Surrogate autonomous vehicle data

Abstract

A navigation request is detected. The navigation request includes a destination for an autonomous vehicle from a starting location. A route is identified from the starting location to the destination based on the navigation request. A regulator lookup related to the route is performed based on the navigation request. The regulator lookup relates to an owner of privacy data of one or more regulators. In response to the regulator lookup, a conditional allowance related to the route is received. The conditional allowance indicates that one or more autonomous vehicle sensors of the autonomous vehicle can not capture data related to a first asset of a first regulator. The first asset is located proximate to the route. In response to the conditional allowance, the one or more autonomous vehicle sensors of the autonomous vehicle are limited.

Description

Background Technology

[0001] This disclosure relates to autonomous vehicles, and more specifically, to protecting location privacy while providing accurate data for the movement and navigation of autonomous vehicles.

[0002] Autonomous vehicles may include both driven and flying vehicles. Autonomous vehicles can operate by capturing information about their path and can be configured to avoid objects on and around that path. For privacy reasons, autonomous vehicles may not be permitted to operate in certain locations. Summary of the Invention

[0003] According to embodiments, a method, system, and computer program product are disclosed. A navigation request is detected. The navigation request includes the destination of an autonomous vehicle from a starting point. A route from the starting point to the destination is identified based on the navigation request. A regulator lookup related to the route is performed based on the navigation request. The regulator lookup relates to the owner of privacy data of one or more regulators. In response to the regulator lookup, a conditional allowance related to the route is received. The conditional allowance indicates that one or more autonomous vehicle sensors of the autonomous vehicle may not capture data related to a first asset of a first regulator. The first asset is located near the route. In response to the conditional allowance, one or more autonomous vehicle sensors of the autonomous vehicle are restricted.

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

[0005] The above description is not intended to depict every illustrated embodiment or implementation of this disclosure. Attached Figure Description

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

[0007] Figure 1 Representative key components of an example computer system that can be used according to some embodiments of this disclosure are depicted.

[0008] Figure 2A An example third-party external sensor system (TESS) operating with an autonomous vehicle in an example vehicle environment, consistent with some embodiments of this disclosure, is depicted.

[0009] Figure 2BAn example vehicle environment is depicted, in which TESS operates on an autonomous vehicle to protect the privacy of one or more assets.

[0010] Figure 2C The autonomous mobility data captured by TESS's autonomous vehicles is depicted.

[0011] Figure 3 Alternative data captured by TESS is depicted, consistent with some embodiments of this disclosure.

[0012] Figure 4 An example system diagram of TESS consistent with some embodiments of this disclosure is depicted.

[0013] Figure 5 Example methods for performing TESS are described that are consistent with some embodiments of this disclosure.

[0014] Figure 6 Example methods for performing TESS are described that are consistent with some embodiments of this disclosure.

[0015] Figure 7 Example methods for performing TESS are described that are consistent with some embodiments of this disclosure.

[0016] While the invention may have various modifications and alternatives, its details have been illustrated by example in the accompanying drawings and will be described in detail. However, it should be understood that the purpose is not to limit the invention to the specific embodiments described. Rather, the invention is intended to cover all modifications, equivalents, and alternatives that fall within its scope. Detailed Implementation

[0017] This disclosure relates to autonomous vehicles; and more specifically, to protecting location privacy while providing accurate data for the movement and navigation of autonomous vehicles. While this disclosure is not necessarily limited to these applications, various aspects of this disclosure can be understood by discussing various examples in this context.

[0018] Autonomous vehicles are becoming increasingly common throughout society. Autonomous vehicles can include small, driverless autonomous vehicles (e.g., drones) and passenger vehicles (e.g., autonomous cars). Autonomous vehicles can be equipped with various sensors to observe their routes or paths and to avoid objects. For example, an autonomous truck can navigate or otherwise move along predefined or undefined routes while acquiring zonal information about people and other vehicles. Sensors can be one or more devices, such as cameras for operation, lidar, microphones, speakers, etc. During navigation or movement around the world, autonomous vehicles can use sensors to operate safely. For example, autonomous vehicles can use sensors to stop, avoid, or otherwise refrain from contact with people, cars, street signs, animals, birds, and other vehicles. In another example, autonomous vehicles can use sensors to acquire and generate autonomous movement data to stay within lanes or avoid driving on sidewalks or outside boundaries or other visible areas.

[0019] In some cases, it may be necessary to capture and preserve autonomous mobility data. For example, autonomous vehicle manufacturers and autonomous software manufacturers may need autonomous mobility data for auditing and analysis. Analysis of autonomous mobility data can be valuable for improving the mobility performed by autonomous vehicles. If autonomous mobility data is not recorded for later analysis, certain defects or errors in autonomous vehicle mobility may not be captured or detected. In another example, autonomous mobility data may need to be provided to law enforcement agencies or arbitrators to analyze incidents. Autonomous vehicles may malfunction, which may require arbitration. Legislators can make this process easier to arbitrate by requiring the preservation and access of autonomous vehicle data used for movement and navigation in various areas for a period of time after autonomous mobility is performed (e.g., days, months, years).

[0020] Conversely, certain regulations and practical barriers may make the capture of sensor data impossible. Various regulations can be implemented or imposed on areas traversed by manned or autonomous vehicles. One such regulation might restrict the capture of information by both manned and autonomous vehicles in certain regulated areas. For example, a regulated area might be a restricted area of ​​a government national park or property. In another example, autonomous vehicles may be subject to flight-related regulations based on regional and national aviation legislation that restricts the capture of raw visual data. Raw visual data can include pixel or radar images of a specific area, which may or may not be acquired by cameras, lidar, or other sensors. Furthermore, if movement and navigation are performed based on sensor-based environmental capture, regulations may only offer a binary choice of whether passage should be permitted.

[0021] Some privacy-preserving techniques may involve obscuring captured sensor data. This has several drawbacks. Privacy-preserving techniques can be implemented to prevent autonomous vehicles from having their information audited later to determine safe passage and accurate navigation. The lack of imaging data may not only prevent engineers and legislators from conducting proper audits, but it may also hinder the actual technical operation of autonomous vehicles. For example, to navigate autonomous vehicles, sensors such as cameras, radar, and lidar are used to capture the world they are navigating. If sensors are obscured, blurred, or otherwise have data corresponding to people, animals, and other vehicles removed, the autonomous vehicle may fail to pass the data detection algorithms. The detection algorithms performed can be machine learning, neural networks, image processing, and object detection based on raw values. For example, captured video streams, photographs, or other images with pixel values ​​depicting one or more moving entities. If parts of an image change from their raw sensor values, image analysis may fail to accurately detect, classify, or place objects within the image scene. Therefore, the autonomous vehicle may incorrectly identify objects in the wrong locations, or in some cases, fail to identify objects at all.

[0022] Furthermore, in areas where binary rules prohibit the capture of information about certain regions, autonomous vehicle operation may become impossible. Even if partial imagery is permitted, navigation by an autonomous vehicle without sensor data of the entire environment is impractical. For example, if there is a rule allowing a vehicle to pass through the outer edge of a specific area, provided it does not attempt to take pictures in a direction towards the center of that area, an autonomous vehicle would struggle to traverse only by capturing images of half the area around it. Therefore, without receiving object data from the vehicle's sensors, autonomous vehicle operation is practically impossible.

[0023] Third-party external sensor systems (TESS) can be operated to overcome practical problems in autonomous vehicle operation across various environments. TESS operates by providing moving object data to the autonomous vehicle. Specifically, TESS can operate by providing moving object data to the autonomous vehicle as it approaches or nears an asset. TESS can be operated by the asset's supervisor. For example, the supervisor could be an asset owner, such as an individual. In another example, the supervisor could be an asset controller, such as a designated contractor or third party contracted to operate the asset alongside the asset owner. TESS can also be operated by a central server system. For example, real-time data on stationary and moving entities can be transmitted from the asset to a central server. Entities can include one or more real-world entities, such as people, animals, or trees, that have not yet been processed and identified as stationary or moving objects.

[0024] TESS can operate by directly restricting the operation of one or more sensors of an autonomous vehicle. For example, a regulator or server can override control of one or more sensors of the autonomous vehicle. TESS can also operate indirectly by restricting the operation of one or more sensors of an autonomous vehicle. For example, a regulator or server can send a command to the autonomous vehicle. In response to this command, the autonomous vehicle can restrict one or more sensors, causing it to fail to comply with the command to receive sensor data from one or more sensors. Restrictions on sensor operation can occur based on the location of the autonomous vehicle, such as on or near an asset, or otherwise in the vicinity of an asset. Restrictions on sensor operation can occur temporarily, such as lasting for a certain number of minutes or seconds. Such restrictions can include erasing a portion of a video signal, such as erasing from the autonomous vehicle's camera. Such restrictions can include erasing an entire image, frame, or series of images from a video signal.

[0025] TESS operates by preventing autonomous vehicles from disobeying regulations. For example, a regulator or server can instruct or attempt to restrict sensor capture from one or more sensors of an autonomous vehicle. Vehicle sensors that may be restricted can include image sensors, radar sensors, lidar sensors, GPS sensors, and other relevant navigation and mobility sensors. In response, the autonomous vehicle may disobey or ignore the regulator. In response, the regulator or server can instruct the autonomous vehicle to change course and leave the regulated area or cease autonomous operation. The regulated area can be certain streets, paths, or boundaries of land or water. The regulated area can also be certain airspace.

[0026] TESS can provide alternative data for navigation, mobility, and telemetry purposes. For example, a regulator or server can provide one or more of the following sensor data: partial images of certain areas that are grayed out; and processed, distorted, or altered images that obscure, blur, remove, or otherwise obscure portions of assets or individuals. In some embodiments, a 3D model can be provided to the autonomous vehicle. TESS can be provided depending on the type of scenario information that the autonomous vehicle can understand, decode, or otherwise access. In some embodiments, a 3D model rendered as an image or an image with a 2D overlay can be provided to the autonomous vehicle. The model or overlay may be larger than the object obscured by the model or overlay. The additional size of the model or overlay may prevent the autonomous vehicle or a party examining the alternative data from identifying the object. The autonomous vehicle can use relevant techniques such as edge detection or object identification to analyze the alternative model or overlay to determine a general version of a moving object within the area. For example, a child and one of the parents may be walking on a sidewalk, and TESS might operate by rendering two 3D models of the child and one of the parents that are blurred. The alternative data from TESS can be used by the autonomous vehicle for navigation to avoid, move around, or otherwise navigate safely in environments with stationary and moving entities. TESS can perform the generation of alternative data continuously or repeatedly. For example, TESS can provide alternative data for mobile entities represented as mobile objects in real time or near real time, every 16 milliseconds, every 16.68 milliseconds, or every second.

[0027] Figure 1 Representative key components of an example computer system 100 (or computer) that can be used according to some embodiments of this disclosure are depicted. It should be understood that individual components may vary in complexity, number, type, and / or configuration. The specific examples disclosed are for illustrative purposes only and are not necessarily the only such variations. Computer system 100 may include processor 110, memory 120, input / output interfaces (hereinafter I / O or I / O interface) 130, and main bus 140. Main bus 140 may provide communication paths for other components of computer system 100. In some embodiments, main bus 140 may be connected to other components, such as a dedicated digital signal processor (not shown).

[0028] The processor 110 of the computer system 100 may include one or more cores 112A, 112B, 112C, 112D (collectively referred to as 112). The processor 110 may also include one or more memory buffers or caches (not shown) that provide temporary storage for instructions and data for the core 112. The core 112 may execute instructions on input provided from a cache or memory 120 and output results to the cache or memory. The core 112 may include one or more circuits configured to perform one or more methods consistent with embodiments of this 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.

[0029] The memory 120 of the computer system 100 may include a memory controller 122. In some embodiments, the memory 120 may include a random access semiconductor memory, storage device, or 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 communicate with the processor 110 to facilitate the storage and retrieval of information in the memory 120. The memory controller 122 may communicate with the I / O interface 130 to facilitate the storage and retrieval of inputs or outputs in the memory 120.

[0030] I / O interface 130 may include I / O bus 150, terminal interface 152, memory interface 154, I / O device interface 156, and network interface 158. I / O interface 130 can connect host bus 140 to I / O bus 150. I / O interface 130 can route instructions and data from processor 110 and memory 120 to various interfaces of I / O bus 150. I / O interface 130 can also route instructions and data from various interfaces of I / O bus 150 to processor 110 and memory 120. The various interfaces may include terminal interface 152, memory interface 154, I / O device interface 156, and network interface 158. In some embodiments, the various interfaces may include a subset of the aforementioned interfaces (e.g., embedded computer systems in industrial applications may not include terminal interface 152 and memory interface 154).

[0031] The logical modules throughout computer system 100—including, but not limited to, memory 120, processor 110, and I / O interface 130—can transmit failures and changes of one or more components to a hypervisor or operating system (not shown). The hypervisor or operating system can allocate various resources available in computer system 100 and track the location of data in memory 120 and the location of processes assigned to the various cores 112. In embodiments that combine or rearrange elements, aspects and capabilities of the logical modules can be combined or redistributed. These variations will be apparent to those skilled in the art.

[0032] Figure 2A An example TESS 200, consistent with some embodiments of this disclosure, is depicted operating in an example vehicle environment 220 with an autonomous vehicle 210. The vehicle environment 220 may include: a street traversing a block having multiple driving and parking lanes 222-1, 222-2, 222-3, and 222-4 (collectively referred to as lanes 222); a series of lane markers 224-1, 224-2, and 224-3 (collectively referred to as markers 224); walkways (or sidewalks) for pedestrian movement, including walkways 226-1 and 226-2 (collectively referred to as walkways 226); and three assets 228-1, 228-2, and 228-3 (collectively referred to as 228).

[0033] Lane 222 can be designated for two-way traffic flow and parking of stationary vehicles. For example, lane 222 can be designated for the following purposes: lane 222-1 can be designated for left-hand street parking; lane 222-2 can be designated for oncoming traffic from the left; lane 222-3 can be used for driving and autonomous navigation of the autonomous vehicle 210; and lane 222-4 can be designated for right-hand street parking. Identification 224 can visually indicate the division between lanes 222 in the vehicle environment 220. For example, identification 224 can include the following: lane identification 224-1 can indicate the boundary between lanes 222-1 and lane 222-2; lane identification 224-2 can indicate the boundary between lanes 222-2 and lane 222-3; and lane identification 224-3 can indicate the boundary between lanes 222-3 and lane 222-4. Lane marking 224 can be configured to indicate to the driver and autonomous vehicle that the boundary should not be crossed or should only be crossed when performing a very specific action (e.g., vehicle overtaking, transition from parking to driving, lane guidance to orient within the lane, transition from driving to parking, turning into another lane not shown).

[0034] Asset 228 can be immovable property, including land, buildings, parks, bodies of water, rocks, walls, or other stationary areas owned and controlled by asset owners (such as individuals, corporations, and government entities). Within Asset 228, Asset 228-2 can be an asset owned and controlled by a regulator. For example, Asset 228-2 could be a corporate building owned and controlled by a company that manages the viewing and capture of image data by autonomous vehicles near Asset 228-2.

[0035] Vehicle environment 220 may also include multiple moving and stationary entities independent of asset 228. For example, stationary entities include: stationary entities 230-1, 230-2, and 230-3 (collectively referred to as 230). Stationary entities 230-1 and 230-2 may be trees or shrubs near driveway 222 of vehicle environment 220. Stationary entity 230-3 may be a sign visually indicating to entities within vehicle environment 220 one or more rules, laws, or regulations that entities within vehicle environment must comply with. Other stationary entities 230 may be considered, such as rock formations, hillsides, regulations, or monuments. Moving entities may include: vehicles 240-1 and 240-2 (collectively referred to as vehicle 240); and pedestrians 242-1, 242-2, and 242-3 (collectively referred to as pedestrian 242). Although not shown, other moving entities may be envisioned in vehicle environment 220. For example, bicycles, dogs, cats, deer, motorcycles, etc., may be entities identified as moving objects that may be present in vehicle environment 220.

[0036] TESS 200 may include collaborative operations between autonomous vehicle 210 and the regulator of asset 228-2. For example, autonomous vehicle 210 may include autonomous vehicle sensors 212 for capturing data and telemetry from the vehicle environment 220. Sensor 212 is capable of capturing data within arc 214. Arc 214 may be... Figure 2A The data is defined as two separate sub-arcs, sub-arc 214-1 and sub-arc 214-2. The autonomous vehicle 210 can use this data and telemetry to generate autonomous mobility data for navigating and moving around the vehicle environment 220 without contacting other objects. For example, data in the form of visual data, depth sensing data, echolocation data, etc., can be received from the autonomous vehicle sensor 212. The autonomous vehicle sensor 212 can be a radar sensor, and the autonomous mobility data can be radar readings indicating the time it takes for signals to bounce off various entities (e.g., stationary entities 230, vehicles 240, and pedestrians 242) in the vehicle environment 220. The autonomous vehicle sensor 212 can 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).

[0037] TESS 200 can also operate based on data obtained from the regulator of asset 228-2. For example, asset 228-2 may include: sensors 250-1 and 250-2 (collectively referred to as sensors 250) configured to capture and record the vehicle environment 220; and a regulator system 260 configured to process the data captured by sensors 250. The regulator system 260 may be a computer configured to send and receive data and generate alternative data (e.g., ...). Figure 1 (Computer 100). Sensor 250 may include image sensors, radar sensors, lidar sensors, and other related sensors, which are particularly well-suited for capturing raw sensor data of an environment that would otherwise be limited. For example, sensor 250-1 may be configured to capture an area delineated by a straight line extending from sensor 250-1. Furthermore, sensor 250-2 may be configured to capture an area delineated by a straight line extending from sensor 250-2.

[0038] Figure 2B An example vehicle environment 220 is depicted, in which TESS 200 operates on an autonomous vehicle 210 to protect the privacy of one or more assets. TESS 200 can be configured to control the capture or recording of one or more autonomous vehicle sensors on certain vehicles. For example, autonomous vehicle 210 can be configured to capture only a portion of the vehicle environment 220. TESS 200 can instruct autonomous vehicle 210 to completely limit (e.g., by disabling) the overall functionality of certain autonomous vehicle sensors. For example, TESS 200 can instruct autonomous vehicle 210 not to record, capture, or otherwise acquire sensor data.

[0039] In some embodiments, TESS 200 may instruct the autonomous vehicle 210 to partially restrict the functionality of certain autonomous vehicle sensors. For example, TESS 200 may instruct the autonomous vehicle 210 to restrict the functionality of autonomous vehicle sensor 212 such that only a portion of arc 214 is captured. In response, the autonomous vehicle 210 may capture sub-arc 214-1 in an unchanged state. Furthermore, the autonomous vehicle 210 may capture sub-arc 214-2 in a restricted state. For example, sub-arc 214-2 may obscure portions of the sensor data corresponding to one or more stationary or moving entities in the vehicle environment 220. In some embodiments, sub-arc 214-2 may not be captured, and any sensor data from sensor 212 may not be viewable, captured, stored, or otherwise analyzed within sub-arc 214-2. Therefore, autonomous vehicle 210 may capture sensor data from autonomous vehicle sensor 212 only for the following entities: lanes 222-1 and 222-2; signs 224-1 and 224-2; walkways 226-1; assets 228-1 and 228-2; stationary entity 230-1; and moving entities, including vehicles 240-1 and pedestrians 242-1. Autonomous vehicle 210 may also capture only a portion of the vehicle environment 220. For example, autonomous vehicle sensor 212 may not capture all lanes 222-3.

[0040] Figure 2C Autonomous movement data 270 captured by the autonomous vehicle 210 of TESS 200 is depicted. Autonomous movement data 270 can be captured from vehicle environment 220 from sensor data. Autonomous movement data 270 can be object data corresponding to various objects located within vehicle environment 220. Autonomous movement data can include the following: guiding objects 272-1 and 272-2 (collectively referred to as 272) corresponding to lanes 222-1 and 222-2 and identifiers 224-1 and 224-2, respectively; and real-world objects 274-1, 274-2, and 274-3 (collectively referred to as 274). Real-world object 274 can be an autonomous movement data representation of entities in vehicle environment 220. For example, real-world object 274-1 can be a representation of pedestrian 242-1; real-world object 274-2 can be a representation of vehicle 240-1; and real-world object 274-3 can be a representation of stationary entity 230-1. The format of autonomous mobile data 270 can be exported based on performing image analysis, and can be related object formats such as wireframes, rendered polygons, point clouds, etc.

[0041] Based on guide object 272 and real-world object 274, the autonomous vehicle 210 of TESS 200 can successfully navigate the vehicle environment 220. For example, the autonomous vehicle 210 can stop, decelerate, change course, or otherwise react based on the presence or movement of real-world object 274, which is continuously updated based on autonomous movement data received from the autonomous vehicle's sensors 212. In another example, the autonomous vehicle 210 can orient itself and move along a path based on guide objects 272-1 and 272-2.

[0042] Figure 3 Alternative data 300 captured by TESS 200 is depicted, consistent with some embodiments of this disclosure. Alternative data 300 may be captured by the regulator of assets 228-2. For example, one or more objects may be captured by sensor 250 and provided to autonomous vehicle 210 by TESS 200. The objects provided by sensor 250 may be in a format consistent with those locally captured 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 prior to navigation using alternative data 300. For example, alternative data 300 may be in a relevant object format, such as wireframe, rendered polygon, point cloud, etc. Alternative data 300 may not be in the form of the original sensor data, such that any images of entities such as people, cars, animals, and assets captured by sensor 250 may not be provided to autonomous vehicle 210. Alternative data 300 may be saved or retained by the autonomous vehicle and may be used to determine the performance of autonomous vehicle 210 in navigating within vehicle environment 220.

[0043] Alternative data 300 may include guide objects 310-1 and 310-2 corresponding to identifiers 224-2 and 224-3 and lanes 222-3 and 222-4, respectively. Guide object 310-1 may be encoded using markers, variables, or other data elements. Autonomous vehicle 210 may interpret this marker as corresponding to an object interpretable based on permitted sensor data. Based on this marker, autonomous vehicle 210 can combine alternative data 300 with autonomous movement data 270 locally captured by autonomous vehicle sensors 212 to generate a complete picture of the vehicle environment 220. Alternative data 300 may include real-world objects 320-1, 320-2, 330, 340, and 350. Real-world objects 320-1 and 320-2 (collectively referred to as object 320) may correspond to pedestrians 242-2 and 242-3, respectively. Real-world object 330 may correspond to stationary entity 230-2. Real-world object 340 may correspond to vehicle 240-2. Real-world object 350 may correspond to stationary entity 230-3. Real-world object 350 may be encoded using data, variables, metadata, or other values ​​that convey information about stationary entity 230-3. For example, stationary entity 230-3 may be a flag that conveys movement-based rules (such as speed limits) to autonomous vehicle 210.

[0044] Figure 4 An example system diagram 400 of TESS consistent with some embodiments of this disclosure is depicted. System diagram 400 may describe one or more components of TESS. For example, system diagram 400 may include the following: autonomous vehicle 410, supervisor system 430, and central server 460. Autonomous vehicle 410, supervisor system 430, and central server 460 may be configured with one or more units or modules. Units and modules may include software (e.g., operating system, management program, daemon, job) and hardware (e.g., one or more processing circuits) configured to perform one or more TESS operations. Figure 1 Computer 100) or combination thereof (e.g., firmware, field-programmable gate array).

[0045] The autonomous vehicle 410 may include the following: 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 multiple autonomous vehicle sensors 420-1 to 420-N (collectively referred to as 420).

[0046] Vehicle management unit 412 can be configured to operate autonomous vehicle 410 and interface with regulator system 430 and central server 460. For example, vehicle management unit 412 can be configured to perform communication with central server 460. Vehicle management unit 412 can be configured to perform route-related regulator lookups. Regulator lookups may involve requesting the owner of privacy data from one or more regulators. The owner could be central server 460. The owner of the privacy data could be a regulator, and the lookup can be directed to regulator communication unit 414 to be sent to regulator system 430. Vehicle management unit 412 can also be configured to supervise the operation of other units of autonomous vehicle 410. Specifically, vehicle management unit 412 can be configured for route management, including receiving requests to travel to various destinations and requests for routes from central server 460. Vehicle management unit 412 can also be configured to capture and receive data from one or more on-board equipment management systems and make high-level decisions regarding autonomous vehicle 410. For example, the vehicle management unit 412 can be configured to decide to maintain a route, decide to request a different route, and check vehicle status, such as battery or fuel status.

[0047] The regulator communication unit 414 of the autonomous vehicle 410 can be configured to relay information to the vehicle management unit 412 and the regulator system 430. For example, the regulator communication unit 414 can be configured to transmit advanced sensor information, such as the status of alternative data being received, to the vehicle management unit 412. The regulator communication unit 414 can also be configured to receive alternative data and autonomous driving commands from the regulator system 430.

[0048] The vehicle control unit 416 of the autonomous vehicle 410 can be configured to receive commands and perform autonomous navigation for the autonomous vehicle 410. The vehicle control unit 416 can receive commands from the vehicle management unit 412. The vehicle control unit 416 can be configured to receive commands and alternative data (e.g., via the regulator communication unit 414) from the regulator system 430. The vehicle control unit 416 can be configured to operate based on sensor data (such as autonomous movement data acquired from the sensor processor unit 418).

[0049] Sensor processor unit 418 can be configured to acquire sensor data from autonomous vehicle sensor 420 and can be configured to detect, determine, or otherwise analyze image data. The sensor processor can be configured as an image processor. An image processor can be a combination of hardware and software, such as an application-specific integrated circuit (ASIC).

[0050] The supervisor system 430 may include a control and authentication unit 432, a vehicle communication unit 434, and an alternative data acquisition unit 436. The supervisor system 430 may be associated with assets such as buildings, land, water bodies, controlled areas, etc. Associated assets 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 on the asset. For example, the asset associated with and controlled by the supervisor system 430 may be a theme park, and the sensors 440 may be asset cameras owned and controlled by the theme park. Asset cameras may be configured to capture movement of a row of customers located in a parking lot or around the vicinity of the park's exterior (e.g., gates and adjacent streets and roads). The supervisor system 430 may also be communicatively coupled to a vehicle data control information database 450 (or a vehicle database). The vehicle database 450 may include one or more sensor profiles (e.g., camera specifications, LiDAR layout diagrams, radar blueprints). The vehicle database 450 may be organized, referenced, or entered based on various models of autonomous vehicles. For example, the autonomous vehicle 410 may have a specific configuration or layout of the autonomous vehicle sensor 420 and the processing capability of the sensor processor unit 418.

[0051] Control and authentication unit 432 can communicate with central server 460. The authentication unit can notify various autonomous vehicles that may or may not request to travel near the assets of regulator system 430 (e.g., areas adjacent to the assets, or roads, streets, bodies of water, or land or assets near the assets). Control and authentication unit 432 can also be configured to authenticate, approve, or otherwise allow central server 460 access to various vehicles. Control and authentication unit 432 can also be configured to proactively communicate with central server 460 in response to detecting the presence of unauthorized vehicles (e.g., autonomous or otherwise) entering the vicinity. In some embodiments, control and authentication unit 432 can request central server 460 to change routes, delay, or otherwise alter the passage of vehicles that may or may not be permitted to travel near the assets of regulator system 430.

[0052] Vehicle communication unit 434 can operate in conjunction with control and authentication unit 432. For example, vehicle communication unit 434 can operate by receiving the status of one or more nearby or soon-to-be-nearby vehicles. Vehicle communication unit 434 can also operate by instructing or otherwise performing guidance and TESS with the autonomous vehicle. For example, vehicle communication unit 434 can transmit various guidance, navigation, or movement instructions or commands to autonomous vehicle 410. Vehicle communication unit 434 can collect real-time sensor information (e.g., sensor data) based on sensor information received from sensor 440 or from the supervisor communication unit 414 of autonomous vehicle 410. Based on the sensor data, vehicle communication unit 434 can generate autonomous movement data.

[0053] Alternative data generation unit 436 can generate autonomous movement data for vehicle communication unit 434. Alternative data generation unit 436 may include one or more processing circuits configured to analyze sensor data from sensor 440 and perform object recognition. Alternative data generation unit 436 may include an image processor. Alternative data generation unit 436 can be configured to generate alternative data for autonomous vehicle 410 in real-time or near real-time. Vehicle communication unit 434 may generate instructions to delay autonomous movement or guidance until alternative data is generated and provided to the autonomous vehicle (e.g., a 30-second delay before performing autonomous movement and navigation). Alternative data generation unit 436 may convert or format alternative data based on sensor profile information stored in vehicle database 450 (e.g., formatting sensor data from sensor 440 into a format understandable by vehicle sensor processor 418).

[0054] The image processor and alternative data generation unit 436 of the sensor processor 418 can be configured to perform various image analysis techniques. These image analysis techniques can be based on machine learning and / or deep learning. These techniques may include, but are not limited to, region-based convolutional neural networks (R-CNN), You Only See Once (YOLO), edge matching, clustering, grayscale matching, gradient matching, invariant models, geometric hashing, scale-invariant feature transform (SIFT), accelerated robust feature transform (SURF), oriented gradient histogram (HOG) features, and single-shot multi-box detector (SSD). In some embodiments, the image processor can be configured to assist in identifying entities in the environment (e.g., analyzing facial images of animals, people, children, and other moving objects using models built based on training data, or analyzing images of cars using models built based on training data of trucks, cars, buses, and other vehicles).

[0055] In some embodiments, object detection algorithms, such as R-CNN, YOLO, SSD, SIFT, HOG features, or other machine learning and / or deep learning object detection algorithms, can be used to identify objects. The output of the object detection algorithm may include one or more identities of one or more corresponding objects and corresponding matching determinism. For example, sensor data from entities of autonomous vehicle sensor 420 or sensor 440, such as a child running, an adult walking, and an animal moving, can be analyzed. Using the relevant object detection algorithms, various entities can be identified as objects with boundaries, bounding boxes, polygons, wireframes, etc.

[0056] In some embodiments, a supervised machine learning model built using training data can be used to determine the features of an object. For example, an image can be input into the supervised machine learning model, and various classifications detected in the image can be output by the model. For example, features such as the object's material (e.g., cloth, metal, plastic, etc.), shape, size, color, and other characteristics can be output by the supervised machine learning model. Furthermore, the identification of objects (e.g., trees, faces, dogs, etc.) can be output as classifications determined by the supervised machine learning model. For example, if an autonomous vehicle 410 takes an image of another vehicle, the supervised machine learning algorithm can be configured to output the identity of the object (e.g., a car) and various characteristics of their vehicle (e.g., model, brand, color, etc.).

[0057] In some embodiments, photogrammetry can be used to determine the characteristics of an object. For example, photogrammetry can be used to approximate the shape and size of an object. In some embodiments, the characteristics of an object can be identified by referencing an ontology. For example, if an object is identified (e.g., using R-CNN), the object's identity can be referenced in an ontology to determine the object's corresponding attributes. An ontology can indicate attributes such as color, size, shape, and purpose of the object. An ontology can indicate the relationship of an object to one or more regulations or rules, such as a sign describing a speed limit, a sign setting conditional restrictions during the presence of a child, and a second object that is simultaneously identified and classified as a child.

[0058] Features may include the object's shape, object size (e.g., height, length, and width), number of objects (e.g., the number of headlights indicating a bus), object color, and / or other object attributes. In some embodiments, the output may generate a list including the object's identity and / or features (e.g., cotton shirt, metal glasses, animal fur, adult human height, etc.). In some embodiments, the output may include an indication that the object's identity or features are unknown. In some embodiments, various objects, object attributes, and relationships between objects (e.g., hierarchical and direct relationships) may be represented in a knowledge graph (KG) structure. Objects may be matched with other objects based on shared features (e.g., skin color of a person's cheeks and chin, reflectivity of a car windshield, pattern or repetitive signal strength indicating a certain color of a street dividing line or lane marking), relationships with other objects (e.g., eyes belong to the face), or objects belonging to the same category (e.g., two vehicles are currently moving vehicles, while a third vehicle is a stationary entity).

[0059] Central server 460 can be communicatively coupled to autonomous vehicle 410 and supervisor system 430. Central server 460 may include route guidance unit 462 and supervisory area unit 464. Route guidance unit 462 can be configured to receive, process, and respond to requests from autonomous vehicle (e.g., autonomous vehicle 410). Route guidance unit 462 can be configured to provide autonomous vehicle with directions, paths, roads, streets, and other information based on map data 466. Route guidance unit 462 can also be configured to monitor and track the location information of the supervisor along the route of the currently operating vehicle. For example, supervisor system 430 can query central server 460 for the status of any vehicle routed to the vicinity of supervisor system 430's assets. Route guidance unit 462 can respond to the query by providing the status of autonomous vehicle 410 (e.g., when autonomous vehicle 410 approaches or becomes close to supervisor system 430's assets). Route guidance unit 462 can be configured to transmit route-related conditions. For example, conditions may allow one or more autonomous vehicle sensors 420 of autonomous vehicle 410 to not capture data related to one or more assets located near the route.

[0060] The supervisory area unit 464 may communicate only with the supervisor system and the route guidance unit 462. The route guidance unit 462 may make requests to the supervisory area unit 464. The supervisor system may also issue requests to the supervisory area unit 464. These requests may take the form of a request to determine whether the autonomous vehicle's route should pass through or near the boundary of a supervised asset. These requests may be in the form of updates to the supervisory area information database 468. The supervisory area unit 464 can manage supervised asset information and can store and update asset information in the supervisory area information database 468. Based on this information, the supervisory area unit 464 can update the route guidance unit 462 and notify the route guidance unit 462 whether the autonomous vehicle should pass within the path of the supervised asset.

[0061] Figure 5 An example method 500 for performing a TESS, consistent with some embodiments of this disclosure, is described. The TESS can be performed by a processing device of a computer system (e.g., computer 100). When an autonomous vehicle requests a route (navigation request) (e.g., Figure 2A When the autonomous vehicle (210) is in operation, TESS can begin at 510. This request can be sent by the autonomous vehicle and can also be sent to a central server (e.g., [example server name]). Figure 4 (Central server 460). Route requests may include vehicle information of the autonomous vehicle. Vehicle information may include one or more of the following: the current location of the autonomous vehicle; the 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 pattern information. Pattern information may include parameters of alternative data that can be processed by the autonomous vehicle.

[0062] In 512, routes can be generated for autonomous vehicles. These routes can be generated by a central server (e.g., Figure 4 The route is generated by a central server (460). Route generation may include one or more route factors (e.g., traffic, speed limits, alternative routes, weather patterns, etc.). Route generation may be influenced by regulators. For example, regulators may send suggestions to the central server, and the central server may adjust the route during route generation. Route generation may be based on querying a map database (e.g., [database name missing]). Figure 4 Map data 466).

[0063] At 514, routes can be generated for one or more regulatory zones 514. Searching for regulatory zones 514 can be based on searches within predefined thresholds. For example, a predefined threshold could be searching for regulatory zones within one mile of the route. In another example, a predefined threshold could be searching for regulatory zones within one-third of the route. Searching for regulatory zones 514 can also be searching for regulatory zones adjacent to the route, such as streets along the route that are adjacent to a regulatory zone.

[0064] If a monitored area is found in the search at 516:Y (Yes), vehicle information can be received at 520. Vehicle information can be received by the monitor that is found to be adjacent to the generated route. For example, given... Figure 4 Based on the determination that autonomous vehicle 410 has a generated route that includes a portion of an asset controlled by regulator system 430, regulator system 430 can receive vehicle information. The received vehicle information may be an unchanged version of the vehicle information received at 510 along with the route request. 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 stripped by a central server before being received at 520. At 522, vehicle controllability can be determined. This determination can be made by a regulator (e.g., regulator system 430). This determination can be made by analyzing information received by the autonomous vehicle, for example, by analyzing a list of one or more sensors and a list of the capabilities of one or more sensors. If it is determined at 524: N (No) that the vehicle is uncontrollable, the route can be regenerated at 512. At 512, the route can be regenerated by rerouting the autonomous vehicle away from the vicinity of the asset.

[0065] If it is determined at 524:Y (Yes) that the vehicle is controllable, then at 526 the regulator's asset can be scanned to obtain alternative data. The scan for alternative data could be a scan of data with sufficient resolution. For example, a camera located on the asset is capable of capturing entities with sufficient pixel resolution. The scan could include determining that sensors are capable of capturing entities around the asset at a sufficient frame rate to capture the movement or speed of the entities. For example, image processing and detection could determine that the asset has a person capable of moving at 27 miles per hour. In another example, the scan could include detecting a vehicle capable of moving at 100 miles per hour. Based on the speed capabilities of entities near the asset, the regulator can determine the presence of sensors with a frame rate capable of capturing entities.

[0066] Scanning for alternative data can be performed by the regulator who owns and controls the asset and the asset's sensors. For example, regulator system 430 may be configured to observe sensor 440 and determine that the alternative data is sufficient. Scanning for alternative data may include scanning the data that imparts pattern information received from the autonomous vehicle. Since pattern information may include the format of the data, the scan may be a scan of data that conforms to the format requested / required by the autonomous vehicle. In some embodiments, the data may need to be converted, and scanning for alternative data may include scanning processing power, memory bandwidth, or other computing resources available to perform conversion to the autonomous vehicle's local format. Scanning for alternative data may include determining that the regulator can provide data of sufficient quality and that the regulator can provide data that conforms to the pattern required by the autonomous vehicle. If no alternative data exists for the asset at 528:N (No), a route can be regenerated at 512. At 512, the route can be regenerated by rerouting the autonomous vehicle away from the vicinity of the asset. If alternative data exists at 528:Y (Yes), the alternative data can be formatted and sent at 530. The alternative data can be transmitted back to the central server for transmission to the autonomous vehicle. Alternative data can be directly transmitted to autonomous vehicles.

[0067] After the alternative data is transmitted to the vehicle, a regeneration route 514 can be studied for additional monitored areas. If no monitored area is found at 516:N (No), the vehicle can be guided at 518. Route selection for the autonomous vehicle at 518 may include granting permission for the autonomous vehicle to begin autonomous movement. Method 500 ends after the vehicle route is determined at 518.

[0068] Figure 6 An example method 600 for performing a TESS, consistent with some embodiments of this disclosure, is depicted. The TESS can be performed by a computer system (e.g., Figure 1 The method is executed by the processing device of the computer 100. Method 600 can be executed after method 500, for example, after the autonomous vehicle starts the route generated by the central server.

[0069] When identifying the regulatory area at 610, method 600 begins. This area can be controlled by autonomous vehicles (e.g., Figure 4 Autonomous vehicles (410) can be identified based on a central server. Figure 4The autonomous vehicle identifies the area by receiving route information from the central server (460). Identification of the autonomous vehicle can 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 geolocate the autonomous vehicle. The autonomous vehicle may also have received information about the latitude and longitude of the monitored area from the central server. Identification of the autonomous vehicle can also be based on receiving notifications from the regulator. For example, the regulator may broadcast a signal notifying nearby autonomous vehicles that they have requested authentication before passing through the vicinity.

[0070] In section 612, information about autonomous vehicles can be sent to regulators. This information may include: the autonomous vehicle's identifier; the autonomous vehicle's equipment information; the autonomous vehicle's location; the autonomous vehicle's sensor data; and pattern information of potential alternative data that can be received by the autonomous vehicle.

[0071] At 620, permission for the autonomous vehicle to be near the regulator's assets can be confirmed. This permission can be confirmed based on information received from the autonomous vehicle. This permission can be granted by the regulator (e.g., Figure 4 The regulator system (430) confirms. If the regulator does not grant permission at 622:N (No), the regulator can notify the autonomous vehicle at 624. This notification can instruct the autonomous vehicle to prohibit passage near the regulator's assets. Based on receiving the notification, the autonomous vehicle can request a new route at 614, and method 600 can end.

[0072] If the regulator does grant permission at 622:Y (Yes), then at 626, the regulator can transmit a confirmation of permission to the autonomous vehicle. The permission confirmation at 626 may include the commencement of alternative data configured based on the scheme information sent at 612. Based on the received permission confirmation, at 616, the autonomous vehicle can begin operation in a coordinated manner. Coordinated operation may include receiving alternative data from the regulator. For example, the regulator system 630 may continuously send alternative data and control information to the autonomous vehicle. After the autonomous vehicle begins coordinated operation with the regulator at 616, method 600 can terminate.

[0073] Figure 7 An example method 700 for performing a TESS, consistent with some embodiments of this disclosure, is depicted. The TESS can be performed by a computer system (e.g., Figure 1 The method 700 can be executed by the processing device of a computer 100. In some embodiments of executing TESS, the method 700 can be executed by an autonomous vehicle (e.g., the autonomous vehicle 210 of Figure 2) and a supervisor (e.g., Figure 4The method is executed in combination with the supervisory system 430. Method 700 can be executed after method 600. For example, method 700 begins when the autonomous vehicle enters the supervisory area. Entering the supervisory area can be near the vicinity of the supervisory area.

[0074] At 712, the autonomous vehicle can transmit its current position and other telemetry information (e.g., speed, altitude, pitch, autonomous vehicle sensor status) to the regulator. For example, autonomous vehicle 410 can transmit its position and telemetry information to regulator system 430. At 720, the regulator can begin attempting assistance with the autonomous vehicle. Attempting assistance may include scanning the received position information. Attempting assistance may include determining that the autonomous vehicle can perform autonomous movement and navigation without using or limiting the capabilities of one or more of its autonomous vehicle sensors. If at 722: N (No) it is determined that the autonomous vehicle cannot be assisted, then at 724, the regulator can request the autonomous vehicle to leave the regulated area. Based on the received request to leave the regulated area, the autonomous vehicle can adjust its route to leave the regulated area as quickly as possible. At 730, after receiving the leave request from the regulator, the autonomous vehicle can leave the regulated area 730.

[0075] If the autonomous vehicle is assisted at 722:Y (Yes), then at 740, the regulator can determine whether the autonomous vehicle is operational based on alternative data. This determination can be based on whether the autonomous vehicle is configured to receive alternative data from the regulator. This determination can also be based on whether the autonomous vehicle is configured to synthesize the alternative data with one or more autonomous vehicle data generated by the autonomous vehicle from one or more autonomous vehicle sensors.

[0076] If the autonomous vehicle is capable of operating based on alternative data at 740:Y (Yes), the alternative data operation can begin at 750. The alternative data operation can be performed continuously. For example, it can be performed every second, every 100 milliseconds, 60 times per second, etc. The alternative data operation may include transmitting alternative data to the autonomous vehicle. Before being transmitted to the autonomous vehicle, the alternative data may be converted by the regulator based on received pattern information. In some embodiments, the autonomous vehicle may perform the conversion of the alternative data for autonomous movement and navigation. The alternative data operation may also include restricting the autonomous vehicle's sensors. For example, the regulator may send a disable signal, a restriction signal, or another signal, and in response, the autonomous vehicle may disable or restrict one or more autonomous vehicle sensors from capturing sensor data. At 730, the alternative data operation can be performed by the regulator sending the alternative data and the autonomous vehicle navigating in the environment until the autonomous vehicle leaves the regulated area.

[0077] If the autonomous vehicle cannot operate based on alternative data at point 740:N (No), then at point 760 the autonomous vehicle traverses the monitored area based on remote operation. Remote operation 760 may include continuously performing autonomous vehicle navigation and movement to a regulator or central server based on unloaded sensor data and telemetry information. The regulator or central server may perform autonomous driving, and the autonomous vehicle may not be able to capture, record, or otherwise analyze sensor data from its sensors. At point 730, remote operation may continue until the autonomous vehicle leaves the monitored area. After the autonomous vehicle leaves the area at point 730, method 700 may terminate.

[0078] This invention can be any possible system, method, and / or computer program product at any level of integration technical detail. The computer program product may include one or more computer-readable storage media having computer-readable program instructions thereon for causing a processor to execute aspects of the invention.

[0079] Computer-readable storage media can be tangible devices capable of retaining and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices such as punched cards or raised structures in recesses on which instructions are recorded, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as being a transient signal, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0080] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or downloaded via a network to an external computer or external storage device, such as the Internet, a local area network (LAN), a wide area network (WAN), and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the corresponding computing / processing device.

[0081] Computer-readable program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk and C++, 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 case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet provided by an Internet service provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute the computer-readable program instructions by utilizing the status information of the computer-readable program instructions to personalize the electronic circuitry, thereby performing aspects of the invention.

[0082] This document describes various aspects of the invention 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, can be implemented by computer-readable program instructions.

[0083] These computer-readable program instructions may be provided to a processor of a computer or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored includes an article of manufacture comprising instructions for implementing aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0084] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device, thereby producing a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus or other device, perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0085] The flowcharts and block diagrams in the accompanying drawings 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 a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions marked in the blocks may occur in a non-linear order. For example, two blocks shown consecutively may actually be completed as a single step, executed simultaneously or substantially simultaneously with partial or complete time overlap, or these blocks may sometimes be executed in reverse order, depending on the functions involved. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a system based on dedicated hardware that performs the specified function or action or executes a combination of dedicated hardware and computer instructions.

[0086] For illustrative purposes, various embodiments of this disclosure have been described, but these descriptions are not intended to be exhaustive or limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been chosen to explain the principles of the embodiments, their practical application, or technical improvements to technologies found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0087] For illustrative purposes, various embodiments of this disclosure have been described, but these descriptions are not intended to be exhaustive or limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been chosen to explain the principles of the embodiments, their practical application, or technical improvements to technologies found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method comprising: Detect navigation requests, the navigation requests including the destination of the autonomous vehicle from its starting position; Based on the navigation request, identify the route from the starting location to the destination; Based on the navigation request, perform a regulator lookup related to the route. The regulators mentioned above are looking for the owner of the privacy data requested by the first of one or more regulators; In response to the regulator's search, a condition related to the route is received, which instructs one or more autonomous vehicle sensors of the autonomous vehicle not to capture data related to a first asset of the first regulator, the first asset being located near the route; as well as In response to the conditions allowing, one or more autonomous vehicle sensors of the autonomous vehicle are restricted, wherein the restriction includes restricting the capture of the first asset by the one or more autonomous vehicle sensors. The owner of the privacy data of the first regulator is the first regulator, and The execution of the regulator search includes: Receive a response from the central server, the response indicating that the first regulator is associated with the condition allowing; as well as Based on the response, the first regulator is prompted if the conditions permit.

2. The method according to claim 1, further comprising: Autonomous movement on the route is controlled based on autonomous movement data received from one or more autonomous vehicle sensors; During the autonomous movement, the autonomous vehicle's approach to the first asset of the first regulator is detected; and In response to detecting the proximity, a portion of the video signal from the camera of the one or more autonomous vehicle sensors is erased; Generate alternative data related to the first asset of the first regulator; as well as Replace a portion of the autonomous movement data, wherein the portion of the autonomous movement data corresponds to the camera having the replacement data.

3. The method according to claim 2, wherein, The portion of the video signal is the entire image of the video signal.

4. The method according to claim 2, wherein, The steps for generating the alternative data include: Request moving object data generated by the first asset camera owned and controlled by the first regulator.

5. The method according to claim 4, wherein, The mobile object data is selected from the group including people walking or running, animals crossing vehicle paths, another autonomous vehicle driving, another autonomous vehicle flying, street signs, and lane guidance.

6. The method according to claim 1, further comprising: The system receives a second condition that allows it to instruct the autonomous vehicle's sensors not to capture data relating to a second asset of the second of the one or more regulators, the second asset being located near the route. Autonomous movement on the route is controlled based on autonomous movement data received from one or more autonomous vehicle sensors of the autonomous vehicle. During the autonomous movement, the autonomous vehicle's approach to the second asset of the second regulator is detected; and In response to detecting the proximity, the first radar sensor among the one or more autonomous vehicle sensors of the autonomous vehicle is disabled; Generate alternative data related to the second asset of the second regulator; as well as Replace a portion of the autonomous movement data, wherein the portion of the autonomous movement data corresponds to the first radar sensor having the replacement data.

7. The method according to claim 6, wherein, The steps for generating the alternative data include: The request is for moving object data generated from the second radar sensor of a second autonomous vehicle owned and controlled by the second regulator.

8. The method according to claim 1, wherein, The regulator's search includes querying whether the route is close to any regulated area, and the criteria allow indicating that multiple regulated areas are close to the route.

9. The method according to claim 1, wherein, The first regulator is the central server.

10. A system comprising: A memory containing one or more instructions; as well as A processor, communicatively coupled to the memory, is configured to: in response to reading the one or more instructions. Detect navigation requests, the navigation requests including the destination of the autonomous vehicle from its starting position; Based on the navigation request, identify the route from the starting location to the destination; Based on the navigation request, a regulator lookup related to the route is performed, the regulator lookup being related to the owner of the privacy data of the first regulator among one or more regulators; In response to the regulator's search, a condition related to the route is received, which instructs one or more autonomous vehicle sensors of the autonomous vehicle not to capture data related to a first asset of the first regulator, the first asset being located near the route; and In response to the stated conditions, one or more autonomous vehicle sensors of the autonomous vehicle are restricted, wherein restricting the one or more autonomous vehicle sensors includes restricting the one or more autonomous vehicle sensors from capturing the first asset. The owner of the privacy data of the first regulator is the first regulator, and The execution of the regulator search includes: Receive a response from the central server, the response indicating that the first of the one or more regulators is associated with the condition allowing; as well as Based on the response, the first regulator is prompted if the conditions permit.

11. The system according to claim 10, wherein, The processor is also configured to: Autonomous movement is performed on the route based on autonomous movement data received from one or more autonomous vehicle sensors; During the autonomous movement, the autonomous vehicle's approach to the first asset of the first regulator is detected; and In response to detecting the proximity, a portion of the video signal from the camera of the one or more autonomous vehicle sensors is erased; Generate approximate alternative data for the first asset of the first regulator; and Replace a portion of the autonomous movement data, wherein the portion of the autonomous movement data corresponds to the camera having the replacement data.

12. The system according to claim 11, wherein, The portion of the video signal is the entire image of the video signal.

13. The system of claim 11, wherein generating the alternative data comprises: Request moving object data generated by the asset camera owned and controlled by the first regulator.

14. The system according to claim 13, wherein, The mobile object data is selected from the group including people walking, children running on the sidewalk, animals crossing the road, another autonomous vehicle driving, another autonomous vehicle flying, street signs, and lane guidance.

15. A computer program product, the computer program product comprising: One or more computer-readable storage media; as well as Program instructions co-stored on the one or more computer-readable storage media, the program instructions being configured to: Detect navigation requests, the navigation requests including the destination of the autonomous vehicle from its starting position; Based on the navigation request, identify the route from the starting location to the destination; Based on the navigation request, a regulator lookup related to the route is performed, the regulator lookup being related to the owner of the privacy data of the first regulator among one or more regulators; In response to the regulator's search, a condition related to the route is received, which instructs one or more autonomous vehicle sensors of the autonomous vehicle not to capture data related to a first asset of the first regulator, the first asset being located near the route; In response to the stated conditions, one or more autonomous vehicle sensors of the autonomous vehicle are restricted, wherein restricting the one or more autonomous vehicle sensors includes restricting the one or more autonomous vehicle sensors from capturing the first asset. The owner of the privacy data of the first regulator is the first regulator, and The execution of the regulator search includes: Receive a response from the central server, the response indicating that the first of the one or more regulators is associated with the condition allowing; as well as Based on the response, the first regulator is prompted if the conditions permit.

16. The computer program product according to claim 15, wherein, The regulator's search includes querying whether the route is close to any regulated area, and the criteria allow indicating that multiple regulated areas are close to the route.

17. The computer program product according to claim 15, wherein, The program instructions are also configured as follows: The system receives a second condition that allows it to instruct the autonomous vehicle's sensors not to capture data relating to a second asset of the second of the one or more regulators, the second asset being located near the route. Autonomous movement is performed on the route based on autonomous movement data received from the one or more sensors; During the autonomous movement, the autonomous vehicle's approach to the second asset of the second regulator is detected; and In response to detecting the proximity, the first radar sensor among the one or more autonomous vehicle sensors of the autonomous vehicle is disabled; Generate alternative data related to the second asset of the second regulator; and Replace a portion of the autonomous movement data, wherein the portion of the autonomous movement data corresponds to the first radar sensor having the replacement data.

18. The computer program product according to claim 17, wherein, Generating the alternative data includes: The request is for moving object data generated from the second radar sensor of a second autonomous vehicle owned and controlled by the second regulator.

19. The computer program product according to claim 15, wherein, The first regulator is the central server.

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