Associating object detection for sensor data processing in autonomous systems and applications

Abstract

The disclosure relates to associating object detection for sensor data processing in autonomous systems and applications. In various examples, sensor data processing is described herein that associates object detection for autonomous and semi-autonomous systems and applications. Systems and methods described herein may group object detection (e.g., echo, etc.) detected using multiple sensors (e.g., ultrasonic sensors, sonar sensors, etc.), and then process the object detection using the grouping to perform one or more tasks, such as object or feature detection. In some examples, object detections are grouped using one or more configurations, such as an order associated with analyzing sensor data generated using sensors and / or a threshold distance associated with determining that object detections are associated with the same object. Further, respective groups may be generated for one or more (e.g., each) detected objects so that the locations of the objects around the machine may be determined.

Description

Background Art

[0001] Generating maps or other representations of the environment (e.g., free space maps and occupancy maps) is crucial for autonomous and / or semi-autonomous machine navigation. For example, these dynamically generated representations of the environment may include a bird's-eye view (BEV) perspective of the area surrounding the machine, where the BEV representation indicates the location of static and / or dynamic objects close to the machine and / or indicates drivable areas (e.g., free space) and non-drivable areas in the environment. Thus, the machine can use these representations to determine the location of objects in the environment and where the machine can navigate, and can use this information to determine planning and control actions for safely navigating through the environment.

[0002] In some examples, conventional systems may generate these representations using specific types of sensor data, such as ultrasonic data generated using a machine's ultrasonic sensors. Based on the one-dimensional output represented by the ultrasonic data, conventional systems may need to group object detections (e.g., echoes) detected using multiple ultrasonic sensors and then process the grouped object detections to determine the location of the objects in the environment. Therefore, these conventional systems may use various techniques to perform grouping. For example, a conventional system may use a brute force technique in which the conventional system processes each combination of object detections detected using the ultrasonic sensors. While this technique can determine all locations of objects located in the environment, it may also require significant computing resources and / or latency depending on the amount of ultrasonic data and / or the number of object detections that need to be processed.

[0003] Therefore, another conventional system may use a closest object technique, in which only the closest object detection represented by the ultrasonic data is processed using each combination to identify objects located close to the machine. For example, if a first object is closer to the machine than a second object, where both objects are detected using the same ultrasonic sensor, then the conventional system may only process that specific object detection to determine the location of the first object. Therefore, while this technique can determine the location of the closest object within the environment while reducing the amount of computing resources required for processing compared to brute-force techniques, this technique may not be able to determine the location of certain objects within the environment, such as objects located behind other objects. As a result, the machine may not have all the information it needs to navigate, such as the locations of all surrounding objects.

[0004] Therefore, another conventional system may use a partitioning technique that groups ultrasonic sensors based on their location on the machine. For example, a conventional system may group ultrasonic sensors located on the left front of the machine into a first group, ultrasonic sensors located on the right front of the machine into a second group, and so on. The conventional system may then use these groups so that object detection results from one set of ultrasonic sensors are processed without processing object detection results from other ultrasonic sensors. While this technique can detect the location of objects at varying distances from the machine while reducing the amount of computing resources required for processing compared to brute-force techniques, it may not be able to detect objects located between ultrasonic sensor groups. As a result, the machine may also lack all the information necessary for navigation, such as the locations of all surrounding objects. Summary of the Invention

[0005] Embodiments of the present disclosure relate to associating object detections for use in sensor data processing with autonomous and semi-autonomous systems and applications. The systems and methods described herein can group object detection results (e.g., echoes, etc.) detected using multiple sensors (e.g., ultrasonic sensors, sonar sensors, etc.) based at least on one or more configurations (e.g., based on the order of the sensors and / or a threshold distance between object detections), and then use the groupings to process the object detection results to perform one or more tasks, such as object detection. For example, sensor data generated using a sensor can be processed to determine whether the sensor data indicates an object detection. Based on one or more configurations, additional sensor data generated using one or more additional sensors can then be processed to determine whether the additional sensor data represents one or more object detections of the same object, such as by using one or more distance thresholds. These object detections can then be grouped, and the grouped object detections can then be processed to determine at least one location of the object in the environment. In addition, a similar process can be used to determine additional object detection groups and / or other locations of objects within the environment.

[0006] Compared to conventional systems (e.g., conventional systems that perform brute force techniques), the system of the present disclosure groups object detections and then uses these groups to process object detections. As described herein, this can reduce the amount of computing resources required to process sensor data because all object detection combinations do not need to be processed while still allowing all objects around the machine to be detected. In addition, compared to conventional systems (e.g., conventional systems that perform the nearest object technique described above), the system of the present disclosure is able to generate groups associated with objects located at different distances from the machine and then use these groups to determine the location of these objects. In this way, objects located near the machine can be detected as well as objects located behind these objects and / or farther away from the machine.

[0007] Furthermore, compared to conventional systems (e.g., conventional systems that perform partitioning techniques), the system of the present disclosure uses multiple factors (e.g., the order associated with the sensors and the distance threshold settings) to group object detections. Thus, as described in more detail herein, the system of the present disclosure is able to dynamically generate groups using various combinations of sensors on the machine, rather than using static groups that include specific (e.g., set or predetermined) sensors as conventional systems do. In this way, the system of the present disclosure can detect objects located anywhere relative to the machine because a group can be dynamically generated for each object. Thus, this provides an improvement over conventional systems, as conventional systems can only detect objects located within the environmental area covered by the static groups, but cannot detect objects located within the environmental area between the static groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present systems and methods for correlating object detection for sensor data processing for autonomous and semi-autonomous systems and applications are described in detail below with reference to the accompanying drawings, in which:

[0009] Figure 1 An example data flow diagram illustrating a process of grouping object detections and then using the group to process the object detections to perform one or more tasks in accordance with some embodiments of the present disclosure;

[0010] Figure 2 shows an example of a machine including a sensor according to some embodiments of the present disclosure;

[0011] Figures 3A-3C shows an example of generating a group associated with object detection according to some embodiments of the present disclosure;

[0012] Figure 4 shows an example of processing object detections associated with a group to determine information associated with the object, according to some embodiments of the present disclosure;

[0013] Figure 5 A flow chart illustrating a method for grouping object detections associated with ultrasound data and then using the grouping to perform one or more tasks according to some embodiments of the present disclosure is shown;

[0014] Figure 6 shows a flow chart of a method for grouping object detections detected using multiple sensors according to some embodiments of the present disclosure;

[0015] Figure 7A is an illustration of an example autonomous vehicle according to some embodiments of the present disclosure;

[0016] Figure 7B According to some embodiments of the present disclosure Figure 7AExamples of camera positions and fields of view for autonomous vehicles;

[0017] Figure 7C According to some embodiments of the present disclosure Figure 7A a block diagram of an example system architecture for an example autonomous vehicle;

[0018] Figure 7D is a cloud-based server and Figure 7A System diagram of an example of communication between autonomous vehicles;

[0019] Figure 8 is a block diagram of an example computing device suitable for implementing some embodiments of the present disclosure; and

[0020] Figure 9 is a block diagram of an example data center suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0021] Systems and methods are disclosed relating to sensor data processing that associates object detection for use in autonomous and semi-autonomous systems and applications. Although the present disclosure may be described with respect to an example autonomous or semi-autonomous vehicle or machine 700 (referred to herein alternatively as "vehicle 700," "ego vehicle 700," "ego machine 700," or "machine 700"), the examples are described with respect to Figures 7A-7D 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 adaptive 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, underwater vehicles, drones, and / or other vehicle types. Furthermore, while the present disclosure may be described with respect to sensor data processing and / or object detection, this is not intended to be limiting, and the systems and methods described herein may be used in augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and / or any other technology field in which object detection and / or map creation may be used.

[0022] For example, the system can receive sensor data generated by sensors of a machine navigating in an environment. As described herein, sensor data can include, but is not limited to, ultrasonic data generated using one or more ultrasonic sensors, sonar data generated using one or more sonar sensors, image data generated using one or more image sensors, LiDAR data generated using one or more LiDAR sensors, RADAR data generated using one or more RADAR sensors, and / or any other type of sensor data generated using any other type of sensor. As described herein, sensor data can indicate one or more locations of one or more objects located in the environment. For example, with respect to ultrasonic data, sensor data can represent a one-dimensional representation indicating one or more distances to an object. For example, sensor data can represent a histogram associated with multiple bins (e.g., 50 bins, 100 bins, 200 bins, 300 bins, 320 bins, 400 bins, etc.), wherein each bin is associated with a corresponding distance within the environment. Additionally, the histogram may indicate amplitude values ​​associated with the frequency signal, where one or more peak amplitude values ​​(eg, echoes) associated with the frequency signal may indicate a distance of an object within the environment.

[0023] The system may also receive, acquire, generate, and / or store data (referred to in some examples as "configuration data") representing one or more configuration parameters associated with grouping different combinations of object detections (e.g., echoes, etc.) detected using at least sensor data. For example, in some examples, the configuration parameters may indicate an order (e.g., a pattern, a period, etc.) associated with analyzing sensor data instances generated using different sensors. For the first example, the order may indicate a starting sensor on the machine, such as the left front sensor (and / or any other sensor), and a direction around the machine, such as clockwise or counterclockwise. For the second example, the order may indicate the first sensor, then the second sensor, then the third sensor, then the fourth sensor, and so on. Furthermore, in some examples, the configuration parameters may indicate additional information, such as a distance threshold for associating object detections (e.g., echoes), a minimum number of object detections to be processed for a group, and / or a maximum number of object detections that a group may include.

[0024] The system can then use the configuration data to determine one or more groups associated with object detection. For example, to start a group, the system can process first sensor data generated using a first sensor (e.g., a starting sensor indicated by a configuration parameter). In some examples, when the group is started, the system can only process specific types of sensor data, such as sensor data associated with a primary measurement of the first sensor. As described herein, the sensor data can be associated with a primary measurement based on the first sensor both emitting an output (e.g., ultrasound, etc.) and receiving an input associated with the output (e.g., one or more echoes). However, in other examples, when the group is started, the system can process multiple types of sensor data, such as sensor data associated with a primary measurement and sensor data associated with a secondary measurement. As described herein, the sensor data can be associated with a secondary measurement based at least on another sensor emitting an output but the first sensor still receiving an input associated with the output.

[0025] In any example, based at least on processing the first sensor data, the system may determine whether the first sensor data represents a first object detection. For example, if the first sensor data includes ultrasonic data, the system may determine that the first sensor data represents a first object detection based at least on the first sensor data representing an echo (e.g., when the frequency signal meets (e.g., is equal to or greater than) a threshold amplitude value and / or indicates a peak). If the system determines that the first sensor data does not represent a first object detection, the system may not generate a group and / or move to the next sensor using the sequence. However, if the system determines that the first sensor data represents a first object detection, the system may generate a group corresponding to the object. Furthermore, the system may associate the first object detection and / or the first sensor data with the group.

[0026] The system can then use this order to process second sensor data generated using a second sensor (e.g., a clockwise adjacent sensor relative to the first sensor). In some examples, the second sensor data can be associated with a primary measurement or a secondary measurement. Based at least on this processing, the system can determine whether the second sensor data represents a second object detection (e.g., an echo) associated with the same object as the first object detection of the first sensor data. As described herein, in some examples, the system can determine that the second object detection is associated with the same object based at least on a distance associated with the second object detection being within a threshold distance relative to the distance associated with the first object detection. If the system determines that the second sensor data does not represent a second object detection associated with the same object, the system can avoid associating the second object detection and / or the second sensor data with the group, and / or the system terminates the process of generating the group. However, if the system determines that the second sensor data represents a second object detection associated with the same object, the system can associate the second object detection and / or the second sensor data with the group.

[0027] The system can then continue this technique of sequentially processing sensor data instances generated using one or more additional sensors to associate one or more additional object detections and / or one or more additional sensor data instances with the group. For example, the system can continue this technique until the system processes sensor data that does not represent object detections associated with the same object, until the system associates a maximum number of object detections with the group, until the system processes a threshold number of sensor data instances, until the system processes sensor data generated by all sensors, and / or until one or more additional and / or replacement events occur.

[0028] In some examples, the system may then perform one or more additional and / or alternative checks on the group. For example, if the group is not associated with at least a minimum number of object detections, the system may determine that the group is not valid, or if the group is associated with at least a minimum number of object detections, the system may determine that the group is valid. For example, if the minimum number of object detections includes three object detections, the system may determine that the group is valid as long as the group is associated with three or more object detections.

[0029] The system may then process the object detections and / or sensor data associated with the group (e.g., if the group is found to be valid) to determine information associated with the object. For example, the system may process the object detections and / or sensor data to determine the location of the object in the environment (e.g., a two-dimensional location, a three-dimensional location, a relative location, etc.), a classification associated with the object, a probability associated with the location, a probability associated with the classification, and / or any other information. In some examples, the system may use various techniques to determine the information, such as by processing the sensor data and / or object detections using one or more machine learning models, one or more neural networks, one or more algorithms, one or more modules, and / or any other processing components. For example, if the system is determining a location associated with an object, the system may determine the location based at least on processing the object detections using one or more trilateration algorithms.

[0030] In some examples, the system can then perform a similar process to determine information associated with any number of objects located in the environment. For the first example, if the system determines that the first sensor data represents another object detection associated with another object, the system can perform a similar process to generate a group for the other object and / or use the group to determine information associated with the other object. For the second example, the system can process the sensor data associated with the next sensor in the sequence, such as the second sensor in the above example. If the system determines that the second sensor data represents an object detection associated with the object and / or another object detection associated with another object, the system can perform a similar process to generate a group for the object and / or the other object and / or use the group to determine information associated with the object and / or the other object.

[0031] In some examples, the system can use information associated with an object to perform additional processes. For example, the system can use the information to generate a representation associated with the environment, such as an occupancy map, a height map, a distance map, and / or any other type of representation. Additionally, the system can use the information and / or the representation to determine one or more operations for navigating the machine. For example, the system can use the information and / or the representation to determine a trajectory for the machine and then cause the machine to navigate along the trajectory. While this is merely one example of one or more operations that the system can cause the machine to perform using information and / or representations, in other examples, the system can cause the machine to perform additional and / or alternative operations.

[0032] 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 adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying boats, boats, space shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, engineering vehicles, underwater vehicles, 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 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.

[0033] 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 implementing large language models (LLMs), systems implementing one or more visual language models (VLMs), 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 for 3D assets, systems for performing generative AI operations, systems implemented at least in part using cloud computing resources, and / or other types of systems.

[0034] refer to Figure 1 , Figure 1 An example data flow diagram of a process 100 for grouping object detections and then processing the object detections using these groups to perform one or more tasks according to some embodiments of the present invention is shown. It should be understood that this arrangement and other arrangements described herein are presented as examples only. 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 some 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 with other components and 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 implemented by a processor executing instructions stored in a memory. In some embodiments, the systems, methods, and processes described herein may be implemented using Figures 7A-7D Example autonomous vehicle 700, Figure 8 The example computing device 800 of FIG. 800 , and / or Figure 9 The example data center 900 may be performed using components, features, and / or functions similar to the components, features, and / or functions of the example data center 900.

[0035] Process 100 may include a grouping component 102 receiving sensor data 104 generated using one or more sensors 106 of a machine (e.g., autonomous vehicle 700). As described herein, sensor data 104 may include, but is not limited to, ultrasonic data 104 generated using one or more ultrasonic sensors 106, sonar data 104 generated using one or more sonar sensors 106, image data 104 generated using one or more image sensors 106, LiDAR data 104 generated using one or more LiDAR sensors 106, RADAR data 104 generated using one or more RADAR sensors 106, and / or any other type of sensor data generated using any other type of sensor. As described herein, sensor data 104 may indicate one or more locations of one or more objects located within an environment. For example, in the case of ultrasonic data, sensor data 104 may represent a one-dimensional representation indicating one or more distances to an object. For example, the sensor data 104 may represent a histogram associated with a plurality of bins (e.g., 50 bins, 100 bins, 200 bins, 300 bins, 320 bins, 400 bins, etc.), wherein each bin is associated with a corresponding distance within the environment. Furthermore, the histogram may indicate amplitude values ​​associated with the frequency signal, wherein one or more peak amplitude values ​​associated with the frequency signal (e.g., echoes) may indicate a distance associated with an object within the environment.

[0036] The process 100 may also include the grouping component 102 receiving, acquiring, generating, and / or storing configuration data 108 representing one or more configuration parameters associated with grouping different combinations of object detections detected using the sensor data 104. For example, the configuration parameters may indicate an order (e.g., a pattern, a period, etc.) associated with analyzing instances of the sensor data 104 generated using the sensors 106. For a first example, the order may indicate a starting sensor 106 on the machine, such as the left front sensor (and / or any other sensor), and a direction around the machine, such as clockwise or counterclockwise. For a second example, the order may indicate a first sensor 106, followed by a second sensor 106, followed by a third sensor 106, followed by a fourth sensor 106, and so on. While these are just a few examples of orders that the configuration data 108 may represent, in other examples, the configuration data 108 may represent additional and / or alternative orders associated with the sensors 106.

[0037] For example, Figure 2 An example of a machine 202 including sensors 204(1)-204(6) (also referred to singly as "sensor 204" or plurally as "sensors 204") is shown in accordance with some embodiments of the present disclosure.Figure 2 The example of FIG. 20 shows six sensors 204 located at the front of machine 202. In other examples, machine 202 may include any other number of sensors located at any other location on machine 202. Additionally, as shown, sensors 204(1)-204(6) may include fields of view (FOVs) 206(1)-206(6), respectively (also referred to as "FOV 206" or "FOVs 206"), where FOVs 206 at least partially overlap one another. In some examples, sensors 204 may each include a particular type of sensor, such as an ultrasonic sensor. However, in other examples, one or more sensors 204 may include one or more additional and / or alternative types of sensors.

[0038] Thus, the machine 202 and / or the sensor 204 may be associated with configuration data (e.g., configuration data 108) that indicates an order associated with instances of processing sensor data generated using the sensor 204. For a first example, the configuration data may indicate the first sensor 204(1), followed by the second sensor 204(2), followed by the third sensor 204(3), followed by the fourth sensor 204(4), followed by the fifth sensor 204(5), and finally the sixth sensor 204(6). For a second example, the configuration data may indicate the sixth sensor 204(6), followed by the fifth sensor 204(5), followed by the fourth sensor 204(4), followed by the third sensor 204(3), followed by the second sensor 204(2), and finally the first sensor 204(1). However, for a third example, the configuration data may indicate starting with the first sensor 204(1) and then moving in a clockwise direction relative to the machine 202. While these are just a few examples of sequences that may be associated with sensor 204 , in other examples, sensor 204 may be associated with one or more additional and / or alternative modes.

[0039] review Figure 1 In some examples, the configuration parameters may further indicate a threshold distance for grouping object detections. As described herein, the threshold distance may include, but is not limited to, 10 centimeters, 30 centimeters, 50 centimeters, 1 meter, and / or any other distance. In some examples, the configuration parameters may further indicate a minimum number of object detections for generating a group and / or a maximum number of object detections that may be associated with a group. As described herein, the minimum number of object detections may include, but is not limited to, one object detection, two object detections, three object detections, four object detections, and / or any other number of object detections. Additionally, the maximum number of object detections may include, but is not limited to, four object detections, five object detections, six object detections, seven object detections, and / or any other number of object detections.

[0040] The process 100 may include a grouping component 102 processing sensor data 104 and / or configuration data 108 to group object detections into one or more groups. For example, to start a group, the grouping component 102 may process first sensor data 104 generated using a first sensor 106 (e.g., a starting sensor indicated by the configuration data 108). In some examples, when starting the group, the system may only process a specific type of sensor data 104, such as sensor data 104 associated with a primary measurement of the first sensor 106. As described herein, the sensor data 104 may be associated with the primary measurement based at least on the fact that the first sensor 106 both transmits an output (e.g., ultrasound, etc.) and receives an input associated with the output (e.g., one or more echoes). However, in other examples, when starting the group, the grouping component 102 may process multiple types of sensor data 104, such as sensor data 104 associated with the primary measurement and sensor data 104 associated with the secondary measurement. As described herein, sensor data 104 may be associated with a secondary measurement based at least on another sensor 106 transmitting an output but the first sensor 106 still receiving an input associated with the output.

[0041] In any example, based at least on processing the first sensor data 104, the grouping component 102 can determine whether the first sensor data 104 represents a first object detection (e.g., a first echo). For example, if the first sensor data 104 includes ultrasonic data, the grouping component 102 can determine that the first sensor data 104 represents a first object detection based at least on the first sensor data 104 representing an echo, such as when the frequency signal meets (e.g., is equal to or greater than) a threshold amplitude value and / or indicates a peak. If the grouping component 102 determines that the first sensor data 104 does not represent a first object detection, the grouping component 102 can not generate a group and / or move to the next sensor 106 using the sequence. However, if the grouping component 102 determines that the first sensor data 104 represents a first object detection, the grouping component 102 can generate a group associated with the object. Furthermore, the grouping component 102 can associate the first object detection and / or the first sensor data 104 with the group.

[0042] The grouping component 102 can then use this order to process second sensor data 104 generated using a second sensor 106 (e.g., a clockwise adjacent sensor 106 relative to the first sensor 106). In some examples, the second sensor data 104 can be associated with a primary measurement or a secondary measurement. Based at least on this processing, the grouping component 102 can determine whether the second sensor data 104 represents a second object detection (e.g., a second echo) associated with the same object as the first object detection of the first sensor data 104. As described herein, in some examples, the grouping component 102 can determine that the second object detection is associated with the same object based at least on a distance associated with the second object detection being within a threshold distance relative to a distance associated with the first object detection.

[0043] If the grouping component 102 determines that the second sensor data 104 does not represent a second object detection associated with the same object, the grouping component 102 can refrain from associating the second object detection and / or the second sensor data 104 with the group, and / or the grouping component 102 can terminate the process of generating the group. However, if the grouping component 102 determines that the second sensor data 104 represents a second object detection associated with the same object, the grouping component 102 can associate the second object detection and / or the second sensor data 104 with the group.

[0044] The grouping component 102 can then sequentially continue this technique of processing instances of the sensor data 104 generated using one or more additional sensors 106 to associate one or more additional object detections and / or one or more additional instances of the sensor data 104 with the group. For example, the grouping component 102 can continue this technique until the grouping component 102 processes instances of the sensor data 104 that do not represent object detections associated with the same object, until the grouping component 102 adds a maximum number of object detections to the group, until the grouping component 102 processes a threshold number of instances of the sensor data 104, until the grouping component 102 processes sensor data 104 generated by all sensors 106, and / or until one or more additional and / or replacement events occur.

[0045] In some examples, the grouping component 102 can then perform one or more additional and / or alternative checks on the group. For example, if the group is associated with a number of object detections that is less than a minimum number of object detections, the grouping component 102 can determine that the group is not valid. Additionally, if the group is associated with a number of object detections that is equal to or greater than the minimum number of object detections, the grouping component 102 can determine that the group is valid.

[0046] For example, Figures 3A-3C An example of generating a group associated with object detection according to some embodiments of the present disclosure is shown.Figure 3A As shown in the example of , at least the first object 302(1) can be located within at least the first FOV 206(1) of the first sensor 204(1) and the second FOV 206(2) of the second sensor 204(2). In addition, the second object 302(2) can be located within at least the first FOV 206(1) of the first sensor 204(1), the second FOV 206(2) of the second sensor 204(2), and the third FOV 206(3) of the third sensor 204(3). Although Figure 3A The example shows two objects 302(1)-302(2) located in the environment, but in other examples, any number of objects may be present in the environment. Figure 3A The example shows that objects 302(1)-302(2) include pedestrians, but in other examples, the objects may include any other type of object.

[0047] like Figure 3B As shown in the example of , the first sensor 204(1) can generate first sensor data 304(1) associated with a primary measurement. The first sensor data 304(1) can represent a first histogram associated with a plurality of bins 306(1) and indicate an amplitude value 308(1) associated with a first frequency signal 310(1). Additionally, the second sensor 204(2) can generate second sensor data 304(2) associated with a secondary measurement (e.g., the first sensor 204(1) may have emitted an output). The second sensor data 304(2) can represent a second histogram associated with a plurality of bins 306(2) and indicate an amplitude value 308(2) associated with a second frequency signal 310(2). Additionally, the second sensor 204(2) can also generate third sensor data 304(3) associated with the primary measurement. The third sensor data 304(3) can represent a third histogram associated with a plurality of bins 306(3) and indicate an amplitude value 308(3) associated with a third frequency signal 310(3). Additionally, the third sensor 204(3) may generate fourth sensor data 304(4) associated with the primary measurement. The fourth sensor data 304(4) may represent a fourth histogram associated with a number of bins 306(4) and indicate amplitude values ​​308(4) associated with a fourth frequency signal 310(4).

[0048] Thus, grouping component 102 can process first sensor data 304(1) and, based at least on the processing, determine that first sensor data 304(1) represents a first object detection 312(1) at a peak associated with first frequency signal 310(1). For example, first object detection 312(1) can be associated with first object 302(1). Based at least on first object detection 312(1), grouping component 102 can generate a first group and associate first sensor data 304(1) and / or first object detection 312(1) with the first group.

[0049] Based at least on the order associated with the sensors 204, the grouping component 102 may then process the second sensor data 304(2) and determine, based at least on the processing, that the second sensor data 304(2) represents a second object detection 312(2) at a peak associated with the second frequency signal 310(2) and a third object detection 312(3) at another peak associated with the second frequency signal 310(2). The grouping component 102 may then determine that the second object detection 312(2) is within a threshold distance 314(1)-314(2) of the first object detection 312(1). Based at least on the determination, the grouping component 102 may associate the second sensor data 304(2) and / or the second object detection 312(2) with the first group. In some examples, the grouping component 102 may also determine that the third object detection 312(3) is outside the threshold distance 314(1)-314(2) of the first object detection 312(1). Based at least on this determination, grouping component 102 can not associate third object detection 312(3) with the first group.

[0050] Next, and based at least on the order, grouping component 102 can process third sensor data 304(3) and, based at least on the processing, determine that third sensor data 304(3) represents a fourth object detection 312(4) at a peak associated with third frequency signal 310(3) and a fifth object detection 312(5) at another peak associated with third frequency signal 310(3). Grouping component 102 can then determine that fourth object detection 312(4) is within a threshold distance 314(1)-314(2) of first object detection 312(1). Based at least on the determination, grouping component 102 can associate third sensor data 304(3) and / or fourth object detection 312(4) with the first group. In some examples, grouping component 102 can also determine that fifth object detection 312(5) is outside a threshold distance 314(1)-314(2) of first object detection 312(1). Based at least on this determination, grouping component 102 can not associate fifth object detection 312(5) with the first group.

[0051] Next, based at least on the order, grouping component 102 may process fourth sensor data 304(4) and determine, based at least on the processing, that fourth sensor data 304(4) represents a sixth object detection 312(6) at a peak associated with fourth frequency signal 310(4). Grouping component 102 may then determine that sixth object detection 312(6) is outside a threshold distance 314(1)-314(2) of first object detection 312(1). Based at least on the determination, grouping component 102 may not associate fourth sensor data 304(4) and / or sixth sensor detection 312(6) with the first group, and / or grouping component 102 may determine that the first group is complete.

[0052] As described herein, grouping component 102 may further process first sensor data 304(1) and, based at least on the processing, determine that first sensor data 304(1) represents a seventh object detection 312(7) at another peak associated with first frequency signal 310(1). For example, seventh object detection 312(7) may be associated with second object 302(2). Based at least on seventh object detection 312(7), grouping component 102 may generate a second group and associate first sensor data 304(1) and / or seventh object detection 312(7) with the second group. Additionally, grouping component 102 may perform a similar process as described herein for the first group to associate second sensor data 304(2) with the second group, associate third object detection 312(3) with the second group, associate third sensor data 304(3) with the second group, associate fifth object detection 312(5) with the second group, associate fourth sensor data 304(4) with the second group, and / or associate sixth object detection 312(6) with the second group.

[0053] In some examples, after processing all sensor detections of the first sensor data 304(1), and based at least on the order, the grouping component 102 can move to processing the second sensor data 304(2) to determine whether to generate one or more new groups. For example, Figure 3C As shown in the example of , grouping component 102 can process second sensor data 304(2) and, based at least on the processing, determine that second sensor data 304(2) represents a second object detection 312(2) at a peak associated with second frequency signal 310(2). For example, second object detection 312(2) can be associated with first object 302(1). Based at least on second object detection 312(2), grouping component 102 can generate a third group and associate second sensor data 304(2) and / or second object detection 312(2) with the third group.

[0054] Next, based at least on the order, grouping component 102 can process third sensor data 304(3) and determine, based at least on the processing, that third sensor data 304(3) represents a fourth object detection 312(4) at a peak associated with third frequency signal 310(3) and a fifth object detection 312(5) at another peak associated with third frequency signal 310(3). Grouping component 102 can then determine that fourth object detection 312(4) is within a threshold distance 314(1)-314(2) of second object detection 312(2). Based at least on the determination, grouping component 102 can associate third sensor data 304(3) and / or fourth object detection 312(4) with the third group. In some examples, grouping component 102 can also determine that fifth object detection 312(5) is outside a threshold distance 314(1)-314(2) of second object detection 312(2). Based at least on this determination, grouping component 102 can not associate fifth object detection 312(5) with the second group.

[0055] Next, and based at least on the order, grouping component 102 may process fourth sensor data 304(4) and, based at least on the processing, determine that fourth sensor data 304(4) represents a sixth object detection 312(6) at a peak associated with fourth frequency signal 310(4). Grouping component 102 may then determine that sixth object detection 312(6) is outside of threshold distance 314(1)-314(2) of second object detection 312(2). Based at least on the determination, grouping component 102 may not associate fourth sensor data 304(4) and / or sixth sensor detection 312(6) with the third group, and / or grouping component 102 may determine that the third group is complete.

[0056] In some examples, grouping component 102 can determine that the third group is not valid based on at least the third group being associated with only two sensor data instances 304(2)-304(3) and / or two object detections 312(2) and 312(4). For example, in these examples, the minimum number of object detections for validating a group can include three object detections. However, in other examples, grouping component 102 can still determine that the third group is valid based on at least the third group being associated with two sensor data instances 304(2)-304(3) and / or two object detections 312(2) and 312(4). For example, in these examples, the minimum number of object detections for validating a group can include two object detections.

[0057] As described herein, grouping component 102 may further process second sensor data 304(2) and, based at least on the processing, determine that second sensor data 304(2) represents a third object detection 312(3) at another peak associated with second frequency signal 310(2). For example, third object detection 312(3) may be associated with second object 302(2). Based at least on third object detection 312(3), grouping component 102 may generate a fourth group and associate second sensor data 304(2) and / or third object detection 312(3) with the fourth group. Additionally, grouping component 102 may perform a process similar to the process described herein with respect to the first group to associate third sensor data 304(3) with the fourth group, associate fifth object detection 312(5) with the fourth group, associate fourth sensor data 304(4) with the fourth group, and / or associate sixth object detection 312(6) with the fourth group.

[0058] In some examples, the grouping component 102 can then continue to process: the third sensor data 304(3) associated with the third sensor 204(3) to determine whether to generate one or more new groups, and / or the fourth sensor data 304(4) associated with the fourth sensor 204(4) to determine whether to generate one or more new groups. As described herein, the grouping component 102 can use the order associated with the sensors 204 when determining which sensors 204 to process and / or the period in which to process the sensors 204.

[0059] review Figure 1 For example, process 100 may include grouping component 102 generating and / or outputting grouped data 110 representing a group. As described herein, for a group, grouped data 110 may represent at least an identifier associated with the group, an identifier of an instance of sensor data 104 associated with the group, an identifier of an object detection associated with the group, and / or any other information associated with the group. Process 100 may then include processing component 112 processing at least a portion of sensor data 104 and at least a portion of grouped data 110 to perform one or more tasks.

[0060] For example, processing component 112 can include and / or use one or more machine learning models, one or more neural networks, one or more algorithms, one or more modules, and / or any other type of processing component configured to process sensor data 104 to perform a task. As described herein, in some examples, the task can include, but is not limited to, object detection, object tracking, object classification, map (and / or any other type of representation) generation, and / or any other task. Additionally, processing component 112 can use grouping data 110 such that processing component 112 is able to process different object detections associated with individual groups. For example, processing component 112 can process a first object detection associated with a first group to generate first output data 114 representing first information associated with a first object, process a second object detection associated with a second group to generate second output data 114 representing second information associated with a second group, process a third object detection associated with a third group to generate third output data 114 representing third information associated with a third object, and so on.

[0061] For example, Figure 4 An example of processing object detections associated with groups to determine information associated with objects is shown in accordance with some embodiments of the present disclosure. In Figure 4 In the example of FIG. 3, processing component 112 can use grouping data to identify a first group associated with at least first sensor data 304(1), first object detection 312(1), second sensor data 304(2), second object detection 312(2), third sensor data 304(3), and / or fourth object detection 312(4). Processing component 112 can then process first sensor data 304(1), first object detection 312(1), second sensor data 304(2), second object detection 312(2), third sensor data 304(3), and / or fourth object detection 312(4) using one or more techniques (e.g., one or more trilateration algorithms) to determine a first location 402(1) associated with first object 302(1).

[0062] In addition, processing component 112 may use the grouped data to identify a fourth group that includes at least second sensor data 304(2), third object detection 312(3), third sensor data 304(3), fifth object detection 312(5), fourth sensor data 304(4), and / or sixth object detection 312(6). Processing component 112 may then process second sensor data 304(2), third object detection 312(3), third sensor data 304(3), fifth object detection 312(5), fourth sensor data 304(4), and / or sixth object detection 312(6) using one or more techniques (e.g., one or more trilateration algorithms) to determine a second location 402(2) associated with second object 302(2).

[0063] In further detail regarding processing the fourth group, processing component 112 may use third object detection 312(3) as represented by detection 404(1), fifth object detection 312(5) as represented by detection 404(2), and sixth object detection 312(6) as represented by detection 404(3). Processing component 112 may then process detections 404(1)-404(3), for example, by using one or more trilateration algorithms (and / or any other type of algorithm) to determine a second location 402(2) associated with second object 302(2). While these are just a few example techniques of how processing component 112 may process sensor data 304(1)-304(4) and / or object detections 312(1)-312(7) based on at least one or more groupings to perform particular tasks, in other examples, processing component 112 may process sensor data 304(1)-304(4) and / or object detections 312(1)-312(7) based on at least one grouping to perform one or more additional and / or alternative tasks.

[0064] Now refer to Figure 5 and Figure 6 , each block of the methods 500 and 600 described herein comprises a computational process that can be performed using any combination of hardware, firmware, and / or software. For example, a processor may perform various functions by executing instructions stored in a memory. The methods 500 and 600 may also be embodied as computer-usable instructions stored on a computer storage medium. The methods 500 and 600 may be provided by a standalone 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. Furthermore, the methods 500 and 600 are directed to Figure 1 However, these methods 500 and 600 may additionally or alternatively be performed by any one system or any combination of systems, including but not limited to the systems described herein.

[0065] Figure 5 A flow chart is shown illustrating a method 500 for grouping object detections associated with ultrasonic data and then using the grouping to perform one or more tasks, according to some embodiments of the present disclosure. At block B502, the method 500 may include acquiring first sensor data generated using a first ultrasonic sensor of a machine and second sensor data generated using a second ultrasonic sensor of the machine. For example, the grouping component 102 may receive first sensor data 104 generated using a first sensor 106 and second sensor data 104 generated using a second sensor 106, where the first sensor 106 and the second sensor 106 may include ultrasonic sensors. In some examples, the grouping component 102 may also receive configuration data 108 representing configuration parameters for grouping at least the first sensor data 104 and the second sensor data 104.

[0066] At block B504, method 500 may include determining to associate the first sensor data with a group corresponding to an object based at least on the first sensor data indicating a first object detection associated with a first distance from the machine. For example, grouping component 102 may process first sensor data 104 to determine that first sensor data 104 indicates a first object detection. As described herein, in some examples, first sensor data 104 may represent at least a first frequency signal, wherein a peak associated with the first frequency signal indicates a first object detection (e.g., a first echo) at a first distance. Based at least on this determination, grouping component 102 may create a group corresponding to the object and then associate the first sensor data 104 and / or the first object detection with the group.

[0067] Method 500 may include, at block B506, determining that the second sensor data indicates a second object detection associated with a second distance from the machine. For example, grouping component 102 may process second sensor data 104 to determine that second sensor data 104 indicates a second object detection. As described herein, in some examples, second sensor data 104 may represent at least a second frequency signal, wherein a peak associated with the second frequency signal indicates a second object detection (e.g., a second echo) at a second distance. Additionally, in some examples, grouping component 102 may use configuration data 108 to determine that second sensor data 104 is processed after first sensor data 104.

[0068] Method 500 may include, at block B508, determining to associate a second object detection with the group based at least on the second distance being within a threshold distance relative to the first distance. For example, grouping component 102 may determine that the second distance is within a threshold distance for the first distance, where the threshold distance may be represented by configuration data 108. Based at least on determining that the second distance is within the threshold distance relative to the first distance, grouping component 102 may associate second sensor data 104 and / or a second object detection with the group. Additionally, grouping component 102 may perform a similar process to associate one or more additional sensor data 104 instances and / or one or more additional object detections with the group.

[0069] Method 500 may include, at block B510, determining a location associated with the object based at least on the group and using at least the first object detection and the second object detection. For example, processing component 112 may determine to process sensor data 104 and / or object detection associated with the group to determine information associated with the object, such as a location associated with the object. In some examples, processing component 112 may process sensor data 104 and / or object detection using a machine learning model, a neural network, an algorithm, a module, and / or any other component configured to determine information. For example, processing component 112 may determine the location by processing sensor data 104 and / or object detection using one or more trilateration algorithms.

[0070] Method 500 may include causing the machine to perform one or more operations based at least on the location associated with the object at block B512. For example, the machine may determine the operation based at least on the location associated with the object. As described herein, in some examples, the operation may include causing the machine to navigate one or more trajectories in the environment, for example, to avoid a collision with the object.

[0071] Figure 6 A flow chart of a method 600 for grouping object detections detected using multiple sensors is shown in accordance with some embodiments of the present disclosure. The method 600 may include, at block B602, obtaining sensor data generated using a sensor of a machine. For example, the grouping component 102 may receive sensor data 104 generated using a sensor 106 of a machine. As described herein, in some examples, the sensor data 104 may include a 1D representation generated using a particular type of sensor (e.g., an ultrasonic sensor, a sonar sensor, etc.). However, in other examples, the sensor data 104 may be generated using any other type of sensor (e.g., an image sensor, a LiDAR sensor, a RADAR sensor, etc.).

[0072] Method 600 can include, at block B604, determining that a portion of sensor data associated with the sensor indicates an object detection corresponding to an object. For example, grouping component 102 can process sensor data 104 and, based at least on the processing, determine that the portion of sensor data 104 represents an object detection. For example, if sensor data 104 includes ultrasonic data, grouping component 102 can determine that the ultrasonic data represents an echo associated with an object located within the environment.

[0073] Method 600 can include, at block B606, associating at least one of the portion of the sensor data or the object detection with a group corresponding to the object. For example, based at least on the object detection, grouping component 102 can generate a group corresponding to the object. Grouping component 102 can then associate at least one of the portion of the sensor data 104 or the object detection with the group.

[0074] Method 600, at block B608, may include determining that an additional portion of sensor data associated with the additional sensor indicates an additional object detection. For example, grouping component 102 may use one or more techniques to identify the additional sensor 106, such as configuration data 108 indicating an order associated with processing sensor data 104 generated using sensor 106. For example, configuration data 108 may indicate that the additional portion of sensor data 104 generated using the additional sensor 106 is processed after the portion of sensor data 104 generated using sensor 106 is processed. Based at least on this processing, grouping component 102 may determine that the additional portion of sensor data 104 indicates an additional object detection. For example, if sensor data 104 again includes ultrasonic data, grouping component 102 may determine that the ultrasonic data indicates an additional echo associated with an object and / or another object located in the environment.

[0075] Method 600 may include, at block B610, determining whether the additional object detection is associated with the object. For example, grouping component 102 may determine whether the additional object detection is also associated with the object. As described herein, in some examples, grouping component 102 may determine that the additional object detection is also associated with the object based at least on the distance associated with the additional object detection being within a threshold distance relative to the distance associated with the object detection. Alternatively, in some examples, grouping component 102 may determine that the additional object detection is not associated with the object based at least on the distance associated with the additional object detection being outside a threshold distance relative to the distance associated with the object detection.

[0076] If it is determined at block B610 that the additional object detection is associated with the object, then the method 600 at block B612 may include associating at least one of the additional portion of the sensor data or the additional object detection with the group. For example, if the grouping component 102 determines that the additional object detection is also associated with the object, then the grouping component 102 may associate at least one of the additional portion of the sensor data 104 or the additional object detection with the group. Additionally, as Figure 6 As further shown in the example of , method 600 may be repeated starting at block B 608 to associate one or more additional portions of sensor data 104 and / or one or more additional object detections with a group.

[0077] However, if it is determined at block B610 that additional object detections are not associated with the object, the method 600 may include performing one or more operations using the group at block B614. For example, in some examples, based at least on determining that additional object detections are not associated with the object, the grouping component 102 may determine that the group is complete. The processing component 112 may then use the grouped data 110 and the sensor data 104 associated with the group to perform operations. For example, the processing component 112 may use the grouped data 110 and the sensor data 104 to determine a location associated with an object in the environment, generate a map (and / or other type of representation) representing information associated with the environment, determine a trajectory for the machine to navigate in the environment, and / or perform any other operation.

[0078] Example autonomous vehicle

[0079] Figure 7A7 is an illustration of an example autonomous vehicle 700 according to some embodiments of the present disclosure. Autonomous vehicle 700 (alternatively referred to herein as "vehicle 700") may include, but is not limited to, a passenger vehicle, such as a car, a truck, a bus, an emergency vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police car, an ambulance, a boat, an engineering vehicle, an underwater vessel, a robotic vehicle, a drone, an airplane, a vehicle coupled to a trailer (e.g., a semi-tractor semi-trailer truck for hauling freight), and / or other types of vehicles (e.g., unmanned and / or capable of accommodating 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 such standards). The vehicle 700 is capable of implementing functionality consistent with one or more of Levels 3 to 5 of the autonomous driving levels. The vehicle 700 is capable of implementing functionality consistent with one or more of Levels 1 to 5 of the automated driving levels. For example, depending on the embodiment, the vehicle 700 is capable of driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5) capabilities. The term "autonomy" as used herein may include any and / or all types of autonomy of 700 or other machines, such as full autonomy, high autonomy, conditional autonomy, partial autonomy, assisted autonomy, semi-autonomy, primary autonomy, or other names.

[0080] Vehicle 700 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other vehicle components. Vehicle 700 may include a propulsion system 750, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. Propulsion system 750 may be connected to a drivetrain of vehicle 700, which may include a transmission, to achieve propulsion of vehicle 700. Propulsion system 750 may be controlled in response to receiving a signal from throttle / accelerator 752.

[0081] A steering system 754, which may include a steering wheel, may be used to steer the vehicle 700 (e.g., along a desired path or route) when the propulsion system 750 is operating (e.g., when the vehicle is in motion). The steering system 754 may receive signals from a steering actuator 756. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0082] Brake sensor system 746 may be used to operate vehicle brakes in response to receiving signals from brake actuator 748 and / or brake sensors.

[0083] May include one or more system on chip (SoC) 704 ( Figure 7C ) and / or one or more GPUs can provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 700. For example, the one or more controllers can send signals to operate the vehicle brakes via one or more brake actuators 748, to operate the steering system 754 via one or more steering actuators 756, and to operate the propulsion system 750 via one or more throttles / accelerators 752. The one or more controllers 736 can 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 enable autonomous driving and / or assist a human driver in driving the vehicle 700. The one or more controllers 736 can include a first controller 736 for autonomous driving functions, a second controller 736 for functional safety functions, a third controller 736 for artificial intelligence functions (e.g., computer vision), a fourth controller 736 for infotainment functions, a fifth controller 736 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 736 may handle two or more of the above functions, two or more controllers 736 may handle a single function, and / or any combination thereof.

[0084] The one or more controllers 736 may provide signals for controlling one or more components and / or systems of the vehicle 700 in response to sensor data (e.g., sensor inputs) received from one or more sensors. The sensor data may be received from, for example and without limitation, a global navigation satellite system ("GNSS") sensor 758 (e.g., a global positioning system sensor), a RADAR sensor 760, an ultrasonic sensor 762, a LIDAR sensor 764, an inertial measurement unit (IMU) sensor 766 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 796, a stereo camera 768, a wide-angle camera 770 (e.g., a fisheye camera), an infrared camera 772, a surround camera 774 (e.g., a 360-degree camera), a long-range and / or mid-range camera 798, a speed sensor 744 (e.g., for measuring the velocity of the vehicle 700), a vibration sensor 742, a steering sensor 740, a brake sensor (e.g., as part of a brake sensor system 546), and / or other sensor types.

[0085] One or more of the controllers 736 may receive input (e.g., represented by input data) from the instrument cluster 732 of the vehicle 700 and provide output (e.g., represented by output data, display data, etc.) via a human machine interface (HMI) display 734, an audible annunciator, a speaker, and / or via other components of the vehicle 700. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Figure 7C The HMI display 734 may include information such as a high-definition (HD) map 722 of the vehicle 700, location data (e.g., the location of the vehicle 700 on the map), directions, locations of other vehicles (e.g., an occupancy grid), information about objects and object states as sensed by the controller 736, and the like. For example, the HMI display 734 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.).

[0086] The vehicle 700 further includes a network interface 724 that can communicate over one or more networks using one or more wireless antennas 726 and / or a modem. For example, the network interface 724 can be capable of communicating over 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 726 can 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 (LPWANs) such as LoRaWAN, SigFox, etc.

[0087] Figure 7B For use according to some embodiments of the present disclosure Figure 7A An example of camera positions and fields of view for an autonomous vehicle 700 is shown. The cameras and respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or alternative cameras may be included and / or located at different locations on the vehicle 700.

[0088] The camera type used 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 700. The camera may operate under Automotive Safety Integrity Level (ASIL) B and / or under another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120fps, 240fps, and the like, 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.

[0089] 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).

[0090] One or more of the cameras can 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 could interfere with the camera's ability to capture image data. With respect to the wing mirror mounting assembly, the wing mirror assembly can be custom 3D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras can be integrated into the wing mirror. For side-view cameras, one or more cameras can also be integrated into the four pillars at each corner of the cabin.

[0091] A camera with a field of view that includes a portion of the environment in front of the vehicle 700 (e.g., a front-facing camera) can be used for surround vision 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 736 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.

[0092] A variety of cameras can 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 can be a wide-angle camera 770, which can be used to sense objects entering the field of view from the periphery (e.g., pedestrians, intersection traffic, or bicycles). Although Figure 7B The figure shows only one wide-angle camera, but there can be any number (including zero) of wide-angle cameras 770 on the vehicle 700. In addition, any number of long-range cameras 798 (e.g., a pair of long-view stereo cameras) can be used for depth-based object detection, especially for objects for which neural networks have not yet been trained. Long-range cameras 798 can also be used for object detection and classification and basic object tracking.

[0093] Any number of stereo cameras 768 may also be included in the front configuration. In at least one embodiment, one or more stereo cameras 768 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. Alternative stereo cameras 768 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 768 may be used in addition to or alternatively to those described herein.

[0094] Cameras with a field of view that includes portions of the environment to the sides of the vehicle 700 (e.g., side-view cameras) can be used for surround viewing, providing information used to create and update occupancy grids and generate side impact collision warnings. For example, surround cameras 774 (e.g., Figure 7B Four surround cameras 774 (shown in FIG) can be placed on the vehicle 700. The surround cameras 774 can include a wide-angle camera 770, a fisheye camera, a 360-degree camera, and / or the like. For example, the four fisheye cameras can be placed on the front, rear, and sides of the vehicle. In an alternative arrangement, the vehicle can use three surround cameras 774 (e.g., left, right, and rear), and can utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround-view camera.

[0095] A camera having a field of view that includes a portion of the environment behind the vehicle 700 (e.g., a rearview 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-facing cameras as described herein (e.g., long-range and / or mid-range cameras 798, stereo cameras 768, infrared cameras 772, etc.).

[0096] Figure 7C For use according to some embodiments of the present disclosure Figure 7A700 . It will 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 conjunction with other components, and in any appropriate combination and location. The various functions described herein as being performed by entities may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in memory.

[0097] Figure 7C Each of the components, features, and systems of the vehicle 700 is illustrated as being connected via a bus 702. The bus 702 may include a controller area network (CAN) data interface (alternatively, referred to herein as a "CAN bus"). The CAN may be a network internal to the vehicle 700 that assists in controlling various features and functions of the vehicle 700, such as 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., a CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine revolutions per minute (RPM), button positions, and / or other vehicle status indicators. The CAN bus may be ASIL B compliant.

[0098] Although bus 702 is described here as a CAN bus, this is not intended to be limiting. For example, FlexRay and / or Ethernet may be used in addition to or in lieu of a CAN bus. Furthermore, although bus 702 is represented by a single line, this is not intended to be limiting. For example, there may be any number of buses 702, 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 702 may be used to perform different functions and / or may be used for redundancy. For example, a first bus 702 may be used for collision avoidance functionality, and a second bus 702 may be used for drive control. In any example, each bus 702 may communicate with any component of vehicle 700, and two or more buses 702 may communicate with the same component. In some examples, each SoC 704, each controller 736, and / or each computer within the vehicle may have access to the same input data (e.g., input from sensors on vehicle 700) and may be connected to a common bus such as a CAN bus.

[0099] The vehicle 700 may include one or more controllers 736, such as those described herein. Figure 7A Controller 736 may be used for a variety of functions. Controller 736 may be coupled to any of the various other components and systems of vehicle 700 and may be used for control of vehicle 700, artificial intelligence of vehicle 700, infotainment for vehicle 700, and / or the like.

[0100] The vehicle 700 may include one or more system-on-chips (SoCs) 704. The SoC 704 may include a CPU 706, a GPU 708, a processor 710, a cache 712, an accelerator 714, a data store 716, and / or other components and features not shown. The SoC 704 may be used to control the vehicle 700 in a variety of platforms and systems. For example, the one or more SoCs 704 may be combined with an HD map 722 in a system (e.g., a system of the vehicle 700), which may be downloaded from one or more servers (e.g., a server) via a network interface 724. Figure 7D one or more servers 778) to obtain map refreshes and / or updates.

[0101] The CPU 706 may include a CPU cluster or CPU complex (alternatively, referred to herein as a "CCPLEX"). The CPU 706 may include multiple cores and / or L2 caches. For example, in some embodiments, the CPU 706 may include eight cores in a coherent multiprocessor configuration. In some embodiments, the CPU 706 may include four dual-core clusters, each of which has a dedicated L2 cache (e.g., a 2MB L2 cache). The CPU 706 (e.g., CCPLEX) may be configured to support simultaneous cluster operations such that any combination of CPU 706 clusters can be active at any given time.

[0102] The CPU 706 may implement power management capabilities including one or more of the following features: each hardware block may be automatically clock gated when idle to conserve dynamic power; each core clock may be gated when the core is not actively executing instructions 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. The CPU 706 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wakeup 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 this work being offloaded to the microcode.

[0103] The GPU 708 may include an integrated GPU (alternatively referred to herein as an "iGPU"). The GPU 708 may be programmable and efficient for parallel workloads. In some examples, the GPU 708 may use an enhanced tensor instruction set. The GPU 708 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB of storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB of storage capacity). In some embodiments, the GPU 708 may include at least eight streaming microprocessors. The GPU 708 may use a computing application programming interface (API). In addition, the GPU 708 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0104] In the case of automotive and embedded use, GPU 708 can be power optimized to achieve optimal performance. For example, GPU 708 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 708 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, warp scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor may include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of computation and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. Streaming microprocessors may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0105] The GPU 708 can include high bandwidth memory (HBM) and / or a 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), can be used in addition to or in lieu of HBM memory.

[0106] The GPU 708 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 services (ATS) support may be used to allow the GPU 708 to directly access the CPU 706 page tables. In such an example, when the GPU 708 memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU 706. In response, the CPU 706 may look up the virtual-to-physical mapping for the address in its page table and transmit the translation back to the GPU 708. In this way, unified memory technology may allow a single unified virtual address space to be used for memory of both the CPU 706 and the GPU 708, thereby simplifying GPU 708 programming and porting applications to the GPU 708.

[0107] In addition, GPU 708 can include access counters that can track how often GPU 708 accesses the memory of other processors. The access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0108] SoC 704 may include any number of caches 712, including those described herein. For example, cache 712 may include an L3 cache available to both CPU 706 and GPU 708 (e.g., connected to both CPU 706 and GPU 708). Cache 712 may include a write-back cache that can track the state of lines, for example, 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.

[0109] SoC 704 may include one or more arithmetic logic units (ALUs) that may be used to perform processing for any of a variety of tasks or operations related to vehicle 700, such as processing a DNN. Furthermore, SoC 704 may include a floating point unit (FPU) or other math coprocessor or digital coprocessor type for performing mathematical operations within the system. For example, SoC 704 may include one or more FPUs integrated as execution units within CPU 706 and / or GPU 708.

[0110] SoC 704 may include one or more accelerators 714 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, SoC 704 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. The large on-chip memory (e.g., 4MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to supplement GPU 708 and offload some tasks of GPU 708 (e.g., freeing up more cycles of GPU 708 to perform other tasks). As an example, accelerator 714 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).

[0111] The accelerator 714 (e.g., a hardware acceleration cluster) may include a deep learning accelerator (DLA). The DLA may include one or more tensor processing units (TPUs) that can be configured to provide an additional 10 trillion operations per second for deep learning applications and 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 exceed the performance of the CPU. The TPU may perform several functions, including a single-instance convolution function, support for INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0112] 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: CNNs for object recognition and detection using data from camera sensors; CNNs for distance estimation using data from camera sensors; CNNs for emergency vehicle detection and recognition and detection using data from microphones; CNNs for facial recognition and vehicle owner identification using data from camera sensors; and / or CNNs for safety and / or security-related events.

[0113] The DLA can perform any function of the GPU 708, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 708. For example, the designer can focus the processing of CNNs and floating-point operations on the DLA and leave other functions to the GPU 708 and / or other accelerators 714.

[0114] The accelerator 714 (e.g., a hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may be alternatively referred to herein as a computer vision accelerator. The PVA may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (ADAS), autonomous driving, and / or augmented reality (AR) and / or virtual reality (VR) applications. The PVA may provide a balance between performance and flexibility. For example, each PVA may include, 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.

[0115] 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 memory devices. For example, the RISC core can include an instruction cache and / or tightly coupled RAM.

[0116] The DMA can enable components of the PVA to access system memory independently of the CPU 706. The DMA can 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 can support addressing in up to six or more dimensions, which can include block width, block height, block depth, horizontal block stride, vertical block stride, and / or depth stride.

[0117] A vector processor can be a programmable processor that can be designed to efficiently and flexibly execute 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 speed.

[0118] Each of the vector processors can include an instruction cache and can be coupled to dedicated memory. As a result, in some examples, each of the vector processors can be configured to execute independently of the other vector processors. In other examples, the vector processors included in a particular PVA can be configured to employ data parallelism. For example, in some embodiments, multiple vector processors included in a single PVA can execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA can execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on sequential images or portions of images. Among other things, any number of PVAs can be included in a hardware acceleration cluster, and any number of vector processors can be included in each of these PVAs. In addition, the PVAs can include additional error correction code (ECC) memory to enhance overall system security.

[0119] The accelerator 714 (e.g., a hardware acceleration cluster) may include an on-chip computer vision network and SRAM to provide high bandwidth, low latency SRAM for the accelerator 714. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and without limitation, 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).

[0120] The on-chip computer vision network can include an interface that ensures that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such an interface can provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst-based communication for continuous data transmission. This type of interface can comply with ISO 26262 or IEC 61508 standards, but other standards and protocols can also be used.

[0121] In some examples, SoC 704 may include a real-time ray tracing hardware accelerator such as that described in U.S. patent application Ser. No. 16 / 101,232 filed on Aug. 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and extent of objects (e.g., within a world model) in order to generate real-time visualization simulations for use in 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 positioning and / or other functions, and / or for other uses. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0122] The accelerator 714 (e.g., a hardware accelerator cluster) has a wide range of uses in autonomous driving. The PVA can be a programmable vision 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-intensive or intensive rule computations, and even on small data sets that require predictable runtimes with low latency and low power. Therefore, in the context of platforms for autonomous vehicles, the PVA is designed to run classic computer vision algorithms because they are efficient at object detection and integer math operations.

[0123] For example, according to one embodiment of the technology, PVA is used to perform computer stereo vision. In some examples, a semi-global matching-based algorithm can be used, but this is not intended to be limiting. Many applications for Level 3-5 autonomous driving require on-the-fly 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.

[0124] In some examples, PVA can be used to perform dense optical flow, by processing raw RADAR data (e.g., using a 4D Fast Fourier Transform) to provide processed RADAR. In other examples, PVA is used for time-of-flight depth processing, by processing raw time-of-flight data to provide processed time-of-flight data.

[0125] The 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 confidence values ​​can be interpreted as probabilities, 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 positives versus false positives. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positives. In an automatic emergency braking (AEB) system, a false positive detection could cause the vehicle to automatically apply emergency braking, which is clearly undesirable. Therefore, only the most confident detections should be considered triggers for AEB. The DLA can run a neural network to regress the confidence value. This neural network can take as its input at least some subset of parameters, such as bounding box dimensions, a ground plane estimate obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 766 output related to the orientation and distance of the vehicle 700, and 3D position estimates of objects obtained from the neural network and / or other sensors (e.g., LIDAR sensor 764 or RADAR sensor 760).

[0126] SoC 704 may include one or more data stores 716 (e.g., memory). Data stores 716 may be on-chip memory of SoC 704 that may store neural networks to be executed on the GPU and / or DLA. In some examples, for redundancy and safety, data stores 716 may be large enough to store multiple instances of the neural network. Data stores 716 may include L2 or L3 cache 712. References to data stores 716 may include references to memory associated with the PVA, DLA, and / or other accelerators 714 as described herein.

[0127] SoC 704 may include one or more processors 710 (e.g., embedded processors). Processor 710 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 704 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 704 thermal and temperature sensor management, and / or SoC 704 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to temperature, and SoC 704 may use the ring oscillator to detect the temperature of CPU 706, GPU 708, and / or accelerator 714. 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 704 in a lower power state and / or place vehicle 700 in a driver safety parking mode (e.g., to safely park vehicle 700).

[0128] The processor 710 may further include a set of embedded processors that may function as an audio processing engine. The audio processing engine may be an audio subsystem that allows for 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.

[0129] The processor 710 may further include an always-on processor engine that may 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.

[0130] Processor 710 may further include a safety cluster engine, which 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 (such as 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.

[0131] Processor 710 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0132] Processor 710 may further include a high dynamic range signal processor, which may include an image signal processor, which is a hardware engine that is part of a camera processing pipeline.

[0133] The processor 710 may include a video image compositer, which may be a processing block (e.g., implemented on a microprocessor) that implements the video post-processing functions required by the video playback application to produce the final image for the player window. The video image compositer may perform lens distortion correction for the wide-angle camera 770, the surround camera 774, and / or for the in-cab monitoring camera sensor. The in-cab monitoring 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.

[0134] The video image compositer can include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, in the presence of motion in the video, the noise reduction appropriately weights spatial information and downweights information provided by neighboring 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 can use information from previous images to reduce noise in the current image.

[0135] The video image compositor can also be configured to perform stereo rectification on the input stereo footage frames. The video image compositor can further be used for user interface composition when the operating system desktop is in use and the GPU 708 does not need to continuously render new surfaces. Even when the GPU 708 is powered on and active for 3D rendering, the video image compositor can be used to offload the GPU 708 to improve performance and responsiveness.

[0136] The SoC 704 may further include a mobile industry processor interface (MIPI) camera serial interface, a high-speed interface for receiving video and input from a camera, and / or a video input block that may be used for camera and related pixel input functions. The SoC 704 may further include an input / output controller that may be controlled by software and may be used to receive I / O signals that are not assigned to a specific role.

[0137] The SoC 704 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and / or other devices. The SoC 704 may be used to process data from cameras (connected via Gigabit multimedia serial links and Ethernet), sensors (e.g., LIDAR sensor 764, RADAR sensor 760, etc., which may be connected via Ethernet), data from the bus 702 (e.g., vehicle 700 speed, steering wheel position, etc.), and data from the GNSS sensor 758 (connected via Ethernet or a CAN bus). The SoC 704 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines and which may be used to free the CPU 706 from routine data management tasks.

[0138] SoC 704 can be an end-to-end platform with a flexible architecture that spans Levels 3-5 of automation, providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS technologies for diversity and redundancy, along with deep learning tools to provide a platform for a flexible and reliable driving software stack. SoC 704 can be faster, more reliable, and even more energy- and space-efficient than conventional systems. For example, when combined with CPU 706, GPU 708, and data storage 716, accelerator 714 can provide a fast and efficient platform for Levels 3-5 autonomous vehicles.

[0139] This technology therefore provides capabilities and functionality that cannot be achieved with 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 execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, a requirement for in-vehicle ADAS applications and practical Level 3-5 autonomous vehicles.

[0140] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a hardware acceleration cluster, the technology described herein allows for multiple neural networks to be executed simultaneously and / or sequentially, and the results to be combined to achieve Level 3-5 autonomous driving capabilities. For example, a CNN executed on a DLA or dGPU (e.g., GPU 720) can 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 can further include a neural network capable of recognizing, interpreting, and providing semantic understanding of the signs, and passing that semantic understanding to a path planning module running on the CPU complex.

[0141] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks can be run simultaneously. For example, a warning sign consisting of "Caution: Flashing lights indicate icing conditions" along with a light can be interpreted by several neural networks, either independently or collectively. The sign itself can be identified as a traffic sign by a first neural network deployed (e.g., a trained neural network), and the text "Flashing lights indicate icing conditions" can be interpreted by a second neural network deployed, which 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 can be identified by operating a third neural network deployed over multiple frames, which informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks can run simultaneously, for example, within the DLA and / or on GPU 708.

[0142] In some examples, a CNN for facial recognition and owner recognition can use data from a camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 700. 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 704 provides security against theft and / or carjacking.

[0143] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 796 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 704 uses CNNs 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 speed of emergency vehicles (for example, 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 758. 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 762, the control program can be used to execute emergency vehicle safety routines to slow the vehicle, pull over, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0144] The vehicle may include a CPU 718 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 704 via a high-speed interconnect (e.g., PCIe). The CPU 718 may include, for example, an X86 processor. The CPU 718 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 704, and / or monitoring the status and health of the controller 736 and / or the infotainment SoC 730.

[0145] The vehicle 700 may include a GPU 720 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 704 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 720 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 on input (e.g., sensor data) from sensors of the vehicle 700.

[0146] The vehicle 700 may further include a network interface 724, which may include one or more wireless antennas 726 (e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface 724 may be used to enable wireless connections to the cloud (e.g., to a server 778 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 may be established between the two vehicles, and / or an indirect link may be established (e.g., across a network and through the Internet). The direct link may be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link may provide the vehicle 700 with information about vehicles approaching the vehicle 700 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 700). This functionality may be part of the cooperative adaptive cruise control functionality of the vehicle 700.

[0147] The network interface 724 may include a SoC that provides modulation and demodulation functions and enables the controller 736 to communicate over a wireless network. The network interface 724 may include an RF front-end for up-conversion from baseband to RF and down-conversion from RF to baseband. The frequency conversion can be performed by well-known processes and / or can be performed using a super-heterodyne process. In some examples, the RF front-end function can be provided by a separate chip. The network interface may include wireless functions for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0148] The vehicle 700 may further include data storage 728, which may include off-chip storage (e.g., outside the SoC 704). The data storage 728 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, a hard disk, and / or other components and / or devices that can store at least one bit of data.

[0149] The vehicle 700 may further include a GNSS sensor 758. The GNSS sensor 758 (e.g., GPS, assisted GPS sensor, differential GPS (DGPS) sensor, etc.) is used to assist with mapping, perception, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 758 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0150] The vehicle 700 may further include a RADAR sensor 760. The RADAR sensor 760 may be used by the vehicle 700 for remote vehicle detection even in darkness and / or in adverse weather conditions. The RADAR functional safety level may be ASIL B. The RADAR sensor 760 may use CAN and / or bus 702 (e.g., to transmit data generated by the RADAR sensor 760) for control and access to object tracking data, and in some examples access Ethernet to access raw data. A variety of RADAR sensor types may be used. For example and without limitation, the RADAR sensor 760 may be suitable for front, rear, and side RADAR use. In some examples, a pulsed Doppler RADAR sensor is used.

[0151] The RADAR sensor 760 can 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 so on. In some examples, the long-range RADAR can be used for adaptive cruise control functions. The long-range RADAR system can provide a wide field of view (e.g., within 250m) achieved through two or more independent browsing. The RADAR sensor 760 can help distinguish between static objects and moving objects and can be used by the ADAS system for emergency braking assistance and forward collision warning. 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 the example with six antennas, the central four antennas can create a focused beam pattern that is designed to record the surroundings of the vehicle 700 at a higher rate with minimal traffic interference from adjacent lanes. The other two antennas can expand the field of view, making it possible to quickly detect vehicles entering or leaving the lane of the vehicle 700.

[0152] As one example, a mid-range RADAR system can include a range of up to 760 m (front) or 80 m (rear) and a field of view of up to 42 degrees (front) or 750 degrees (rear). A short-range RADAR system can include, but is not limited to, RADAR sensors designed to be mounted at both ends of the rear bumper. When mounted at both ends of the rear bumper, such a RADAR sensor system can create two beams that continuously monitor the rear and blind spots to the sides of the vehicle.

[0153] A short-range RADAR system can be used in an ADAS system for blind spot detection and / or lane change assist.

[0154] The vehicle 700 can further include ultrasonic sensors 762. The ultrasonic sensors 762, which can be placed on the front, rear, and / or sides of the vehicle 700, can be used for parking assist and / or to create and update an occupancy grid. A wide variety of ultrasonic sensors 762 can be used, and different ultrasonic sensors 762 can be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensors 762 can operate at an ASIL B functional safety level.

[0155] The vehicle 700 can include LIDAR sensors 764. The LIDAR sensors 764 can be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensors 764 can be at an ASIL B functional safety level. In some examples, the vehicle 700 can include multiple LIDAR sensors 764 (e.g., two, four, six, etc.) that can use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0156] In some examples, the LIDAR sensors 764 can be capable of providing a list of objects and their distances for a 360-degree field of view. A commercially available LIDAR sensor 764 can have, for example, an advertised range of approximately 700 m, a precision of 2 cm - 3 cm, and support for a 700 Mbps Ethernet connection. In some examples, one or more flush-mounted LIDAR sensors 764 can be used. In such examples, the LIDAR sensors 764 can be implemented as small devices that can be embedded into the front, rear, sides, and / or corners of the vehicle 700. In such examples, the LIDAR sensors 764 can provide a field of view of up to 120 degrees horizontal and 35 degrees vertical with a range of 200 m, even for low reflectivity objects. Front-mounted LIDAR sensors 764 can be configured for a horizontal field of view between 45 degrees and 135 degrees.

[0157] In some examples, LIDAR technologies such as 3D flash LIDAR may also be used. 3D flash LIDAR uses flashes of laser as an emission source to illuminate the vehicle's surroundings up to about 200 m. 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 a highly accurate and distortion-free image 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 700. Available 3D flash LIDAR systems include solid-state 3D staring array LIDAR cameras (e.g., non-browsing LIDAR devices) with no moving parts other than a fan. 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 764 may be less susceptible to motion blur, vibration, and / or shock.

[0158] The vehicle may further include an IMU sensor 766. In some examples, the IMU sensor 766 may be located at the center of the rear axle of the vehicle 700. The IMU sensor 766 may include, for example and without limitation, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, such as in a six-axis application, the IMU sensor 766 may include an accelerometer and a gyroscope, while in a nine-axis application, the IMU sensor 766 may include an accelerometer, a gyroscope, and a magnetometer.

[0159] In some embodiments, the IMU sensor 766 can be implemented as a miniature, high-performance GPS-assisted inertial navigation system (GPS / INS) that combines micro-electromechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. Thus, in some examples, the IMU sensor 766 can enable the vehicle 700 to estimate heading without the need for input from a magnetic sensor by directly observing and correlating velocity changes from the GPS to the IMU sensor 766. In some examples, the IMU sensor 766 and the GNSS sensor 758 can be combined into a single integrated unit.

[0160] The vehicle may include microphones 796 positioned in and / or around the vehicle 700. The microphones 796 may be used for, among other things, emergency vehicle detection and identification.

[0161] The vehicle may further include any number of camera types, including stereo cameras 768, wide angle cameras 770, infrared cameras 772, surround cameras 774, long and / or medium range cameras 798, and / or other camera types. These cameras may be used to capture image data around the entire periphery of the vehicle 700. The type of camera used depends on the embodiment and the requirements of the vehicle 700, and any combination of camera types may be used to provide the necessary coverage around the vehicle 700. Additionally, the number of cameras may 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 may support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may include a 10GbE GPIO (100GbE) ... Figure 7A and Figure 7B Described in more detail.

[0162] Vehicle 700 may further include a vibration sensor 742. Vibration sensor 742 can measure vibrations of vehicle components, such as axles. For example, changes in vibration can indicate changes in the road surface. In another example, when two or more vibration sensors 742 are used, the difference between the vibrations can 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).

[0163] The vehicle 700 may include an ADAS system 738. In some examples, the ADAS system 738 may include a SoC. The ADAS system 738 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.

[0164] The ACC system can utilize RADAR sensors 760, LIDAR sensors 764, and / or cameras. The ACC system can include longitudinal ACC and / or lateral ACC. Longitudinal ACC monitors and controls the distance to the vehicle immediately in front of vehicle 700, automatically adjusting the vehicle speed to maintain a safe distance from the vehicle in front. Lateral ACC maintains distance and, when necessary, recommends that vehicle 700 change lanes. Lateral ACC is related to other ADAS applications such as LCA and CWS.

[0165] 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 the network interface 724 and / or wireless antenna 726. 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 700 and in the same lane as it), while the I2V communication concept provides information about traffic further ahead. The CACC system can include either or both of the I2V and V2V information sources. Given information about the vehicle ahead of the vehicle 700, CACC can be more reliable, and it has the potential to improve the smoothness of traffic flow and reduce road congestion.

[0166] 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-facing camera and / or RADAR sensor 760 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, audible, visual warnings, vibrations, and / or rapid brake pulses.

[0167] 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-facing camera and / or RADAR sensor 760 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 can include technologies such as dynamic brake support and / or collision approach braking.

[0168] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 700 crosses a lane marking. When the driver indicates an intention to leave the lane by activating a turn signal, the LDW system is deactivated. The LDW system may utilize 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 vibration components.

[0169] The LKA system is a variation of the LDW system. If the vehicle 700 begins to leave its lane, the LKA system provides steering input or braking to correct the vehicle 700.

[0170] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, audible, and / or tactile alerts to indicate that merging or changing lanes is unsafe. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use a rear-facing camera and / or RADAR sensor 760 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibration component.

[0171] The RCTW system can provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear-mounted camera while the vehicle 700 is in reverse. Some RCTW systems include automatic emergency braking (AEB) to ensure that the vehicle brakes are applied to avoid a collision. The RCTW system can use one or more rear-mounted RADAR sensors 760 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibration component.

[0172] Conventional ADAS systems may be prone to false positive results, which may be annoying and distracting to the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether a safety condition actually exists and take action accordingly. However, in the autonomous vehicle 700, in the event of conflicting results, the vehicle 700 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 736 or the second controller 736). For example, in some embodiments, the ADAS system 738 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 failures in perception and dynamic driving tasks. The output from the ADAS system 738 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 reconcile the conflict to ensure safe operation.

[0173] In some examples, the primary computer can be configured to provide a confidence score to the supervisory MCU, indicating the primary computer's confidence in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the primary computer's direction, regardless of whether the secondary computer provides conflicting or inconsistent results. In the event that the confidence score does not meet the threshold and the primary and secondary computers indicate different results (e.g., a conflict), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0174] The supervisory MCU can be configured to run a neural network that is trained and configured to determine, based on outputs from the primary and secondary computers, conditions under which the secondary computer provides a false alarm. Thus, the neural network in the supervisory MCU can learn when the output of the secondary computer can be trusted and when it cannot. For example, when the secondary computer is a RADAR-based FCW system, the neural network in the supervisory MCU can learn when the FCW system is identifying a metal object that is not actually a danger, such as a drain grate or manhole cover that triggers an alarm. Similarly, when the secondary computer is a camera-based LDW system, the neural network in the supervisory MCU can learn to disregard the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In embodiments that include a neural network running on the supervisory MCU, the supervisory MCU can include at least one of a DLA or a GPU suitable for running the neural network with associated memory. In preferred embodiments, the supervisory MCU can include and / or be included as a component of the SoC 704.

[0175] In other examples, the ADAS system 738 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 with respect to failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or bug 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 did not cause a substantial error.

[0176] In some examples, the output of the ADAS system 738 can be fed into the primary computer's perception block and / or the primary computer's dynamic driving task block. For example, if the ADAS system 738 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information when identifying the object. In other examples, the secondary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0177] The vehicle 700 may further include an infotainment SoC 730 (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 separate components. The infotainment SoC 730 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, Wi-Fi, etc.), and / or information services (e.g., a 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 700. For example, the infotainment SoC 730 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connectivity, an onboard computer, in-car entertainment, Wi-Fi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 734, 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 730 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 738, 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.

[0178] The infotainment SoC 730 may include GPU functionality. The infotainment SoC 730 may communicate with other devices, systems, and / or components of the vehicle 700 via a bus 702 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 730 may be coupled to a supervisory MCU so that in the event of a failure of a primary controller 736 (e.g., a primary and / or backup computer of the vehicle 700), the infotainment system's GPU may perform some self-driving functions. In such an example, the infotainment SoC 730 may place the vehicle 700 in a driver-safe parking mode as described herein.

[0179] The vehicle 700 may further include an instrument cluster 732 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 732 may include a controller and / or a supercomputer (e.g., a separate controller or a supercomputer). The instrument cluster 732 may include a set of instruments, such as a speedometer, fuel level, 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 check light, airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and / or shared between the infotainment SoC 730 and the instrument cluster 732. In other words, the instrument cluster 732 may be included as part of the infotainment SoC 730, or vice versa.

[0180] Figure 7D For cloud-based servers and Figure 7A Schematic diagram of a system for communicating between an example autonomous vehicle 700. System 776 may include a server 778, a network 790, and a vehicle including vehicle 700. Server 778 may include multiple GPUs 784(A)-784(H) (collectively referred to herein as GPUs 784), PCIe switches 782(A)-782(H) (collectively referred to herein as PCIe switches 782), and / or CPUs 780(A)-780(B) (collectively referred to herein as CPUs 780). GPUs 784, CPUs 780, and PCIe switches may be interconnected with a high-speed interconnect and / or PCIe connection 786, such as, for example and without limitation, the NVLink interface 788 developed by NVIDIA. In some examples, GPUs 784 are connected via NVLink and / or NVSwitch SoCs, and GPUs 784 and PCIe switches 782 are connected via PCIe interconnects. Although eight GPUs 784, two CPUs 780, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the servers 778 can include any number of GPUs 784, CPUs 780, and / or PCIe switches. For example, each of the servers 778 can include eight, sixteen, thirty-two, and / or more GPUs 784.

[0181] The server 778 can receive image data from vehicles over the network 790, the image data representing images showing unexpected or changing road conditions such as a road work that has recently started. The server 778 can transmit neural networks 792, updated neural networks 792, and / or map information 794, including information about traffic and road conditions, to vehicles over the network 790. Updates to the map information 794 can include updates to the HD map 722, e.g., information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, the neural networks 792, updated neural networks 792, and / or map information 794 can have been generated from experience using training performed at a data center (e.g., using the server 778 and / or other servers) and / or from data received from any number of vehicles in the environment.

[0182] The server 778 can be used to train machine learning models (e.g., neural networks) based on training data. The training data can be generated by vehicles and / or can be generated in simulations (e.g., using game engines). In some examples, the training data is labeled (e.g., in cases where the neural network benefits from supervised learning) and / or undergoes other pre-processing, while in other examples, the training data is not labeled and / or pre-processed (e.g., in cases where the neural network does not require supervised learning). The training can be performed according to any class or more classes of machine learning techniques, including but not limited to the following classes: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including spare dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations thereof. Once the machine learning models are trained, the machine learning models can be used by vehicles (e.g., transmitted to vehicles over the network 790), and / or the machine learning models can be used by the server 778 to remotely monitor vehicles.

[0183] In some examples, the server 778 can receive data from vehicles and apply the data to the latest real-time neural networks for real-time intelligent inference. The server 778 can include deep learning supercomputers and / or specialized AI computers powered by GPUs 784, such as the DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server 778 can include deep learning infrastructure of a data center that is powered by CPUs only.

[0184] The deep learning infrastructure of server 778 may be capable of rapid real-time reasoning, and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 700. For example, the deep learning infrastructure may receive periodic updates from vehicle 700, such as an image sequence and / or objects that vehicle 700 has located in the image sequence (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 700, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 700 has malfunctioned, server 778 may transmit a signal to vehicle 700 instructing the fail-safe computer of vehicle 700 to take control, notify passengers, and complete a safe parking maneuver.

[0185] For inference, server 778 may include a GPU 784 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT 3). The combination of GPU-powered servers and inference acceleration can enable real-time responses. In other examples, such as where performance is less important, CPU, FPGA, and other processor-powered servers can be used for inference.

[0186] Example computing device

[0187] Figure 8 FIG2 is a block diagram of an example computing device 800 suitable for implementing some embodiments of the present disclosure. The computing device 800 may include an interconnect system 802 that directly or indirectly couples the following devices: memory 804, one or more central processing units (CPUs) 806, one or more graphics processing units (GPUs) 808, a communication interface 810, input / output (I / O) ports 812, an I / O component 814, a power supply 816, one or more presentation components 818 (e.g., a display), and one or more logic units 820. In at least one embodiment, the computing device 800 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, the one or more GPUs 808 may include one or more vGPUs, the one or more CPUs 806 may include one or more vCPUs, and / or the one or more logic units 820 may include one or more virtual logic units. Thus, computing device 800 may include discrete components (eg, a complete GPU dedicated to computing device 800 ), virtual components (eg, a portion of a GPU dedicated to computing device 800 ), or a combination thereof.

[0188] although Figure 8various blocks are shown as being connected via the interconnection system 802 having a line, but this is intended to be a simplified representation of a more complex connection that can be present. For example, in some embodiments, a rendering component 818, such as a display device, can be considered an I / O component 814 (e.g., if the display is a touchscreen). As another example, CPU 806 and / or GPU 808 can include memory (e.g., memory 804 can represent a storage device separate from the memory of GPU 808, CPU 806, and / or other components). In other words, Figure 8 The computing device of FIG. 8 is merely illustrative. Distinction is not made between a “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “hand-held device,” “game console,” “electronic control unit (ECU),” “virtual reality system,” and / or other device or system types, as all are contemplated within the scope of FIG. 8. Figure 8 The computing device of FIG. 8 is merely illustrative. Distinction is not made between a “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “hand-held device,” “game console,” “electronic control unit (ECU),” “virtual reality system,” and / or other device or system types, as all are contemplated within the scope of FIG. 8.

[0189] The interconnection system 802 can 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 802 can include one or more link or bus types, such as an Industry Standard Architecture (ISA) bus, an Extended Industry Standard Architecture (EISA) bus, a Video Electronics Standards Association (VESA) bus, a Peripheral Component Interconnect (PCI) bus, a Peripheral Component Interconnect Express (PCIe) bus, and / or another type of bus or link. In some embodiments, there are direct connections between components. As an example, CPU 806 can be directly connected to memory 804. Also, CPU 806 can be directly connected to GPU 808. Where there are direct or point-to-point connections between components, the interconnection system 802 can include a PCIe link to perform the connection. In these examples, a PCI bus need not be included in the computing device 800.

[0190] The memory 804 can include any of a wide variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computing device 800. By way of example, and not limitation, computer-readable media can comprise computer storage media and communication media.

[0191] Computer storage media may include volatile and non-volatile media and / or removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 804 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system). Computer storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage devices, 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 that can be accessed by the computing device 800. As used herein, computer storage media does not include signals themselves.

[0192] 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 transmission mechanism, and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in such a manner as to encode information into 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. Any combination of the above should also be included within the scope of computer-readable media.

[0193] The CPU 806 can be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 800 to perform one or more of the methods and / or processes described herein. Each of the CPUs 806 can include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of processing a large number of software threads simultaneously. The CPU 806 can include any type of processor and can include different types of processors, depending on the type of computing device 800 implemented (e.g., a processor with fewer cores for mobile devices and a processor with more cores for servers). For example, depending on the type of computing device 800, the processor can be an Advanced RISC (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). In addition to one or more microprocessors or supplementary coprocessors such as math coprocessors, the computing device 800 can also include one or more CPUs 806.

[0194] In addition to or in place of the CPU 806, the GPU 808 may also be configured to execute at least some computer-readable instructions to control one or more components of the computing device 800 to perform one or more of the methods and / or processes described herein. One or more GPUs 808 may be integrated GPUs (e.g., with one or more CPUs 806) and / or one or more GPUs 808 may be discrete GPUs. In embodiments, one or more GPUs 808 may be coprocessors to one or more CPUs 806. The computing device 800 may use the GPU 808 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, the GPU 808 may be used for general-purpose computing on a GPU (GPGPU). The GPU 808 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. The GPU 808 may generate pixel data for outputting an image in response to a rendering command (e.g., a rendering command received from the CPU 806 via a host interface). The GPU 808 may include graphics memory such as display memory for storing pixel data or any other suitable data (e.g., GPGPU data). Display memory can be included as part of memory 804. GPU 808 can include two or more GPUs operating in parallel (e.g., via a link). The link can connect the GPUs directly (e.g., using NVLINK) or through a switch (e.g., using NVSwitch). When combined, each GPU 808 can generate pixel data or GPGPU data for different portions or different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU can include its own memory or can share memory with other GPUs.

[0195] In addition to or in lieu of the CPU 806 and / or GPU 808, the logic unit 820 may be configured to execute at least some computer-readable instructions to control one or more components of the computing device 800 to perform one or more methods and / or processes described herein. In embodiments, the CPU 806, GPU 808, and / or logic unit 820 may perform any combination of methods, processes, and / or portions thereof, either separately or in conjunction. The one or more logic units 820 may be part of and / or integrated within the one or more CPUs 806 and / or the one or more GPUs 808, and / or the one or more logic units 820 may be discrete components of or otherwise external to the CPU 806 and / or GPU 808. In embodiments, the one or more logic units 820 may be processors of the one or more CPUs 806 and / or the one or more GPUs 808.

[0196] Examples of the logic unit 820 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 traversal unit (TTU), an artificial intelligence accelerator (AIA), a deep learning accelerator (DLA), an arithmetic logic unit (ALU), an application-specific integrated circuit (ASIC), a floating point unit (FPU), an input / output (I / O) element, a peripheral component interconnect (PCI) or a peripheral component interconnect express (PCIe) element, etc.

[0197] The communication interface 810 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 800 to communicate with other computing devices via an electronic communication network, including wired and / or wireless communications. The communication interface 810 may include components and functionality that enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, the logic unit 820 and / or the communication interface 810 may include one or more data processing units (DPUs) to transmit data received over the network and / or through the interconnect system 802 directly to one or more GPUs 808 (e.g., memory in a GPU 808).

[0198] The I / O ports 812 can enable the computing device 800 to be logically coupled to other devices including I / O components 814, presentation components 818, and / or other components, some of which can be built into (e.g., integrated into) the computing device 800. Illustrative I / O components 814 include a microphone, a mouse, a keyboard, a joystick, a game pad, a game controller, a satellite dish, a browser, a printer, a wireless device, and the like. The I / O components 814 can provide a natural user interface (NUI) that processes user-generated air gestures, voice, or other physiological input. In some instances, the input can be transmitted to an appropriate network element for further processing. The NUI can implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition on and adjacent to the screen, air gestures, head and eye tracking, and touch recognition associated with the display of the computing device 800 (as described in more detail below). The computing device 800 can include a depth camera such as a stereo camera system, an infrared camera system, an RGB camera system, touch screen technology, and combinations thereof for gesture detection and recognition. Additionally, computing device 800 may include an accelerometer or gyroscope to enable motion detection (e.g., as part of an inertial measurement unit (IMU)). In some examples, the output of the accelerometer or gyroscope may be used by computing device 800 to render immersive augmented or virtual reality.

[0199] The power supply 816 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 816 may provide power to the computing device 800 to enable the components of the computing device 800 to operate.

[0200] The presentation component 818 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), speakers, and / or other presentation components. The presentation component 818 may receive data from other components (e.g., the GPU 808, the CPU 806, the DPU, etc.) and output the data (e.g., as images, video, sound, etc.).

[0201] Sample Data Center

[0202] Figure 9 An example data center 900 is shown, which may be used in at least one embodiment of the present disclosure. The data center 900 may include a data center infrastructure layer 910, a framework layer 920, a software layer 930, and an application layer 940.

[0203] like Figure 9As shown, the data center infrastructure layer 910 may include a resource coordinator 912, grouped computing resources 914, and node computing resources ("node CRs") 916(1)-916(N), where "N" represents any complete positive integer. In at least one embodiment, the node CRs 916(1)-916(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state drives or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules, and cooling modules. In some embodiments, one or more of the node CRs 916(1)-916(N) may correspond to a server having one or more of the above-mentioned computing resources. Furthermore, in some embodiments, nodes CR916(1)-916(N) may include one or more virtual components, such as vGPUs, vCPUs, etc., and / or one or more of nodes CR916(1)-916(N) may correspond to virtual machines (VMs).

[0204] In at least one embodiment, the computing resources 914 of grouping can include the separate grouping (not shown) of the node CR916 housed in one or more racks, or can be housed in many racks (also not shown) in the data center of each geographical location. The separate grouping of the node CR916 in the computing resources 914 of grouping can include the computing, network, memory or storage resources that can be configured or assigned to support the grouping of one or more workloads. In at least one embodiment, several node CR916 comprising CPU, GPU, DPU and / or other processors can be grouped in one or more racks to provide computing resources to support one or more workloads. One or more racks can also include any number of power modules, cooling modules and / or network switches in any combination.

[0205] Resource coordinator 912 may configure or otherwise control one or more nodes CR 916(1)-916(N) and / or grouped computing resources 914. In at least one embodiment, resource coordinator 912 may comprise a software design infrastructure (SDI) management entity for data center 900. Resource coordinator 912 may comprise hardware, software, or some combination thereof.

[0206] In at least one embodiment, Figure 9As shown, the framework layer 920 may include a job scheduler 933, a configuration manager 934, a resource manager 936, and a distributed file system 938. The framework layer 920 may include a framework that supports the software 932 of the software layer 930 and / or one or more applications 942 of the application layer 940. The software 932 or the application 942 may include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 920 may be, but is not limited to, a free and open source software network application framework, such as Apache Spark, which can utilize the distributed file system 938 for large-scale data processing (e.g., "big data"). TM (hereinafter referred to as "Spark"). In at least one embodiment, the job scheduler 933 may include a Spark driver to facilitate scheduling of workloads supported by various layers of the data center 900. In at least one embodiment, the configuration manager 934 may be capable of configuring different layers, such as the software layer 930 and the framework layer 920 including Spark and a distributed file system 938 for supporting large-scale data processing. The resource manager 936 can manage the mapping or allocation of clustered or grouped computing resources used to support the distributed file system 938 and the job scheduler 933. In at least one embodiment, the clustered or grouped computing resources may include grouped computing resources 914 at the data center infrastructure layer 910. The resource manager 936 can coordinate with the resource coordinator 912 to manage these mapped or allocated computing resources.

[0207] In at least one embodiment, the software 932 included in the software layer 930 may include software used by at least a portion of the node CRs 916(1)-916(N), the grouped computing resources 914, and / or the distributed file system 938 of the framework layer 920. 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.

[0208] In at least one embodiment, the one or more applications 942 included in the application layer 940 may include one or more types of applications used by at least a portion of the node CRs 916(1)-916(N), the grouped computing resources 914, and / or the distributed file system 938 of the framework layer 920. The one or more types of applications may include, but are not limited to, any number of genomics 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.

[0209] In at least one embodiment, any of configuration manager 934, resource manager 936, and resource coordinator 912 can implement any number and type of self-modification actions based on any number and type of data acquired in any technically feasible manner. The self-modification actions can relieve the data center operator of data center 900 from making potentially poor configuration decisions and can avoid underutilized and / or poorly performing portions of the data center.

[0210] The data center 900 may include tools, services, software, or other resources for training one or more machine learning models or using one or more machine learning models to predict or infer information according to one or more embodiments described herein. For example, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using the software and computing resources described above with respect to the data center 900. In at least one embodiment, using the weight parameters calculated by one or more training techniques, the resources described above with respect to the data center 900 may be used to infer or predict information using a trained machine learning model corresponding to one or more neural networks, such as but not limited to those described herein.

[0211] In at least one embodiment, the data center 900 can use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, and / or other hardware (or corresponding virtual computing resources) to use the above resources to perform training and / or reasoning. In addition, one or more of the above software and / or hardware resources can be configured as a service to allow users to train or perform information reasoning, such as image recognition, speech recognition, or other artificial intelligence services.

[0212] Sample network environment

[0213] 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: Figure 8 The backend device 900 may be implemented on one or more instances of the computing device 800 of the embodiment of the present invention—for example, each device may include similar components, features and / or functions of the computing device 800. In addition, in the case of implementing a backend device (e.g., a server, NAS, etc.), the backend device may be included as part of the data center 900, examples of which are described herein with respect to Figure 9 Describe in more detail.

[0214] The components of the network environment can communicate with each other through the network, which can be wired, wireless, or both. The network can include multiple networks, or a network of 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 (e.g., the Internet and / or the 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.

[0215] 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 with respect to the server may be implemented on any number of client devices.

[0216] 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. The 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, which may include one or more core network servers and / or edge servers. The framework layer may include a framework for supporting software at the software layer and / or one or more applications at the application layer. The software or application may include network-based service software or application programs, respectively. In an embodiment, one or more client devices may use network-based service software or application programs (e.g., by accessing the service software and / or application programs via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a type of free and open source software network application framework that may, for example, use a distributed file system for large-scale data processing (e.g., "big data").

[0217] 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 described herein (or one or more portions thereof). Any of these various 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).

[0218] Client devices may include Figure 8 The client device 800 may be embodied as a personal computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smartwatch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality head-mounted display, a global positioning system (GPS) or device, a video player, a camera, a surveillance device or system, a vehicle, a watercraft, an aircraft, a virtual machine, a drone, a robot, a handheld communication device, a hospital device, a gaming device or system, an entertainment system, an in-vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these described devices, or any other suitable device.

[0219] The present disclosure can be described in the general context of machine-usable instructions or computer code 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. Generally, program modules including routines, programs, objects, components, data structures, etc. refer to code that performs a specific task or implements a specific abstract data type. The present disclosure can be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure can also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked through a communication network.

[0220] As used herein, the statement "and / or" with respect to two or more elements should be interpreted as referring to only one element or 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.

[0221] 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 inventors have contemplated that the claimed subject matter may also be embodied in other ways to include steps that are 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.

[0222] Example paragraph

[0223] A: A method comprising: obtaining first sensor data generated by a first ultrasonic sensor of a machine and second sensor data generated by a second ultrasonic sensor of the machine; determining to associate the first object detection with a group corresponding to an object based at least on the first sensor data indicating a first object detection associated with a first distance from the machine; determining that the second sensor data indicates a second object detection associated with a second distance from the machine; determining to associate the second object detection with the group based at least on the second distance being within a threshold distance relative to the first distance; determining a position associated with the object based at least on the group and using at least the first object detection and the second object detection; and causing the machine to perform one or more operations based at least on the position associated with the object.

[0224] B: The method of paragraph A further includes: determining an order in which the second sensor data is analyzed after the first sensor data based at least on one or more configurations associated with at least the first ultrasonic sensor and the second ultrasonic sensor, wherein the second sensor data is determined to indicate the second object detection based at least on the order.

[0225] C: The method of paragraph A or paragraph B, further comprising: obtaining third sensor data generated using a third ultrasonic sensor of the machine; determining that the third sensor data indicates a third object detection associated with a third distance from the machine; and avoiding associating the third object detection with the group based at least on the third distance being outside the threshold distance relative to the first distance.

[0226] D: A method as in any of paragraphs AC, wherein the first sensor data includes a first primary measurement result and the second sensor data includes a second primary measurement result, and wherein the method further comprises: obtaining third sensor data generated using at least one of the first ultrasonic sensor or the second ultrasonic sensor, the third sensor data including a secondary measurement result; determining that the third sensor data indicates a third object detection associated with a third distance from the machine; and determining to associate the third object detection with the group based at least on the third distance being within the threshold distance relative to the first distance, wherein determining the position associated with the object also uses the third object detection.

[0227] E: The method of any of paragraphs AD further includes: determining to associate the third object detection with a second group corresponding to a second object based at least on the first sensor data indicating a third object detection associated with a third distance from the machine; determining that the second sensor data indicates a fourth object detection associated with a fourth distance from the machine; determining to associate the fourth object detection with the second group based at least on the fourth distance being within the threshold distance relative to the third distance; and determining a second position associated with the second object based at least on the second group and using at least the third object detection and the fourth object detection, wherein causing the machine to perform one or more operations is also based at least on the second position associated with the second object.

[0228] F: A method as in any of paragraphs AE, wherein determining the position associated with the object comprises determining the position associated with the object based at least on the group and processing the first object detection and the second object detection using one or more trilateration algorithms.

[0229] G: A system comprising: one or more processors for: obtaining first sensor data generated using a first sensor of a machine and second sensor data generated using a second sensor of the machine; determining that the first sensor data indicates a first object detection at a first distance; determining that the second sensor data indicates a second object detection at a second distance, the second distance being within a threshold distance relative to the first distance; determining a location associated with an object based at least on the second distance being within the threshold distance relative to the first distance and using the first object detection and the second object detection; and causing one or more operations to be performed based at least on the location associated with the object.

[0230] H: A system as in paragraph G, wherein the one or more processors are further used to: generate a group corresponding to the object based at least on the first sensor data indicating the first object detection; associate at least one of the first sensor data or the first object detection with the group, and associate at least one of the second sensor data or the second object detection with the group based at least on the second distance being within the threshold distance relative to the first distance, wherein the determination of the position associated with the object is based at least on the group.

[0231] I: A system as in paragraph G or paragraph H, wherein the one or more processors are further used to: determine that the second sensor includes the next sensor to be analyzed after the first sensor, wherein determining that the second sensor data indicates the second object detection at the second distance within the threshold distance relative to the first distance is based at least on the second sensor including the next sensor to be analyzed after the first sensor.

[0232] J: A system as in paragraph I, wherein determining that the second sensor comprises the next sensor to be analyzed after the first sensor is based at least on one or more of: an order associated with the sensors of the machine, the order indicating that the second sensor comprises the next sensor to be analyzed after the first sensor; or the second sensor comprises an adjacent sensor of the first sensor.

[0233] K: A system as in any of paragraphs GJ, wherein the one or more processors are further used to: obtain third sensor data generated using a third sensor of the machine; determine that the third sensor data indicates a third object detection at a third distance outside the threshold distance relative to the first distance; and avoid using the third object detection to determine the position associated with the object based at least on the third distance being outside the threshold distance relative to the first distance.

[0234] L: A system as in any of paragraphs GK, wherein the first sensor data includes a first primary measurement result and the second sensor data includes a second primary measurement result, and wherein the one or more processors are further used to: obtain third sensor data generated using at least one of the first sensor or the second sensor, the third sensor data including a secondary measurement result; and determine that the third sensor data indicates a third object detection at a third distance within the threshold distance relative to the first distance, wherein the position associated with the object is determined also using the third object detection based at least on the third distance being within the threshold distance relative to the first distance.

[0235] M: A system as in any of paragraphs GL, wherein the one or more processors are further used to: obtain third sensor data generated using a third sensor of the machine; and determine that the third sensor data indicates a third object detection at a third distance within the threshold distance relative to the first distance, wherein the position associated with the object is determined also using the third object detection based at least on the third distance being within the threshold distance relative to the first distance.

[0236] N: A system as in any of paragraphs GM, wherein the one or more processors are further used to: obtain third sensor data generated using a third sensor of the machine; determine that the second sensor data indicates a third object detection at a third distance; determine that the third sensor data indicates a fourth object detection at a fourth distance within the threshold distance relative to the third distance; and determine a second position associated with a second object based at least on the fourth distance being within the threshold distance relative to the third distance and using the third object detection and the fourth object detection, wherein one or more operations are further performed based at least on the second position associated with the second object.

[0237] O: A system as in any of paragraphs GN, wherein the one or more processors are further used to: associate at least the first object detection and the second object detection with a group corresponding to the object based at least on the second distance being within the threshold distance relative to the first distance; and determine that the group includes at least one of a number of object detections greater than a first threshold number or a number of object detections less than a second threshold number, wherein the determination of the position associated with the object is based at least on the group including at least one of a number of object detections greater than the first threshold number or a number of object detections less than the second threshold number.

[0238] P: A system as in any of paragraphs GO, wherein determining the position associated with the object comprises determining the position associated with the object based at least on the second distance being within the threshold distance relative to the first distance and processing the first object detection and the second object detection using one or more trilateration algorithms.

[0239] Q: A system as in any of paragraphs GP, wherein performing the one or more operations includes at least one of: generating a map that at least indicates the location associated with the object; or causing the machine to navigate based at least on the location associated with the object.

[0240] R: A system as in any of paragraphs GQ, wherein the system is included in at least one of the following: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation of 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative AI operations; a system for performing operations using one or more large language models (LLMs); a system for performing operations using one or more visual language models (VLMs); a system for performing one or more conversational AI operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system comprising one or more virtual machines (VMs); a system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources.

[0241] S: One or more processors, comprising: processing circuitry for causing one or more operations of a machine to be performed based at least on a position associated with an object, wherein the position associated with the object is determined based at least on associating a first object detection determined using a first ultrasonic sensor of the machine with a second object detection determined using a second ultrasonic sensor of the machine, the association being based at least on at least one of: a first position of the first ultrasonic sensor relative to a second position of the second ultrasonic sensor, or a first distance associated with the first object detection relative to a second distance associated with the second object detection.

[0242] T: One or more processors as in paragraph S, wherein the one or more processors are included in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation of 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative AI operations; a system for performing operations using one or more large language models (LLMs); a system for performing operations using one or more visual language models (VLMs); a system for performing one or more conversational AI operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system comprising one or more virtual machines (VMs); a system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources.

[0243] Any, some, and / or all of the features of one aspect of the present disclosure may be applied to other aspects of the present disclosure in any appropriate combination or subcombination. In particular, device aspects may be applied to method aspects, and vice versa. It should also be understood that specific combinations of the various features described and defined in any aspect or embodiment of the present disclosure may be implemented and / or provided and / or used independently.

[0244] Various features described in the specification as optional (for example, through the use of "may" or "can") can be combined into a single embodiment, and / or any combination of features can be combined to form various embodiments that rely on combinations of these various optional features.

Claims

1. A method comprising: acquiring first sensor data generated using a first ultrasonic sensor of a machine and second sensor data generated using a second ultrasonic sensor of the machine; determining to associate the first object detection with a group corresponding to an object based on at least the first sensor data indicating a first object detection associated with a first distance from the machine; determining that the second sensor data indicates a second object detection associated with a second distance from the machine; determining to associate the second object detection with the group based at least on the second distance being within a threshold distance relative to the first distance; determining a location associated with the object based on at least the group and using at least the first object detection and the second object detection; as well as The machine is caused to perform one or more operations based at least on the location associated with the object.

2. The method according to claim 1, further comprising: determining an order including analyzing the second sensor data after the first sensor data based at least on one or more configurations associated with at least the first ultrasonic sensor and the second ultrasonic sensor, Wherein, it is determined based at least on the order that the second sensor data indicates the second object detection.

3. The method according to claim 1, further comprising: acquiring third sensor data generated using a third ultrasonic sensor of the machine; determining that the third sensor data indicates a third object detection associated with a third distance from the machine; and Based at least on the third distance being outside the threshold distance relative to the first distance, associating the third object detection with the group is avoided.

4. The method of claim 1 , wherein the first sensor data comprises a first primary measurement and the second sensor data comprises a second primary measurement, and wherein the method further comprises: acquiring third sensor data generated using at least one of the first ultrasonic sensor or the second ultrasonic sensor, the third sensor data comprising a secondary measurement; determining that the third sensor data indicates a third object detection associated with a third distance from the machine; and determining to associate the third object detection with the group based at least on the third distance being within the threshold distance relative to the first distance, Wherein determining the location associated with the object further uses the third object detection.

5. The method according to claim 1, further comprising: determining to associate the third object detection with a second group corresponding to a second object based at least on the first sensor data indicating a third object detection associated with a third distance from the machine; determining that the second sensor data indicates a fourth object detection associated with a fourth distance from the machine; determining to associate the fourth object detection with the second group based at least on the fourth distance being within the threshold distance relative to the third distance; as well as determining a second location associated with the second object based on at least the second set and using at least the third object detection and the fourth object detection, Wherein causing the machine to perform one or more operations is further based on at least the second location associated with the second object.

6. The method of claim 1 , wherein determining the location associated with the object comprises: The location associated with the object is determined based on at least the group and processing the first object detection and the second object detection using one or more trilateration algorithms.

7. A system comprising: One or more processors for: acquiring first sensor data generated using a first sensor of a machine and second sensor data generated using a second sensor of the machine; determining that the first sensor data indicates a first object detection at a first distance; determining that the second sensor data indicates a second object detection at a second distance, the second distance being within a threshold distance relative to the first distance; determining a location associated with an object based at least on the second distance being within the threshold distance relative to the first distance and using the first object detection and the second object detection; as well as One or more operations are caused to be performed based at least on the location associated with the object.

8. The system of claim 7, wherein the one or more processors are further configured to: generating a group corresponding to the object based on at least the first sensor data indicating the first object detection; associating at least one of the first sensor data or the first object detection with the group, and associating at least one of the second sensor data or the second object detection with the group based at least on the second distance being within the threshold distance relative to the first distance, Wherein the location associated with the object is determined based at least on the group.

9. The system of claim 7, wherein the one or more processors are further configured to: determining that the second sensor comprises the next sensor to be analyzed after the first sensor, Wherein determining that the second sensor data indicates the second object detection at the second distance within the threshold distance relative to the first distance is based at least on the second sensor comprising the next sensor to be analyzed after the first sensor.

10. The system of claim 9, wherein determining that the second sensor comprises the next sensor to be analyzed after the first sensor is based at least on one or more of: an order associated with sensors of the machine, the order indicating that the second sensor comprises the next sensor to be analyzed after the first sensor; or The second sensor includes an adjacent sensor to the first sensor.

11. The system of claim 7, wherein the one or more processors are further configured to: acquiring third sensor data generated using a third sensor of the machine; determining that the third sensor data indicates a third object detection at a third distance outside the threshold distance relative to the first distance; and Based at least on the third distance being outside the threshold distance relative to the first distance, utilizing the third object detection to determine the location associated with the object is avoided.

12. The system of claim 7, wherein the first sensor data comprises a first primary measurement and the second sensor data comprises a second primary measurement, and wherein the one or more processors are further configured to: obtaining third sensor data generated using at least one of the first sensor or the second sensor, the third sensor data comprising a secondary measurement; and determining that the third sensor data indicates a third object detection at a third distance within the threshold distance relative to the first distance, Wherein determining the location associated with the object further uses the third object detection based at least on the third distance being within the threshold distance relative to the first distance.

13. The system of claim 7, wherein the one or more processors are further configured to: acquiring third sensor data generated using a third sensor of the machine; and determining that the third sensor data indicates a third object detection at a third distance within the threshold distance relative to the first distance, Wherein determining the location associated with the object further uses the third object detection based at least on the third distance being within the threshold distance relative to the first distance.

14. The system of claim 7, wherein the one or more processors are further configured to: acquiring third sensor data generated using a third sensor of the machine; determining that the second sensor data indicates a third object detection at a third distance; determining that the third sensor data indicates a fourth object detection at a fourth distance within the threshold distance relative to the third distance; as well as determining a second location associated with a second object based at least on the fourth distance being within the threshold distance relative to the third distance and using the third object detection and the fourth object detection, Wherein one or more operations are also caused to be performed based on at least the second location associated with the second object.

15. The system of claim 7, wherein the one or more processors are further configured to: associating at least the first object detection and the second object detection with a group corresponding to the object based at least on the second distance being within the threshold distance relative to the first distance; and determining that the group includes at least one of greater than a first threshold number of object detections or less than a second threshold number of object detections, Wherein determination of the location associated with the object is based at least on the group including at least one of greater than the first threshold number of object detections or less than the second threshold number of object detections.

16. The system of claim 7, wherein determining the location associated with the object comprises: The location associated with the object is determined based at least on the second distance being within the threshold distance relative to the first distance and processing the first object detection and the second object detection using one or more trilateration algorithms.

17. The system of claim 7, wherein performing the one or more operations comprises at least one of: generating a map indicating at least the location associated with the object; or The machine is caused to navigate based on at least the location associated with the object.

18. The system of claim 7, wherein the system 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 one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; a system for performing one or more deep learning operations; Systems implemented using edge devices; Systems implemented using robots; a system for performing one or more AI-generating operations; A system for performing operations using one or more large language models (LLMs); A system for performing operations using one or more visual language models (VLMs); A system for performing one or more conversational AI operations; Systems for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; 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. One or more processors comprising: Processing circuitry for causing one or more operations of a machine to be performed based at least on a position associated with an object, wherein the position associated with the object is determined based at least on correlating a first object detection determined using a first ultrasonic sensor of the machine with a second object detection determined using a second ultrasonic sensor of the machine, the correlation being based on at least at least one of: a first position of the first ultrasonic sensor relative to a second position of the second ultrasonic sensor, or a first distance associated with the first object detection relative to a second distance associated with the second object detection.

20. The one or more processors of claim 19, wherein the one or more processors are included in at least one of: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; a system for performing one or more deep learning operations; Systems implemented using edge devices; Systems implemented using robots; A system for performing one or more generative AI operations; A system for performing operations using one or more large language models (LLMs); A system for performing operations using one or more visual language models (VLMs); A system for performing one or more conversational AI operations; Systems for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; 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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