OPTIMIZING DATA TRANSFER IN LOCATION-BASED SYSTEMS

Abstract

In various examples, a technique for managing data uploads from location-based systems includes determining a set of attributes associated with a set of data uploaded using a group of location-based systems in a geographic region. The technique also includes calculating a set of upload control parameters for the geographic region based at least on the set of attributes. The technique further includes receiving a request from a location-based system indicating the geographic region.The technique additionally includes sending the set of upload control parameters within one or more control layers included in map data for the geographic region to the location-based system in response to the request, wherein the location-based system regulates the upload of additional data associated with the geographic region based at least on the one or more control layers.

Description

[0001] Modern vehicles are typically equipped with sensors and communication systems that collect and transmit data about their surroundings. This data may include, but is not limited to, images, point clouds, road conditions, traffic patterns, weather conditions, and / or other information that can be used to evaluate and / or improve vehicle operation and navigation. For example, a fleet of autonomous or semi-autonomous vehicles or other types of machines within a geographic or other physical area may continuously collect data and transmit it to a central server. This data can be used to troubleshoot the vehicles, train machine learning models for vehicle operation, and / or perform other tasks.

[0002] However, the volume of data collected by a large vehicle fleet can quickly exceed a limited bandwidth for transmitting data from the fleet. If this bandwidth quota is exceeded, further data transmissions may be throttled or truncated. Furthermore, the value or significance of a particular set of collected data may vary depending on factors such as (but not limited to) the geographical location of the respective vehicle, the type of data collected, the use cases associated with the collected data, and / or the duplication of the collected data in other vehicles. Consequently, selective transmission of data from vehicles in the fleet may be necessary to efficiently and effectively utilize available network bandwidth and / or other limited resources.

[0003] Existing approaches to controlling data transmission from vehicle fleets involve a distributed configuration system that sends instructions to upload various types and amounts of data to the vehicles via a push or pull mechanism. These existing approaches can also, or instead, evaluate the data uploaded by the vehicles and send messages to throttle additional uploads from the vehicles. However, these existing approaches require frequent communication with the vehicles, necessitating a resource-intensive online infrastructure that may be difficult to maintain and / or scale. These existing approaches may also require the creation and definition of individual "campaigns" that represent different configurations or policies for uploading data from the vehicles.Each campaign may additionally require the creation and / or deployment of custom code, retry mechanisms, redundancies, databases, and / or caching layers to implement the campaign, which is both time- and resource-intensive.

[0004] There is therefore a need for more effective techniques to manage data transmission in large vehicle fleets. SUMMARY

[0005] Embodiments of the present disclosure relate to optimizing data transmission in location-based, hereinafter also "location-based," systems. The techniques described herein include determining a set of attributes associated with a set of data uploaded using a set of location-based systems in a geographic region. The technique also includes calculating a set of upload control parameters for the geographic region based at least on the set of attributes. The technique further includes receiving a request from a location-based system indicating the geographic region.The technique additionally includes sending the set of upload control parameters within one or more control layers included in map data for the geographic region to the location-based system in response to the request, wherein the location-based system controls the upload of additional data associated with the geographic region based at least on the one or more control layers.

[0006] A technical advantage of the described techniques over previous approaches is the ability to define and implement different policies for controlling data uploads from a fleet of vehicles. This allows the limited bandwidth allocated for these uploads to be used efficiently, effectively, and responsively. Furthermore, the described techniques allow parts of the control layers for controlling data uploads to be selectively requested from the vehicles, reducing communication with the vehicles and the resource overhead associated with an online infrastructure for conducting this communication.Because the schemas for the control layers are distributed along with the control layers, the vehicles can adapt the data upload to the parameters in the control layers without the need to create and distribute special code, retry mechanisms, redundancies, databases, and / or caching layers.

[0007] The invention is defined by the claims. To illustrate the invention, aspects and embodiments are described herein, which may or may not fall within the scope of the claims.

[0008] The disclosure extends to all novel aspects or features described and / or illustrated herein.

[0009] Further features of the disclosure are characterized by the independent and dependent claims.

[0010] Any feature of one aspect of the disclosure may be applied to other aspects of the disclosure, in any suitable combination. In particular, method aspects may be applied to device or system aspects, and vice versa.

[0011] Furthermore, functions implemented in hardware may also be implemented in software, and vice versa. Any reference to software and hardware functions herein should be interpreted accordingly.

[0012] Any system or device feature described here may also be provided as a method feature, and vice versa. System and / or device aspects described functionally (including means plus functional features) may alternatively be expressed by their corresponding structure, e.g., by an appropriately programmed processor and associated memory.

[0013] It should also be appreciated that certain combinations of the various features described and defined in each aspect of the disclosure may be implemented and / or provided and / or used independently of one another.

[0014] The disclosure also provides computer programs and computer program products comprising software code that, when executed on a data processing device, is adapted to perform any of the methods described herein and / or embody any of the device and system features described herein, including any or all component steps of a method.

[0015] The disclosure also provides a computer or computer system (including networked or distributed systems) having an operating system that supports a computer program for performing the methods described herein and / or for embodying the device or system features described herein.

[0016] The disclosure also provides a computer-readable medium on which one or more of the aforementioned computer programs are stored.

[0017] The disclosure also provides a signal carrying one or more of the aforementioned computer programs.

[0018] The disclosure extends to methods and / or devices and / or systems as described herein with reference to the accompanying drawings.

[0019] Aspects and embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present systems and methods for optimizing data transmission in location-based systems are described in detail below with reference to the attached figures, where: Fig. 1 illustrates a computing device configured to implement one or more aspects of various embodiments; Fig. 2 is a more detailed representation of the management engine, the processing engine and the tracking engine of Fig. 1, according to various embodiments; Fig. 3A illustrates an example schema for a control layer used to control data uploads from a number of location-based systems, according to various embodiments; Fig. 3B illustrates an example schema for a control layer used to control data uploads from a number of location-based systems, according to various embodiments; Fig. 4 shows a flowchart of a method for managing data transmission in a number of location-based systems according to various embodiments; Fig. 5A is an illustration of an example of an autonomous vehicle in accordance with some embodiments of the present disclosure Fig. Figure 5B is an example of camera positions and fields of view for the autonomous vehicle from Fig. 5A, in accordance with some embodiments of the present disclosure; Fig. 5C is a block diagram of an example system architecture for the example autonomous vehicle of Fig. 5A, in accordance with some embodiments of the present disclosure Fig. Figure 5D is a system diagram for the communication between the cloud-based server(s) and the autonomous example vehicle from Fig. 5A, in accordance with some embodiments of the present disclosure; Fig. 6 is a block diagram of an example computing device suitable for use in implementing some embodiments of the present disclosure; and Fig. 7 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 described that optimize data transmission in autonomous and semi-autonomous systems and applications. Although the present disclosure is described with respect to an example of an autonomous or semi-autonomous vehicle or machine 500 (also referred to herein as "vehicle 500," "ego-vehicle 500," "machine 500," or "ego-machine 500") and described by way of example with reference to Fig. 5A-5D ), this is not to be understood as limiting. For example, the systems and methods described herein may be used without limitation by location-based systems capable of determining, calculating, and / or utilizing the geographic position of a person, a mobile device, and / or a moving object. These location-based systems may be implemented in 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, guided and unguided robots or robotic platforms, warehouse vehicles, off-highway vehicles, vehicles coupled to one or more trailers, aircraft, boats, shuttles, emergency vehicles, motorcycles, electric or motorized bicycles, airplanes, construction vehicles, underwater vehicles, drones, and / or other types of vehicles. Although the present disclosure is described with respect to data transmission by autonomous or semi-autonomous machines and applications, this is not intended to be limiting.The systems and methods described herein may be used in the areas of augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, mobile devices, and / or any other technology area where data transmission management may be used.

[0022] As previously mentioned, the volume of data collected by a large vehicle fleet can quickly exceed a limited bandwidth for transmitting data from the fleet. If this bandwidth quota is exceeded, further data transmissions may be throttled or truncated. Furthermore, the value or significance of a particular set of collected data may vary depending on factors such as (but not limited to) the geographic location of the respective vehicle, the type of data collected, the use cases associated with the collected data, and / or the duplication of the collected data in other vehicles.

[0023] To improve data transmission in vehicle fleets and / or other location-based systems with limited bandwidth, the described techniques provide guidance for uploading data from vehicles in a fleet across a series of geospatially indexed control layers. A vehicle can request one or more control layers from a server and / or a central distribution system and selectively upload data according to the information in the control layer(s).Each control layer may specify, for a given geographic region, the types of data that can be uploaded, the amount of data to be uploaded, the frequency of data uploading, the number of uploads, situations in which data should or should not be uploaded, times at which the data should or should not be uploaded, probabilities or priorities associated with uploading those data types, settings to enable or disable data uploading, and / or other parameters that can be used to control the uploading of data from that geographic region.A specific control layer can be added, removed, and / or updated in real-time or near-real-time to accommodate new use cases and / or changes to existing use cases related to the data being uploaded, the types and amounts of data already uploaded for the geographic region, and / or other factors. A specific control layer can also be distributed with a schema that allows a vehicle to interpret data uploads and adapt them to the control layer's parameters.

[0024] A technical advantage of the described techniques over previous approaches is the ability to define and implement different policies for controlling data uploads from a fleet of vehicles. This allows the limited bandwidth available for these uploads to be used efficiently, effectively, and responsively. Furthermore, the described techniques allow parts of the control layers for controlling data uploads to be selectively requested from the vehicles, reducing communication with the vehicles and the resource overhead associated with an online infrastructure for conducting this communication.Because the schemas for the control layers are distributed along with the control layers, the vehicles can adapt the data upload to the parameters in the control layers without the need to create and distribute special code, retry mechanisms, redundancies, databases, and / or caching layers.

[0025] Fig. 1 illustrates a computing device 100 configured to implement one or more aspects of various embodiments. In at least one embodiment, computing device 100 includes a desktop computer, a laptop computer, a smartphone, a personal digital assistant (PDA), a tablet computer, a server, one or more virtual machines, an embedded system, a system(s) on a chip(s), an in-vehicle computing device, and / or any other type of computing device configured to receive input, process data, and optionally display images, and suitable for performing one or more embodiments. Computing device 100 is configured to operate a management engine 122, a processing engine 124, and a tracking engine 126, which may be located in memory 116.It should be noted that the computing device described herein is an example, and that all other technically possible configurations are within the scope of the present disclosure. For example, multiple instances of management engine 122, processing engine 124, and tracking engine 126 may be executed on a number of nodes in a distributed and / or cloud computing system to implement the functionality of computing device 100.

[0026] In one or more embodiments, computing device 100 includes, without limitation, an interconnect (bus) 112 connecting one or more processors 102, an input / output (I / O) device interface 104 coupled to one or more input / output (I / O) devices 108, memory 116, storage 114, and / or a network interface 106. Processor(s) 102 may be any suitable processor implemented as a central processing unit (CPU), graphics processing unit (GPU), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), artificial intelligence (AI) accelerator, parallel processing unit (PPU), data processing unit (DPU), any other type of processing unit, or a combination of different processing units, such as a CPU configured to operate in conjunction with a GPU.In general, processor(s) 102 may comprise any technically feasible hardware device capable of processing data and / or executing software applications. For the purposes of this disclosure, the computing elements depicted in computing device 100 may correspond to a physical computing system (e.g., a system in a data center) and / or a virtual computing instance executing in a computing cloud.

[0027] In at least one embodiment, I / O devices 108 include devices that can receive input, such as a keyboard, a mouse, a touchscreen, a touchpad, a VR / MR / AR headset, a gesture recognition system, and / or a microphone, as well as devices that can provide output, such as one or more display devices, one or more haptic devices, and / or one or more speakers. Furthermore, I / O devices 108 may include devices that can both receive input and provide output, such as a touchscreen, a Universal Serial Bus (USB) port, etc. I / O devices 108 may be configured to receive various types of input from an end user (e.g., a designer) of the computing device 100 and also provide various types of output to the end user of the computing device 100, such as displayed digital images or digital videos or text.In some embodiments, one or more of the I / O devices 108 are configured to connect the computing device 100 to a network 110.

[0028] In one embodiment, network 110 is any technically feasible type of communications network that enables the exchange of data between computing device 100 and internal, local, remote, or external units or devices, such as a web server or other networked computing device. Network 110 may be, for example, a wide area network (WAN), a local area network (LAN), a wireless network (e.g., Wi-Fi), a cellular network, and / or the Internet, to name a few.

[0029] In at least one embodiment, storage 114 includes non-volatile storage for applications and data and may include fixed or removable disk drives, flash memory devices, and CD-ROM, DVD-ROM, Blu-ray, HD-DVD, or other magnetic, optical, or solid-state storage devices. Management engine 122, processing engine 124, and tracking engine 126 may be stored in storage 114 and loaded into memory 116 during execution.

[0030] In one or more embodiments, memory 116 includes a random access memory (RAM) module, a flash memory device, and / or another type of storage device, or a combination thereof. Processor(s) 102, I / O device interface 104, and network interface 106 may be configured to read and write data to memory 116. Memory 116 may contain various software programs executable by processor(s) 102, as well as application data associated with those software programs, including management engine 122, processing engine 124, and tracking engine 126.

[0031] The management engine 122, the processing engine 124, and the tracking engine 126 include functionality for managing the upload of data from a group of location-based systems. For example, the management engine 122, the processing engine 124, and the tracking engine 126 can be used to control the upload of images, point clouds, sensor data, telemetry data, event data, user interaction data, and / or other data types from a fleet of vehicles. This allows data uploads to efficiently utilize the limited bandwidth allocated to the fleet, reduce duplication of uploaded data, scale with the number of vehicles in the fleet, efficiently communicate changes to data upload controls and / or policies to the fleet, and / or increase the quality and / or variety of data uploaded by the vehicles.The operation of the management engine 122, the processing engine 124 and the tracking engine 126 is described in more detail below.

[0032] Fig. Figure 2 is a more detailed illustration of the management engine 122, the processing engine 124 and the tracking engine 126 of Fig. 1, according to various embodiments. As previously mentioned, management engine 122, processing engine 124, and tracking engine 126 may be used to manage data upload from a number of location-based systems 232(1)-232(W) (each of which is referred to herein as location-based system 232). For example, management engine 122, processing engine 124, and tracking engine 126 may be used to increase and / or decrease the upload of data from a fleet of vehicles operating in one or more geographic regions.

[0033] The tracking engine 126 generates events 210(1)-210(Y) (each individually referred to herein as an event 210) that track data uploads 206(1)-206(X) (each individually referred to herein as a data upload 206) by location-based systems 232. For example, the tracking engine 126 may receive and / or detect data uploads 206 of images, point clouds, sensor data, telemetry data, event data, user interaction data, and / or other data types from a fleet of vehicles. The tracking engine 126 may also generate a separate event 210 for each data upload 206 and publish that event 210 to one or more topics within a distributed messaging system.

[0034] Each event 210 generated by a corresponding data upload 206 may contain metadata related to the data upload 206. For example, the tracking engine 126 may associate a particular event 210 representing a data upload 206 with data such as (but not limited to) a unique identifier for the event, a timestamp, an amount of data uploaded, one or more types of data included in that data upload 206, a location and / or geographic region from which the uploaded data was collected, a type of location-based system 232 (e.g., a semi-autonomous vehicle, an autonomous vehicle, a mobile device, etc.) from which the data was uploaded, an identifier for that location-based system 232, an identifier for a user associated with that location-based system 232 (e.g., a user of a mobile device, a driver or passenger of a vehicle, etc.), hardware and / or software components used to collect the data, time and / or bandwidth consumed by this data upload 206, and / or other contextual information associated with this data upload 206.

[0035] The tracking engine 126 may also generate and / or publish additional events 210 based on aggregations and / or analyses of events 210 representing individual data uploads 206. For example, the tracking engine 126 may aggregate multiple events 210 representing individual data uploads 206 from a particular geographic region, a particular type of data, and / or over a particular time period into one or more events representing the number of those data uploads 206, the amount of data uploaded, the amount or proportion of allocated bandwidth consumed by those data uploads 206, and / or other metrics or statistics associated with those data uploads 206.The tracking engine 126 may also or instead perform additional aggregations of events 210 representing multiple data uploads 206 by individual location-based systems 232, types of location-based systems 232, and / or other dimensions.

[0036] The management engine 122 analyzes the events 210 to determine one or more sets of data upload attributes 208(1)-208(0) (each individually referred to herein as data upload attributes 208) associated with data uploads 206. In some embodiments, the data upload attributes 208 include attributes of data uploads 206 that are relevant to and / or may be used to control subsequent data uploads 206 from location-based systems 232.For example, a set of data upload attributes 208 may be calculated for each data upload 206, a set of data uploads 206 from a particular location-based system 232, a group of location-based systems 232, and / or a type of location-based system 232, a set of data uploads 206 from a particular geographic region and / or location, a set of data uploads 206 represented by one or more events 210, a particular time period, and / or other selection and / or aggregation of data uploads 206.The data upload attributes 208 include, among others, the number of data uploads 206 from a particular geographic region, from one or more location-based systems 232 and / or over a particular period of time; the amount of data included in the data uploads 206 from a particular geographic region, from one or more location-based systems 232 and / or over a particular period of time; one or more types of data (e.g., images, point clouds, road conditions, traffic patterns, weather conditions, roadway geometries, traffic signs, traffic rules, etc.) included in data uploads 206 from a particular geographic region, from one or more location-based systems 232 and / or over a particular period of time; and / or one or more properties of data (e.g.,, types of objects depicted in images and / or point clouds, situations and / or scenarios in which the data was collected, specific values in the data, etc.) included in data uploads 206 from a particular geographic region, from one or more location-based systems 232, and / or over a particular period of time.

[0037] The management engine 122 also determines one or more sets of data upload requests 202(1)-202(M) (each individually referred to herein as data upload requests 202) associated with location-based systems 232. In some embodiments, the data upload requests 202 include restrictions associated with data uploads 206 by location-based systems 232. For example, each set of data upload requests 202 may specify a particular geographic region, a particular location-based system 232 (e.g., a unique identifier for a vehicle), a type of location-based system 232 (e.g.,a category of location-based system 232, one or more hardware and / or software components included in one or more location-based systems 232, a model of location-based system 232, a company and / or facility associated with the manufacture or operation of one or more location-based systems 232, etc.), a group of location-based systems 232 (e.g., a particular fleet or subset of vehicles), a type of data included in data uploads 206 (e.g., sensor data, telemetry data, images, weather conditions, road conditions, traffic conditions, log data, synthetic / simulated data, real-world data, etc.), a characteristic of data included in data uploads 206, a time period over which data uploads 206 are to be collected, and / or another dimension associated with data uploads 206.Each set of data upload requests 202 may also include (but are not limited to) a minimum, maximum, and / or desired number of data uploads 206 associated with a given set of dimensions, a minimum, maximum, and / or desired amount of data to be included in data uploads 206 associated with a given set of dimensions, and / or other values that may be used to control data uploads 206 associated with a given set of dimensions.

[0038] In some embodiments, the management engine 122 derives a particular set of data upload requests 202 from one or more data collection campaigns 218(1)-218(X) (each individually referred to herein as a data collection campaign 218) stored in a database 216. Each data collection campaign 218 may represent a request from an entity (e.g., a user, a team, an organization, etc.) for data from one or more location-based systems 232. For example, an entity may specify one or more data collection campaigns 218 via a user interface, an application programming interface (API), and / or another type of interface to the database 216.In each data collection campaign 218, the entity may specify one or more sets of coordinates, a map tile, a region of a geofence, and / or other representation of a geographic location and / or region from which data is to be collected. The entity may also specify a specific number of data uploads 206, a specific amount of data to be uploaded, a type of data to be uploaded, a type of location-based system 232 from which data is to be uploaded, a time period over which data is to be uploaded, one or more scenarios or conditions under which data is to be uploaded, and / or other attributes associated with the requested data. Upon completion of the creation of a particular data collection campaign 218, that data collection campaign 218 may be stored in the database 216 for later retrieval and use by the management engine 122 and / or other components.

[0039] To generate data upload requests 202 from data collection campaigns 218, the management engine 122 may group the data collection campaigns 218 by geography, type of location-based system 232, type of data, and / or other dimensions. For each group of data collection campaigns 218, the management engine 122 may aggregate the number of data uploads 206, the amount of data to be uploaded, and / or other numeric values corresponding to the requested data volumes associated with the corresponding dimension(s). The management engine 122 may then populate a corresponding set of data upload requests 202 with the dimensions and numeric values.

[0040] The management engine 122 compares the data upload attributes 208 to the data upload requests 202 and uses the results of the comparison to generate one or more sets of upload control parameters 212(1)-212(N) (each individually referred to herein as upload control parameters 212). For example, the management engine 122 may generate a new set of upload control parameters 212 for a particular location, geographic region, location-based system 232, type of location-based system 232, group of location-based systems, time period, and / or other dimension at regular intervals (e.g., every time a certain number of seconds or minutes have elapsed) and / or on-demand (e.g., whenever a new data upload 206 associated with the dimension(s) is received).To calculate the new upload control parameters 212, the management engine 122 may match locations, geographic regions, location-based system identifiers 232, location-based system types 232, timestamps, data types, and / or other dimensions included in the data upload attributes 208 of one or more data uploads 206 with the corresponding dimensions included in one or more sets of data upload requests 202. The management engine 122 may also use the data upload attributes 208 to compare the amount of data, the number of data uploads 206, and / or other quantities associated with the data uploads 206 with the requested amount of data, the number of data uploads 206, and / or other quantities requested in the corresponding data upload requests 202.The management engine 122 may additionally calculate one or more differences, ratios, proportions, and / or other statistics between the quantities of the data uploads 206 and the corresponding requested quantities in the data upload requests 202. The management engine 122 may then convert the statistics into upload control parameters 212, which may be used to control subsequent data uploads 206 associated with the corresponding dimensions.For example, the management engine 122 may calculate upload control parameters 212 that may be used to adjust the rate of data uploads 206, the frequency of data uploads 206, the probabilities of data uploads 206, and / or the amount of data included in each data uploads 206 associated with a given set of dimensions based on one or more statistics calculated between the amounts of existing data uploads 206 associated with the set of dimensions and the requested amounts of data for the same set of dimensions.

[0041] In one or more embodiments, upload control parameters 212 include rules, instructions, and / or other data that can be used to control subsequent data uploads 206 from location-based systems 232. For example, upload control parameters 212 can include a binary value used to enable or disable data uploads 206 for a particular geographic region, from one or more location-based systems 232, from one or more types of location-based systems 232, for a particular type of data, for a particular scenario and / or condition under which data was collected, and / or for any other set or combination of dimensions associated with data uploads 206.In another example, the upload control parameters 212 may include a numeric value representing a "probability" of uploading data associated with a particular geographic region, a particular location-based system 232, a set of location-based systems 232, a type of location-based system 232, and / or another set of dimensions. In a third example, the upload control parameters 212 may include one or more formulas, rules, heuristics, decision trees, machine learning models, and / or other mechanisms that can be used to control the frequency of the data uploads 206, the amount of data included in the data uploads 206, the types of data included in the data uploads 206, and / or other characteristics of the data uploads 206 from the location-based systems 232.

[0042] The processing engine 124 receives the upload control parameters 212 from the management engine 122. The processing engine 124 also populates a series of control layers 204(1)-204(Z) (each of which is individually referred to herein as a control layer 204) included in the map data 220 with upload control parameters 212. For example, the processing engine 124 may create one or more new control layers 204 to store one or more new sets of upload control parameters 212 received from the management engine 122. The processing engine 124 may also, or instead, replace older upload control parameters 212 in one or more existing layers 204 with the new upload control parameters 212. The processing engine 124 may also or instead delete a particular control layer 204 after a period of time associated with the corresponding upload control parameters 212 has expired.

[0043] In one or more embodiments, the map data 220 includes geographically indexed data divided along spatial boundaries. For example, the map data 220 may be divided into individual map tiles representing contiguous geographic regions within a map. These map tiles may correspond to grid cells, rectangles, squares, hexagons, and / or other shapes. These map tiles may also be defined at multiple "zoom" levels representing different levels of detail. For example, a map tile with a low zoom level may represent a relatively large area such as a city, while a map tile with a high zoom level may represent a single building or city block.

[0044] The map data 220 may also, or instead, contain non-polygonal representations of geographic locations and / or regions. For example, the map data 220 may contain a curve traversing a map. Each point on the curve may be indexed by a corresponding number and represent a specific location on the map.

[0045] The map data 220 may additionally be divided into multiple layers representing different data types, such as (but not limited to) images, roads, highways, road markings, traffic signs, radar points, traffic conditions, weather conditions, network conditions, landmarks, topography, and / or metadata. These layers may be transmitted to location-based systems 232 so that location-based systems 232 can provide geolocation, navigation, planning, and / or other types of location-based services or functions. These layers may also include one or more control layers 204 that can be used to control the operation of location-based systems 232, including (but not limited to) controlling data uploads 206 of different types of data and / or from different geographic locations by location-based systems 232.

[0046] The processing engine 122 also includes and / or generates schemas 224(1)-224(P), individually referred to herein as schema 224, for control layers 204 in the map data 220. Each schema 224 may define one or more fields in one or more corresponding control layers 204 and how the fields are to be used to control data uploads 206.

[0047] Fig. 3A shows an example scheme 224 for a control layer (e.g., the control layers 204 of Fig. 2) used to control data uploads from a number of location-based systems, according to various embodiments. As described in Fig. As shown in Figure 3A, the example schema 224 comprises two parts, 302 and 304. Part 302 contains a field "SSRTriggerCondition," enumerated as a byte data type with three possible values: "None" (which is set to 0), "Partial," and "All." This field "SSRTriggerCondition" thus defines possible trigger conditions for uploading data from a location-based system.

[0048] The value "None" can indicate that no data uploads should be performed. For example, the value "None" can be set for a specific geographic region or location if no data uploads are requested for that geographic region or location and / or if the requested number of data uploads for that geographic region or location has already been reached.

[0049] The "Partial" value can indicate that data uploads should be performed selectively or partially. For example, the "Partial" value can be set for a specific geographic region or location after a certain proportion of the requested amount of data uploads has been received. The "Partial" value can represent a certain predefined probability for uploading data from the geographic region or location. A location-aware system in the geographic region or location can perform a virtual weighted coin toss and decide whether or not to upload data from the geographic region or location based on this probability.The predefined probability may additionally be adjusted and / or modified based on the proportion of requested data uploaded, the type of data being uploaded, a particular location-based system and / or the type of location-based system associated with the data, and / or other factors.

[0050] The "All" value can mean that all data should be uploaded. For example, the "All" value can be set for a specific geographic region or location after a significant amount of data has been requested from that geographic region or location and / or when no data from that geographic region or location has yet been uploaded.

[0051] Part 304 specifies a table named "SSRControlsV1Layer," which contains a field named "values" corresponding to an array of "SSRTriggerCondition" values. If map data containing the control layer is divided into a grid of map tiles, the array can be indexed using the formula [row*grid_size + col], where "row" is a row index for a specific map tile in the grid, "col" is a column index for the map tile, and "grid_size" is the number of map tiles along a dimension within the grid. Thus, the "values" field can store different trigger conditions for individual map tiles in the grid.

[0052] Part 304 also contains a field named "zoom_level," which corresponds to a uint8 data type. The "zoom_level" field can represent a zoom level, which can be used to derive the "grid_size" and the number of map tiles with different "SSRTriggerCondition" values. For example, if the grid has a total of 21 zoom levels and "zoom_level" is set to 16 out of 21, "grid_size" can be calculated as 2^(21-16) = 32, and the "values" array can contain 32x32 = 1024 different "SSRTriggerCondition" values for 1024 different map tiles at zoom level 16.

[0053] Fig. 3B shows an example scheme 224 for a control layer (e.g., the control layers 204 of Fig. 2) used to control data uploads from a number of location-based systems, according to various embodiments. As described in Fig. As shown in Figure 3B, the example schema 224 contains a part 306 that defines a table "MapStreamControlsV1Layer" with two fields. The first field is called "pvalues" and corresponds to an array of uint8 values. If map data containing the control layer is divided into a grid of map tiles, the "pvalues" array can be used like the "values" array in Fig. 3A can be indexed using the formula [row*grid_size + col], where "row" is a row index for a specific map tile in the grid, "col" is a column index for the map tile, and "grid_size" is the number of map tiles along a dimension within the grid. Thus, the "pvalues" field can store various values used to control the data upload of individual map fields in the grid.

[0054] Each element of the "pvalues" array can contain an integer between 0 and 255. A value of 255 means that no data from the corresponding geographic region or location should be uploaded. A value of 0 means that all data from the corresponding geographic region or location should be uploaded. Values between 0 and 255 indicate the probability that data from the corresponding geographic region or location will be uploaded, with lower values indicating a higher probability. The probability that data from the geographic region or location will be uploaded can be calculated, for example, using the formula [1 - pvalue / 255], where "pvalue" is an element from the "pvalues" array corresponding to the geographic region or location.A location-based system can use this probability to perform a virtual weighted coin toss that determines whether or not to upload data from the geographic region or location. Each element in the "pvalues" array can be additionally updated based on the proportion of requested data uploaded from the geographic region or location, the type of data to be uploaded from the geographic region or location, a particular location-based system, and / or the type of location-based system associated with the data, and / or other factors.

[0055] The second field in the "MapStreamControlsV1Layer" table is called "zoom_level" and is a uint8 data type. The "zoom_level" field can represent a zoom level, which can be used to derive the "grid_size" and the number of distinct values in the "pvalues" array. For example, if the grid has 21 zoom levels and "zoom_level" is set to 16 of 21, "grid_size" can be calculated as 2^(21-16) = 32, and the "pvalues" array can contain 32x32 = 1024 distinct probability values for 1024 different map tiles at zoom level 16.

[0056] To respond to the discussion in Fig. 2, the processing engine 122 transmits control layers 204 and / or the corresponding schemas 224 to location-based systems 232 to control data uploads 206 by location-based systems 232. In some embodiments, a particular location-based system 232 retrieves a portion of the map data 220 (e.g., one or more control layers 204, map tiles, etc.) and / or a corresponding schema 224 by sending a request to the processing engine 124 specifying the portion of the map data 220 and / or the schema 224. The processing engine 124 processes the request by matching parameters of the request with the portion of map data 220 and / or the schema 224 and sending a response containing the portion of map data 220 and / or the schema 224 to the location-based system 232.

[0057] For example, the processing engine 122 may receive a request from a particular location-based system 232 specifying a set of coordinates, a map tile, and / or other representation of a geographic location and / or geographic region. The request may also, or instead, include an identifier for the location-based system 232, an identifier for a group of location-based systems 232 to which the location-based system 232 belongs, and / or a type of location-based system 232. The request may also, or instead, specify a type of data, a particular data collection campaign 218, and / or a particular control layer 204 within the map data 220.The processing engine 122 may perform a search of the map data 220 based on the parameters included in the request and retrieve one or more map tiles and / or one or more control layers 204 within the map tile(s) that match the parameters. The processing engine 122 may then transmit a response to the location-based system 232 that includes the map tile(s) and / or the control layer(s) 204. The processing engine 122 may optionally transmit a corresponding schema 224 for the map tile(s) and / or the control layer(s) 204 in the same response (e.g., if the data in the map tile(s) and / or the control layer(s) 204 is associated with a new schema 224 that has not been previously used by the location-based system 232).The processing engine 122 may alternatively transmit the corresponding schema 224 in response to receiving a separate request specifying that schema 224.

[0058] In one or more embodiments, location-based systems 232 transmit requests for control layers 204 in map data 220 and / or the corresponding schemas 224 to processing engine 124 when they detect changes to those control layers 204 and / or schemas 224. For example, a location-based system 232 may periodically and / or continuously query and / or receive from processing engine 124 a checksum, timestamp, and / or other representation of the most recent and / or up-to-date set of control layers 204 for a geographic location and / or region of interest (e.g., the current geographic location and / or region of that location-based system 232, a projected future geographic location and / or region of location-based system 232, etc.).If the representation indicates that the location-based system 232 does not have the most recent and / or up-to-date set of control layers 204, the location-based system 232 may request the most recent control layers 204 (and optionally the corresponding schema(s) 224) for the geographic location and / or geographic region from the processing engine 124. In this way, location-based systems 232 may perform data uploads 206 with the most recent upload control parameters 212, control layers 204, and / or schemas 224 without requiring changes to the software in location-based systems 232 and / or frequent communication between the processing engine 124 and location-based systems 232.

[0059] It should be understood that these and other arrangements described herein are only examples. Other arrangements and elements (e.g., machines, interfaces, functions, arrangements, groupings of functions, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Furthermore, many of the elements described herein are functional units that may be implemented as individual or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions performed by units described herein may be performed by hardware, firmware, and / or software. For example, various functions may be performed by a processor executing instructions stored in memory.In some embodiments, the systems, methods, and processes described herein may be implemented with similar components, features, and / or functions as those of the example autonomous vehicle 500 of FIG. Fig. 5A-5D, the example computer device 600 of Fig. 6 and / or the example data center 700 of Fig. 7.

[0060] Fig. 4 shows that each block of the method 400 described herein comprises a computational process that may be performed using any combination of hardware, firmware, and / or software. For example, various functions may be performed by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media. The methods 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 for another product, to name a few. Furthermore, the method 400 is exemplified for the system of Fig. 1. However, these methods may additionally or alternatively be performed by any system or combination of systems, including, but not limited to, the systems described herein.

[0061] Fig. 4 shows a flowchart of a method 400 for managing data transmission in a number of location-based systems according to various embodiments. As shown in Fig. 4, the method 400 begins with operation 402, in which the management engine 122 determines the attributes associated with the data uploads of a group of location-based systems in one or more geographic regions. For example, the management engine 122 may receive events representing data uploads by the location-based systems from a distributed messaging system. The management engine 122 may calculate a different set of attributes for each data upload, a set of data uploads from a particular location-based system and / or a set of location-based systems, a set of data uploads received over a particular period of time, a set of data uploads associated with a geographic region, and / or other selection or aggregation of data uploads.Attributes include, but are not limited to, the number of data uploads from a particular geographic region, from one or more location-based systems, and / or over a particular period of time; the amount of data contained in the data uploads from a particular geographic region, from one or more location-based systems, and / or over a particular period of time; one or more types of data (e.g., images, point clouds, road conditions, traffic patterns, weather conditions, roadway geometries, traffic signs, traffic rules, etc.) contained in data uploads from a particular geographic region, from one or more location-based systems, and / or over a particular period of time; and / or one or more properties of data contained in data uploads from a particular geographic region, from one or more location-based systems, and / or over a particular period of time.

[0062] In operation 404, the management engine 122 determines one or more sets of data upload requests associated with the geographic region(s). Each set of data upload requests 202 may specify a particular geographic region, a particular location-based system (e.g., a unique identifier for a vehicle), a type of location-based system (e.g., a category of location-based system, one or more hardware and / or software components included in one or more location-based systems, a model of location-based system, a company and / or entity associated with the manufacture or operation of one or more location-based systems, etc.), a type of data included in data uploads (e.g., sensor data, telemetry data, images, weather conditions, road conditions, traffic conditions, log data, synthetic / simulated data, real-world data, etc.).), a characteristic of the data included in the data uploads (e.g., objects depicted in images, certain weather or road conditions, etc.), a time period over which data uploads are to be collected, and / or another dimension associated with the data uploads. Each set of data upload requirements may also include (but is not limited to) a minimum, maximum, and / or desired number of data uploads associated with a given set of dimensions, a minimum, maximum, and / or desired amount of data to be included in data uploads associated with a given set of dimensions, and / or other values that may be used to control data uploads associated with a given set of dimensions.

[0063] In operation 406, the management engine 122 calculates one or more sets of upload control parameters for the geographic region(s) based on a comparison of the attributes and the data upload requests. For example, the management engine 122 may determine, from the data upload requests for a particular geographic region, a counter for the number of data uploads to be collected from that region. The management engine 122 may decrement this counter by a field in the attributes that indicates the number of data uploads for the geographic region. The management engine 122 may then calculate a probability and / or other value based on the counter to be used to control the rate and / or probability of subsequent data uploads associated with the geographic region.

[0064] In operation 408, the processing engine 124 populates one or more control layers in the map data for the geographic region(s) with the upload control parameters. For example, the processing engine 124 may store, in the control layer(s), a probability, one or more rules, and / or other values for controlling the rate and / or probability of subsequent data uploads associated with each geographic region in an array element and / or other field associated with the geographic region. The processing engine 124 may also specify a particular location-based system, a group of location-based systems, a type of location-based system, a time period, a data type, a characteristic of data, and / or other dimensions to which the control layer applies.

[0065] In operation 410, the processing engine 124 determines whether or not one or more requests for one or more control layers have been received from one or more location-based systems. For example, a particular location-based system may transmit the request periodically and / or upon detecting a change in a control layer relevant to the location-based system, data collected by the location-based system, and / or a current or future location of the location-based system.The request may include parameters specifying a particular geographic region, an identifier for the location-based system, a type of location-based system, a group of location-based systems to which the location-based system belongs, a type of data that can be uploaded, a characteristic of data that can be uploaded, a time period over which data uploads should be collected, and / or another dimension associated with data uploads by the location-based system. If the processing engine 124 determines that no requests for control layers have been received, the processing engine 124 does not transmit any control layers to location-based systems.

[0066] When the processing engine 124 determines that one or more requests for control layers have been received, the processing engine 124 performs operation 412, where the processing engine 124 matches one or more parameters of each request with a portion of the control layer(s). For example, the processing engine 124 may search the map data for a geographic region, an identifier for the location-based system, a type of location-based system, a group of location-based systems to which the location-based system belongs, a type of data that can be uploaded, a characteristic of data that can be uploaded, a time period over which to collect data uploads, and / or another dimension specified in each request.

[0067] In operation 414, the processing engine 124 transmits the corresponding portion of the control layer(s) and / or a schema for the portion in a response to each request. For example, the processing engine 124 may transmit one or more control layers storing probabilities, binary values, and / or other fields that can be used to control data uploads to each location-based system from which a request was received. The processing engine 124 may also transmit a schema for the field(s) and / or control layer(s) with the portion if the schema was requested by the corresponding location-based system, if the schema for the portion is new and / or has changed, and / or based on other factors or conditions.

[0068] In operation 416, the management engine 124 and / or the processing engine 124 determine whether or not to continue managing the data uploads. For example, the management engine 124 and / or the processing engine 124 may determine to continue managing the data uploads while data is being uploaded from the location-based systems and / or while data collection campaigns for the data uploads are being conducted.

[0069] While managing data uploads, management engine 122 and processing engine 124 repeat operations 402, 404, 406, 408, 410, 412, 414, and 416 periodically and / or continuously. For example, management engine 122 may perform operations 402, 404, and 406 to generate current attributes, data upload requests, and upload control parameters for the data upload from the on-premises systems. Processing engine 124 may then perform operations 408, 410, 412, and / or 414 to communicate the latest upload control parameters to the on-premises systems.The management engine 122 and the processing engine 124 may continue with method 400 to manage data uploads from the location-based systems until no more data uploads are performed by the location-based systems, data collection campaigns are no longer used to control and / or prioritize data uploads from the location-based systems, and / or until one or more other conditions are met.

[0070] The systems and methods described herein may be used, without limitation, by non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), guided and unguided robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, aircraft, boats, shuttles, emergency vehicles, motorcycles, electric or motorized bicycles, airplanes, construction vehicles, underwater vehicles, drones, and / or other types of vehicles.Furthermore, the systems and methods described herein may be used for a variety of purposes, including, without limitation, machine control, machine locomotion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actor simulation and / or digital twinning, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing, and / or other suitable applications.

[0071] The described embodiments may be included in a variety of different systems, such as automotive systems (e.g., a control system for an autonomous or semi-autonomous machine, a perception system for an autonomous or semi-autonomous machine), systems implemented with a robot, aviation systems, media systems, boat systems, systems for monitoring smart areas, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented with an edge device, systems containing one or more virtual machines (VMs), systems for generating synthetic data, systems implemented at least partially in a data center, systems for performing conversational AI operations, systems for performing light transport simulations,Systems for collaborative content creation for 3D assets, systems implemented at least partially using cloud computing resources, and / or other types of systems. EXAMPLE AUTONOMOUS VEHICLE

[0072] Fig. 5A is an illustration of an example of an autonomous vehicle 500 in accordance with some embodiments of the present disclosure. The autonomous vehicle 500 (alternatively referred to herein as "vehicle 500") may be, without limitation, a passenger vehicle, such as a passenger car, a truck, a bus, an emergency vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire engine, a police vehicle, an ambulance, a boat, a construction vehicle, an underwater vehicle, a robotic vehicle, a drone, an aircraft, a vehicle coupled to a trailer (e.g., a semi-trailer truck for transporting goods), and / or another type of vehicle (e.g., an unmanned vehicle and / or a vehicle with one or more passengers).Autonomous vehicles are generally described in terms of levels of automation defined by the National Highway Traffic Safety Administration (NHTSA), a division of the U.S. Department of Transportation, and the Society of Automotive Engineers (SAE) "Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles" (Standard No. J3016-201806, published June 15, 2018, Standard No. J3016-201609, published September 30, 2016, and prior and future versions of this standard). The vehicle 500 may exhibit capabilities according to one or more of the Levels 3 through 5 of the autonomous driving levels. The vehicle 500 may exhibit capabilities according to one or more of the Levels 1 - Level 5 of the autonomous driving levels.For example, depending on the embodiment, the vehicle 500 may provide driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5). The term "autonomous" as used herein may encompass any and / or all types of autonomy for the vehicle 500 or other machine, such as fully autonomous, highly autonomous, conditionally autonomous, partially autonomous, assisted autonomy, partially autonomous, primarily autonomous, or another designation.

[0073] The vehicle 500 may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle 500 may include a propulsion system 550, such as an internal combustion engine, a hybrid electric power plant, a pure electric motor, and / or another type of propulsion system. The propulsion system 550 may be connected to a drivetrain of the vehicle 500, which may include a transmission to enable propulsion of the vehicle 500. The propulsion system 550 may be controlled in response to receiving signals from the throttle / accelerator device 552.

[0074] A steering system 554, which may include a steering wheel, may be used to steer the vehicle 500 (e.g., along a desired path or route) when the propulsion system 550 is operating (e.g., when the vehicle is moving). The steering system 554 may receive signals from a steering actuator 556. The steering wheel may be optional in full automation (Level 5).

[0075] The brake sensor system 546 may be used to apply the vehicle brakes in response to receiving signals from the brake actuators 548 and / or the brake sensors.

[0076] The control unit(s) 536, which may comprise one or more System-on-Chip (SoCs) 504 ( Fig. 5C) and / or GPU(s), may send signals (e.g., representative of commands) to one or more components and / or systems of the vehicle 500. For example, the control unit(s) may send signals to apply the vehicle brakes via one or more brake actuators 548, to apply the steering system 554 via one or more steering actuators 556, to apply the propulsion system 550 via one or more throttle / accelerator devices 552. The control unit(s) 536 may include one or more integrated computing devices (e.g., supercomputers) that process sensor signals and issue operational commands (e.g., signals representing commands) to enable autonomous driving and / or to assist a human driver in driving the vehicle 500.The control unit(s) 536 may include a first control unit 536 for autonomous driving functions, a second control unit 536 for functional safety functions, a third control unit 536 for artificial intelligence functions (e.g., computer vision), a fourth control unit 536 for infotainment functions, a fifth control unit 536 for emergency redundancy, and / or other control units. In some examples, a single control unit 536 may perform two or more of the above functions, two or more control units 536 may perform a single function, and / or any combination thereof.

[0077] The control unit(s) 536 may provide the signals to control one or more components and / or systems of the vehicle 500 in response to sensor data received from one or more sensors (e.g., sensor inputs). The sensor data may, for example and without limitation, come from GNSS sensors 558 (e.g., Global Positioning System sensors), RADAR sensors 560, ultrasonic sensors 562, LiDAR sensors 564, IMU sensors 566 (e.g., accelerometers, gyroscopes, magnetic compasses, magnetometers, etc.), microphone(s) 596, stereo camera(s) 568, wide-angle camera(s) 570 (e.g., fisheye cameras), infrared camera(s) 572, environmental camera(s) 574 (e.g., 360-degree cameras), long-range and / or medium-range camera(s) 598, speed sensor(s) 544 (e.g., for measuring the speed of the vehicle 500), vibration sensor(s) 542, steering sensor(s) 540, brake sensor(s) (e.g., as part of the brake sensor system 546) and / or other sensor types.

[0078] One or more of the control units 536 may receive inputs (e.g., in the form of input data) from an instrument cluster 532 of the vehicle 500 and provide outputs (e.g., in the form of output data, display data, etc.) via a human-machine interface (HMI) display 534, an audible annunciator, a speaker, and / or via other components of the vehicle 500. The outputs may include information such as vehicle speed vector, speed, time, map data (e.g., the high definition ("HD") map 522 of Fig. 5C), location data (e.g., the location of the vehicle 500, e.g., on a map), direction, location of other vehicles (e.g., an occupancy grid), information about objects and the status of objects as perceived by the controller(s) 536, etc. For example, the HMI display 534 may display information about the presence of one or more objects (e.g., a road sign, a warning sign, a changing traffic light, etc.) and / or information about driving maneuvers the vehicle has performed, is currently performing, or will perform (e.g., change lanes now, take exit 34B in two miles, etc.).

[0079] The vehicle 500 also includes a network interface 524 that may utilize one or more wireless antenna(s) 526 and / or modem(s) to communicate over one or more networks. For example, the network interface 524 may enable communication over Long-Term Evolution ("LTE"), Wideband Code Division Multiple Access ("WCDMA"), Universal Mobile Telecommunications System ("UMTS"), Global System for Mobile Communication ("GSM"), IMT-CDMA Multi-Carrier ("CDMA2000"), etc. The wireless antenna(s) 526 may also enable communication between surrounding objects (e.g., vehicles, mobile devices, etc.) using local area networks such as Bluetooth, Bluetooth Low Energy ("LE"), Z-Wave, ZigBee, etc., and / or Low Power Wide Area Networks ("LPWANs") such as LoRaWAN, SigFox, etc.

[0080] Fig. 5B is an example of camera positions and fields of view for the autonomous vehicle 500 of Fig. 5A, in accordance with some embodiments of the present disclosure. The cameras and the respective fields of view are an example of one embodiment and are not intended to be limiting. For example, additional and / or alternative cameras may be present and / or the cameras may be located at various locations on the vehicle 500.

[0081] The camera types for the cameras may include, but are not limited to, digital cameras that can be adapted for use with the components and / or systems of the vehicle 500. The camera(s) may operate at Security Level B (ASIL) and / or another ASIL. The camera types may have any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc., depending on the embodiment. The cameras may use rolling shutter, global shutter, another shutter type, or a combination thereof. In some examples, the color filter array may include a red-clear-clear-clear color filter array (RCCC), a red-clear-clear-blue color filter array (RCCB), a red-blue-green-clear color filter array (RBGC), a Foveon X3 color filter array, a Bayer sensor color filter array (RGGB), a monochrome sensor color filter array, and / or another type of color filter array.In some embodiments, cameras with clear pixels, such as cameras with an RCCC, an RCCB, and / or an RBGC color filter array, may be used to increase light sensitivity.

[0082] In some examples, one or more of the cameras can be used to run advanced driver assistance systems (ADAS) (e.g., as part of a redundant or fail-safe design). For example, a multifunction mono camera can be installed, providing functions such as lane departure warning, traffic sign assist, and intelligent headlight control. One or more of the cameras (e.g., all cameras) can simultaneously record and provide image data (e.g., video).

[0083] One or more of the cameras can be mounted in a bracket, e.g., a custom-made (three-dimensionally ("3D") printed) mount, to eliminate stray light and reflections from inside the vehicle (e.g., reflections from the dashboard reflected in the windshield mirrors) that could interfere with the camera's image data acquisition. Regarding the mounting of the exterior mirrors, the exterior mirrors can be custom 3D printed so that the camera mounting plate is adapted to the shape of the exterior mirror. In some examples, the camera can be integrated into the exterior mirror. For side-view cameras, the cameras can also be integrated into the four pillars at each corner of the cabin.

[0084] Cameras with a field of view that includes portions of the environment in front of the vehicle 500 (e.g., forward-facing cameras) can be used for the surrounding view to identify forward paths and obstacles and, with the assistance of one or more controllers 536 and / or control SoCs, provide information critical for establishing an occupancy grid and / or determining preferred vehicle paths. Forward-facing cameras can be used for many of the same ADAS functions as LiDAR, including emergency braking, pedestrian detection, and collision avoidance. Forward-facing cameras can also be used for ADAS features and systems such as lane departure warnings ("LDW"), autonomous cruise control ("ACC"), and / or other features such as traffic sign recognition.

[0085] A variety of cameras can be used in a forward-facing configuration, e.g., a monocular camera platform containing a CMOS color image sensor. Another example is the wide-angle camera(s) 570, which can be used to detect objects coming into view from the periphery (e.g., pedestrians, crossing vehicles, or bicycles). Although Fig. 5B depicts only one wide-angle camera, the vehicle 500 may be equipped with any number (including zero) of wide-angle cameras 570. Furthermore, any number of wide-angle cameras 598 (e.g., a pair of wide-angle stereo cameras) may be used for depth-based object detection, particularly for objects for which a neural network has not yet been trained. The long-range camera(s) 598 may also be used for object detection and classification, as well as basic object tracking.

[0086] Any number of stereo cameras 568 may also be included in a forward-facing configuration. In at least one embodiment, one or more of the stereo camera(s) 568 may include an integrated control unit comprising a scalable processing unit that can provide programmable logic ("FPGA") and a multi-core microprocessor with an integrated Controller Area Network ("CAN") or Ethernet interface on a single chip. Such a unit may be used to create a 3D map of the vehicle's surroundings, including a distance estimate for all points in the image. An alternative stereo camera(s) 568 may include a compact stereo vision sensor(s) containing two camera lenses (one each on the left and right) and an image processing chip that measures the distance between the vehicle and the target object and processes the generated information (e.g.,Metadata) is used to activate the autonomous emergency braking and lane departure warning functions. In addition to or as an alternative to the stereo cameras described here, other types of stereo cameras 568 may also be used.

[0087] Cameras with a field of view that includes parts of the environment to the side of the vehicle 500 (e.g., side cameras) can be used for the environmental view and provide information used to create and update the occupancy grid and to generate collision warnings in the event of a side impact. For example, the environmental camera(s) 574 (e.g., four environmental cameras 574, as shown in Fig. 5B) may be positioned on the vehicle 500. The surround camera(s) 574 may include wide-angle camera(s) 570, fisheye camera(s), 360-degree camera(s), and / or the like. For example, four fisheye cameras may be mounted on the front, rear, and sides of the vehicle. In an alternative arrangement, the vehicle may utilize three surround cameras 574 (e.g., left, right, and rear) and employ one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0088] Cameras with a field of view that includes portions of the environment behind the vehicle 500 (e.g., rearview cameras) may be used for parking assistance, surrounding view, rear collision warnings, and occupancy grid creation and updating. A variety of cameras may be used, including, but not limited to, cameras that are also suitable as forward-facing cameras (e.g., long-range and / or mid-range camera(s) 598, stereo camera(s) 568, infrared camera(s) 572, etc.), as described herein.

[0089] Fig. 5C is a block diagram of an example system architecture for the example autonomous vehicle 500 of Fig. 5A, in accordance with some embodiments of the present disclosure. It should be understood that these and other arrangements described herein are only examples. Other configurations and elements (e.g., engines, interfaces, functions, arrangements, groupings of functions, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Furthermore, many of the elements described herein are functional units that may be implemented as individual or distributed components, or in conjunction with other components, and in any suitable combination and location. Various functions performed by units described herein may be performed by hardware, firmware, and / or software. For example, various functions may be performed by a processor executing instructions stored in memory.

[0090] All components, functions and systems of the vehicle 500 in Fig. 5C are connected via bus 502. Bus 502 may include a Controller Area Network (CAN) data interface (also referred to herein as a "CAN bus"). A CAN bus may be a network within vehicle 500 used to control various features and functions of vehicle 500, such as the application of brakes, acceleration, braking, steering, windshield wipers, etc. A CAN bus may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., a CAN ID). The CAN bus may be read to determine steering wheel angle, vehicle speed, engine speed (RPM), button positions, and / or other vehicle status indicators. The CAN bus may be ASIL B compliant.

[0091] Although bus 502 is described herein as a CAN bus, this is not a limitation. For example, FlexRay and / or Ethernet may be used in addition to or alternatively to the CAN bus. Even though bus 502 is represented by a single wire, this is not a limitation. For example, there may be any number of buses 502, which may include one or more CAN buses, one or more FlexRay buses, one or more Ethernet buses, and / or one or more other bus types using a different protocol. In some examples, two or more buses 502 may be used to perform different functions and / or provide redundancy. For example, a first bus 502 may be used for collision avoidance and a second bus 502 may be used for propulsion control.In each example, each bus 502 may communicate with any component of the vehicle 500, and two or more buses 502 may communicate with the same components. In some examples, each SoC 504, each control unit 536, and / or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle 500) and be connected to a common bus, such as the CAN bus.

[0092] The vehicle 500 may include one or more control units 536 as described herein in Fig. 5A. The control unit(s) 536 may be used for a variety of functions. The control unit(s) 536 may be coupled to the various other components and systems of the vehicle 500 and may be used for controlling the vehicle 500, the artificial intelligence of the vehicle 500, the infotainment of the vehicle 500, and / or the like.

[0093] The vehicle 500 may include a system(s) on a chip (SoC) 504. The SoC 504 may include CPU(s) 506, GPU(s) 508, processor(s) 510, cache(s) 512, accelerator 514, data storage 516, and / or other components and functions not shown. The SoC(s) 504 may be used to control the vehicle 500 in a variety of platforms and systems. For example, the SoC(s) 504 may be combined in a system (e.g., the system of the vehicle 500) with an HD card 522 that may be accessed via a network interface 524 from one or more servers (e.g., server(s) 578 from Fig. 5D) can receive map updates and / or updates.

[0094] The CPU(s) 506 may comprise a CPU cluster or CPU complex (alternatively referred to herein as a "CCPLEX"). The CPU(s) 506 may include multiple cores and / or L2 caches. For example, in some embodiments, the CPU(s) 506 may comprise eight cores in a coherent multiprocessor configuration. In some embodiments, the CPU(s) 506 may comprise four dual-core clusters, each cluster having a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s) 506 (e.g., the CCPLEX) may be configured to support concurrent operation of clusters, such that any combination of clusters of the CPU(s) 506 may be active at any given time.

[0095] The CPU(s) 506 may implement power management features that include one or more of the following: clock gating may be automatically enabled for individual hardware blocks when the hardware blocks are idle to conserve dynamic power; power gating may be enabled for each core when the core is not actively executing instructions due to the execution of WFI / WFE instructions; power gating may be enabled for each core independently; clock gating may be enabled for each core cluster independently if all cores have clock gating or power gating active; and / or power gating may be enabled for each core cluster independently if all cores have power gating active.The CPU(s) 506 may also implement an enhanced power state management algorithm, where permissible power states and expected wake-up times are determined, and the hardware / microcode determines the best power state for the core, cluster, and CCPLEX. The processor cores may support simplified power state entry sequences in software, offloading the work to the microcode.

[0096] The GPU(s) 508 may include an integrated GPU (alternatively referred to herein as an "iGPU"). The GPU(s) 508 may be programmable and efficient for parallel workloads. The GPU(s) 508 may, in some examples, utilize an enhanced Tensor instruction set. The GPU(s) 508 may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB of memory capacity) and two or more of the streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512 KB of memory capacity). In some embodiments, the GPU(s) 508 may include at least eight streaming microprocessors. The GPU(s) 508 may utilize the application programming interface(s) (API(s)) of the computing application. In addition, the GPU(s) 508 may utilize one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0097] The graphics processor(s) 508 may be optimized for best performance in automotive and embedded use cases. For example, the GPU(s) 508 may be fabricated on a fin field-effect transistor (FinFET). However, this is not a limitation, and the GPU(s) 508 may also be fabricated using other semiconductor fabrication techniques. Each streaming microprocessor may include a number of mixed-precision compute cores divided into multiple blocks. For example, without loss of generality, 64 PF32 cores and 32 PF64 cores may be divided into four processing blocks. In such an example, each processing block can be assigned 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two NVIDIA TENSOR COREs with mixed precision for deep learning matrix arithmetic, an L0 instruction cache, a warp scheduler, a dispatch unit, and / or a 64 KB register file.Additionally, streaming microprocessors can include independent parallel integer and floating-point datapaths to enable efficient execution of workloads with a mix of computations and addressing calculations. Streaming microprocessors can include independent thread scheduling to enable finer synchronization and collaboration between parallel threads. Streaming microprocessors can include a combined L1 data cache and shared memory unit to improve performance while simplifying programming.

[0098] The graphics processor(s) 508 may include high-bandwidth memory (HBM) and / or a 16 GB HBM2 memory subsystem to provide, in some examples, a peak memory bandwidth of approximately 900 GB / second. In some examples, synchronous graphics random-access memory (SGRAM), e.g., synchronous graphics double-data-rate random-access memory type 5 (GDDR5), may be used in addition to or as an alternative to HBM memory.

[0099] The GPU(s) 508 may include unified memory technology with access counters to enable more accurate migration of memory pages to the processor that accesses them most frequently, thereby improving efficiency for memory regions shared between processors. In some examples, address translation service (ATS) support may be used to allow the GPU(s) 508 to directly access the page tables of the CPU(s) 506. In such examples, if the memory management unit (MMU) of the GPU(s) 508 detects a fault, an address translation request may be communicated to the CPU(s) 506. In response, the CPU(s) 506 may look up the virtual-to-physical mapping for the address in its page tables and pass the translation back to the GPU(s) 508.Unified Memory technology enables a single, unified virtual address space for the memory of the CPU(s) 506 and the GPU(s) 508, thus simplifying the programming of the GPU(s) 508 and the porting of applications to the GPU(s) 508.

[0100] Additionally, the GPU(s) 508 may include an access counter that tracks the frequency with which the GPU(s) 508 accesses the memory of other processors. The access counter may help ensure that memory pages are moved to the physical memory of the processor that accesses those pages most frequently.

[0101] The SoC(s) 504 may include any number of cache(s) 512, including those described herein. For example, the cache(s) 512 may include an L3 cache available to both the CPU(s) 506 and the GPU(s) 508 (e.g., connected to both the CPU(s) 506 and the GPU(s) 508). The cache(s) 512 may include a write-back cache that can track line states, e.g., by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache may be 4 MB or more, depending on the embodiment, although smaller cache sizes may be used.

[0102] The SoC(s) 504 may include an arithmetic logic unit (ALU(s)) that may be utilized in performing processing related to any of the various tasks or operations of the vehicle 500, such as processing DNNs. Additionally, the SoC(s) 504 may include one or more floating-point units (FPU(s))—or other mathematical coprocessors or numerical coprocessors—for performing mathematical operations within the system. For example, the SoC(s) 504 may include one or more FPUs integrated as execution units within a CPU(s) 506 and / or GPU(s) 508.

[0103] The SoC(s) 504 may include one or more accelerators 514 (e.g., hardware accelerators, software accelerators, or a combination thereof). The SoC(s) 504 may include, for example, a hardware acceleration cluster comprising optimized hardware accelerators and / or a large on-chip memory. The large on-chip memory (e.g., 4 MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other computations. The hardware acceleration cluster may be used to complement the GPU(s) 508 and offload some of the tasks from the GPU(s) 508 (e.g., to free up more GPU(s) 508 cycles to perform other tasks). For example, the accelerator(s) 514 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are stable enough to be accelerated.The term “CNN” as used here can encompass all types of CNNs, including region-based or regional convolutional neural networks (RCNs) and fast RCNs (e.g., for object detection).

[0104] The accelerator(s) 514 (e.g., the hardware acceleration cluster) may include a deep learning accelerator (DLA). The DLA(s) may include one or more tensor processing units (TPUs), which may be configured to provide an additional ten trillion operations per second for deep learning applications and inferencing. The TPUs may be accelerators configured and optimized to perform image processing functions (e.g., for CNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specific number of neural network types and floating-point operations, as well as for inferencing. The design of the DLA(s) may provide more performance per millimeter than a general-purpose GPU, far exceeding the performance of a CPU.The TPU(s) can perform several functions, including a single-instance convolution function supporting, for example, INT8, INT16, and FP16 data types for both features and weights, as well as post-processing functions.

[0105] The DLA(s) may quickly and efficiently execute neural networks, in particular CNNs, on processed or unprocessed data for a variety of functions, including, for example and without limitation: a CNN for object identification and recognition using data from camera sensors; a CNN for distance estimation using data from camera sensors; a CNN for emergency vehicle detection and identification using data from microphones; a CNN for facial recognition and vehicle owner identification using data from camera sensors; and / or a CNN for safety-related events.

[0106] The DLA(s) can perform any function of the GPU(s) 508, and by using an inference accelerator, for example, a developer can use either the DLA(s) or the GPU(s) 508 for any function. For example, the developer can focus the processing of CNNs and floating-point operations on the DLA(s) and leave other functions to the GPU(s) 508 and / or other accelerators 514.

[0107] The accelerator(s) 514 (e.g., the hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA(s) may be developed 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(s) may provide a balance between performance and flexibility. For example, and without limitation, each PVA may include any number of reduced instruction set computers (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0108] The RISC cores may interact with image sensors (e.g., the image sensors of one of the cameras described herein), image signal processors, and / or the like. Each of the RISC cores may include any amount of memory. The RISC cores may use a variety of protocols depending on the embodiment. In some examples, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented with one or more integrated circuits, application-specific integrated circuits (ASICs), and / or memory devices. The RISC cores may include, for example, an instruction cache and / or tightly coupled RAM.

[0109] The DMA may enable components of the PVA(s) to access system memory independently of the CPU(s) 506. The DMA may support any number of features designed to optimize the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support up to six or more dimensions of addressing, including block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0110] The vector processors may be programmable processors that can be designed to efficiently and flexibly execute the programming of computer vision algorithms and provide signal processing functions. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem may act as the primary processing unit of the PVA and may include a vector processing unit (VPU), an instruction cache, and / or vector memory (e.g., VMEM). A VPU core may include a digital signal processor, such as a single instruction multiple data (SIMD) and very long instruction word (VLIW) digital signal processor. Combining SIMD and VLIW can increase throughput and speed.

[0111] Each of the vector processors may include an instruction cache and be connected to dedicated memory. Therefore, in some examples, each of the vector processors may be configured to operate independently of the other vector processors. In other examples, the vector processors included in a particular PVA may be configured to use data parallelism. For example, in some embodiments, the multiple vector processors in a single PVA may execute the same computer vision algorithm, but for different regions of an image. In other examples, the vector processors included in a particular PVA may concurrently execute different computer vision algorithms on the same image, or even execute different algorithms on consecutive images or portions of an image.Among other things, any number of PVAs can be included in the hardware acceleration cluster, and any number of vector processors can be included in each of the PVAs. Furthermore, the PVA(s) can contain additional ECC (Error Correcting Code) memory to increase system security.

[0112] The accelerator(s) 514 (e.g., the hardware acceleration cluster) may include an on-chip computer vision network and SRAM to provide high-bandwidth, low-latency SRAM to the accelerator(s) 514. In some examples, the on-chip memory may include at least 4 MB of SRAM, consisting of, for example, and without limitation, eight field-configurable memory blocks accessible 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 the DLA may access the memory through a backbone that provides high-speed access to the memory to the PVA and the DLA. The backbone may include an on-chip computer vision network that connects the PVA and the DLA to the memory (e.g., through the APB).

[0113] The on-chip computer vision network can include an interface that determines whether both the PVA and the DLA are delivering ready and valid signals before transmitting control signals / addresses / data. Such an interface can provide separate phases and channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface can comply with ISO 26262 or IEC 61508, although other standards and protocols can also be used.

[0114] In some examples, the SoC(s) 504 may include a real-time ray tracing hardware accelerator, as described in U.S. Patent Application No. 16 / 101,232, filed August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the positions and extents of objects (e.g., within a world model), generate real-time visualization simulations, interpret radar signals, synthesize and / or analyze sound propagation, simulate sonar systems, general wave propagation simulation, compare with lidar data for localization and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing-related operations.

[0115] The accelerator(s) 514 (e.g., the hardware accelerator cluster) have a wide range of uses for autonomous driving. The PVA can be a programmable image processing accelerator that can be used for critical processing steps in ADAS and autonomous vehicles. The capabilities of the PVA are well suited to algorithmic areas that require predictable processing with low power and low latency. In other words, the PVA is well suited for semi-dense or dense regular computations, even on small datasets, that require predictable runtimes with low latency and low power. In the context of autonomous vehicle platforms, the PVAs are therefore designed to execute classical computer vision algorithms because they are efficient at object detection and operate with integer mathematical methods.

[0116] For example, in one embodiment of the technology, the PVA is used to perform computer stereo vision. In some examples, a semi-global matching algorithm may be used, although this is not a limitation. Many Level 3-5 autonomous driving applications require motion estimation / stereo matching while driving (e.g., structure from motion, pedestrian detection, lane detection, etc.). The PVA can perform a computer stereo vision function based on inputs from two monocular cameras.

[0117] In some examples, PVA can be used to enable dense optical flow after processing raw radar data (e.g., with a 4D Fast Fourier Transform) to obtain processed radar data. In other examples, PVA is used for depth-of-flight processing, e.g., by processing raw time-of-flight data to yield processed time-of-flight data.

[0118] Any type of network can be powered by the DLA to improve control and driving safety, e.g., a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability, or as the relative "weight" of each detection compared to other detections. This confidence value allows the system to make further decisions about which detections should be considered true positives and which should be considered false positives. For example, the system can set a confidence threshold and only consider detections that exceed this threshold as true positives. In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable.Therefore, only the most confident detections should be considered as triggers for AEB. The DLA may employ a neural network to regress the confidence value. The neural network may take as input at least a subset of parameters, such as the dimensions of the bounding box, the ground plane estimate obtained (e.g., from another subsystem), the output of the IMU (Inertial Measurement Unit) sensor 566 correlated with the orientation of the vehicle 500, the distance, the 3D position estimates of the object obtained by the neural network and / or other sensors (e.g., LiDAR sensor(s) 564 or RADAR sensor(s) 560), and others.

[0119] The SoC(s) 504 may include data storage 516 (e.g., memory). The data storage(s) 516 may be on-chip memory of the SoC(s) 504, in which neural networks to be executed on the GPU and / or the DLA may be stored. In some examples, the capacity of the data storage(s) 516 may be large enough to store multiple instances of neural networks for redundancy and security. The data storage(s) 512 may include L2 or L3 cache(s) 512. The reference to the data storage(s) 516 may also include a reference to the memory associated with the PVA, DLA, and / or other accelerators 514, as described herein.

[0120] The SoC(s) 504 may include one or more processor(s) 510 (e.g., embedded processors). The processor(s) 510 may include a boot and power management processor, which may be a dedicated processor and subsystem to handle the boot and power management functions and associated security enforcement. The boot and power management processor may be part of the boot sequence of the SoC(s) 504 and may provide runtime power management services. The boot and power management processor may provide clock and voltage programming, support for transitions to a low-power state, management of SoC(s) 504 temperatures and temperature sensors, and / or management of the SoC(s) 504 power states.Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to temperature, and the SoC(s) 504 may use the ring oscillators to sense the temperatures of the CPU(s) 506, GPU(s) 508, and / or accelerator 514. If temperatures are determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine and place the SoC(s) 504 into a lower-power state and / or place the vehicle 500 into a chauffeur-to-safe-stop mode (e.g., bring the vehicle 500 to a safe stop).

[0121] Processor(s) 510 may also include a number of embedded processors that can serve as an audio processing engine. The audio processing engine may be an audio subsystem that enables full hardware support for multi-channel audio across multiple interfaces, as well as a wide and flexible range of audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core with a digital signal processor with dedicated RAM.

[0122] The processor(s) 510 may also include an "always on" processor engine that may provide the necessary hardware functions to support low-power sensor management and wake-up functions. The "always on" processor engine may include a processor core, tightly coupled memory, supporting peripherals (e.g., timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0123] The processor(s) 510 may also include a security cluster engine, which includes a dedicated processor subsystem for security management of automotive applications. The security cluster engine may include two or more processor cores, tightly coupled memory, supporting peripherals (e.g., timers, an interrupt controller, etc.), and / or routing logic. In a security mode, the two or more cores may operate in a lockstep mode, functioning as a single core with comparison logic to detect differences between their operations.

[0124] The processor(s) 510 may also include a real-time camera engine that includes a dedicated processor subsystem for managing the real-time camera.

[0125] The processor(s) 510 may also include a high dynamic range signal processor, which may include an image signal processor that is a hardware engine that is part of the camera processing pipeline.

[0126] The processor(s) 510 may include a video image compositor, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required by a video playback application to generate the final image for the player window. The video image compositor may perform lens distortion correction on the wide-angle camera(s) 570, the surround camera(s) 574, and / or the in-cabin surveillance camera sensors. The in-cabin surveillance camera sensor is preferably monitored by a neural network running on another instance of the Advanced SoC and configured to detect and respond to events in the cabin.An in-cabin system can perform lip reading to activate cellular service and place a call, dictate emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or enable voice-activated web browsing. Certain functions are available to the driver only when the vehicle is operating in autonomous mode and are disabled otherwise.

[0127] The video compositor can incorporate enhanced temporal noise reduction to reduce both spatial and temporal noise. For example, if motion is present in a video, the noise reduction weights the spatial information accordingly, reducing the weight of information provided by neighboring frames. If an image or part of an image contains no motion, the video compositor's temporal noise reduction can use information from the previous frame to reduce noise in the current frame.

[0128] The video image compositor can also be configured to perform stereo rectification of the input stereo image frames. The video image compositor can also be used for user interface design when the operating system desktop is in use and the GPU(s) 508 do not need to constantly render new surfaces. Even when the graphics processor(s) 508 are turned on and actively performing 3D rendering, the video image compositor can be used to offload the graphics processor(s) 508, thus improving performance and responsiveness.

[0129] The SoC(s) 504 may also include a serial MIPI (Mobile Industry Processor Interface) camera interface for receiving video and input from cameras, a high-speed interface, and / or a video input block that may be used for camera and related pixel input functions. The SoC(s) 504 may also include an input / output controller that may be controlled by software and used to receive I / O signals not associated with a specific function.

[0130] The SoC(s) 504 may also include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and / or other devices. The SoC(s) 504 may be used to process data from cameras (e.g., via Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LiDAR sensor(s) 564, RADAR sensor(s) 560, etc., which may be connected via Ethernet), data from bus 502 (e.g., vehicle speed 500, steering wheel position, etc.), and data from GNSS sensor(s) 558 (e.g., connected via Ethernet or CAN bus). The SoC(s) 504 may also include dedicated high-performance mass storage controllers, which may include their own DMA engines and which may be used to offload routine data management tasks from the CPU(s) 506.The SoC(s) 504 may also determine whether or not to upload data from various sources based on one or more control layers within the map data received from a remote location, as described above with reference to . Fig. 1-4 was explained.

[0131] The SoC(s) 504 may be an end-to-end platform with a flexible architecture spanning automation levels 3 through 5, thereby providing a comprehensive functional safety architecture that leverages computer vision and ADAS techniques for diversity and redundancy, and provides a platform for a flexible, reliable driving software stack along with deep learning tools. The SoC(s) 504 may be faster, more reliable, and even more power and space efficient than conventional systems. For example, the accelerator(s) 514, in combination with the CPU(s) 506, the GPU(s) 508, and the data storage(s) 516, may form a fast, efficient platform for Level 3-5 autonomous vehicles.

[0132] The technology thus offers capabilities and functions 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 variety of processing algorithms on a wide range of visual data. However, CPUs are often unable to meet the performance requirements of many image processing applications, e.g., in terms of execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, which is a prerequisite for in-vehicle ADAS applications and a requirement for practical Level 3-5 autonomous vehicles.

[0133] In contrast to conventional systems, the technology described here enables the simultaneous and / or sequential execution of multiple neural networks and the combination of the results to enable Level 3-5 autonomous driving functions by providing a CPU complex, a GPU complex, and a hardware acceleration cluster. For example, a CNN running on the DLA or dGPU (e.g., the GPU(s) 520) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, even those for which the neural network has not been specifically trained. The DLA may also include a neural network capable of identifying, interpreting, and semantically understanding the signs and passing this semantic understanding to the path planning modules running on the CPU complex.

[0134] Another example is that multiple neural networks can run simultaneously, as required for Level 3, 4, or 5 driving. A warning sign reading "Caution: Flashing lights indicate black ice," along with an electric light, can be interpreted independently or jointly by multiple neural networks. The sign itself can be identified as a traffic sign by a first deployed neural network (e.g., a trained neural network), and the text "Flashing lights indicate black ice" can be interpreted by a second deployed neural network, which informs the vehicle's path-planning software (preferably running on the CPU complex) that the flashing lights indicate black ice.The turn signal can be identified by running a third neural network over multiple frames, where the third neural network informs the vehicle's path planning software about the presence (or absence) of turn signals. All three neural networks can run simultaneously, e.g., within the DLA and / or on the GPU(s) 508.

[0135] In some examples, a facial recognition and vehicle owner identification CNN may use data from camera sensors to identify the presence of an authorized driver and / or owner of vehicle 500. The "always on" sensor processing engine may be used to unlock the vehicle when the owner approaches the driver's door and turns on the lights, and in security mode, to disable the vehicle when the owner exits the vehicle. In this way, the SoC(s) 504 provide security against theft and / or carjacking.

[0136] In another example, a CNN for emergency vehicle detection and identification may use data from microphones 596 to detect and identify emergency vehicle sirens. Unlike conventional systems that use general classifiers to detect sirens and manually extract features, the SoC(s) 504 utilize the CNN to classify ambient and urban noise, as well as visual data. In a preferred embodiment, the CNN running on the DLA is trained to detect the relative approach speed of the emergency vehicle (e.g., using the Doppler effect). The CNN may also be trained to detect emergency vehicles specific to the local region in which the vehicle is traveling, as identified by the GNSS sensors 558.For example, the CNN will attempt to detect European sirens when deployed in Europe and only North American sirens when deployed in the United States. Once an emergency vehicle is detected, a control program can be used to execute an emergency vehicle safety routine, slow the vehicle, pull over to the side of the road, park the vehicle, and / or idle the vehicle using ultrasonic sensors 562 until the emergency vehicle(s) have passed.

[0137] The vehicle may include one or more CPU(s) 518 (e.g., discrete CPU(s) or dCPU(s)) that may be connected to the SoC(s) 504 via a high-speed connection (e.g., PCIe). The CPU(s) 518 may be, for example, an x86 processor. The CPU(s) 518 may be used to perform any of a variety of functions, including reconciling potentially conflicting results between ADAS sensors and the SoC(s) 504 and / or monitoring the status and health of the controller(s) 536 and / or the infotainment SoC 530, for example.

[0138] The vehicle 500 may include one or more GPU(s) 520 (e.g., discrete GPU(s) or dGPU(s)) that may be connected to the SoC(s) 504 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s) 520 may provide additional artificial intelligence functionality, e.g., by executing redundant and / or distinct neural networks, and may be used to train and / or update neural networks based on inputs (e.g., sensor data) from sensors of the vehicle 500.

[0139] The vehicle 500 may also include the network interface 524, which may include one or more wireless antennas 526 (e.g., one or more wireless antennas for various communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface 524 may be used to enable a wireless connection over the Internet to the cloud (e.g., to the server(s) 578 and / or other network devices), to other vehicles, and / or to computing devices (e.g., passenger client devices). To communicate with other vehicles, a direct connection may be established between the two vehicles and / or an indirect connection (e.g., via networks and the Internet). Direct connections may be established via a vehicle-to-vehicle communication link.The vehicle-to-vehicle communication link may provide information to the vehicle 500 about vehicles in the vicinity of the vehicle 500 (e.g., vehicles in front of, beside, and / or behind the vehicle 500). This function may be part of a cooperative adaptive cruise control function of the vehicle 500.

[0140] The network interface 524 may include an SoC that provides modulation and demodulation functions and enables the controller(s) 536 to communicate over wireless networks. The network interface 524 may include a radio frequency front-end for upconversion from baseband to radio frequency and downconversion from radio frequency to baseband. The frequency conversions may be performed using known methods and / or super-heterodyne techniques. In some examples, the radio frequency front-end functionality may be provided by a separate chip. The network interface may include wireless functions for communication over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0141] The vehicle 500 may also include data storage 528 that may be stored off-chip (e.g., outside the SoC(s) 504). The data storage 528 may include one or more memory elements such as RAM, SRAM, DRAM, VRAM, flash, hard drives, and / or other components and / or devices capable of storing at least one bit of data.

[0142] The vehicle 500 may also include GNSS sensor(s) 558. The GNSS sensor(s) 558 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS), etc.) assist in mapping, sensing, occupancy grid creation, and / or path planning. Any number of GNSS sensors 558 may be used, e.g., a GPS with a USB port and an Ethernet-to-serial (RS-232) bridge.

[0143] The vehicle 500 may also include RADAR sensor(s) 560. The RADAR sensor(s) 560 may be used by the vehicle 500 for vehicle detection over long distances, even in darkness and / or poor weather conditions. The RADAR sensor(s) 560 may use the CAN bus and / or bus 502 (e.g., for transmitting the data generated by the RADAR sensor(s) 560) for control and access to object tracking data, with access to the raw data via Ethernet in some examples. A variety of RADAR sensor types may be used. For example, and without limitation, the RADAR sensor (or sensors) 560 may be suitable for front, rear, and side RADAR deployment. In some examples, pulse Doppler RADAR sensors are used.

[0144] The RADAR sensor(s) 560 may include various configurations, e.g., long range with a narrow field of view, short range with a wide field of view, short range with side coverage, etc. In some examples, long range RADAR may be used for adaptive cruise control. Long range RADAR systems may provide a wide field of view realized by two or more independent scans, e.g., within a range of 250 m. The RADAR sensor(s) 560 may help distinguish between static and moving objects and may be used by ADAS systems for emergency braking and forward collision warning. Long range RADAR sensors may include monostatic multimodal RADARs with multiple (e.g., six or more) fixed RADAR antennas and a high-speed CAN and FlexRay interface.In a six-antenna example, the middle four antennas can create a focused beam pattern that covers the vehicle's surroundings at higher speeds with minimal interference from traffic in adjacent lanes. The other two antennas can expand the field of view so that vehicles entering or exiting the lane of vehicle 500 can be quickly detected.

[0145] Medium-range radar systems, for example, can have a range of up to 560 m (front) or 80 m (rear) and a field of view of up to 42 degrees (front) or 550 degrees (rear). Short-range radar systems include radar sensors that can be mounted on either end of the rear bumper. When installed on either end of the rear bumper, such a radar sensor system can create two beams that continuously monitor the blind spot behind and to the side of the vehicle.

[0146] Short-range radar systems can be used in an ADAS system for blind spot detection and / or as a lane change assistant.

[0147] The vehicle 500 may also include ultrasonic sensor(s) 562. The ultrasonic sensor(s) 562, which may be mounted at the front, rear, and / or sides of the vehicle 500, may be used for parking assistance and / or for creating and updating an occupancy grid. A variety of ultrasonic sensors 562 may be used, and different ultrasonic sensors 562 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensor(s) 562 may operate according to functional safety levels of ASIL B.

[0148] The vehicle 500 may include LiDAR sensor(s) 564. The LiDAR sensor(s) 564 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LiDAR sensor(s) 564 may be ASIL B functional safety level. In some examples, the vehicle 500 may include multiple LiDAR sensors 564 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to deliver data to a Gigabit Ethernet switch).

[0149] In some examples, the LiDAR sensor(s) 564 may be capable of providing a list of objects and their distances for a 360-degree field of view. Commercially available LiDAR sensors 564 may have a range of approximately 500 m, with an accuracy of 2 cm-3 cm, and with support for a 500 Mbps Ethernet connection, for example. In some examples, one or more non-protruding LiDAR sensors 564 may be used. In such examples, the LiDAR sensor (or LiDAR sensors) 564 may be implemented as a small device that may be embedded in the front, rear, sides, and / or corners of the vehicle 500. In such examples, the LiDAR sensor(s) 564 may provide a horizontal field of view of up to 120 degrees and a vertical field of view of up to 35 degrees, with a range of 200 m, even for objects with low reflectivity.The front-mounted LiDAR sensor(s) 564 can be configured for a horizontal field of view between 45 degrees and 135 degrees.

[0150] In some examples, LiDAR technologies such as 3D Flash LiDAR may also be used. 3D Flash LiDAR uses a laser flash as a transmission source to illuminate the vehicle's surroundings up to a distance of approximately 200 m. A Flash LiDAR unit contains a receptor that records the time of flight of the laser pulse and the reflected light on each pixel, which in turn corresponds to the distance between the vehicle and the objects. Flash LiDAR can enable highly precise and distortion-free images of the surroundings to be created with each laser flash. In some examples, four Flash LiDAR sensors may be deployed, one on each side of the vehicle. Available 3D Flash LiDAR systems include a fixed 3D star array LiDAR camera, which contains no moving parts other than a fan (e.g., a non-scanning LiDAR device).The Flash LiDAR device can use a 5-nanosecond Class I (eye-safe) laser pulse per image and capture the reflected laser light as 3D range point clouds and co-registered intensity data. By using Flash LiDAR and being a solid-state device with no moving parts, the 564 LiDAR sensor(s) are less susceptible to motion blur, vibration, and / or shock.

[0151] The vehicle may also include IMU sensor(s) 566. In some examples, the IMU sensor(s) 566 may be located at the center of the rear axle of the vehicle 500. The IMU sensor(s) 566 may include, for example and without limitation, one or more accelerometers, one or more magnetometers, one or more gyroscopes, one or more magnetic compasses, and / or other types of sensors. In some examples, such as in six-axis applications, the IMU sensor(s) 566 may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s) 566 may include accelerometers, gyroscopes, and magnetometers.

[0152] In some embodiments, the IMU sensor(s) 566 may be implemented as a miniaturized, high-performance GPS-based inertial navigation system (GPS / INS) that combines microelectromechanical (MEMS) inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. In some examples, the IMU sensors 566 may enable the vehicle 500 to estimate heading without requiring input from a magnetic sensor by directly observing velocity changes from the GPS and correlating them with the IMU sensors 566. In some examples, the IMU sensor(s) 566 and the GNSS sensor(s) 558 may be combined into a single integrated unit.

[0153] The vehicle may include one or more microphones 596 mounted in and / or around the vehicle 500. The microphone(s) 596 may be used, among other things, for detecting and identifying emergency vehicles.

[0154] The vehicle may also include any number of camera types, including stereo camera(s) 568, wide-angle camera(s) 570, infrared camera(s) 572, surround camera(s) 574, long-range and / or medium-range camera(s) 598, and / or other camera types. The cameras may be used to capture image data around the entire perimeter of the vehicle 500. The type of cameras used depends on the embodiments and requirements of the vehicle 500, and any combination of camera types may be used to provide the necessary coverage around the vehicle 500. The number of cameras may also 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. The cameras may support, for example and without limitation, Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet.Each of the cameras is described here with reference to . Fig. 5A and Fig. 5B is described in more detail.

[0155] The vehicle 500 may also include one or more vibration sensors 542. The vibration sensor(s) 542 may measure vibrations from components of the vehicle, such as the axle(s). Changes in vibration may, for example, indicate a change in the road surface. In another example, when using two or more vibration sensors 542, the differences between the vibrations may be used to determine the friction or slippage of the road surface (e.g., if the difference in vibration is between a driven axle and a free-spinning axle).

[0156] The vehicle 500 may include an ADAS system 538. The ADAS system 538 may include an SoC in some examples. The ADAS system 538 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 keep assist (LKA), blind spot warning (BSW), rear cross traffic alert (RCTW), forward collision warning (CWS), lane centering (LC), and / or other features and functions.

[0157] The ACC systems may use radar sensor(s) 560, LiDAR sensor(s) 564, and / or one or more cameras. The ACC systems may include longitudinal ACC and / or lateral ACC. Longitudinal ACC monitors and controls the distance to the vehicle immediately in front of vehicle 500 and automatically adjusts vehicle speed to maintain a safe distance from preceding vehicles. Lateral ACC maintains the distance and advises vehicle 500 to change lanes if necessary. Lateral ACC is related to other ADAS applications such as LCA and CWS.

[0158] CACC utilizes information from other vehicles, which may be received via the network interface 524 and / or the wireless antenna(s) 526 from other vehicles over a wireless connection or indirectly via a network connection (e.g., over the Internet). Direct connections may be established via a vehicle-to-vehicle (V2V) communication link, while indirect connections may be an infrastructure-to-vehicle (I2V) communication link. In general, the V2V communication concept provides information about the immediately preceding vehicles (e.g., vehicles immediately in front of and in the same lane as vehicle 500), while the I2V communication concept provides information about traffic further ahead. CACC systems may include one or both I2V and V2V information sources.Given the information about the vehicles ahead of vehicle 500, CACC can be more reliable and has the potential to improve traffic flow and reduce congestion on the road.

[0159] FCW systems are designed to warn the driver of a hazard so they can take corrective action. FCW systems utilize a forward-facing camera and / or radar sensor(s) 560 coupled with a dedicated processor, DSP, FPGA, and / or ASIC that is electrically connected to provide feedback to the driver, e.g., via a display, speaker, and / or vibrating component. FCW systems can provide a warning, e.g., in the form of a sound, a visual warning, a vibration, and / or a rapid braking pulse.

[0160] AEB systems detect an impending forward collision with another vehicle or object and can automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use forward-facing camera(s) and / or radar sensor(s) 560 connected to a dedicated processor, DSP, FPGA, and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision. If the driver does not take corrective action, the AEB system can automatically apply the brakes to prevent or at least mitigate the effects of the predicted collision. AEB systems may incorporate techniques such as dynamic brake support and / or crash-preparatory braking.

[0161] Lane departure warning systems warn the driver visually, audibly, and / or tactilely, e.g., through vibrations of the steering wheel or seat, when the vehicle crosses the lane markings. Lane departure warning systems will not activate if the driver indicates an intentional lane departure by activating the turn signal. LDW systems may use forward-facing cameras connected to a dedicated processor, DSP, FPGA, and / or ASIC, which is electrically connected to driver feedback, e.g., a display, speaker, and / or vibrating component.

[0162] LKA systems are a variant of LDW systems. LKA systems correct the vehicle 500 through steering inputs or braking when the vehicle 500 leaves the lane.

[0163] BSW systems detect and warn the driver of vehicles in the vehicle's blind spot. BSW systems can provide visual, audible, and / or tactile warnings that merging or changing lanes is unsafe. The system can provide an additional warning when the driver activates a turn signal. BSW systems can utilize rear-facing camera(s) and / or radar sensor(s) 560 coupled with a dedicated processor, DSP, FPGA, and / or ASIC electrically connected to driver feedback, e.g., a display, speaker, and / or vibrating component.

[0164] RCTW systems can provide visual, audible, and / or tactile notification when an object is detected outside the range of the rear camera while the vehicle 500 is reversing. Some RCTW systems include AEB to ensure the vehicle brakes are applied to avoid a crash. RCTW systems can utilize one or more rear-facing RADAR sensors 560 coupled to a dedicated processor, DSP, FPGA, and / or ASIC electrically connected to driver feedback, such as a display, speaker, and / or vibrating component.

[0165] Conventional ADAS systems can produce false positive results, which can be annoying and distracting for the driver, but are generally not catastrophic because ADAS systems warn the driver and give them the opportunity to decide whether a safety-threatening situation truly exists and act accordingly. However, in an autonomous vehicle 500, in the event of conflicting results, the vehicle 500 must decide for itself whether to consider the result of a primary or a secondary computer (e.g., a first control unit 536 or a second control unit 536). In some embodiments, the ADAS system 538 may, for example, be a backup and / or secondary computer that provides perception information to a rationality module of the backup computer.The backup computer's rationality monitor can run redundant software on hardware components to detect errors in perception and dynamic driving tasks. The outputs of the ADAS system 538 can be forwarded to a monitoring MCU. If the outputs of the primary computer and the secondary computer conflict, the monitoring MCU must determine how to resolve the conflict to ensure safe operation.

[0166] In some examples, the primary computer may be configured to provide the monitoring MCU with a confidence value indicating the primary computer's confidence in the chosen outcome. If the confidence value exceeds a threshold, the monitoring MCU may follow the primary computer's instruction regardless of whether the secondary computer provides a conflicting or inconsistent outcome. If the confidence value does not reach the threshold and the primary and secondary computers provide different outcomes (e.g., a conflict), the monitoring MCU may mediate between the computers to determine the correct outcome.

[0167] The monitoring MCU can be configured to run one or more neural networks trained and configured to determine, based on the outputs of the primary computer and the secondary computer, the conditions under which the secondary computer triggers false alarms. This allows the neural network in the monitoring MCU to learn when the output of the secondary computer can and cannot be trusted. For example, if the secondary computer is a RADAR-based FCW system, a neural network in the monitoring MCU can learn when the FCW system identifies metallic objects that are not actually hazardous, such as a drain grate or manhole cover, which triggers an alarm.If the secondary computer is a camera-based lane departure warning system, a neural network in the monitoring MCU can learn to override the lane departure warning system when cyclists or pedestrians are present and lane departure is actually the safest maneuver. In embodiments where a neural network runs on the monitoring MCU, the monitoring MCU can include at least one DLA or a GPU suitable for operating the neural network(s) with associated memory. In preferred embodiments, the monitoring MCU can comprise and / or be included as a component of the SoC(s) 504.

[0168] In other examples, the ADAS system 538 may include a secondary computer that executes the ADAS functionality using conventional computer vision rules. Thus, the secondary computer can use classic computer vision (if-then) rules, and the presence of one or more neural networks in the monitoring MCU can improve reliability, safety, and performance. Thus, the diverse implementation and intentional non-identity make the overall system more fault-tolerant, especially against errors caused by software features (or software-hardware interfaces).For example, if a software bug occurs in the primary computer's software and the non-identical software code on the secondary computer produces the same overall result, the monitoring MCU can assume with greater confidence that the overall result is correct and that the bug in the primary computer's software or hardware does not cause a significant failure.

[0169] In some examples, the output of the ADAS system 538 may be fed into the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, if the ADAS system 538 displays a collision warning due to an immediately preceding object, the perception block may use this information in identifying objects. In other examples, the secondary computer may have its own neural network trained to reduce the risk of false alarms, as described herein.

[0170] The vehicle 500 may also include the infotainment SoC 530 (e.g., an in-vehicle infotainment (IVI) system). Although the infotainment system is illustrated and described as an SoC, it need not be a SoC, but may consist of two or more discrete components. The infotainment SoC 530 may include a combination of hardware and software responsible for providing audio (e.g., music, personal digital assistant, navigation directions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and / or information services (e.g., navigation systems, parking assistance, a radio data system, vehicle-related information such as fuel level, total distance traveled, brake fluid level, fuel level, oil level, door open / close, air filter information, etc.) to the vehicle 500.The infotainment SoC 530 may include, for example, radios, record players, navigation systems, video players, USB and Bluetooth connections, car computers, in-car entertainment, Wi-Fi, steering wheel audio controls, a hands-free system, a heads-up display (HUD), an HMI display 534, a telematics device, a control panel (e.g., for controlling and / or interacting with various components, functions, and / or systems), and / or other components. The infotainment SoC 530 may also be used to provide one or more users of the vehicle with information (e.g., visual and / or audible), such as information from the ADAS system 538, autonomous driving information (e.g., planned vehicle maneuvers, trajectories), information about the environment (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0171] The infotainment SoC 530 may include GPU functions. The infotainment SoC 530 may communicate with other devices, systems, and / or components of the vehicle 500 via the bus 502 (e.g., CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 530 may be coupled to a supervisory MCU so that the infotainment system's GPU can perform some self-driving functions if the primary control unit(s) 536 (e.g., the primary and / or backup computers of the vehicle 500) fail. In such an example, the infotainment SoC 530 may place the vehicle 500 into a safe-stop chauffeur mode, as described herein.

[0172] The vehicle 500 may also include an instrument cluster 532 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 532 may include a control unit and / or a supercomputer (e.g., a discrete control unit or a supercomputer). The instrument cluster 532 may include a number of instruments, such as a speedometer, fuel level, oil pressure, tachometer, odometer, turn signals, shift position indicator, seat belt warning light(s), parking brake warning light(s), engine malfunction light(s), airbag system (SRS) information, lighting controls, safety system controls, navigation information, etc. In some examples, information from the infotainment SoC 530 and the instrument cluster 532 may be displayed and / or shared. In other words, the instrument cluster 532 may be part of the infotainment SoC 530, or vice versa.

[0173] Fig. 5D is a system diagram for communication between the cloud-based server(s) and the example autonomous vehicle 500 of Fig. 5A, in accordance with some embodiments of the present disclosure. The system 576 may include the server(s) 578, the network(s) 590, and vehicles, including the vehicle 500. The server(s) 578 may include a plurality of GPUs 584(A)-584(H) (collectively referred to herein as GPUs 584), PCIe switches 582(A)-582(H) (collectively referred to herein as PCIe switches 582), and / or CPUs 580(A)-580(B) (collectively referred to herein as CPUs 580). The GPUs 584, the CPUs 580, and the PCIe switches may be interconnected via high-speed interconnects such as the NVLink interfaces 588 developed by NVIDIA and / or PCIe interconnects 586. In some examples, the GPUs 584 are connected via NVLink and / or NVSwitch SoC, and the GPUs 584 and the PCIe switches 582 are connected via PCIe connections. Although eight GPUs 584, two CPUs 580, and two PCIe switches are depicted, this is not a limitation.Depending on the embodiment, each of the servers 578 may include any number of GPUs 584, CPUs 580, and / or PCIe switches. For example, the servers 578 may each include eight, sixteen, thirty-two, and / or more GPUs 584.

[0174] The server(s) 578 may receive, via the network(s) 590 and from the vehicles, image data representative of images depicting unexpected or changed road conditions, such as recently commenced roadwork. The server(s) 578 may transmit, via the network(s) 590 and to the vehicles, neural networks 592, updated neural networks 592, and / or map information 594, including information about traffic and road conditions. The updates to the map information 594 may include updates to the HD map 522, such as information about construction, potholes, detours, flooding, and / or other obstacles.In some examples, the neural networks 592, the updated neural networks 592, and / or the map information 594 may be the result of new training and / or new experience contained in the data received from any number of vehicles in the environment and / or based on training performed in a data center (e.g., using the server(s) 578 and / or other servers).

[0175] The server(s) 578 may be used to train machine learning models (e.g., neural networks) based on training data. The training data may be generated by the vehicles and / or in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., when the neural network benefits from supervised learning) and / or subjected to other preprocessing, while in other examples, the training data is not tagged and / or preprocessed (e.g., when the neural network does not require supervised learning).Training may be performed using one or more classes of machine learning techniques, including, but not limited to, 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, nonlinear dimensionality reduction, representation learning (including sparse dictionary learning), rule-based machine learning, anomaly detection, and any variations or combinations thereof. Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., by transmitting them to the vehicles via the network(s) 590) and / or the machine learning models may be used by the server(s) 578 to remotely monitor the vehicles.

[0176] In some examples, servers 578 may receive data from the vehicles and apply the data to real-time neural networks to draw intelligent conclusions in real time. Server(s) 578 may include deep learning supercomputers and / or dedicated AI computers powered by GPU(s) 584, such as the DGX and DGX Station machines developed by NVIDIA. However, in some examples, servers 578 may also include deep learning infrastructures using only CPU-powered data centers.

[0177] The deep learning infrastructure of server(s) 578 may be capable of rapid, real-time inference and may utilize this capability to evaluate and verify the state of the processors, software, and / or associated hardware in vehicle 500. For example, the deep learning infrastructure may receive regular updates from vehicle 500, such as an image sequence and / or objects that vehicle 500 has located in that image sequence (e.g., via computer vision and / or other machine object classification techniques). The deep learning infrastructure may run its own neural network to identify the objects and compare them to the objects identified by vehicle 500.If the results do not match and the infrastructure concludes that the AI in the vehicle 500 is not functioning properly, the server(s) 578 may send a signal to the vehicle 500 instructing a fail-safe computer of the vehicle 500 to take control, notify the passengers, and perform a safe parking maneuver.

[0178] For inference, server 578 may include GPU(s) 584 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-driven servers and inference acceleration may enable real-time responsiveness. In other examples, e.g., where performance is less critical, servers powered by CPUs, FPGAs, and other processors may be used for inference. EXAMPLE COMPUTER DEVICE

[0179] Fig. 6 is a block diagram of an example computing device 600 suitable for use in implementing some embodiments of the present disclosure. Computing device 600 may include an interconnect system 602 that directly or indirectly interconnects the following devices: memory 604, one or more central processing units (CPUs) 606, one or more graphics processing units (GPUs) 608, a communications interface 610, input / output (I / O) ports 612, input / output components 614, a power supply 616, one or more presentation components 618 (e.g., display(s)), and one or more logic units 620. In at least one embodiment, computing device(s) 600 may include one or more virtual machines (VMs), and / or each of the components thereof may include virtual components (e.g., virtual hardware components).As non-limiting examples, one or more GPUs 608 may include one or more vGPUs, one or more CPUs 606 may include one or more vCPUs, and / or one or more logic units 620 may include one or more virtual logic units. A computing device (or multiple computing devices) 600 may include discrete components (e.g., a full GPU for the computing device 600), virtual components (e.g., a portion of a GPU for the computing device 600), or a combination thereof.

[0180] Although the different blocks in Fig. 6 are shown as being connected with lines via the interconnect system 602, this is not to be understood as a limitation and is for clarity only. For example, in some embodiments, a presentation component 618, such as a display, may be considered an I / O component 614 (e.g., if the display is a touchscreen). Another example is that the CPUs 606 and / or the GPUs 608 may include memory (e.g., the memory 604 may represent any memory device in addition to the memory of the GPUs 608, the CPUs 606, and / or other components). In other words, the computing device of Fig. 6 is merely illustrative. It does not distinguish between categories such as ‘workstation’, ‘server’, ‘laptop’, ‘desktop’, ‘tablet’, ‘client device’, ‘mobile device’, ‘handheld device’, ‘game console’, ‘electronic control unit (ECU)’, ‘virtual reality system’ and / or other device or system types, since all components of the computing device are Fig. 6 should be considered.

[0181] The interconnect system 602 may represent one or more connections or buses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system 602 may include one or more bus or connection 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 bus or connection type. In some embodiments, there are direct connections between the components. For example, the CPU 606 may be directly connected to the memory 604. Additionally, the CPU 606 may be directly connected to the GPU 608. For direct or point-to-point connections between components, the interconnect system 602 may include a PCIe connection to establish the connection.In these examples, the computing device 600 may not necessarily include a PCI bus.

[0182] The storage 604 may consist of a variety of computer-readable media. The computer-readable media may be any available media accessible by the computing device 600. The computer-readable media may include both volatile and non-volatile media, as well as removable and non-removable media. By way of example and without limitation, the computer-readable media may include computer storage media and communication media.

[0183] The computer storage media may include both volatile and non-volatile media and / or removable and non-removable media, as implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, and / or other types of data. For example, the memory 604 may store computer-readable instructions (e.g., programs and / or program elements such as an operating system). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other storage technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by the computing device 600. As used herein, computer storage media does not, per se, include signals.

[0184] Computer storage media may embody computer-readable instructions, data structures, program modules, and / or other data types in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any media for transmitting information. The term "modulated data signal" may refer to a signal in which one or more of its properties are adjusted or altered to encode information in the signal. Examples of computer storage media include wired media, such as a wired network or a direct wired connection, and wireless media, such as acoustic, RF, infrared, and other wireless media. Combinations of the above media should also be considered computer-readable media.

[0185] The CPU(s) 606 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 600 and to perform one or more of the methods and / or processes described herein. The CPU(s) 606 may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of concurrently executing a plurality of software threads. The CPU(s) 606 may include any type of processor and may include different types of processors depending on the type of computing device 600 implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers).Depending on the type of computing device 600, the processor may be, for example, an Advanced RISC Machines (ARM) processor using Reduced Instruction Set Computing (RISC) or an x86 processor using Complex Instruction Set Computing (CISC). Computing device 600 may include one or more CPUs 606 in addition to one or more microprocessors or additional coprocessors, such as math coprocessors.

[0186] In addition to or alternatively to the CPU(s) 606, the GPU(s) 608 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 600 to perform one or more of the methods and / or processes described herein. One or more of the GPU(s) 608 may be an integrated GPU (e.g., with one or more of the CPU(s) 606 and / or one or more of the GPU(s) 608 may be a discrete GPU). In certain embodiments, one or more of the GPU(s) 608 may be a coprocessor of one or more of the CPU(s) 606. The GPU(s) 608 may be used by the computing device 600 to render graphics (e.g., 3D graphics) or to perform general-purpose computations. The GPU(s) 608 may be used, for example, for General-Purpose Computing on GPUs (GPGPU).The GPU(s) 608 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. The GPU(s) 608 may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s) 606 received via a host interface). The GPU(s) 608 may include graphics memory, e.g., display memory, for storing pixel data or other suitable data, such as GPGPU data. The display memory may be part of the memory 604. The GPU(s) 608 may include two or more GPUs operating in parallel (e.g., via a link). The link may connect the GPUs directly (e.g., using NVLINK) or connect the GPUs via a switch (e.g., using NVSwitch). When used together, each GPU 608 can generate pixel data or GPGPU data for different parts of an output or for different outputs (e.g.a first GPU for a first image and a second GPU for a second image). Each GPU can have its own memory or share memory with other GPUs.

[0187] In addition to or alternatively to the CPU(s) 606 and / or the GPU(s) 608, the logic unit(s) 620 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 600 to perform one or more of the methods and / or processes described herein. In embodiments, the CPU(s) 606, the GPU(s) 608, and / or the logic unit(s) 620 may discretely or jointly execute any combination of the methods, processes, and / or portions thereof. One or more of the logic units 620 may be part of one or more of the CPU(s) 606 and / or the GPU(s) 608, and / or one or more of the logic units 620 may be discrete components or otherwise external to the CPU(s) 606 and / or the GPU(s) 608.In certain embodiments, one or more of the logic units 620 may be a coprocessor of one or more of the CPU(s) 606 and / or one or more of the GPU(s) 608.

[0188] Examples of the logical unit(s) 620 include one or more processing cores and / or components thereof, such as data processing units (DPUs), tensor cores (TCs), tensor processing units (TPUs), pixel visual cores (PVCs), vision processing units (VPUs), graphics processing clusters (GPCs), texture processing clusters (TPCs), streaming multiprocessors (SMs), tree traversal units (TTUs), artificial intelligence accelerators (AIAs), deep learning accelerators (DLAs), arithmetic logic units (ALUs), application-specific integrated circuits (ASICs), floating point units (FPUs), input / output (I / O) elements, peripheral component interconnect (PCI) or peripheral component interconnect express (PCIe) elements, and / or the like.

[0189] The communication interface 610 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 600 to communicate with other computing devices over an electronic communication network, including wired and / or wireless communication. The communication interface 610 may include components and functions that enable communication over a variety of networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communication 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(s) 620 and / or the communication interface 610 may include one or more data processing units (DPUs) to transfer the data received over a network and / or via the interconnect system 602 directly to one or more GPU(s) 608 (e.g., a memory). As described above with reference to FIG. Fig. 1-4, this data may be selectively sent to the computing device 600 based on probabilities and / or other values contained in the control layers of the map data.

[0190] Through the I / O ports 612, the computing device 600 can be logically connected to other devices, including the I / O components 614, the presentation component(s) 618, and / or other components, some of which may be built into (e.g., integrated) the computing device 600. Example I / O components 614 include a microphone, a mouse, a keyboard, a joystick, a gamepad, a game controller, a satellite dish, a scanner, a printer, a wireless device, etc. The I / O components 614 can provide a natural user interface (NUI) that processes air gestures, voice input, or other physiological input from a user. In some cases, the inputs can be transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, pen recognition, facial recognition, biometric recognition, both on-screen and off-screen gesture recognition, air gestures, head and eye tracking, and touch detection (as described in more detail below) in conjunction with a display of computing device 600. Computing device 600 may include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations thereof, for gesture recognition and capture. Additionally, computing device 600 may include accelerometers or gyroscopes (e.g., as part of an inertial measurement unit (IMU)) that enable the detection of motion. In some examples, the output of the accelerometers or gyroscopes from computing device 600 may be used to present immersive augmented reality or virtual reality.

[0191] Power supply 616 may be a hardwired power supply, a battery power supply, or a combination thereof. Power supply 616 may supply power to computing device 600 so that components of computing device 600 can operate.

[0192] The presentation component(s) 618 may include a display (e.g., a monitor, a touchscreen, a television screen, a heads-up display (HUD), other display types, or a combination thereof), speakers, and / or other presentation components. The presentation component(s) 618 may receive data from other components (e.g., the GPU(s) 608, the CPU(s) 606, DPUs, etc.) and output the data (e.g., as an image, video, audio, etc.). EXAMPLE OF A DATA CENTER

[0193] Fig. Figure 7 shows an example of a data center 700 that may be used in at least one embodiment of the present disclosure. The data center 700 may include a data center infrastructure layer 710, a framework layer 720, a software layer 730, and / or an application layer 740.

[0194] As in Fig. 7, the infrastructure layer of the data center 710 may include a resource orchestrator 712, clustered computing resources 714, and node computing resources (“KKR”) 716(1)-716(N), where “N” represents any natural number. In at least one embodiment, the KRRs 716(1)-716(N) may be any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state or disk drives), network input / output devices (NW I / O), network switches, virtual machines (VMs), power modules and / or cooling modules, etc. In some embodiments, one or more KKRs among the KKRs 716(1)-716(N) may correspond to a server having one or more of the above-mentioned computing resources.Furthermore, in some embodiments, the KRRs 716(1)-716(N) may include one or more virtual components, such as vGPUs, vCPUs, and / or the like, and / or one or more of the KRRs 716(1)-716(N) may correspond to a virtual machine (VM).

[0195] In at least one embodiment, the grouped computing resources 714 may include separate groupings of KRRs 716 housed in one or more racks (not shown) or in many racks in data centers in different geographical locations (also not shown). Separate groupings of KRRs 716 within the grouped computing resources 714 may include grouped computing, networking, and / or memory resources that may be configured or allocated to support one or more workloads. In at least one embodiment, multiple KRRs 716 with CPUs, GPUs, DPUs, and / or other processors may be grouped in one or more racks to provide computing resources to support one or more workloads. The one or more racks may also include any number of power modules, cooling modules, and / or network switches in any combination.

[0196] Resource orchestrator 712 may configure or otherwise control one or more KRRs 716(1)-716(N) and / or grouped computing resources 714. In at least one embodiment, resource orchestrator 712 may be a management entity for the software design infrastructure (SDI) of data center 700. Resource orchestrator 712 may be hardware, software, or a combination thereof.

[0197] In at least one embodiment, as in Fig. 7, the framework layer 720 may include a job scheduler 733, a configuration manager 734, a resource manager 736, and / or a distributed file system 738. The framework layer 720 may include a framework to support the software 732 of the software layer 730 and / or one or more applications 742 of the application layer 740. The software 732 or the application(s) 742 may include web-based service software or applications such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 720 may be some type of free and open source software web application framework such as, but not limited to, Apache Spark™ (hereinafter "Spark"), which may utilize the distributed file system 738 for processing large amounts of data (e.g., "Big Data").In at least one embodiment, the job scheduler 733 may include a Spark driver to facilitate the scheduling of workloads supported by various layers of the data center 700. The configuration manager 734 may be capable of configuring various layers, such as the software layer 730 and the framework layer 720, including Spark and the distributed file system 738, to support the processing of large amounts of data. The resource manager 736 may manage clustered or grouped computing resources allocated to support the distributed file system 738 and the job scheduler 733. In at least one embodiment, the clustered or grouped computing resources may include the clustered computing resources 714 at the infrastructure layer 710 of the data center.The resource manager 736 may coordinate with the resource orchestrator 712 to manage these allocated or assigned computing resources.

[0198] In at least one embodiment, the software 732 included in software layer 730 may include software used by at least portions of KRRs 716(1)-716(N), clustered computing resources 714, and / or distributed file system 738 of framework layer 720. One or more types of software may include, but are not limited to, web searching software, email virus scanning software, database software, and video content streaming software.

[0199] In at least one embodiment, the application(s) 742 included in the application layer 740 may include one or more types of applications used by at least portions of the KKRs 716(1)-716(N), the clustered compute resources 714, and / or the distributed file system 738 of the framework layer 720. One or more types of applications may include, but are not limited to, any number of genomic applications, cognitive computation, 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 connection with one or more embodiments.

[0200] In at least one embodiment, the configuration manager 734, the resource manager 736, and the resource orchestrator 712 may perform any number and type of modifying actions based on any amount and type of data collected in any technically feasible manner. Self-modifying actions may relieve the operator of a data center 700 from potentially making poor configuration decisions and potentially avoid underutilized and / or malfunctioning parts of a data center.

[0201] Data center 700 may include tools, services, software, or other resources to train one or more machine learning models or to predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, one or more machine learning models may be trained by calculating weighting parameters according to a neural network architecture using software and / or computing resources described above with respect to data center 700.In at least one embodiment, trained or deployed machine learning models corresponding to one or more neural networks may be used to infer or predict information using the resources described above with respect to data center 700 by using weighting parameters calculated by one or more training techniques, such as, but not limited to, those described herein.

[0202] In at least one embodiment, the data center 700 may utilize CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, and / or other hardware (or corresponding virtual computing resources) to perform training and / or inference with the resources described above. Furthermore, one or more of the software and / or hardware resources described above may be configured as a service that enables the user to train or infer information, such as image recognition, speech recognition, or other artificial intelligence services.

[0203] The data center 700 may include tools, services, software, or other resources to implement one or more instances of the tracking engine 126, management engine 122, and processing engine 124. These instances may be used to manage data uploads from location-based systems to the data center 700, as described above with reference to Fig. 1-4 is explained. EXAMPLE OF NETWORK ENVIRONMENTS

[0204] Network environments suitable for implementing embodiments of the disclosure may include one or more client devices, servers, network-attached storage (NAS), other backend devices, and / or other device types. The client devices, servers, and / or other device types (e.g., each individual device) may be implemented on one or more instances of the computing device(s) 600 of Fig. 6 - e.g., each device may include similar components, features, and / or functions of the computing device(s) 600. If backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may also be part of a data center 700, an example of which is shown in Fig. 7 is described in more detail.

[0205] The components of a network environment can communicate with each other over one or more networks, which can be wired, wireless, or both. The network can comprise multiple networks or a network of networks. For example, the network can comprise one or more wide area networks (WANs), one or more local area networks (LANs), one or more public networks such as the Internet and / or a public switched telephone network (PSTN), and / or one or more private networks. If the network comprises a wireless telecommunications network, components such as a base station, a communications tower, or even access points (as well as other components) can provide wireless connectivity.

[0206] Compatible network environments include one or more peer-to-peer network environments—in which case, a server may not be included in a network environment—and one or more client-server network environments—in which case, one or more servers may be included in a network environment. In peer-to-peer network environments, the functionality described here can be implemented with respect to one or more servers on any number of client devices.

[0207] In at least one embodiment, a network environment may include one or more cloud-based network environments, a distributed computing environment, a combination thereof, etc. A cloud-based 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. A framework layer may include a framework for supporting software of a software layer and / or one or more applications of an application layer. The software or application(s) may each include web-based service software or applications. In certain embodiments, one or more of the client devices may utilize the web-based service software or applications (e.g.,by accessing the service software and / or applications via one or more application programming interfaces (APIs). The framework layer can be, among other things, a type of free and open-source software framework for web applications that, for example, uses a distributed file system for processing large amounts of data (e.g., "Big Data").

[0208] A cloud-based network environment may provide cloud computing and / or cloud storage performing any combination of the computing and / or data storage functions described herein (or one or more portions thereof). Each of these various functions may be distributed across multiple locations from central servers or core servers (e.g., from one or more data centers that may be located across a state, region, country, globe, etc.). When a connection to a user (e.g., a client device) is relatively close to one or more edge servers, one or more core servers may delegate at least some functionality to the edge server(s). A cloud-based network environment may be private (e.g., restricted to a single organization), public (e.g., available to many organizations), and / or a combination thereof (e.g., a hybrid cloud environment).

[0209] The client device(s) may include at least some of the components, features, and functions of the Fig.6. A client device may be, for example, a personal computer (PC), a laptop, a mobile device, a smartphone, a tablet computer, a smartwatch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality headset, a global positioning system (GPS) or device, a video player, a video camera, a surveillance device or system, a vehicle, a boat, an aircraft, a virtual machine, a drone, a robot, a handheld communication device, a hospital device, a gaming device or system, an entertainment system, a vehicle computing system, an embedded system controller, a remote control, an appliance, a consumer electronics device, a workstation, an edge device, any combination of these described devices, or any other suitable device.

[0210] In summary, the described techniques provide a path for uploading data from vehicles in a fleet (or other types of location-based systems) through a series of geospatially indexed control layers. A vehicle can request one or more control layers from a server and / or a central distribution system and selectively upload data according to the information in the control layer(s).Each control layer may specify, for a given geographic region, the types of data that can be uploaded, the amount of data to be uploaded, the frequency of data uploads, the number of uploads, situations in which data should or should not be uploaded, times at which the data should or should not be uploaded, probabilities or priorities associated with uploading those data types, settings to enable or disable data uploads, and / or other parameters that can be used to control the uploads of data from that geographic region.A specific control layer can be added, removed, and / or updated in real-time or near-real-time to accommodate new use cases and / or changes to existing use cases related to the data being uploaded, the types and amounts of data already uploaded for the geographic region, and / or other factors. A specific control layer can also be distributed with a schema that allows a vehicle to interpret data uploads and adapt them to the control layer's parameters.

[0211] A technical advantage of the described techniques over previous approaches is the ability to define and implement different policies for controlling a fleet's data uploads. This allows the limited bandwidth available for these uploads to be used efficiently, effectively, and responsively. Furthermore, the described techniques allow parts of the control layers for controlling data uploads to be selectively requested from the vehicles, reducing communication with the vehicles and the resource overhead associated with an online infrastructure for conducting this communication.Because the schemas for the control layers are distributed along with the control layers, the vehicles can adapt the data upload to the parameters in the control layers without the need to create and distribute special code, retry mechanisms, redundancies, databases, and / or caching layers.

[0212] The disclosure of this application also includes the following numbered clauses: 1. In some embodiments, a method comprises determining a set of attributes associated with a set of data uploaded using a set of location-based systems in a geographic region; calculating a set of upload control parameters for the geographic region based at least on the set of attributes; receiving a request from a location-based system indicating the geographic region; and sending the set of upload control parameters within one or more control layers included in map data for the geographic region to the location-based system in response to the request, wherein the location-based system controls the upload of additional data associated with the geographic region based at least on the one or more control layers. 2. The method of clause 1, comprising determining a second set of attributes associated with a second set of data uploaded using a second set of location-based systems in a second geographic region; generating a second set of upload control parameters for the second geographic region based on at least the second set of attributes; and sending the second set of upload control parameters within the one or more control layers in response to a second request specifying the second geographic region. 3. The method of any of clauses 1-2, comprising sending a schema associated with the set of upload control parameters to the location-based system. 4. The method of any of clauses 1-3, wherein calculating the set of upload control parameters comprises determining a set of data upload requirements associated with the geographical region and generating the set of upload control parameters based at least on a comparison of the set of attributes and the set of data upload requirements. 5. The method of any of clauses 1-4, wherein determining the set of data upload requests comprises associating the geographic region with one or more data collection campaigns; and aggregating the set of data upload requests from the one or more data collection campaigns. 6. Methods according to any of clauses 1-5, wherein the one or more data collection campaigns cover a period during which data are to be collected from the geographical region. 7. The method of any of clauses 1-6, wherein the set of attributes comprises at least one of the following characteristics: the number of uploads, the size of the dataset, one or more types of data included in the dataset, or one or more properties of the dataset. 8. The method of any of clauses 1-7, wherein the set of upload control parameters comprises at least one of the following: the probability of uploading, the type of data to be uploaded, the amount of data to be uploaded, a set of conditions that trigger a data upload, or a set of conditions that trigger the disabling of a data upload. 9. The method of any of clauses 1-8, wherein the geographic region is specified in the request using at least one of a set of coordinates, a map tile, a zoom level associated with a map tile, a point on a curve, a cell in a grid, or a geofence. 10. A method according to any one of clauses 1-9, wherein the set of location-based systems comprises at least one of the following systems: 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 for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting virtual reality, augmented reality, or mixed reality content; a system implemented using a robot; a system for performing one or more conversational AI operations;a system for performing one or more generative AI operations; a system implementing one or more large language models (LLMs); a system for generating synthetic data; a system including one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. 11. In some embodiments, one or more processors include one or more circuits to perform operations including: determining a set of attributes associated with a set of data uploaded using a set of location-based systems in a geographic region; calculating a set of upload control parameters for the geographic region based at least on the set of attributes; receiving a request from a location-based system indicating the geographic region;and sending the set of upload control parameters within one or more control layers included in map data for the geographic region to the location-based system in response to the request, wherein the location-based system controls the upload of additional data associated with the geographic region based at least on the one or more control layers; 12. One or more processors of clause 11, wherein the operations further comprise: determining a second set of attributes associated with a second set of data uploaded using a second set of location-based systems in a second geographic region; generating a second set of upload control parameters for the second geographic region based on at least the second set of attributes; and sending the second set of upload control parameters within the one or more control layers in response to a second request specifying the second geographic region. 13. One or more processors according to any one of clauses 11-12, wherein the operations further comprise sending a schema associated with the one or more control layers to the location-based system. 14. One or more processors according to any one of clauses 11-13, wherein the one or more control layers comprise a first control layer containing a first subset of the set of upload control parameters and a second control layer containing a second subset of the set of upload control parameters. 15. One or more processors of any of clauses 11-14, wherein calculating the set of upload control parameters comprises calculating an upload probability included in the set of upload control parameters based at least on a comparison of a number of data uploads associated with the dataset and a number of requested uploads associated with the geographic region. 16. 16. One or more processors according to any of clauses 11-15, wherein the data set comprises at least one of the following: sensor data, log data, telemetry data, simulated data or development data. 17. One or more processors of any of clauses 11-16, wherein the set of upload control parameters is calculated based on at least one of the following: receipt of one or more new uploads associated with the geographical region or a frequency associated with updating the set of upload control parameters. 18. One or more processors according to any of clauses 11-17, wherein the one or more processors are included in at least one of the following systems: 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 for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting virtual reality, augmented reality, or mixed reality content; a system implemented using a robot;a system for performing one or more conversational AI operations; a system for performing one or more generative AI operations; a system implementing one or more large language models (LLMs); a system for generating synthetic data; a system including one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. 19. In some embodiments, a system comprises one or more processors for performing operations including calculating a set of upload control parameters for a geographic region based at least on a set of attributes associated with a set of data uploaded using a set of location-based systems in a geographic region; sending the set of upload control parameters within one or more control layers included in map data for the geographic region to a location-based system, and in response to a request specifying the geographic region from the location-based system, the location-based system controlling the upload of additional data associated with the geographic region based at least on the one or more control layers. 20. A system according to clause 19, wherein the system comprises at least one of the following systems: 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 for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting virtual reality, augmented reality, or mixed reality content; a system implemented using a robot; a system for performing one or more conversational AI operations;a system for performing one or more generative AI operations; a system implementing one or more large language models (LLMs); a system for generating synthetic data; a system including one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.

[0213] The disclosure may be described in the general context of computer code or machine-usable instructions, including computer-executable instructions such as program modules executed by a computer or other machine, such as a personal data assistant or other handheld device. In general, program modules, including routines, programs, objects, components, data structures, etc., refer to code that performs specific tasks or implements specific abstract data types. The disclosure may be implemented in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The disclosure may also be applied in distributed computing environments where tasks are performed by remotely controlled devices connected via a communications network.

[0214] As used herein, any reference to "and / or" in reference to two or more elements should be interpreted to mean only one element or a combination of elements. For example, "Element A, Element B, and / or Element C" may include only Element A, only Element B, only Element C, Element A and Element B, Element A and Element C, Element B and Element C, or both Elements A, B, and C. Furthermore, "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.

[0215] The subject matter of the present disclosure is described herein with a certain degree of particularity in order to satisfy legal requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter may be embodied in other ways to incorporate various steps or combinations of steps similar to those described herein in connection with other present or future technologies. Although the terms "step" and / or "block" are used herein to refer to various elements of the method employed, the terms should not be interpreted to imply any particular ordering among or between various steps described herein unless the order of each step is expressly described.

[0216] It is to be understood that the aspects and embodiments described above are only exemplary and that changes in detail may be made within the scope of the claims.

[0217] Each apparatus, method, and feature described in the specification and (where appropriate) in the claims and drawings may be provided independently or in any suitable combination.

[0218] The reference numbers used in the claims are for illustrative purposes only and do not limit the scope of the claims. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 16 / 101,232

[0114] Cited non-patent literature

[0000] SAE) “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201806, published on June 15, 2018, Standard No. J3016-201609, published on September 30, 2016

[0072]

Claims

[1] Method comprising: Determining a set of attributes associated with a set of data uploaded using a set of location-based systems in a geographic region; Calculating a set of upload control parameters for the geographical region based at least on the set of attributes; Receiving a request from a location-based system specifying the geographic region; and Sending the set of upload control parameters within one or more control layers contained in the map data for the geographical region to the location-based system in response to the request, wherein the location-based system controls an upload of additional data associated with the geographic region based at least on the one or more control layers. [2] A method according to claim 1, comprising: Determining a second set of attributes associated with a second set of data uploaded using a second set of location-based systems in a second geographic region; Generating a second set of upload control parameters for the second geographic region based on at least the second set of attributes; and Sending the second set of upload control parameters within the one or more control layers in response to a second request specifying the second geographic region. [3] The method of any preceding claim, comprising sending a schema associated with the set of upload control parameters to the location-based system. [4] The method according to any one of the preceding claims, wherein calculating the set of upload control parameters comprises: Determining a set of data upload requirements associated with the geographic region; and Generating the set of upload control parameters based at least on a comparison of the set of attributes and the set of data upload requirements. [5] The method of claim 4, wherein determining the set of data upload requirements comprises: Assigning the geographic region to one or more data collection campaigns; and Compiling data upload requests from one or more data collection campaigns. [6] The method of claim 5, wherein the one or more data collection campaigns comprise a period of time over which data from the geographic region is to be collected. [7] A method according to any preceding claim, wherein the set of attributes comprises at least one of the following features: a number of uploads, a size of the data set, one or more data types included in the data set, or one or more properties of the data set. [8] A method according to any preceding claim, wherein the set of upload control parameters comprises at least one of the following features: probability of upload, type of data to be uploaded, amount of data to be uploaded, set of conditions that trigger a data upload, or set of conditions that trigger disabling a data upload. [9] A method according to any preceding claim, wherein the geographical region is specified in the request by at least one of a set of coordinates, a map tile, a zoom level associated with a map tile, a point on a curve, a cell in a grid, and a geofence. [10] A method according to any preceding claim, wherein the set of location-based systems comprises at least one of the following systems: Control system for an autonomous or semi-autonomous machine; Perception system for an autonomous or semi-autonomous machine; System for performing one or more simulation operations; System for performing one or more digital twin operations; System for performing light transport simulations; System for collaborative content creation for 3D assets; System for performing one or more deep learning operations; System implemented with an edge device; System for generating or displaying virtual reality, augmented reality or mixed reality content; System implemented with the help of a robot; System for performing one or more conversational AI operations; System for performing one or more generative AI operations; System that implements one or more large language models (LLMs); System for generating synthetic data; System that contains one or more virtual machines (VMs); System that is at least partially implemented in a data center; or System implemented at least in part using cloud computing resources. [11] One or more processors comprising: one or more circuits for performing operations, comprising: Determining a set of attributes associated with a set of data uploaded using a set of location-based systems in a geographic region; Calculating a set of upload control parameters for the geographic region based at least on the set of attributes; Receiving a request from a location-based system specifying the geographic region; and Sending the set of upload control parameters within one or more control layers contained in the map data for the geographical region to the location-based system in response to the request, wherein the location-based system controls the upload of additional data associated with the geographic region based at least on the one or more control layers. [12] One or more processors according to claim 11, wherein the operations further comprise: Determining a second set of attributes associated with a second set of data uploaded using a second set of location-based systems in a second geographic region Generating a second set of upload control parameters for the second geographic region based on at least the second set of attributes; and Sending the second set of upload control parameters within the one or more control layers in response to a second request specifying the second geographic region. [13] One or more processors according to claim 11 or 12, wherein the operations further comprise sending a schema associated with the one or more control layers to the location-based system. [14] One or more processors according to any one of claims 11-13, wherein the one or more control layers comprise a first control layer containing a first subset of the set of upload control parameters and a second control layer containing a second subset of the set of upload control parameters. [15] One or more processors according to any one of claims 11-14, wherein calculating the set of upload control parameters comprises calculating an upload probability included in the set of upload control parameters based at least on a comparison of a number of data uploads associated with the data set and a number of requested uploads associated with the geographic region. [16] One or more processors according to any one of claims 11-15, wherein the data set comprises at least one of the following: sensor data, log data, telemetry data, simulated data or development data. [17] One or more processors according to any one of claims 11-16, wherein the set of upload control parameters is calculated based on at least one of the following: receipt of one or more new uploads associated with the geographical region or a frequency associated with updating the set of upload control parameters. [18] One or more processors according to any one of claims 11-17, wherein one or more processors comprise at least one of the following features: Control system for an autonomous or semi-autonomous machine; Perception system for an autonomous or semi-autonomous machine; System for performing one or more simulation operations; System for performing one or more digital twin operations; System for performing light transport simulations; System for collaborative content creation for 3D assets; System for performing one or more deep learning operations; System implemented with an edge device; System for generating or displaying virtual reality, augmented reality or mixed reality content; System implemented with the help of a robot; System for performing one or more conversational AI operations; System for performing one or more generative AI operations; System that implements one or more large language models (LLMs); System for generating synthetic data; System that contains one or more virtual machines (VMs); System that is at least partially implemented in a data center; or System implemented at least in part using cloud computing resources. [19] System comprising: one or more processors for performing operations, comprising: Calculating a set of upload control parameters for a geographic region based on at least one set of attributes associated with a set of data uploaded using a set of location-based systems in a geographic region; Sending the set of upload control parameters within one or more control layers contained in the map data for the geographical region to a location-based system and in response to a request specifying the geographical region from the location-based system, wherein the location-based system controls the upload of additional data associated with the geographic region based at least on the one or more control layers. [20] The system of claim 19, wherein the system comprises at least one of the following elements: Control system for an autonomous or semi-autonomous machine; Perception system for an autonomous or semi-autonomous machine; System for performing one or more simulation operations; System for performing one or more digital twin operations; System for performing light transport simulations; System for collaborative content creation for 3D assets; System for performing one or more deep learning operations; System implemented with an edge device; System for generating or displaying virtual reality, augmented reality or mixed reality content; System implemented with the help of a robot; System for performing one or more conversational AI operations; System for performing one or more generative AI operations; System that implements one or more large language models (LLMs); System for generating synthetic data; System that contains one or more virtual machines (VMs); System that is at least partially implemented in a data center; or System implemented at least in part using cloud computing resources.

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