Method and system for detecting objects within a vehicle and storage medium

Abstract

Methods and systems for detecting an object within a vehicle and storage media are provided. The method can include emitting at least one audible signal within the vehicle during at least one first time interval, measuring a second audible signal emitted by the object within the vehicle during a second time interval subsequent to the at least one first time interval, wherein the emission of the second audible signal is caused by the emission of the at least one first audible signal, determining a location of the object within the vehicle based on the measurement of the second audible signal, and generating an alert to a user indicating the location of the object. Systems and computer program products are also provided.

Description

Technical Field

[0001] This disclosure relates to techniques for detecting objects within a vehicle. Background Technology

[0002] Vehicles can be used to transport people from one location to another. For example, a person can enter the passenger compartment of a vehicle and (e.g., by manually driving the vehicle and / or instructing the vehicle's autonomous systems to navigate it to its destination) use the vehicle to travel to the destination.

[0003] In some implementations, a person may misplace or otherwise leave objects in the vehicle. For example, a person may enter the vehicle with an object (e.g., a personal item such as a bag, phone, etc.) but leave the vehicle without taking the object with them. Summary of the Invention

[0004] According to one aspect of the present invention, a method for detecting objects within a vehicle is provided, comprising: emitting at least one first auditory signal within the vehicle by an object detection system of the vehicle during at least one first time interval; measuring, by the object detection system, a second auditory signal emitted by an object within the vehicle during a second time interval following the at least one first time interval, wherein the emission of the second auditory signal is caused by the emission of the at least one first auditory signal; determining, by the object detection system, the position of the object within the vehicle based on the measurement result of the second auditory signal; and generating, by the object detection system, an alarm indicating the position of the object to a user.

[0005] According to another aspect of the present invention, a system for detecting objects within a vehicle is provided, comprising: at least one processor; and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: emit at least one first auditory signal within the vehicle during at least one first time interval; measure a second auditory signal emitted by an object within the vehicle during a second time interval following the at least one first time interval, wherein the emission of the second auditory signal is caused by the emission of the at least one first auditory signal; determine the position of the object within the vehicle based on the measurement result of the second auditory signal; and generate an alarm for a user indicating the position of the object.

[0006] According to another aspect of the invention, at least one non-transitory storage medium is provided, which stores instructions that, when executed by at least one processor, cause the at least one processor to: emit at least one first auditory signal within a vehicle during at least one first time interval; measure a second auditory signal emitted by an object within the vehicle during a second time interval following the at least one first time interval, wherein the emission of the second auditory signal is caused by the emission of the at least one first auditory signal; determine the position of the object within the vehicle based on the measurement result of the second auditory signal; and generate an alarm for a user indicating the position of the object. Attached Figure Description

[0007] Figure 1 It is an example environment that can realize a vehicle that includes one or more components of an autonomous system;

[0008] Figure 2 It is a diagram of one or more systems that include autonomous vehicles;

[0009] Figure 3 yes Figure 1 and Figure 2 A diagram of one or more devices and / or one or more system components;

[0010] Figure 4 This is a diagram of some components of an object detection system;

[0011] Figure 5A and 5B This is a diagram illustrating an example operation of an object detection system;

[0012] Figure 6A This is a diagram of an example sinusoidal scanning signal;

[0013] Figure 6B This is a diagram of an example impulse response signal;

[0014] Figure 7A This is a diagram illustrating the implementation of a neural network;

[0015] Figure 7B and 7C This is a diagram illustrating example operations of a neural network;

[0016] Figure 8 This is a flowchart for the process of detecting objects inside a vehicle.

[0017] Specific implementation

[0018] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of this disclosure. However, it will be apparent that the embodiments described herein can be practiced without these specific details. In some instances, well-known constructions and apparatuses are illustrated in block diagram form to avoid unnecessarily obscuring aspects of this disclosure.

[0019] In the accompanying drawings, for ease of description, specific arrangements or orders of schematic elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements) are illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the drawings is not intended to imply a requirement for a particular processing order or sequence, or separation of processes. Furthermore, unless explicitly described, the inclusion of schematic elements in the drawings is not intended to imply that such elements are required in all embodiments, nor is it intended to imply that features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0020] Furthermore, in the accompanying drawings, connecting elements (such as solid or dashed lines or arrows) are used to illustrate connections, relationships, or associations between or among two or more other schematic elements. The absence of any such connecting element does not imply that connections, relationships, or associations cannot exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the content of this disclosure. Additionally, for ease of illustration, a single connecting element may be used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents communication of signals, data, or instructions (e.g., "software instructions"), those skilled in the art will understand that such an element may represent one or more signal paths (e.g., a bus) that may be necessary to influence the communication.

[0021] Although the terms "first," "second," and / or "third," etc., are used to describe various elements, these elements should not be limited by these terms. The terms "first," "second," and / or "third" are used only to distinguish one element from another. For example, without departing from the scope of the described embodiments, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact.

[0022] The terminology used in the description of the various embodiments described herein is included for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments described and the appended claims, the singular forms “a,” “an,” and “the” are also intended to include the plural forms and may be used interchangeably with “one or more” or “at least one” unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that when the terms “comprising,” “including,” “possessing,” and / or “having” are used in this specification, they specifically indicate the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] As used herein, the terms "communication" and "to communicate" refer to at least one of the following: receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands). For a unit (e.g., an apparatus, system, component of an apparatus or system, and / or combinations thereof) that wants to communicate with another unit, this means that the unit is able to receive information directly or indirectly from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is essentially wired and / or wireless. Furthermore, two units can communicate with each other even if the transmitted information can be modified, processed, relayed, and / or routed between the first and second units. For example, the first unit can communicate with the second unit even if it passively receives information and does not actively transmit information to the second unit. As another example, the first unit can communicate with the second unit if at least one intermediary unit (e.g., a third unit located between the first and second units) processes information received from the first unit and transmits the processed information to the second unit. In some embodiments, a message may refer to a network packet that includes data (e.g., a data packet, etc.).

[0024] As used herein, depending on the context, the term "if" may optionally be interpreted as "when," "in," "in response to being determined," and / or "in response to being detected," etc. Similarly, depending on the context, the phrases "if determined" or "if [the stated condition or event] is detected" may optionally be interpreted as "in response to being determined," "in response to being determined," "or" "in response to being detected," and / or "in response to being detected," etc. Furthermore, as used herein, the terms "have," "possess," or "own," etc., are intended to be open-ended terms. Additionally, unless explicitly stated otherwise, the phrase "based on" is intended to mean "at least partially based on."

[0025] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to those skilled in the art that the various embodiments described can be practiced without these specific details. In other instances, well-known methods, processes, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0026] General Overview

[0027] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement techniques for using sound to detect objects within a vehicle. In an example implementation, the object detection system emits sound within the vehicle over a period of time, causing objects within the vehicle to resonate at one or more specific frequencies. The system detects the resonance of these objects and (e.g., using a machine learning system) determines the location and / or identification of these objects based on the resonance. Furthermore, the object detection system notifies the user (e.g., by displaying an auditory alarm or electronic message on the user's mobile device) to retrieve the object.

[0028] Some advantages of these technologies include enabling vehicles to detect objects within the vehicle, allowing occupants to retrieve objects before they are lost. In some implementations, the system can detect objects in locations that might be difficult to detect using other sensor technologies (e.g., under the seat or in a back pocket, where they might be hidden from view by a camera).

[0029] Furthermore, these technologies enable vehicles to detect objects using components that may already be included in the vehicle in other ways. For example, a vehicle will typically include speakers (e.g., for playing audio to occupants) and microphones (e.g., for detecting verbal commands from users and / or enabling users to communicate voice-to-speech with other users). These components can be additionally used to detect objects within the vehicle. This can be beneficial in, for example, reducing or eliminating the need for dedicated sensors solely for object detection. Consequently, the cost and / or complexity of manufacturing vehicles is reduced.

[0030] Furthermore, these technologies can reduce the likelihood of passengers unintentionally leaving items in the vehicle after boarding. Because they can reduce delays associated with users retrieving lost items and / or the vehicle returning lost items to passengers or service stations, these technologies may be particularly advantageous in vehicles shared by several different users (e.g., autonomous vehicles used in ride-sharing services). Consequently, vehicles can be operated more efficiently.

[0031] In some embodiments, the techniques described herein can be implemented within a vehicle, such as a vehicle with an autonomous system (e.g., an autonomous vehicle) and / or a vehicle without an autonomous system.

[0032] Now for reference Figure 1 Example environment 100 is illustrated, in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118. Vehicles 102a-102n, vehicle-to-infrastructure (V2I) device 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via wired connections, wireless connections, or a combination of wired and wireless connections (e.g., establishing connections for communication, etc.). In some embodiments, objects 104a-104n are interconnected with at least one of vehicles 102a-102n, vehicle-to-infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 via wired connection, wireless connection, or a combination of wired and wireless connection.

[0033] Vehicles 102a-102n (specifically referred to as vehicle 102 and collectively as vehicle 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicle 102 is configured to communicate with V2I device 110, remote AV system 114, queue management system 116 and / or V2I system 118 via network 112. In some embodiments, vehicle 102 includes cars, buses, trucks and / or trains, etc. In some embodiments, vehicle 102 is associated with vehicle 200 described herein (see Figure 2 The vehicles 102 are the same as or similar to autonomous vehicles 202. In some embodiments, vehicles 200 in a group of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicles 102 travel along corresponding routes 106a-106n (each individually referred to as route 106 and collectively as route 106). In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[0034] Objects 104a-104n (each individually referred to as object 104 and collectively as object 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., a building, a sign, a fire hydrant, etc.). Each object 104 (e.g., located at a fixed position and for a period of time) is either stationary or (e.g., having a speed and associated with at least one trajectory) moving. In some embodiments, object 104 is associated with a corresponding location in area 108.

[0035] Routes 106a-106n (each individually referred to as Route 106 and collectively as Route 106) are each associated with (e.g., defining) a series of actions (also referred to as trajectories) along which the connecting AV can navigate. Each Route 106 begins with an initial state (e.g., a state corresponding to a first spatiotemporal location and / or speed, etc.) and ends with a final target state (e.g., a state corresponding to a second spatiotemporal location different from the first spatiotemporal location) or a target area (e.g., a subspace of an acceptable state (e.g., a termination state)). In some embodiments, a first state includes a location where one or more individuals will board the AV, and a second state or area includes a location where one or more individuals boarding the AV will disembark. In some embodiments, Route 106 includes multiple acceptable state sequences (e.g., multiple spatiotemporal location sequences) associated with multiple trajectories (e.g., defining multiple trajectories). In the example, Route 106 includes only high-level actions or imprecise state locations, such as a series of connecting roads indicating a change of direction at a roadway intersection. Additionally or alternatively, route 106 may include more precise actions or states, such as, for example, specific target lanes or precise locations within a lane area and target rates at those locations. In the example, route 106 includes multiple precise state sequences along at least one high-level action with a finite look-ahead horizon leading to an intermediate target, wherein the cumulative combination of successive iterations of the finite horizon state sequences corresponds to multiple trajectories that collectively form a high-level route terminating at a final target state or region.

[0036] Region 108 includes a physical area (e.g., a geographic region) that the vehicle 102 can navigate. In the example, region 108 includes at least one state (e.g., a country, a province, a single state among multiple states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, region 108 includes at least one named arterial road (referred to herein as a "road"), such as a highway, interstate highway, park road, city street, etc. Additionally or alternatively, in some examples, region 108 includes at least one unnamed road, such as a driving lane, a section of a parking lot, a section of vacant land and / or undeveloped area, dirt road, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In the example, a road includes at least one lane associated with at least one lane marking (e.g., identified based on at least one lane marking).

[0037] The Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as a Vehicle-to-Everything (V2X) device) includes at least one device configured to communicate with vehicle 102 and / or V2I infrastructure system 118. In some embodiments, the V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, the V2I device 110 includes radio frequency identification (RFID) devices, signs, cameras (e.g., two-dimensional (2D) and / or three-dimensional (3D) cameras), lane markings, streetlights, parking meters, etc. In some embodiments, the V2I device 110 is configured to communicate directly with vehicle 102. Additionally or alternatively, in some embodiments, the V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, and / or queue management system 116 via V2I system 118. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.

[0038] Network 112 includes one or more wired and / or wireless networks. In the example, network 112 includes cellular networks (e.g., Long Term Evolution (LTE) networks, third-generation (3G) networks, fourth-generation (4G) networks, fifth-generation (5G) networks, Code Division Multiple Access (CDMA) networks, etc.), Public Land Mobile Networks (PLMNs), Local Area Networks (LANs), Wide Area Networks (WANs), Metropolitan Area Networks (MANs), telephone networks (e.g., Public Switched Telephone Networks (PSTN)), private networks, self-organizing networks, intranets, the Internet, fiber-based networks, cloud computing networks, etc., and / or combinations of some or all of these networks.

[0039] The remote AV system 114 includes at least one device configured to communicate with vehicle 102, V2I device 110, network 112, queue management system 116, and / or V2I system 118 via network 112. In examples, the remote AV system 114 includes a server, server group, and / or other similar devices. In some embodiments, the remote AV system 114 is located in the same location as the queue management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle, including autonomous systems, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing. In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the lifespan of the vehicle.

[0040] The queue management system 116 includes at least one device configured to communicate with vehicle 102, V2I device 110, remote AV system 114, and / or V2I infrastructure system 118. In examples, the queue management system 116 includes servers, server groups, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ride-sharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including and / or not including autonomous systems)).

[0041] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the queue management system 116 via a network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection different from the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipality or private entity (e.g., a private entity maintaining the V2I device 110).

[0042] supply Figure 1 The number and arrangement of the elements are shown as examples. (and) Figure 1 Compared to the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements arranged differently. Additionally or alternatively, at least one element of environment 100 may be described as being composed of… Figure 1 One or more functions performed by at least one different element of environment 100. Additionally or alternatively, at least one group of elements of environment 100 may perform one or more functions described as performed by at least one different group of elements of environment 100.

[0043] Now for reference Figure 2 The vehicle 200 includes an autonomous system 202, a powertrain control system 204, a steering control system 206, a braking system 208, and an object detection system 210. In some embodiments, the vehicle 200 and the vehicle 102 (see...) Figure 1The vehicle 200 is similar to or the same as the vehicle in question. In some embodiments, the vehicle 200 has autonomous capabilities (e.g., implementing at least one function, feature, and / or device that enables the vehicle 200 to operate partially or fully without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon human intervention) and / or highly autonomous vehicles (e.g., vehicles that abandon human intervention in certain situations)). For a detailed description of fully autonomous and highly autonomous vehicles, refer to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road MotorVehicle Automated Driving Systems, the entire contents of which are incorporated herein by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ride-sharing company.

[0044] Autonomous system 202 includes a sensor suite comprising one or more devices such as camera 202a, LiDAR sensor 202b, radar sensor 202c, and microphone 202d. In some embodiments, autonomous system 202 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance traveled by vehicle 200). In some embodiments, autonomous system 202 uses one or more devices included in autonomous system 202 to generate data associated with environment 100 as described herein. The data generated by one or more devices of autonomous system 202 may be used by one or more systems as described herein to observe the environment in which vehicle 200 is located (e.g., environment 100). In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle computing 202f, and safety controller 202g.

[0045] Camera 202a includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3At least one means of communicating with the same or similar bus as bus 302. Camera 202a includes at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, and / or an event camera, etc.) for capturing images of physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, camera 202a generates camera data as output. In some examples, camera 202a generates camera data including image data associated with an image. In this example, the image data may specify at least one parameter corresponding to the image (e.g., image characteristics such as exposure, brightness, etc., and / or image timestamp, etc.). In such examples, the image may be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, camera 202a includes multiple independent cameras configured (e.g., positioned on) a vehicle to capture images for stereoscopic imaging (stereoscopic vision). In some examples, camera 202a includes generating image data and transmitting the image data to an autonomous vehicle computing 202f and / or a queue management system (e.g., with...). Figure 1 The queue management system 116 (same as or similar to a queue management system) has multiple cameras. In such an example, the autonomous vehicle calculation 202f determines the depth of one or more objects in the fields of view of at least two of the multiple cameras based on image data from at least two cameras. In some embodiments, camera 202a is configured to capture images of objects within a distance relative to camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Therefore, camera 202a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to camera 202a.

[0046] In embodiments, camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs, and / or other physical objects providing visual navigation information. In some embodiments, camera 202a generates traffic light data associated with one or more images. In some examples, camera 202a generates TLD data associated with one or more images, including formats such as RAW, JPEG, and / or PNG. In some embodiments, camera 202a, which generates TLD data, differs from other systems containing cameras described herein in that camera 202a may include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fisheye lens, and / or a lens with an angle of view of about 120 degrees or greater) to generate images associated with as many physical objects as possible.

[0047] The laser detection and ranging (LiDAR) sensor 202b includes components configured to communicate with a communication device 202e, an autonomous vehicle computing unit 202f, and / or a safety controller 202g via a bus (e.g., with...). Figure 3 At least one device communicating with the same or similar bus 302. The LiDAR sensor 202b includes a system configured to emit light from a emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object it encounters. The LiDAR sensor 202b also includes at least one photosensor that detects the light after it has encountered a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., point cloud and / or combined point cloud, etc.) representing objects included in the field of view of the LiDAR sensor 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b generates an image representing the boundaries of a physical object and / or the surface of the physical object (e.g., the topology of the surface). In such examples, the image is used to determine the boundaries of the physical object within the field of view of the LiDAR sensor 202b.

[0048] The radio detection and ranging (radar) sensor 202c includes components configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., with...). Figure 3 At least one device that communicates with the same or similar bus (bus 302). The radar sensor 202c includes a system configured to emit (pulsed or continuous) radio waves. The radio waves emitted by the radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves emitted by the radar sensor 202c encounter a physical object and are reflected back to the radar sensor 202c. In some embodiments, the radio waves emitted by the radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with the radar sensor 202c generates a signal representing objects included in the field of view of the radar sensor 202c. For example, at least one data processing system associated with the radar sensor 202c generates an image representing the boundaries of physical objects and / or the surfaces of physical objects (e.g., surface topology). In some examples, this image is used to determine the boundaries of physical objects in the field of view of the radar sensor 202c.

[0049] Microphone 202d includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3 At least one device that communicates with the same or similar bus as bus 302. Microphone 202d includes one or more microphones (e.g., array microphones and / or external microphones, etc.) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphone 202d includes transducer devices and / or similar devices. In some embodiments, one or more systems described herein can receive data generated by microphone 202d and determine the position (e.g., distance, etc.) of an object relative to vehicle 200 based on the audio signal associated with the data.

[0050] The communication device 202e includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, an autonomous vehicle computing system 202f, a safety controller 202g, a drive-by-wire (DBW) system 202h, and / or an object detection system 210. For example, the communication device 202e may include communication with… Figure 3 The communication device 202e is the same as or similar to the communication interface 314. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device for enabling wireless communication of data between vehicles).

[0051] The autonomous vehicle computing 202f includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, a security controller 202g, and / or a DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as client devices, mobile devices (e.g., cellular phones and / or tablets) and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units). In some embodiments, the autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., with...). Figure 1 Remote AV systems 114 are the same as or similar to autonomous vehicle systems), queue management systems (e.g., with...). Figure 1 The queue management system 116 is the same as or similar to the queue management system 116), and V2I devices (e.g., with Figure 1 V2I devices (same as or similar to V2I devices 110) and / or V2I systems (e.g., with V2I devices 110) Figure 1 The V2I system 118 communicates with the same or similar V2I system.

[0052] The safety controller 202g includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, an autonomous vehicle computing system 202f, and / or a DBW system 202h. In some examples, the safety controller 202g includes one or more controllers (electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate the vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). In some embodiments, the safety controller 202g is configured to generate control signals that take precedence over (e.g., override) the control signals generated and / or transmitted by the autonomous vehicle computing system 202f.

[0053] The DBW system 202h includes at least one device configured to communicate with the communication device 202e and / or the autonomous vehicle computing 202f. In some examples, the DBW system 202h includes one or more controllers (e.g., electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate the vehicle 200, including one or more devices (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). Additionally or alternatively, one or more controllers of the DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device (e.g., turn signals, headlights, door locks, and / or windshield wipers, etc.) of the vehicle 200.

[0054] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives control signals from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a certain direction, decelerate in a certain direction, make a left turn and / or make a right turn, etc. In examples, the powertrain control system 204 increases, keeps the same, or decreases the energy (e.g., fuel and / or electricity, etc.) supplied to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0055] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 causes the two front wheels and / or the two rear wheels of the vehicle 200 to turn left or right, thereby causing the vehicle 200 to turn left or right.

[0056] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or keep the vehicle 200 stationary. In some examples, the braking system 208 includes at least one controller and / or actuator configured to close one or more calipers associated with one or more wheels of the vehicle 200 on the respective rotor of the vehicle 200. Additionally or alternatively, in some examples, the braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0057] In some embodiments, the vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring the nature of the state or conditions of the vehicle 200. In some examples, the vehicle 200 includes platform sensors such as a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), a wheel rate sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor.

[0058] Furthermore, the object detection system 210 includes at least one device configured to detect objects within the vehicle 200 and generate a notification related to the detected objects to at least one user. As an example, the object detection system 210 can detect objects brought into the vehicle 200 by occupants (e.g., in the passenger compartment of the vehicle 200 and / or storage compartments such as luggage compartments of the vehicle 200) and objects remaining within the vehicle 200 after the occupants have left the vehicle 200. In some embodiments, the object detection system 210 can detect objects by inducing resonance in objects within the vehicle 200 (e.g., using sound), detecting resonance using one or more microphones, and identifying and locating the objects based on the detected resonance. Furthermore, in some embodiments, the object detection system 210 can detect objects based on sensor data obtained from a camera 202a, a LiDAR system 202b, a radar sensor 202c, a microphone 202d, and / or any other sensor of the vehicle 200. In addition, the object detection system 210 can generate notifications for the occupants, such as informing them that an object has been left inside the vehicle 200.

[0059] In some embodiments, the object detection system 210 may be implemented at least partially as one or more components of the autonomous system 202. In some embodiments, the object detection system 210 may be implemented at least partially as one or more components or devices that are separate from and different from the autonomous system 202.

[0060] For example, refer to Figure 4-8 Further details relating to the object detection system 210 are described below.

[0061] Now for reference Figure 3 The diagram illustrates a schematic of device 300. In some embodiments, device 300 corresponds to: at least one device of vehicle 200 and / or vehicle 102 (e.g., at least one device of the system of vehicle 102), at least one device of remote AV system 114, queue management system 116, V2I system 118, and / or one or more devices of network 112 (e.g., one or more devices of the system of network 112). In some embodiments, one or more devices of vehicle 102 and / or 200 (e.g., one or more devices of the system of vehicle 102 and 200 such as autonomous system 202, object detection system 210, etc.), remote AV system 114, queue management system 116, V2I system 118, and / or one or more devices of network 112 (e.g., one or more devices of the system of network 112) include at least one device 300 and / or at least one component of device 300.

[0062] like Figure 3 As shown, the device 300 includes a bus 302, a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, and a communication interface 314.

[0063] Bus 302 includes components for communication between the components of the licensed device 300. In some embodiments, processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, processor 304 includes a processor (e.g., a central processing unit (CPU), graphics processing unit (GPU), and / or accelerated processing unit (APU), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field-programmable gate array (FPGA) and / or application-specific integrated circuit (ASIC), etc.). Memory 306 includes random access memory (RAM), read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by processor 304.

[0064] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes hard disks (e.g., magnetic disks, optical disks, magneto-optical disks, and / or solid-state disks), compact discs (CDs), digital versatile discs (DVDs), floppy disks, cassette tapes, magnetic tapes, CD-ROMs, RAM, PROMs, EPROMs, FLASH-EPROMs, NV-RAMs, and / or other types of computer-readable media, and corresponding drives.

[0065] Input interface 310 includes components that enable the device 300 to receive information, such as via user input (e.g., a touchscreen display, keyboard, keypad, mouse, buttons, switches, microphone, and / or camera). Additionally or alternatively, in some embodiments, input interface 310 includes sensors for sensing information (e.g., a Global Positioning System (GPS) receiver, accelerometer, gyroscope, and / or actuator). Output interface 312 includes components for providing output information from device 300 (e.g., a display, speaker, and / or one or more light-emitting diodes (LEDs)).

[0066] In some embodiments, the communication interface 314 includes transceiver-like components (e.g., a transceiver and / or separate receivers and transmitters) that enable the licensing device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. In some examples, the communication interface 314 enables the licensing device 300 to receive information from and / or provide information to another device. In some examples, the communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, etc. Interfaces and / or cellular network interfaces, etc.

[0067] In some embodiments, device 300 performs one or more of the processes described herein. Device 300 performs these processes based on software instructions stored in a computer-readable medium, such as memory 305 and / or storage component 308, executed by processor 304. Computer-readable medium (e.g., non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes storage space located within a single physical storage device or storage space distributed across multiple physical storage devices.

[0068] In some embodiments, software instructions are read from another computer-readable medium or from another device via communication interface 314 into memory 306 and / or storage component 308. When executed, the software instructions stored in memory 306 and / or storage component 308 cause processor 304 to perform one or more processes described herein. Additionally or alternatively, hard-wired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Therefore, unless explicitly stated otherwise, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0069] The memory 306 and / or storage component 308 include a data storage unit or at least one data structure (e.g., a database). The device 300 is capable of receiving information from the data storage unit or at least one data structure in the memory 306 or storage component 308, storing the information in the data storage unit or at least one data structure, communicating information to the data storage unit or at least one data structure, or searching for information stored in the data storage unit or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0070] In some embodiments, device 300 is configured to execute software instructions stored in the memory of memory 306 and / or another device (e.g., another device identical or similar to device 300). As used herein, the term "module" refers to at least one instruction stored in the memory of memory 306 and / or the other device, which, when executed by the processor of processor 304 and / or the processor of another device (e.g., another device identical or similar to device 300), causes device 300 (e.g., at least one component of device 300) to perform one or more processes as described herein. In some embodiments, modules are implemented in software, firmware, and / or hardware, etc.

[0071] supply Figure 3 The number and arrangement of components are illustrated as examples. In some embodiments, with Figure 3 Compared to the illustrated components, device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a group of components of device 300 (e.g., one or more components) may perform one or more functions described as being performed by another component or another group of components of device 300.

[0072] Example Object Detection System

[0073] Figure 4The object detection system 210 is shown in more detail in various aspects. The object detection system 210 includes one or more speakers 402a-402n, one or more microphones 404a-404n, and one or more cameras 406a-406n. Furthermore, the object detection system 210 includes object locator circuitry 408, a database 410, and notification circuitry 412.

[0074] Typically, the object detection system 210 is configured to detect objects (e.g., object 450) inside the vehicle 200 based on sound, images, and / or video representing the interior of the vehicle 200. Furthermore, the object detection system 210 is configured to generate a notification to at least one user related to the detected object. As an example, the object detection system 210 can detect objects brought into the vehicle 200 by an occupant (e.g., in the occupant compartment of the vehicle 200 and / or in storage compartments such as luggage compartments of the vehicle 200) and left inside the vehicle 200 after the occupant leaves the vehicle 200. Additionally, the object detection system 210 can generate a notification to the occupant that they have left an object inside the vehicle 200 (e.g., enabling the occupant to retrieve the object).

[0075] In some embodiments, the object detection system 210 can detect objects at least partially based on sound. For example, the object locator circuit 408 can generate an audio output signal (e.g., digital and / or analog audio signal) for one or more speakers 402a-402n and instruct the speakers 402a-402n to emit sound within the vehicle 200 according to the generated audio output signal. Figure 4 and Figure 5A As shown, the emitted sound (e.g., sound wave 452) propagates through the interior of the vehicle 200 and impacts one or more objects (e.g., object 450) inside the vehicle 200.

[0076] In some embodiments, at least some of the speakers 402a-402n may be positioned inside the vehicle 200 or otherwise directed towards the vehicle 200. As an example, such as Figure 5A As shown, at least some of the speakers 402a-402n may be positioned in or otherwise directed toward the passenger compartment 502 of the vehicle (e.g., the cabin or seating area of ​​the vehicle 200). For example, at least some of the speakers 402a-402n may be located on or near the dashboard, console, pillars, doors, ceiling, floor, and / or interior of the vehicle 200. As another example, at least some of the speakers 402a-402n may be positioned in or otherwise directed toward storage compartments of the vehicle, such as the rear luggage compartment, front luggage compartment, glove compartment, etc.

[0077] In some embodiments, at least some of the speakers 402a-402n may be implemented as part of the entertainment system of the vehicle 200. For example, in addition to the operations described herein, at least some of the speakers 402a-402n may also be configured to play back audio content, such as audio from a radio, an audio playback device (e.g., a cassette player, CD player, etc.), a video playback device (e.g., a DVD player, Blu-ray player), a personal electronic device (e.g., a smartphone, wearable device, etc.), or any other device configured to output audio content.

[0078] In at least some embodiments, the sound emitted by the loudspeakers 402a-402n can cause the object 450 to resonate at a specific frequency or frequency range. For example, due to the physical properties of the object 450, the object 450 may have one or more inherent vibration frequencies. Furthermore, the impact of the sound wave 452 on the object 450 can cause the object 450 to vibrate at these natural frequencies (or their harmonics). This vibration may be further amplified, for example, by repeated or continuous impacts of sound on the object 450 over a period of time (e.g., causing the object 450 to resonate at a specific frequency or frequency range). In some embodiments, the resonance may continue even after the loudspeakers 402a-402n cease to emit sound (e.g., due to momentum). This resonance can further cause the sound wave 454 to propagate from the object 450 (e.g., as...). Figure 4 and Figure 5B (As shown).

[0079] The resonance of object 450 (e.g., in the form of sound wave 454) is detected by one or more microphones 404a-404n. For example, microphones 404a-404n may generate audio recording signals representing sound propagating inside vehicle 200 and provide at least some of the audio recording signals to object locator circuit 408. In some embodiments, microphones 404a-404n may generate audio recording signals before, during, and / or after sound is emitted by speakers 402a-402n.

[0080] In some embodiments, at least some of the microphones 404a-404n may also be located inside the vehicle 200 or otherwise pointed towards the interior of the vehicle 200. As an example, such as Figure 5BAs shown, at least some of the microphones 404a-404n may be located in or otherwise directed toward the passenger compartment 502 of the vehicle. For example, at least some of the microphones 404a-404n may be located in or near the dashboard, console, pillars, doors, ceiling, floor, and / or interior of the vehicle 200. As another example, at least some of the microphones 404a-404n may be located in or otherwise directed toward storage compartments of the vehicle, such as the rear luggage compartment, front luggage compartment, glove compartment, etc.

[0081] In some embodiments, at least some of the microphones 404a-404n may be implemented as part of the communication system of the vehicle 200. For example, in addition to the operations described herein, at least some of the microphones 404a-404n may also be configured to record occupant voice and provide at least a portion of the recorded data to a wireless communication device such as a smartphone, tablet, etc. (e.g., to facilitate occupant participation in telephone calls, video calls, etc.). As another example, in addition to the operations described herein, at least some of the microphones 404a-404n may also be configured to record occupant voice and provide at least a portion of the recorded data to a voice-enabled control system of the vehicle 200 (e.g., a control system configured to control one or more components of the vehicle 200 based on occupant verbal commands). In some implementations, at least some of the microphones 404a-404n may include references. Figure 2 One or more of the microphones 202d described.

[0082] Furthermore, object 450 can also be detected by one or more cameras 406a-406n. For example, cameras 406a-406n can generate one or more images and / or videos of the interior of vehicle 200 and provide at least some of the images and / or videos to object locator circuit 408.

[0083] In some embodiments, at least some of the cameras 406a-406n may also be located inside or otherwise pointed towards the interior of the vehicle 200. As an example, at least some of the microphones 404a-404n may be located in or pointed towards the passenger compartment 502 of the vehicle. For example, at least some of the cameras 406a-406n may be located on or near the dashboard, console, pillars, doors, ceiling, floor, and / or interior of the vehicle 200. As another example, at least some of the cameras 406a-406n may be located in or otherwise pointed towards storage compartments of the vehicle, such as the rear luggage compartment, front luggage compartment, glove compartment, etc.

[0084] In some embodiments, at least some of the cameras 406a-406n may also be implemented as part of the communication system of the vehicle 200. For example, in addition to the operations described herein, at least some of the cameras 406a-406n may also be configured to record images and / or videos of occupants and provide at least a portion of the images and / or videos to a wireless communication device such as a smartphone, tablet, etc. (e.g., to facilitate occupants participating in video calls). As another example, in addition to the operations described herein, at least some of the cameras may also be configured to record the exterior of the vehicle 200 (e.g., to facilitate autonomous operation of the vehicle, such as autonomous navigation). In some implementations, at least some of the cameras 406a-406n may include references Figure 2 One or more cameras described in camera 202a.

[0085] The object locator circuit 408 determines one or more characteristics of the object 450 based on audio recording signals received from microphones 404a-404n and / or images and / or videos received from cameras 406a-406n.

[0086] As an example, object locator circuit 408 can determine the position of object 450 inside vehicle 200 based on audio recording signals received from microphones 404a-404n and / or images and / or video received from cameras 406a-406n. For example, object locator circuit 408 can output data indicating the general area where the object is located (e.g., in a specific seat of vehicle 200, in a specific footwell of vehicle 200, under a specific seat of vehicle 200, in a specific storage compartment of vehicle 200, in a specific seat back pocket of vehicle 200, etc.). As another example, object locator circuit 408 can output data indicating a set of spatial coordinates (e.g., a set of x, y, and z coordinates relative to the vehicle) representing the position of object 450.

[0087] In some implementations, the object locator circuit 408 can determine the position of the object 450 at least in part by acquiring multiple audio recording signals using microphones 404a-404n located at different positions within the vehicle 200 and performing triangulation on the resonance source. For example, the object locator circuit 408 can determine the direction of the resonance relative to the position of the microphone that generated the audio recording signal for each audio recording signal. Furthermore, the object locator circuit 408 can determine a position or region consistent with the determined directions (e.g., for the intersection or intersection region of the determined directions). The object locator circuit 408 can then identify this position or region as the position of the object 450.

[0088] As another example, object locator circuit 408 can determine the identifier or type of object 450. For example, object locator circuit 408 can output data indicating whether object 450 is a backpack, wallet, handbag, purse, briefcase, briefcase, luggage, clothing (e.g., coat, shirt, trousers, hat, etc.), electronic device (e.g., computer, smartphone, tablet, headphones, earphones, etc.), glasses, sports equipment (e.g., ball, bat, racket, golf club, helmet, etc.), tools (e.g., hammer, wrench, screwdriver, etc.), jewelry (e.g., ring, watch, earrings, necklace, etc.) and / or any other type of object.

[0089] In some implementations, the object locator circuit 408 can determine the identification or type of the object 450 based at least in part on the audio recording signal. For example, different types of objects may have specific acoustic characteristics that differ from other types of objects. Therefore, different types of objects can be distinguished from each other based on their acoustic characteristics.

[0090] As an example, a first type of object may have one or more first inherent vibration frequencies and may resonate at one or more first resonant frequencies in response to the sound emitted by speakers 402a-402n. Furthermore, the first type of object (e.g., due to the physical characteristics of this type of object) may suppress or attenuate certain frequencies of sound. Similarly, a second type of object may have one or more second inherent vibration frequencies and may resonate at one or more second resonant frequencies in response to the sound emitted by speakers 402a-402n. Furthermore, the second type of object (e.g., due to the physical characteristics of this type of object) may suppress or attenuate certain other frequencies of sound. The object locator circuit 408 may distinguish between the two types of objects (e.g., by determining the resonant frequencies and / or suppressed frequencies, and identifying object types with the same or similar characteristics) based on spectral analysis of the audio recording signal.

[0091] Furthermore, in some implementations, the object locator circuit 408 can determine the identity or type of the object 450 based at least in part on images and / or videos acquired by cameras 406a-406n. For example, the object locator circuit 408 can use computer vision and / or image classification systems to interpret the images and / or videos and identify the objects depicted therein.

[0092] In some implementations, the object locator circuit 408 can acquire audio recordings, images, and / or video from inside the vehicle 200 in a "basic" or "default" state (e.g., when no foreign objects are left inside the vehicle by the occupants). Furthermore, the object locator circuit 408 can acquire additional audio recordings, images, and / or video from inside the vehicle 200 (e.g., during use of the vehicle 200) and identify changes in the recorded sound, images, and / or video.

[0093] For example, this technique can be advantageous in enabling the object locator circuit 408 to distinguish between structures that are part of the vehicle itself (e.g., seats, seat belts, dashboards, consoles, pillars, doors, roofs, floors, etc.) and objects brought into and left in the vehicle by the occupants. For instance, structures that are part of the vehicle may also exhibit specific resonances in response to sound emitted by speakers 402a-402n. The object locator circuit 408 can identify these resonances (e.g., based on data obtained when the vehicle is in a basic or default state) and filter out (or otherwise ignore) them when locating and / or identifying objects within the vehicle.

[0094] In some embodiments, the object locator circuit 408 may make at least some of the determinations described herein based on one or more machine learning models. For example, a machine learning model may be trained to receive input data (e.g., data received from microphones 404a-404n and / or cameras 406a-406n) and generate output data based on the input data associated with one or more predictions regarding the location and / or identification of the object 450.

[0095] As an example, a machine learning model can be trained using training data (e.g., training data stored in database 410) relating to one or more additional objects located or previously located on vehicle 200 or other vehicles. These additional objects may include objects previously detected and / or identified by object detection system 210. These additional objects may also include objects previously detected and / or identified by another system (e.g., another object detection system 210).

[0096] For each additional object, the training data may include reference data. Figure 4 The input information described is similar to other input information. For example, training data may include data acquired by one or more microphones (e.g., audio recordings representing the resonance of the object) and / or data acquired by cameras (e.g., images and / or videos of the interior of the vehicle) while the object is inside the vehicle.

[0097] Furthermore, for each additional object, the training data may include data representing the object's position within vehicle 200 (or other vehicle) at the time the sensor measurements were obtained. For example, database 410 may indicate the general area where a particular object is located at the time the sensor measurements were obtained. As another example, the training data may indicate a set of spatial coordinates representing the position of a particular object at the time the sensor measurements were obtained.

[0098] In addition, for each additional object, the training data may include data representing the acoustic properties of that object. For example, the training data may indicate one or more intrinsic frequencies of the object, the object's... of One or more resonant frequencies and the acoustic damping properties of the object.

[0099] In addition, for each additional object, the training data may include data representing the object's identifier or type. For example, the training data may indicate whether a particular object is a backpack, wallet, handbag, purse, briefcase, briefcase, luggage, clothing, electronic device, glasses, sports equipment, tools, jewelry, and / or any other type of object.

[0100] Based on the training data, machine learning models can be trained to identify the correlations, relationships, and / or trends between (i) the input data, (ii) the location of the object within the vehicle, and / or (iii) the object's identifier.

[0101] refer to Figures 7A-7C The example machine learning model is described in further detail.

[0102] In some embodiments, the object detection system 210 may be configured (e.g., in database 410) to store information related to object 450 for future retrieval and / or processing. As an example, object locator circuit 408 may transmit information related to object 450 (such as the determined location of object 450 and / or the type of object 450) to database 410 for storage. As another example, object locator circuit 408 may transmit at least some of the sensor information obtained about object 450 (e.g., audio recording signals, images, video, etc.) to database 410 for storage.

[0103] Furthermore, the object detection system 210 can be configured to generate a notification to at least one user related to a detected object. As an example, the object locator circuit 408 can provide the notification circuit 412 with the location and / or type of the object 450. Additionally, the object locator circuit 408 can provide the notification circuit 412 with one or more images and / or videos of the object 450. The notification circuit 412 can generate one or more notifications to the user (e.g., notifying the user that the object 450 has been left in the vehicle 200). Furthermore, the notification circuit 412 can include information related to the object 450 in the notification, such as the location of the object 450, the type of the object 450, an image of the object 450, and / or a video of the object 450.

[0104] In some implementations, notifications may include email messages, chat messages, text messages (e.g., SMS service messages), direct messages, and / or any other type of electronic message. In some implementations, notifications may include telephone calls, voice calls, video calls, or any other type of audio and / or video communication. In some implementations, notifications may include audio alarms (e.g., warning sounds) and / or haptic alarms (e.g., vibrations or pulses) presented using electronic devices such as smartphones, tablets, and / or wearable computers. In some implementations, notifications may include visual alarms (e.g., pop-up notifications or notification icons) presented using electronic devices. In some implementations, notifications may include audio alarms (e.g., warning sounds) output to the external environment of the vehicle 200 (e.g., via a speaker or horn of the vehicle 200).

[0105] In some implementations, the object detection system 210 can determine that the object 450 has been left in the vehicle 200 by a specific user and generate one or more notifications for that user.

[0106] As an example, object detection system 210 can (e.g., based on usage or service records of vehicle 200, such as records related to bookings of vehicle 200 as part of a ridesharing service) determine that: object 450 has been brought into vehicle 200 at a specific time, and that a specific user has entered vehicle 200 during that time. Object detection system 210 can determine, at least in part, that a user is likely the owner of object 450 based on this determination, and can generate a notification to that user regarding object 450.

[0107] As another example, object detection system 210 may (e.g., based on usage or service records of vehicle 200, such as records related to bookings of vehicle 200 as part of a ridesharing service) determine that object 450 was detected in vehicle 200 at a specific time, and that a specific user was already riding in vehicle 200 during that time. Object detection system 210 may determine, at least in part, based on this determination, that the user is likely the owner of object 450, and may generate a notification to that user regarding object 450.

[0108] As another example, object detection system 210 may (e.g., based on audio, images, and / or video acquired by sensors of vehicle 200 during a specific period of time) determine that object 450 has been detected at a specific location within the vehicle and at that time, and that a user was already seated at or around that location. Based at least in part on this determination, object detection system 210 may determine that the user is likely the owner of object 450 and may generate a notification to that user regarding object 450.

[0109] As another example, object detection system 210 (e.g., based on usage or service records of vehicle 200, such as records related to bookings of vehicle 200 as part of a ridesharing service) detects object 450 in vehicle 200, and a specific user is a passenger at the most recent vehicle time. Object detection system 210 may determine, at least in part, that the user is likely the owner of object 450 based on this determination, and may generate a notification to that user regarding object 450.

[0110] As described above, the object detection system 210 can induce resonance in the object 450 using sound. In some implementations, the object detection system 210 can induce resonance in the object 450, at least in part, by causing specific speakers 402a-402n of the vehicle 200 (one or more) to emit specific sounds within a specific time period, based on a specific number of repetitions and / or based on a specific repetition frequency. As an example, the object detection system 210 can cause a subset of speakers 402a-402n to be selected and a specific audio output signal to be provided to the selected subset of speakers. Furthermore, the object detection system 210 can cause the selected speakers to emit sound based on a specific audio output signal that starts at a specific start time and ends at a specific end time. Additionally, the object detection system 210 can cause the selected speakers to emit sound based on a specific number of repetitions and frequency.

[0111] In some implementations, a subset of speakers, audio output signals, time intervals, number of repetitions, and / or repetition frequencies can be selected empirically. For example, experiments can be conducted (e.g., by the developers of object detection system 210) to identify specific combinations of speakers, audio output signals, time intervals, number of repetitions, and / or repetition frequencies, which can improve the accuracy and / or sensitivity of the object detection system in distinguishing different object locations and / or different object types.

[0112] In some implementations, machine learning can be used to select subsets of speakers, audio output signals, time intervals, number of repetitions, and / or repetition frequencies. For example, machine learning models can be used to identify specific combinations of speakers, audio output signals, time intervals, number of repetitions, and / or repetition frequencies, which can improve the accuracy and / or sensitivity of object detection systems in distinguishing different object locations and / or different object types.

[0113] Typically, audio output signals may include ultrasonic spectral components, spectral components within the range of human hearing, and / or infrasound spectral components.

[0114] In some implementations, the audio output signal may include a sinusoidal signal having a single frequency or several frequencies (e.g., one or more frequency ranges). In some embodiments, the audio output signal may include a sinusoidal sweep signal (e.g., a sinusoidal signal having a frequency that varies over time).

[0115] Figure 6A An example sinusoidal scanning signal 600 is shown. In this example, the frequency of the sinusoidal scanning signal 600 increases monotonically with time (e.g., from a first lower frequency to a second higher frequency). However, instead of... Figure 6A In addition to the sinusoidal scanning signal 600 shown, different sinusoidal scanning signals can also be used. For example, the frequency of the sinusoidal scanning signal can decrease monotonically over time. As another example, the sinusoidal scanning signal can include time intervals of frequency increase and other time intervals of frequency decrease.

[0116] In some implementations, the audio output signal may include an impulse response signal (e.g., a signal having local intensity peaks or pulses consistent with or approximating the impulse response function (IRF)). Figure 6B Example impulse response signal 610 is shown in the figure.

[0117] Although the examples described herein relate to identifying objects already inside vehicle 200, in some implementations, the systems and techniques described herein can also be used to identify objects already outside vehicle 200. For example, at least some of the speakers 402a-402n can be configured to output sound to the external environment of vehicle 200. Furthermore, at least some of the microphones 404a-404n can be configured to generate audio recording signals representing sounds in the external environment. Additionally, at least some of the cameras 406a-406n can be configured to generate images and / or videos representing the external environment. Object detection system 210 can (e.g., in a manner similar to that described above) locate and / or identify objects based on audio recording signals, images, and / or videos.

[0118] At least some of the techniques described herein can be implemented using one or more machine learning models. As an example, Figure 7 illustrates an implementation of a machine learning model. More specifically, it illustrates an implementation of a convolutional neural network (CNN) 720. For illustrative purposes, the following description of CNN 720 will concern the implementation of CNN 720 via object detection system 400. However, it will be understood that in some examples, CNN 720 (e.g., one or more components of CNN 720) is implemented by systems other than object detection system 400 or other than object detection system 400 (such as autonomous vehicle computation 202f, etc.). Although CNN 720 includes certain features as described herein, these features are provided for illustrative purposes and are not intended to limit this disclosure.

[0119] CNN 720 includes multiple convolutional layers comprising a first convolutional layer 722, a second convolutional layer 724, and a convolutional layer 726. In some embodiments, CNN 720 includes a subsampling layer 728 (sometimes referred to as a pooling layer). In some embodiments, subsampling layer 728 and / or other subsampling layers have a dimension smaller than that of the upstream system (i.e., the number of nodes). By means of subsampling layer 728 having a dimension smaller than that of the upstream layers, CNN 720 combines the amount of data associated with the initial input and / or output of the upstream layers, thereby reducing the computational cost required for downstream convolution operations in CNN 720. Additionally or alternatively, subsampling layer 728 is associated with at least one subsampling function (e.g., configured to perform at least one subsampling function) (as described below). Figure 7B and Figure 7C As described, the CNN 720 combines the amount of data associated with the initial input.

[0120] Based on the object detection system 210 providing corresponding inputs and / or outputs associated with each of the first convolutional layer 722, the second convolutional layer 724, and the convolutional layer 726 to generate corresponding outputs, the object detection system 210 performs convolution operations. In some examples, based on the object detection system 210 providing data as input to the first convolutional layer 722, the second convolutional layer 724, and the convolutional layer 726, the object detection system 210 implements a CNN 720. In such examples, based on the object detection system 210 receiving data from one or more different systems (e.g., microphones 404a-404n, cameras 406a-406n, database 410, etc.), the object detection system 210 provides the data as input to the first convolutional layer 722, the second convolutional layer 724, and the convolutional layer 726. The following is about Figure 7B Includes a detailed explanation of convolution operations.

[0121] In some embodiments, the object detection system 210 provides data associated with an input (referred to as initial input) to a first convolutional layer 722, and uses the first convolutional layer 722 to generate data associated with an output. In some embodiments, the object detection system 210 provides the output generated by the convolutional layers as input to different convolutional layers. For example, the object detection system 210 provides the output of the first convolutional layer 722 as input to a subsampling layer 728, a second convolutional layer 724, and / or a convolutional layer 726. In such an example, the first convolutional layer 722 is referred to as the upstream layer, and the subsampling layer 728, the second convolutional layer 724, and / or the convolutional layer 726 are referred to as downstream layers. Similarly, in some embodiments, the object detection system 210 provides the output of the subsampling layer 728 to the second convolutional layer 724 and / or the convolutional layer 726, and in this example, the subsampling layer 728 will be referred to as the upstream layer, and the second convolutional layer 724 and / or the convolutional layer 726 will be referred to as the downstream layer.

[0122] In some embodiments, before the object detection system 210 provides input to the CNN 720, the object detection system 210 processes the data associated with the input provided to the CNN 720. For example, the object detection system 210 processes the data associated with the input provided to the CNN 720 based on the normalization of sensor data (e.g., audio data, image data, and / or video data, etc.) by the object detection system 210.

[0123] In some embodiments, the CNN 720 generates output based on convolution operations associated with each convolutional layer performed by the object detection system 210. In some examples, the CNN 720 generates output based on convolution operations associated with each convolutional layer and an initial input performed by the object detection system 210. In some embodiments, the object detection system 210 generates output and provides that output to a fully connected layer 730. In some examples, the object detection system 210 provides the output of convolutional layer 426 to the fully connected layer 730, wherein the fully connected layer 730 includes data associated with a plurality of feature values ​​referred to as F1, F2, ..., FN. In this example, the output of convolutional layer 726 includes data associated with a plurality of output feature values ​​representing a prediction.

[0124] In some embodiments, the object detection system 210 identifies a prediction from among multiple predictions based on a feature value identified as the highest probability of being the correct prediction among multiple predictions. For example, if the fully connected layer 730 includes feature values ​​F1, F2, ..., FN and F1 is the largest feature value, the object detection system 210 identifies the prediction associated with F1 as the correct prediction among multiple predictions. In some embodiments, the object detection system 210 trains CNN 720 to generate predictions. In some examples, the object detection system 210 trains CNN 720 to generate predictions based on training data associated with predictions provided to CNN 720.

[0125] Predictions may include, for example, the predicted location of an object within the vehicle 200. As another example, predictions may include predicted characteristics of the object (e.g., the object's type, category, or identifier).

[0126] Now for reference Figure 7B and Figure 7C A diagram illustrating example operation of CNN 740 using object detection system 210. In some embodiments, CNN 740 (e.g., one or more components of CNN 740) and CNN 720 (e.g., one or more components of CNN 720) (see...) Figure 7A (Same or similar)

[0127] In step 750, the object detection system 210 provides data as input to the CNN 740 (step 750). For example, the object detection system 210 can provide data obtained by one or more of microphones 404a-404n and / or cameras 406a-406n. As another example, the object detection system 210 can provide data received from database 410.

[0128] In step 755, CNN 740 executes a first convolution function. For example, CNN 740 executes the first convolution function based on the values ​​representing input data being provided as input to one or more neurons (not explicitly illustrated) included in the first convolutional layer 742. As an example, a value representing an image or video may correspond to a value representing a region (sometimes called a receptive field) of the image or video. As another example, a value representing an audio signal may correspond to a value representing a portion of the audio signal (e.g., a specific time portion and / or a specific spectral portion). As yet another example, a value representing some other sensor measurement may correspond to a value representing a portion of that sensor measurement (e.g., a specific time portion and / or a specific spectral portion).

[0129] In some embodiments, each neuron is associated with a filter (not explicitly illustrated). A filter (sometimes called a kernel) can be represented as an array of values ​​corresponding in size to the values ​​provided as input to the neuron. In one example, the filter may be configured to identify edges in an image (e.g., horizontal lines, vertical lines, and / or straight lines, etc.). In successive convolutional layers, the filters associated with neurons may be configured to successively identify more complex patterns in the image (e.g., arcs and / or objects, etc.). In another example, the filter may be configured to identify spectral portions of an audio signal (e.g., portions of the audio signal corresponding to a specific frequency and / or frequency range). In successive convolutional layers, the filters associated with neurons may be configured to successively identify more complex patterns in the audio signal (e.g., patterns indicating the location, identification, or type of an audio source, etc.).

[0130] In some embodiments, the CNN 740 performs a first convolution function by multiplying the values ​​of each neuron in one or more neurons included in the first convolutional layer 742, which are provided as input, with the values ​​of the filters corresponding to each of the neurons in the same or more neurons. For example, the CNN 740 may multiply the values ​​of each neuron in one or more neurons included in the first convolutional layer 742, which are provided as input, with the values ​​of the filters corresponding to each of the neurons in the same or more neurons to generate a single value or an array of values ​​as output. In some embodiments, the collective output of the neurons in the first convolutional layer 742 is referred to as the convolutional output. In some embodiments, when the neurons have the same filters, the convolutional output is referred to as a feature map.

[0131] In some embodiments, the CNN 740 provides the outputs of each neuron in the first convolutional layer 742 to neurons in downstream layers. For clarity, an upstream layer can be a layer that transmits data to a different layer (referred to as a downstream layer). For example, the CNN 740 may provide the outputs of each neuron in the first convolutional layer 742 to the corresponding neurons in a subsampling layer. In this example, the CNN 740 provides the outputs of each neuron in the first convolutional layer 742 to the corresponding neurons in the first subsampling layer 744. In some embodiments, the CNN 740 adds a bias value to the set of all values ​​provided to the neurons in the downstream layer. For example, the CNN 740 adds a bias value to the set of all values ​​provided to the neurons in the first subsampling layer 744. In such an example, the CNN 740 determines the final values ​​to be provided to the neurons in the first subsampling layer 744 based on the set of all values ​​provided to the neurons and the activation function associated with each neuron in the first subsampling layer 744.

[0132] In step 760, CNN 740 executes a first subsampling function. For example, CNN 740 may execute the first subsampling function based on the values ​​provided by CNN 740 from the output of the first convolutional layer 742 to the corresponding neurons of the first subsampling layer 744. In some embodiments, CNN 740 executes the first subsampling function based on an aggregation function. In one example, CNN 740 executes the first subsampling function based on determining the maximum input (called the max pooling function) among the values ​​provided to a given neuron. In another example, CNN 740 executes the first subsampling function based on determining the average input (called the average pooling function) among the values ​​provided to a given neuron. In some embodiments, based on the values ​​provided by CNN 740 to the individual neurons of the first subsampling layer 744, CNN 740 generates an output, which is sometimes referred to as the subsampling convolution output.

[0133] In step 765, CNN 740 executes a second convolution function. In some embodiments, CNN 740 executes the second convolution function in a manner similar to how CNN 740 executes the first convolution function described above. In some embodiments, CNN 740 executes the second convolution function based on CNN 740 providing the value output by the first subsampling layer 744 as input to one or more neurons (not explicitly illustrated) included in the second convolutional layer 746. In some embodiments, as described above, each neuron in the second convolutional layer 746 is associated with a filter. As described above, the filter (one or more) associated with the second convolutional layer 746 can be configured to recognize more complex patterns compared to the filter associated with the first convolutional layer 742.

[0134] In some embodiments, the CNN 740 performs a second convolution function by multiplying the values ​​of each neuron in one or more neurons included in the second convolutional layer 746 as input with the values ​​of the filters corresponding to each of those neurons. For example, the CNN 740 may multiply the values ​​of each neuron in one or more neurons included in the second convolutional layer 746 as input with the values ​​of the filters corresponding to those neurons to generate a single value or an array of values ​​as output.

[0135] In some embodiments, CNN 740 provides the outputs of each neuron in the second convolutional layer 746 to neurons in downstream layers. For example, CNN 740 may provide the outputs of each neuron in the first convolutional layer 742 to the corresponding neurons in the subsampling layer. In this example, CNN 740 provides the outputs of each neuron in the first convolutional layer 742 to the corresponding neurons in the second subsampling layer 748. In some embodiments, CNN 740 adds a bias value to the set of all values ​​provided to the neurons in the downstream layers. For example, CNN 740 adds a bias value to the set of all values ​​provided to the neurons in the second subsampling layer 748. In such an example, CNN 740 determines the final values ​​provided to the neurons in the second subsampling layer 748 based on the set of all values ​​provided to the neurons and the activation function associated with each neuron in the second subsampling layer 748.

[0136] In step 770, CNN 740 executes a second subsampling function. For example, based on the values ​​provided by CNN 740 from the output of the second convolutional layer 746 to the corresponding neurons of the second subsampling layer 748, CNN 740 may execute the second subsampling function. In some embodiments, CNN 740 executes the second subsampling function based on the use of an aggregation function. In the example, as described above, CNN 740 executes a first subsampling function based on determining the maximum or average input among the values ​​provided to a given neuron. In some embodiments, CNN 740 generates an output based on the values ​​provided to the individual neurons of the second subsampling layer 748.

[0137] In step 775, CNN 740 provides the outputs of each neuron in the second subsampling layer 748 to the fully connected layer 749. For example, CNN 740 provides the outputs of each neuron in the second subsampling layer 748 to the fully connected layer 749 so that the fully connected layer 749 generates an output. In some embodiments, the fully connected layer 749 is configured to generate an output associated with a prediction (sometimes called a classification).

[0138] As an example, the output may include a prediction of the object's position within the vehicle 200. For instance, the output may indicate the general area where the object is located (e.g., in a specific seat of the vehicle, in a specific footwell of the vehicle, under a specific seat of the vehicle, in a specific storage compartment of the vehicle, in a specific seat back pocket of the vehicle, etc.). As another example, the output may indicate a set of spatial coordinates representing the object's position (e.g., a set of x, y, and z coordinates relative to the vehicle).

[0139] As an example, the output may include predictions related to the identification or type of objects inside vehicle 200. For instance, the output may indicate whether an object is a backpack, wallet, handbag, purse, briefcase, briefcase, luggage, clothing (e.g., coat, shirt, trousers, hat, etc.), electronic device (e.g., computer, smartphone, tablet, headphones, earphones, etc.), glasses, sports equipment (e.g., ball, bat, racket, golf club, helmet, etc.), tool (e.g., hammer, wrench, screwdriver, etc.), jewelry (e.g., ring, watch, earring, necklace, etc.) and / or any other type of object.

[0140] In some embodiments, the object detection system 210 performs one or more operations and / or provides data associated with the prediction to the different systems described herein.

[0141] Now for reference Figure 8 The diagram illustrates a flowchart of a process 800 for detecting objects within a vehicle. In some embodiments, one or more steps described with respect to process 800 (e.g., wholly and / or partially) are performed by object detection system 210. Additionally or alternatively, in some embodiments, one or more steps described with respect to process 800 (e.g., wholly and / or partially) are performed by other means or groups of means separate from or including object detection system 210 (such as computer systems located remotely from the vehicle, e.g., server computers and / or cloud computing systems).

[0142] Continue to refer to Figure 8 The object detection system of the vehicle emits at least one auditory signal within the vehicle during at least one first time interval (box 802). In some implementations, the vehicle may be an autonomous vehicle.

[0143] In some implementations, at least one auditory signal may include one or more ultrasonic signals, signals within the range of human hearing, and / or infrasound signals. In some implementations, at least one auditory signal may include at least one sinusoidal signal, a sinusoidal scanning signal, and / or an impulse response signal.

[0144] In some implementations, at least one auditory signal may be emitted during a single time interval. In some implementations, at least one auditory signal may be emitted during multiple first time intervals.

[0145] In some implementations, the system can determine that the user has left the vehicle and issue at least one first auditory signal after determining that the user has left the vehicle.

[0146] Continue to refer to Figure 8 The system measures a second auditory signal emitted by an object within the vehicle during a second time interval following at least a first time interval (box 804). The emission of the second auditory signal is caused by the emission of at least one first auditory signal. For example, the second auditory signal may correspond to the resonance of the object caused by the first auditory signal.

[0147] Continue to refer to Figure 8 The system determines the object's position within the vehicle based on measurements of the second auditory signal (box 806). In some implementations, the system may make this determination based on machine learning and / or signal triangulation.

[0148] Continue to refer to Figure 8 The system generates an alert for the user indicating the location of the object (box 808).

[0149] In some implementations, generating an alarm may include emitting a third auditory signal from outside the vehicle.

[0150] In some implementations, generating an alert may include transmitting an electronic message to a mobile device associated with the user.

[0151] In some implementations, the system can also determine the type of an object based on measurements of the second auditory signal.

[0152] In some implementations, the location of an object and the type of the object can be determined based on a neural network that takes the measurement of a second signal as input.

[0153] In some implementations, the system can also acquire image data (e.g., images and / or videos) relating to the interior of the vehicle from one or more image sensors (e.g., still cameras, video cameras, etc.). The location of objects can be further determined based on the image data.

[0154] In some implementations, multiple speakers within the vehicle can simultaneously emit at least one auditory signal. Furthermore, multiple microphones within the vehicle can simultaneously measure a second auditory signal. Additionally, the object's position can be determined, at least in part, by triangulation based on the measurements of the second auditory signal from multiple microphones.

[0155] In the preceding description, aspects and embodiments of this disclosure have been described with reference to numerous specific details, which may vary from implementation to implementation. Therefore, the specification and drawings should be considered illustrative rather than restrictive. The sole and exclusive indication of the scope of this invention, and what the applicant expects to be the scope of this invention, is the literal and equivalent scope of the claims published from this application in the specific form of the published claims, including any subsequent amendments. Any definitions of terms expressly set forth herein for inclusion in such claims should be taken as meaning as such terms are used in the claims. Furthermore, when the term “comprising” is used in the preceding specification or appended claims, what follows that phrase may be an additional step or entity, or a sub-step / sub-entity of a previously stated step or entity.

Claims

1. A method for detecting objects within a vehicle, comprising: The object detection system of the vehicle emits at least one first auditory signal within the vehicle during at least one first time interval; The object detection system measures a second auditory signal emitted by an object within the vehicle during a second time interval following the at least one first time interval, wherein the emission of the second auditory signal is caused by the emission of the at least one first auditory signal, and wherein the second auditory signal corresponds to a resonance of the object in response to the at least one first auditory signal; The object detection system determines the position of the object within the vehicle based on the measurement results of the second auditory signal; and The object detection system generates an alert for the user indicating the location of the object.

2. The method according to claim 1, wherein, Generating the alarm includes: A third auditory signal is emitted from outside the vehicle.

3. The method according to claim 1, wherein, Generating the alarm includes: Transmit electronic messages to mobile devices associated with users.

4. The method according to claim 1, further comprising: It was determined that the user had left the vehicle, and Specifically, after determining that the user has left the vehicle, at least one first auditory signal is emitted.

5. The method according to claim 1, wherein, During multiple first time intervals, multiple first auditory signals are emitted within the vehicle.

6. The method according to claim 1, wherein, The at least one first auditory signal includes a sine wave signal.

7. The method according to claim 1, wherein, The at least one first auditory signal includes a sinusoidal scanning signal.

8. The method according to claim 1, wherein, The at least one first auditory signal includes an impulse response signal.

9. The method according to claim 1, further comprising: The type of the object is determined based on the measurement results of the second auditory signal.

10. The method according to claim 9, wherein, The location of the object and the type of the object are at least one determined based on a neural network that takes the measurement results of the second auditory signal as input.

11. The method according to claim 1, further comprising: The object detection system acquires image data relating to the interior of the vehicle from one or more image sensors, and The location of the object is further determined based on the image data.

12. The method according to claim 1, wherein, Emitting at least one auditory signal includes: The at least one auditory signal is emitted simultaneously using multiple speakers within the vehicle.

13. The method according to claim 1, wherein, Measuring the second auditory signal includes: The second auditory signal is measured simultaneously using multiple microphones within the vehicle.

14. The method according to claim 13, wherein, Determining the location of the object includes: The position of the object is triangulated based on the measurement results of the second auditory signal from the multiple microphones.

15. A system for detecting objects within a vehicle, comprising: At least one processor; as well as At least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: At least one first auditory signal is emitted within the vehicle during at least one first time interval; Measure a second auditory signal emitted by an object within the vehicle during a second time interval following the at least one first time interval, wherein the emission of the second auditory signal is caused by the emission of the at least one first auditory signal, and wherein the second auditory signal corresponds to a resonance of the object in response to the at least one first auditory signal; The position of the object within the vehicle is determined based on the measurement results of the second auditory signal; and Generate an alert for the user indicating the location of the object.

16. At least one non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to: At least one first auditory signal is emitted within the vehicle during at least one first time interval; Measure a second auditory signal emitted by an object within the vehicle during a second time interval following the at least one first time interval, wherein the emission of the second auditory signal is caused by the emission of the at least one first auditory signal, and wherein, The second auditory signal corresponds to the resonance of the object in response to the at least one first auditory signal; The position of the object within the vehicle is determined based on the measurement results of the second auditory signal; as well as Generate an alert for the user indicating the location of the object.

17. A computer program product comprising a program that, when executed by a computer, implements the steps of the method according to any one of claims 1 to 14.

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