Restrictions on access to vehicle-related information

Abstract

To restrict access to vehicle-associated information based on privacy criteria.SOLUTION: In some examples, a system receives vehicle-associated information from a data source associated with a vehicle, and restricts access to the vehicle-associated information based on at least one privacy criterion selected from among a machine learning use criterion relating to the use of the vehicle-associated information by a machine learning model, a vehicle motion criterion relating to a movement status of the vehicle, and a person identification information criterion relating to identification information of a person in the vehicle.SELECTED DRAWING: Figure 2

Description

Background Art

[0001] Detailed Description of the Invention A vehicle can include or receive data from a source that provides vehicle-related information. The data source can exist inside or outside the vehicle. An entity can attempt to access vehicle-related information regardless of whether it exists inside or outside the vehicle.

[0002] To protect privacy (e.g., the privacy of passengers in the vehicle, the privacy of the vehicle owner, etc.), to provide security, or for other purposes, it may be desirable to restrict access to vehicle-related information. In some embodiments, access to vehicle-related information can be based on permissions associated with a requesting entity such as a user, program, or machine. The access permission can define whether an entity is permitted to access vehicle-related information. The access permission can be an all-or-nothing permission, i.e., the requesting entity can either have full access to the vehicle-related information or no access to the vehicle-related information. The coarseness of the access control using permissions can result in either an overly large amount of vehicle-related information being exposed or an overly small amount of vehicle-related information being provided.

[0003] Examples of vehicle-related information include, but are not limited to, data from vehicle sensors (e.g., speedometer for measuring vehicle speed, accelerometer for measuring vehicle acceleration, vehicle fluid monitoring sensors, tire pressure sensors, temperature sensors, pressure sensors, humidity sensors, engine speed sensors, etc.), vehicle location data, still or video images (inside or outside the vehicle), data stored in storage media within the vehicle, identification information relating to vehicle occupants (driver or passengers), data relating to the use of vehicle safety equipment (e.g., seat belts, anti-lock brakes, collision avoidance control systems, etc.), data relating to facial recognition or other biometric authentication data, data relating to vehicle occupants (e.g., weight, seat position, etc.), or any or any combination of any other vehicle-related information.

[0004] Vehicle-related information may be generated from data sources within the vehicle, or alternatively, or in addition, from data sources outside the vehicle. For example, data sources may be components such as roadside units (RSUs) or remote servers. Embodiments of RSUs may include traffic lights, electronic road signs, or any other electronic devices located on or near the road through which a vehicle is traveling. Remote servers may refer to computer systems (including one or more computers), such as computer systems that are components of clouds, data centers, or web environments. The present invention provides, for example, the following items: (Item 1) A non-transient machine-readable storage medium, wherein the non-transient machine-readable storage medium includes instructions, and the instructions, in response to execution, are transmitted to the system. Receiving vehicle-related information from data sources associated with the vehicle, Restricting access to the above vehicle-related information based on at least one privacy criterion selected from among machine learning usage criteria related to the use of the above vehicle-related information by machine learning models, vehicle motion criteria related to the vehicle's movement status, or person identification criteria related to the identification information of persons inside the above vehicle. A non-transient machine-readable memory medium that enables the following. (Item 2) The non-transient machine-readable storage medium described above, wherein, upon execution of the above instruction, causes the system to restrict access to the vehicle-related information based on the machine learning usage criteria by reducing the sampling rate of the vehicle-related information to the machine learning model. (Item 3) The above instructions, in accordance with their execution, cause the system to further restrict access to the vehicle-related information by blocking access to the vehicle-related information based on location-based criteria if the vehicle has a specified relevance to the geofence, as described in any one of the above items. (Item 4) The above instruction, upon execution, will affect the above system. To access the above vehicle-related information, the entity receives requests and The determination that the above entity has permission to access the above vehicle-related information, In response to the determination that the above entity has the above permission, it is necessary to determine whether the above vehicle has the prescribed relevance to the above geofence, If the above vehicle has a relation to the above geofence as defined above, then the above entity shall be prevented from accessing the above vehicle-related information. A non-transient machine-readable storage medium described in any one of the above items, which enables the following to occur. (Item 5) The above instruction, upon execution, will affect the above system. If the above vehicle does not have the above-mentioned relevance to the above geofence, the above entity shall be allowed to access the above-mentioned vehicle-related information. A non-transient machine-readable storage medium described in any one of the above items, which enables the following to occur. (Item 6) A non-transient machine-readable storage medium as described in any one of the above items, which, upon execution of the above instruction, causes the system to restrict access to the vehicle-related information by preventing access to the vehicle-related information based on the vehicle motion criteria when the vehicle is moving. (Item 7) A non-transient machine-readable storage medium as described in any one of the above items, which, upon execution of the above instruction, causes the system to restrict access to the vehicle-related information by preventing access to the vehicle-related information based on the vehicle motion criteria if the vehicle is not moving. (Item 8) A non-transient machine-readable storage medium as described in any one of the above items, which, upon execution of the above instruction, causes the system to restrict access to the vehicle-related information by blocking access to the vehicle-related information based on the vehicle motion criteria if the vehicle's speed has a specified correlation with a speed threshold. (Item 9) The above-mentioned vehicle-related information comprises image data captured by the vehicle's camera, and the above-mentioned command, upon execution, causes the system to restrict access to the video data based on the vehicle motion criteria, as described in any one of the above items, on a non-transient machine-readable storage medium as described in any one of the above items. (Item 10) A non-transient machine-readable storage medium as described in any one of the above items, which, upon execution of the above instruction, causes the system to restrict access to the vehicle-related information by blocking access to the vehicle-related information based on the person identification information criteria if the identification information of the person in the vehicle matches the prescribed identification information. (Item 11) A non-transient machine-readable storage medium as described in any one of the above items, which, upon execution of the above instruction, causes the above system to restrict access to the above vehicle-related information by allowing access to the above vehicle-related information based on the above person identification information criteria if the above identification information of the above person in the above vehicle differs from the above prescribed identification information. (Item 12) The identification information specified in the above provisions of the above personal identification information standards pertains to the driver or occupant of the above vehicle, and is a non-transient machine-readable storage medium as described in any one of the above items. (Item 13) A non-transient machine-readable storage medium as described in any one of the above items, which, upon execution of the above instruction, causes the above system to restrict access to the above seat belt information based on a seat belt information access criterion that stipulates that seat belt information will not be provided for drivers or passengers of a specified category. (Item 14) A non-transient machine-readable storage medium as described in any one of the above items, which, upon execution of the above instruction, causes the above system to restrict access to the above vehicle-related information by obscuring the person identification information within the above vehicle-related information. (Item 15) The above system is a non-transient machine-readable storage medium as described in any one of the above items, which is a component of the above vehicle. (Item 16) The above system is a non-transient machine-readable storage medium located remotely from the above vehicle, as described in any one of the above items. (Item 17) The above data source is a non-transient, machine-readable storage medium located inside the above vehicle, as described in any one of the above items. (Item 18) The above data source is a non-transient, machine-readable storage medium located outside the above vehicle, as described in any one of the above items. (Item 19) A computer system, the above computer system is One or more hardware processors, A non-transient storage medium for storing instructions executable on one or more of the above-mentioned hardware processors, wherein the instructions are: Receiving vehicle-related information from data sources associated with the vehicle, Restricting access to the vehicle-related information based on at least one privacy criterion selected from a machine learning usage criterion related to the use of the vehicle-related information by a machine learning model, a vehicle motion criterion related to the movement status of the vehicle, or a person identification information criterion related to the identification information of a person within the vehicle A non-transitory storage medium that causes the above to be performed A computer system comprising the above (Item 20) A method of a computer system, comprising: Receiving vehicle-related information from a data source associated with a vehicle in the computer system; Restricting access to the vehicle-related information by the computer system based on at least one privacy criterion selected from a machine learning usage criterion related to the use of the vehicle-related information by a machine learning model, a vehicle motion criterion related to the movement status of the vehicle, or a person identification information criterion related to the identification information of a person within the vehicle A method comprising the above (Abstract) In some embodiments, the system receives vehicle-related information from a data source associated with a vehicle and restricts access to the vehicle-related information based on at least one privacy criterion selected from a machine learning usage criterion related to the use of the vehicle-related information by a machine learning model, a vehicle motion criterion related to the movement status of the vehicle, or a person identification information criterion related to the identification information of a person within the vehicle

Brief Description of the Drawings

[0005] Some implementations of the present disclosure are described with respect to the following figures

[0006] [Figure 1] FIG. 1 is a block diagram of a vehicle and a vehicle-related information filtering engine according to some embodiments

[0007] [Figure 2]FIG. 2 is a flow diagram of a process according to some embodiments.

[0008] [Figure 3] FIG. 3 is a block diagram of a computer system according to some embodiments.

[0009] Throughout the drawings, like reference numerals designate similar, but not necessarily the same, elements. The figures are not necessarily to scale, and the sizes of some components may be exaggerated to more clearly illustrate the embodiments shown. Further, the drawings provide embodiments and / or implementations consistent with the description, however, the description is not limited to the embodiments and / or implementations provided in the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] DETAILED DESCRIPTION In the present disclosure, the use of the terms “a,” “an,” or “the” is intended to include the plural as well, unless the context clearly indicates otherwise. Also, as used in the present disclosure, the terms “includes,” “including,” “comprises,” “comprising,” “have,” or “having” specify the presence of the recited elements, but do not preclude the presence or addition of other elements.

[0011] Figure 1 is a block diagram of an exemplary array, including a vehicle 102 having a vehicle-related information filtering engine 104, as in some implementations of the present disclosure. The vehicle-related information filtering engine 104 is used to control access by entities (users, programs, and / or machines) to vehicle-related information from various data sources. Entities that may request access to vehicle-related information may include entities inside or outside the vehicle 102. Controlling access to vehicle-related information to various entities can provide some or all of the following benefits: data privacy is enhanced by reducing the sampling rate at which vehicle-related information is provided to entities; data privacy is enhanced by controlling the time at which vehicle-related information is made available to entities based on one or more criteria (e.g., vehicle motion status or engine ignition status, vehicle speed, time, location, occupant identification information, etc.).

[0012] Figure 1 shows the vehicle-related information filtering engine 104 located inside the vehicle 102, but in other embodiments, the vehicle-related information filtering engine 104 may be located outside the vehicle 102. For example, the vehicle-related information filtering engine 104 may be a component such as an RSU 116 or a remote server 118.

[0013] As used herein, “engine” may refer to a hardware processing circuit, which may include any or any combination of a microprocessor, a core of a multicore microprocessor, a microcontroller, a programmable integrated circuit, a programmable gate array, or any other hardware processing circuit. Alternatively, “engine” may refer to a combination of a hardware processing circuit and machine-readable instructions (software and / or firmware) that can be executed on the hardware processing circuit.

[0014] Various exemplary data sources for vehicle-related information are depicted in Figure 1. The data sources are located within the vehicle 102 and may include sensors 106, cameras 108, data loggers 109, a Global Positioning System (GPS) receiver 110 for receiving location data from GPS satellites, a storage system 112, or other data sources. External data sources that may provide vehicle-related information may include components such as an RSU 116 or a remote server 118.

[0015] The sensor 106 within the vehicle 102 can be coupled to individual vehicle subsystems 107, such as the vehicle engine, vehicle transmission, brakes, tires, sound system, battery, suspension, navigation system, climate control system, seat belts, airbags, collision prevention system, or any combination of any other vehicle subsystems.

[0016] Sensor 106 is used to measure metrics that represent the characteristics of the vehicle subsystem 107, including characteristics related to the operation of the vehicle subsystem 107, wear of the vehicle subsystem 107, errors or faults within the vehicle subsystem 107, etc.

[0017] In further embodiments, the sensor 106 may be used to perform environmental measurements of the environment either inside and / or outside the vehicle 102. Examples of environmental metrics that can be measured by the environmental sensor include, namely, any or any combination of, temperature, pressure, humidity, etc. The sensor may also be used to detect the condition of the road over which the vehicle 102 is traveling, such as whether potholes are present in the road, whether the road is paved or unpaved, etc.

[0018] Camera 108 of vehicle 102 is used to capture images, including still and / or video images. Camera 108 can be used to capture images of objects inside vehicle 102 (e.g., occupants inside vehicle 102, inanimate objects inside vehicle 102, etc.) or images of objects outside vehicle 102 (e.g., the environment on the four sides of vehicle 102, including any other vehicles or people that may be present in close proximity to vehicle 102).

[0019] The data logger 109 may include hardware or machine-readable instructions for logging various data of the vehicle 102, such as data related to the vehicle's operation. The data logger 109 can store the logged data in the storage system 112.

[0020] The GPS receiver 110 can provide location data related to the vehicle 102. Although described as a GPS receiver, in other embodiments, a location receiver (different from a GPS receiver) may receive location data from other types of satellites or from other location systems such as base stations in a cellular network.

[0021] The storage system 112 of the vehicle 102 can be used to store vehicle-related information 114, which may be provided by various data sources, including a sensor 106, a camera 108, a data logger 109, and a GPS receiver 110. The storage system 112 can be implemented using one or more storage devices, such as disk-based storage devices and solid-state drives.

[0022] The vehicle-related information 114 stored in the memory system 112 may also include vehicle-related information received from data sources outside the vehicle 102, including the RSU 116 and the remote server 118. Embodiments of vehicle-related information that may be received from the RSU 116 include, namely, any or any combination of the following: the current state of traffic lights (e.g., whether the lights are displaying red, yellow, or blue), images obtained by the RSU 116's camera, and roadway-related traffic information. The RSU 116 may be located at the intersection of multiple roadways, along the side of the roadway, or at other locations near the roadway.

[0023] The vehicle-related information that may be provided by the remote server 118 may include any or a combination of the following: roadway traffic information, control information for controlling the direction of the vehicle 102, etc.

[0024] The RSU 116 and the remote server 118 can communicate with the vehicle 102 via individual wireless links 120 and 122, such as wireless links in a cellular network or a wireless local area network (WAN).

[0025] Vehicle 102 includes a communication interface 124 that enables vehicle 102 to communicate wirelessly with other endpoints such as RSU 116 and remote server 118. The communication interface 124 may include signal transceivers for transmitting and receiving signals, and one or more protocol layers that govern the protocol of the information communicated over wireless links 120 and 122.

[0026] Vehicle 102 includes a vehicle network 125 that enables various components of vehicle 102 to communicate with one another. The vehicle network 125 may include a wired network and / or a wireless network.

[0027] According to some implementations of this disclosure, the vehicle-related information filtering engine 104 controls access to vehicle-related information from various data sources, including any of the aforementioned.

[0028] The vehicle-related information filtering engine 104 can use access control rule information 126 stored in the vehicle's memory 128. The memory 128 can be implemented using one or more memory devices such as a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, or a flash memory device.

[0029] The access control rule information 126 includes various access rules governing access to various fragments of vehicle-related information provided by any or a combination of internal or external data sources. In response to a request from an entity (internal or external to vehicle 102) regarding a given fragment of vehicle-related information, or when an entity requests to be notified when the vehicle-related information changes state, the vehicle-related information filtering engine 104 accesses one or more access rules in the access control rule information 126 to determine whether and when to authorize the entity to access the given fragment of vehicle-related information. In some cases, the entity may be an application or program internal or external to vehicle 102. The access control rule information 126 may include separate access control rules for different entities.

[0030] Access control rule information 126 can be provided to the vehicle 102 from an external source such as a remote server 118. In some embodiments, the access control rule information 126 can be dynamically updated over time.

[0031] Various examples of access rules are provided below.

[0032] Access rules for machine learning operations

[0033] Vehicle 102 also includes a computer 130 on which a machine learning (ML) model 132 can be run. A machine learning model refers to a model that can make predictions based on input data, such as based on input vehicle-related information 134 from one or more data sources. The machine learning model 132 can generate outputs that include predictions based on the input vehicle-related information 134. For example, the machine learning model 132 can classify the input vehicle-related information 134 into selected categories of several different categories. In another embodiment, the machine learning model 132 can generate output values ​​that make predictions (e.g., vehicle 102 may experience a breakdown, vehicle 102 may be about to collide with another object, whether an object is approaching vehicle 102, identification information or presence of occupants in vehicle 102), indications of actions to be taken (e.g., pay when vehicle 102 approaches the parking lot exit gate, apply the brakes, etc.).

[0034] The machine learning model 132 can be trained to make its predictions. Training may be based on training data, which includes various fragments of vehicle-related information and labels assigned to the fragments of vehicle-related information. The assigned labels may be provided by a human or other entity and may indicate a category associated with each fragment of vehicle-related information, an output value associated with each fragment of vehicle-related information, etc. In the embodiment, the training data may be provided to a computer 130, and the machine learning model 132 may be trained on the training data. The machine learning model 132 can be continuously trained and updated.

[0035] According to some implementations of this disclosure, the vehicle-related information filtering engine 104 can control the characteristics of the input vehicle-related information 134 provided as input to the machine learning model 132 based on the machine learning model-related access rules of the access control rule information 126. For example, the machine learning model-related access rules may include an access rule that specifies that only a specified subset of vehicle-related information should be provided to the machine learning model 132.

[0036] In some applications, the machine learning model 132 does not need to be provided with all of the specified vehicle-related information in order to perform calculations. For example, the specified vehicle-related information may include images (still and / or video images) of the environment surrounding the vehicle 102. The machine learning model 132 may also be a theft detection machine learning model for predicting whether theft is occurring against the vehicle 102.

[0037] To predict whether a theft has occurred, the theft detection machine learning model can detect the presence of one or more people around the vehicle based on images obtained by camera 108, and can receive sensor data indicating intrusion, such as sound data indicating shattered glass, doors being opened without the use of a key or fob, etc. The output provided by the theft detection machine learning model is a theft indicator, which can be set to a "true" value (indicating that a vehicle theft event has occurred) or a "false" value (indicating that a vehicle theft event has not occurred). To make its prediction, the theft detection machine learning model may not need to be provided with all of the images or all of the sensor data obtained by camera 108. Rather, the theft detection machine learning model may be able to make its prediction based on sampling of images and / or sensor data. "Sampling" of data refers to a selection of fewer samples than all of the available data. Providing data sampling to the theft detection machine learning model reduces the amount of personal information made available to the application that invokes the theft detection machine learning model, while still enabling the theft detection machine learning model to provide its prediction.

[0038] As an example, access rules related to a machine learning model can define a sampling rate for specified vehicle-related information (including, for example, the image and / or sensor data mentioned above) that will be provided to the theft detection machine learning model. The sampling rate can be defined as a ratio, for example, the vehicle-related information filtering engine 104 can select a specified ratio of all samples of the specified vehicle-related information that will be provided to the theft detection machine learning model. For example, if the access rules related to the machine learning model define a sampling rate of 10%, the vehicle-related information filtering engine 104 will provide one of the 10 samples of the specified vehicle-related information as the vehicle-related information 134 input to the theft detection machine learning model. In some cases, the sampling rate may vary based on factors such as the current time, the location of the vehicle, or whether there are moving objects around the vehicle. If the vehicle is located in an area with a high crime rate, or if the current time falls within a time cycle in which vehicle thefts frequently occur (e.g., 12:00 AM to 5:00 AM), the sampling rate may be increased. Otherwise, a lower sampling rate may be used. When vehicle sensors detect moving objects around a parked, unoccupied vehicle, the sampling rate is increased, allowing for better capture of potential theft events.

[0039] Providing a theft detection machine learning model with all available samples of specified vehicle-related information may raise privacy concerns because an excessive amount of information may be provided to the model. Additionally, the theft detection machine learning model may be running within an application (e.g., a synthetic sensor) that confidentially collects information for other purposes. Better privacy can be achieved by limiting the data available to the application. The theft detection machine learning model can accurately predict the presence of a theft event based on only a subset of all available samples of specified vehicle-related information.

[0040] In other embodiments, other types of machine learning models 132 may be executed by the computer 130, accompanied by corresponding access rules defined in the access control rule information 126 relating to such other types of machine learning models 132. Embodiments of other types of machine learning models 132 may include machine learning models for detecting the presence and / or identification information of occupants in a vehicle 102, or machine learning models for making decisions when making payments, etc. The access control rule information 126 may include access rules that specify different sampling rates relating to different machine learning models 132 executed by the computer 130.

[0041] In other applications, such as when the machine learning model 132 is used to detect whether a collision with another vehicle or person is imminent, the machine learning model 132 may be provided with a continuous stream of vehicle-related information (i.e., all available samples of vehicle-related information, such as images captured by the camera 108 of objects in front of the vehicle 102).

[0042] In further embodiments, alternative or additional access rules may be included within the access control rule information 126 relating to the machine learning model. For example, the access rules relating to the machine learning model may include any or any combination of the following: the type of vehicle-related information to be provided to the machine learning model (e.g., a first type of vehicle-related information should be provided, but a second type should not be provided), the time at which the vehicle-related information should be provided to the machine learning model, and the start and stop criteria for providing the vehicle-related information to the machine learning model (e.g., when the start criterion is met, the vehicle-related information is provided to the machine learning model, and when the stop criterion is met, the vehicle-related information is not provided). For example, the access rules relating to the machine learning model may stipulate that, when vehicle 102 is used during the daytime, specified vehicle-related information should not be provided during the daytime but should be provided at night to protect the privacy of the driver or other users of vehicle 102.

[0043] Exercise-based access rules

[0044] In a further embodiment, the access control rule information 126 may include access rules based on the motion status of the vehicle 102 ("motion-based access rules"). For example, an application (which may be launched inside or outside the vehicle 102) may base its actions on detecting objects inside or outside the vehicle while the vehicle is stationary. For example, the application may record images collected by the camera 108 while the vehicle 102 is stationary (e.g., parked in a parking lot). In another embodiment, an application (such as a machine learning model) may perform theft detection of the vehicle 102 by capturing the behavior of a person moving around the vehicle 102 while the vehicle 102 is stationary.

[0045] In such embodiments, motion-based access rules may stipulate that when the vehicle 102 is not moving, an application (e.g., a theft detection application) should provide images collected by the camera 108, but when the vehicle 102 is moving or the engine ignition is on (but the vehicle is not moving), the application should not provide images collected by the camera 108.

[0046] The vehicle-related information filtering engine 104 can monitor the speed of vehicle 102 based on sensor data (such as from a speedometer), and can provide images (or other vehicle-related information) to the application only when vehicle 102 is not moving (i.e., when the speed of vehicle 102 is zero).

[0047] In other embodiments, a further application (which may be initiated inside or outside the vehicle 102) may perform calculations when the vehicle 102 is moving (for example, having a speed faster than a specified threshold). For example, the further application may calculate the average speed of the vehicle 102 when the vehicle is moving (however, the calculated average speed would not take into account the zero speed of the vehicle 102 while it is stationary). In such embodiments, a motion-based access rule may stipulate that if the speed of the vehicle 102 is faster than a specified threshold, speed data should be provided to the further application.

[0048] Driver identification-based access rules

[0049] In a further embodiment, a driver behavior program (running either inside or outside the vehicle 102) can track the driving behavior of the driver of the vehicle 102. The driving behavior of the driver of the vehicle 102 can be based on measurement data from sensors 106 in the vehicle 102 (for example, sensors 106 may indicate the speed and acceleration of the vehicle 102, which may indicate the driver's aggressiveness). The driver behavior program can also track the location of the vehicle 102 and determine where the driver drove the vehicle 102.

[0050] In some embodiments, the ability to track a driver's driving behavior can be based on driver identification information. Access control rule information 126 may include driver identification-based access rules that prevent the driver behavior program from accessing specified vehicle-related information (such as speed, acceleration, and location) if the driver has first identification information (e.g., an adult in the family), but may allow access to specified vehicle-related information for any other driver who has different identification information (e.g., a minor in the family). In that case, parents can use the driver behavior program to monitor whether the minor is exhibiting good driving behavior.

[0051] Therefore, the vehicle-related information filtering engine 104 can use driver identification information-based access rules to prevent specified vehicle-related information from being provided to the driver behavior program in response to detecting that the driver has first identification information (for example, based on facial recognition of the driver, or based on biometric authentication data such as the driver's fingerprint data). The vehicle-related information filtering engine 104 can use driver identification information-based access rules to communicate specified vehicle-related information to the driver behavior program in response to detecting that the driver has identification information different from the first identification information.

[0052] Speed-based access rules

[0053] In a further embodiment, the access control rule information 126 may include a speed-based access rule that disables access to vehicle speed information (from the speedometer) if the speed of the vehicle 102 exceeds a first threshold. In other words, based on the speed-based access rule, the vehicle-related information filtering engine 104 may allow a speed monitoring program (running inside or outside the vehicle 102) to access the vehicle speed information if the speed of the vehicle 102 does not exceed the first threshold. The vehicle-related information filtering engine 104 may allow the speed monitoring program to disable access to the speed information if the speed of the vehicle 102 exceeds the first threshold.

[0054] In other embodiments, the access control rule information 126 may include a speed-based access rule that disables access to vehicle speed information (from the speedometer) if the vehicle speed 102 is slower than a second threshold. In some cases, different applications / programs may have different access control rules (e.g., different filtering requirements). Each application / program may send its filtering requirements (e.g., filtering start / stop criteria, sampling rate) to the access control rule information 126 so that the vehicle-related information filtering engine 104 can provide the corresponding filtered data. Some applications / programs may not be associated with access control rules and therefore receive unfiltered data (i.e., data not affected by filtering by the vehicle-related information filtering engine 104).

[0055] Crew member identifier-based access rules

[0056] In further embodiments, an occupant tracking program (running inside or outside the vehicle 102) may track the identification information of occupants inside the vehicle 102, for example, for identification purposes. In some cases, it may not be desirable to track the identification information of any occupant of the vehicle 102, for example, for privacy reasons.

[0057] The access control rule information 126 may include an occupant identifier-based access rule that specifies that if an occupant in the vehicle 102 has a first identification information, user identification information data (e.g., an image of the inside of the vehicle 102, user identification information derived from biometric authentication or facial recognition processes, etc.) will not be provided for occupant tracking.

[0058] Therefore, the vehicle-related information filtering engine 104 can use occupant identification information-based access rules to prevent specified vehicle-related information from being provided to the occupant tracking program in response to detecting that an occupant in the vehicle 102 has first identification information. The vehicle-related information filtering engine 104 can use occupant identification information-based access rules to communicate specified vehicle-related information to the occupant tracking program in response to detecting that all occupants inside the vehicle 102 have identification information different from the first identification information.

[0059] Seatbelt Information Access Rules

[0060] In further embodiments, a seatbelt tracking program (running either inside or outside vehicle 102) may want to verify that occupants inside vehicle 102 are wearing their seatbelts. In some embodiments, certain categories of drivers or passengers may be exempt from the requirement to wear seatbelts.

[0061] In such embodiments, the access control rule information 126 may include a seat belt information access rule that specifies that seat belt information is not provided for drivers or passengers of a specified category.

[0062] The vehicle-related information filtering engine 104 can identify any occupant of the vehicle 102 that belongs to a specified category, and can use seat belt information access rules to prevent seat belt information (for example, information from seat belt sensors indicating whether the seat belt is engaged) from being communicated to the seat belt tracking program of any occupant in the specified category. The vehicle-related information filtering engine 104 can perform recognition of occupant identification information and, based on the recognized identification information, can determine (for example, using correlation information) whether the occupant with the identification information belongs to a specified category.

[0063] The vehicle-related information filtering engine 104 can enable the seat belt tracking program to communicate seat belt information about any occupant that does not fall into a specified category.

[0064] Geofence-based access rules

[0065] In some embodiments, a location determination program (running inside or outside the vehicle 102) can query location information (e.g., GPS information) to determine the location of the vehicle 102. For privacy reasons, it may be undesirable to allow the location of the vehicle 102 to be communicated to the location determination program unless the vehicle 102 is inside (or outside) a geofence. A “geofence” can refer to information that defines a geographic area (which may consist of a single geographic area or multiple geographic areas).

[0066] The access control rule information 126 may include geofence-based access rules that control the communication of vehicle location data to the location determination program based on the relevance of the vehicle 102's current location to the geofence. For example, a geofence-based access rule may stipulate that if vehicle 102 is inside or outside the geofence, vehicle location data will not be communicated to the location determination program.

[0067] The vehicle-related information filtering engine 104 can use geofence-based access rules to prevent vehicle location data from being communicated to the location determination program if vehicle 102 is currently inside or outside a geofence. However, the vehicle-related information filtering engine 104 can also use geofence-based access rules to allow vehicle location data to be communicated to the location determination program if vehicle 102 is currently outside or inside a geofence. Restricting the provision of vehicle location data to the location determination program according to geofence-based access rules can be used to achieve the goal of preventing the location determination program from receiving data to determine a specific location, such as home or work.

[0068] person Blurring of identifying information (PII)

[0069] In further embodiments, the vehicle-related information filtering engine 104 may obscure portions of personal identification information (PII) when certain criteria are met. Embodiments of PII may include any or any combination of the following: user identification information, a user's facial image, user's biometric data, etc. Obscuring portions of PII may include deleting portions of PII, replacing portions of PII with different data, or concealing portions of PII. For example, portions of PII may be obscure if the PII falls into a specified category.

[0070] Further Examples

[0071] It should be noted that the vehicle-related information filtering engine 104 may apply several different access rules (e.g., any of the aforementioned) within the access control rule information 126 to restrict the communication of vehicle-related information to entities, whether inside or outside the vehicle 102.

[0072] Figure 2 is a flowchart of process 200 according to several embodiments, which may be implemented by a computer system. The computer system may include one or more computers and may be located inside or outside a vehicle (e.g., vehicle 102).

[0073] Process 200 includes receiving vehicle-related information from a data source associated with the vehicle (in 202). The data source associated with the vehicle may reside inside or outside the vehicle. It should also be noted that the vehicle-related information from the data source may be stored in a storage system prior to its use by entities inside or outside the vehicle.

[0074] Process 200 includes receiving requests from entities (in 204) for access to vehicle-related information. In some embodiments, the entities may include either the machine learning model 132 in Figure 1 or one of the programs discussed above.

[0075] In response to the request, process 200 includes reading access control rule information (e.g., 126 in Figure 1) (in 206) and determining (in 208) whether the entity should be authorized to access vehicle-related information based on the access control rule information. Tasks 206 and 208 can be performed, for example, by the vehicle-related information filtering engine 104.

[0076] Based on the decision, process 200 may restrict access to vehicle-related information (in 210) according to access control rule information. In some embodiments, the access control rule information includes at least one privacy criterion selected from machine learning usage criteria related to the use of vehicle-related information by machine learning models, vehicle motion criteria related to the vehicle's movement status, or person identification criteria related to the identification information of persons in the vehicle. In other embodiments, the access control rule information may include additional or alternative criteria related to access to vehicle-related information.

[0077] In some embodiments, machine learning usage criteria may include access rules relating to machine learning operations, such as any of those discussed above. Vehicle motion criteria may include motion-based access rules, such as any of those discussed above. Person identification criteria may include driver identification-based access rules and / or occupant identifier-based access rules, such as any of those discussed above.

[0078] In further embodiments, access control rule information includes location-based criteria such as geofence-based access rules. Process 200 can restrict access to vehicle-related information by blocking access to the vehicle-related information based on location-based criteria if the vehicle has a specified relevance to a geofence (inside or outside of it).

[0079] In such further embodiments, process 200 may receive a request from an entity to access vehicle-related information, determine that the entity has permission to access the vehicle-related information, and in response to the entity's determination that it has permission, determine whether the vehicle has a specified relevance to the geofence, and if the vehicle has a specified relevance to the geofence, prevent the entity from accessing the vehicle-related information.

[0080] Process 200 allows entities to access vehicle-related information if the vehicle does not have a specified relevance to the geofence.

[0081] In some embodiments, process 200 restricts access to vehicle-related information by blocking access to the vehicle-related information based on vehicle motion criteria when the vehicle is in motion. In further embodiments, process 200 restricts access to vehicle-related information by blocking access to the vehicle-related information based on vehicle motion criteria when the vehicle is not in motion.

[0082] In some embodiments, process 200 restricts access to vehicle-related information by blocking access to vehicle-related information based on vehicle motion criteria if the vehicle speed has a predetermined correlation with a speed threshold (exceeding or being slower than it).

[0083] In some embodiments, process 200 restricts access to vehicle-related information by blocking access to the vehicle-related information based on a person identification criterion if the identification information of a person in the vehicle matches a defined identification criterion. Process 200 allows access to the vehicle-related information if the identification information of a person in the vehicle differs from the defined identification criterion.

[0084] In some embodiments, process 200 restricts access to seat belt information based on seat belt information access criteria that specify that seat belt information is not provided to drivers or passengers in a designated category.

[0085] Figure 3 is a block diagram of a computer system 300, which is a component of a vehicle such as vehicle 102 in Figure 1, or may be located outside of it. The computer system 300 includes one or more hardware processors 302. The hardware processors may include microprocessors, cores of multicore microprocessors, microcontrollers, programmable integrated circuits, programmable gate arrays, or other hardware processing circuits.

[0086] The computer system 300 includes a non-transient machine-readable or computer-readable storage medium 304 that stores machine-readable instructions executable on one or more hardware processors 302 for performing various tasks. The machine-readable instructions include vehicle-related information filtering instructions 306 for performing access control of vehicle-related information. The vehicle-related information filtering instructions 306 may be, for example, instructions of the vehicle-related information filtering engine 104 in Figure 1.

[0087] The vehicle-related information filtering command 306 can perform access control of vehicle-related information based on access control rule information 308 stored in the memory 310 of the computer system 300.

[0088] The storage medium (e.g., 304) may include, namely, semiconductor memory devices such as dynamic or static random access memory (DRAM or SRAM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM), and flash memory or other types of non-volatile memory devices; magnetic disks such as fixed, floppy, and removable disks; other magnetic media including tapes; optical media such as compact discs (CDs) or digital video discs (DVDs); or any or any combination of other types of storage devices. Note that the instructions discussed above may be provided on a single computer-readable or machine-readable storage medium, or alternatively, on multiple computer-readable or machine-readable storage media distributed within a large system potentially having multiple nodes. Such computer-readable or machine-readable storage media or multiple media are considered components of an article (or product). An article or product may refer to any single or multiple manufactured components. The storage medium or multiple mediums may be located either within a machine that invokes machine-readable instructions, or in a remote facility from which machine-readable instructions can be downloaded over a network for execution.

[0089] Numerous details are provided in the foregoing description to provide an understanding of the subject matter disclosed herein. However, implementations may be practiced without some of these details. Other implementations may include modifications and variations from the details discussed above. The appended claims are intended to cover such modifications and variations.

Claims

1. A non-transient machine-readable storage medium, wherein the non-transient machine-readable storage medium includes instructions, The aforementioned instruction, upon execution, Receiving vehicle-related information from data sources associated with the vehicle, Restricting access to the vehicle-related information based on at least one privacy criterion, wherein the at least one privacy criterion includes a machine learning usage criterion related to the use of the vehicle-related information by a machine learning model. Let the system do it, Restricting the machine learning model's access to the vehicle-related information means that Based on the aforementioned machine learning usage standards, permission is granted to provide a first type of information as input to the machine learning model, Based on the aforementioned machine learning usage criteria, the second type of information is not provided as input to the machine learning model, provided that the second type of information is different from the first type of information. Includes, The instruction causes the system to restrict access to the vehicle-related information based on the machine learning usage criteria by controlling the sampling rate of the vehicle-related information input to the machine learning model, in response to execution, wherein the sampling rate is the ratio of the vehicle-related information input to the machine learning model to all of the received vehicle-related information. This is a non-transient, machine-readable storage medium.

2. Controlling the sampling rate means Based on the first location of the vehicle, a first sampling rate is set for the vehicle-related information input to the machine learning model. Setting a second sampling rate for the vehicle-related information input to the machine learning model based on a second location of the vehicle, wherein the second location is different from the first location, and the second sampling rate is different from the first sampling rate. A non-transient, machine-readable storage medium according to claim 1, including the above.

3. The non-transient, machine-readable storage medium according to claim 1, wherein the instruction, upon execution, causes the system to further restrict access to the vehicle-related information on a location-based basis by blocking access to the vehicle-related information if the vehicle has a specified relevance to a geofence.

4. The aforementioned instruction, upon execution, To access the aforementioned vehicle-related information, the entity receives requests from the entity, Determining that the entity has permission to access the vehicle-related information, In response to the determination that the entity has the permit, the determination of whether the vehicle has the relevant relevance to the geofence as defined above, If the vehicle has the aforementioned relation to the geofence, access to the vehicle-related information by the entity shall be prevented. A non-transient, machine-readable storage medium according to claim 3, which causes the system to perform the above action.

5. The aforementioned instruction, upon execution, If the vehicle does not have the aforementioned relation to the geofence, the entity shall be allowed to access the vehicle-related information. A non-transient, machine-readable storage medium according to claim 4, which causes the system to perform the above action.

6. The non-transient, machine-readable storage medium according to claim 1, wherein, upon execution of the instruction, the system causes the system to further restrict access to the vehicle-related information based on a vehicle motion criterion by preventing access to the vehicle-related information when the vehicle is moving, the vehicle motion criterion being included in the at least one privacy criterion.

7. The non-transient, machine-readable storage medium according to claim 1, wherein, upon execution of the instruction, the system causes to further restrict access to the vehicle-related information based on a vehicle motion criterion by preventing access to the vehicle-related information if the vehicle is not moving, the vehicle motion criterion being included in the at least one privacy criterion.

8. The non-transient, machine-readable storage medium according to claim 1, wherein, upon execution, the instruction causes the system to further restrict access to the vehicle-related information based on a vehicle motion criterion by blocking access to the vehicle-related information if the vehicle's speed has a predetermined correlation with a speed threshold, the vehicle motion criterion being included in the at least one privacy criterion.

9. The non-transient, machine-readable storage medium according to claim 1, wherein controlling the sampling rate includes adjusting the sampling rate of the vehicle-related information input to the machine learning model according to the current time.

10. Controlling the sampling rate means Based on the detection of moving objects around the vehicle by the vehicle sensor, a first sampling rate is set for the vehicle-related information input to the machine learning model. Based on the fact that the vehicle sensor does not detect the moving object around the vehicle, a second sampling rate is set for the vehicle-related information input to the machine learning model, wherein the second sampling rate is different from the first sampling rate. A non-transient, machine-readable storage medium according to claim 1, including the above.

11. The non-transient, machine-readable storage medium according to claim 1, wherein the instruction, upon execution, causes the system to further restrict access to the vehicle-related information based on a person identification criterion, by controlling access to the vehicle-related information based on comparing the identification information of a person in the vehicle with a specified identification criterion, the person identification criterion being included in the at least one privacy criterion.

12. The non-transient, machine-readable storage medium according to claim 11, wherein the identification information specified in the aforementioned personal identification information standards relates to the driver or occupant of the vehicle.

13. A non-transient machine-readable storage medium, wherein the non-transient machine-readable storage medium includes instructions, The aforementioned instruction, upon execution, Receiving vehicle-related information from data sources associated with the vehicle, Restricting access to the vehicle-related information based on at least one privacy criterion, wherein the at least one privacy criterion includes a machine learning usage criterion related to the use of the vehicle-related information by a machine learning model. Let the system do it, Restricting the machine learning model's access to the vehicle-related information means that Based on the aforementioned machine learning usage standards, permission is granted to provide a first type of information as input to the machine learning model, Based on the aforementioned machine learning usage criteria, the second type of information is not provided as input to the machine learning model, provided that the second type of information is different from the first type of information. Includes, A non-transient, machine-readable storage medium, which, upon execution of the instruction, causes the system to restrict access to the seat belt information based on a seat belt information access criterion that stipulates that the seat belt information will not be provided to drivers or passengers of a specified category.

14. The non-transient, machine-readable storage medium according to claim 1, wherein the instruction causes the system to restrict access to the vehicle-related information by obscuring person identification information within the vehicle-related information, in response to execution of the instruction.

15. The system is a non-transient, machine-readable storage medium according to claim 1, which is part of the vehicle or located remotely from the vehicle.

16. Controlling the sampling rate means The detection of the vehicle being parked and unoccupied, Based on the detection that the vehicle is parked and unoccupied, the sampling rate of the vehicle-related information input to the machine learning model is increased. A non-transient, machine-readable storage medium according to claim 1, including the above.

17. The data source is a non-transient, machine-readable storage medium according to claim 1, which is located inside or outside the vehicle.

18. A computer system, wherein the computer system is One or more hardware processors, A non-transient memory medium that stores commands and Equipped with, The aforementioned instruction is, Receiving vehicle-related information from data sources associated with the vehicle, Restricting access to the vehicle-related information based on at least one privacy criterion, wherein the at least one privacy criterion includes a machine learning usage criterion relating to the use of the vehicle-related information by a machine learning model, and restricting the machine learning model's access to the vehicle-related information includes controlling the sampling rate of the vehicle-related information input to the machine learning model according to the current time. The above is executable on one or more hardware processors, Controlling the sampling rate means Based on the fact that the current time is within a first time period, a first sampling rate is set for the vehicle-related information input to the machine learning model. Based on the fact that the current time is within a second time period, a second sampling rate for the vehicle-related information input to the machine learning model is set, wherein the second time period is different from the first time period, and the second sampling rate is different from the first sampling rate. A computer system, including a computer system.

19. A computer system method, wherein the method is In the aforementioned computer system, vehicle-related information is received from a data source associated with the vehicle, The computer system restricts access to the vehicle-related information based on at least one privacy criterion, wherein the at least one privacy criterion includes a machine learning usage criterion relating to the use of the vehicle-related information by a machine learning model, and the restriction of the machine learning model's access to the vehicle-related information includes controlling the sampling rate of the vehicle-related information input to the machine learning model. Includes, Controlling the sampling rate means Based on the detection of moving objects around the vehicle by the vehicle sensor, a first sampling rate is set for the vehicle-related information input to the machine learning model. Based on the fact that the vehicle sensor does not detect the moving object around the vehicle, a second sampling rate is set for the vehicle-related information input to the machine learning model, wherein the second sampling rate is different from the first sampling rate. Methods that include...

20. The computer system according to claim 18, wherein the instruction is executable on one or more hardware processors to adjust the sampling rate based on the vehicle sensor detecting moving objects around the vehicle.

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