Aggregation of data representing geographic region

Abstract

Methods and systems are provided for aggregating data associated with various geographic regions for trajectory determination and high definition map generation. The methods and systems may include obtaining first data associated with a first area external to the vehicle, converting the first data associated with the first area from a first format to a second format, transmitting the converted first data and a query associated with the second area, receiving second data specific to the second area in response to the inquiry, aggregating the second data specific to the second area with the first data, determining, using at least one processor, a trajectory of the vehicle within the physical space based on the aggregation of the second data with the first data, and / or generating a graphical representation for use by a display of the vehicle based on second data aggregated with the first data.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of priority under 35 U.S.C.§ 119(e) to U.S. Provisional Application No. 63 / 416,463, filed on October 14, 2022, entitled "GRAPHICAL REPRESENTATION GENERATION BASED ON AGGREGATION OF PROCESSED DATA OF GEOGRAPHICAL AREAS" and U.S. Utility Application No. 18 / 091,786, filed on December 30, 2022, entitled "AGGREGATION OF DATA REPRESENTING GEOGRAPHICAL AREAS", the subject matter of which is hereby incorporated by reference in its entirety. Background Art

[0003] Vehicles such as autonomous vehicles use sensors to detect data of various objects within a specific proximity of their surrounding environment and use this data to generate high - definition maps for navigation. In addition, autonomous vehicles can access data from various sources external to these vehicles to assist in navigation. Brief Description of the Drawings

[0004] Figure 1 is an example environment of a vehicle that can implement one or more components of an autonomous system;

[0005] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0006] Figure 3 is Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0007] Figure 4 is a diagram of certain components of an autonomous system;

[0008] Figure 5 depicts an example embodiment of a system configured to aggregate processed data of various geographical areas for the purpose of determining the trajectory of a vehicle in physical space and generating one or more graphical representations for use on a display of the vehicle;

[0009] Figure 6A depicts an example implementation of the system of the present disclosure according to one or more embodiments described and illustrated herein, in which two vehicles obtain and process data of different geographical areas;

[0010] Figure 6B Depicts an example embodiment in accordance with one or more embodiments described and illustrated herein, in which a vehicle transmits an interrogation and receives a response that enables determination of information related to an object outside the field of view of a camera of the vehicle;

[0011] Figure 7 Depicts an example of generating and outputting a high-definition map on a display of a vehicle; and

[0012] Figure 8 Is a flowchart of a process for aggregating processed data of various geographic regions for the purpose of determining a trajectory of a vehicle in physical space and generating one or more graphical representations for use on a display of the vehicle. Detailed Description

[0013] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, that the embodiments described herein may be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0014] In the drawings, for ease of description, a specific arrangement or order of illustrative elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that unless explicitly described, the specific order or arrangement of the illustrative elements in the drawings is not intended to imply a required processing order or sequence, or separation of processes. Additionally, unless explicitly described, including illustrative elements in the drawings is not intended to imply that such elements are required in all embodiments, nor that the features represented by such elements cannot be included in some embodiments or combined with other elements in some embodiments.

[0015] Furthermore, in the drawings, connecting elements (such as solid lines, dashed lines, or arrows, etc.) are used to illustrate a connection, relationship, or association between two or more other illustrative elements or among them. The absence of any such connecting element is not intended to imply that a connection, relationship, or association cannot exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the present disclosure. Additionally, for ease of illustration, a single connecting element may be used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents the communication of a signal, data, or instruction (e.g., "software instruction"), those skilled in the art should understand that such an element may represent one or more signal paths (e.g., a bus) required for the communication.

[0016] Although terms such as "first", "second", and / or "third" are used to describe various elements, these elements should not be limited by these terms. The terms "first", "second", and / or "third" are only used to distinguish one element from another. For example, without departing from the scope of the described embodiments, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact.

[0017] The terms used in the description of the various embodiments herein are included only for the purpose of describing a particular embodiment and are not intended to be limiting. As used in the description of the various embodiments and the appended claims, the singular forms "a", "an", and "the" are also intended to include the plural forms and may be used interchangeably with "one or more than one" or "at least one", unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that when the terms "comprises", "comprising", "includes", and / or "having" are used in this specification, it specifies the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0018] As used herein, the terms "communicate" and "communicating" refer to at least one of receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof, etc.) that is to communicate with another unit, this means that the unit can directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is essentially wired and / or wireless. Additionally, even if the information transmitted can be modified, processed, relayed, and / or routed between a first unit and a second unit, the two units can still communicate with each other. For example, even if a first unit receives information passively and does not actively transmit information to a second unit, the first unit can still communicate with the second unit. As another example, if at least one intermediate unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message may refer to a network packet (e.g., a data packet, etc.) that includes data.

[0019] As used herein, depending on the context, the term "if" is optionally interpreted to mean "when", "at the time of", "in response to determining that", and / or "in response to detecting", etc. Similarly, depending on the context, the phrase "if it has been determined" or "if [the stated condition or event] is detected" is optionally interpreted to mean "at the time of determining...", "in response to determining that", or "at the time of detecting [the stated condition or event]" and / or "in response to detecting [the stated condition or event]", etc. Further, as used herein, the terms "has", "having", or "owns", etc. are intended to be open-ended terms. Additionally, unless otherwise expressly stated, the phrase "based on" is intended to mean "at least in part based on".

[0020] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0021] General Overview

[0022] Aspects and / or embodiments, systems, methods, and computer program products described herein relate to a vehicle that determines a trajectory of the vehicle in physical space and generates a map based on an aggregation of data related to visible street portions of the vehicle and data related to non-visible street portions of the vehicle. The generated map enables a first vehicle to identify objects (e.g., pedestrians located near a building corner) that are difficult for a camera of the first vehicle to obtain or are outside the visual range of the camera. Data processing that occurs locally at each vehicle (e.g., which includes the first vehicle) before transmission to a server or one or more additional vehicles includes steps for converting captured data from, for example, JPEG, RAW, or PNG format into a specific numerical representation (e.g., floating-point values). The floating-point values are numerical or digital representations that are utilized to describe characteristics of one or more objects included in the image data associated with the image. In some aspects, the floating-point values represent or characterize the position, orientation, and various attributes of objects in images and video streams, etc. In some aspects, the floating-point values represent the coordinates of the object (e.g., x coordinate, y coordinate, z coordinate) and the width and length of the object, etc. In some aspects, the objects included in the captured images are, for example, pedestrians, buildings, traffic lights, and traffic signs, etc. The floating-point values also represent the speed of the object in the x direction, y direction, or z direction. Additionally, data related to non-visible street portions of the vehicle is obtained from an external source such as a server, which in turn may have obtained the data from a second vehicle that previously traveled in the vicinity of the street portion. Alternatively, in some embodiments, the first vehicle directly obtains data related to non-visible street portions of the first vehicle from the second vehicle, and the second vehicle operates to convert captured image data of an area in the vicinity of the second vehicle into floating-point values. Then, the second vehicle directly transmits these floating-point values to the first vehicle.

[0023] Implementations of the systems, methods, and computer program products described herein provide numerous advantages for technologies that are used to determine, for example, the trajectory of a vehicle in physical space based on the aggregation of processed data from various geographic regions and to generate one or more graphical representations for use on a display of the vehicle. Some of these advantages include enabling the vehicle to determine a driving trajectory and to generate in real time an accurate high-definition ("HD") map of areas beyond the visual range of cameras deployed on the vehicle and / or areas that are difficult to obtain with such cameras, for example, while avoiding blind spots, obstructions, and various network inaccuracies. Additionally, the step of locally processing the captured data by each vehicle prior to transmitting the data facilitates the efficient sharing of a large amount of information related to objects present in various geographic regions. In particular, the processing of the captured data involves steps for transforming the data captured by a camera (e.g., converting the data from an image data format to floating-point values). Such transformation facilitates resource-efficient data sharing. Further, implementations of the systems, methods, and computer program products described herein enable the sharing of image data in a unified data structure (i.e., in the form of floating-point values representing data such as objects and maps).

[0024] Now refer to Figure 1 , to illustrate example environment 100, in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, environment 100 includes vehicles 102a - 102n, objects 104a - 104n, routes 106a - 106n, region 108, vehicle-to-infrastructure (V2I) devices 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118. Vehicles 102a - 102n, vehicle-to-infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via a wired connection, a wireless connection, or a combination of wired and wireless connections (e.g., establishing a connection for communication, etc.). In some embodiments, objects 104a - 104n are interconnected with at least one of vehicles 102a - 102n, vehicle-to-infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0025] Vehicles 102a - 102n (individually referred to as vehicle 102 and collectively referred to as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicle 102 is configured to communicate with V2I device 110, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, vehicle 102 includes a car, a bus, a truck, and / or a train, etc. In some embodiments, vehicle 102 is the same as or similar to vehicle 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicle 102 travels along corresponding routes 106a - 106n (individually referred to as route 106 and collectively referred to as routes 106). In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[0026] Objects 104a - 104n (individually referred to as object 104 and collectively referred to as objects 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., a building, a sign, a fire hydrant, etc.). Each object 104 (e.g., located at a fixed location and over a period of time) is stationary or (e.g., has a speed and is associated with at least one trajectory) moving. In some embodiments, object 104 is associated with a corresponding location in region 108.

[0027] Routes 106a - 106n (individually referred to as Route 106 and collectively as Routes 106) are each associated with (e.g., define) a series of actions (also referred to as a trajectory) along which an AV can be navigated. Each Route 106 begins at an initial state (e.g., a state corresponding to a first spatio - temporal location and / or speed, etc.) and ends at a final target state (e.g., a state corresponding to a second spatio - temporal location different from the first) or a target zone (e.g., a subspace of acceptable states (e.g., a termination state)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or zone includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, Route 106 includes multiple acceptable state sequences (e.g., multiple spatio - temporal location sequences) that are associated with (e.g., define) multiple trajectories. In an example, Route 106 includes only high - level actions or imprecise state locations, such as a series of connected roads indicating a direction change at a roadway intersection, etc. Additionally or alternatively, Route 106 can include more precise actions or states, such as, for example, a specific target lane or precise location within a lane region and a target rate at those locations. In an example, Route 106 includes multiple precise state sequences along at least one high - level action with a finite look - ahead horizon to reach an intermediate target, where the combination of successive iterations of the finite - horizon state sequences cumulatively corresponds to multiple trajectories that together form a high - level route terminating at the final target state or zone.

[0028] Region 108 includes a physical area (e.g., a geographic area) in which vehicle 102 can be navigated. In an example, Region 108 includes at least one state (e.g., a country, a province, an individual state among multiple states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, Region 108 includes at least one named arterial road (referred to herein as a “road”), such as a highway, an interstate highway, a parkway, an urban street, etc. Additionally or alternatively, in some examples, Region 108 includes at least one unnamed road, such as a lane, a section of a parking lot, a section of an open space and / or undeveloped area, a dirt road, etc. In some embodiments, a road includes at least one lane (e.g., the portion of the road through which vehicle 102 can pass). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0029] A vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-infrastructure or vehicle-to-everything (V2X) device) includes at least one device configured to communicate with a vehicle 102 and / or a V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, a remote AV system 114, a queue management system 116, and / or the V2I system 118 via a network 112. In some embodiments, the V2I device 110 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), lane markings, street lights, a parking meter, etc. In some embodiments, the V2I device 110 is configured to communicate directly with the vehicle 102. Additionally or alternatively, in some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, and / or the queue management system 116 via the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the V2I system 118 via the network 112.

[0030] The network 112 includes one or more wired and / or wireless networks. In an example, the network 112 includes a cellular network (e.g., a Long-Term Evolution (LTE) network, a third-generation (3G) network, a fourth-generation (4G) network, a fifth-generation (5G) network, a Code Division Multiple Access (CDMA) network, etc.), a Public Land Mobile Network (PLMN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a telephone network (e.g., a Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber-based network, a cloud computing network, etc., and / or a combination of some or all of these networks.

[0031] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the queue management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the queue management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including an autonomous system, an autonomous vehicle computer, and / or software implemented by the autonomous vehicle computer). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0032] The queue management system 116 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the V2I system 118. In an example, the queue management system 116 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including autonomous systems and / or vehicles not including autonomous systems), etc.).

[0033] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the queue management system 116 via the network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection different from the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipal authority or a private institution (e.g., a private institution for maintaining the V2I device 110, etc.).

[0034] Provide Figure 1 The number and arrangement of the illustrated elements are provided as an example. Compared with Figure 1 the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements in a different arrangement. Additionally or alternatively, at least one element of the environment 100 may perform one or more functions described as being performed by Figure 1 at least one different element. Additionally or alternatively, at least one set of elements of the environment 100 may perform one or more functions described as being performed by at least one different set of elements of the environment 100.

[0035] Now referring to Figure 2 , the vehicle 200 (which may be the same as or similar to Figure 1 the vehicle 102) includes an autonomous system 202, a powertrain control system 204, a steering control system 206, and a braking system 208, or is associated with the autonomous system 202, the powertrain control system 204, the steering control system 206, and the braking system 208. In some embodiments, the vehicle 200 is the same as the vehicle 102 (see Figure 1)Same or similar. In some embodiments, the autonomous system 202 is configured to endow the vehicle 200 with autonomous driving capabilities (e.g., implement at least one of the following driving functions, features, and / or devices that are automatic or based on maneuvering actions, and the at least one driving function, feature, and / or device enables the vehicle 200 to operate partially or fully without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations, such as level 3 ADS-operated vehicles, etc.), etc.). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to enable the vehicle 200 to operate in road traffic and continuously perform part or all of the dynamic driving task (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) that includes driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference can be made to SAE International standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire content of which is incorporated by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ridesharing company.

[0036] The autonomous system 202 includes a sensor suite that includes one or more devices such as a camera 202a, a LiDAR sensor 202b, a Radar sensor 202c, and a microphone 202d. In some embodiments, the autonomous system 202 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance the vehicle 200 has traveled, etc.). In some embodiments, the autonomous system 202 uses one or more devices included in the autonomous system 202 to generate data associated with the environment 100 described herein. The data generated by one or more devices of the autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which the vehicle 200 is located. In some embodiments, the autonomous system 202 includes a communication device 202e, an autonomous vehicle computing 202f, a drive-by-wire (DBW) system 202h, and a safety controller 202g.

[0037] The camera 202a includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus 302 that is the same as or similar to Figure 3 the bus). The camera 202a includes at least one camera (e.g., a digital camera using an optical sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, and / or an event camera, etc.) for capturing images including physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, the camera 202a generates camera data as output. In some examples, the camera 202a generates camera data including image data associated with the image. In such an example, the image data may specify at least one parameter corresponding to the image (e.g., image characteristics such as exposure, brightness, etc., and / or an image timestamp, etc.). In such an example, the image may be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a includes a plurality of independent cameras configured (e.g., positioned) on the vehicle for capturing images for the purpose of stereoscopic vision (stereo vision). In some examples, the camera 202a includes generating image data and transmitting the image data to the autonomous vehicle computing 202f and / or a queue management system (e.g., the same as Figure 1Multiple cameras of the same or similar queue management system as queue management system 116. In such an example, the autonomous vehicle computing 202f determines the depth to one or more objects in the fields of view of at least two of the multiple cameras based on image data from at least two cameras. In some embodiments, the camera 202a is configured to capture images of objects within a distance relative to the camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Thus, the camera 202a includes features such as sensors and lenses that are optimized for sensing objects at one or more distances relative to the camera 202a.

[0038] In an embodiment, the camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs, and / or other physical objects that provide visual navigation information. In some embodiments, the camera 202a generates traffic light data associated with one or more images. In some examples, the camera 202a generates TLD (Traffic Light Detection) data associated with one or more images including a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a that generates TLD data is different from other systems incorporating cameras described herein in that the camera 202a may include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fish-eye lens, and / or a lens with a viewing angle of about 120 degrees or greater, etc.) to generate images related to as many physical objects as possible.

[0039] The Light Detection and Ranging (LiDAR) sensor 202b includes being configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., with Figure 3at least one device configured to communicate via a bus (e.g., a bus identical or similar to bus 302). The LiDAR sensor 202b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object it encounters. The LiDAR sensor 202b also includes at least one light detector that detects the light after the light emitted from the light emitter encounters a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing the objects included in the field of view of the LiDAR sensor 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In such examples, the image is used to determine the boundary of the physical object in the field of view of the LiDAR sensor 202b.

[0040] A Radio Detection and Ranging (Radar) sensor 202c includes at least one device configured to communicate with a communication device 202e, an autonomous vehicle computer 202f, and / or a safety controller 202g via a bus (e.g., a bus identical or similar to Figure 3 bus 302). The Radar sensor 202c includes a system configured to emit (pulsed or continuous) radio waves. The radio waves emitted by the Radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves emitted by the Radar sensor 202c encounter a physical object and are reflected back to the Radar sensor 202c. In some embodiments, the radio waves emitted by the Radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with the Radar sensor 202c generates a signal representing the objects included in the field of view of the Radar sensor 202c. For example, at least one data processing system associated with the Radar sensor 202c generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundary of the physical object in the field of view of the Radar sensor 202c.

[0041] The microphone 202d includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus the same or similar to Figure 3 bus 302). The microphone 202d includes one or more microphones (e.g., an array microphone and / or an external microphone, etc.) that capture an audio signal and generate data associated with (e.g., representing) the audio signal. In some examples, the microphone 202d includes a transducer device and / or a similar device. In some embodiments, one or more of the systems described herein may receive the data generated by the microphone 202d and determine the position (e.g., distance, etc.) of an object relative to the vehicle 200 based on the audio signal associated with the data.

[0042] The communication device 202e includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the DBW (drive-by-wire) system 202h. For example, the communication device 202e may include a device the same or similar to Figure 3 communication interface 314. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device for enabling wireless communication of data between vehicles).

[0043] The autonomous vehicle computing 202f includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the safety controller 202g, and / or the DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as a client device, a mobile device (e.g., a cellular phone and / or a tablet, etc.), and / or a server (e.g., a computing device including one or more central processing units and / or graphics processing units, etc.). In some embodiments, the autonomous vehicle computing 202f is the same or similar to the autonomous vehicle computing 400 described herein. Additionally or alternatively, in some embodiments, the autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to Figure 1 the remote AV system 114), a queue management system (e.g., a queue management system the same or similar to Figure 1 queue management system 116), a V2I device (e.g., a V2I device the same or similar to Figure 1 V2I device 110), and / or a V2I system (e.g., a V2I system the same or similar to Figure 1communicate with a V2I system 118 that is the same as or similar to the V2I system).

[0044] The safety controller 202g includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the autonomous vehicle computing 202f, and / or the DBW system 202h. In some examples, the safety controller 202g includes one or more controllers (such as an electrical controller and / or an electromechanical controller, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). In some embodiments, the safety controller 202g is configured to generate control signals that take precedence over (e.g., override) the control signals generated and / or transmitted by the autonomous vehicle computing 202f.

[0045] The DBW system 202h includes at least one device configured to communicate with the communication device 202e and / or the autonomous vehicle computing 202f. In some examples, the DBW system 202h includes one or more controllers (such as an electrical controller and / or an electromechanical controller, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). Additionally or alternatively, one or more controllers of the DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device of the vehicle 200 (such as turn signals, headlights, door locks, and / or windshield wipers, etc.).

[0046] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives control signals from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to perform longitudinal vehicle movements (such as starting to move forward, stopping moving forward, starting to move backward, stopping moving backward, accelerating in a certain direction, decelerating in a certain direction, etc.) or lateral vehicle movements (such as making a left turn and / or making a right turn, etc.). In an example, the powertrain control system 204 increases, maintains the same, or decreases the energy (such as fuel and / or electricity, etc.) provided to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0047] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates two front wheels and / or two rear wheels of the vehicle 200 left or right to turn the vehicle 200 left or right. In other words, the steering control system 206 causes the activities required to regulate the y-axis component of the vehicle's movement.

[0048] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or hold the vehicle 200 stationary. In some examples, the braking system 208 includes at least one controller and / or actuator configured to close one or more calipers associated with one or more wheels of the vehicle 200 on the corresponding rotors of the vehicle 200. Additionally or alternatively, in some examples, the braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0049] In some embodiments, the vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring the nature of the state or condition of the vehicle 200. In some examples, the vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor. Although the braking system 208 is illustrated as being proximal to the vehicle 200 Figure 2 in, the braking system 208 can be located anywhere in the vehicle 200.

[0050] Now refer to Figure 3, A schematic diagram of an exemplary device 300. As illustrated, device 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of the system of vehicle 102); at least one device of vehicle 200 (e.g., at least one device of the system of vehicle 200); and / or one or more devices of network 112 (e.g., one or more devices of the system of network 112). In some embodiments, one or more devices of vehicle 102 (e.g., at least one device of the system of vehicle 102), one or more devices of vehicle 200, and / or one or more devices of network 112 (e.g., one or more devices of the system of network 112) include at least one device 300 and / or at least one component of device 300. As Figure 3 shown, device 300 includes a bus 302, a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, and a communication interface 314.

[0051] Bus 302 includes components that permit communication between the components of device 300. In some cases, processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application specific integrated circuit (ASIC), etc.). Memory 306 includes random access memory (RAM), read only memory (ROM), and / or another type of dynamic and / or static storage device that stores data and / or instructions for use by processor 304 (e.g., flash memory, magnetic memory, and / or optical memory, etc.).

[0052] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cassette tape, a magnetic tape, a CD-ROM, a RAM, a PROM, an EPROM, a FLASH-EPROM, an NV-RAM, and / or another type of computer readable medium, and corresponding drives.

[0053] The input interface 310 includes components of the enabling device 300 that receive information via a user input (e.g., a touchscreen display, keyboard, keypad, mouse, button, switch, microphone, and / or camera, etc.). Additionally or alternatively, in some embodiments, the input interface 310 includes sensors for sensing information (e.g., a Global Positioning System (GPS) receiver, accelerometer, gyroscope, and / or actuator, etc.). The output interface 312 includes components for providing output information from the device 300 (e.g., a display, speaker, and / or one or more Light Emitting Diodes (LEDs), etc.).

[0054] In some embodiments, the communication interface 314 includes transceiver-like components that enable the device 300 to communicate with other devices via a wired connection, wireless connection, or a combination of wired and wireless connections (e.g., a transceiver and / or separate receiver and transmitter, etc.). In some examples, the communication interface 314 enables the device 300 to receive information from another device and / or provide information to another device. In some examples, the communication interface 314 includes an Ethernet interface, optical interface, coaxial interface, infrared interface, Radio Frequency (RF) interface, Universal Serial Bus (USB) interface, interface, and / or a cellular network interface, etc.

[0055] In some embodiments, the device 300 performs one or more of the processes described herein. The device 300 performs these processes based on software instructions stored in a computer-readable medium such as the memory 306 and / or the storage component 308 by the processor 304. A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes a storage space located within a single physical storage device or a storage space distributed across multiple physical storage devices.

[0056] In some embodiments, software instructions are read into the memory 306 and / or the storage component 308 from another computer-readable medium or from another device via the communication interface 314. When executed, the software instructions stored in the memory 306 and / or the storage component 308 cause the processor 304 to perform one or more of the processes described herein. Additionally or alternatively, hardwired circuitry is used instead of or in combination with software instructions to perform one or more of the processes described herein. Thus, unless otherwise explicitly stated, the embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0057] The memory 306 and / or the storage component 308 includes a data storage or at least one data structure (e.g., a database, etc.). The apparatus 300 is capable of receiving information from the data storage or at least one data structure in the memory 306 or the storage component 308, storing the information in the data storage or at least one data structure, communicating the information to the data storage or at least one data structure, or searching for the information stored in the data storage or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0058] In some embodiments, the apparatus 300 is configured to execute software instructions stored in the memory 306 and / or the memory of another apparatus (e.g., another apparatus that is the same as or similar to the apparatus 300). As used herein, the term "module" refers to at least one instruction stored in the memory 306 and / or the memory of another apparatus, which, when executed by the processor 304 and / or the processor of another apparatus (e.g., another apparatus that is the same as or similar to the apparatus 300), causes the apparatus 300 (e.g., at least one component of the apparatus 300) to perform one or more than one process described herein. In some embodiments, the module is implemented in software, firmware, and / or hardware, etc.

[0059] Provided Figure 3 The number and arrangement of the illustrated components are by way of example. In some embodiments, compared with Figure 3 the illustrated components, the apparatus 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a set of components of the apparatus 300 (e.g., one or more than one component) may perform one or more than one function described as being performed by another component or another set of components of the apparatus 300.

[0060] Now refer to Figure 4, an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack") is illustrated. As illustrated, the autonomous vehicle computing 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a localization system 406 (sometimes referred to as a localization module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in and / or implemented in an automatic navigation system of the vehicle (e.g., the autonomous vehicle computing 202f of the vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in one or more independent systems (e.g., one or more systems identical or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in one or more independent systems located in the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 400 are implemented in software (e.g., software instructions stored in a memory), computer hardware (e.g., via a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), and / or a field programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. It will also be understood that, in some embodiments, the autonomous vehicle computing 400 is configured to communicate with remote systems (e.g., an autonomous vehicle system identical or similar to the remote AV system 114, a queue management system identical or similar to the queue management system 116, and / or a V2I system identical or similar to the V2I system 118, etc.).

[0061] In some embodiments, the perception system 402 receives data associated with at least one physical object in the environment (e.g., data used by the perception system 402 to detect at least one physical object), and classifies the at least one physical object. In some examples, the perception system 402 receives image data captured by at least one camera (e.g., camera 202a), the image being associated with one or more physical objects within the field of view of the at least one camera (e.g., representing the one or more physical objects). In such examples, the perception system 402 classifies the at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of the physical objects by the perception system 402, the perception system 402 transmits data associated with the classification of the physical objects to the planning system 404.

[0062] In some embodiments, the planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel toward the destination. In some embodiments, the planning system 404 periodically or continuously receives data from the perception system 402 (e.g., the data associated with the classification of the physical objects described above), and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 402. In other words, the planning system 404 can perform tasks related to the tactical functions required to operate the vehicle 102 in road traffic. Tactical efforts involve maneuvering the vehicle in traffic during the journey, which includes but is not limited to deciding whether and when to overtake another vehicle, change lanes, or select an appropriate speed, acceleration, deceleration, etc. In some embodiments, the planning system 404 receives data associated with the updated position of the vehicle (e.g., vehicle 102) from the positioning system 406, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 406.

[0063] In some embodiments, the positioning system 406 receives data associated with (e.g., indicative of) the location of a vehicle (e.g., vehicle 102) in an area. In some examples, the positioning system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 202b). In certain examples, the positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 406 generates a combined point cloud based on the respective point clouds. In these examples, the positioning system 406 compares the at least one point cloud or the combined point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in the database 410. Then, based on the positioning system 406 comparing the at least one point cloud or the combined point cloud with the map, the positioning system 406 determines the location of the vehicle in the area. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of roadway geometry, a map depicting the connectivity of the road network, a map depicting the physical properties of roadways (such as traffic speed, traffic flow, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction or the type and location of lane markings, or a combination thereof, etc.), and a map depicting the spatial location of road features (such as crosswalks, traffic signs or various other types of driving signal lights, etc.). In some embodiments, the map is generated in real time based on data received by the perception system.

[0064] In another example, the positioning system 406 receives Global Navigation Satellite System (GNSS) data generated by a Global Positioning System (GPS) receiver. In some examples, the positioning system 406 receives GNSS data associated with the location of a vehicle in an area, and the positioning system 406 determines the latitude and longitude (i.e., coordinates) of the vehicle in the area. In such examples, the positioning system 406 determines the location of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, the positioning system 406 generates data associated with the location of the vehicle. In some examples, based on the positioning system 406 determining the location of the vehicle, the positioning system 406 generates data associated with the location of the vehicle. In such examples, the data associated with the location of the vehicle includes data associated with one or more semantic properties corresponding to the location of the vehicle.

[0065] In some embodiments, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle. In some examples, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle by generating and transmitting control signals to cause the powertrain control system (e.g., the DBW system 202h and / or the powertrain control system 204, etc.), the steering control system (e.g., the steering control system 206), and / or the braking system (e.g., the braking system 208) to operate. For example, the control system 408 is configured to perform operational functions such as lateral vehicle motion control or longitudinal vehicle motion control. Lateral vehicle motion control causes activities required to regulate the y-axis component of the vehicle motion. Longitudinal vehicle motion control causes activities required to regulate the x-axis component of the vehicle motion. In an example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.) to change states.

[0066] In some embodiments, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model individually or in combination with one or more of the above systems. In some examples, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in the environment, etc.).

[0067] The database 410 stores data transmitted to, received from, and / or updated by the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408. In some examples, the database 410 includes a storage component of at least one system for storing operation-related data and / or software and using the autonomous vehicle computing 400 (e.g., associated with Figure 3the same or similar storage components as the storage component 308). In some embodiments, the database 410 stores data associated with 2D and / or 3D maps of at least one region. In some examples, the database 410 stores data associated with 2D and / or 3D maps of a part of a city, multiple parts of multiple cities, multiple cities, counties, states, and / or countries (e.g., nations), etc. In such examples, a vehicle (e.g., a vehicle the same or similar to the vehicle 102 and / or the vehicle 200) can drive along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, back roads, and / or off-road paths, etc.), and cause at least one LiDAR sensor (e.g., a LiDAR sensor the same or similar to the LiDAR sensor 202b) to generate data associated with an image representing the objects included in the field of view of the at least one LiDAR sensor.

[0068] In some embodiments, the database 410 can be implemented across multiple devices. In some examples, the database 410 is included in a vehicle (e.g., a vehicle the same or similar to the vehicle 102 and / or the vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to the remote AV system 114), a queue management system (e.g., a queue management system the same or similar to Figure 1 the queue management system 116) and / or a V2I system (e.g., a V2I system the same or similar to Figure 1 the V2I system 118), etc.

[0069] Figure 5 An example embodiment of a system depicting a system configured to aggregate processed data of various geographic regions is described. The aggregated data can be used, for example, to determine the trajectory of a vehicle within physical space or to generate one or more graphical representations for use on a display of the vehicle. In an embodiment, Figure 5Depict multiple vehicles 502, 504, 506, and 508, which are communicatively coupled to each other and to a server 514 via, for example, a network 112. In an embodiment, the network 112 utilizes communication protocols based on, for example, 5G and C-V2X. Broadly speaking, each of the vehicles 502, 504, 506, and 508 may include a plurality of cameras disposed thereon, which are configured to capture images of various objects within the respective surrounding environments of these vehicles. For example, each of the vehicles 502, 504, 506, and 508 captures images of various objects (such as pedestrians, traffic lights, additional vehicles, and building signs, etc.), and stores the captured data (such as image data) in the database 410 of each of these vehicles in various formats (such as RAW format, JPEG format, and PNG format, etc.).

[0070] In addition, the captured data is each associated with a timestamp or a predefined time period. In an embodiment, the data captured by the respective cameras of each of the vehicles 502, 504, 506, and 508 is further processed by a perception system 402 of an autonomous vehicle computing 400 ( Figure 4 ) included as part of each of the vehicles 502, 504, 506, and 508. The perception system 402 operates to classify one or more of the various objects within the surrounding environment of each vehicle based on various groupings. For example, an object can be classified as a bicycle, a pedestrian, a traffic light, another vehicle, a building sign, etc. In an embodiment, the perception system 402 operates to convert the format of the image data associated with the captured image (such as RAW format, JPEG format, and PNG format, etc.) into a numerical representation (such as a floating-point value), and stores these floating-point values in the database 410 of the autonomous vehicle computing 400. In an embodiment, these floating-point values are also routed by the autonomous vehicle computing 400 to a communication device 202e (included as part of each of the vehicles 502, 504, 506, and 508), which communicates these floating-point values to other vehicles and / or the server 514 via, for example, the network 112. Note that the server 514 is the same as or equivalent to a remote autonomous vehicle (AV) system 114, a queue management system, and / or a V2I system.

[0071] In an example operation of the system of the present disclosure, the communication device 202e of vehicle 502 transmits an interrogation directly to one or more of vehicles 504, 506, and 508 via network 112 to determine information related to the area around vehicles 504, 506, and 508. In particular, the interrogation may include a request for information related to objects within a specific proximity range (e.g., 25 - 50 meters) of each of the vehicles 504, 506, and 508. In an embodiment, vehicle 502 transmits such an interrogation via network 112 to server 514, which in turn transmits the interrogation to each of vehicles 504, 506, and 508. In response, the sensing systems in each of vehicles 504, 506, and 508 capture image data of their respective surrounding environments (e.g., in the nearby range of 25 - 50 meters), classify the various objects included in the image data according to various groupings (e.g., objects may be classified as bicycles, pedestrians, traffic lights, other vehicles, and construction signs, etc.), and convert the format of the image data from, for example, RAW format, JPEG format, and PNG format, etc. to a numerical format such as floating-point values. In an embodiment, the autonomous vehicle computing 400 routes the generated floating-point values from the sensing system 402 to the communication device 202e installed in each of vehicles 504, 506, and 508. In an embodiment, then, the communication device 202e of each of vehicles 504, 506, and 508 transmits these floating-point values, for example, directly to the communication device 202e of vehicle 502 (the vehicle that requested the interrogation), or transmits these floating-point values to the communication device 202e via server 514.

[0072] In an embodiment, after the communication device 202e of vehicle 502 receives floating point values from each of vehicles 504, 506, and 508, these values are routed to the sensing system 402 and database 410 of vehicle 502. In an embodiment, the sensing system 402 converts the floating point values into various image formats (e.g., RAW format, JPEG format, PNG format, etc.), and thereby can access information related to various objects within the vicinity (e.g., 25 - 50 meters) of each of vehicles 504, 506, and 508. In an embodiment, the sensing system 402, operating either alone or in conjunction with the planning system 404, operates to aggregate the image data of each of vehicles 504, 506, and 508 together in addition to aggregating the image data of each of vehicles 504, 506, and 508 with the image data captured by the cameras of vehicle 502. Then, the aggregated data is routed by the sensing system 402 of vehicle 502 to the planning system 404 of vehicle 502, and the aggregated data incorporates data of objects included in the surrounding environment of vehicles 504, 506, and 508 as well as data of objects included in the surrounding environment of vehicle 502. In an embodiment, the transmission of queries made by vehicle 502 and the transmission and reception of data (e.g., floating point values) are performed using one or more Internet devices based on the Global System for Mobile Communications (“GSM”) module.

[0073] In an embodiment, the planning system 404 utilizes the aggregated image data to determine the trajectory of vehicle 502 from a source location to a destination location. Additionally, in an embodiment, vehicle 502 periodically (e.g., directly or via a server) transmits a plurality of such queries to other vehicles in order to receive data (in the form of floating point values) of the surrounding environments of vehicles 504, 506, and 508 at various locations along the determined specific trajectory. Based on the responses to these periodic queries, the sensing system 402 can also aggregate the image data approximately in real-time and route the image data to the planning system 404, which in turn operates to plan the vehicle trajectory or modify an existing trajectory based on the aggregated data. In an embodiment, the aggregated data is output approximately in real-time on the display of vehicle 502. In an embodiment, note that, as described above, vehicles 504, 506, and 508 can also transmit queries, aggregate data, and determine trajectories in a manner similar to vehicle 502, directly to other vehicles.

[0074] Figure 6AAn example implementation of a system of the present disclosure depicting one or more embodiments described and illustrated herein, in which two vehicles acquire and process data from different geographical regions. As illustrated, vehicle 508 is shown traveling through a particular intersection adjacent to skyscrapers 604, 606. Additionally, vehicle 508 includes a plurality of sensors (e.g., cameras) disposed external to vehicle 508 that acquire external environment data by capturing images within a first range 608 and a second range 610 (e.g., 25 - 50 meters) around vehicle 508. Note that the value of 25 - 50 meters is for illustrative purposes only as the cameras can be designed to capture nearer or farther images. As illustrated, in the first range 608, vehicle 508 can capture images of children 609 and 611 at the intersection (e.g., the first region). Note that due to skyscrapers 604 and 606, the presence of these children 609 and 611 (e.g., one or more first objects) is blocked by skyscrapers 604 and 606.

[0075] After capturing the images, vehicle 508 locally stores the image data representative of these images in database 410 of autonomous vehicle computing 400 of vehicle 508. In some embodiments, the cameras capture images in the form of a live video stream of the surrounding environment. In some embodiments, as described above, the perception system 402 of vehicle 508 transforms the format of the captured images (which can be, for example, RAW format, JPEG format, PNG format, etc.) into a numerical format such as floating - point values. These floating - point values represent, in a memory - or resource - efficient manner, dimensions of various objects in the image, colors of the image, and other characteristics. Thereafter, instead of transmitting the captured images, vehicle 508 directly transmits the floating - point values to other vehicles 502, 504, and 506. In an embodiment, the floating - point values are transmitted to server 514, which routes the floating - point values via network 112 to each of vehicles 502, 504, and 506. In this way, vehicle 508 shares data related to various objects within a particular proximity of vehicle 508 with server 514 and / or other vehicles.

[0076] In Figure 6AIn the example shown, the vehicle 504 illustrated as traveling behind the vehicle 508 operates in a manner similar to the operation of the vehicle 508. For example, the vehicle 504 includes one or more sensors (such as cameras) disposed on the outer surface of the vehicle 504, and the one or more sensors obtain data on the environment around the vehicle 504 (such as within a third range 600 and a fourth range 602 around the vehicle 504 (e.g., within 25-50 meters) and the like). The fourth range 602 may include other vehicles 502 (e.g., a second object), and the third range 600 may be part of a skyscraper 604. In an embodiment, the vehicle 504 locally stores the data associated with the third range 600 and the fourth range 602 in, for example, the database 410 of the vehicle 504. Thereafter, the perception system 402 of the vehicle 504 transforms the data on the environment around the vehicle 504 from, for example, RAW format, JPEG format, and PNG format, etc., into a numerical format such as floating-point values. These floating-point values are transmitted by the vehicle 504 directly or via the server 514 to each of the other vehicles 502, 506, and 508 approximately in real time.

[0077] Figure 6B An exemplary embodiment is depicted in which the vehicle 504 transmits an inquiry to the server 514 to determine information related to various objects within a specific distance of a particular location (e.g., the location through which the vehicle 504 is traveling within a short time frame). In particular, the vehicle 504 may transmit a query to determine the types of objects present in the area 50 meters in front of the vehicle 504. As Figure 6A illustrated, such an area may correspond to a location previously traveled through by the vehicle 508. Accordingly, the data transmitted by the vehicle 508 may include information that can assist the vehicle 504 in effective navigation (i.e., information related to the presence of a child outside the visual range of the camera of the vehicle 504).

[0078] Upon receiving a query, server 514 transmits data received from vehicle 508 to vehicle 504, e.g., in real time. In some embodiments, upon receiving a query, the perception system 402 of vehicle 508 operates to transform image data captured by one or more cameras of vehicle 508 into floating-point values, which are then routed from the perception system 402 to the communication device 202e of vehicle 508. In an embodiment, the communication device 202e of vehicle 508 transmits the floating-point values to server 514, which then transmits these floating-point values to the communication device 202e of vehicle 504. In an embodiment, vehicle 504 may also communicate image data captured by one or more cameras included in vehicle 504 to server 514. Server 514 then aggregates the floating-point values received from vehicle 504 and vehicle 508 and transmits the aggregated floating-point values (aggregated data 620) to the querying vehicle (vehicle 504).

[0079] The aggregated data 620 is received by the communication device 202e of vehicle 504 and is routed to the perception system 402 of the autonomous vehicle computing 400 of vehicle 504. As described above, the perception system 402 operates to convert the received aggregated data 620 (e.g., all floating-point values associated with vehicle 504 and vehicle 508) into other image data formats (e.g., RAW format, JPEG format, and PNG format, etc.). The data in the image format is routed to the planning system 404 of vehicle 504. The planning system 404 uses the converted data to determine the trajectory of vehicle 504 within the physical space, modify an existing trajectory, and / or generate a high-definition map and use the map for navigation. In some embodiments, the map is output on a display included inside vehicle 504. In an embodiment, note that the respective high-definition maps generated by each vehicle are stored in server 514.

[0080] Alternatively, the communication device 202e of vehicle 508 directly transmits the floating-point value to the communication device 202e of other vehicles 504 (e.g., the inquiring vehicle). The vehicle 504 that receives the floating-point value from vehicle 508 uses the sensing system 402 to convert the floating-point value received from vehicle 508 into image data corresponding to the image format, and aggregates the converted image data with the image data captured by one or more cameras of vehicle 504. In this way, in this embodiment, vehicle 504 generates aggregated data 620, which includes image data from both vehicles 504 and 508. Thereafter, as described above, the aggregated data 620 is routed from the sensing system 402 to the planning system 404, which uses the converted data to determine the trajectory of vehicle 504 in physical space, modify the existing trajectory, and / or generate a high-definition map and use the map for navigation.

[0081] Note that the respective locations of vehicle 504 and vehicle 508 can be identified by using GPS. For example, both vehicles 504 and 508 use GPS to identify their respective locations, and operate to communicate their respective locations to each other, communicate their respective locations to other vehicles and the server 514, etc. In an embodiment, one or more locations of each of vehicle 504 and vehicle 508 can be broadcast, for example, at a preset interval, throughout the time that vehicles 504 and 508 are traveling. In addition, these locations are broadcast (e.g., at a preset interval or approximately in real time) to the server 514 that runs the publisher / subscriber software application.

[0082] In an embodiment, an operator associated with a vehicle can purchase a subscription to a publisher / subscriber software application, which enables the subscribed vehicle to access, near real-time, image data (e.g., floating point values) associated with the surroundings of multiple vehicles traveling at various locations. In this way, the publisher / subscriber software operates as part of a platform that enables the aggregation (unification) of data (e.g., floating point values) to augment current data (e.g., map data) generated by a particular vehicle. These vehicles operate to transmit data (e.g., floating point values) representing various objects within a particular proximity of each of these vehicles to server 514 and / or other vehicles. Then, a particular vehicle accesses data for the surrounding areas of all other vehicles traveling at various locations. Additionally, in an embodiment, the publisher / subscriber software application operates as part of a platform (e.g., a common application programming interface or (“API”)) that enables the sharing of image data in a unified data structure (i.e., in the form of floating point values representing data such as objects and maps). Note that the operations described in this disclosure can be performed using the autonomous vehicle computing 400 without using additional computing devices on each of these vehicles.

[0083] Figure 7 An example of a high-definition map generated and output on a display 702 included within vehicle 504 is depicted. In an embodiment, after receiving the aggregated data 620, a processor 304 of vehicle 504 can perform one or more transformation operations on the floating point values included in the aggregated data 620, the one or more transformation operations including converting the format of these floating point values into, for example, a RAW format, a JPEG format, or a PNG format representative of image and video streams, etc. As described above, such a conversion can be performed by the perception system 402 of vehicle 504. After such a conversion, the autonomous vehicle computing 400 utilizes the converted data to generate a high-definition map and output it on the display 702. As illustrated, the map output on the display 702 can include skyscrapers 604 and 606 at an intersection, vehicle 504, and children 609 and 611. Accordingly, vehicle 504 can now be aware that the intersection is a busy intersection, and / or that such an area has a likelihood of pedestrian collisions above a particular threshold.

[0084] Now refer to Figure 8, an example flowchart of a process for aggregating processed data of various geographic regions for the purpose of determining the trajectory of a vehicle in physical space and generating one or more graphical representations for use on a display of the vehicle. In some embodiments, one or more of the steps described for this process (e.g., fully and / or partially, etc.) are performed by the autonomous vehicle computing 400. Additionally or alternatively, in some embodiments, one or more of the steps described for this process (e.g., fully and / or partially, etc.) are performed by other devices or groups of devices that are separate from or include the autonomous vehicle computing 400.

[0085] Specifically, in block 802, first data associated with a first region external to the vehicle is obtained. In an embodiment, the vehicle 200 uses the camera 202a to obtain the first data associated with the first region external to the vehicle. The vehicle 200 is the same as or similar to Figure 1 the vehicle 102a.

[0086] In block 804, the first data associated with the first region is converted from a first format to a second format. The first format may correspond to, for example, JPEG, Raw, and PNG formats, etc., and the second format may correspond to, for example, a numerical representation of floating-point values. Note that other representations of each of the first and second formats are also considered. The perception system 402 of the autonomous vehicle computing 400 included as part of the vehicle 200 may perform the conversion of the first data from the first format to the second format.

[0087] In block 806, the converted first data and an inquiry associated with a second region external to the vehicle are transmitted to a server (e.g., the remote AV system 114, the queue management system 116, and / or the vehicle-to-infrastructure system 118), or directly transmitted to other vehicles. For example, the vehicle 102a may transmit an inquiry and floating-point values representing various objects within the vicinity of the vehicle (e.g., the vehicle 102a) to the server or directly to other vehicles (e.g., the vehicle 102b) to determine information related to various objects in a region that is outside the visual range of the camera of the other vehicle (e.g., the vehicle 102b) or beyond the visual range of the camera. In an embodiment, the transmission of the inquiry and the converted first data may be performed by the communication device 202e included as part of the autonomous system 202 of the vehicle 200. Note that the vehicle 200 is the same as or similar to Figure 1 the vehicles illustrated (e.g., 102a and 102b, etc.).

[0088] In an embodiment, the query associated with the second region is a request for mapping the environment within a particular vicinity of other vehicles. Such information can enable the vehicle to navigate through the second region in a manner that enables avoidance of collisions and the like. In an embodiment, note that the first distance associated with the first region relative to the vehicle in block 802 is less than the second distance associated with the second region, which may be associated with other vehicles (e.g., a second vehicle). For example, a first vehicle may be traveling at a particular location on a street and capture images of various objects within the vicinity of the first vehicle, and communicate a query to a second vehicle (e.g., 50 meters in front of the first vehicle) that is traveling, for the purpose of determining information regarding various objects (such as individuals and other vehicles, etc.) in areas that are difficult for the camera of the first vehicle to obtain.

[0089] In block 808, the vehicle may receive second data specific to the second region in response to the query. In particular, Figure 2 the communication device 202e of the vehicle 200 illustrated in may receive a floating-point value that is used to describe various objects that exist in a region (e.g., the region where the query is sent) that is outside the visual range of the camera of the vehicle. In an embodiment, the vehicle receives this information directly from other vehicles or from a server.

[0090] In block 810, the vehicle may aggregate the second data specific to the second region with first data stored in the database of the vehicle. In an embodiment, note that the step of aggregating the first data and the second data is performed by a server, which may transmit the aggregated first data and second data to the vehicle. Alternatively, the step of aggregating the first data and the second data is performed by the querying vehicle (e.g., the first vehicle), which receives data in the form of a floating-point value from the second vehicle. In an embodiment, the first vehicle may convert the floating-point value received from the second vehicle into an image data format and aggregate it with the image data captured by one or more cameras of the first vehicle. Then, the first vehicle routes the aggregated image data to, for example, the planning system of the first vehicle.

[0091] In block 812, the planning system 404 of the autonomous vehicle computing 400 corresponding to the autonomous vehicle computing 202f of the vehicle 200 determines the vehicle (e.g., corresponding to Figure 1 vehicles 102a and 102b, etc.) based on the aggregation of the second data and the first data Figure 2The trajectory of the vehicle 200) within the physical space. In an embodiment, the planning system 404 of the autonomous vehicle computing 400 of the vehicle utilizes the aggregation of the first data and the second data to determine a specific route from a source location to a destination location and to modify an existing route to avoid collisions, etc.

[0092] In addition, the planning system 404 utilizes the aggregation of the second data and the first data to generate a graphical representation for use on a display of the vehicle (e.g., vehicles 102a and 102b, etc.). For example, upon receiving floating point values corresponding to a second region, the vehicle (e.g., the perception system 402 of the vehicle) may convert these floating point values into an image format (e.g., JPEG, Raw, and PNG formats, etc.) and combine this information with the image data initially captured by the vehicle. The planning system 404 of the vehicle may generate a high-definition map based on this combination, which includes objects beyond the visual range of the vehicle's camera. In an embodiment, an example graphical representation includes a first object derived from data associated with a first region and a second object derived from second data specific to the second region. In an embodiment, the graphical representation may be a high-definition digital map output on a display of the first vehicle.

[0093] In an embodiment, the high-definition digital map displays various objects in the first region and various additional objects included in the second region as part of the same map. In other embodiments, the display of the vehicle may include a user interface, where a route is illustrated adjacent to these objects on the user interface. Other variations of the user interface are also contemplated. Note that the objects may include, for example, pedestrians, traffic lights, additional vehicles, buildings, and a plurality of other concerns such as restaurants and grocery stores. Data associated with each of these objects may be output on the display of the vehicle. It should also be noted that the numerical values corresponding to the various floating point values described in block 804 represent various characteristics of the above objects. For example, the floating point values describe the dimensions of various objects, such as one or more of width, length, and height, etc. In addition, if one or more of these objects are other vehicles, the characteristics also correspond to the speed and acceleration of these vehicles, etc. In an embodiment, orientation information, classification information (e.g., whether the object is a person or a machine, and whether the object is stationary or moving, etc.), and coordinates are also captured by the floating point values.

[0094] According to some non-limiting embodiments or examples, a method is provided, which includes:

[0095] Using at least one processor, obtaining first data associated with a first region external to the vehicle from at least one sensor of the vehicle;

[0096] Using the at least one processor, convert the first data associated with the first region from a first format to a second format;

[0097] Using the at least one processor, transmit the converted first data and an inquiry associated with a second region outside the vehicle to a server outside the vehicle;

[0098] Using the at least one processor, receive second data specific to the second region in response to the inquiry from the server;

[0099] Using the at least one processor, aggregate the second data specific to the second region with the first data; and

[0100] Using the at least one processor, determine a trajectory of the vehicle in physical space based on the aggregation of the second data and the first data.

[0101] According to some non - limiting embodiments or examples, a system is provided that includes: at least one processor of a vehicle, and at least one non - transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations including:

[0102] Using the at least one processor, obtain first data associated with a first region outside the vehicle from at least one sensor of the vehicle;

[0103] Using the at least one processor, convert the first data associated with the first region from a first format to a second format;

[0104] Using the at least one processor, transmit the converted first data and an inquiry associated with a second region outside the vehicle to a server outside the vehicle;

[0105] Using the at least one processor, receive second data specific to the second region in response to the inquiry from the server;

[0106] Using the at least one processor, aggregate the second data specific to the second region with the first data; and

[0107] Using the at least one processor, determine a trajectory of the vehicle in physical space based on the aggregation of the second data and the first data.

[0108] Other non - limiting aspects or embodiments are set forth in the numbered clauses below:

[0109] Clause 1: A method includes: using at least one processor to obtain first data associated with a first area outside the vehicle from at least one sensor of the vehicle; using the at least one processor to convert the first data associated with the first area from a first format to a second format; using the at least one processor to transmit the converted first data and an inquiry associated with a second area outside the vehicle to a server outside the vehicle; using the at least one processor to receive second data specific to the second area in response to the inquiry from the server; using the at least one processor to aggregate the second data specific to the second area with the first data; and using the at least one processor to determine a trajectory of the vehicle in physical space based on the aggregation of the second data and the first data.

[0110] Clause 2: The method according to Clause 1 further includes: generating a graphical representation for use by a display of the vehicle based on the second data aggregated with the first data, the graphical representation including a first object derived from data associated with the first area and a second object derived from the second data specific to the second area, wherein the graphical representation is a digital map generated based on the first area and the second area.

[0111] Clause 3: The method according to Clause 2 further includes: outputting the graphical representation on the display, the output including outputting the digital map to include the first object and the second object.

[0112] Clause 4: The method according to Clause 3 further includes: receiving third data specific to the second area; and updating the graphical representation to include a third object derived from the third data.

[0113] Clause 5: The method according to any one of Clauses 1 to 4, wherein the at least one sensor is a camera; and wherein obtaining the first data associated with the first area outside the vehicle includes: capturing, by the camera, at least one image within a vicinity of the vehicle in the first area outside the vehicle.

[0114] Clause 6: The method according to any one of Clauses 1 to 5, wherein the first format corresponds to at least one of a RAW format, a JPEG format, and a PNG.

[0115] Clause 7: The method according to any one of Clauses 1 to 6, wherein the second format corresponds to a floating-point value.

[0116] Clause 8: The method according to Clause 7, wherein obtaining the first data associated with the first area outside the vehicle includes: capturing, by a camera, at least one image within the vicinity of the vehicle in the first area outside the vehicle, wherein the floating-point value represents characteristics of an object included in the at least one image.

[0117] Clause 9: The method according to Clause 8, wherein the object includes one or more of a pedestrian, a traffic light, an additional vehicle, and a building sign.

[0118] Clause 10: The method according to Clause 8, wherein the characteristics of the object include one or more of a width, a length, a height, a speed, an acceleration, an orientation, a classification, and coordinates associated with the object.

[0119] Clause 11: A system, comprising: at least one processor of a vehicle, and at least one non-transitory storage medium storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations, the operations including: using the at least one processor, obtaining first data associated with a first area outside the vehicle from at least one sensor of the vehicle; using the at least one processor, converting the first data associated with the first area from a first format to a second format; using the at least one processor, transmitting the converted first data and an inquiry associated with a second area outside the vehicle to a server outside the vehicle; using the at least one processor, receiving from the server second data specific to the second area in response to the inquiry; using the at least one processor, aggregating the second data specific to the second area with the first data; and using the at least one processor, determining a trajectory of the vehicle within physical space based on the aggregation of the second data and the first data.

[0120] Clause 12: The system according to Clause 11, wherein the inquiry associated with the second area is a request for mapping the environment associated with the second area, and the first area is at a first distance relative to the vehicle, and the second area is at a second distance relative to the vehicle, wherein the second distance is greater than the first distance.

[0121] Clause 13: The system according to Clause 11 or Clause 12, wherein the operation further includes: generating a graphical representation for use on a display of the vehicle based on the second data aggregated with the first data, the graphical representation including a first object derived from data associated with the first region and a second object derived from the second data specific to the second region, wherein the graphical representation is a digital map generated based on the first region and the second region; and outputting the graphical representation on the display, the output including outputting the digital map to include the first object and the second object.

[0122] Clause 14: The system according to any one of Clauses 11 to 13, wherein the operation further includes: receiving third data specific to the second region; and updating the graphical representation to include a third object derived from the third data.

[0123] Clause 15: The system according to any one of Clauses 11 to 14, wherein the at least one sensor is a camera; and wherein obtaining the first data associated with the first region outside the vehicle includes: capturing, by the camera, at least one image within a vicinity of the vehicle in the first region outside the vehicle.

[0124] Clause 16: The system according to any one of Clauses 11 to 15, wherein the first format corresponds to at least one of RAW format, JPEG format, and PNG.

[0125] Clause 17: The system according to any one of Clauses 11 to 16, wherein the second format corresponds to floating-point values.

[0126] Clause 18: The system according to Clause 17, wherein one of the operations for obtaining the first region outside the vehicle includes: capturing, by a camera, at least one image within a vicinity of the vehicle in the first region outside the vehicle, wherein the floating-point values represent characteristics of the objects included in the at least one image.

[0127] Clause 19: The system according to Clause 18, wherein the object includes one or more of a pedestrian, a traffic light, an additional vehicle, and a building sign.

[0128] Clause 20: The system according to Clause 18, wherein the characteristics of the object include one or more of width, length, height, speed, acceleration, orientation, classification, and coordinates associated with the object.

[0129] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details, which may vary depending on the implementation. Accordingly, the specification and drawings are to be regarded as illustrative rather than in a limiting sense. The sole and exclusive indication of the scope of the invention, and what the applicant regards as the scope of the invention, is the literal and equivalent scope of the claims as issued from this application in the specific form of the issued claims, including any subsequent amendments. Any definitions expressly set forth herein for terms to be included in such claims shall be controlling as to the meaning of such terms as used in the claims. Additionally, when the term "further comprises" is used in a previous specification or the appended claims, the recitation following this phrase may be additional steps or entities, or sub-steps / sub-entities of the previously recited steps or entities.

Claims

1. A method, comprising: Using at least one processor, obtaining first data associated with a first area outside the vehicle from at least one sensor of the vehicle; Using the at least one processor, converting the first data associated with the first area from a first format to a second format; Using the at least one processor, transmitting the converted first data and an inquiry associated with a second area outside the vehicle to an additional vehicle outside the vehicle; Using the at least one processor, receiving second data specific to the second area in response to the inquiry; Using the at least one processor, aggregating the second data specific to the second area with the first data; And Using the at least one processor, determining a trajectory of the vehicle in physical space based on the aggregation of the second data and the first data.

2. The method according to claim 1 further comprises: Generating a graphical representation for use by a display of the vehicle based on the second data aggregated with the first data, the graphical representation including a first object derived from data associated with the first area and a second object derived from the second data specific to the second area, wherein the graphical representation is a digital map generated based on the first area and the second area.

3. The method according to claim 2, further comprising: Outputting the graphical representation on the display, the output including outputting the digital map to include the first object and the second object.

4. The method according to claim 3, further comprising: Receiving third data specific to the second area; And Updating the graphical representation to include a third object derived from the third data.

5. The method according to any one of claims 1 to 4, wherein The at least one sensor is a camera; And Wherein obtaining the first data associated with the first area outside the vehicle includes: capturing at least one image within a vicinity of the vehicle in the first area outside the vehicle by the camera.

6. The method according to any one of claims 1 to 5, wherein The first format corresponds to at least one of a RAW format, a JPEG format, and a PNG format.

7. The method according to any one of claims 1 to 6, wherein The second format corresponds to floating-point values.

8. The method according to claim 7, wherein Obtaining the first data associated with the first area outside the vehicle includes: Capturing at least one image within a vicinity of the vehicle in the first area outside the vehicle by a camera, wherein the floating-point values represent characteristics of objects included in the at least one image.

9. The method according to claim 8, wherein The objects include one or more of pedestrians, traffic lights, additional vehicles, and building signs.

10. The method according to claim 8, wherein, The characteristics of the objects include one or more of a width, a length, a height, a speed, an acceleration, an orientation, a classification, and coordinates associated with the object.

11. A system, comprising: At least one processor of a vehicle, and At least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations including: Using the at least one processor, obtaining first data associated with a first region external to the vehicle from at least one sensor of the vehicle; Using the at least one processor, converting the first data associated with the first region from a first format to a second format; Using the at least one processor, transmitting the converted first data and an inquiry associated with a second region external to the vehicle; Using the at least one processor, receiving second data specific to the second region in response to the inquiry; Using the at least one processor, aggregating the second data specific to the second region with the first data; and Using the at least one processor, determining a trajectory of the vehicle in physical space based on the aggregation of the second data and the first data.

12. The system according to claim 11, wherein, The inquiry associated with the second region is a request for mapping an environment associated with the second region, and the first region is at a first distance relative to the vehicle, and the second region is at a second distance relative to the vehicle, wherein the second distance is greater than the first distance.

13. The system according to claim 11 or 12, wherein, The operations further include: Generating a graphical representation for use by a display of the vehicle based on the second data aggregated with the first data, the graphical representation including a first object derived from data associated with the first region and a second object derived from the second data specific to the second region, wherein the graphical representation is a digital map generated based on the first region and the second region; and Outputting the graphical representation on the display, the output including outputting the digital map to include the first object and the second object.

14. The system according to claim 13, wherein, The operations further include: Receiving third data specific to the second region; and Updating the graphical representation to include a third object derived from the third data.

15. The system according to any one of claims 11 to 14, wherein, The at least one sensor is a camera; And Wherein obtaining the first data associated with the first region external to the vehicle includes: capturing, by the camera, at least one image within a vicinity of the vehicle in the first region external to the vehicle.

16. The system according to any one of claims 11 to 15, wherein, The first format corresponds to at least one of a RAW format, a JPEG format, and a PNG format.

17. The system according to any one of claims 11 to 16, wherein, The second format corresponds to a floating-point value.

18. The system according to claim 17, wherein, One of the operations for obtaining the first region external to the vehicle includes: Capturing, by a camera, at least one image within a vicinity of the vehicle in the first region external to the vehicle, wherein the floating-point value represents a characteristic of an object included in the at least one image.

19. The system according to claim 18, wherein The object includes one or more of a pedestrian, a traffic light, an additional vehicle, and a building sign.

20. The system according to claim 18, wherein, The characteristics of the object include one or more of the width, length, height, speed, acceleration, orientation, classification, and coordinates associated with the object.

Images