Navigation method, system, and program for autonomous vehicle

Abstract

A method for navigating an autonomous vehicle is provided. First, a navigation request is detected. The navigation request includes a destination for the autonomous vehicle from a starting location, and a route from the starting location to the destination is identified based on the navigation request. A regulator match associated with the route is performed based on the navigation request, the regulator match associated with an owner of privacy data for one or more regulators. In response to the regulator match, a conditional authorization associated with the route is received, the conditional authorization indicating that one or more autonomous vehicle sensors of the autonomous vehicle should not capture data associated with a first property of the first regulator, the first property being located near the route, and in response to the conditional authorization, the one or more autonomous vehicle sensors of the autonomous vehicle are restricted.

Description

[Technical field]

[0001] The present disclosure relates to autonomous vehicles, and more particularly, to protecting location privacy while providing accurate data for autonomous vehicle movement and navigation. [Background technology]

[0002] Autonomous vehicles may include driving and flying vehicles. Autonomous vehicles may operate by capturing information about a route and may be configured to avoid objects on and around the route. Autonomous vehicles may not be permitted to operate in certain locations for privacy reasons. Summary of the Invention

[0003] According to an embodiment, a method, system, and computer program product are disclosed. A navigation request is detected. The navigation request includes a destination of the autonomous vehicle from a starting location. Based on the navigation request, a route from the starting location to the destination is identified. Based on the navigation request, a regulator match associated with the route is performed. The regulator match is associated with an owner of privacy data for one or more regulators. In response to the regulator match, a conditional authorization associated with the route is received. The conditional authorization indicates that one or more autonomous vehicle sensors of the autonomous vehicle should not capture data associated with a first property of the first regulator. The first property is located in a vicinity of the route. In response to the conditional authorization, one or more autonomous vehicle sensors of the autonomous vehicle are restricted.

[0004] In some embodiments, restricting the one or more autonomous vehicle sensors includes restricting capture of the first property by the one or more autonomous vehicle sensors.

[0005] The above summary is not intended to describe each illustrated embodiment or every implementation of the present disclosure.

[0006] The drawings included in this application are incorporated in and form a part of the specification. The drawings illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The drawings are merely illustrative of certain embodiments and are not intended to limit the present disclosure. [Brief description of the drawings]

[0007] [Figure 1] 1 illustrates representative major components of an exemplary computer system that may be used in accordance with some embodiments of the present disclosure. [Figure 2A] 1 illustrates an exemplary third party external sensor system (TESS) operating with an autonomous vehicle in an exemplary vehicular environment consistent with certain embodiments of the present disclosure. [Figure 2B] 1 illustrates an example vehicular environment in which a TESS operates in an autonomous vehicle to protect the privacy of one or more properties. [Figure 2C] This shows autonomous movement data captured by TESS's autonomous vehicle. [Diagram 3] 1 illustrates alternative data captured by TESS, consistent with certain embodiments of the present disclosure. [Figure 4] FIG. 1 shows an exemplary system diagram of a TESS, consistent with certain embodiments of the present disclosure. [Diagram 5] 1 illustrates an example method for performing TESS, consistent with certain embodiments of the present disclosure. [Figure 6] 1 illustrates an example method for performing TESS, consistent with certain embodiments of the present disclosure. [Figure 7] 1 illustrates an example method for performing TESS, consistent with certain embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] While the invention is subject to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It is to be understood, however, that there is no intention to limit the invention to the particular embodiments described. On the contrary, it is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention.

[0009] Aspects of the present disclosure relate to autonomous vehicles, and more particularly to preserving location privacy while providing accurate data for autonomous vehicle movement and navigation. Although the present disclosure is not necessarily limited to such applications, various aspects of the present disclosure may be understood through the discussion of various examples using this context.

[0010] Autonomous vehicles are becoming more and more prevalent throughout society. Autonomous vehicles may include small unmanned autonomous vehicles (e.g., drones) and passenger vehicles (e.g., autonomous cars). Autonomous vehicles may be equipped with various sensors to make observations about the route or path the vehicle travels and to avoid objects. For example, an autonomous truck may navigate or otherwise move along a predefined or undefined route while acquiring area information of people and other vehicles. The sensor may be one or more devices such as a camera, lidar, microphone, speaker, etc. used in the operation. An autonomous vehicle may use sensors to perform safe operations during navigation or movement in the world. For example, an autonomous vehicle may use sensors to stop, avoid or otherwise avoid contact with people, cars, road signs, animals, birds, other vehicles. In another example, an autonomous vehicle may use sensors to acquire and generate autonomous movement data to stay within a lane, or to avoid driving on a sidewalk, or to stay outside a boundary or other visual area.

[0011] Certain conditions may require the capture and storage of autonomous movement data. For example, autonomous movement data may be required for autonomous vehicle manufacturers and autonomous driving software manufacturers to conduct research and analysis. Analysis of autonomous movement data may be beneficial to improve the movements performed by the autonomous vehicle. If the autonomous movement data is not recorded for later analysis, certain defects or errors in the autonomous vehicle's movements may not be captured or detected. In another example, it may be necessary to provide autonomous movement data to law enforcement or arbitration agencies to analyze the incident. An autonomous vehicle may malfunction and the situation may need to be referred to arbitration. Legislators may make the process easier to arbitrate by requiring that the autonomous vehicle data used by the autonomous vehicle to move and navigate in various areas be stored and accessible for a period of time (e.g., days, months, years) after the autonomous movement is performed.

[0012] Conversely, certain regulatory and practical impediments may preclude the capture of sensor data. Various regulations may be implemented or imposed on areas through which manned or unmanned vehicles are routed. One such regulation may be that manned and unmanned automobiles may be restricted from capturing information in certain restricted areas. For example, the restricted area may be a restricted area of ​​a government national park or national property. In another example, an unmanned vehicle may be subject to flight restrictions pursuant to local and national aviation laws that restrict the capture of live visual data. Live visual data may include pixel or radar images of a particular area that may or may not be captured by a camera, lidar, or other sensor. Furthermore, regulations may only dictate a binary choice of whether passage should be permitted or not when travel and navigation is based on sensor-based capture of the environment.

[0013] Some privacy preservation techniques may include obscuring or blurring the captured sensor data. This has several drawbacks. Some privacy preservation techniques may prevent the autonomous vehicle from being later surveyed and collected to determine safe passage and accurate navigation. Lack of imaging data may not only preclude proper surveys by engineers and legislators, but may also impede the actual technical operations performed by the autonomous vehicle. For example, an autonomous vehicle may use sensors such as cameras, radar, lidar, etc. to capture the world the vehicle is navigating in order to navigate. If the sensors have obscured, blurred, or otherwise removed data corresponding to people, animals, and other vehicles, the autonomous vehicle may fail detection algorithms on the data. The detection algorithms performed may be machine learning, neural networks, image processing, and object detection on raw values. Take for example a captured video stream, photo, or other image with pixel values ​​depicting one or more moving entities. If a portion of an image is altered from its raw sensor values, image analysis may not be able to accurately detect, classify, or identify objects within the image scene, and as a result, the autonomous vehicle may misidentify objects in an incorrect location, or in some cases not identify the object at all.

[0014] Furthermore, binary regulations may preclude operation of an autonomous vehicle in areas where capture of information of a particular area is not permitted. Even if partial imagery is permitted, it may be impractical for an autonomous vehicle to navigate without sensor data of the entire surroundings of the autonomous vehicle. For example, if regulations permit a vehicle to travel around the outer edge of a particular area, provided that the vehicle does not attempt to capture images toward the center of the area, it may be difficult for an autonomous vehicle to navigate by capturing only half of the area around the autonomous vehicle. As a result, it may be difficult for an autonomous vehicle to operate without object data received from the autonomous vehicle sensors.

[0015] A third-party external sensor system (TESS) may operate to overcome practical problems of autonomous vehicle operation in various environments. A TESS may operate by providing moving object data to an autonomous vehicle. A TESS may operate by providing moving object data to an autonomous vehicle as the autonomous vehicle approaches or approaches a vicinity of a property. A TESS may be operated by a regulator of the property. For example, the regulator may be the owner of the property, such as an individual. In another example, the regulator may be a manager of the property, such as a designated contractor or a third party that has contracted with the owner of the property to operate the property. A TESS may be operated by a central server system. For example, real-time data of stationary and moving entities may be transmitted from the property to the central server. The entities may include one or more real-world entities, such as people, animals, trees, etc., that have not yet been processed and identified as stationary or moving objects.

[0016] The TESS may operate by directly restricting the operation of one or more sensors of the autonomous vehicle. For example, a regulator or server may override the control of one or more sensors of the autonomous vehicle. The TESS may operate indirectly by restricting the operation of one or more sensors of the autonomous vehicle. For example, a regulator or server may transmit an instruction to the autonomous vehicle. In response to the instruction, the autonomous vehicle may restrict one or more sensors such that the autonomous vehicle does not receive sensor data from one or more sensors of the autonomous vehicle in accordance with the instruction. The restriction of the sensor operation may be based on the location of the autonomous vehicle, such as being at or near a particular property or otherwise in the vicinity. The restriction of the sensor operation may be temporary, such as for a period of minutes or seconds. The restriction may include erasing a portion of a video signal, such as from a camera of the autonomous vehicle. The restriction may include erasing an entire image, frame, or image sequence from the video signal.

[0017] TESS may operate by preventing disobedience of the autonomous vehicle. For example, a regulator or server may command or attempt to restrict sensor capture by one or more sensors of the autonomous vehicle. Restricted vehicle sensors may include image sensors, radar sensors, lidar sensors, global positioning sensors (GPS), and other related navigation and movement sensors. In response, the autonomous vehicle may disobey or otherwise ignore the regulator. In response, the regulator or server may command the autonomous vehicle to reroute away from the restricted area or to cease autonomous operation. The restricted area may be a particular street, route, or boundary of a land or water body. The restricted area may be a particular airspace.

[0018] TESS may provide alternative data for navigation, movement, and telemetry applications. For example, a regulator or server may provide one or more of the following sensor data: partial images with certain areas greyed out, occluding, blurring, removing, or otherwise obscuring portions of a property or individual, processed, transformed, or altered images. In some embodiments, a three-dimensional model may be provided to the autonomous vehicle. TESS may be provided depending on the type of scheme information the autonomous vehicle can understand, decode, or otherwise access. In some embodiments, a three-dimensional model that is rendered on the image or an image with a two-dimensional overlay may be provided to the autonomous vehicle. The model or overlay may be larger than the object obscured by the model or overlay. The extra size of the model or overlay may prevent the autonomous vehicle or a party reviewing the alternative data from determining the object. The alternative model or overlay may be analyzed by the autonomous vehicle using related techniques such as edge detection or object identification to determine that a generic version of the moving object is within the area. For example, a child and a parent may be walking down a sidewalk, and TESS may operate by rendering two three-dimensional models that obscure the child and the parent. TESS's proxy data may be used for navigation by an autonomous vehicle to avoid, move around, or otherwise safely navigate an environment of stationary and moving entities. TESS may generate proxy data continuously or iteratively. For example, TESS may provide proxy data for moving entities depicted as moving objects in real-time or near real-time every 16 milliseconds, every 16.68 milliseconds, or every second.

[0019] FIG. 1 illustrates representative major components of an exemplary computer system 100 (or computer) that may be used in accordance with some embodiments of the present disclosure. It should be understood that the individual components may vary in complexity, number, type, or configuration, or combinations thereof. The specific examples disclosed are merely for illustrative purposes and are not necessarily limited to such variations. The computer system 100 may include a processor 110, a memory 120, an input / output interface (I / O or I / O interface herein) 130, and a main bus 140. The main bus 140 may provide a communication path for other components of the computer system 100. In some embodiments, the main bus 140 may connect to other components, such as a dedicated digital signal processor (not shown).

[0020] The processor 110 of the computer system 100 may be comprised of one or more cores 112A, 112B, 112C, 112D (collectively 112). The processor 110 may further include one or more memory buffers or caches (not shown) that provide temporary storage of instructions and data for the cores 112. The cores 112 may perform instructions on inputs provided from the cache or memory 120 and output results to the cache or memory. The cores 112 may be comprised of one or more circuits configured to perform one or more methods consistent with embodiments of the present disclosure. In some embodiments, the computer system 100 may include multiple processors 110. In some embodiments, the computer system 100 may be a single processor 110 having a single core 112.

[0021] The memory 120 of the computer system 100 may include a memory controller 122. In some embodiments, the memory 120 may comprise a random access semiconductor memory, a storage device, or a storage medium (volatile or non-volatile) for storing data and programs. In some embodiments, the memory may be in the form of a module (e.g., a dual in-line memory module). The memory controller 122 may be in communication with the processor 110 to facilitate storage and retrieval of information in the memory 120. The memory controller 122 may be in communication with the I / O interface 130 to facilitate storage and retrieval of inputs or outputs in the memory 120.

[0022] The I / O interface 130 may include an I / O bus 150, a terminal interface 152, a storage interface 154, an I / O device interface 156, and a network interface 158. The I / O interface 130 may connect the main bus 140 to the I / O bus 150. The I / O interface 130 may direct instructions and data from the processor 110 and memory 120 to various interfaces of the I / O bus 150. The I / O interface 130 may direct instructions and data from the various interfaces of the I / O bus 150 to the processor 110 and memory 120. The various interfaces may include a terminal interface 152, a storage interface 154, an I / O device interface 156, and a network interface 158. In some embodiments, the various interfaces may include a subset of the above interfaces (e.g., an embedded computer system in an industrial application may not include the terminal interface 152 and the storage interface 154).

[0023] Logical modules throughout computer system 100, including but not limited to memory 120, processor 110, and I / O interface 130, may communicate failures and changes in one or more components to a hypervisor or operating system (not shown). The hypervisor or operating system may allocate the various resources available in computer system 100 and track the location of data in memory 120 and processes assigned to the various cores 112. In embodiments that combine or rearrange elements, aspects and functions of the logical modules may be combined or redistributed. These variations will be apparent to those skilled in the art.

[0024] 2A illustrates an example TESS 200 operating with an autonomous vehicle 210 in an example vehicle environment 220 consistent with certain embodiments of the present disclosure. The vehicle environment 220 may include the following: a neighborhood street having a number of travel and parking lanes 222-1, 222-2, 222-3, and 222-4 (collectively, lanes 222), a series of lane markings 224-1, 224-2, and 224-3 (collectively, markings 224), a sidewalk (or footpath) for pedestrian travel including sidewalks 226-1 and 226-2 (collectively, sidewalks 226), and three properties 228-1, 228-2, and 228-3 (collectively, 228).

[0025] Lanes 222 may be defined for two-way traffic flow and parking of stationary vehicles. For example, lanes 222 may be defined for the following: lane 222-1 may be defined for left-side street parking, lane 222-2 may be defined for oncoming traffic flow on the left, lane 222-3 may be defined for driving and autonomous navigation by autonomous vehicle 210, and lane 222-4 may be defined for right-side street parking. Markings 224 may visually indicate the divisions between lanes 222 of vehicle environment 220. For example, markings 224 may include the following: That is, lane marking 224-1 may indicate the boundary between lanes 222-1 and 222-2, lane marking 224-2 may indicate the boundary between lanes 222-2 and 222-3, and lane marking 224-3 may indicate the boundary between lanes 222-3 and 222-4. Lane markings 224 may be configured to indicate to the driver and autonomous vehicle boundaries that should not be crossed or that should only be crossed when performing very specific actions (e.g., passing a vehicle, transitioning from park to drive, lane guidance to guide into a lane, transitioning from drive to park, or changing into another lane not shown).

[0026] Property 228 may be real property including land, structures, parks, bodies of water, rock formations, walls, or other stationary areas owned and controlled by property owners, such as individuals, businesses, and governmental entities. Of property 228, property 228-2 may be property owned and controlled by a regulator. For example, property 228-2 may be a corporate building owned and controlled by a company that regulates the viewing and capture of image data by autonomous vehicles in the vicinity of property 228-2.

[0027] The vehicle environment 220 may further include a number of moving and stationary entities unrelated to the property 228. For example, the stationary entities include the following: stationary entities 230-1, 230-2, and 230-3 (collectively 230). The stationary entities 230-1 and 230-2 may be trees or shrubs near the lane 222 of the vehicle environment 220. The stationary entity 230-3 may be a sign that visually indicates to the entities in the vehicle environment 220 one or more rules, laws, or regulations that the entities in the vehicle environment should follow. Other stationary entities 230 such as rock formations, mountainsides, statues, or monuments may be envisioned. The moving entities may include the following: vehicles 240-1 and 240-2 (collectively vehicles 240) and pedestrians 242-1, 242-2, and 242-3 (collectively pedestrians 242). Although not shown, other moving entities may be envisioned in the vehicle environment 220. For example, bicycles, dogs, cats, deer, motorcycles, etc. may be entities identified as moving objects that may be present in the vehicular environment 220 .

[0028] TESS 200 may include a collective cooperative effort by autonomous vehicle 210 and regulators of property 228-2. For example, autonomous vehicle 210 may include autonomous vehicle sensors 212 for capturing data and telemetry from vehicle environment 220. Sensors 212 may be capable of capturing data within arc 214. Arc 214 may be defined by two separate sub-arcs, shown in FIG. 2A as sub-arc 214-1 and sub-arc 214-2. The data and telemetry may be used by autonomous vehicle 210 to generate autonomous movement data used for navigation and movement in vehicle environment 220 and to avoid contact with other objects. For example, data may be received from autonomous vehicle sensors 212 in the form of visual data, depth sensing data, echolocation data, etc. The autonomous vehicle sensor 212 may be a radar sensor, and the autonomous movement data may be radar measurements indicative of time of flight of signals reflecting off various entities (e.g., stationary entities 230, vehicles 240, and pedestrians 242) in the vehicle environment 220. The autonomous vehicle sensor 212 may be a visible light camera configured to capture images of the vehicle environment 220 (e.g., in the form of red, green, and blue pixels).

[0029] TESS 200 may operate based on data obtained from a regulator of property 228-2. For example, property 228-2 may include the following: sensors 250-1 and 250-2 (collectively sensors 250) configured to capture and record vehicle environment 220, and regulator system 260 configured to process data captured by sensors 250. Regulator system 260 may be a computer (e.g., computer 100 of FIG. 1) configured to transmit and receive data and generate alternative data. Sensors 250 may include image, radar, lidar, and other related sensors specifically adapted to capture live sensor data of an otherwise restricted environment. For example, sensor 250-1 may be configured to capture an area indicated by a straight line extending from sensor 250-1. Additionally, sensor 250-2 may be configured to capture an area indicated by a straight line extending from sensor 250-2.

[0030] 2B illustrates an example vehicle environment 220 in which the TESS 200 operates in an autonomous vehicle 210 to protect the privacy of one or more properties. The TESS 200 may be configured to control the capture or recording of one or more autonomous vehicle sensors of a particular vehicle. For example, the autonomous vehicle 210 may be configured to capture only a portion of the vehicle environment 220. The TESS 200 may instruct the autonomous vehicle 210 to completely limit the functionality (e.g., by disabling) of an entire particular autonomous vehicle sensor. For example, the TESS 200 may instruct the autonomous vehicle 210 not to record, capture, or otherwise obtain sensor data.

[0031] In some embodiments, TESS 200 may instruct autonomous vehicle 210 to partially restrict the functionality of a particular autonomous vehicle sensor. For example, TESS 200 may instruct autonomous vehicle 210 to restrict the functionality of autonomous vehicle sensor 212 such that only a portion of arc 214 is captured. Accordingly, autonomous vehicle 210 may capture partial arc 214-1 in an unaltered state. Additionally, autonomous vehicle 210 may capture partial arc 214-2 in a restricted state. For example, partial arc 214-2 may have a blurred portion of sensor data corresponding to one or more stationary or moving entities in vehicle environment 220. In some embodiments, partial arc 214-2 may not be captured, and no sensor data from sensor 212 may be able to view, capture, store, or otherwise analyze the sensor data within partial arc 214-2. As a result, autonomous vehicle 210 may only capture sensor data from autonomous vehicle sensors 212 related to the following entities: lanes 222-1 and 222-2, markings 224-1 and 224-2, sidewalks 226-1, property 228-1 and 228-2, stationary entities 230-1, and moving entities, including vehicles 240-1 and pedestrians 242-1. Autonomous vehicle 210 may only partially capture some of vehicle environment 220. For example, autonomous vehicle sensors 212 may not be able to capture all of lane 222-3.

[0032] 2C illustrates autonomous movement data 270 captured by autonomous vehicle 210 of TESS 200. Autonomous movement data 270 may be captured from vehicle environment 220 from sensor data. Autonomous movement data 270 may be object data corresponding to various objects disposed within vehicle environment 220. Autonomous movement data may include the following: guiding objects 272-1 and 272-2 (collectively 272), which correspond to lanes 222-1 and 222-2 and markings 224-1 and 224-2, respectively; real-world objects 274-1, 274-2, and 274-3 (collectively 274). Real-world objects 274 may be autonomous movement data representations of entities of vehicle environment 220. For example, real-world object 274-1 may be a representation of pedestrian 242-1, real-world object 274-2 may be a representation of vehicle 240-1, real-world object 274-3 may be a representation of stationary entity 230-1, etc. The format of autonomous movement data 270 may be derived based on performing image analysis and may be in a relevant object format such as wireframe, rendered polygon, point cloud, etc.

[0033] Based on the guiding object 272 and the real-world object 274, the autonomous vehicle 210 of the TESS 200 may successfully navigate the vehicle environment 220. For example, the autonomous vehicle 210 may stop, slow down, change course, or otherwise react based on the presence or movement of the real-world object 274, which updates continuously based on the autonomous movement data received from the autonomous vehicle sensors 212. In another example, the autonomous vehicle 210 may guide itself and move along a path based on the guiding objects 272-1 and 272-2.

[0034] FIG. 3 illustrates alternative data 300 captured by TESS 200 consistent with some embodiments of the present disclosure. The alternative data 300 may be captured by a regulator of property 228-2. For example, one or more objects may be captured by sensor 250 and provided by TESS 200 to autonomous vehicle 210. The objects provided by sensor 250 may be in a format that matches those captured in situ by autonomous vehicle 210. In some embodiments, the objects provided by sensor 250 may be in a secondary format, and autonomous vehicle 210 may provide a format conversion before utilizing alternative data 300 for navigation. For example, alternative data 300 may be in a related object format, such as wireframe, rendered polygon, point cloud, etc. Alternative data 300 may not be in the form of raw sensor data, and thus, any images of entities, such as people, cars, animals, and property captured by sensor 250 may not be provided to autonomous vehicle 210. The proxy data 300 may be stored or maintained by the autonomous vehicle and used to determine the performance of the autonomous vehicle 210 navigating the vehicle environment 220 .

[0035] The alternative data 300 may include guiding objects 310-1 and 310-2 corresponding to signs 224-2 and 224-3 and lanes 222-3 and 222-4, respectively. The guiding object 310-1 may be encoded with flags, variables, or other data elements. The flags may be understood by the autonomous vehicle 210 as corresponding to objects that are understandable based on authorized sensor data. Based on the flags, the autonomous vehicle 210 may be able to combine the alternative data 300 with autonomous movement data 270 captured in situ by the autonomous vehicle sensors 212 to generate a complete picture of the vehicle environment 220. The alternative data 300 may include real-world objects 320-1, 320-2, 330, 340, and 350. The real-world objects 320-1 and 320-2 (collectively objects 320) may correspond to pedestrians 242-2 and 242-3, respectively. The real-world object 330 may correspond to the stationary entity 230-2. The real-world object 340 may correspond to the vehicle 240-2. The real-world object 350 may correspond to the stationary entity 230-3. The real-world object 350 may be encoded with data, variables, metadata, or other values ​​that communicate information about the stationary entity 230-3. For example, the stationary entity 230-3 may be a sign that communicates a movement-based restriction, such as a speed limit, to the autonomous vehicle 210.

[0036] FIG. 4 illustrates an example system diagram 400 of a TESS consistent with some embodiments of the present disclosure. The system diagram 400 may illustrate one or more components of a TESS. For example, the system diagram 400 may include the following: an autonomous vehicle 410, a regulator system 430, and a central server 460. The autonomous vehicle 410, the regulator system 430, and the central server 460 may be configured with one or more units or modules. The units and modules may include software (e.g., operating system, hypervisor, daemons, jobs), hardware (e.g., one or more processing circuits, computer 100 of FIG. 1), or a combination (e.g., firmware, field programmable gate array) configured to perform one or more operations of the TESS.

[0037] The autonomous vehicle 410 may include a vehicle management unit 412, a regulator communication unit 414, a vehicle control unit 416, and a sensor processor unit 418. The autonomous vehicle may include a number of autonomous vehicle sensors 420-1 through 420-N (collectively 420).

[0038] The vehicle management unit 412 may be configured to operate the autonomous vehicle 410 and interface with the regulator system 430 and the central server 460. For example, the vehicle management unit 412 may be configured to communicate with the central server 460. The vehicle management unit 412 may be configured to perform regulator checks related to a route. The regulator checks may be related to making requests to one or more regulator owners of privacy data. The owner may be the central server 460. The privacy data owner may be a regulator, and the checks may be directed to the regulator communication unit 414 to be transmitted to the regulator system 430. The vehicle management unit 412 may further be configured to monitor the operation of other units of the autonomous vehicle 410. In particular, the vehicle management unit 412 may be configured to perform driving route management, including receiving requests to drive to various destinations and requests to request routes from the central server 460. The vehicle management unit 412 may be configured to capture and receive one or more on-board device management systems and make high-level decisions regarding the autonomous vehicle 410. For example, the vehicle management unit 412 may be configured to make decisions to stay on route, to request a different route, check vehicle status such as battery or fuel status, etc.

[0039] The regulator communication unit 414 of the autonomous vehicle 410 may be configured to relay information to the vehicle management unit 412 and the regulator system 430. For example, the regulator communication unit 414 may be configured to transmit high level sensor information, such as the status of alternative data being received at the vehicle management unit 412. The regulator communication unit 414 may be configured to receive alternative data and autonomous driving commands from the regulator system 430.

[0040] The vehicle control unit 416 of the autonomous vehicle 410 may be configured to receive commands and perform autonomous navigation for the autonomous vehicle 410. The vehicle control unit 416 may receive commands from the vehicle management unit 412. The vehicle control unit 416 may be configured to receive commands and alternative data from the regulator system 430 (e.g., via the regulator communication unit 414). The vehicle control unit 416 may be configured to act based on sensor data, such as autonomous movement data obtained from the sensor processor unit 418.

[0041] The sensor processor unit 418 may be configured to obtain sensor data from the autonomous vehicle sensors 420 and may be configured to detect, determine, or otherwise analyze image data. The sensor processor may be configured as an image processor. The image processor may be a collection of hardware and software, such as an application specific integrated circuit.

[0042] The regulator system 430 may include the following: a control and authentication unit 432, a vehicle communication unit 434, and an alternative data acquisition unit 436. The regulator system 430 may be associated with a property, such as a building, land, water, territory, etc. The associated property may include one or more sensors 440-1 to 440-N (440) configured to capture and transmit object information corresponding to one or more real-world entities that may be located at the property. For example, the property associated and controlled by the regulator system 430 may be a theme park, and the sensors 440 may be property cameras owned and controlled by the theme park. The property cameras may be configured to capture patron queues located in parking lots, or pedestrian movement around the park's exterior vicinity (e.g., gates and nearby streets and roads). The regulator system 430 may be communicatively coupled to a vehicle data control information database 450 (or a vehicle database). Vehicle database 450 may include one or more sensor profiles (e.g., camera specifications, lidar layouts, radar blueprints). Vehicle database 450 may be organized, referenced, or populated based on various models of autonomous vehicles. For example, autonomous vehicle 410 may have a particular configuration or layout of autonomous vehicle sensors 420 and sensor processor unit 418 processing capabilities.

[0043] The control and authentication unit 432 may communicate with the central server 460, and the authentication unit may be notified of various autonomous vehicles that may or may not be requesting to travel near the property of the regulator system 430 (e.g., areas adjacent to the property, or roads, streets, bodies of water, or land at or near the property). The control and authentication unit 432 may be configured to authenticate, approve, or otherwise grant the central server 460 access to various vehicles. The control and authentication unit 432 may be configured to proactively communicate with the central server 460 in response to detecting the presence of a vehicle (e.g., autonomous or otherwise) that is not authorized to be in the vicinity. The control and authentication unit 432, in some embodiments, may request the central server 460 to reroute, delay, or otherwise change the traffic of vehicles that may or may not be allowed to travel near the property of the regulator system 430.

[0044] The vehicle communication unit 434 may operate in conjunction with the control and authentication unit 432. For example, the vehicle communication unit 434 may operate by receiving the status of one or more nearby or soon-to-be nearby vehicles. The vehicle communication unit 434 may operate by commanding or otherwise performing guidance and TESS with the autonomous vehicle. For example, the vehicle communication unit 434 may communicate various guidance, navigation, or travel commands or instructions to the autonomous vehicle 410. The vehicle communication unit 434 may collect real-time sensor information (e.g., sensor data) based on sensor information received from the sensors 440 or from the regulator communication unit 414 of the autonomous vehicle 410. Based on the sensor data, the vehicle communication unit 434 may generate autonomous travel data.

[0045] The alternative data generation unit 436 may generate autonomous movement data for the vehicle communication unit 434. The alternative data generation unit 436 may include one or more processing circuits configured to analyze sensor data from the sensors 440 and perform object recognition. The alternative data generation unit 436 may include an image processor. The alternative data generation unit 436 may be configured to generate alternative data for the autonomous vehicle 410 in real-time or near real-time. The vehicle communication unit 434 may generate instructions to delay autonomous movement or guidance (e.g., a 30 second delay before autonomous movement and navigation) until alternative data is generated and provided to the autonomous vehicle. The alternative data generation unit 436 may convert or format the alternative data (e.g., format sensor data from the sensors 440 into a format understandable by the vehicle sensor processor 418) based on sensor profile information stored in the vehicle database 450.

[0046] The image processors of the sensor processor 418 and the alternative data generation unit 436 may be configured to perform various image analysis techniques. The image analysis techniques may be machine learning and / or deep learning based techniques. These techniques may include, but are not limited to, region-based convolutional neural networks (R-CNN), you only look once (YOLO), edge matching, clustering, grayscale matching, gradient matching, invariance models, geometric hashing, scale invariant feature transform (SIFT), speeded up robust features (SURF), histogram of oriented gradients (HOG) features, and single shot multibox detector (SSD). In some embodiments, the image processor may be configured to help identify one of the entities in the environment (e.g., by analyzing facial images with models built with training data of animals, people, children, and other moving objects, by analyzing automobile images with models built with training data of trucks, cars, buses, and other vehicles).

[0047] In some embodiments, objects may be identified using an object detection algorithm, such as R-CNN, YOLO, SSD, SIFT, Hog features, or other machine learning and / or deep learning object detection algorithms. The output of the object detection algorithm may include one or more identities of one or more respective objects with corresponding match certainties. For example, sensor data from the autonomous vehicle sensor 420 or sensor 440 may be analyzed for entities such as a running child, a walking adult, a moving animal, etc. Using the associated object detection algorithm, various entities may be identified as objects having boundaries, bounding boxes, polygons, wireframes, etc.

[0048] In some embodiments, the characteristics of an object may be determined using a supervised machine learning model constructed using training data. For example, an image may be input to the supervised machine learning model, and various classifications detected in the image may be output by the model. For example, characteristics such as object material (e.g., fabric, metal, plastic, etc.), shape, size, color, and other characteristics may be output by the supervised machine learning model. Additionally, the identity of the object (e.g., tree, human face, dog, etc.) may be output as a classification determined by the supervised machine learning model. For example, if the autonomous vehicle 410 captures an image of another vehicle, the supervised machine learning algorithm may be configured to output the identity of the object (e.g., automobile) along with various characteristics of the vehicle (e.g., model, make, color, etc.).

[0049] In some embodiments, the characteristics of the object may be determined using photogrammetry techniques. For example, the shape and dimensions of the object may be approximated using photogrammetry techniques. In some embodiments, the characteristics of the object may be identified by referencing an ontology. For example, when an object is identified (e.g., using R-CNN), the identity of the object may be referenced in the ontology to determine the corresponding attributes of the object. The ontology may indicate attributes such as the color, size, shape, use, etc. of the object. The ontology may indicate the relationship of the object to one or more regulations or rules, such as, for example, a sign indicating a speed limit, a conditional restriction while a child is present, and a sign to simultaneously place a second object identified and classified as a child.

[0050] The characteristics may include object shape, object dimensions (e.g., height, length, and width), number of objects (e.g., number of headlights indicating a passenger vehicle), color of the object, or other attributes of the object, or combinations thereof. In some embodiments, the output may generate a list including the object's identity and / or characteristics (e.g., cotton shirt, metallic glasses, animal fur, adult height, etc.). In some embodiments, the output may include an indication that the object's identity or characteristics is unknown. In some embodiments, the various objects, object attributes, and relationships between the objects (e.g., hierarchical and direct relationships) may be represented within a knowledge graph (KG) structure. Objects may be matched with other objects based on common characteristics (e.g., the skin color of a person's cheeks and the skin color of a person's chin, the reflectivity of a car's windshield, a particular color pattern or repeating signal strength indicating a road boundary or lane marking), relationships with other objects (e.g., eyes belong to a face), or objects belonging to the same classification (e.g., two vehicles are currently moving vehicles while a third vehicle is a stationary entity).

[0051] The central server 460 may be communicatively coupled to the autonomous vehicle 410 and the regulator system 430. The central server 460 may include a route guidance unit 462 and a regulated area unit 464. The route guidance unit 462 may be configured to receive, process, and respond to requests from an autonomous vehicle (e.g., the autonomous vehicle 410). The route guidance unit 462 may be configured to provide directions, routes, roads, streets, and other information to the autonomous vehicle based on map data 466. The route guidance unit 462 may be configured to monitor and track location information along a route of a vehicle currently operating for the regulator. For example, a query may be made by the regulator system 430 to the central server 460 regarding the status of any vehicle routed near the regulator system 430's premises. In response to the query, the route guidance unit 462 may provide the status of the autonomous vehicle 410 (e.g., as the autonomous vehicle 410 approaches or approaches the regulator system 430's premises). Route guidance unit 462 may be configured to transmit conditional permissions related to the route. For example, the conditional permissions may indicate that one or more autonomous vehicle sensors 420 of autonomous vehicle 410 should not capture data related to one or more properties located in the vicinity of the route.

[0052] The regulatory domain unit 464 may communicate only with the regulator system and the route guidance unit 462. The route guidance unit 462 may make requests to the regulatory domain unit 464. The regulator system may make requests to the regulatory domain unit 464. The request may be in the form of a request to determine whether the route of the autonomous vehicle passes within or near the boundaries of a restricted property. The request may be in the form of an update to the regulatory domain information database 468. The regulatory domain unit 464 may manage the restricted property information and may store and update the property information in the regulatory domain information database 468. Based on this information, the regulatory domain unit 464 may update the route guidance unit 462 and may inform the route guidance unit 462 when the autonomous vehicle passes within a path of a restricted property.

[0053] FIG. 5 illustrates an example method 500 for performing TESS consistent with some embodiments of the present disclosure. TESS may be executed by a processing device of a computer system (e.g., computer 100). TESS may begin at 510 when a route (navigation request) is requested from an autonomous vehicle (e.g., autonomous vehicle 210 of FIG. 2A). The request may be sent by the autonomous vehicle and sent to a central server (e.g., central server 460 of FIG. 4). The route request may include vehicle information of the autonomous vehicle. The vehicle information may include one or more of the following: a current location of the autonomous vehicle, a requested destination of the autonomous vehicle, a list of one or more sensors of the autonomous vehicle. The list of one or more sensors may include a list of sensor capabilities and specifications. The list of one or more sensors may include schema information. The schema information may include parameters of alternative data that the autonomous vehicle can process.

[0054] At 512, a route may be generated for the autonomous vehicle. The route may be generated by a central server (e.g., central server 460 of FIG. 4). The generation of the route may include one or more route factors (e.g., traffic volume, speed limits, alternatives, weather patterns, etc.). The generation of the route may be influenced based on regulators. For example, regulators may send suggestions to the central server, which may adjust the route during route generation. The generation of the route may be based on queries to a map database (e.g., map data 466 of FIG. 4).

[0055] At 514, the generated route may be searched for one or more regulatory areas 514. The search for regulatory areas 514 may be based on a search within a predetermined threshold. For example, the predetermined threshold may be a search for regulatory areas within one mile (1.61 km) of the route. In another example, the predetermined threshold may be a search for regulatory areas within one-third of a mile (536 m) of the route. The search for regulatory areas 514 may be a search for regulatory areas proximate to the route, such as streets along the route proximate to regulatory areas.

[0056] If a regulated area is found by the search at Y of 516, the vehicle information may be received at 520. The vehicle information may be received by a regulator found to be proximate to the generated route. For example, in FIG. 4, the vehicle information may be received by the regulator system 430 based on a determination that the autonomous vehicle 410 has a route generated that includes a portion of the route near a property under the control of the regulator system 430. The received vehicle information may be an unmodified version of the vehicle information received with the route request at 510. In some embodiments, the received status may include a modified version of the vehicle status. For example, identifying personal information included in the vehicle information may be removed by the central server before being received at 520. At 522, the controllability of the vehicle may be determined. This determination may be made by a regulator (e.g., the regulator system 430). This determination may be made by analyzing the received information of the autonomous vehicle. For example, by analyzing a list of one or more sensors and a list of one or more sensor functions. If, at 524N, it is determined that the vehicle is not controllable, the route may be regenerated at 512. The route may be regenerated at 512 by rerouting the autonomous vehicle away from the vicinity of the property.

[0057] If, at Y of 524, it is determined that the vehicle is controllable, then, at 526, a scan for alternative data may be performed on the regulator's property. The scan for alternative data may be a scan for data with sufficient resolution, such as a camera located on the property capable of capturing the entity with sufficient pixel resolution. The scan may include a determination that a sensor is capable of capturing the entity around the property at a frame rate sufficient to capture the entity's movement or speed. For example, image processing and detection may determine that the property has a human capable of moving at 27 miles per hour (43.5 km). In another example, the scan may include detection of a vehicle capable of moving at 100 miles per hour (161 km). Based on the speed of the entity in the vicinity of the property, the regulator may determine that a sensor is present with a frame rate capable of capturing the entity.

[0058] The scan for alternative data may be performed by a regulator that owns and controls the property and the sensors at the property. For example, regulator system 430 may be configured to monitor sensors 440 and make a determination that alternative data is sufficient. The scan for alternative data may include a scan for data that matches the schema information received from the autonomous vehicle. Because the schema information may include a format of the data, the scan may be a scan for data that fits the format requested / required by the autonomous vehicle. In some embodiments, data may need to be converted and the scan for alternative data may include a scan for available processing power, memory bandwidth, or other computational resources to perform the conversion to the native format of the autonomous vehicle. The scan for alternative data may include a determination that the regulator can provide data of sufficient quality and that the regulator can provide data that fits the required schema of the autonomous vehicle. If alternative data does not exist for the property at 528N, a route may be regenerated at 512. The route may be regenerated at 512 by rerouting the autonomous vehicle away from the vicinity of the property. At Y of 528, if alternative data exists, the alternative data may be formatted and transmitted at 530. The alternative data may be transmitted back to a central server for transmission to the autonomous vehicle. The alternative data may also be transmitted directly to the autonomous vehicle.

[0059] After transmitting the alternative data to the vehicle, the regenerated route may be re-searched for additional regulated areas 514. If no regulated areas are found at 516N, a vehicle route determination may be performed at 518. The autonomous vehicle route determination at 518 may include providing permission for the autonomous vehicle to begin autonomous travel. The method 500 ends after the vehicle route determination at 518.

[0060] 6 illustrates an example method 600 for performing TESS consistent with some embodiments of the present disclosure. TESS may be performed by a processing device of a computer system (e.g., computer 100 of FIG. 1). Method 600 may be performed after method 500, such as after an autonomous vehicle has begun a route generated by a central server.

[0061] The method 600 begins when a restricted area is recognized at 610. The area may be recognized by an autonomous vehicle (e.g., autonomous vehicle 410 of FIG. 4). The area may be recognized by the autonomous vehicle based on route information received by a central server (e.g., central server 460 of FIG. 4). The recognition of the autonomous vehicle may be based on one or more sensors of the autonomous vehicle. For example, the autonomous vehicle may have a GPS sensor configured to provide guidance and identify a geographic location of the autonomous vehicle. The autonomous vehicle may have received information about the latitude and longitude of the restricted area from the central server. The recognition of the autonomous vehicle may be based on receiving a notification from a regulator. For example, the regulator may broadcast a signal to nearby autonomous vehicles notifying them that the regulator requires authorization before the autonomous vehicle can travel in the vicinity.

[0062] At 612, information about the autonomous vehicle may be transmitted to a regulator. The information may include the following: an identifier for the autonomous vehicle, device information for the autonomous vehicle, a location of the autonomous vehicle, sensor data for the autonomous vehicle, and schema information for alternative data that may be received by the autonomous vehicle.

[0063] At 620, permission for the autonomous vehicle to be near the regulator's property may be confirmed. Permission may be confirmed based on information received from the autonomous vehicle. Permission may be confirmed by a regulator (e.g., regulator system 430 of FIG. 4). If the regulator does not grant permission at 622N, a notification may be provided by the regulator to the autonomous vehicle at 624. The notification may indicate that the autonomous vehicle is prohibited from traveling near the regulator's property. Based on receiving the notification, the autonomous vehicle may request a new route at 614, and method 600 may end.

[0064] If the regulator grants permission at Y of 622, the regulator may send a confirmation of permission to the autonomous vehicle at 626. The confirmation of permission at 626 may include initiating substitute data configured based on the scheme information sent at 612. Based on receiving the confirmation of permission, the autonomous vehicle may initiate a cooperative operation at 616. The cooperative operation may include receiving substitute data from the regulator. For example, the regulator system 630 may continuously transmit the substitute data and control information to the autonomous vehicle. After the autonomous vehicle initiates a cooperative operation with the regulator at 616, method 600 may end.

[0065] FIG. 7 illustrates an example method 700 for performing TESS consistent with some embodiments of the present disclosure. TESS may be performed by a processing device of a computer system (e.g., computer 100 of FIG. 1). Method 700 may be performed in some embodiments performing TESS by a combination of an autonomous vehicle (e.g., autonomous vehicle 210 of FIG. 2) and a regulator (e.g., regulator system 430 of FIG. 4). Method 700 may be performed after method 600. For example, method 700 begins when the autonomous vehicle enters a restricted area. Entering the restricted area may be entering a vicinity of the restricted area.

[0066] At 712, the autonomous vehicle may transmit its current location and other telemetry information (e.g., speed, altitude, pitch, autonomous vehicle sensor states) to the regulator. For example, the autonomous vehicle 410 may transmit the location and telemetry information to the regulator system 430. At 720, the regulator may initiate an attempt to assist the autonomous vehicle. The assistance attempt may include scanning the received location information. The assistance attempt may include determining that the autonomous vehicle is capable of autonomous travel and navigation without or with limited functionality of one or more autonomous vehicle sensors of the autonomous vehicle. If at 722N it is determined that the autonomous vehicle is not capable of assistance, at 724, the regulator may request the autonomous vehicle to leave the restricted area. Based on receiving the request to leave the restricted area, the autonomous vehicle may adjust its route to leave the restricted area as quickly as possible. At 730, the autonomous vehicle may leave the restricted area 730 after receiving the request to leave from the regulator.

[0067] If the autonomous vehicle is capable of assistance at Y of 722, then at 740 the regulator may determine whether the autonomous vehicle is capable of functioning based on the alternative data manipulation. The determination may be based on whether the autonomous vehicle is configured to receive the alternative data from the regulator. The determination may be based on whether the autonomous vehicle is configured to combine the alternative data with one or more autonomous vehicle data generated by the autonomous vehicle from one or more autonomous vehicle sensors.

[0068] If, at 740Y, the autonomous vehicle is capable of functioning based on the alternate data operation, at 750, the alternate data operation may commence. The alternate data operation may be performed continuously. For example, the alternate data operation may be performed every second, every 100 milliseconds, 60 times per second, etc. The alternate data operation may include transmitting alternate data to the autonomous vehicle. The alternate data may be transformed by the regulator based on the received schema information before being transmitted to the autonomous vehicle. In some embodiments, the autonomous vehicle may transform the alternate data for use in autonomous travel and navigation. The alternate data operation may further include restricting autonomous vehicle sensors of the autonomous vehicle. For example, the regulator may transmit a disable signal, a limit signal, or another signal that may cause the autonomous vehicle to disable or limit one or more of the autonomous vehicle sensors from capturing sensor data. The alternate data operation may be performed by the regulator transmitting the alternate data and the autonomous vehicle navigating the environment until the autonomous vehicle leaves the restricted area at 730.

[0069] At 740N, if the autonomous vehicle is not capable of functioning based on the alternative data operations, the autonomous vehicle navigates the restricted area based on teleoperation 760. Teleoperation 760 may include navigating and moving the autonomous vehicle based on the autonomous vehicle continuously offloading sensor data and telemetry information to a regulator or central server. The regulator or central server may perform the autonomous operation, and the autonomous vehicle may not capture, record, or otherwise be able to analyze sensor data of the autonomous vehicle sensors. Teleoperation may be performed until the autonomous vehicle leaves the restricted area at 730. After the autonomous vehicle leaves the area at 730, method 700 may end.

[0070] The present invention may be a system, method, and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions for causing a processor to implement aspects of the present invention.

[0071] A computer readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. A non-exhaustive list of more specific examples of computer readable storage media includes the following: portable computer diskettes, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROMs or flash memories), static random access memories (SRAMs), portable compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or ridge-in-groove structures having instructions recorded thereon, and any suitable combination of the above. As used herein, a computer-readable storage medium should not be construed as being a transient signal per se, such as electric waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted down an electrical wire.

[0072] The computer readable program instructions described herein may be downloaded from a computer readable storage medium into each computing / processing device or to an external computer or storage device via a network, such as the Internet, a local area network, a wide area network, or a wireless network, or a combination thereof. The network may include copper transmission cables, optical transmission fiber, wireless transmission, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof. A network adapter card or network interface in each computing / processing device receives the computer readable program instructions from the network and forwards the computer readable program instructions to a computer readable storage medium in the respective computing / processing device for storage.

[0073] The computer readable program instructions for implementing the operations of the present invention may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source or object code written in any combination of one or more programming languages, including object oriented programming languages ​​such as Smalltalk, C++, etc., and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or a connection may be made to an external computer (e.g., via the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry to perform aspects of the invention.

[0074] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, are implemented by computer readable program instructions.

[0075] These computer readable program instructions may be provided to a computer processor or other programmable data processing apparatus to produce a machine, such that the instructions, executed by the computer processor or other programmable data processing apparatus, form means for performing the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams. These computer readable program instructions may be stored in a computer readable storage medium that may direct a computer, programmable data processing apparatus, or other device, or combination thereof, to function in a particular manner, such that the computer readable storage medium having the instructions stored thereon comprises an article of manufacture including instructions that perform aspects of the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams.

[0076] The computer-readable program instructions may be loaded into a computer, other programmable data processing apparatus, or other device such that the instructions, which execute on a computer, other programmable data processing apparatus, or other device, perform the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams, thereby causing a series of operational steps to be performed on the computer, other programmable apparatus, or other device to generate a computer-implemented process.

[0077] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions described in the blocks may occur in an order other than that described in the figures. For example, two blocks shown in succession may actually be realized as one step, may be executed simultaneously, substantially simultaneously, partially or fully overlapping in time, or the blocks may possibly be executed in reverse order depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, are implemented by a special purpose hardware-based system that performs the specified functions or operations or realizes a combination of special purpose hardware and computer instructions.

[0078] The description of various embodiments of the present disclosure is presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Numerous modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terms used in this specification are selected to explain the principles of the embodiments, practical applications, or technical improvements to the technology found in the market, or to enable other skilled in the art to understand the embodiments disclosed herein.

[0079] The description of various embodiments of the present disclosure is presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Numerous modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terms used in this specification are selected to explain the principles of the embodiments, practical applications, or technical improvements to the technology found in the market, or to enable other skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for computer-based information processing, comprising the steps of: Detecting a navigation request, the navigation request including a destination of the autonomous vehicle from a starting location; determining a route from the starting point to the destination based on the navigation request; performing a regulator check related to the route based on the navigation request, the regulator check being related to an owner of privacy data for one or more regulators; receiving a conditional authorization associated with the route in response to the regulator match, the conditional authorization indicating that one or more autonomous vehicle sensors of the autonomous vehicle should not capture data associated with a first property of a first regulator, the first property being located near the route; in response to the conditional authorization, restricting the one or more autonomous vehicle sensors of the autonomous vehicle, the restriction including restricting capture of the first property by the one or more autonomous vehicle sensors; controlling autonomous movement along the route based on autonomous movement data received from the one or more autonomous vehicle sensors; detecting a vicinity of the first property of the first regulator during the autonomous movement; blanking a portion of a video signal of a camera of the one or more autonomous vehicle sensors in response to detecting the vicinity; generating proxy data for the vicinity of the first property of the first regulator, the generating of the proxy data including requesting moving object data generated from first property cameras owned and controlled by the first regulator; replacing a portion of the autonomous movement data corresponding to the camera with the alternative data; The method includes:

2. The method of claim 1 , wherein the portion of the video signal is an entire image of the video signal.

3. 2. The method of claim 1, wherein the moving object data is selected from the group consisting of a person walking or running, an animal crossing a vehicle path, another autonomous vehicle driving, another autonomous vehicle flying, a road sign, and lane guidance.

4. receiving a second conditional authorization indicating that one or more sensors of the autonomous vehicle shall not capture data related to a second property of a second regulator, the second property being located near the route; controlling autonomous movement along the route based on autonomous movement data received from the one or more sensors; detecting a vicinity of the second property of the second regulator during the autonomous movement; in response to detecting the vicinity, disabling a first radar sensor of the one or more sensors; generating alternative data for the vicinity of the second property of the second regulator; replacing a portion of the autonomous movement data corresponding to the first radar sensor with the substitute data; The method of claim 1 further comprising:

5. The generating of the alternative data includes: and requesting moving object data generated from a second radar sensor of a second autonomous vehicle, the second autonomous vehicle being owned and controlled by the second regulator. The method of claim 4 , comprising:

6. The method of claim 1 , wherein the regulator check includes querying whether the route is proximate to any regulatory domains, and the conditional authorization indicates that multiple regulatory domains are proximate to the route.

7. The method of claim 1 , wherein the owner of privacy data for the one or more regulators is a central server.

8. The owner of privacy data for the first regulator is the first regulator, and performing the regulator match includes: receiving a response from a central server, the response indicating that the first regulator is associated with the conditional authorization; prompting the first regulator for the conditional authorization based on the response; and The method of claim 1 , comprising:

9. 1. A system comprising: a memory for storing one or more instructions; a processor communicatively coupled to the memory, the processor responding to reading the one or more instructions by: Detecting a navigation request, the navigation request including a destination of the autonomous vehicle from a starting location; determining a route from the starting point to the destination based on the navigation request; performing a regulator check related to the route based on the navigation request, the regulator check being related to an owner of privacy data for one or more regulators; receiving a conditional authorization associated with the route in response to the regulator match, the conditional authorization indicating that one or more autonomous vehicle sensors of the autonomous vehicle should not capture data associated with a first property of a first regulator, the first property being located near the route; in response to the conditional authorization, restricting the one or more autonomous vehicle sensors of the autonomous vehicle, the restricting comprising restricting capture of the first property by the one or more autonomous vehicle sensors; performing autonomous movement along the route based on autonomous movement data received from the one or more autonomous vehicle sensors; detecting a vicinity of the first property of the first regulator during the autonomous movement; blanking a portion of a video signal of a camera of the one or more autonomous vehicle sensors in response to detecting the vicinity; generating proxy data for the vicinity of the first property of the first regulator, the generating of the proxy data including requesting moving object data generated from first property cameras owned and controlled by the first regulator; replacing a portion of the autonomous movement data corresponding to the camera with the alternative data; The processor is configured to: A system comprising:

10. The system of claim 9 , wherein the portion of the video signal is an entire image of the video signal.

11. 10. The system of claim 9, wherein the moving object data is selected from the group consisting of a person walking, a child running on a sidewalk, an animal crossing a road, another autonomous vehicle driving, another autonomous vehicle flying, a road sign, and lane guidance.

12. A computer program product for causing a computer to execute the method according to any one of claims 1 to 8.

13. 13. A storage medium having the computer program of claim 12 stored on one or more computer readable storage media.

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