User authentication for vehicle access and in-cabin experience using infrared imaging

Abstract

The invention relates to user authentication for vehicle access and in-cabin experience using infrared imaging. In various examples, infrared image data may be used to detect subcutaneous features (e.g., palm vein topology) of a person (e.g., a person requesting access to a vehicle, a vehicle occupant) and authenticate a user based on the detected subcutaneous features. For example, infrared image data representing one or more acquired subcutaneous features (e.g., topologies that are authenticating veins and / or other blood vessels in areas of the palm, hand, neck, forearm, face, fingertip, eye, etc. of the user may be generated. Hand and / or palm detection may be applied to detect a region depicting a user's hand or palm, and the region (or some subset thereof) may be segmented to generate a representation of the acquired vein topology. The acquired vein topology may be compared to one or more reference vein topologies stored in a database to determine whether the acquired vein topology matches one of the reference vein topologies.

Description

Background Art

[0001] Vehicle designs often rely on some type of authentication to ensure that only authorized users can access the vehicle. Traditional physical keys are one of the most straightforward authentication methods. The owner or authorized operator of the vehicle uses a unique key that fits into the vehicle lock to gain access. Remote keyless entry systems (e.g., key fobs or smart keys) can grant vehicle access by transmitting a radio frequency signal that communicates with the electronics in the vehicle to unlock the doors when the user approaches the vehicle. Some keyless entry and start (e.g., push-to-start) systems use proximity sensors to detect the presence of an authorized key fob and unlock the doors when the user presses a button on the door handle while the key fob is near the vehicle. Push-to-start systems detect whether an authorized key fob is inside the vehicle and start the engine when the user presses the start button while the key fob is inside the vehicle. Some modern vehicles are equipped with biometric authentication systems, such as fingerprint scanners or facial recognition systems. These systems can identify authorized users based on their unique fingerprints or detected facial features and can grant access to the vehicle accordingly.

[0002] One of the major drawbacks of existing biometric authentication systems stems from their reliance on salient physical features for user authentication. For example, conventional facial recognition systems in existing vehicles can be defrauded by placing a photograph or three-dimensional (3D) silhouette of the vehicle owner in front of the system's camera, thereby tricking the system into falsely authenticating an intruder as the owner. In another example, conventional facial recognition systems may be less accurate and fail in situations where salient physical features are obscured (e.g., when the user is wearing a mask or other facial covering). As a result, existing biometric authentication technologies are susceptible to hacking and errors that could compromise the safety of the vehicle and / or its passengers. Therefore, there is a need for improved biometric authentication technologies that can reduce the risk of unauthorized entry and / or increase the convenience of entering and operating a vehicle. Summary of the invention

[0003] Embodiments of the present disclosure relate to personal (e.g., subcutaneous) authentication for vehicle access and in-cabin experience. Disclosed are systems and methods for using infrared image data to detect one or more subcutaneous features (e.g., a representation of vein topology, such as a palm vein map) of a person (e.g., a person requesting entry into a vehicle, a vehicle occupant) and authenticate the person based on the detected subcutaneous features.

[0004] Compared to conventional systems (such as the above-mentioned systems), the infrared camera can use a near-infrared light source (e.g., a wavelength of 850 nanometers, 940 nanometers, etc.) to generate infrared image data representing one or more acquired subcutaneous features (e.g., the topology of veins and / or other blood vessels in the palm, hand, neck, forearm, face, fingertip, eye, etc. of the user being authenticated). In this way, the infrared image data can be segmented to generate a representation of one or more acquired subcutaneous features (e.g., a binary map representing the segmented veins), and the acquired subcutaneous features can be matched with corresponding reference subcutaneous features stored in a database to authenticate the user.

[0005] In example embodiments involving access to an interior space (e.g., a vehicle cabin), an image acquisition loop may be initiated upon detection of a request to unlock a door (e.g., via a key fob or key fob, by pressing a button (e.g., on a door handle)), upon detection of the presence of an authorized key fob or key fob via a proximity sensor, and / or otherwise. Taking palm vein authentication as an example, a user being authenticated may place their hand in front of an infrared camera positioned and / or oriented to view the exterior of the vehicle, and the infrared camera may be used to generate an infrared image of the user's hand. Hand and / or palm detection may be applied to the infrared image to detect an area depicting the user's hand or palm, and that area (or some subset thereof) may be segmented to generate a representation of the acquired vein topology.

[0006] In example embodiments involving authentication of an occupant of an interior space, an image acquisition loop may be initiated when a door to the interior space is unlocked, when the presence of an authorized key fob or key fob is detected within the interior space via a proximity sensor, when a body gesture is detected (e.g., via an OMS sensor) indicating that one or more key points representing a limb, hand, wrist, and / or palm of the occupant are within a threshold distance of an infrared camera, and / or other circumstances. Taking palm vein authentication as an example, the user being authenticated may place their hand in front of an infrared camera (e.g., a driver monitoring system camera located in a steering column), the infrared camera may be used to generate an infrared image of the user's hand, hand detection and / or palm detection may be applied to the infrared image to detect an area depicting the user's hand and / or palm, and that area (or some subset thereof) may be segmented to generate a representation of the acquired vein topology.

[0007] The detected vein topology obtained from the user being authenticated can be compared with one or more reference vein topologies stored in a database to determine whether the detected vein topology matches one of the reference vein topologies. For example, the acquired (e.g., binary) palm vein topology can be aligned with a reference (e.g., binary) palm vein topology, and the acquired palm vein topology and the reference palm vein topology can be applied to one or more machine learning models (e.g., discriminator networks) to generate an indication of whether the corresponding areas of the acquired palm vein topology and the reference palm vein topology are predicted to match. A measure of the likelihood and / or similarity of the match (e.g., the number of matching areas) can be determined and compared with a specified threshold (e.g., which may depend on the resolution of the palm vein topology and / or the compared area). If the measure of the likelihood and / or similarity of the match (e.g., the number of matching areas) reaches or exceeds the specified threshold, the user being authenticated can pass the authentication. Otherwise, the acquired palm vein topology can be tested with another reference palm vein topology in the database, for example, until the user passes the authentication or there are no remaining reference palm vein topologies available for testing.

[0008] Thus, depending on the application, one or more responsive actions may be taken in response to authenticating the user (e.g., unlocking the doors; updating one or more vehicle, cabin, console, and / or other settings to reflect saved values ​​associated with the corresponding user profile, such as seat position, mirror orientation, steering wheel position, heating and / or cooling settings, audio and / or volume presets, driving modes, console display settings, driver assist settings, vehicle lighting, etc.; and / or other actions). BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present system and method for subcutaneous authentication will be described in detail below with reference to the accompanying drawings, in which:

[0010] Figure 1 is a data flow diagram illustrating an example palm vein authentication pipeline according to some embodiments of the present disclosure;

[0011] Figure 2A shows an example placement of a driver monitoring system camera within a steering column, and Figure 2B is an example infrared image of a user's palm being authenticated as seen from the perspective of a driver monitoring system camera within a steering column according to some embodiments of the present disclosure;

[0012] Figure 3 Some embodiments of the present disclosure are shown Figure 2B Example region of interest in an example infrared image of;

[0013] Figure 4 shows an example representation of a segmented vein topology according to some embodiments of the present disclosure;

[0014] Figure 5A and Figure 5B shows an example representation of a correspondence between an acquired vein topology and a reference vein topology according to some embodiments of the present disclosure;

[0015] Figure 6 is a flow chart illustrating a method of performing one or more actions based at least on matching one or more acquired representations of subcutaneous features with corresponding representations of one or more reference subcutaneous features according to some embodiments of the present disclosure;

[0016] Figure 7 is a flow chart illustrating a method for comparing an acquired vein topology with a reference vein topology according to some embodiments of the present disclosure;

[0017] Fig. 8A is an illustration of an example autonomous vehicle according to some embodiments of the present disclosure;

[0018] Figure 8B According to some embodiments of the present disclosure Fig. 8A Examples of autonomous vehicle camera positions and fields of view;

[0019] Figure 8C According to some embodiments of the present disclosure Fig. 8A a block diagram of an example system architecture for an example autonomous vehicle;

[0020] Fig.8D According to some embodiments of the present disclosure, a method for Fig. 8A System diagram of an example of communication between autonomous vehicles;

[0021] Fig. 9 is a block diagram of an example computing device suitable for implementing some embodiments of the present disclosure; and

[0022] Fig.10 is a block diagram of an example data center suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0023] Disclosed are systems and methods related to user authentication for vehicle access and in-cabin experience using infrared imaging. For example, disclosed are systems and methods for detecting one or more subcutaneous features (e.g., a representation of vein topology, such as a palm vein map) of a person (e.g., a person requesting entry into a vehicle, a vehicle occupant) using infrared image data and authenticating the person based on the detected (one or more) subcutaneous features. The present technology can be used to authenticate a person and / or take some responsive action (e.g., unlocking a door; updating one or more vehicle, cabin, console, and / or other settings to reflect saved values ​​associated with or otherwise customized for a corresponding user profile, such as seat position, mirror orientation, steering wheel position, heating and / or cooling settings, audio and / or volume presets, driving modes, console display settings, driver assistance settings, vehicle lighting, etc.; and / or other actions) in systems with surround visualization, occupant monitoring (e.g., driver and / or passenger monitoring), and / or other types of systems.

[0024] Although the present disclosure may be directed to an example autonomous or semi-autonomous vehicle or machine 800 (referred to herein alternatively as “vehicle 800 ” or “ego machine 800 ”), the example Figures 8A-8D For example, the systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more advanced driver assistance systems (ADAS)), autonomous vehicles or machines, manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles connected to one or more trailers, aircraft, boats, space shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, airplanes, engineering vehicles, trains, underwater vehicles, remotely controlled vehicles (e.g., drones), and / or other vehicle types. In addition, while the present disclosure may be described with respect to subcutaneous (e.g., vein topology, such as palm veins) authentication for vehicle access and in-cabin experience, this is not intended to be limiting, and the systems and methods described herein may be used for augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and / or any other technology space where authentication may be used.

[0025] At a high level, one or more infrared (IR) sensors (e.g., IR cameras, combined RGB / infrared (RGB / IR) cameras) may be positioned to observe a user being authenticated. In an example embodiment involving vehicle entry, one or more IR sensors may be positioned to observe a person requesting vehicle entry. In an example embodiment involving an occupant monitoring system (OMS), one or more IR sensors may be positioned to observe one or more occupants within a vehicle cabin, cockpit, or other interior space. For example, the OMS may include a driver monitoring system (DMS), a system for monitoring non-driver occupants, or a system for monitoring driver occupants and / or non-driver occupants. The IR sensor may use a near infrared (NIR) light source (e.g., a wavelength of 850 nanometers, 940 nanometers, etc.) to generate IR image data representing one or more acquired subcutaneous features (e.g., the topology of veins and / or other blood vessels in areas such as the palm, hand, neck, forearm, face, fingertips, eyes, etc. of the user being authenticated). Thus, the IR image data may be segmented to generate representations of one or more acquired subcutaneous features (eg, a binary map representing segmented veins), and the acquired subcutaneous features may be matched with corresponding reference subcutaneous features stored in a database to authenticate the user.

[0026] Taking palm vein authentication as an example, in some embodiments, an image acquisition loop can be initiated to generate one (or more) IR images and detect whether a suitable palm vein topology is visible in the IR image. In an example embodiment involving entry into an interior space (e.g., a vehicle cabin), an image acquisition loop can be initiated upon detecting a request to unlock a door (e.g., by a key fob or smart key, by pressing a button, such as a button on a door handle), upon detecting the presence of an authorized key fob or smart key by a proximity sensor, and / or other means. In an example embodiment involving authentication of an occupant of an interior space, an image acquisition loop can be initiated when a door of the interior space is unlocked, upon detecting the presence of an authorized key fob or smart key in the interior space by a proximity sensor, upon detecting (e.g., by an OMS sensor) a body gesture indicating that one or more key points representing a limb, hand, wrist, and / or palm of the occupant are within a threshold distance of an infrared camera, and / or other means. Once initiated, the image acquisition loop may (e.g., repeatedly) generate an IR image, detect the presence of a hand or palm in the IR image, normalize the posture of the detected hand or palm (e.g., by aligning the detected posture to face the IR camera and distorting the IR image accordingly), and / or detect the presence of veins in one or more regions of the detected hand or palm. In some embodiments, the image acquisition loop may test the detection accuracy of the hand, palm, and / or vein detection according to corresponding confidence thresholds. If the hand, palm, and / or vein topology is not detected with at least a minimum confidence, the image acquisition loop may restart the loop and acquire a new IR image. In some embodiments, the image acquisition loop may provide feedback on a display visible to the authenticating user, including posture guidance instructions on how to position their hand to increase the likelihood of positive detection (e.g., move the hand closer to the camera, or move the hand further away from the camera), where the instructions may be based on detection accuracy, the size and / or location of the region in the IR image where the detected hand or palm is detected, the detected posture (e.g., indicating the position of the occupant's limb, hand, wrist, and / or palm relative to the IR camera), and / or other features from previous loop iterations. Thus, the image acquisition loop may iterate at any specified frame rate, for example, until the vein topology is detected with at least a minimum confidence.

[0027] In some embodiments, the detected area predicted to contain the hand and / or palm (e.g., the predicted and / or normalized bounding box or other bounding shape) can be reduced, fine-tuned and / or otherwise processed to identify one or more regions of interest (e.g., subset regions of the detected area), and palm vein detection can be run on the predicted area and / or the identified region of interest. For example, most palms may be located in a subset of the hand (e.g., the lower 50% of the bounding box of the hand, a subset region of size determined based on the correlation between the palm size and the relationship between the index finger and the little finger, etc.), so one or more corresponding subset regions of the predicted bounding box or other bounding shape can be identified. In some embodiments, palm vein detection can be run separately for different regions of interest (e.g., scanning one or more kernels on the row of the predicted bounding box to test different subset regions, performing a first pass on a larger region of interest, performing a second pass on a smaller region of interest (e.g., a subset region identified by the first pass), etc.). In this way, one or more subset regions of the predicted bounding box or other bounding shape can be identified, and palm vein detection can be run on the subset regions.

[0028] In some embodiments, an IR image (e.g., obtained by multiplying an original IR image by a binary mask corresponding to an identified region of interest, rotated to align the identified region of interest with an input grid (e.g., a rectangular grid)) can be segmented to extract a representation of a detected vein topology (e.g., a palm vein topology), which can include a binary value representing whether each pixel is predicted to depict a detected vein. The detected vein topology obtained from the user being authenticated can be compared with one or more reference vein topologies stored in a database to determine whether the detected vein topology matches one of the reference vein topologies. For example, the obtained (e.g., binary) palm vein topology can be aligned with a reference (e.g., binary) palm vein topology, and the obtained palm vein topology and the reference palm vein topology can be applied to one or more machine learning models (e.g., a discriminator network) to generate a representation of whether corresponding regions of the obtained palm vein topology and the reference palm vein topology are predicted to match. A measure of the likelihood and / or similarity of a match (e.g., the number of matching regions) can be determined and compared to a specified threshold (e.g., which may depend on the resolution of the palm vein topology and / or the region being compared). If the match probability and / or similarity metric (e.g., the number of matching areas) reaches or exceeds a specified threshold, the authenticating user may be authenticated. Otherwise, the acquired palm vein topology may be tested against another reference palm vein topology in the database, for example, until the user is authenticated or there are no remaining reference palm vein topologies to test.

[0029] Thus, the techniques described herein may be used to authenticate a person and / or take some responsive action (e.g., unlock a door; update one or more vehicle, cabin, console, and / or other settings to reflect a saved value associated with a corresponding user profile, and / or other actions). The present techniques improve upon conventional biometric authentication techniques in terms of security and privacy. For example, subcutaneous authentication is more powerful than conventional facial recognition systems because subcutaneous features (e.g., acquired palm vein topology) are only visible under infrared illumination and are therefore more difficult to spoof than, for example, facial recognition systems that rely on visible light. In addition, embodiments that authenticate based on infrared illumination of unobstructed subcutaneous features (e.g., detected from a palm, hand, neck, forearm, unobstructed facial area, fingertips, eyes, etc.) avoid failures that occur when conventional solutions attempt to authenticate based on obstructed physical attributes (e.g., when a user is wearing a mask or other facial covering). In addition, embodiments that rely on a user to position themselves in front of a camera to trigger an authentication request provide additional flexibility by allowing the user to choose whether to authenticate, as opposed to conventional facial recognition techniques, where there is no way to evade the camera and trigger an authentication request. Thus, the techniques described herein amount to improved biometric authentication techniques that may reduce the risk of unauthorized entry and / or increase the ease of entering and operating a vehicle.

[0030] refer to Figure 1 , Figure 1 is an example palm vein authentication pipeline 100 according to some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are presented only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of the arrangements and elements shown, and certain elements may be omitted entirely. In addition, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and may be implemented in any suitable combination and location. The various functions performed by the entities described herein may be performed by hardware, firmware, and / or software. For example, the various functions may be performed by a processor executing instructions stored in a memory. In some embodiments, the systems, methods, and processes described herein may be used with Figures 8A-8D Example of autonomous vehicle 800, Fig. 9 The example computing device 900 and / or Fig.10 The example data center 1000 may be implemented with similar components, features and / or functions.

[0031] exist Figure 1In the illustrated embodiment, the palm vein authentication pipeline 100 includes an IR camera 105, a loop control component 110, an image acquisition component 120, a region of interest generator 130, a palm vein detector 140, and an authentication component 150. At a high level, the loop control component 110 may initiate and control an image acquisition loop in which the image acquisition component 120 may generate IR image data representing a portion of a user being authenticated (e.g., palm, hand, neck, forearm, face, fingertips, eyes, etc.) using the IR camera 105 and identify and / or locate a detected hand or detected palm depicted in the IR image data, the region of interest generator 130 may identify certain regions of the detected hand or detected palm, and the palm vein detector 140 may segment a vein topology (or some other subcutaneous feature visible under infrared illumination) from the identified regions of the detected hand or detected palm. The loop control component 110 may iterate the loop at any specified rate until a detected hand or palm region and / or segmented vein topology is obtained for which the detection accuracy meets or exceeds a threshold confidence level. Therefore, the authentication component 150 can compare the acquired vein topology with one or more reference vein topologies 160 representing authorized users to determine whether the user being authenticated is an authorized user. If so, the authentication component 150 can determine that the user being authenticated has been authenticated and perform certain responsive operations, such as granting access rights (e.g., unlocking one or more vehicle locks 170, applying one or more vehicle settings 180 associated with the profile of the authenticated user, etc.).

[0032] The IR camera 105 (e.g., which may be a dedicated IR camera, an RGB / IR camera, and / or some other device including one or more IR sensors) may include or otherwise coordinate with a near infrared (NIR) light source (e.g., having a wavelength of 850 nanometers, 940 nanometers, etc.) to illuminate a target area in the field of view of the IR camera 105. Thus, the IR camera 105 may include one or more IR sensors that may be used to observe IR light (e.g., radiated and / or reflected IR light) and generate IR image data representative of a scene and / or object(s) in the field of view of the IR camera 105. In general, the IR image data (e.g., an IR image) may include pixels storing values ​​corresponding to detected temperatures, and the IR image data may be in the form of a grayscale image or a false color representation, where different grayscales or different colors are used to represent different temperatures.

[0033] The infrared camera 105 can be positioned and / or oriented to observe the authenticating user in various ways depending on the application (e.g., security applications, observing a body area for one or more subcutaneous features, etc.) and / or embodiment. For example, in some embodiments observing the topology of veins and / or other blood vessels in a specific area of ​​the authenticating user's body, the IR camera 105 can be positioned in a manner that is accessible to the authenticating user and / or facilitates positioning a significant portion of the authenticating user's body in the field of view and within an appropriate distance from the IR camera 105 (e.g., raising their palm or forearm close to the camera; placing their face, neck, or eyes in front of the camera; etc.). Taking the example of securing access (e.g., to a physical space such as a room, building, or cubicle; to a software or electronic device such as a computing or storage device; to an (e.g., access-controlled) account; to an online service; to a database or record; etc.) through subcutaneous authentication, the IR camera 105 can be placed on a wall or other component of a protected physical space, in a computing or storage device for requesting access (e.g., an IR web camera, an IR smartphone camera, etc.), and / or elsewhere. In an example embodiment involving vehicle entry, the IR camera 105 may be a surround view camera (e.g., equipped with an infrared sensing function). Figures 8A-8D The IR camera 105 may be part of an OMS or DMS, such as a surround view camera 874 of the vehicle 800 and / or may be otherwise positioned to observe a person requesting vehicle entry. In example embodiments involving authorizing the start of a vehicle engine or detecting a particular identity and loading one or more saved vehicle settings associated with that identity, the IR camera 105 may be part of an OMS or DMS. Vehicle manufacturers tend to vary the number of OMS cameras depending on the model and trim level. Base models typically have an IR camera facing the driver (e.g., located on the steering column, vehicle pillar, or within the infotainment console). Higher trim levels may include any number of additional cameras (e.g., one in the steering column facing the driver, one in a rearview mirror facing the driver or cabin, one in a vehicle pillar facing a particular row of seats, etc.). Figure 2A An example implementation is shown in which the IR camera 105 corresponds to a DMS camera 210 placed within a steering column. This is by way of example only, and other types of IR cameras and / or IR sensors may be positioned and / or oriented within the scope of the present disclosure.

[0034] In some embodiments, the cycle control component 110 can initiate and / or control an image acquisition loop that repeatedly generates IR image data (e.g., IR images) using the IR camera 105 and / or otherwise accesses IR image data generated by the IR camera 105. In example embodiments involving access to an interior space (e.g., a vehicle cabin), the cycle control component 110 can initiate an image acquisition loop when the cycle control component 110 or some other component detects a request to unlock a door (e.g., via a key fob or key fob, by pressing a button (e.g., on a door handle)), when the cycle control component 110 or some other component detects the presence of an authorized key fob or key fob via a proximity sensor, and / or otherwise. In example embodiments involving authentication of an occupant of an interior space (e.g., associated with authorization to start a vehicle engine, detection of a particular identity, and loading one or more saved vehicle settings associated with the identity, etc.), an image acquisition loop may be initiated when the loop control component 110 or some other component detects that a door to the interior space is unlocked, when the loop control component 110 or some other component detects, via a proximity sensor, the presence of an authorized key fob or key fob within the interior space, when the loop control component 110 or some other component detects (e.g., via an OMS sensor, which may be a different camera than the IR camera 105) a body gesture indicating that one or more key points representing a limb, hand, wrist, and / or palm of the occupant are within a threshold distance of the IR camera 105, and / or other situations. In example embodiments involving body gesture detection, an OMS camera (e.g., in a rearview mirror) may be used to detect a body gesture of a vehicle operator, and the loop control component 110 may evaluate the detected body gesture to determine when the detected body gesture indicates that the vehicle operator has positioned his or her hand or palm within a threshold distance of a DMS camera in the steering column.

[0035] Thus, the authenticating user may position himself and / or a significant portion of his body within the field of view of the IR camera 105. In some embodiments (e.g., when the loop control component 110 initiates an image acquisition loop), the image acquisition component 120 may use the IR camera 105 to generate and / or otherwise access IR image data representing a portion of the authenticating user (e.g., a palm, hand, neck, forearm, face, fingertips, eyes, etc.). For example, Figure 2B is an example infrared image of the palm of the user being authenticated that may be generated according to some embodiments of the present disclosure. In some embodiments, to facilitate determining whether a suitable image of a significant portion of the user's body being authenticated has been generated, Figure 1The image acquisition component 120 can identify and / or locate the detected hand or detected palm depicted in the IR image data, and the loop control component 110 can test the detection accuracy of the detected hand or detected palm. If the loop control component 110 determines that the detection accuracy is below a minimum confidence level, the loop control component 110 can acquire a new IR image (e.g., at a specified frame rate).

[0036] exist Figure 1 In the illustrated embodiment, the subcutaneous features used for authentication may include palm vein topology, and thus the image acquisition component 120 may include a palm detection network 122 and a normalization component 124. At a high level, the palm detection network 122 (or hand detection network) may include one or more machine learning models (e.g., one or more deep neural networks or "DNNs") that detect and / or locate a hand or palm in the IR image data (e.g., a bounding box or other bounding shape and / or one or more key points representing a pose of the hand or palm in the IR image data), and the normalization component 124 may normalize the detected pose of the hand or palm (e.g., by aligning the detected pose to face the IR camera 105 and warping the IR image accordingly).

[0037] In some embodiments, the palm detection network 122 (and / or any other machine learning model and / or detection network described herein, such as the palm vein detection network 144 and / or the palm vein comparison network 154) may be implemented using a DNN, such as a convolutional neural network (CNN). Although some embodiments describe the use of a neural network to implement the palm detection network 122, the palm vein detection network 144, and / or the palm vein comparison network 154, this is not limiting. For example, but not limited to, the palm detection network 122, the palm vein detection network 144 and / or the palm vein comparison network 154 may include any type of multiple different networks or machine learning models, such as machine learning models using linear regression, logistic regression, decision trees, support vector machines (SVM), naive Bayes, k-nearest neighbors (Knn), K-means clustering, random forests, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., autoencoders, convolutions, transformers, loops, perceptrons, long / short term memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolution, generative adversarial, liquid machines, etc.), and / or other types of machine learning models.

[0038] In some embodiments, the palm detection network 122 (and / or any other machine learning model and / or detection network described herein, such as the palm vein detection network 144 and / or the palm vein comparison network 154) may include a common backbone (or laminar flow) having several input and / or output heads (or at least partially discrete laminar flows) for accepting different inputs and / or predicting different outputs. For example, the palm detection network 122 may include, but is not limited to, a feature extractor (e.g., a DNN, an encoder / decoder, etc.) comprising convolutional layers, pooling layers, and / or other layer types, wherein the output of the feature extractor may be provided as input to multiple output heads (e.g., classification and / or regression heads) for detecting and / or classifying pixels that may depict a detected hand or palm (or one or more key points or other parts thereof), detecting and / or regressing coordinates of an enclosing shape (or one or more key points or other parts) of a detected hand and / or palm, and the like. Thus, any known post-processing technique may be applied to decode the predicted classification data and / or regression data to generate a bounding box, closed polyline, and / or other bounding shape that identifies the position, size, and / or orientation of one or more key points of the detected hand or palm and / or represents the posture of the detected hand or palm. These and other architectures described herein are merely examples, and other architectures may be implemented within the scope of the present disclosure. Although some embodiments are described with respect to hand or palm detection, some embodiments may additionally or alternatively detect and / or locate other parts of the body, such as based on the location of authenticated subcutaneous features (e.g., neck, forearms, face, fingertips, eyes, significant portions thereof, etc.).

[0039] In some embodiments, the loop control component 110 can test the detection accuracy of a detected hand or a detected palm. For example, the palm detection network 122 can predict classification data (e.g., binary classification of the entire IR image, classification data for each pixel) that represents the likelihood that the IR image and / or each individual pixel depicts a hand and / or a palm. Therefore, the loop control component 110 can identify a prediction confidence (e.g., for the binary classification of the entire IR image predicted by the palm detection network 122, an aggregate and / or statistical value representing the prediction confidence of multiple per-pixel classifications, such as the average prediction confidence of pixels located within the predicted bounding box, etc.), and the loop control component 110 can use the identified prediction confidence as the detection accuracy and test the prediction confidence compared to a specified confidence threshold. If the loop control component 110 determines that the detection accuracy is below a minimum confidence level, the loop control component 110 can acquire a new IR image (e.g., at a specified frame rate). If the loop control component 110 determines that the detection accuracy meets or exceeds the minimum confidence level, the loop control component 110 can proceed to the next step in the process (e.g., in the palm vein authentication pipeline 100). Although the application of this threshold is described as occurring at a specific step in the process, this is not the case, and the application of this threshold and / or other thresholds described herein may occur at any step in the process.

[0040] In some embodiments, the image acquisition component 120 includes a normalization component 124. For example, some embodiments may attempt to detect one or more subcutaneous features in a particular part of the body (such as a hand or palm), and certain postures may facilitate positive detection. For example, by positioning an open palm in a position facing the IR camera 105, positive detection may be facilitated. Therefore, in some embodiments, the normalization component 124 can align the detected posture (e.g., represented as 2D and / or 3D coordinates of multiple key points detected by the palm detection network 122) with a specified posture (e.g., facing the IR camera 105), for example, by fitting the detected key points to a 3D model of the hand and rotating the 3D model to have the specified posture. In this way, the normalization component 124 can warp the IR image data (and / or corresponding predicted classification data, regression data, bounding shape, etc.) to the rotated key points to generate normalized IR image data representing the specified posture.

[0041] In some embodiments, the image acquisition component 120 includes a region of interest generator 130. The region of interest generator 130 can generate a representation of one or more reduced, fine-tuned and / or otherwise identified regions of interest (e.g., subset regions) of a detected hand or palm (or other region of the user's body being authenticated) represented in the (e.g., normalized) IR image data. Taking the detected hand as an example, most palms may be located in a subset of the hand (e.g., the lower 50% of the bounding box of the hand, a subset region whose size is determined based on the correlation between the size of the palm and the relationship between the index finger and the pinky finger, etc.), so the region of interest generator 130 can identify one or more corresponding subset regions of the (e.g., normalized) bounding box or other bounding shape that is predicted to contain the detected hand (e.g., by scaling the predicted bounding box in one or more dimensions by a specified percentage or calculated ratio). Figure 3 An example region of interest 310 (eg, representing the palm of a user being authenticated) is shown according to some embodiments of the present disclosure. Figure 2B A subset of the example infrared images.

[0042] Thus, the region of interest generator 130 may provide representations of one or more regions of interest of the detected hand or palm, and the palm vein detector 140 may detect the presence of and / or otherwise generate representations of the detected veins within the detected hand or palm and / or one or more regions of interest therein (representing predicted bounding boxes or other bounding shapes of the detected hand or palm and / or one or more regions of interest therein). In some embodiments, the palm vein detector 140 may be performed separately for different regions of interest (e.g., scanning one or more kernels over rows of predicted bounding boxes to test different subset regions, performing a first pass on larger regions of interest, performing a second pass on smaller regions of interest (e.g., subset regions identified by the first pass), etc.). In this way, the region of interest generator 130 can identify representations of one or more subset regions of the detected hand or palm (e.g., a predicted bounding box or other enclosing shape representing the detected hand or palm) and provide the representations of one or more subset regions of the detected hand or palm (e.g., a predicted bounding box or other enclosing shape representing the detected hand or palm) to the palm vein detector 140, and the palm vein detector 140 can process the detected hand or palm and / or the one or more subset regions to detect (e.g., segment out) veins represented in the detected hand or palm and / or the one or more subset regions.

[0043] exist Figure 1In the example shown in , the palm vein detector 140 includes an input generator 142 and a palm vein detection network 144. Given a representation representing a detected hand and / or palm and / or a subset region thereof (e.g., representing a binary mask representing a detected hand and / or palm and / or a subset region thereof), the input generator 142 can generate and / or otherwise prepare IR image data representing the detected hand, palm and / or a subset region in a format accepted by the palm vein detection network 144. For example, the input generator 142 can multiply the IR image data (e.g., an IR image generated using the IR camera 105, normalized IR image data generated by the normalization component 124) by a representation of a region to be tested (e.g., Figure 3 The method may also include applying a binary mask of the region of interest 310 of the palm vein detection network 144 to the palm vein detection network 144, rotating the (e.g., obtained) IR image data to align the tested area with the rectangular grid, and / or applying the (e.g., obtained) IR image data to the palm vein detection network 144.

[0044] Thus, the palm vein detection network 144 may segment (e.g., obtained) input IR image data to generate a representation of one or more detected veins depicted in the input IR image data. For example, the palm vein detection network 144 may include, but is not limited to, a feature extractor (e.g., a DNN, an encoder / decoder, etc.) comprising convolutional layers, pooling layers, and / or other layer types, wherein the output of the feature extractor may be provided to a classification head for detecting and / or classifying pixels that may depict a vein (or some other blood vessel or other subcutaneous feature visible in IR illumination). In some embodiments, the palm vein detection network 144 may apply class segmentation (e.g., by applying a threshold per-pixel prediction confidence) to identify pixels that may depict a vein. Thus, the palm vein detection network 144 may predict, segment out, and / or otherwise generate a (e.g., binary) representation of the acquired (e.g., palm) vein topology for use in authenticating a user. Figure 4 An example representation of a segmented palm vein topology 410 is shown in accordance with some embodiments of the present disclosure.

[0045] In some embodiments, the loop control component 110 can test the detection accuracy of the detected veins. For example, the palm vein detection network 144 can predict classification data (e.g., binary classification of the entire input IR image, classification data per pixel) that represents the likelihood that the input IR image and / or each individual pixel depicts the detected vein. Therefore, the loop control component 110 can identify a prediction confidence (e.g., for the binary classification of the entire input IR image predicted by the vein detection network 144, an aggregate and / or statistical value representing the prediction confidence of multiple per-pixel classifications, such as the average prediction confidence of the pixels within the tested area, etc.), and the loop control component 110 can use the identified prediction confidence as the detection accuracy and test the prediction confidence compared to a specified confidence threshold. If the loop control component 110 determines that the detection accuracy is below a minimum confidence level, the loop control component 110 can acquire a new IR image. If the loop control component 110 determines that the detection accuracy meets or exceeds the minimum confidence level, the loop control component 110 can proceed to the next step in the process (eg, in the palm vein authentication pipeline 100).

[0046] In some embodiments, when the loop control component 110 triggers or is otherwise associated with triggering the image acquisition component 120 to acquire another frame of IR image data, the loop control component 110 may trigger the presentation of feedback on a display visible to the authenticating user with coaching instructions on how to position their hand (or other part of their body) to increase the likelihood of positive detection (e.g., move your hand closer to the camera or further away from the camera, move your hand to the left or right, move your hand up or down, rotate your hand to face the camera, etc.). The loop control component 110 may determine which instructions to provide based on detection accuracy (e.g., detection accuracy of the palm detection network 122, palm vein detection network 144), the size and / or location of the area of ​​the IR image where the detected hand or palm is detected (e.g., by the palm detection network 122), the detected posture (e.g., indicating the position of the occupant's limb, hand, wrist, and / or palm relative to the IR camera), and / or other features from previous loop iterations. In an example embodiment involving body gesture detection, an OMS camera (e.g., in a rearview mirror) may be used to detect a body gesture of a vehicle operator, and the loop control component 110 may evaluate the detected body gesture to detect the distance and / or positioning of the vehicle operator's hand relative to the DMS camera in the steering column. Thus, the loop control component 110 may determine in which direction the vehicle operator should move his or her hand and / or any suggested changes to his or her gesture orientation, and the loop control component 110 may trigger a display to display corresponding instructions. Thus, the loop control component 110 may trigger any number of iterations of an image acquisition loop running at any specified frame rate (e.g., 5 frames per second (fps), 10 fps, 20, 30, etc.), for example, until the vein topology (or other subcutaneous features) is detected with at least a minimum confidence level.

[0047] In some embodiments, the authentication component 150 may compare the acquired palm vein topology of the authenticating user with one or more reference palm vein topologies 160 representing authorized users to determine whether the authenticating user is an authorized user. The reference vein palm vein topology 160 may be registered using any suitable technique. For example, the vehicle owner and / or authorized user may create or update a user profile (e.g., by interacting with a display such as an infotainment console). As part of the process, the vehicle owner and / or authorized user may be prompted to register their palm vein topology (or other subcutaneous features), in which case a representation of their palm vein topology may be obtained using the techniques described herein (e.g., using the circulatory control component 110, the image acquisition component 120, the region of interest generator 130, and / or the palm vein detector 140). In some embodiments, one or more cabin, console, and / or other settings may be saved and / or otherwise associated with their user profile, such as seat position, mirror orientation, steering wheel position, heating and / or cooling settings, audio and / or volume presets, driving modes, console display settings, driver assistance settings, vehicle lighting, and / or other.

[0048] Thus, the authentication component 150 may access one of the reference palm vein topologies 160 registered in a database (e.g., resident in the vehicle, at some remote location accessible via a wireless communication network) and compare the acquired vein topology with the palm reference vein topology 160. Figure 1 In the example shown, the authentication component 150 includes an input generator 152 , a palm vein comparison network 154 , and a matching component 156 .

[0049] In some embodiments, the input generator 152 may align the (e.g., binary) representation of the acquired palm vein topology with the reference palm vein topology (e.g., rotate one or both to align with the input (e.g., rectangular) grid, rotate one or both to have comparable poses, center one or both in or otherwise position them in a particular region (e.g., central region) of the frame, etc.). Thus, the input generator 152 may apply the acquired palm vein topology and the reference palm vein topology to the palm vein comparison network 154 to generate a representation of whether corresponding regions of the acquired palm vein topology and the reference palm vein topology are predicted to match, and the matching component 156 may decode the output of the palm vein comparison network 154 to determine whether the acquired palm vein topology and the reference palm vein topology are predicted to match.

[0050] For example, the palm vein comparison network 154 may include one or more machine learning models (e.g., a discriminator network) that compares the corresponding regions of the acquired palm vein topology and the reference palm vein topology (e.g., a binary mask representing the acquired palm vein topology and the reference palm vein topology) and generates an indication of whether the corresponding regions match. For example, the palm vein comparison network 154 may include, but is not limited to, one or more feature extractors (e.g., a DNN, an encoder / decoder, etc.), including convolutional layers, pooling layers, and / or other layer types, for extracting and / or comparing features of the acquired palm vein topology and the reference palm vein topology. The palm vein comparison network 154 may predict a correspondence graph that quantifies the similarity and / or likelihood of the corresponding regions of the acquired palm vein topology and the reference palm vein topology matching. For example, each element in the correspondence graph may quantify the similarity and / or likelihood of matching between a portion of a topology (e.g., a binary mask representing a topology) and a corresponding portion of another topology (e.g., a binary mask representing another topology) within a receptive field having a specified size (e.g., 5x5 pixels). As a non-limiting example, the representation of the palm vein topology may have a pixel resolution of 630x430, and the palm vein comparison network 154 may predict a correspondence map having a resolution of 126x86 based on comparing the receptive fields of the corresponding 5x5 pixel regions of the acquired palm vein topology and the reference palm vein topology. Thus, each element in the predicted output (e.g., the predicted correspondence map) may quantify the likelihood that the corresponding regions of the acquired palm vein topology and the reference palm vein topology match.

[0051] Thus, the matching component 156 can quantify the number of matching regions, for example, by applying a threshold confidence (e.g., 85%) to each element of the predicted output (e.g., the predicted correspondence map). In this way, the matching component 156 can identify the number of matching regions between the acquired palm vein topology and the reference palm vein topology, and the matching component 156 can compare the number to a specified threshold, which may depend on the resolution of the palm vein topology, the size of the receptive field, the body region being compared, and / or other factors. Continuing with the above example, using a palm vein topology with a pixel resolution of 630x430 and a correspondence map with a resolution of 126x86, a comparison between an unmatched acquired vein topology and a reference vein topology may result in approximately 20 matching regions, while a comparison between a matched acquired vein topology and a reference vein topology may result in approximately 100 matches, so a threshold such as 70 can be applied. Figure 5A is a visualization of an example correspondence 530 between an unmatched acquired vein topology 510 and a reference vein topology 520, and Figure 5Bis a visualization of an example correspondence 550 between a matched acquired vein topology 510 and a reference vein topology 540 .

[0052] Thus, if the matching component 156 can determine whether the number of matching areas meets or exceeds a specified threshold, and if so, the matching component 156 can determine that the user being authenticated is an authorized user and is therefore authenticated. Otherwise, the authentication component 150 can test the acquired palm vein topology against another reference vein topology 160 in the database, for example, until the user is authenticated or there are no remaining reference palm vein topologies to test. If the authentication component 150 (e.g., the matching component 156) determines that the user has been authenticated, the authentication component 150 can perform a responsive action, such as granting access rights (e.g., triggering one or more actuators to unlock one or more corresponding vehicle locks 170, applying one or more saved settings associated with the profile of the authenticated user (e.g., vehicle settings 180), etc.).

[0053] Reference now Figure 6 and Figure 7 , each block of methods 600 and 700 described herein includes a computing process that can be performed using any combination of hardware, firmware, and / or software. For example, various functions can be performed by a processor executing instructions stored in a memory. These methods can also be embodied as computer-usable instructions stored on a computer storage medium. These methods can be provided by a stand-alone application, a service or a hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, methods 600 and 700 are relative to Figure 1 The palm vein authentication pipeline 100 is described by way of example. However, these methods may additionally or alternatively be performed by any one system or any combination of systems, including but not limited to the systems described herein.

[0054] Figure 6 6 is a flow chart illustrating a method 600 for performing one or more actions based at least on matching one or more acquired representations of subcutaneous features with corresponding representations of one or more reference subcutaneous features according to some embodiments of the present disclosure. The method 600 includes, at block B602, generating IR image data representing an area where a user is being authenticated. For example, relative to Figure 1 , the image acquisition component 120 can use the IR camera 105 to generate IR image data representing a portion of the user being authenticated (e.g., palm, hand, neck, forearm, face, fingertips, eyes, etc.).

[0055] Method 600 includes, at block B604, generating a representation of one or more acquired subcutaneous features based at least on at least a portion of the segmented IR image data. Figure 1 , the image acquisition component 120 (e.g., the palm detection network 122) can identify and / or locate the detected hand or detected palm depicted in the IR image data, and / or the palm vein detector 140 can segment out the vein topology (or some other subcutaneous feature visible in infrared illumination) from the IR image data and / or a portion thereof. In some embodiments, the region of interest generator 130 can identify a representation of one or more subset regions of the detected hand or palm (e.g., a predicted bounding box or other bounding shape representing the detected hand or palm) and provide the representation of one or more subset regions of the detected hand or palm (e.g., a predicted bounding box or other bounding shape representing the detected hand or palm) to the palm vein detector 140, and the palm vein detector 140 can process the detected hand or palm and / or the one or more subset regions to detect (e.g., segment out) the veins represented in the detected hand or palm and / or the one or more subset regions.

[0056] Method 600 includes, at block B606, performing one or more actions based at least on matching one or more acquired representations of subcutaneous features with corresponding representations of one or more reference subcutaneous features stored in a database. Figure 1 , the authentication component 150 can compare the acquired palm vein topology of the authenticating user with one or more reference palm vein topologies 160 representing authorized users to determine whether the authenticating user is an authorized user. If the authentication component 150 (e.g., the matching component 156) determines that the user has been authenticated, the authentication component 150 can perform a response action, such as granting access rights (e.g., triggering one or more actuators to unlock one or more corresponding vehicle locks 170, applying one or more saved settings associated with the profile of the authenticated user (e.g., vehicle settings 180), etc.).

[0057] Figure 7 7 is a flow chart showing a method 700 for comparing an acquired vein topology with a reference vein topology according to some embodiments of the present disclosure. The method 700 includes, at block B702: starting IR image acquisition. For example, relative to Figure 1 , the loop control component 110 can initiate and / or control an image acquisition loop, wherein the image acquisition component 120 uses the IR camera 105 to generate and / or otherwise access IR image data representing a portion of the user being authenticated (e.g., a palm, hand, neck, forearm, face, fingertips, eyes, etc.).

[0058] Method 700 includes, at block B704, detecting a hand or palm represented in the acquired IR image. Figure 1, the image acquisition component 120 (eg, the palm detection network 122 ) may identify and / or locate a detected hand or detected palm depicted in the acquired IR image.

[0059] Method 700 tests at block B706 whether the detection accuracy is above a specified threshold. Figure 1 , the loop control component 110 may test the detection accuracy of the detected hand or detected palm detected at box B704. For example, the loop control component 110 may identify a prediction confidence (e.g., for a binary classification of the entire IR image predicted by the palm detection network 122, an aggregate and / or statistical value representing the prediction confidence of multiple per-pixel classifications, such as an average prediction confidence of pixels located within a prediction bounding box, etc.), and the loop control component 110 may use the identified prediction confidence as the detection accuracy and test the prediction confidence against a specified confidence threshold. If the detection accuracy does not meet the specified threshold, the method 700 returns to box B702 to obtain a new IR image. Otherwise, the method 700 proceeds to box B708.

[0060] Method 700 includes, at block B708, segmenting veins to obtain a vein topology for the user being authenticated. Figure 1 , the palm vein detector 140 may segment out the vein topology from the acquired IR image data and / or a portion thereof. In some embodiments, the region of interest generator 130 may identify and provide to the palm vein detector 140 a representation of one or more subset regions of the detected hand or palm (e.g., a bounding box or other bounding shape representing a prediction), and the palm vein detector 140 may process the detected hand or palm and / or the one or more subset regions to detect (e.g., segment out) veins represented in the detected hand or palm and / or the one or more subset regions.

[0061] Method 700 tests at block B710 whether the detection accuracy is above a specified threshold. Figure 1 , the loop control component 110 can test the detection accuracy of the detected veins segmented at block B708. For example, the loop control component 110 can identify a prediction confidence (e.g., for a binary classification of the entire input IR image predicted by the vein detection network 144, an aggregate and / or statistical value representing the prediction confidence of multiple per-pixel classifications, such as an average prediction confidence of pixels within the tested area, etc.), and the loop control component 110 can use the identified prediction confidence as the detection accuracy and test the prediction confidence against a specified confidence threshold. If the detection accuracy does not meet the specified threshold, the method 700 returns to block B702 to obtain a new IR image. Otherwise, the method 700 proceeds to block B712.

[0062] Method 700 includes, at block B712, comparing the acquired vein topology to a reference vein topology. Figure 1 , the input generator 152 can apply the representations of the acquired palm vein topology and the reference palm vein topology to the palm vein comparison network 154 to generate a representation of whether the corresponding areas of the acquired palm vein topology and the reference palm vein topology are predicted to match, for example, a correspondence map that quantifies the likelihood that the corresponding areas of the acquired palm vein topology and the reference palm vein topology match.

[0063] Method 700 includes, at block B714, determining whether the acquired vein topology matches the reference vein topology. Figure 1 , the matching component 156 can quantify the number of matching areas, for example, by applying a threshold confidence (e.g., 85%) to each element of the predicted output (e.g., the predicted correspondence map). In this way, the matching component 156 can identify the number of matching areas between the acquired palm vein topology and the reference palm vein topology, and the matching component 156 can compare the number to a specified threshold. If the number of matching areas does not reach the specified threshold, the method 700 can return to box B712 to test the acquired palm vein topology against another reference vein topology (if there are any remaining to test). If the number of matching areas does reach the specified threshold, the method 700 includes determining at box B716 that the user has been authenticated.

[0064] The systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles connected to one or more trailers, flying boats, ships, space shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, airplanes, engineering vehicles, trains, underwater vehicles, remotely controlled vehicles (e.g., drones), and / or other vehicle types. In addition, the systems and methods described herein may be used for a variety of purposes, such as, but not limited to, machine control, machine motion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or participant simulation and / or digital twins, data center processing, conversational AI (artificial intelligence), light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation of 3D assets, cloud computing, and / or any other suitable application.

[0065] The disclosed embodiments can be included in a variety of different systems, such as automotive systems (e.g., control systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aviation systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems including one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least in part in a data center, systems for performing conversational AI operations, systems for performing light transport simulations, systems for performing collaborative content creation of 3D assets, systems implemented at least in part using cloud computing resources, and / or other types of systems.

[0066] Example Autonomous Vehicle

[0067] Fig. 8Ais an illustration of an example autonomous vehicle 800 according to some embodiments of the present disclosure. Autonomous vehicle 800 (alternatively referred to herein as "vehicle 800") may include, but is not limited to, a passenger vehicle such as a car, a truck, a bus, a first response vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police vehicle, an ambulance, a boat, a construction vehicle, an underwater vessel, a robotic vehicle, a drone, an airplane, a vehicle coupled to a trailer (e.g., a semi-tractor truck for hauling cargo), and / or another type of vehicle (e.g., a vehicle that is unmanned and / or accommodates one or more passengers). Autonomous vehicles are generally described in terms of automation levels 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, issued on June 15, 2018, Standard No. J3016-201609, issued on September 30, 2016, and previous and future versions of the standard). The vehicle 800 may be capable of implementing one or more functions of Level 3-5 that conform to the autonomous driving level. The vehicle 800 may be capable of implementing one or more functions of Level 1-5 according to the autonomous driving level. For example, depending on the embodiment, the vehicle 800 may be capable of driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5). As used herein, the term “autonomy” may include any and / or all types of autonomy of a vehicle 800 or other machine, such as fully autonomous, highly autonomous, conditionally autonomous, partially autonomous, assisted autonomous, semi-autonomous, primarily autonomous, or other names.

[0068] The vehicle 800 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. The vehicle 800 may include a propulsion system 850, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. The propulsion system 850 may be connected to a drive train of the vehicle 800, which may include a transmission, to achieve propulsion of the vehicle 800. The propulsion system 850 may be controlled in response to receiving a signal from a throttle / accelerator 852.

[0069] A steering system 854, which may include a steering wheel, may be used to steer the vehicle 800 (e.g., along a desired path or route) when the propulsion system 850 is operating (e.g., when the vehicle is in motion). The steering system 854 may receive signals from a steering actuator 856. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0070] Brake sensor system 846 may be used to operate vehicle brakes in response to receiving signals from brake actuator 848 and / or brake sensors.

[0071] May include one or more system on chip (SoC) 804 ( Figure 8C ) and / or one or more controllers 836 of one or more GPUs can provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 800. For example, the one or more controllers can send signals to operate vehicle brakes via one or more brake actuators 848, operate the steering system 854 via one or more steering actuators 856, and operate the propulsion system 850 via one or more throttles / accelerators 852. The one or more controllers 836 may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals and output operating commands (e.g., signals representing commands) to achieve autonomous driving and / or assist a human driver in driving the vehicle 800. The one or more controllers 836 may include a first controller 836 for autonomous driving functions, a second controller 836 for functional safety functions, a third controller 836 for artificial intelligence functions (e.g., computer vision), a fourth controller 836 for infotainment functions, a fifth controller 836 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 836 may handle two or more of the above functions, two or more controllers 836 may handle a single function, and / or any combination thereof.

[0072] The one or more controllers 836 may provide signals for controlling one or more components and / or systems of the vehicle 800 in response to sensor data (eg, sensor input) received from one or more sensors. Sensor data may be received from, for example and without limitation, global navigation satellite system (“GNSS”) sensors 858 (e.g., global positioning system sensors), RADAR sensors 860 , ultrasonic sensors 862 , LIDAR sensors 864 , inertial measurement unit (IMU) sensors 866 (e.g., accelerometers, gyroscopes, magnetic compasses, magnetometers, etc.), microphones 896 , stereo cameras 868 , wide angle cameras 870 (e.g., fisheye cameras), infrared cameras 872 , surround cameras 874 (e.g., 360 degree cameras), long-range and / or mid-range cameras 898 , speed sensors 844 (e.g., for measuring the velocity of the vehicle 800 ), vibration sensors 842 , steering sensors 840 , brake sensors (e.g., as part of a brake sensor system 846 ), one or more occupant monitoring system (OMS) sensors 801 (e.g., one or more interior cameras), and / or other sensor types.

[0073] One or more of the controllers 836 may receive inputs (e.g., represented by input data) from the instrument cluster 832 of the vehicle 800 and provide outputs (e.g., represented by output data, display data, etc.) via a human machine interface (HMI) display 834, an audible annunciator, a speaker, and / or via other components of the vehicle 800. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Figure 8C The HMI display 834 may include information such as a high-definition (“HD”) map 822 of the vehicle 800 , location data (e.g., the location of the vehicle 800 on the map), directions, locations of other vehicles (e.g., an occupancy grid), information about objects and states of objects as sensed by the controller 836 , and the like. For example, the HMI display 834 may display information about the presence of one or more objects (e.g., street signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that the vehicle has made, is making, or will make (e.g., changing lanes now, leaving 34B in two miles, etc.).

[0074] The vehicle 800 also includes a network interface 824 that can communicate via one or more networks using one or more wireless antennas 826 and / or a modem. For example, the network interface 824 may be capable of communicating via Long Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile Communications (“GSM”), IMT-CDMA Multi-Carrier (“CDMA2000”), etc. The one or more wireless antennas 826 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.) using one or more local area networks such as Bluetooth, Bluetooth Low Energy (“LE”), Z-wave, ZigBee, etc., and / or one or more low power wide area networks (“LPWAN”) such as LoRaWAN, SigFox, etc.

[0075] Figure 8B For use according to some embodiments of the present disclosure Fig. 8A 800. The cameras and respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or replaceable cameras may be included and / or the cameras may be located at different locations on the vehicle 800.

[0076] The camera type for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 800. The camera may operate at an automotive safety integrity level (ASIL) B and / or at another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120fps, 240fps, 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 examples, the color filter array may include a red-white-white-white (RCCC) color filter array, a red-white-white-blue (RCCB) color filter array, a red-blue-green-white (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, a clear pixel camera such as a camera with an RCCC, RCCB, and / or RBGC color filter array may be used in an effort to improve light sensitivity.

[0077] In some examples, one or more of the cameras can 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 monocular camera can be installed to provide functions including lane departure warning, traffic sign assistance, and intelligent headlight control. One or more of the cameras (e.g., all of the cameras) can simultaneously record and provide image data (e.g., video).

[0078] One or more of the cameras may be mounted in a mounting assembly such as a custom designed (three-dimensional ("3D") printed) assembly to cut out stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirror) that may interfere with the camera's image data capture capabilities. With respect to the wing mirror mounting assembly, the wing mirror assembly may be custom 3D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras may be integrated into the wing mirror. For the side view cameras, one or more cameras may also be integrated into the four pillars at each corner of the cab.

[0079] A camera (e.g., a front-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 800 can be used for surround view to help identify the forward path and obstacles, as well as assist in providing information critical to generating an occupancy grid and / or determining a preferred vehicle path with the help of one or more controllers 836 and / or control SoCs. The front-facing camera can be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. The front-facing camera can also be used for ADAS functions and systems, including lane departure warning ("LDW"), autonomous cruise control ("ACC"), and / or other functions such as traffic sign recognition.

[0080] A variety of cameras may be used in the front-facing configuration, including, for example, a monocular camera platform including a complementary metal oxide semiconductor ("CMOS") color imager. Another example may be a wide-angle camera 870, which may be used to sense objects (e.g., pedestrians, intersection traffic, or bicycles) entering the field of view from the periphery. Figure 8B Only one wide-angle camera is shown in the figure, but there may be any number (including zero) of wide-angle cameras 870 on the vehicle 800. In addition, any number of long-range cameras 898 (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not been trained. Long-range cameras 898 may also be used for object detection and classification and basic object tracking.

[0081] Any number of stereo cameras 868 may also be included in the front configuration. In at least one embodiment, one or more stereo cameras 868 may include an integrated control unit including a scalable processing unit that may provide a multi-core microprocessor and programmable logic ("FPGA") with an integrated controller area network ("CAN") or Ethernet interface on a single chip. Such a unit can be used to generate a 3D map of the vehicle environment, including distance estimates for all points in the image. An alternative stereo camera 868 may include a compact stereo vision sensor that may include two camera lenses (one on the left and one on the right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. Other types of stereo cameras 868 may be used in addition to or alternatively to those described herein.

[0082] A camera having a field of view that includes a portion of the environment to the side of the vehicle 800 (e.g., a side view camera) can be used for surround viewing, providing information used to create and update the occupancy grid and generate side impact collision warnings. Figure 8B The four surround cameras 874 shown in FIG. 8 may be placed on the vehicle 800. The surround cameras 874 may include a wide-angle camera 870, a fisheye camera, a 360-degree camera, and / or the like. For example, the four fisheye cameras may be placed in front, behind, and on the sides of the vehicle. In an alternative arrangement, the vehicle may use three surround cameras 874 (e.g., left, right, and rear), and may utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0083] A camera having a field of view that includes a portion of the environment behind the vehicle 800 (e.g., a rear view camera) can be used to assist with parking, surround view, rear collision warning, and creating and updating occupancy grids. A variety of cameras can be used, including but not limited to cameras that are also suitable as front cameras as described herein (e.g., long-range and / or mid-range cameras 898, stereo cameras 868, infrared cameras 872, etc.).

[0084] A camera (e.g., one or more OMS sensors 801) whose field of view includes a portion of the interior environment of the cabin of the vehicle 800 may be used as part of an occupant monitoring system (OMS), such as, but not limited to, a driver monitoring system (DMS). For example, an OMS sensor (e.g., OMS sensor 801) may be used (e.g., by a controller 836) to track the gaze direction, head posture, and / or blinking of an occupant and / or driver. The gaze information may be used to determine the attention level of an occupant or driver (e.g., to detect drowsiness, fatigue, and / or distraction), and / or to take responsive actions to prevent harm to an occupant or operator. In some embodiments, data from the OMS sensor may be used to implement gaze control operations triggered by a driver and / or non-driver passenger, such as, but not limited to, adjusting cabin temperature and / or airflow, opening and closing windows, controlling cabin lighting, controlling an entertainment system, adjusting rearview mirrors, adjusting seat positions, and / or other operations. In some embodiments, the OMS may be used for applications such as determining when an object and / or passenger remains in the cabin (e.g., by detecting the presence of an occupant after the driver leaves the vehicle).

[0085] Figure 8C For use according to some embodiments of the present disclosure Fig. 8A Block diagram of an example system architecture for an example autonomous vehicle 800. It should be understood that this arrangement and other arrangements described herein are set forth merely as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any appropriate combination and location. The various functions described herein as being performed by an entity may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory.

[0086] Figure 8C Each of the components, features, and systems of the vehicle 800 is illustrated as being connected via a bus 802. The bus 802 may include a controller area network (CAN) data interface (alternatively, referred to herein as a "CAN bus"). The CAN may be a network inside the vehicle 800 that assists in controlling various features and functions of the vehicle 800, such as the actuation of brakes, acceleration, braking, steering, windshield wipers, and the like. The CAN bus may be configured to have tens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compatible.

[0087] Although bus 802 is described as a CAN bus here, this is not intended to be limiting. For example, in addition to or alternatively to the CAN bus, FlexRay and / or Ethernet can be used. In addition, although bus 802 is represented by a single line, this is not intended to be limiting. For example, there can be any number of buses 802, which may include 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 examples, two or more buses 802 can be used to perform different functions, and / or can be used for redundancy. For example, a first bus 802 can be used for a collision avoidance function, and a second bus 802 can be used for drive control. In any example, each bus 802 can communicate with any component of the vehicle 800, and two or more buses 802 can communicate with the same component. In some examples, each SoC 804, each controller 836, and / or each computer in the vehicle can have access to the same input data (e.g., input from a sensor of the vehicle 800), and can be connected to a common bus such as a CAN bus.

[0088] The vehicle 800 may include one or more controllers 836, such as those described herein. Fig. 8A Those controllers described. Controller 836 can be used for a variety of functions. Controller 836 can be coupled to any other different components and systems of vehicle 800, and can be used for control of vehicle 800, artificial intelligence of vehicle 800, infotainment for vehicle 800, and / or the like.

[0089] The vehicle 800 may include one or more system on chip (SoC) 804. The SoC 804 may include a CPU 806, a GPU 808, a processor 810, a cache 812, an accelerator 814, a data store 816, and / or other components and features not shown. The SoC 804 may be used to control the vehicle 800 in a variety of platforms and systems. For example, one or more SoCs 804 may be combined with an HD map 822 in a system (e.g., a system of the vehicle 800), and the HD map may be downloaded from one or more servers (e.g., a server) via a network interface 824. Fig.8D one or more servers 878) to obtain map refreshes and / or updates.

[0090] The CPU 806 may include a CPU cluster or CPU complex (alternatively, referred to herein as a "CCPLEX"). The CPU 806 may include multiple cores and / or L2 caches. For example, in some embodiments, the CPU 806 may include eight cores in a coherent multiprocessor configuration. In some embodiments, the CPU 806 may include four dual-core clusters, each of which has a dedicated L2 cache (e.g., a 2MB L2 cache). The CPU 806 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, such that any combination of clusters of the CPU 806 can be active at any given time.

[0091] CPU 806 may implement power management capabilities including one or more of the following features: each hardware block may be automatically clock gated when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to the execution of WFI / WFE instructions; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. CPU 806 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wake-up times are specified, and hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core may support a simplified power state entry sequence in software, with the work being offloaded to the microcode.

[0092] The GPU 808 may include an integrated GPU (alternatively, referred to herein as an "iGPU"). The GPU 808 may be programmable and efficient for parallel workloads. In some examples, the GPU 808 may use an enhanced tensor instruction set. The GPU 808 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB storage capacity). In some embodiments, the GPU 808 may include at least eight streaming microprocessors. The GPU 808 may use a computing application programming interface (API). In addition, the GPU 808 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0093] In the case of automotive and embedded use, GPU 808 can be power optimized to achieve optimal performance. For example, GPU 808 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 808 can be manufactured using other semiconductor manufacturing processes. Each streaming microprocessor can merge several mixed precision processing cores divided into multiple blocks. For example and without limitation, 64 PF32 cores and 32 PF64 cores can be divided into four processing blocks. In such an example, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed precision NVIDIA tensor cores for deep learning matrix arithmetic, L0 instruction cache, thread beam (warp) scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor can include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of calculations and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. A streaming microprocessor may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0094] The GPU 808 may include a high bandwidth memory (HBM) and / or 16GB HBM2 memory subsystem that provides a peak memory bandwidth of approximately 900 GB / s in some examples. In some examples, synchronous graphics random access memory (SGRAM), such as fifth generation graphics double data rate synchronous random access memory (GDDR5), may be used in addition to or in lieu of HBM memory.

[0095] The GPU 808 may include unified memory technology that includes access counters to allow memory pages to be more accurately migrated to the processor that accesses them most frequently, thereby improving the efficiency of memory ranges shared between processors. In some examples, address translation service (ATS) support can be used to allow the GPU 808 to directly access the CPU 806 page table. In such an example, when the GPU 808 memory management unit (MMU) experiences a miss, an address translation request can be transmitted to the CPU 806. In response, the CPU 806 can look up the virtual-physical mapping for the address in its page table and transmit the translation back to the GPU 808. In this way, unified memory technology can allow a single unified virtual address space to be used for the memory of both the CPU 806 and the GPU 808, thereby simplifying GPU 808 programming and porting applications to the GPU 808.

[0096] In addition, GPU 808 may include access counters that can track how often GPU 808 accesses the memory of other processors. Access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0097] SoC 804 may include any number of caches 812, including those described herein. For example, cache 812 may include an L3 cache available to both CPU 806 and GPU 808 (e.g., connected to both CPU 806 and GPU 808). Cache 812 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4MB or more, although smaller cache sizes may also be used.

[0098] The SoC 804 may include an arithmetic logic unit (ALU) that may be utilized in performing any of a variety of tasks or operations with respect to the vehicle 800, such as processing a DNN. Additionally, the SoC 804 may include a floating point unit (FPU) (or other math coprocessor or digital coprocessor type) for performing mathematical operations within the system. For example, the SoC 804 may include one or more FPUs integrated as execution units within the CPU 806 and / or GPU 808.

[0099] SoC 804 may include one or more accelerators 814 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, SoC 804 may include a hardware accelerator cluster, which may include optimized hardware accelerators and / or large on-chip memory. The large on-chip memory (e.g., 4MB SRAM) may enable the hardware accelerator cluster to accelerate neural networks and other calculations. The hardware accelerator cluster may be used to supplement GPU 808 and offload some tasks of GPU 808 (e.g., free up more cycles of GPU 808 for performing other tasks). As an example, accelerator 814 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are sufficiently stable to be easily controlled for acceleration. When used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0100] The accelerator 814 (e.g., a hardware accelerator cluster) may include a deep learning accelerator (DLA). The DLA may include one or more tensor processing units (TPUs) that may be configured to provide an additional 10 trillion operations per second for deep learning applications and reasoning. The TPU may be an accelerator configured to perform image processing functions (e.g., for CNN, RCNN, etc.) and optimized for performing image processing functions. The DLA may be further optimized for a specific set of neural network types and floating point operations and reasoning. The design of the DLA may provide higher performance per millimeter than a general-purpose GPU, and far exceeds the performance of the CPU. The TPU may perform several functions, including a single instance convolution function, support, for example, INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0101] The DLA can quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a wide variety of functions, such as, but not limited to: a CNN for object recognition and detection using data from a camera sensor; a CNN for distance estimation using data from a camera sensor; a CNN for emergency vehicle detection and identification and detection using data from a microphone; a CNN for facial recognition and vehicle owner identification using data from a camera sensor; and / or a CNN for safety and / or security-related events.

[0102] The DLA can perform any function of the GPU 808, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 808. For example, the designer can focus the processing of CNNs and floating point operations on the DLA, and leave other functions to the GPU 808 and / or other accelerators 814.

[0103] The accelerator 814 (e.g., a hardware accelerator cluster) may include a programmable vision accelerator (PVA), which may be alternatively referred to as a computer vision accelerator herein. 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, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0104] The RISC core can interact with an image sensor (e.g., an image sensor of any camera described herein), an image signal processor, and / or the like. Each of these RISC cores can include any amount of memory. Depending on the embodiment, the RISC core can use any of a number of protocols. In some examples, the RISC core can execute a real-time operating system (RTOS). The RISC core can be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and / or storage devices. For example, the RISC core can include an instruction cache and / or a tightly coupled RAM.

[0105] The DMA may enable components of the PVA to access system memory independently of the CPU 806. The DMA may support any number of features used to provide optimizations for the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support addressing in up to six or more dimensions, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0106] The vector processor can be a programmable processor that can be designed to efficiently and flexibly perform programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA can include a PVA core and two vector processing subsystem partitions. The PVA core can include a processor subsystem, one or more DMA engines (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem can operate as the main processing engine of the PVA, and can include a vector processing unit (VPU), an instruction cache, and / or a vector memory (e.g., VMEM). The VPU core can include a digital signal processor, such as, for example, a single instruction multiple data (SIMD), a very long instruction word (VLIW) digital signal processor. The combination of SIMD and VLIW can enhance throughput and rate.

[0107] Each of the vector processors may include an instruction cache and may be coupled to a dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors 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 examples, 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 sequence images or portions of an image. Among other things, any number of PVAs may be included in a hardware accelerator cluster, and any number of vector processors may be included in each of these PVAs. In addition, the PVA may include additional error correction code (ECC) memory to enhance overall system security.

[0108] The accelerator 814 (e.g., a hardware accelerator cluster) may include an on-chip computer vision network and SRAM to provide high bandwidth, low latency SRAM for the accelerator 814. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and not limited to, eight field-configurable memory blocks that can be accessed by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides high-speed memory access to the PVA and DLA. The backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using APB).

[0109] The on-chip computer vision network may include an interface that determines that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such an interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface may comply with ISO 26262 or IEC 61508 standards, but other standards and protocols may also be used.

[0110] In some examples, SoC 804 may include a real-time ray tracing hardware accelerator such as described in U.S. Patent Application No. 16 / 101,232 filed on August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for SONAR system simulations, for general wave propagation simulations, for comparison with LIDAR data for the purpose of positioning and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0111] The accelerator 814 (e.g., a cluster of hardware accelerators) has a wide range of uses in autonomous driving. The PVA can be a programmable visual accelerator that can be used in key processing stages in ADAS and autonomous vehicles. The capabilities of the PVA are a good match for algorithmic domains that require predictable processing, low power, and low latency. In other words, the PVA performs well on semi-dense or dense rule calculations, and even on small data sets that require predictable runtimes with low latency and low power. Therefore, in the context of a platform for autonomous vehicles, the PVA is designed to run classic computer vision algorithms because they are effective at object detection and integer math.

[0112] For example, according to one embodiment of the technology, PVA is used to perform computer stereo vision. In some examples, algorithms based on semi-global matching can be used, but this is not intended to be limiting. Many applications for level 3-5 autonomous driving require instant motion estimation / stereo matching (e.g., structure from motion, pedestrian recognition, lane detection, etc.). PVA can perform computer stereo vision functions on input from two monocular cameras.

[0113] In some examples, PVA can be used to perform dense optical flow. According to the process raw RADAR data (e.g., using 4D fast Fourier transform) to provide processed RADAR. In other examples, PVA is used for time-of-flight depth processing, which is for example by processing raw time-of-flight data to provide processed time-of-flight data.

[0114] DLA can be used to run 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 a relative "weight" of each detection compared to other detections. This confidence value enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positive detections. In an automatic emergency braking (AEB) system, a false positive detection will cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detection should be considered a trigger for AEB. DLA can run a neural network for regressing confidence values. The neural network can take at least some subset of parameters as its input, such as bounding box dimensions, ground plane estimates obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 866 output related to vehicle 800 orientation and distance, 3D position estimates of objects obtained from neural networks and / or other sensors (e.g., LIDAR sensor 864 or RADAR sensor 860), etc.

[0115] SoC 804 may include one or more data stores 816 (e.g., memory). Data store 816 may be on-chip memory of SoC 804 that may store neural networks to be executed on the GPU and / or DLA. In some examples, data store 816 may be large enough to store multiple instances of the neural network for redundancy and safety. Data store 816 may include L2 or L3 cache 812. References to data store 816 may include references to memory associated with PVA, DLA, and / or other accelerators 814 as described herein.

[0116] SoC 804 may include one or more processors 810 (e.g., embedded processors). Processor 810 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management functions and related safety implementations. The boot and power management processor may be part of the SoC 804 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, auxiliary system low power state transitions, SoC 804 thermal and temperature sensor management, and / or SoC 804 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC 804 may use the ring oscillator to detect the temperature of CPU 806, GPU 808, and / or accelerator 814. If it is determined that the temperature exceeds a threshold, the boot and power management processor may enter a temperature fault routine and place SoC 804 in a lower power state and / or place the vehicle 800 in a driver safety parking mode (e.g., parking the vehicle 800 safely).

[0117] The processor 810 may also include a set of embedded processors that can be used as an audio processing engine. The audio processing engine can be an audio subsystem that allows full hardware support for multi-channel audio through multiple interfaces and a wide range of flexible audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core having a digital signal processor with dedicated RAM.

[0118] The processor 810 may also include an always-on processor engine that can provide the necessary hardware features to support low-power sensor management and wake-up use cases. The always-on processor engine may include a processor core, tightly coupled RAM, supporting peripherals (such as timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0119] The processor 810 may also include a safety cluster engine that includes a dedicated processor subsystem that handles safety management of 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 act as a single core with comparison logic to detect any differences between their operations.

[0120] Processor 810 may also include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0121] Processor 810 may also 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.

[0122] The processor 810 may include a video image compositer, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required for the video playback application to produce a final image for the player window. The video image compositer may perform lens distortion correction for the wide-angle camera 870, the surround camera 874, and / or for an in-cab surveillance camera sensor. The in-cab surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC, configured to recognize in-cab events and respond accordingly. The in-cab system may perform lip reading to activate mobile phone service and place calls, dictate emails, change vehicle destinations, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other circumstances.

[0123] The video image compositer may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, in the case of motion in the video, the noise reduction appropriately weights the spatial information and reduces the weight of information provided by adjacent frames. In the case where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositer may use information from previous images to reduce noise in the current image.

[0124] The video image compositor may also be configured to perform stereoscopic rectification on the input stereoscopic footage frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use and the GPU 808 does not need to continuously render new surfaces. Even when the GPU 808 is powered on and active for 3D rendering, the video image compositor may be used to offload the GPU 808 to improve performance and responsiveness.

[0125] The SoC 804 may also include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from a camera, a high-speed interface, and / or a video input block that may be used for camera and related pixel input functions. The SoC 804 may also include an input / output controller that may be controlled by software and may be used to receive I / O signals that are not committed to a specific role.

[0126] SoC 804 may also include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management and / or other devices. SoC 804 may be used to process data from cameras (connected via Gigabit multimedia serial links and Ethernet), sensors (e.g., LIDAR sensor 864, RADAR sensor 860, etc., which may be connected via Ethernet), data from bus 802 (e.g., speed of vehicle 800, steering wheel position, etc.), data from GNSS sensor 858 (connected via Ethernet or CAN bus). SoC 804 may also include dedicated high-performance mass storage controllers, which may include their own DMA engines, and which may be used to free up CPU 806 from routine data management tasks.

[0127] SoC 804 can be an end-to-end platform with a flexible architecture that spans automation levels 3-5, providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS technologies to achieve diversity and redundancy, together with deep learning tools to provide a platform for a flexible and reliable driving software stack. SoC 804 can be faster, more reliable, and even more energy efficient and space efficient than conventional systems. For example, when combined with CPU 806, GPU 808, and data storage 816, accelerator 814 can provide a fast and efficient platform for level 3-5 autonomous vehicles.

[0128] The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms can be executed on CPUs, which can be configured using high-level programming languages ​​such as the C programming language to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs often fail to meet the performance requirements of many computer vision applications, such as those related to, for example, execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, which is a requirement for in-vehicle ADAS applications and a requirement for practical Level 3-5 autonomous vehicles.

[0129] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a cluster of hardware accelerators, the techniques described herein allow multiple neural networks to be executed simultaneously and / or sequentially, and the results to be combined together to achieve Level 3-5 autonomous driving functions. For example, a CNN executed on a DLA or dGPU (e.g., GPU 820) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which a neural network has not been specifically trained. The DLA may also include a neural network that can recognize, interpret, and provide semantic understanding of the signs, and pass that semantic understanding to a path planning module running on the CPU complex.

[0130] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks may be running simultaneously. For example, a warning sign consisting of "Caution: Flashing Lights Indicate Icing Conditions" along with electric lights may be interpreted by several neural networks independently or collectively. The sign itself may be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing Lights Indicate Icing Conditions" may be interpreted by a deployed second neural network that informs the vehicle's path planning software (preferably executing on a CPU complex) that icing conditions exist when the flashing lights are detected. The flashing lights may be identified by operating a deployed third neural network over multiple frames that informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks may be running simultaneously, for example, within the DLA and / or on the GPU 808.

[0131] In some examples, a CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 800. The always-on sensor processing engine can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's door, and in security mode, disable the vehicle when the owner leaves the vehicle. In this way, the SoC 804 provides security against theft and / or carjacking.

[0132] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 896 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 804 uses CNN to classify environmental and urban sounds and to classify visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing rate 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 vehicle is operating, as identified by the GNSS sensor 858. Thus, for example, when operating in Europe, the CNN will seek to detect European sirens, and when in the United States, the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, with the assistance of the ultrasonic sensor 862, the control program can be used to execute emergency vehicle safety routines to slow the vehicle, drive to the side of the road, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0133] The vehicle may include a CPU 818 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 804 via a high-speed interconnect (e.g., PCIe). The CPU 818 may include, for example, an X86 processor. The CPU 818 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 804, and / or monitoring the status and health of the controller 836 and / or the infotainment SoC 830.

[0134] The vehicle 800 may include a GPU 820 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 804 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 820 may provide additional artificial intelligence functionality, for example, by executing redundant and / or different neural networks, and may be used to train and / or update the neural network based at least in part on input from sensors of the vehicle 800 (e.g., sensor data).

[0135] The vehicle 800 may also include a network interface 824, which may include one or more wireless antennas 826 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). The network interface 824 can be used to enable wireless connections to the cloud (e.g., with a server 878 and / or other network devices), to other vehicles, and / or to computing devices (e.g., a passenger's client device) via the Internet. In order to communicate with other vehicles, a direct link can be established between the two vehicles, and / or an indirect link can be established (e.g., across a network and through the Internet). The direct link can be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link can provide the vehicle 800 with information about vehicles approaching the vehicle 800 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 800). This functionality can be part of the collaborative adaptive cruise control functionality of the vehicle 800.

[0136] The network interface 824 may include a SoC that provides modulation and demodulation functions and enables the controller 836 to communicate over a wireless network. The network interface 824 may include a radio frequency front end for up-conversion from baseband to radio frequency and down-conversion from radio frequency to baseband. The frequency conversion may be performed by a known process and / or may be performed using a super-heterodyne process. In some examples, the radio frequency front end function may be provided by a separate chip. The network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0137] The vehicle 800 may also include data storage 828, which may include off-chip storage (e.g., outside the SoC 804). The data storage 828 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, hard disk, and / or other components and / or devices that can store at least one bit of data.

[0138] The vehicle 800 may also include a GNSS sensor 858. The GNSS sensor 858 (e.g., GPS, assisted GPS sensor, differential GPS (DGPS) sensor, etc.) is used to assist in mapping, perception, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 858 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0139] The vehicle 800 may also include a RADAR sensor 860. The RADAR sensor 860 may be used by the vehicle 800 for remote vehicle detection even in darkness and / or inclement weather conditions. The RADAR functional safety level may be ASIL B. The RADAR sensor 860 may use CAN and / or bus 802 (e.g., to transmit data generated by the RADAR sensor 860) for control and access to object tracking data, accessing Ethernet in some examples to access raw data. A variety of RADAR sensor types may be used. For example and without limitation, the RADAR sensor 860 may be suitable for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0140] The RADAR sensor 860 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, and the like. In some examples, the long-range RADAR may be used for adaptive cruise control functions. The long-range RADAR system may provide a wide field of view (e.g., within a range of 250m) achieved by two or more independent scans. The RADAR sensor 860 may help distinguish between static objects and moving objects, and may be used by the ADAS system for emergency braking assistance and forward collision warnings. The long-range RADAR sensor may include a single-station multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In an example with six antennas, the central four antennas may create a focused beam pattern designed to record the surroundings of the vehicle 800 at a higher rate with minimal traffic interference from adjacent lanes. The other two antennas may expand the field of view, making it possible to quickly detect vehicles entering or leaving the lane of the vehicle 800.

[0141] As an example, a medium-range RADAR system may include a range of up to 860m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 850 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on both ends of a rear bumper. When mounted on both ends of a rear bumper, such a RADAR sensor system may create two beams that continuously monitor the blind spots behind and beside the vehicle.

[0142] Short-range RADAR systems can be used in ADAS systems for blind spot detection and / or lane change assistance.

[0143] The vehicle 800 may also include ultrasonic sensors 862. Ultrasonic sensors 862, which may be placed on the front, rear, and / or sides of the vehicle 800, may be used for parking assistance and / or creating and updating occupancy grids. A variety of ultrasonic sensors 862 may be used, and different ultrasonic sensors 862 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensors 862 may operate at a functional safety level of ASIL B.

[0144] The vehicle 800 may include a LIDAR sensor 864. The LIDAR sensor 864 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 864 may be ASIL B for functional safety level. In some examples, the vehicle 800 may include multiple LIDAR sensors 864 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0145] In some examples, the LIDAR sensor 864 may be able to provide a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensors 864 may have, for example, an advertised range of approximately 800m, an accuracy of 2cm-3cm, and support for 800Mbps Ethernet connections. In some examples, one or more non-protruding LIDAR sensors 864 may be used. In such examples, the LIDAR sensor 864 may be implemented as a small device that can be embedded in the front, back, side, and / or corner of the vehicle 800. In such examples, the LIDAR sensor 864 may provide a field of view of up to 120 degrees horizontally and 35 degrees vertically, with a range of 200m, even for low reflectivity objects. The front-mounted LIDAR sensor 864 may be configured for a horizontal field of view between 45 and 135 degrees.

[0146] In some examples, LIDAR technologies such as 3D flash LIDAR may also be used. 3D flash LIDAR uses a flash of laser as an emission source to illuminate the vehicle's surroundings up to about 200m. The flash LIDAR unit includes a receiver that records the laser pulse transmission time and the reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LIDAR can allow highly accurate and distortion-free images of the surrounding environment to be generated with each laser flash. In some examples, four flash LIDAR sensors can be deployed, one on each side of the vehicle 800. Available 3D flash LIDAR systems include solid-state 3D staring array LIDAR cameras (e.g., non-scanning LIDAR devices) with no moving parts other than fans. The flash LIDAR device can use 5 nanosecond Class I (eye-safe) laser pulses per frame, and can capture the reflected laser light in the form of a 3D range point cloud and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor 864 may be less susceptible to motion blur, vibration, and / or shock.

[0147] The vehicle may also include an IMU sensor 866. In some examples, the IMU sensor 866 may be located at the center of the rear axle of the vehicle 800. The IMU sensor 866 may include, for example and without limitation, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, for example, in a six-axis application, the IMU sensor 866 may include an accelerometer and a gyroscope, while in a nine-axis application, the IMU sensor 866 may include an accelerometer, a gyroscope, and a magnetometer.

[0148] In some embodiments, the IMU sensor 866 can be implemented as a miniature high-performance GPS-assisted inertial navigation system (GPS / INS) that combines a micro-electromechanical system (MEMS) inertial sensor, a high-sensitivity GPS receiver, and an advanced Kalman filter algorithm to provide estimates of position, velocity, and attitude. Thus, in some examples, the IMU sensor 866 can enable the vehicle 800 to estimate heading by directly observing and correlating velocity changes from the GPS to the IMU sensor 866 without the need for input from a magnetic sensor. In some examples, the IMU sensor 866 and the GNSS sensor 858 can be combined into a single integrated unit.

[0149] The vehicle may include microphones 896 positioned in and / or around the vehicle 800. The microphones 896 may be used for, among other things, emergency vehicle detection and identification.

[0150] The vehicle may also include any number of camera types, including a stereo camera 868, a wide-angle camera 870, an infrared camera 872, a surround camera 874, a long-range and / or mid-range camera 898, and / or other camera types. These cameras can be used to capture image data around the entire periphery of the vehicle 800. The type of camera used depends on the embodiment and the requirements of the vehicle 800, and any combination of camera types can be used to provide the necessary coverage around the vehicle 800. In addition, the number of cameras can vary depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or another number of cameras. As an example and not limitation, the cameras can support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may be capable of supporting Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Fig. 8A and Figure 8B Described in more detail.

[0151] The vehicle 800 may also include a vibration sensor 842. The vibration sensor 842 may measure vibrations of components of the vehicle, such as axles. For example, changes in vibration may indicate changes in the road surface. In another example, when two or more vibration sensors 842 are used, the difference between the vibrations may be used to determine friction or slippage of the road surface (e.g., when there is a vibration difference between a powered drive shaft and a freely rotating shaft).

[0152] The vehicle 800 may include an ADAS system 838. In some examples, the ADAS system 838 may include a SoC. The ADAS system 838 may include autonomous / adaptive / automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward collision warning (FCW), automatic emergency braking (AEB), lane departure warning (LDW), lane keeping assist (LKA), blind spot warning (BSW), rear cross traffic warning (RCTW), collision warning system (CWS), lane centering (LC), and / or other features and functions.

[0153] The ACC system may use a RADAR sensor 860, a LIDAR sensor 864, and / or a camera. The ACC system may include a longitudinal ACC and / or a lateral ACC. The longitudinal ACC monitors and controls the distance to the vehicle immediately in front of the vehicle 800, and automatically adjusts the vehicle speed to maintain a safe distance from the vehicle in front. The lateral ACC performs distance keeping and suggests that the vehicle 800 change lanes when necessary. The lateral ACC is related to other ADAS applications such as LCA and CWS.

[0154] CACC uses information from other vehicles, which can be received from other vehicles indirectly via a wireless link or through a network connection (e.g., through the Internet) via a network interface 824 and / or a wireless antenna 826. A direct link can be provided by a vehicle-to-vehicle (V2V) communication link, while an indirect link can be an infrastructure-to-vehicle (I2V) communication link. Typically, the V2V communication concept provides information about the vehicle immediately ahead (e.g., the vehicle immediately ahead of the vehicle 800 and in the same lane as it), while the I2V communication concept provides information about traffic farther ahead. The CACC system may include either or both of the I2V and V2V information sources. Given information about the vehicle ahead of the vehicle 800, CACC can be more reliable, and it is possible to improve the smoothness of traffic flow and reduce road congestion.

[0155] The FCW system is designed to alert the driver to hazards so that the driver can take corrective action. The FCW system uses a front camera and / or RADAR sensor 860 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibrating components. The FCW system can provide warnings in the form of, for example, sound, visual warnings, vibrations and / or rapid brake pulses.

[0156] The AEB system detects an impending forward collision with another vehicle or other object and can automatically apply the brakes if the driver does not take corrective action within specified time or distance parameters. The AEB system can use a front camera and / or RADAR sensor 860 coupled to a dedicated processor, DSP, FPGA and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes in an effort to prevent or at least mitigate the effects of the predicted collision. The AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0157] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 800 crosses a lane marking. When the driver indicates an intention to leave the lane, by activating a turn signal, the LDW system is not activated. The LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0158] The LKA system is a variation of the LDW system. If the vehicle 800 begins to leave the lane, the LKA system provides steering input or braking to correct the vehicle 800.

[0159] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, auditory and / or tactile alerts to indicate that it is unsafe to merge or change lanes. 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 860 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibration components.

[0160] The RCTW system may provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 800 is in reverse. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. The RCTW system may use one or more rear RADAR sensors 860 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0161] Conventional ADAS systems may be prone to false positive results, which may annoy and distract the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether the safety condition really exists and take action accordingly. However, in the autonomous vehicle 800, in the case of conflicting results, the vehicle 800 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 836 or the second controller 836). For example, in some embodiments, the ADAS system 838 can be a backup and / or auxiliary computer for providing perception information to the backup computer rationality module. The backup computer rationality monitor can run redundant and diverse software on hardware components to detect faults in perception and dynamic driving tasks. The output from the ADAS system 838 can be provided to the supervisory MCU. If the outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to coordinate the conflict to ensure safe operation.

[0162] In some examples, the master computer can be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the direction of the master computer, regardless of whether the auxiliary computer provides conflicting or inconsistent results. In the case where the confidence score does not meet the threshold and the master computer and the auxiliary computer indicate different results (e.g., conflicts), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0163] The supervisory MCU may be configured to run a neural network that is trained and configured to determine conditions under which the auxiliary computer provides a false alarm based, at least in part, on outputs from the primary computer and the auxiliary computer. Thus, the neural network in the supervisory MCU may learn when the output of the auxiliary computer may be trusted and when it may not. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU may learn when the FCW system is identifying a metal object that is not actually dangerous, such as a drainage grate or manhole cover that triggers an alarm. Similarly, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In an embodiment that includes a neural network running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or a GPU suitable for running the neural network with associated memory. In a preferred embodiment, the supervisory MCU may include a component of the SoC 804 and / or be included as a component of the SoC 804.

[0164] In other examples, the ADAS system 838 may include an auxiliary computer that uses traditional computer vision rules to perform ADAS functions. In this way, the auxiliary computer can use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or error in the software running on the main computer and the non-identical software code running on the auxiliary computer provides the same overall result, the supervisory MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware on the main computer does not cause a substantial error.

[0165] In some examples, the output of the ADAS system 838 can be fed to the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, if the ADAS system 838 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information in identifying the object. In other examples, the auxiliary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0166] The vehicle 800 may also include an infotainment SoC 830 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as an SoC, the infotainment system may not be an SoC and may include two or more discrete components. The infotainment SoC 830 may include a combination of hardware and software that can be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, WiFi, etc.), and / or information services (e.g., navigation system, rear parking assistance, radio data system, vehicle-related information such as fuel level, total distance covered, brake fuel level, oil level, door open / closed, air filter information, etc.) to the vehicle 800. For example, the infotainment SoC 830 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connectivity, an onboard computer, onboard entertainment, WiFi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 834, a telematics device, a control panel (e.g., for controlling and / or interacting with various components, features, and / or systems), and / or other components. The infotainment SoC 830 may further be used to provide information (e.g., visual and / or auditory) to a user of the vehicle, such as information from an ADAS system 838, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0167] The infotainment SoC 830 may include GPU functionality. The infotainment SoC 830 may communicate with other devices, systems, and / or components of the vehicle 800 via a bus 802 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 830 may be coupled to a supervisory MCU so that in the event of a failure of a master controller 836 (e.g., a main and / or backup computer of the vehicle 800), the GPU of the infotainment system may perform some self-driving functions. In such an example, the infotainment SoC 830 may place the vehicle 800 in a driver safe parking mode as described herein.

[0168] The vehicle 800 may also include an instrument cluster 832 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 832 may include a controller and / or a supercomputer (e.g., a discrete controller or a supercomputer). The instrument cluster 832 may include a set of instruments, such as a speedometer, a fuel level, an oil pressure, a tachometer, an odometer, a turn indicator, a shift position indicator, a seat belt warning light, a parking brake warning light, an engine fault light, an airbag (SRS) system information, lighting controls, safety system controls, navigation information, and the like. In some examples, information may be displayed and / or shared between the infotainment SoC 830 and the instrument cluster 832. In other words, the instrument cluster 832 may be included as part of the infotainment SoC 830, or vice versa.

[0169] Fig.8D For a cloud-based server and Fig. 8A 800. System 876 may include a server 878, a network 890, and a vehicle including the vehicle 800. The server 878 may include a plurality of GPUs 884(A)-884(H) (collectively referred to herein as GPUs 884), PCIe switches 882(A)-882(H) (collectively referred to herein as PCIe switches 882), and / or CPUs 880(A)-880(B) (collectively referred to herein as CPUs 880). The GPUs 884, CPUs 880, and PCIe switches may be interconnected with high-speed interconnects and / or PCIe connections 886 such as, for example and without limitation, the NVLink interface 888 developed by NVIDIA. In some examples, the GPUs 884 are connected via NVLink and / or NVSwitch SoCs, and the GPUs 884 and PCIe switches 882 are connected via PCIe interconnects. Although eight GPUs 884, two CPUs 880, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the servers 878 may include any number of GPUs 884, CPUs 880, and / or PCIe switches. For example, each of the servers 878 may include eight, sixteen, thirty-two, and / or more GPUs 884.

[0170] Server 878 may receive image data over network 890 and from a vehicle, the image data representing images showing unexpected or changed road conditions, such as recently begun road work. Server 878 may transmit neural network 892, updated neural network 892, and / or map information 894, including information about traffic and road conditions, over network 890 and to the vehicle. Updates to map information 894 may include updates to HD map 822, such as information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, neural network 892, updated neural network 892, and / or map information 894 may have been generated from new training and / or data received from any number of vehicles in the environment and / or based on experience from training performed at a data center (e.g., using server 878 and / or other servers).

[0171] Server 878 can be used to train a machine learning model (e.g., a neural network) based on training data. The training data can be generated by the vehicle, and / or can be generated in a simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., when the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other examples, the training data is not labeled and / or preprocessed (e.g., when the neural network does not require supervised learning). Training can be performed according to any one or more categories of machine learning techniques, including but not limited to the following categories: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including main components and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternate dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations thereof. Once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via network 890), and / or the machine learning model can be used by server 878 to remotely monitor the vehicle.

[0172] In some examples, server 878 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. Server 878 may include a deep learning supercomputer and / or a dedicated AI computer powered by GPU 884, such as DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 878 may include a deep learning infrastructure of a data center powered only by CPUs.

[0173] The deep learning infrastructure of server 878 may be capable of rapid real-time inference, and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 800. For example, the deep learning infrastructure may receive periodic updates from vehicle 800, such as a sequence of images and / or objects located in the sequence of images that vehicle 800 has located (e.g., via computer vision and / or other machine learning object classification techniques). The deep learning infrastructure may run its own neural network to identify objects and compare them to the objects identified by vehicle 800, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 800 has failed, then server 878 may transmit a signal to vehicle 800 instructing the fail-safe computer of vehicle 800 to take control, notify passengers, and complete a safe parking maneuver.

[0174] For reasoning, the server 878 may include a GPU 884 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and reasoning acceleration can make real-time responses possible. In other examples, such as when performance is not so important, CPU, FPGA, and other processor-powered servers can be used for reasoning.

[0175] Example computing device

[0176] Fig. 9 900 is a block diagram of an example computing device 900 suitable for implementing some embodiments of the present disclosure. The computing device 900 may include an interconnect system 902 that directly or indirectly couples the following devices: a memory 904, one or more central processing units (CPUs) 906, one or more graphics processing units (GPUs) 908, a communication interface 910, an input / output (I / O) port 912, an input / output component 914, a power supply 916, one or more presentation components 918 (e.g., (one or more) displays) and one or more logic units 920. In at least one embodiment, the (one or more) computing device 900 may include one or more virtual machines (VMs), and / or any of its components may include virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs 908 may include one or more vGPUs, one or more of the CPUs 906 may include one or more vCPUs, and / or one or more of the logic units 920 may include one or more virtual logic units. As such, computing device(s) 900 may include discrete components (e.g., a full GPU dedicated to computing device 900), virtual components (e.g., a portion of a GPU dedicated to computing device 900), or a combination thereof.

[0177] although Fig. 9 The various blocks of are shown as being connected via interconnect system 902 using wires, but this is not intended to be limiting and is for clarity only. For example, in some embodiments, presentation component 918 (such as a display device) may be considered to be I / O component 914 (e.g., if the display is a touch screen). As another example, CPU 906 and / or GPU 908 may include memory (e.g., memory 904 may represent a storage device in addition to the memory of GPU 908, CPU 906, and / or other components). In other words, Fig. 9 The computing devices described herein are illustrative only. No distinction is made between such categories as "workstations," "servers," "laptops," "desktop computers," "tablet computers," "client devices," "mobile devices," "handheld devices," "game consoles," "electronic control units (ECUs)," "virtual reality systems," and / or other device or system types, as all are considered Fig. 9 within the range of computing devices.

[0178] The interconnection system 902 may represent one or more links or buses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnection system 902 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 standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect fast (PCIe) bus, and / or another type of bus or link. In some embodiments, there is a direct connection between components. As an example, CPU 906 may be directly connected to memory 904. Further, CPU 906 may be directly connected to GPU 908. In the case where there is a direct or point-to-point connection between components, the interconnection system 902 may include a PCIe link to perform the connection. In these examples, the PCI bus does not need to be included in the computing device 900.

[0179] The memory 904 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by the computing device 900. Computer-readable media may include volatile and non-volatile media, as well as removable and non-removable media. By way of example and not limitation, computer-readable media may include computer storage media and communication media.

[0180] Computer storage media may include 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 904 may store computer readable instructions (e.g., representing (one or more) programs and / or (one or more) program elements, such as an operating system). Computer storage media may include, but are 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 devices or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by computing device 900. As used herein, computer storage media does not include the signals themselves.

[0181] 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 a manner that encodes the information in the signal. By way of example, and not limitation, computer storage media may include wired media (such as a wired network or a 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.

[0182] The CPU 906 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and / or processes described herein. The CPUs 906 may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of handling numerous software threads simultaneously. The CPU 906 may include any type of processor, and may include different types of processors (e.g., a processor with fewer cores for a mobile device and a processor with more cores for a server) depending on the type of computing device 900 implemented. For example, depending on the type of computing device 900, the processor may be an Advanced RISC Machine (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 900 may also include one or more CPUs 906 in addition to one or more microprocessors or supplementary coprocessors (such as a math coprocessor).

[0183] In addition to or in place of (one or more) CPUs 906, (one or more) GPUs 908 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and / or processes described herein. One or more of the GPUs 908 may be an integrated GPU (e.g., with one or more of the CPUs 906) and / or one or more of the GPUs 908 may be a discrete GPU. In an embodiment, one or more of the GPUs 908 may be a coprocessor of one or more of the CPUs 906. The GPU 908 may be used by the computing device 900 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, the GPU 908 may be used for general-purpose computing on a GPU (GPGPU). The GPU 908 may include hundreds or thousands of cores capable of handling hundreds or thousands of software threads simultaneously. The GPU 908 may generate pixel data for an output image in response to a rendering command (e.g., a rendering command received from the CPU 906 via a host interface). The GPU 908 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 904. The GPU 908 may include two or more GPUs operating in parallel (e.g., via a link). The link may connect the GPUs directly (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU 908 may generate pixel data or GPGPU data for different parts of the output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs.

[0184] In addition to or in lieu of the CPU 906 and / or GPU 908, the logic unit 920 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and / or processes described herein. In embodiments, the CPU(s) 906, the GPU(s) 908, and / or the logic unit(s) 920 may discretely or jointly perform any combination of methods, processes, and / or portions thereof. One or more of the logic units 920 may be a part of and / or integrated into one or more of the CPU 906 and / or GPU 908 and / or one or more of the logic units 920 may be discrete components or otherwise external to the CPU 906 and / or GPU 908. In embodiments, one or more of logic units 920 may be co-processors of one or more of CPUs 906 and / or one or more of GPUs 908 .

[0185] Examples of logic unit 920 include one or more processing cores and / or components thereof, such as a data processing unit (DPU), a tensor core (TC), a tensor processing unit (TPU), a pixel vision core (PVC), a vision processing unit (VPU), a graphics processing cluster (GPC), a texture processing cluster (TPC), a streaming multiprocessor (SM), a tree transverse 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 input / output (I / O) element, a peripheral component interconnect (PCI) or a peripheral component interconnect express (PCIe) element, etc.

[0186] The communication interface 910 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 900 to communicate with other computing devices via an electronic communication network (including wired and / or wireless communications). The communication interface 910 may include components and functions that implement communication through any of a plurality of different networks, such as a wireless network (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), a wired network (e.g., via Ethernet or unlimited band communication), a low power wide area network (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, the one or more logic units 920 and / or the communication interface 910 may include one or more data processing units (DPUs) to transmit data received through the network and / or through the interconnect system 902 directly to one or more GPUs 908 (e.g., memory of).

[0187] The I / O ports 912 may enable the computing device 900 to be logically coupled to other devices including I / O components 914, (one or more) presentation components 918, and / or other components, some of which may be built into (e.g., integrated into) the computing device 900. Illustrative I / O components 914 include microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dishes, scanners, printers, wireless devices, and the like. The I / O components 914 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some cases, the input may be transmitted to an appropriate network element for further processing. The NUI may implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition on and near the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with the display of the computing device 900. The computing device 900 may include a depth camera for gesture detection and recognition, such as a stereo camera system, an infrared camera system, an RGB camera system, touch screen technology, and combinations of these. Additionally, computing device 900 may include an accelerometer or gyroscope that enables detection of motion (e.g., as part of an inertial measurement unit (IMU)). In some examples, computing device 900 may use the output of the accelerometer or gyroscope to render an immersive augmented reality or virtual reality.

[0188] The power supply 916 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 916 may provide power to the computing device 900 to enable the components of the computing device 900 to operate.

[0189] The presentation component 918 may include a display (e.g., a monitor, a touch screen, a television screen, a head-up display (HUD), other display types, or a combination thereof), a speaker, and / or other presentation components. The presentation component 918 may receive data from other components (e.g., GPU 908, CPU 906, DPU, etc.) and output the data (e.g., as an image, video, sound, etc.).

[0190] Sample Data Center

[0191] Fig.10 An example data center 1000 is shown that can be used in at least one embodiment of the present disclosure. The data center 1000 can include a data center infrastructure layer 1010, a framework layer 1020, a software layer 1030, and / or an application layer 1040.

[0192] like Fig.10As shown, the data center infrastructure layer 1010 may include a resource coordinator 1012, grouped computing resources 1014, and node computing resources ("node CRs") 1016(1)-1016(N), where "N" represents any complete positive integer. In at least one embodiment, the node CRs 1016(1)-1016(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memories), storage devices (e.g., solid-state or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules and / or cooling modules, etc. In some embodiments, one or more of the node CRs from the node CRs 1016(1)-1016(N) may correspond to servers having one or more of the above-mentioned computing resources. Furthermore, in some embodiments, nodes CR1016(1)-10161(N) may include one or more virtual components, such as vGPU, vCPU, etc., and / or one or more of nodes CR1016(1)-1016(N) may correspond to a virtual machine (VM).

[0193] In at least one embodiment, the grouped computing resources 1014 may include separate groups of nodes CR1016 housed in one or more racks (not shown), or many racks housed in data centers at different geographical locations (also not shown). The separate groups of nodes CR1016 within the grouped computing resources 1014 may include grouped computing, networks, memory, or storage resources that can be configured or allocated to support one or more workloads. In at least one embodiment, several nodes CR1016 including CPUs, GPUs, DPUs, and / or other processors may be grouped in one or more racks to provide computing resources to support one or more workloads. One or more racks may also include any number of power modules, cooling modules, and / or network switches in any combination.

[0194] Resource coordinator 1012 may configure or otherwise control one or more node CRs 1016(1)-1016(N) and / or grouped computing resources 1014. In at least one embodiment, resource coordinator 1012 may include a software design infrastructure (SDI) management entity for data center 1000. Resource coordinator 1012 may include hardware, software, or some combination thereof.

[0195] In at least one embodiment, Fig.10As shown, the framework layer 1020 may include a job scheduler 1033, a configuration manager 1034, a resource manager 1036, and / or a distributed file system 1038. The framework layer 1020 may include a framework that supports software 1032 of the software layer 1030 and / or one or more applications 1042 of the application layer 1040. The software 1032 or the application 1042 may include network-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 1020 may be, but is not limited to, a free and open source software network application framework (such as Apache Spark) that can utilize the distributed file system 1038 for large-scale data processing (e.g., "big data"). TM (hereinafter referred to as "Spark")). In at least one embodiment, the job scheduler 1033 may include a Spark driver to facilitate scheduling workloads supported by different layers of the data center 1000. The configuration manager 1034 may be able to configure different layers, such as the software layer 1030 and the framework layer 1020 (which includes Spark and a distributed file system 1038 for supporting large-scale data processing). The resource manager 1036 may be able to manage clustered or grouped computing resources mapped to the distributed file system 1038 and the job scheduler 1032 or allocated to support the distributed file system 1038 and the job scheduler 1033. In at least one embodiment, the clustered or grouped computing resources may include grouped computing resources 1014 at the data center infrastructure layer 1010. The resource manager 1036 may coordinate with the resource coordinator 1012 to manage these mapped or allocated computing resources.

[0196] In at least one embodiment, the software 1032 included in the software layer 1030 may include software used by at least a portion of the node CRs 1016(1)-1016(N), the grouped computing resources 1014, and / or the distributed file system 1038 of the framework layer 1020. The one or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0197] In at least one embodiment, the applications 1042 included in the application layer 1040 may include one or more types of applications used by at least a portion of the node CRs 1016(1)-1016(N), the grouped computing resources 1014, and / or the distributed file system 1038 of the framework layer 1020. The one or more types of applications may include, but are not limited to, any number of genomic applications, cognitive computing, and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and / or other machine learning applications used in conjunction with one or more embodiments.

[0198] In at least one embodiment, any of configuration manager 1034, resource manager 1036, and resource coordinator 1012 can implement any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. The self-modification actions can save a data center operator of data center 1000 from making potentially poor configuration decisions and potentially avoiding underutilized and / or poorly performing portions of the data center.

[0199] According to one or more embodiments described herein, data center 1000 may include tools, services, software, or other resources to train one or more machine learning models or use one or more machine learning models to predict or infer information. For example, the (one or more) machine learning models may be trained by calculating weight parameters according to a neural network architecture using the software and / or computing resources described above with respect to data center 1000. In at least one embodiment, a trained or deployed machine learning model corresponding to one or more neural networks may be used to infer or predict information using the resources described above with respect to data center 1000 using weight parameters calculated by one or more training techniques, such as, but not limited to, those described herein.

[0200] In at least one embodiment, the data center 1000 may use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, and / or other hardware (or virtual computing resources corresponding thereto) to perform training and / or inference using the above resources. In addition, one or more software and / or hardware resources described above may be configured to allow a user to train or perform inference services on information, such as image recognition, speech recognition, or other artificial intelligence services.

[0201] Example network environment

[0202] A network environment suitable for implementing embodiments of the present disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and / or other device types. The client devices, servers, and / or other device types (e.g., each device) may be configured to communicate with the client device, server, or other device types. Fig. 9 The backend device 1000 may be implemented on one or more instances of the computing device 900 of the data center 1000 - for example, each device may include similar components, features and / or functions of the computing device 900 of the data center 1000. In addition, in the case of implementing a backend device (e.g., a server, a NAS, etc.), the backend device may be included as part of the data center 1000, and examples of the data center 1000 are described herein. Fig.10 Describe in more detail.

[0203] The components of the network environment can communicate with each other via a network, which can be wired, wireless, or both. The network can include multiple networks or one of multiple networks. For example, the network can include one or more wide area networks (WANs), one or more local area networks (LANs), one or more public networks (such as the Internet and / or a public switched telephone network (PSTN)), and / or one or more private networks. In the case where the network includes a wireless telecommunications network, components such as base stations, communication towers, or even access points (and other components) can provide wireless connections.

[0204] Compatible network environments may include one or more peer-to-peer network environments (in which case the server may not be included in the network environment) and one or more client-server network environments (in which case one or more servers may be included in the network environment). In a peer-to-peer network environment, the functionality described herein for the server may be implemented on any number of client devices.

[0205] In at least one embodiment, the network environment may include one or more cloud-based network environments, distributed computing environments, combinations thereof, and the like. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more servers, and the server may include one or more core network servers and / or edge servers. The framework layer may include a framework for software supporting the software layer and / or one or more applications of the application layer. The software or application may include network-based service software or applications, respectively. In an embodiment, one or more client devices may use network-based service software or applications (e.g., by accessing the service software and / or application via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a free and open source software network application framework that can use a distributed file system for large-scale data processing (e.g., "big data").

[0206] A cloud-based network environment can provide cloud computing and / or cloud storage that performs any combination of the computing and / or data storage functions (or one or more portions thereof) described herein. Any of these different functions can be distributed across multiple locations from a central or core server (e.g., one or more data centers that can be distributed across a state, region, country, global, etc.). If the connection to the user (e.g., client device) is relatively close to an edge server, the core server can assign at least a portion of the functionality to the edge server. A cloud-based network environment can be private (e.g., limited to a single organization), public (e.g., available to many organizations), and / or a combination thereof (e.g., a hybrid cloud environment).

[0207] The client device(s) may include the Fig. 9 At least some of the components, features, and functionality of the described (one or more) example computing device(s) 900. By way of example and not limitation, the client device may be implemented as a personal computer (PC), a laptop computer, a mobile device, a smart phone, a tablet computer, a smart watch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality headset, a global positioning system (GPS) or device, a video player, a camera, a surveillance device or system, a vehicle, a boat, a spacecraft, a virtual machine, a drone, a robot, a handheld communication device, a hospital device, a gaming device or system, an entertainment system, a vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of the depicted devices, or any other suitable device.

[0208] The present disclosure may be described in the general context of machine-usable instructions or computer codes executed by a computer or other machine such as a personal digital assistant or other handheld device, including computer-executable instructions such as program modules. Typically, program modules including routines, programs, objects, components, data structures, etc. refer to codes that perform specific tasks or implement specific abstract data types. The present disclosure may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communication network.

[0209] As used herein, the statement of "and / or" with respect to two or more elements should be interpreted as referring to only one element or a combination of elements. For example, "element A, element B, and / or element C" may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B, and C. In addition, "at least one of element A or element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, "at least one of element A and element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B.

[0210] The subject matter of the present disclosure is described in detail herein to meet statutory requirements. However, the description itself is not intended to limit the scope of the present disclosure. On the contrary, the present inventors have contemplated that the claimed subject matter may also be embodied in other ways to include steps different from the steps described herein in conjunction with other current or future technologies or combinations of similar steps. Moreover, although the terms "step" and / or "block" may be used herein to imply different elements of the method employed, these terms should not be interpreted as implying any particular order among or between the various steps disclosed herein, unless the order of the steps is explicitly described.

Claims

1. A processor, comprising: One or more processing units for: generating infrared (IR) image data representing an area where the user is being authenticated; generating a representation of one or more acquired subcutaneous features based at least on segmenting at least a portion of the IR image data; as well as One or more actions are performed based at least on matching the one or more acquired representations of subcutaneous features with corresponding representations of one or more reference subcutaneous features stored in a database.

2. The processor of claim 1 , wherein the one or more processing units are further configured to initiate an IR image acquisition loop using an IR camera observing an interior space based on at least one of: detecting that one or more doors of the interior space are unlocked, detecting an authorized key via a proximity sensor, or detecting a body gesture indicating that one or more of a limb, hand, wrist, or palm of the user being authenticated is within a threshold distance of the IR camera.

3. The processor of claim 1 , wherein the one or more processing units are further configured to initiate an IR image acquisition loop using an IR camera observing an external space based on at least one of: detecting a request to unlock one or more doors or detecting an authorized key via a proximity sensor.

4. According to the processor of claim 1, the one or more processing units are also used to execute an IR image acquisition loop, which is configured to: generate frames of the IR image data; and test the detection accuracy of corresponding instances of the one or more acquired subcutaneous features represented in the frames of the IR image data compared to a threshold confidence level.

5. According to the processor of claim 1, the one or more processing units are also used to execute an IR image acquisition loop, which is configured to: repeatedly generate instances of the IR image data; apply at least one of hand detection or palm detection to detect a hand area or a palm area represented in the IR image data; apply vein detection to the hand area or the palm area; and test the detection accuracy of at least one of the hand detection, the palm detection or the vein detection.

6. According to the processor of claim 1, the one or more processing units are also used to: generate a representation of the detected hand or the detected palm represented by the IR image data; normalize the posture of the detected hand or the detected palm based at least on distorting the IR image data; and detect veins in one or more areas of the detected hand or the detected palm.

7. A processor according to claim 1, wherein the one or more processing units are further used to: detect a hand region of the IR image data that is predicted to depict a hand; identify a palm region as a subset of the hand region; and apply vein detection to the palm region.

8. The processor of claim 1, wherein: The one or more acquired representations of subcutaneous features include an acquired binary representation of a vein topology of the authenticating user.

9. The processor of claim 1, wherein: The one or more acquired subcutaneous features of the authenticating user include vein topology of a palm, neck, forearm, finger, or eye of the authenticating user.

10. The processor of claim 1, wherein: The one or more acquired representations of subcutaneous features include the acquired palm vein topology of the user being authenticated, the corresponding representations of the one or more reference subcutaneous features include the reference palm vein topology of the authorized user, and the matching includes applying the acquired representations of the palm vein topology and the reference palm vein topology to the discriminator network.

11. The processor of claim 1 , wherein: The one or more actions include updating one or more of: seat position, mirror orientation, steering wheel position, heating or cooling settings, audio or volume presets, driving mode, console display settings, driver assistance settings, or vehicle lighting to reflect one or more corresponding saved values ​​associated with a user profile of the authenticating user.

12. The processor of claim 1, wherein: The one or more actions include unlocking one or more doors.

13. The processor of claim 1, wherein: The processor is included in at least one of the following: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing simulation operations; Systems for performing digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; Systems for performing deep learning operations; A system for performing real-time streaming; Systems for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; Systems implemented using edge devices; Systems implemented using robots; Systems for performing conversational AI operations; Systems for generating synthetic data; Systems for generating synthetic data using AI; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

14. A system comprising one or more processing units for performing one or more actions based at least on matching an acquired representation of a vein topology with a corresponding representation of a reference vein topology stored in a database.

15. According to the system of claim 14, the one or more processing units are also used to execute an IR image acquisition loop, which is configured to: repeatedly generate instances of IR image data; apply hand detection or palm detection to detect a hand area or a palm area represented in the IR image data; apply vein detection to the hand area or the palm area to segment out the acquired vein topology; and test the detection accuracy of at least one of the hand detection, the palm detection or the vein detection.

16. According to the system of claim 14, the one or more processing units are also used to: generate a representation of the detected hand or the detected palm represented by IR image data; normalize the posture of the detected hand or the detected palm based at least on distorting the IR image data; and detect the acquired vein topology in one or more areas of the detected hand or the detected palm.

17. According to the system of claim 14, the one or more processing units are also used to: detect a hand region represented in the IR image data that is predicted to depict a hand; identify a palm region that is a subset of the hand region; and apply vein detection to the palm region to segment out the acquired vein topology.

18. The system of claim 14, wherein: The system comprises at least one of the following: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing simulation operations; Systems for performing digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; Systems for performing deep learning operations; A system for performing real-time streaming; Systems for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; Systems implemented using edge devices; Systems implemented using robots; Systems for performing conversational AI operations; Systems for generating synthetic data; Systems for generating synthetic data using AI; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

19. A method comprising: generating a representation of the acquired vein topology of the user being authenticated based at least on segmenting at least a portion of the IR image data representing an area of ​​the user being authenticated; authenticating the authenticating user based at least on matching the acquired representation of the vein topology with a corresponding representation of a reference vein topology stored in a database; as well as One or more actions are performed based on the authentication of the authenticating user.

20. The method according to claim 19, wherein: The method is performed by at least one of the following: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing simulation operations; Systems for performing digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; Systems for performing deep learning operations; A system for performing real-time streaming; Systems for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; Systems implemented using edge devices; Systems implemented using robots; Systems for performing conversational AI operations; Systems for generating synthetic data; Systems for generating synthetic data using AI; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

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