when operating in autonomous mode and are disabled otherwise.

[0084] The video image combiner may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, when motion occurs in the video, the noise reduction reduces the weight of information provided by adjacent frames and appropriately weights spatial information. When an image or portion of an image does not contain motion, the temporal noise reduction performed by the video image combiner can use information from previous images to reduce noise in the current image.

[0085] The video image compositor may also be configured to perform stereo rectification on the input stereo lens frames. The video image compositor may further be used for user interface compositing when the operating system desktop is in use, and the GPU 408 is not required to continuously render new surfaces. Even when the GPU 408 is powered on and actively performing 3D rendering, the video image compositor may be used to offload the GPU 408 to improve performance and responsiveness.

[0086] The SoC 404 may further include a mobile industry processor interface (MIPI) camera serial interface, a high-speed interface for receiving video and input from a camera, and / or a video input block that may be used for camera and related pixel input functions. The SoC 404 may further include an input / output controller that may be controlled by software and that may be used to receive I / O signals that are not committed to a specific role. The SoC 404 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and / or other devices. The SoC 404 may be used to process data from cameras (e.g., connected via gigabit multimedia serial links and Ethernet), sensors (e.g., LIDAR sensors 464, RADAR sensors 460, etc., which may be connected via Ethernet), data from the bus 402 (e.g., vehicle 400 speed, steering wheel position, etc.), and GNSS sensors 458 (e.g., connected via Ethernet or a CAN bus). The SoC 404 may further include a dedicated high-performance mass storage controller, which may include its own DMA engine and may be used to offload routine data management tasks from the CPU 406.

[0087] The SoC 404 may be an end-to-end platform with a flexible architecture spanning levels 3-5 of automation, thereby providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS techniques for diversity and redundancy, and provides a platform for a flexible, reliable driving software stack along with deep learning tools. The SoC 404 may be faster, more reliable, and more energy- and space-efficient than conventional systems. For example, when the accelerator 414 is combined with the CPU 406, the GPU 408, and the data store 416, it can provide a fast and efficient platform for levels 3-5 of autonomous vehicles.

[0088] This technology therefore offers capabilities and functionality not achievable by conventional systems. For example, computer vision algorithms can be implemented on a central processing unit (CPU), which can be configured using a high-level programming language, such as the C programming language, to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs often cannot meet the performance requirements of many computer vision applications, including those related to execution time and power consumption. Specifically, many CPUs cannot execute complex object detection algorithms in real time, a requirement for in-vehicle ADAS applications and practical Level 3-5 autonomous vehicles.

[0089] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a hardware acceleration cluster, the technology described herein allows multiple neural networks to run simultaneously and / or serially and the results to be combined to enable Level 3-5 autonomous driving capabilities. For example, a CNN running on the DLA or dGPU (e.g., GPU420) can include text and word recognition, enabling the supercomputer to read and understand traffic signs, including signs for which the neural network was not specifically trained. The DLA can further include a neural network that can identify, interpret, and provide a semantic understanding of the signs and pass the semantic understanding to a route planning module running on the CPU complex.

[0090] As another example, multiple neural networks may be run simultaneously, as required for Level 3, 4, or 5 operation. For example, a warning sign consisting of "Caution: Flashing lights indicate icy conditions" along with a lightning flash may be interpreted independently or collectively by several neural networks. The sign itself may be identified as a traffic sign by a first deployed neural network (e.g., a trained neural network), and the text "Flashing lights indicate icy conditions" may be interpreted by a second deployed neural network that notifies the vehicle's route planning software (preferably running on a CPU complex) that icy conditions exist when the flashing light is detected. The flashing light may be identified by running a third deployed neural network over multiple frames, informing the vehicle's route planning software of the presence (or absence) of the flashing light. All three neural networks may be run simultaneously, such as within the DLA and / or on the GPU 408.

[0091] In some instances, a CNN for facial recognition and vehicle owner identification can use data from the camera sensor to identify the presence of a legitimate driver and / or owner of the vehicle 400. An always-on sensor processing engine can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's side door, and in security mode, to disable operation of the vehicle when the owner leaves the vehicle. In this manner, the SoC 404 provides security against theft and / or vehicle hijacking.

[0092] In another example, a CNN for emergency vehicle detection and identification can detect and identify emergency vehicle sirens using data from microphone 496. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 404 uses CNNs for environmental and urban sound classification, as well as visual data classification. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative terminal velocity of emergency vehicles (e.g., by using the Doppler effect). The CNN can also be trained to identify emergency vehicles specific to the local area in which the mobile vehicle is operating, as identified by GNSS sensor 458. Thus, for example, when operating in Europe, the CNN would attempt to detect European sirens, and when in the United States, the CNN would attempt to identify only North American sirens. After an emergency vehicle is detected, a control program can be used to perform emergency vehicle safety routines, such as slowing down the mobile vehicle, stopping it at the side of the road, parking it, and / or idling it, with the assistance of ultrasonic sensor 462, until the emergency vehicle has passed.

[0093] The vehicle may include a CPU 418 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 404 via a high-speed interconnect (e.g., PCIe). The CPU 418 may include, for example, an X86 processor. The CPU 418 may be used to perform any of a variety of functions, including, for example, reconciling potentially inconsistent results between the ADAS sensors and the SoC 404 and / or monitoring the status and health of the controller 436 and / or the infotainment SoC 430.

[0094] Vehicle 400 may include GPU 420 (e.g., a discrete GPU or dGPU) that may be coupled to SoC 404 via a high-speed interconnect (e.g., NVIDIA's NVLINK). GPU 420 may provide additional artificial intelligence functionality, such as by running 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 in vehicle 400.

[0095] The mobile vehicle 400 may further include a network interface 424, which may include one or more wireless antennas 426 (e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface 424 may be used to enable wireless connections with the cloud via the Internet (e.g., with the server 478 and / or other network devices), with other mobile vehicles, and / or with computing devices (e.g., passenger client devices). To communicate with other mobile vehicles, a direct link may be established between the two mobile vehicles and / or an indirect link may be established (e.g., through a network and via the Internet). A direct link may be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link may provide the mobile vehicle 400 with information about mobile vehicles in its vicinity (e.g., vehicles in front of, beside, and / or behind the mobile vehicle 400). This functionality may be part of a cooperative adaptive cruise control function of the mobile vehicle 400.

[0096] The network interface 424 may include an SoC that provides modulation and demodulation functions and enables the controller 436 to communicate over a wireless network. The network interface 424 may include a radio frequency front end for upconversion from baseband to radio frequency and downconversion from radio frequency to baseband. The frequency conversion may be performed through well-known processes and / or may be performed using a superheterodyne process. In some instances, the radio frequency front end functionality may be provided by a separate chip. The network interface may include wireless functionality for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and / or other wireless protocols. Mobile vehicle 400 may further include a data store 428, which may include off-chip (e.g., off-SoC 404) storage. Data store 428 may include one or more memory elements, including RAM, SRAM, DRAM, VRAM, flash, hard disk, and / or other components and / or devices capable of storing at least one bit of data.

[0097] Vehicle 400 may further include GNSS sensors 458 (e.g., GPS and / or assisted GPS sensors) to assist in mapping, perception, occupancy grid generation, and / or route planning functions. Any number of GNSS sensors 458 may be used, including, for example, but not limited to, a GPS using a USB connector with an Ethernet to serial (RS-232) bridge. The mobile vehicle 400 may further include a RADAR sensor 460. The RADAR sensor 460 may be used by the mobile vehicle 400 for long-range mobile vehicle detection, even in darkness and / or severe weather conditions. The RADAR functional safety level may be ASIL B. In some instances, the RADAR sensor 460 may use the CAN and / or bus 402 for control and to access object tracking data (e.g., to transmit data generated by the RADAR sensor 460), with access to Ethernet for accessing raw data. A wide variety of RADAR sensor types may be used. For example, and without limitation, the RADAR sensor 460 may be suitable for front, rear, and side RADAR use. In some instances, a pulse-Doppler RADAR sensor is used.

[0098] The RADAR sensor 460 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, and short-range side coverage. In some instances, long-range RADAR may be used for adaptive cruise control functions. Long-range RADAR systems may provide a wide field of view achieved by two or more independent scans, such as within a 250-meter range. The RADAR sensor 460 may help distinguish between static and moving objects and may be used by ADAS systems for emergency brake assist and forward collision warning. Long-range RADAR sensors may include monostatic multimodal RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In one example with six antennas, the center four antennas may create a focused beam pattern designed to record the surroundings of the moving vehicle 400 at high speeds with minimal interference from traffic in adjacent lanes. The other two antennas may widen the field of view, allowing for rapid detection of moving vehicles entering or leaving the lane of the moving vehicle 400.

[0099] As an example, a medium-range RADAR system may include a range of up to 460 meters (front) or 80 meters (rear) and a field of view of up to 42 degrees (front) or 450 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on either end of a rear bumper. When mounted on either end of a rear bumper, such a RADAR sensor system can create two beams that constantly monitor the blind spots behind and adjacent to a moving vehicle.

[0100] Short-range RADAR systems may be used in ADAS systems for blind spot detection and / or lane change assist.

[0101] The mobile vehicle 400 may further include ultrasonic sensors 462. The ultrasonic sensors 462, which may be positioned on the front, rear, and / or sides of the mobile vehicle 400, may be used for parking assistance and / or for creating and updating an occupancy grid. A variety of ultrasonic sensors 462 may be used, with different ultrasonic sensors 462 being used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensors 462 may operate at an ASIL B functional safety level.

[0102] The mobile vehicle 400 may include a LIDAR sensor 464. The LIDAR sensor 464 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 464 may be functional safety level ASIL B. In some instances, the mobile vehicle 400 may include multiple (e.g., two, four, six, etc.) LIDAR sensors 464 that can use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0103] In some instances, the LIDAR sensor 464 may be capable of providing a list of objects and their distances in a 360-degree field of view. Commercially available LIDAR sensors 464 may have an advertised range of approximately 100 m, for example, with an accuracy of 2 cm to 3 cm and support for a 100 Mbps Ethernet connection. In some instances, one or more non-protruding LIDAR sensors 464 may be used. In such instances, the LIDAR sensor 464 may be implemented as a small device that may be integrated into the front, rear, sides, and / or corners of the vehicle 400. In such instances, the LIDAR sensor 464 may have a range of 200 m, even for low-reflecting objects, and may provide up to a 120-degree horizontal and 35-degree vertical field of view. A front-mounted LIDAR sensor 464 may be configured for a horizontal field of view between 45 and 135 degrees.

[0104] In some instances, LIDAR technology such as 3D flash LIDAR may also be used. 3D flash LIDAR uses a laser flash as a transmitter to illuminate the surroundings of the vehicle up to approximately 200 meters. The flash LIDAR unit includes a receptor that records the laser pulse transit time and the reflected light at each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LIDAR may enable a highly accurate and distortion-free image of the surroundings to be generated with every laser flash. In some instances, four flash LIDAR sensors may be deployed, one on each side of the vehicle 400. Available 3D flash LIDAR systems include solid-state 3D steering array LIDAR cameras (e.g., non-scanning LIDAR devices) with no moving parts other than the blower. Flash LIDAR devices may use 5 nanosecond Class I (eye-safe) laser pulses per frame and may capture reflected laser light in the form of a 3D range point cloud and coregistered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor 464 may be less susceptible to motion blur, vibration, and / or shock.

[0105] The mobile vehicle may further include an IMU sensor 466. In some instances, the IMU sensor 466 may be positioned at the center of the rear axle of the mobile vehicle 400. The IMU sensor 466 may include, for example, but not limited to, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some instances, such as in a six-axis application, the IMU sensor 466 may include an accelerometer and a gyroscope, while in a nine-axis application, the IMU sensor 466 may include an accelerometer, a gyroscope, and a magnetometer.

[0106] In some embodiments, IMU sensor 466 may be implemented as a miniature, high-performance GPS-Aided Inertial Navigation System (GPS / INS) that combines micro-electro-mechanical system (MEMS) inertial sensors, a highly sensitive GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. As such, in some instances, IMU sensor 466 may enable vehicle 400 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating changes in velocity from the GPS to IMU sensor 466. In some instances, IMU sensor 466 and GNSS sensor 458 may be combined in a single integrated unit. The mobile vehicle may include a microphone 496 placed in and / or around the mobile vehicle 400. The microphone 496 may be used for emergency vehicle detection and identification, among other things.

[0107] The vehicle may further include any number of camera types, including stereo cameras 468, wide-view cameras 470, infrared cameras 472, surround cameras 474, long-range and / or mid-range cameras 498, and / or other camera types. The cameras may be used to capture image data around the entire exterior of the vehicle 400. The types of cameras used depend on the implementation and requirements of the vehicle 400, and any combination of camera types may be used to achieve the desired coverage around the vehicle 400. Additionally, the number of cameras may vary depending on the implementation. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or another number of cameras. The cameras may support, by way of example only, Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each camera is described in further detail herein with reference to FIGS. 4A and 4B.

[0108] The vehicle 400 may further include a vibration sensor 442. The vibration sensor 442 may measure vibrations of vehicle components, such as an axle. For example, a change in vibration may indicate a change in the road surface. In another example, when two or more vibration sensors 442 are used, the difference in vibration may be used to determine friction or slippage of the road surface (e.g., when the difference in vibration is between a powered axle and a free-spinning axle).

[0109] The mobile vehicle 400 may include an ADAS system 438. In some instances, the ADAS system 438 may include an SoC. The ADAS system 438 may include autonomous / adaptive / automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward crash warning (FCW), automatic emergency braking (AEB), lane departure warning (LDW), lane keep assist (LKA), blind spot warning (BSW), rear cross-traffic warning (RCTW), collision warning system (CWS), lane centering (LC), and / or other features and functions.

[0110] The ACC system may use a RADAR sensor 460, a LIDAR sensor 464, and / or a camera. The ACC system may include longitudinal ACC and / or lateral ACC. The longitudinal ACC monitors and controls the distance to the vehicle directly ahead of the vehicle 400 and automatically adjusts the vehicle speed to maintain a safe distance from the vehicle ahead. The lateral ACC performs distance keeping and advises the vehicle 400 to change lanes when necessary. The lateral ACC is related to other ADAS applications such as LC and CWS.

[0111] CACC uses information from other moving vehicles, which may be received from other moving vehicles via a wireless link via the network interface 424 and / or wireless antenna 426, or indirectly via a network connection (e.g., via the Internet). A direct link may be provided by a vehicle-to-vehicle (V2V) communication link, while an indirect link may be an infrastructure-to-vehicle (I2V) communication link. Generally, V2V communication concepts provide information about the immediately preceding moving vehicle (e.g., the moving vehicle directly ahead of the moving vehicle 400 that is in the same lane as the moving vehicle 400), while I2V communication concepts provide information about traffic further ahead. A CACC system may include either or both I2V and V2V information sources. Given information about moving vehicles ahead of the moving vehicle 400, CACC may be more reliable, potentially allowing for smoother traffic flow and reducing road congestion.

[0112] FCW systems are designed to warn the driver of hazards so that the driver can take corrective action. FCW systems use forward-facing cameras and / or RADAR sensors 460 coupled to dedicated processors, DSPs, FPGAs, and / or ASICs, electrically coupled to driver feedback such as displays, speakers, and / or vibration components. FCW systems can provide warnings in the form of audio, visual alerts, vibrations, and / or quick brake pulses.

[0113] An AEB system can detect an imminent forward collision with another moving vehicle or other object and automatically apply the brakes if the driver does not take corrective action within specified time or distance parameters. The AEB system can use a forward-facing camera and / or RADAR sensor 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision; if the driver does not take corrective action, the AEB system can automatically apply the brakes as part of an effort to prevent, or at least mitigate, the effects of the predicted collision. The AEB system can include techniques such as dynamic brake support and / or collision imminent braking.

[0114] The LDW system provides visual, audible, and / or tactile warnings, such as vibrations in the steering wheel or seat, to alert the driver when the mobile vehicle 400 crosses a lane marking. The LDW system does not activate when the driver indicates an intentional lane departure by activating a turn signal. The LDW system may use a forward-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC electrically coupled to driver feedback, such as a display, speaker, and / or vibration components.

[0115] The LKA system is a modification of the LDW system, which provides steering input or braking to correct the vehicle 400 if it begins to drift out of its lane. The BSW system detects and warns the driver of a moving vehicle in the vehicle's blind spot. The BSW system can provide visual, audible, and / or tactile warnings to indicate that merging or changing lanes is unsafe. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use a rear-facing camera and / or RADAR sensor 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, electrically coupled to driver feedback, e.g., a display, speaker, and / or vibration component.

[0116] The RCTW system can provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera when the vehicle 400 is backing up. Some RCTW systems include AEB to ensure vehicle brakes are applied to avoid a collision. The RCTW system can use one or more rear-facing RADAR sensors 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, electrically coupled to driver feedback, e.g., a display, speaker, and / or vibration components.

[0117] Because conventional ADAS systems alert the driver and allow the driver to determine whether a safety condition truly exists and act accordingly, conventional ADAS systems can be prone to producing false positives that, while not usually catastrophic, can be annoying and distracting to the driver. However, in an autonomous vehicle 400, when results conflict, the vehicle 400 itself must decide whether to listen to results from a primary computer or a secondary computer (e.g., the first controller 436 or the second controller 436). For example, in some embodiments, the ADAS system 438 may be a backup and / or secondary computer that provides perception information to a backup computer rationality module. The backup computer rationality monitor can run redundant software on hardware components to detect failures in perception and dynamic driving tasks. Output from the ADAS system 438 may be provided to a supervisory MCU. When the outputs from the primary and secondary computers conflict, the supervisory MCU must determine how to reconcile the conflict to ensure safe operation.

[0118] In some instances, the primary computer may be configured to provide a reliability score to the supervising MCU indicating the reliability of the primary computer in a selected outcome. If the reliability score exceeds a threshold, the supervising MCU may follow the primary computer's instructions regardless of whether the secondary computers provide conflicting or inconsistent results. If the reliability score does not meet the threshold, and the primary and secondary computers provide different (e.g., conflicting) results, the supervising MCU may arbitrate between the computers to determine the appropriate outcome.

[0119] The supervisory MCU may be configured to execute a neural network trained and configured to determine, based on outputs from the primary and secondary computers, conditions under which the secondary computer will provide a false alarm. Thus, the neural network in the supervisory MCU can learn when the output of the secondary computer can be trusted and when it cannot be trusted. For example, when the secondary computer is a RADAR-based FCW system, the neural network in the supervisory MCU can learn when the FCW identifies a metal object that is not actually dangerous, such as a sewer grate or manhole cover, which triggers an alarm. Similarly, when the secondary computer is a camera-based LDW system, the neural network in the supervisory MCU can learn to ignore the LDW when a bicyclist or pedestrian is present and lane departure is, in fact, the safest maneuver. In embodiments including a neural network running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or a GPU suitable for executing the neural network with associated memory. In a preferred embodiment, the supervising MCU may comprise and / or be included as a component of the SoC 404 .

[0120] In other instances, the ADAS system 438 may include a secondary computer that performs ADAS functions using traditional rules of computer vision. As such, the secondary computer may use classical computer vision rules (if-then), and the presence of a neural network in the supervisory MCU may improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity may make the overall system more fault-tolerant, particularly to failures caused by software (or software-hardware interface) functions. For example, if a software bug or error exists in software running on the primary computer and non-identical software code running on the secondary computer provides the same overall result, the supervisory MCU may have greater confidence that the overall result is correct and that a bug in the software or hardware used by the primary computer has not caused a critical error.

[0121] In some instances, the output of the ADAS system 438 can be fed to the perception block of the primary computer and / or the dynamic driving task block of the primary computer. For example, if the ADAS system 438 indicates a forward collision warning due to an object directly ahead, the perception block can use this information when identifying the object. In other instances, the secondary computer can have its own neural network that is trained as described herein, thus reducing the risk of false positives.

[0122] The mobile vehicle 400 may further include an infotainment SoC 430 (e.g., an in-vehicle infotainment system (IVI)). Although shown and described as an SoC, the infotainment system need not be an SoC and may include two or more separate components. The infotainment SoC 430 may include a combination of hardware and software that may be used to provide audio (e.g., music, personal digital assistants, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), telephony (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and / or information services (e.g., navigation system, reverse parking assist, wireless data system, vehicle-related information such as fuel level, total distance traveled, brake fuel level, oil level, door opening / closing, air filter information, etc.) to the mobile vehicle 400. For example, the infotainment SoC 430 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connectivity, a car computer, in-car entertainment, Wi-Fi, steering wheel audio controls, hands-free voice control, a heads-up display (HUD), an HMI display 434, telematics devices, a control panel (e.g., for controlling and / or interacting with various components, features, and / or systems), and / or other components. The infotainment SoC 430 may further be used to provide information (e.g., visual and / or audible) to a user of the vehicle, such as information from an ADAS system 438, autonomous driving information such as planned vehicle maneuvers, trajectory, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0123] The infotainment SoC 430 may include GPU functionality. The infotainment SoC 430 may communicate with other devices, systems, and / or components of the mobile vehicle 400 via the bus 402 (e.g., CAN bus, Ethernet, etc.). In some instances, the infotainment SoC 430 may be coupled to a supervisory MCU such that the infotainment system's GPU can perform some self-driving functions in the event of a failure of the primary controller 436 (e.g., the primary and / or backup computer of the mobile vehicle 400). In such instances, the infotainment SoC 430 may place the mobile vehicle 400 in a Chauffeur safe stop mode, as described herein.

[0124] The mobile vehicle 400 may further include an instrument cluster 432 (e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 432 may include a controller and / or a supercomputer (e.g., a separate controller or supercomputer). The instrument cluster 432 may include a set of instruments such as a speedometer, fuel level, oil pressure, a tachometer, an odometer, turn signals, a gear shift position indicator, a seat belt warning light, a parking brake warning light, an engine malfunction light, an airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some instances, information may be displayed and / or shared between the infotainment SoC 430 and the instrument cluster 432. In other words, the instrument cluster 432 may be included as part of the infotainment SoC 430, or vice versa.

[0125] 4D is a system diagram of communication between the cloud-based server and the example autonomous vehicle 400 of FIG. 4A in accordance with some embodiments of the present disclosure. System 476 may include a server 478, a network 490, and a mobile vehicle including the mobile vehicle 400. Server 478 may include multiple GPUs 484(A)-484(H) (collectively referred to herein as GPUs 484), PCIe switches 482(A)-482(H) (collectively referred to herein as PCIe switches 482), and / or CPUs 480(A)-480(B) (collectively referred to herein as CPUs 480). GPUs 484, CPUs 480, and PCIe switches may be interconnected with a high-speed interconnect, such as, but not limited to, an NVLink interface 488 developed by NVIDIA and / or a PCIe connection 486. In some instances, the GPUs 484 are connected via NVLink and / or NVSwitch SoCs, and the GPUs 484 and PCIe switches 482 are connected via PCIe interconnects. While eight GPUs 484, two CPUs 480, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each server 478 may include any number of GPUs 484, CPUs 480, and / or PCIe switches. For example, the servers 478 may each include 8, 16, 32, and / or more GPUs 484.

[0126] Server 478 can receive image data from the mobile vehicles via network 490, representing images showing unexpected or changed road conditions, such as recently started road construction. Server 478 can transmit neural network 492, updated neural network 492, and / or map information 494, including information about traffic and road conditions, to the mobile vehicles via network 490. Updates to map information 494 can include updates to HD map 422, such as information about construction sites, potholes, detours, flooding, and / or other obstacles. In some instances, neural network 492, updated neural network 492, and / or map information 494 may result from new training and / or experience represented in data received from any number of mobile vehicles in the environment and / or based on training performed at a data center (e.g., using server 478 and / or other servers).

[0127] The server 478 may be used to train a machine learning model (e.g., a neural network) based on training data. The training data may be generated by a mobile vehicle and / or generated in a simulation (e.g., using a game engine). In some instances, the training data is tagged (e.g., if the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other instances, the training data is not tagged and / or preprocessed (e.g., if the neural network does not require supervised learning). The training may be performed according to any one or more classes of machine learning techniques, including, but not limited to, the following classes: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including preliminary dictionary learning), rule-based machine learning, anomaly detection, and variations or combinations thereof. After the machine-learned model is traced, it may be used by the vehicle (e.g., transmitted to the vehicle via network 490) and / or it may be used by server 478 to remotely monitor the vehicle.

[0128] In some instances, server 478 can receive data from mobile vehicles and apply the data to state-of-the-art real-time neural networks for real-time intelligent inference. Server 478 can include deep learning supercomputers and / or dedicated AI computers powered by GPUs 484, such as the DGX and DGX Station machines developed by NVIDIA. However, in some instances, server 478 can include deep learning infrastructure that uses only CPU-powered data centers.

[0129] The deep learning infrastructure of server 478 may be capable of rapid real-time inference and may use that capability to evaluate and verify the health of the processor, software, and / or associated hardware within mobile vehicle 400. For example, the deep learning infrastructure may receive periodic updates from mobile vehicle 400 (e.g., via computer vision and / or other machine learning object classification techniques), such as a sequence of images and / or objects where mobile vehicle 400 was located within the sequence of images. The deep learning infrastructure may run its own neural network to identify objects and compare them to objects identified by mobile vehicle 400; if the results are inconsistent and the infrastructure concludes that the AI ​​within mobile vehicle 400 is not functioning properly, server 478 may send a signal to mobile vehicle 400 instructing its failsafe computer to take control, notify passengers, and complete a safe parking maneuver.

[0130] For inference, server 478 may include a GPU 484 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT). The combination of a GPU-powered server and inference acceleration can enable real-time responsiveness. In other instances, such as when less performance is required, servers powered by CPUs, FPGAs, and other processors may be used for inference.

[0131] 5 is a block diagram of an example computing device 500 suitable for use in implementing some embodiments of the present disclosure. The computing device 500 may include an interconnection system 502 that indirectly or directly couples the following devices: memory 504, one or more central processing units (CPUs) 506, one or more graphics processing units (GPUs) 508, a communication interface 510, I / O ports 512, input / output components 514, a power supply 516, one or more presentation components 518 (e.g., displays), and one or more logic units 520.

[0132] While the various blocks in FIG. 5 are depicted as connected by lines via interconnection system 502, this is not intended to be limiting and is merely for clarity. For example, in some embodiments, a presentation component 518, such as a display device, may be considered an I / O component 514 (e.g., if the display is a touch screen). As another example, CPU 506 and / or GPU 508 may include memory (e.g., memory 504 may represent a storage device in addition to the memory of GPU 508, CPU 506, and / or other components). In other words, the computing devices in FIG. 5 are merely exemplary. Categories such as “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “handheld device,” “gaming console,” “electronic control unit (ECU),” “virtual reality system,” “augmented reality system,” and / or other device or system types are all intended to be within the scope of the computing devices in FIG. 5 and therefore will not be distinguished from one another.

[0133] Interconnect system 502 may represent one or more links or buses, such as an address bus, a data bus, a control bus, or a combination thereof. Interconnect system 502 may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standards association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and / or another type of bus or link. In some embodiments, direct connections exist between components. As an example, CPU 506 may be directly connected to memory 504. Further, CPU 506 may be directly connected to GPU 508. When direct or point-to-point connections exist between components, interconnect system 502 may include a PCIe link to implement the connections. In these examples, a PCI bus need not be included in computing device 500.

[0134] Memory 504 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by computing device 500. Computer-readable media may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media.

[0135] Computer storage media may include both volatile and nonvolatile media, and / or removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 504 may store computer-readable instructions (e.g., representing programs and / or program elements), such as an operating system. Computer storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by computing device 500. As used herein, computer storage media does not include the signals themselves.

[0136] Computer storage media may embody computer-readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, computer storage media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

[0137] The CPU 506 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and / or processes described herein. The CPU 506 may include one or more (e.g., 1, 2, 4, 8, 28, 72, etc.) cores, each capable of simultaneously processing multiple software threads. The CPU 506 may include any type of processor, and may include different types of processors depending on the type of computing device 500 implemented (e.g., a processor with fewer cores for a mobile device and a processor with more cores for a server). For example, depending on the type of computing device 500, the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 500 may include one or more CPUs 506 within one or more microprocessors or auxiliary coprocessors, such as computational coprocessors.

[0138] In addition to or instead of CPU 506, GPU 508 may be configured to execute at least some of the computer-readable instructions to control one or more components of computing device 500 to perform one or more of the methods and / or processes described herein. One or more of GPUs 508 may be integrated GPUs (e.g., with one or more of CPUs 506) and / or one or more of GPUs 508 may be discrete GPUs. In an embodiment, one or more of GPUs 508 may be coprocessors of one or more of CPUs 506. GPU 508 may be used by computing device 500 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, GPU 508 may be used with GPGPU (General-Purpose Computing on a GPU) The GPU 508 may be used for graphics processing (GPU). The GPU 508 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. The GPU 508 may generate pixel data for an output image in response to rendering commands (e.g., rendering commands from the CPU 506 received via a host interface). The GPU 508 may include graphics memory, e.g., display memory, for storing pixel data or any other suitable data, e.g., GPGPU data. The display memory may be included as part of the memory 504. GPU 508 may include two or more GPUs operating in parallel (e.g., via links). The links may connect the GPUs directly (e.g., using NVLINK) or may connect the GPUs via a switch (e.g., using NVSwitch). When coupled together, each GPU 508 may generate pixel data or GPGPU data for a different portion of the output or for a different output (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 instead of CPU 506 and / or GPU 508, logic unit 520 may be configured to execute at least some of the computer-readable instructions to control one or more of computing devices 500 to perform one or more of the methods and / or processes described herein. In an embodiment, CPU 506, GPU 508, and / or logic unit 520 may discretely or jointly execute any combination of methods, processes, and / or portions thereof. One or more of logic units 520 may be part of and / or integrated with one or more of CPU 506 and / or GPU 508, and / or one or more of logic units 520 may be discrete components to or otherwise external to CPU 506 and / or GPU 508. In an embodiment, one or more of logic units 520 may be a coprocessor of one or more of CPU 506 and / or GPU 508.

[0139] Examples of logic unit 520 include one or more processing cores and / or components thereof, such as a tensor core (TC), a tensor processing unit (TPU), a pixel visual core (PVC), a vision processing unit (VPU), a graphics processing cluster (GPC), a texture processing cluster (TPC), a streaming multiprocessor (SM), a tree traversal unit (TTU), an artificial intelligence accelerator (AIA), a deep learning accelerator (DLA), an arithmetic logic unit (ALU), an application specific integrated circuit (ASIC), a floating point unit (FPU), an I / O element, a peripheral component interconnect (PCI) or peripheral component interconnect express (PCIe) element, and / or the like.

[0140] The communications interface 510 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 500 to communicate with other computing devices over electronic communications networks, including wired and / or wireless communications. The communications interface 510 may include components and functionality to enable communication over any of several different networks, such as a wireless network (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), a wired network (e.g., communicating over Ethernet or InfiniBand), a low-power wide area network (e.g., LoRaWAN, SigFox, etc.), and / or the Internet.

[0141] The I / O ports 512 may enable the computing device 500 to be logically coupled to other devices, including I / O components 514, presentation components 518, and / or other components, some of which may be built into (e.g., integrated with) the computing device 500. Exemplary I / O components 514 include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I / O components 514 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological input generated by the user. In some cases, the input may be sent to an appropriate network element for further processing. The NUI may implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, on-screen and adjacent-screen gesture recognition, air gestures, head and eye tracking, and touch recognition in connection with the display of the computing device 500 (as described in more detail below). The computing device 500 may include a depth camera, such as a stereoscopic camera system, an infrared camera system, an RGB camera system, touch screen technology, and combinations thereof, for gesture detection and recognition. Additionally, the computing device 500 may include an accelerometer or gyroscope (e.g., as part of an inertia measurement unit (IMU)) to enable detection of movement. In some instances, the output of the accelerometer or gyroscope may be used by the computing device 500 to render immersive augmented or virtual reality.

[0142] The power supply 516 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 516 may provide power to the computing device 500 to enable the components of the computing device 500 to operate. The presentation component 518 may include a display (e.g., a monitor, a touch screen, a television screen, a heads-up display (HUD), other display types, or a combination thereof), speakers, and / or other presentation components. The presentation component 518 can receive data from other components (e.g., the GPU 508, the CPU 506, etc.) and output data (e.g., as images, video, sound, etc.).

[0143] 6 is a flow chart illustrating process steps for determining a seat belt position status according to an embodiment of the present disclosure. Here, the computing device 300 first receives sensor data from a sensor-monitored seat belt 20, e.g., a camera 310, in contact with the driver 50 (step 600). As previously described, this sensor data may be any data, e.g., image data from the camera 310, that can be used to determine the spatial location of a fiducial marker 40. The computing device 300 then determines the position of the seat belt 20, e.g., whether the position of the seat belt 20 falls within one of the aforementioned use cases (step 610). Specifically, the fiducial position determination module 416 may determine the location of each fiducial marker 40 in its input image or other data, and the pattern recognition module 418 may then select a particular classification or seat belt use state (e.g., case on, case off, case under, or case back) from the determined marker 40 location.

[0144] Optionally, computing device 300 can also determine other quantities besides seat belt occupancy status (step 620). For example, device 300 can also determine the size or location of various vehicle components. Fiducial markers 40 can be placed on any vehicle component or any object within the vehicle, and sensor 100 can be directed to capture images or location information of these additional fiducial markers 40. Device 300 can then determine the positional status of these components or objects as described above, classifying them into various states as necessary. For example, fiducial markers 40 can be placed on the backrest portion 60 and / or seatrest portion 70 of a vehicle seat, and device 300 can determine the occupancy status of the driver's (or other passenger's) seat. In this way, device 300 can determine, for example, whether the driver's seat is reclined or has moved too far forward or backward, and can notify the vehicle to take various actions, such as redirecting heating / cooling vents or warning the driver that their seat is in an improper position for driving. Fiducial markers 40 may also be placed on objects within the vehicle, such as a child car seat or booster. Device 300 may then determine the location of these objects within the vehicle according to embodiments of the present disclosure, determining whether the object is in the seat and / or correctly oriented, for example, according to a characteristic distribution of fiducial marker 40 positions when the object is properly positioned. Thus, for example, device 300 may determine whether a child car seat or booster seat is properly installed, whether it has moved position over time, or the like, and may notify the vehicle accordingly. In another embodiment, device 300 may determine whether cargo within a passenger compartment of a vehicle is in a safe position.

[0145] As another example, the device 300 can determine the size of a passenger or driver. Specifically, because the positions of the fiducial markers 40 outline the driver's or passenger's body, the passenger's size can be determined from the seat belt outline, i.e., the positions of the various fiducial markers 40. For example, the machine learning model of the fiducial position detection module 416 can be trained to classify clusters of fiducial marker 40 positions for drivers / passengers of various weights, dimensions, or volumes. Thus, the location of the markers 40 in the input image can be classified according to the driver / passenger weight corresponding to the most closely matching cluster. This can inform the system of the driver / passenger's approximate weight, which can then inform the vehicle, for example, whether an airbag should be deployed in the event of a collision. Alternatively, the vehicle can use that information to adjust how much or how the airbag is deployed. For example, various portions of the vehicle's cocoon airbag can be deployed or not deployed based on the size of the passenger. To aid in more accurate determination of driver / passenger size, fiducial markers 40 may be placed in addition to the seat belt shoulder straps, for example, on the lap belt 30 and / or seat back portion 60.

[0146] As a further example, the position or posture of a passenger can be determined by the position of the fiducial markers as they correspond to passengers moving around in their seats. For example, the machine learning model of the reference position detection module 416 can be trained to classify clusters of fiducial marker 40 positions for drivers / passengers in various postures. Thus, the location of the markers 40 in the input image can be classified according to the driver / passenger posture that corresponds to the most closely matching cluster. This can inform the vehicle, for example, about drivers / passengers who have fallen asleep in their seats and thereby collapsed, children who have tried to stand up in their seats, drivers who are facing backwards and therefore not looking at the road, and the like. By training the machine learning model of the reference position detection module 416 using images of any driver / passenger position or posture of interest, the device 300 can determine any driver / passenger position that can be inferred by seat belt position.

[0147] In some embodiments of the present disclosure, step 620 is optional and may be omitted if necessary. After the seat belt usage status is selected and / or any other driver or passenger count is determined, i.e., when either step 610 or step 620 is completed, system 300 can then initiate vehicle action accordingly (step 620). As previously described, any vehicle action may be initiated. Such vehicle action may include airbag deployment or adjustment (e.g., disabling airbag deployment when a small passenger such as a child is detected), any audible or visual alarm such as a warning light, a bell, or other signal, or a voice or text-based alert. In more urgent situations, perhaps when a seat belt is suddenly unfastened or unbuckled, vehicle action may include braking, ignition shutoff or engine shutdown, autopilot engagement, or the like. In some embodiments, the seat belt usage status may be transmitted away from the vehicle. For example, in a fleet management situation, it may be important to know when drivers are wearing their seat belts or if they are wearing their seat belts properly. Other situations would also benefit from such a solution; for example, monitoring safe driving habits and seat belt use may be important to insurance companies or parents.

[0148] It should be noted that embodiments of the present disclosure enable detection of seat belt position and usage for both the driver and other passengers. That is, any detected quantity and any resulting action of any embodiment of the present disclosure may apply to both the driver and any other vehicle occupant. Thus, while FIG. 1A illustrates detection of driver seat belt position or usage, the disclosed methods and processes may equally apply to any other passengers as well. For example, fiducial markers may be placed on any passenger seat belt, and sensor 100 may be appropriately positioned to capture images of these markers; the image or other sensor data may then be used to determine such passenger's seat belt position and usage. In this manner, a system of embodiments of the present disclosure may determine whether a passenger seat belt is properly fastened or whether one or more passengers have an improperly fastened seat belt; the vehicle may then issue an alert or other message to that passenger or to other passengers in the vehicle.

[0149] It should also be noted that the fiducial marker 40 may be any type and shape of marking capable of conveying spatial location information. As one example, the fiducial marker 40 may be any shape and size and formed in any manner. For example, the marker 40 may be a visible patch or other visible object or shape adhesively bonded, sewn, printed, sprayed, projected, or otherwise formed on the seat belt 20. As another example, the fiducial marker 40 may be visible in any light range. Thus, for example, the fiducial marker 40 may be a marker perceptible in visible light frequencies, such as a visible patch or other indicia. Alternatively, the marker 40 may be perceptible only at frequencies outside the visible light spectrum. For example, the marker 40 may be an ultraviolet, infrared, or near-infrared marking. As one example, the fiducial marker may be first printed with black ink that is visible in infrared and near-infrared light. This black ink may then be overlaid with another layer of ink that appears black in visible light but is translucent in infrared and / or near-infrared light. Thus, fiducial markers formed in this manner are invisible to passengers and drivers, but perceptible to systems of embodiments of the present disclosure.

[0150] It is further noted that the machine learning model of the pattern recognition module 220 can classify the reference positions into other seat belt use cases besides the four specified in conjunction with Figures 2B-2C. As one example, additional cases can be added to capture instances in which the driver 50 or passenger pushes away any portion of the seat belt 20 with his or her hand. Here, the machine learning model of the pattern recognition module 220 can be trained with an input training set of images including images of the driver / passenger pushing away a portion of their seat belt, possibly labeled with the corresponding spatial location of its fiducial marker 40.

[0151] Additionally, machine learning models of embodiments of the present disclosure may be trained to handle at least partial occlusion of one or more of the fiducial markers 40. While not common, such occlusions are expected to occur. For example, a passenger's hair may hang over the sensor 100, preventing it from recognizing the fiducial markers 40; loose clothing or hand gestures may similarly obscure the fiducial markers 40; and the like. Thus, the machine learning models of both the fiducial position determination module 210 and the pattern recognition module 220 may each be trained using images in which at least some of the fiducial markers are partially or fully occluded and / or partial or incomplete fiducial position information. As one example, a portion of the training set of images input to the machine learning models of embodiments of the present disclosure may be images in which at least some of the fiducial markers are partially or fully occluded. In this manner, the machine learning models of embodiments of the present disclosure are trained to handle fiducial marker occlusion, thereby providing more reliable classification results that remain accurate in a variety of real-world situations.

[0152] Furthermore, it should be noted that embodiments of the present disclosure may additionally initiate or direct automated seat belt positioning actions, perhaps in response to properties determined from the detected seat belt position. As previously discussed, detected seat belt position information may be used to infer the size and / or weight of a passenger or driver. Embodiments of the present disclosure further contemplate initiating automatic vehicle adjustments in response to driver or passenger size / weight. For example, the seat belt 20 and / or seat may include known adjustment mechanisms configured to automatically tighten or loosen the seat belt 20 or move the seat in a particular direction. Thus, one or more processors of embodiments of the present disclosure may determine the presence of a child and adjust their seat belt to better accommodate a smaller body, e.g., lower and tighten the seat belt, raise the seat, or the like. As another example, a processor of embodiments of the present disclosure may determine from the seat belt 20 position that a passenger is improperly positioned in their seat. If, when warned, the passenger does not move from their improper position for some period of time, the passenger's seat or seat belt 20 may accommodate the passenger's incorrect position, for example, by adjusting the seat to place the seat belt 20 in the proper position or tension, or the like. Any adjustment of any component of the vehicle may be performed for any detected passenger or driver size or weight.

[0153] FIG. 7 illustrates an exemplary automatic seat belt deployment operation according to an embodiment of the present disclosure. Here, seat belt 20 may include a known automatic retraction mechanism for retracting seat belt 20, for example, in the direction of the upper arrow in FIG. 7 . Similarly, seats 60, 70 may include known actuation mechanisms for moving seats 60, 70 forward / backward and left / right in the direction of the lower right arrow. In addition, buckle 80 may further include actuation mechanisms for moving forward / backward and left / right in the direction of the lower left arrow. Anchor 130 may separately or additionally include actuation mechanisms for moving up / down and forward / backward in the direction of the upper arrow.

[0154] In this manner, the seat belt 20 can be automatically adjusted to correct any detected improper seating position. For example, as shown in FIG. 7, the sensor 100 can detect the seat belt 20 as being in an excessively loose configuration according to a fiducial marker 40 pattern that extends generally vertically along a significant portion of the front of the driver 50, rather than diagonally across the driver 50 as would be appropriate. In response, the vehicle can perform any seat belt 20, seat 60, 70, buckle 80, or anchor 130 actuation actions to adjust the seat belt 20 to its proper position. These actions may include any one or more of: retracting the seat belt 20 to tighten any loose or slack portions extending along the front of the driver 50; actuating the buckle 80 down and / or back to tighten the seat belt 20; similarly actuating the seats 60, 70 forward to tighten the seat belt 20; actuating the anchor 130 up and / or back to tighten the seat belt 20; any combination of any such actuation actions; or any automatic movement of any other vehicle part that may tighten the seat belt 20. Embodiments of the present disclosure contemplate the detection and characterization of any seat belt 20 positions that may be considered improper by the methods and processes of the present disclosure, such as those described above, and correcting or adjusting these improper seat belt 20 positions by adjusting any one or more vehicle components.

[0155] Embodiments of the present disclosure further contemplate the use of other sensors in addition to sensor 100 to generate any type of information that can be used in conjunction with the determined seat belt position of embodiments of the present disclosure to initiate any vehicle action. Any one or more sensors of any type are contemplated. For example, a weight or pressure sensor may be placed in the seat rest portion 70 under the driver 50 to measure the weight of the driver 50, his or her position in the seat, the distribution of his or her weight, or the like. Similarly, other weight or pressure sensors may be placed in other seats to measure the weight of passengers. Thus, the system of embodiments of the present disclosure can determine which seat is occupied and the weight of the passenger or driver occupying the seat. The weight and seat belt 20 position information may then be used together to determine the appropriate tension of the seat belt 20, and the vehicle can then automatically adjust the seat belt to that tension by, for example, adjusting the position of the buckle 80, anchor 130, and / or seats 60, 70.

[0156] Any quantity can be determined and used in conjunction with seat belt 20 position to determine any quantity and to initiate any action in response. As one example, the distribution of passenger weight and their position as determined from fiducial marker 40 position can be used together to determine whether a passenger is sitting improperly, e.g., sitting or leaning too far to one side, leaning too far forward, a child seat being improperly positioned (perhaps using fiducial marker 40 positioned on the child seat), or the like. In response, the vehicle can initiate various actions, such as tightening the seat belt 20 to pull the passenger or driver back into position, adjusting the seat 60, 70 to move the passenger or driver into an appropriate position, or the like. As another example, the weight and fiducial marker 40 position of the driver 50 or other passengers can be used to determine their body dimensions, e.g., height and width. The vehicle can then adjust the position of anchor 130, buckle 80, or the like to more appropriately position the driver / passenger's seat belt 20, for example, by raising anchor 130 to accommodate a taller driver, moving anchor 130 (in the view of FIG. 7 ) to the right to adjust the seat belt 20 for a driver with broader shoulders, and the like. In this manner, driver or passenger properties such as height or other dimensions can be inferred from both fiducial marker 40 position and other sensor data such as weight, and the seat belt 20 can be automatically adjusted to more optimally fit those particular driver / passenger dimensions. Thus, embodiments of the present disclosure enable the seat belt 20 to automatically (and, if necessary, continuously) adapt to a more appropriate position for each driver or passenger, thereby improving safety during vehicle operation.

[0157] Embodiments of the present disclosure additionally contemplate determining properties such as vehicle vibration from the determined positions of fiducial markers 40. More specifically, the frequency and magnitude of the vibration may be determined from the recorded movement of fiducial markers 40 over time. Vehicle vibration may be determined from the movement of fiducial markers 40 in any manner, for example, by first determining a baseline or neural set of marker 40 positions and subsequently tracking the relative movement of this baseline set as a function of time. Marker 40 positions may be determined via any technique, for example, by known computer vision methods for identifying objects and determining their spatial location, machine learning methods using one or more machine learning models trained to identify and determine the location of specific objects, or the like.

[0158] The determined vehicle vibrations may be utilized in any manner. For example, the component frequencies of the determined vibrations and their corresponding magnitudes may be identified in a known manner and used to determine various aspects of vehicle function or load. Thus, for example, baseline frequencies and magnitudes may be identified and recorded as described above, and certain frequencies may be identified as corresponding to sources such as baseline engine operation, road noise, or the like. Systems of embodiments of the present disclosure may then alert the vehicle or passengers to deviations from such baselines. In this manner, systems of embodiments of the present disclosure may identify the occurrence of abnormal engine operation, vehicle component failure, vehicle damage, dangerous or potentially harmful road conditions, and the like, and alert the vehicle or driver to take corrective action.

[0159] The present disclosure may be described in the general context of computer code or machine-usable instructions, including computer-executable instructions, such as program modules, being executed by a computer or other machine, such as a personal digital assistant or other handheld device. Generally, program modules, including routines, programs, objects, components, data structures, etc., refer to code that performs particular tasks or implements particular abstract data types. The present disclosure may be implemented in a variety of configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be implemented in distributed computing environments where tasks are performed by remote processing devices linked through a communications network.

[0160] As used herein, the term "and / or" in reference to two or more elements should be interpreted to mean one element only or a combination of elements. For example, "element A, element B, and / or element C" may include element A only, element B only, element C only, elements A and B, elements A and C, elements B and C, or elements A, B, and C. Additionally, "at least one of element A or element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Furthermore, "at least one of element A and element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B.

[0161] The subject matter of the present disclosure has been described with specificity to meet statutory requirements. However, that description itself is not intended to limit the scope of the disclosure. Rather, the inventors contemplate that the claimed subject matter may be implemented in other ways, including different steps or combinations of steps similar to those described herein, in conjunction with other current or future technologies. Furthermore, although the terms "step" and / or "block" may be used herein to connote different elements of the method used, these terms should not be construed as implying any particular order among the various steps disclosed herein unless and when the order of individual steps is explicitly described.

[0162] For purposes of explanation, the foregoing used specific nomenclature to achieve a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that specific details are not required to practice the methods and systems of the present disclosure. Thus, the foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Numerous modifications and variations are possible in light of the above teachings. For example, any fiducial marker of any size and / or shape and visible in any wavelength of light may be used. Fiducial marker positions may be determined in any manner, and resulting seat belt use cases may be classified in any manner to produce any desired classification. The embodiments were chosen and described to best explain the principles of the present invention and its practical application, thereby enabling those skilled in the art to best utilize the methods and systems of the present disclosure and various embodiments with various modifications as appropriate for the particular use intended. In addition, different features of the various embodiments, whether disclosed or not, may be mixed and matched or otherwise combined to produce further embodiments contemplated by the present disclosure.

Claims

1. A system for determining seat belt position in a vehicle, comprising: a seat belt having a plurality of fiducial markers disposed thereon; a sensor positioned to generate data representative of the fiducial markers of the seat belt; a parallel processing circuit in electronic communication with the sensor, receiving sensor data generated using the sensor; generating an image using the sensor data generated using the sensor indicative of one or more fiducial markers positioned on the seat belt of the vehicle; determining, based at least on the generated image, one or more values ​​indicative of one or more coordinate locations associated with the one or more fiducial markers shown in the image; determining a distribution associated with the one or more reference markers based at least on the one or more values; comparing a first distribution associated with a position of the seat belt when the seat belt is properly used and a second distribution associated with a position of the seat belt when the seat belt is not properly used with the distribution associated with the one or more fiducial markers; determining whether the seat belt position is in a proper use state based at least on comparing the seat belt position to the first distribution and the second distribution; and Initiating an operation of the vehicle according to whether the seat belt position is in a properly used state. a parallel processing circuit configured to perform A system comprising:

2. The determined position of the seat belt is a first position in which the seat belt rests on the passenger's shoulder; a second position in which the seat belt extends under the shoulders of the passenger; a third position in which the seat belt is not fastened by the passenger; or a fourth position in which the seat belt extends behind the passenger; The system of claim 1 , wherein the system is one of:

3. The parallel processing circuitry is further configured to determine the position of the seat belt at least in part according to one or more machine learning models, the one or more machine learning models comprising: a first machine learning model having the data as input and the positions of the fiducial markers as output; or a second machine learning model having as input the positions of the fiducial markers and having as output one or more classifications of the positions of the fiducial markers; The system of claim 1 , comprising one or more of:

4. The system of claim 3 , wherein the one or more machine learning models are trained using data representing occluded one or more of the fiducial markers.

5. the parallel processing circuit further comprises: determining a size or location of the vehicle component from the position of the seat belt; determining the size of a passenger wearing the seat belt from the position of the seat belt; or Determining the position or posture of the passenger wearing the seat belt from the position of the seat belt. The system of claim 1 configured to perform one or more of the following:

6. 6. The system of claim 5, wherein the parallel processing circuitry is further configured to initiate the movement of the vehicle according to one or more of the determined size or position of the component, the determined size of the occupant, or the determined position or attitude of the occupant.

7. The system of claim 1 , wherein the sensor is a camera and the data is image data.

8. 2. The system of claim 1, wherein the action is any one of an airbag deployment, an audible alarm, a visual alarm, braking action, ignition shutoff, or movement of any one or more of a seat belt anchor, a seat belt buckle, or at least a portion of a seat.

9. The system of claim 1 , further comprising an illumination source positioned to illuminate the fiducial marker while the sensor generates the data.

10. The system of claim 9 , wherein the illumination source is an infrared illumination source and the sensor is an infrared sensor.

11. 2. The system of claim 1, wherein different ones of the reference markers are distributed along the length of the shoulder harness of the seat belt to facilitate determining the position of the length of the shoulder harness.

12. A method for determining seat belt position in a vehicle, comprising: receiving sensor data generated using a sensor, the sensor data relating to one or more fiducial markers positioned on the seat belt of the vehicle; determining, based at least on the sensor data, one or more values ​​indicative of one or more spatial locations associated with the one or more fiducial markers within the vehicle; determining a distribution associated with the one or more reference markers based at least on the one or more values; comparing a first distribution associated with a position of the seat belt when the seat belt is properly used and a second distribution associated with a position of the seat belt when the seat belt is not properly used with the distribution associated with the one or more fiducial markers; determining whether the seat belt position is in a proper use state based at least on comparing the seat belt position to the first distribution and the second distribution; and Initiating an operation of the vehicle according to the position of the seat belt and the seat belt being in a properly used state. A method comprising:

13. The seat belt position is a first position in which the seat belt rests on the passenger's shoulder; a second position in which the seat belt extends under the shoulders of the passenger; a third position in which the seat belt is not fastened by the passenger; or a fourth position in which the seat belt extends behind the passenger; 13. The method of claim 12, wherein the

14. The method of claim 12 , further comprising training the one or more machine learning models according to data indicative of one or more of the fiducial markers being occluded.

15. 13. The method of claim 12, wherein the action is any one of an airbag deployment, an audible alarm, a visual alarm, braking action, ignition shutoff, or movement of any one or more of a seat belt anchor, a seat belt buckle, or at least a portion of a seat.

16. 13. The method of claim 12, wherein the fiducial marker is further located on a seat of the vehicle, and the determining step further comprises determining a position of the seat of the vehicle.

17. determining a size or location of a portion of the vehicle from the seat belt position; determining the size of the seat belted passenger from the seat belt position; or Determining the passenger's position or posture from the seat belt position. The method of claim 12 , further comprising one or more of:

18. 20. The method of claim 17, wherein the initiating step further comprises initiating the movement of the vehicle according to one or more of the determined size or position of the portion of the vehicle, the determined size of the passenger, or the determined position or attitude of the passenger.

19. The method of claim 12 , wherein the sensor is a camera and the data is image data.

20. The method of claim 12 , further comprising activating an illumination source to illuminate the fiducial marker while the sensor generates the data.

21. 21. The method of claim 20, wherein the illumination source is an infrared illumination source and the sensor is an infrared sensor.

22. 13. The method of claim 12, wherein the fiducial markers are dispersed along the length of the shoulder harness of the seat belt to facilitate determining the position of the length of the shoulder harness.

Images