SYSTEM AND METHOD FOR DETECTING AND REPORTING EVENTS - Patent application

Abstract

To provide a method for detecting and reporting events.SOLUTION: A method, a system, and a device for detecting and reporting events are provided. The method may include: acquiring sensor data from at least one sensor device in a vehicle that can be implemented by one or more programmed processors in the vehicle; processing the acquired sensor data to determine whether a predetermined event is occurring outside the vehicle; and transmitting information corresponding to the predetermined event to a server based on the determination that the predetermined event is occurring.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] Systems and methods consistent with exemplary embodiments of the present disclosure relate to vehicles, and more particularly, to systems and methods for detecting and reporting events from data acquired by a vehicle. [Background technology]

[0002] Typically, emergency events, such as fires, automobile accidents, personal injuries, and the like, are time-sensitive and require immediate attention from responding authorities (e.g., medical teams, fire departments, police departments, etc.) so that appropriate action can be provided in a timely manner.

[0003] Traditionally, authorities are only alerted to an event after a human observes and identifies the event and then manually contacts the authorities. Furthermore, some emergency events may not be identified by human observation. For example, a call for help may not be heard by a person wearing headphones or who is hard of hearing, an incident occurring behind a vehicle may go unnoticed by the vehicle's driver, and the like.

[0004] Thus, persons may not be able to immediately identify and report an emergency event, and authorities may not receive timely warning and may not be able to provide action in response to the emergency event. As a result, the emergency event may result in injury or death that could be avoided if appropriate action were provided in a timely manner by authorities.

[0005] Additionally, human reports may contain inaccurate information, such as inaccurate event location information, incorrect identification of the event, and the like, which may result in false alarms and waste of agency resources (e.g., time, effort, etc.). Summary of the Invention

[0006] According to embodiments, methods, systems, and devices are provided for automatically detecting and reporting events. For example, exemplary embodiments of the present disclosure provide methods, systems, and devices that utilize an on-board device in a vehicle to automatically detect an event that may require action from authorities, automatically detect associated authorities, and initiate contact with the associated authorities to report the event. Additionally, the methods, systems, and devices of the exemplary embodiments may automatically verify that an event has been accurately detected before reporting the event to authorities.

[0007] According to an embodiment, the method may be implemented by one or more programmed processors in a vehicle, and the method may include acquiring sensor data from at least one sensor device in the vehicle, processing the acquired sensor data to determine whether a predetermined event outside the vehicle is occurring, and based on determining that the predetermined event is occurring, transmitting information corresponding to the predetermined event to a server.

[0008] The transmitted information may include acquired sensor data. The at least one sensor device may include a sensor device for detecting data outside the vehicle and a sensor device for detecting data inside the vehicle.

[0009] The acquired sensor data may include at least one of image data, LiDAR sensor data, accelerometer data, audio data, and infrared image data.

[0010] Processing the acquired sensor data may include inputting a predetermined set of the acquired sensor data into a machine learning model that is trained to detect a predetermined event.

[0011] The captured sensor data may include audio data, and processing the captured sensor data may include determining whether the audio data includes a predetermined keyword or is louder than a predetermined threshold.

[0012] Additionally or alternatively, the acquired sensor data may include infrared image data, and processing the acquired sensor data may include determining whether the infrared image data includes a vehicle-shaped object having a temperature above a predetermined threshold.

[0013] Additionally or alternatively, the acquired sensor data may include LiDAR sensor data, and processing the acquired sensor data may include determining whether a collision between the vehicle and a pedestrian is occurring based on the LiDAR sensor data.

[0014] Additionally, transmitting the information may include transmitting the information to a server configured to determine or confirm whether an event is occurring based on the sensor data received from the multiple vehicles.

[0015] Additionally or alternatively, transmitting the information may include transmitting other sensor data to the server, separate from the acquired sensor data, based on determining that the predetermined event has occurred, which is processed to determine the predetermined event.

[0016] Additionally, processing the acquired sensor data may include processing the acquired sensor data acquired from the multiple sensor devices to determine whether a predetermined event is occurring.

[0017] Additionally or alternatively, processing the acquired sensor data may include inputting the acquired sensor data to a number of event detection modules to determine whether a predetermined event is occurring.

[0018] According to an embodiment, a vehicle may include at least one sensor device, a memory storing instructions, and at least one programmed processor configured to execute the instructions to acquire sensor data from the at least one sensor device, process the acquired sensor data to determine whether a predetermined event outside of the vehicle is occurring, and based on determining that the predetermined event is occurring, transmit information corresponding to the predetermined event to a server.

[0019] The transmitted information may include acquired sensor data. The at least one sensor device may include a sensor device for detecting data outside the vehicle and a sensor device for detecting data inside the vehicle.

[0020] The acquired sensor data may include at least one of image data, LiDAR sensor data, accelerometer data, audio data, and infrared image data.

[0021] The captured sensor data may include audio data, and the at least one programmed processor may be configured to process the captured sensor data by executing instructions to determine whether the audio data includes a predetermined keyword or is louder than a predetermined threshold.

[0022] Additionally or alternatively, the acquired sensor data may include infrared image data, and the at least one programmed processor may be configured to process the acquired sensor data by executing instructions to determine whether the infrared image data includes a vehicle-shaped object having a temperature above a predetermined threshold.

[0023] Additionally or alternatively, the acquired sensor data may include LiDAR sensor data, and the at least one programmed processor may be configured to process the acquired sensor data by executing instructions to determine whether a collision between the vehicle and a pedestrian is occurring based on the LiDAR sensor data.

[0024] Further, the at least one programmed processor may be configured to execute instructions to transmit the information by transmitting the information to a server configured to determine or confirm whether an event is occurring based on the sensor data received from the plurality of vehicles.

[0025] Additional aspects will be set forth in part in the description that follows, and in part will be apparent from the description, or may be realized by practice of the presented embodiments of the present disclosure. [Brief description of the drawings]

[0026] The features, advantages and importance of preferred embodiments of the present disclosure will now be described with reference to the accompanying drawings, in which like reference numerals refer to like elements, and in which: [Figure 1] FIG. 1 illustrates a block diagram of an exemplary system for communication of one or more vehicles with a server according to one or more embodiments. [Diagram 2] FIG. 2 illustrates a diagram of example components of a vehicle, according to one or more embodiments. [Diagram 3] FIG. 3 illustrates an example of a record file containing information of sensor data according to one or more embodiments. [Figure 4] FIG. 4 illustrates a flow diagram of an example method for detecting a predetermined event and providing corresponding information according to one or more embodiments. [Diagram 5] FIG. 5 illustrates a block diagram of an example operation for detecting an event utilizing an event detection module according to one or more embodiments. [Figure 6]FIG. 6 illustrates a block diagram of an example operation for detecting an event using the output of an event detection module according to one or more embodiments. [Figure 7] FIG. 7 illustrates a block diagram of an example operation of utilizing sensor data obtained from multiple sensor devices using an event detection module, according to one or more embodiments. [Figure 8] FIG. 8 illustrates a block diagram of an example operation of utilizing sensor data obtained from multiple sensor devices using multiple event detection modules according to one or more embodiments. [Figure 9] FIG. 9 illustrates an example components diagram of a server according to one or more embodiments. [Figure 10] FIG. 10 illustrates a flow diagram of a method for managing sensor data according to one or more embodiments. [Figure 11] FIG. 11 illustrates a flow diagram of an exemplary method for initiating contact with one or more authorities according to one or more embodiments. [Figure 12] FIG. 12 illustrates an example of a record file containing available authority information according to one or more embodiments. [Figure 13] FIG. 13 illustrates a flow diagram of a method for further processing received sensor data according to one or more embodiments. [Figure 14] FIG. 14 illustrates a block diagram of operations for acquiring sensor data from multiple vehicles according to one or more embodiments. [Figure 15] FIG. 15 illustrates a block diagram of operations for acquiring sensor data from a vehicle and a separate server in accordance with one or more embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] The following detailed description of the preferred embodiments refers to the accompanying drawings. The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementation to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementation. Furthermore, one or more features or components of one embodiment may be incorporated in or combined with another embodiment (or one or more features of another embodiment). Furthermore, in the flowcharts and descriptions of operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be performed (at least partially) simultaneously, and the order of one or more operations may be switched.

[0028] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement the systems and / or methods is not a limitation of the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0029] Although a particular combination of features is recited in a claim and / or disclosed herein, that combination is not intended to limit the disclosure of possible implementations. Indeed, many of the features may be combined in ways not specifically recited in the claims and / or disclosed herein. Although each dependent claim listed below may depend directly on only one claim, the disclosure of possible implementations includes each dependent claim in combination with all other claims in the claim set.

[0030] No element, act, or instruction used herein should be construed as critical or required unless expressly stated otherwise. Additionally, the articles "a" and "an" as used herein are intended to include one or more items and may be used interchangeably with "one or more." When only one item is intended, the term "a" or similar terms are used. Additionally, the terms "has," "have," "having," "include," "including," or the like as used herein are intended to be open-ended terms. Additionally, the phrase "based on" is intended to mean "based at least in part on," unless expressly stated otherwise. Additionally, phrases such as "at least one of [A] and [B]" or "at least one of [A] or [B]" should be understood as including only A, only B, or both A and B.

[0031] References throughout this specification to "one embodiment," "an embodiment," "a non-limiting preferred embodiment," or similar terms mean that the particular feature, structure, or characteristic described in connection with the illustrated embodiment is included in at least one embodiment of the solution. Thus, the phrases "in one embodiment," "in an embodiment," "a non-limiting preferred embodiment," and similar terms throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0032] Furthermore, the described features, advantages, and characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. Those skilled in the art will recognize, in light of the description herein, that the present disclosure may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the present disclosure.

[0033] In one implementation of the disclosure described herein, a display page may include information residing in a memory of a computing device, which may be transmitted from the computing device to a data center over a network, or vice versa. Information may be stored in memory at each computing device, in data storage residing at the edge of the network, or on a server at a data center. A computing device or a mobile device may receive non-transitory computer-readable media, which may include instructions, logic, data, or code that may be stored in a permanent or temporary memory of a mobile device, or that may affect or initiate an action by the mobile device in some way. Similarly, one or more servers may communicate with one or more mobile devices in a network and transmit computer files residing in memory. For example, a network may include the Internet, a wireless communication network, or any other network that connects one or more mobile devices to one or more servers.

[0034] Exemplary embodiments of the present disclosure provide a method and system that utilizes an on-board device in a vehicle to automatically detect an event that may require action from authorities. Furthermore, upon detecting an event, the method and system of the exemplary embodiments may automatically report the event to authorities. Furthermore, the method and system of the exemplary embodiments may verify whether the event is accurately detected before reporting the event to authorities.

[0035] Finally, exemplary embodiments of the present disclosure provide efficient and effective identification and reporting of events. For example, identification and reporting of events may occur with less delay than human identification and reporting. Furthermore, events that are not detected or detectable by humans may be effectively and automatically detected. Furthermore, events may be confirmed or detected with greater accuracy, thereby reducing the rate of false alarms or false reports, thereby preserving agency resources.

[0036] 1 illustrates a block diagram of an example system 100 for one or more vehicles communicating with a server, according to one or more embodiments. With reference to FIG. 1, the system 100 may include a server 110, a network 120, and a number of vehicles (vehicles 130-1 and 130-2).

[0037] Server 110 may be communicatively coupled to a plurality of vehicles via network 120. Server 110 and the plurality of vehicles may be configured to send and receive one or more pieces of information to each other. Information may be exchanged between server 110 and the plurality of vehicles in the form of signals, network data, and any other suitable form.

[0038] The network 120 may include one or more data links that enable the transport of electronic data between the server 110 and the plurality of vehicles (and components or systems contained therein). In this regard, the network 120 may include one or more wired and / or wireless networks. For example, the network 120 may include a cellular network (e.g., a fifth generation (5G) network, a long term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a wireless fidelity (WiFi) network, a private network, a Bluetooth® network, an ad-hoc network, an intranet, the Internet, an optical fiber-based network, or the like, and / or a combination of these or other types of networks. According to an embodiment, the plurality of vehicles may transmit information or data to the server 110 via over-the-air (OTA) transmission using the network 120.

[0039] The server 110 may include one or more devices capable of receiving, generating, storing, processing, computing, and / or providing information or data. According to an embodiment, the server 110 may include a cloud server or a group of cloud servers (e.g., a server cluster, etc.). According to an embodiment, the server 110 may be composed of multiple servers, some of which may be deployed at different locations. For example, the server 110 may include edge servers deployed near the vehicles 130-1 and / or the vehicles 130-2, central servers deployed far from the vehicles 130-1 and / or the vehicles 130-2, and the like. Furthermore, the server 110 may be communicatively connected to one or more devices associated with an authority (e.g., an authority warning system, an emergency event reporting device, etc.). According to an embodiment, the server 110 may be managed or operated by one or more authorities.

[0040] Vehicles 130-1 and 130-2 may include any motorized and / or mechanical machine capable of carrying or transporting people and / or cargo, such as cars, trucks, motorcycles, buses, bicycles, mobility scooters, air vehicles, and the like. Vehicles 130-1 and 130-2 may include one or more components configured to detect one or more events and provide information of the detected events to server 110.

[0041] 2 illustrates a diagram of example components of a vehicle 200, according to one or more embodiments. Vehicle 200 may be similar to vehicle 130-1 and / or vehicle 130-2 in FIG. 1, and thus, it may be understood that descriptions corresponding to vehicle 200 and vehicle 130-1 / vehicle 130-2 may be applicable to one another, unless expressly stated otherwise.

[0042] With reference to FIG. 2, vehicle 200 may include a bus 210, a processor 220, a memory 230, a storage component 240, an input component 250, an output component 260, sensors 270, and a communication interface 280.

[0043] The bus 210 may include one or more components that allow communication between components of the vehicle 200. The processor 220 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 220 may be a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), and / or another type of processing or computational component. In some implementations, the processor 220 may include one or more processors that are programmable to perform functions. The memory 230 may include a random access memory (RAM), a read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) that stores information and / or instructions used by the processor 220.

[0044] Storage component 240 may store information and / or software related to the operation and use of vehicle 200. For example, storage component 240 may include a hard disk (e.g., a magnetic disk, optical disk, magneto-optical disk, and / or solid state disk), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium along with a corresponding drive.

[0045] Input components 250 may include one or more components (e.g., a touch screen display, buttons, switches, a microphone, etc.) that allow vehicle 200 to receive information via user input, etc. According to an embodiment, input components 250 may include at least a portion of sensors 270 (described further below). Output components 260 may include one or more components (e.g., a display, a speaker, one or more light emitting diodes (LEDs), etc.) that provide output information from vehicle 200.

[0046] The sensors 270 may include one or more devices configured to detect, measure, and capture respective data (which may be referred to herein as "sensor data"). For example, the sensors 270 may include an accelerometer that measures and captures data associated with vehicle acceleration / deceleration, vehicle speed, vehicle mileage, and the like; an image sensor (e.g., a camera, etc.) that detects and captures image data inside, outside, around, or near the vehicle; a light detection and ranging (LiDAR) sensor that detects and captures data associated with light in one or more light spectrums, such as the visible spectrum, the infrared spectrum, the ultraviolet spectrum, and / or any other light spectrum; an audio sensor (e.g., a microphone, etc.) that may detect and capture audio data inside and / or outside the vehicle; The sensors may include temperature sensors that measure and capture data associated with the temperature inside and / or outside the vehicle, location sensors (e.g., Global Positioning System (GPS), Inertial Measurement Unit (IMU), etc.) that measure and capture data associated with the location, position, and / or orientation of the vehicle, contact sensors (e.g., pressure detectors, impact detectors, etc.) that detect and capture data associated with parts of the vehicle and between objects, air sensors that measure and capture data associated with the air inside and / or outside the vehicle (e.g., oxygen levels, pollution levels, humidity levels, etc.), and any other sensors suitable for deployment on the vehicle.

[0047] It may be appreciated that some of the aforementioned sensors may operate together to perform certain operations: for example, an accelerometer and a location sensor may operate together to measure the current location of the vehicle and estimate the future location of the vehicle, an image sensor may operate together with an audio sensor to generate a video recording file, a location sensor may provide location and timing information to each of the aforementioned sensors such that data measured / captured by the sensors may be linked to the corresponding location and time measured / captured, and the like.

[0048] Additionally, the sensors 270 may be Internet of Things (IoT) based, allowing the vehicle to communicate with other devices over a network. For example, the vehicle may communicate with one or more external storage media where the measured sensor data is stored internally, may communicate with a server to provide some or all of the measured sensor data for further processing, may communicate with another vehicle to exchange data (e.g., for data validation, data enrichment, data correction, etc.), or the like.

[0049] Additionally, the sensor data may be stored (e.g., stored in memory 230 and / or storage component 240, etc.) in vehicle 200 permanently or semi-permanently for a predetermined period of time. In some embodiments, at least some of the sensor data may be transferred (e.g., by wireless transmission over a network) from vehicle 200 to one or more storage media (e.g., server or cloud storage, etc.). Metadata or logs of the transferred sensor data or other information indicative of the transferred data may be stored in the vehicle or may be implicitly known in the vehicle's control logic. Sensor data may be captured periodically, continuously, intermittently, or based on a trigger event (e.g., loud voices, horn activation, sudden deceleration or hard braking, sudden turns, etc.).

[0050] 3, an example of a record file 300 containing sensor data information is shown, according to one or more embodiments. Record file 300 may include sensor data captured by one or more sensor devices deployed in the vehicle (e.g., sensor 270 in FIG. 2) and associated information such as the location where the data was measured and captured, the time the data was measured and captured, parameters specific to the sensor type (e.g., image type, data source, etc.), and the like.

[0051] The recording file 300 may be stored in the vehicle (e.g., in the memory 230 and / or storage component 240, etc.) and / or in one or more devices external to the vehicle (e.g., a cloud server, an external storage medium, etc.). It may further be understood that multiple recording files may be generated and stored. For example, a first recording file may be generated that records sensor data provided by a temperature sensor, a second recording file may be generated that records sensor data provided by an image sensor, and the like.

[0052] It may further be understood that the recording file may include more / less information than that shown in Figure 3. For example, in cases where the sensor data (e.g., metadata, etc.) is stored in an external storage medium, the recording file may include information of the external storage medium, such as an access link, storage time, and the like.

[0053] Referring back to FIG. 2 , the processor 220 of the vehicle 200 may be configured to process some or all of the sensor data before and / or after storing the sensor data in a storage medium. For example, the processor 220 may be configured to perform one or more of the following operations on some or all of the sensor data: pre-processing (e.g., normalizing, encoding, decoding, enhancing, etc.), transforming (e.g., speech vs. text and / or natural language understanding for audio data), matching (e.g., cataloging, etc.), and filtering (e.g., filtering out some of the data, particularly by detecting or classifying objects, or by comparing a threshold against a threshold (e.g., a threshold volume level for the captured audio data). Additionally, the processor 220 may be configured to anonymize some or all of the received data. For example, the one or more processors may be configured to determine which received data needs to be anonymized (e.g., sensitive information such as characteristics of the vehicle or a user of the vehicle, vehicle driving history, etc.) and perform one or more suitable data anonymizations (e.g., erasing, encrypting, etc.) on the data.

[0054] According to an embodiment, the processor 220 may perform one or more of the aforementioned operations by utilizing one or more artificial intelligence (AI) or machine learning (ML) models (e.g., input at least a portion of the received data to one or more AI / ML models that are trained to perform the aforementioned operations, etc.). The one or more AI / ML models may be pre-trained and stored in one or more storage media, and may be retrieved and utilized by the processor 220 when needed. According to an embodiment, in addition to performing the aforementioned operations by utilizing one or more AI / ML models, the processor 220 may also train one or more AI / ML models with the sensor data. Furthermore, the processor 220 may also detect one or more events (and / or event characteristics associated therewith) by utilizing one or more AI / ML models and / or any suitable rule-based module (e.g., keyword mapping algorithm, etc.).

[0055] The communication interface 280 may include transceiver-like components (e.g., a transceiver and / or separate receiver and transmitter) that enable the vehicle 200 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface 280 may allow the vehicle 200 to receive information from and / or provide information to another device (e.g., the server 110, another vehicle, etc.). For example, the communication interface 280 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like. According to an embodiment, the communication interface 280 may communicatively connect the vehicle 200 (and one or more components included therein) to a server (e.g., the server 110) via a network (e.g., the network 120) to enable the exchange of information or data between the vehicle and the server.

[0056] In view of the above, a vehicle may include components that capture sensor data and communicate the sensor data and / or associated information (e.g., events detected based on the sensor data, etc.) to another device (e.g., a server, another vehicle, etc.).

[0057] The captured sensor data may be utilized for a variety of purposes, including autonomous driving, object detection, voice activation, and the like. According to an exemplary embodiment of the present disclosure, the sensor data may be utilized to detect events that may require action from authorities. Specifically, the sensor data may be processed and analyzed to detect predefined events (and / or predefined event characteristics), and then information corresponding to the predefined events may be transmitted to a server.

[0058] 4 illustrates a flow diagram of an example method 400 for detecting a predetermined event and providing corresponding information according to one or more embodiments. Method 400 may be performed by processor 220 of vehicle 200 based on sensor data captured by sensors 270 and information stored in memory 230 and / or storage component 240 (e.g., recording files, AI models, ML models, rule-based algorithms, etc.).

[0059] 4, at operation S410, the vehicle may acquire one or more sensor data. For example, one or more processors (e.g., processor 220) of the vehicle may be configured to acquire the sensor data stored in one or more storage media (e.g., memory 230, storage component 240, external storage medium, etc.) from the one or more storage media.

[0060] Alternatively or additionally, the one or more processors may be configured to obtain sensor data directly from one or more sensors (e.g., sensor 270) or associated devices in real time or near real time. According to an embodiment, the one or more processors may periodically (or continuously) query or request one or more sensors to provide their respective sensor data.

[0061] According to an embodiment, one or more processors may obtain a recording file (e.g., storage file 300 in FIG. 3 ) from one or more storage media, analyze the recording file to analyze whether an update has occurred in the sensor data (e.g., whether new sensor data is acquired, etc.), and then obtain sensor data associated with the update (e.g., newly added or acquired sensor data, etc.).

[0062] According to an embodiment, the acquired sensor data may include at least one of image data, LiDAR sensor data, accelerometer data, audio data, infrared image data, and data of any sensor suitable for deployment in a vehicle.

[0063] At operation S420, the vehicle may process the acquired sensor data. For example, one or more processors of the vehicle may be configured to analyze the sensor data to determine whether a predetermined event is occurring. The predetermined event may be any event defined as an "emergency" by the vehicle manufacturer, the vehicle owner / operator, authorities, and the like. Additionally, the predetermined event may be an event occurring inside (interior), outside (exterior), and / or near the vehicle.

[0064] For example, the one or more processors may analyze infrared camera data to detect objects (e.g., objects having the shape of a vehicle) that are abnormally hot (e.g., higher than some predefined threshold) indicative of a fire, LiDAR sensor data to detect that a pedestrian is not moving after a collision between the pedestrian and the vehicle, audio data captured by a microphone to detect loud noises, specific keywords inside or outside the vehicle (e.g., "help"), or local sirens / warnings (e.g., from a nearby building), accelerometer and seismometer data to detect earthquakes, image data to detect red color indicating blood or to detect damaged body parts, air quality data to detect gas leaks (e.g., from nearby vehicles and / or buildings), and / or any other suitable sensor data to detect any events inside, outside, and / or near the vehicle.

[0065] According to an embodiment, the acquired sensor data may include voice data, and one or more processors in the vehicle may process the acquired sensor data by determining whether the voice data includes one or more predetermined keywords and / or is louder than a predetermined threshold.

[0066] According to an embodiment, the acquired sensor data may include infrared image data, and one or more processors in the vehicle may process the acquired sensor data by determining whether the infrared image data includes one or more vehicle-shaped objects having a temperature above a predetermined threshold.

[0067] According to an embodiment, the acquired sensor data may include LiDAR sensor data, and one or more processors of the vehicle may process the acquired sensor data by determining whether a collision between the vehicle and a pedestrian is occurring based on the LiDAR sensor data.

[0068] According to an embodiment, one or more processors in the vehicle may process all or a set of sensor data (e.g., a predetermined sample of data points, a predetermined number of data points per predetermined time interval, etc.) to analyze and detect one or more predetermined events.

[0069] For example, the one or more processors may input all or a sequence of sensor data into one or more event detection models or modules to detect one or more events. The one or more event detection models / modules may include any suitable artificial intelligence (AI) / machine learning (ML) models (e.g., supervised models, unsupervised models, etc.), any suitable algorithms (e.g., rule-based algorithms, etc.), or combinations thereof, and may be implemented in the form of computer-executable instructions. Further, the one or more event detection models / modules may be trained to detect one or more predefined events and stored in one or more storage media (e.g., memory 230, storage component 240, etc.) in the vehicle.

[0070] As an example, the one or more processors may utilize one or more rule-based algorithms to compare the sensor data against one or more predefined rules (e.g., detected audio greater than a predefined threshold, detected audio containing a predefined keyword, detected temperature of an object greater than a predefined threshold, etc.). Thus, the one or more processors may determine whether one or more predefined events have occurred based on a determination of whether the sensor data has been collected with the one or more predefined rules.

[0071] As another example, the one or more processors may input all or a set of sensor data into one or more AI / ML models to detect corresponding events. Each of the one or more AI / ML models may be trained to detect or predict the same event, partially different events, or different events. Thus, the one or more processors may determine whether one or more predefined events are occurring by inputting sensor data into one or more AI / ML models and then analyzing the output of the AI / ML models.

[0072] According to an embodiment, one or more processors of a vehicle may utilize one or more event detection modules to detect one or more predefined events (and / or one or more characteristics associated therewith). For example, the one or more processors may input or provide one or more sensor data to the one or more event detection modules and determine from the output of the one or more event detection modules whether the one or more predefined events (and / or one or more predefined event characteristics associated therewith) are occurring. Each of the one or more event detection modules may be a software-based module, a hardware-based module, or a combination thereof, and may be configured to detect the respective event (and / or the predefined event characteristics associated therewith) utilizing one or more rule-based algorithms, one or more AI / ML models, or a combination thereof.

[0073] According to an embodiment, the one or more event detection modules may directly output a result of the event detection (e.g., a given event has / has not occurred, etc.), and the one or more processors may simply utilize said result accordingly. According to another embodiment, the one or more event detection modules may output one or more parameters required to detect an event (e.g., event characteristics, event categorization / classification, event prediction, etc.), and then the one or more processors may determine the result of the event detection based on the one or more parameters.

[0074] An example embodiment for utilizing one or more event detection modules to detect one or more events (and / or one or more predefined event characteristics associated therewith) in operation S420 is described below with reference to FIGS. 5-8.

[0075] Referring first to Figure 5, a block diagram of an example operation for detecting an event utilizing an event detection module is shown, according to one or more embodiments. As shown in Figure 5, sensor data A (i.e., data captured and provided by sensor A) may be provided or input to event detection module 1 by one or more processors in the vehicle, and the output of event detection module 1 may be utilized by the one or more processors to determine whether event 1 is occurring.

[0076] As an example, the event detection module 1 may be utilized to detect one or more objects (and / or one or more characteristics associated with the one or more objects), and the sensor data A may include image data captured from an infrared camera (i.e., an image sensor). According to an embodiment, the sensor data A may be input or provided to the event detection module 1 to determine whether the captured image data includes any objects having a temperature higher than a predetermined threshold. Based on a determination that the output of the event detection module 1 indicates that the image data includes an object having a temperature higher than a predetermined threshold, the one or more processors may determine that an event 1 has occurred, or vice versa. According to another embodiment, the sensor data A may be input or provided to the event detection module 1 to determine whether the captured image data includes any objects having a temperature within a temperature range, and the one or more processors may use the results to determine whether an event 1 has occurred (e.g., whether an object having a temperature within the temperature range is an object having a temperature higher than a predetermined threshold, etc.).

[0077] According to an embodiment, the output of the event detection module may be input or provided to another event detection module to detect another event. For example, referring to Figure 6, a block diagram of an example operation of detecting an event using the output of an event detection module is shown, according to one or more embodiments.

[0078] As shown in Figure 6, the output (shown as "Event 1") of event detection module 1 (described above with reference to Figure 5) may be input or provided to event detection module 2 to detect event 2. It may be understood that the output of event detection module 1 may be provided directly by event detection module 1 to event detection module 2, may be processed by one or more processors in the vehicle and then provided to event detection module 2 by the one or more processors, or the like.

[0079] As an example, assuming event 1 (i.e., the output of event detection module 1) indicates that image data includes an object having a temperature above a predetermined threshold, the event information and associated image data may be input or provided (e.g., by event detection module 1, by one or more processors, etc.) to event detection module 2, and in response, event detection module 2 may detect the type of object. For example, event detection module 2 may detect whether the object is a vehicle, an obstacle, a building, a person, and the like based on the event information and / or the image data.

[0080] According to an embodiment, one or more events (and / or event characteristics associated therewith) may be detected based on sensor data provided by multiple sensors and / or multiple event detection models / modules.

[0081] That is, one or more processors of the vehicle may process sensor data obtained from multiple sensors to determine whether one or more predetermined events (and / or one or more predetermined event characteristics associated therewith) are occurring, or may process sensor data obtained from a single sensor by inputting the obtained sensor data into multiple event detection modules to determine whether one or more predetermined events (and / or one or more predetermined event characteristics associated therewith) are occurring, or may process sensor data from multiple sensors (or devices having similar functionality) by inputting the obtained sensor data into multiple event detection modules to determine whether one or more predetermined events (and / or one or more predetermined event characteristics associated therewith) are occurring.

[0082] For example, referring to FIG. 7 , a block diagram of an example operation of utilizing sensor data obtained from multiple sensor devices using an event detection module is shown, in accordance with one or more embodiments.

[0083] As shown in FIG. 7, sensor data C-D (ie, data provided by sensors C-D, respectively) may be input or provided to at least one of event detection modules 3-4 by one or more processors in the vehicle.

[0084] For example, sensor data C (provided by sensor C) and sensor data D (provided by sensor D) may be input or provided (e.g., simultaneously, consecutively, etc.) to event detection module 3 to detect event 3. By way of example, sensor data C may include impact data (e.g., captured by a contact sensor, etc.) defining an impact / contact of the vehicle with an object, sensor data D may include image data (e.g., captured by one or more image sensors, e.g., an interior camera, a front camera, etc.) defining facial expressions of one or more users (e.g., a vehicle driver, a passenger in the vehicle, a pedestrian near the vehicle, etc.), and event detection module 3 may be utilized to detect whether the vehicle has experienced an unintended impact / contact based on the facial expressions of the one or more users. Based on a determination that the impact data indicates that the impact / contact level exceeds a predetermined threshold, and based on a determination that the image data indicates a particular facial expression of one or more users (e.g., a surprised / frightened facial expression, etc.), event detection module 3 may determine (or output parameters that determine) that an unintended impact / contact (e.g., a vehicle collision, etc.) has occurred (e.g., event 3 has occurred).

[0085] Further, as shown in FIG. 7 , in addition to providing sensor data D to event detection module 3 to detect event 3 (as described above), sensor data D may also be input or provided (e.g., simultaneously, consecutively, etc.) to event detection module 4 to detect event 4.

[0086] As an example, assuming that the sensor data D includes image data defining one or more user facial expressions, in addition to being provided to the event detection module 3 to detect whether the vehicle has received an unintended impact / contact (as described above), the sensor data D may also be provided to the event detection module 4 to detect the status of one or more users (e.g., whether the driver is drowsy, whether the driver is looking at their surroundings and not focused on the road, whether a pedestrian in front of the vehicle is inclined to cross the road, etc.).

[0087] Referring now to FIG. 8, a block diagram of an example operation for utilizing sensor data obtained from multiple sensor devices using multiple event detection modules is shown in accordance with one or more embodiments.

[0088] As shown in FIG. 8, the event 7 may be detected by multiple event detection modules 5-7. As an example, the sensor data E (provided by sensor E) may include accelerometer data, and the event detection module 5 may be utilized to detect whether the vehicle is experiencing a static or dynamic acceleration force higher than a predetermined threshold. Meanwhile, the sensor data F (provided by sensor F) may include seismometer data, and the event detection module 6 may be trained to detect whether the vehicle is experiencing a ground movement / vibration higher than a predetermined threshold. Thus, the output of the event detection module 5 (e.g., that the vehicle is / is not experiencing a static or dynamic acceleration force higher than a predetermined threshold) and the output of the event detection module 6 (e.g., that the vehicle is / is not experiencing a ground movement / vibration higher than a predetermined threshold) may be input or provided to the event detection module 7 (e.g., by one or more processors of the vehicle, by the event detection modules 5-6, etc.) to detect whether an earthquake, excavation, boring, drilling, or similar event is occurring.

[0089] It will be understood that the operations described above with reference to Figures 5-8 are merely examples of processing that may be performed by one or more processors of a vehicle (e.g., in operation S420 in Figure 4) to detect one or more predetermined events (and / or one or more predetermined event characteristics associated therewith), and that other possible arrangements, modifications, and combinations should not be excluded from the scope of the present disclosure.

[0090] Referring back to FIG. 4, upon processing the sensor data to detect one or more predefined events (and / or one or more predefined event characteristics associated therewith), in operation S430, information associated with the one or more predefined events may be provided to a server (e.g., server 110) for further analysis or reporting to authorities.

[0091] For example, one or more processors of the vehicle may transmit a volume of sensor data including the sequence of sensor data used to detect an event, the sequence of sensor data, and sensor data captured before and / or after the sequence, as well as associated event information (e.g., event location information, event time information, event outcome, event characteristics, event predictions, etc.) to a server over a network (e.g., network 120) via a communications interface (e.g., communications interface 280, etc.).

[0092] Additionally, sensor data from one or more other sensors may also be provided to the server. For example, if a predefined keyword (e.g., help) is detected from audio data (captured by an audio sensor), both the audio data and associated image data (e.g., data captured by one or more image sensors at locations near the event location during the event time) may be transmitted to the server.

[0093] Further, the sensor data processed to detect an event may not be transmitted to the server, but instead, a message or information indicative of the event detection (e.g., generated in real time or near real time by one or more processors in the vehicle) and / or other sensor data may be transmitted to the server. For example, if a predetermined keyword (e.g., "help," "injured," etc.) is detected in the voice data, the voice data may not be transmitted to the server, but instead, a short message (e.g., "immediate medical assistance is needed"), information (e.g., a brief description of the detected event), and / or associated image data may be provided to the server.

[0094] 9, a diagram of example components of a server 900 is shown, in accordance with one or more embodiments. The server 900 may be similar to the server 110 in FIG. 1, or similar to any of the servers described above with reference to FIG. 2-8. Thus, descriptions corresponding to the server 900 and the servers described in FIG. 1-8 may be applicable to one another, unless expressly stated otherwise.

[0095] 9, the server 900 may include a bus 910, a processor 920, a memory 930, a storage component 940, an input component 950, an output component 960, and a communication interface 970, each of which may have similar functions and roles as the bus 210, the processor 220, the memory 230, the storage component 240, the input component 250, the output component 260, and the communication interface 280, respectively, described above with reference to FIG 2. Accordingly, redundant descriptions corresponding thereto may be omitted below for brevity.

[0096] Server 900 may be managed or operated by one or more authorities, by one or more vehicle manufacturers (e.g., the manufacturer of the vehicle for which the sensor data is provided), and / or by any suitable party that may appropriately manage or utilize the sensor data and / or event information.

[0097] According to an embodiment, server 900 may be configured to receive sensor data and / or event information from one or more vehicles (e.g., vehicle 130-1, vehicle 130-2, vehicle 200, etc.) and then manage the sensor data and / or event information. For example, server 900 (or its associated processor 920) may be configured to contact authorities based on the received sensor data and / or event information, further process the sensor data and / or event information (described further below), store the sensor data and / or event information (e.g., store as evidence that may be used to investigate or prosecute a crime, such as a hit-and-run or vehicular assault, store as evidence for later determining who is at fault or responsible for a particular case, such as a car accident, etc.), share the sensor data and / or event information with one or more other devices (e.g., other servers, other vehicles, etc.), and the like.

[0098] 10, a flow diagram of a method 1000 for managing sensor data is shown in accordance with one or more embodiments. Method 1000 may be performed by server 110 of FIG. 1 or server 900 of FIG. 9 (or associated processor 920).

[0099] 10, at operation S1010, a server may receive sensor data from one or more vehicles. For example, one or more processors (e.g., processor 920, etc.) of a server (e.g., server 110, server 900, etc.) may be configured to receive sensor data from one or more vehicles (e.g., vehicle 130-1, vehicle 130-2, vehicle 200, etc.) that have detected one or more predefined events (and / or one or more predefined event characteristics thereof).

[0100] The one or more processors of the server may receive the sensor data via a communication interface (e.g., communication interface 970, etc.) over a network (e.g., network 120). Alternatively or additionally, the one or more processors may receive the sensor data via one or more storage media (e.g., memory 930, storage component 940, etc.) configured to acquire the sensor data from the one or more vehicles via the communication interface and store the acquired sensor data internally.

[0101] The received or acquired sensor data may include a series of sensor data used to detect an event (or characteristics thereof) (e.g., used by one or more vehicles, etc.), a volume of sensor data including the series of sensor data, and sensor data captured before and / or after the series, as well as associated event information (e.g., event location information, event time information, etc.).

[0102] At operation S1020, the server may initiate contact with one or more authorities. For example, one or more processors of the server may determine one or more authorities associated with the event, obtain information of the one or more associated authorities, and then provide the event information to the one or more associated authorities. The process of contacting the authorities may be performed automatically by the server. Further description corresponding to the operation of initiating contact with one or more authorities is provided below with reference to FIGS. 11-12.

[0103] 10, upon receiving the sensor data at operation S1010, the server may perform optional operation S1030 in which the received sensor data is further processed prior to initiating contact with one or more associated authorities at operation S1020. For example, at optional operation S1030, one or more processors at the server may verify the event occurrence, enhance the sensor data, and the like. Further description corresponding to operations for further processing the received sensor data is provided below with reference to FIG.

[0104] 11, a flow diagram of an example method 1100 of initiating contact with one or more authorities is shown, according to one or more embodiments. One or more operations of method 1100 may be part of operation S1020 in FIG. 10 and may be performed by one or more processors of a server.

[0105] 11, in operation S1110, one or more authorities associated with the event are determined. For example, one or more processors of the server may be configured to determine, based on the sensor data and / or event information, one or more authorities that are best suited to handle or respond to the event.

[0106] According to an embodiment, the one or more processors may determine a type of event, e.g., a type of incident related to the event, a type of response required by authorities, and the like. For example, the one or more processors may determine that a motor vehicle accident (e.g., an event) involves a personal injury, a hit-and-run incident, and a vehicle fire based on the sensor data and / or the event information.

[0107] According to an embodiment, the one or more processors may determine the type of event utilizing one or more event type detection modules (e.g., one or more modules utilizing one or more rule-based algorithms, one or more AI / ML models, or a combination thereof, etc.). For example, the one or more processors may input or provide sensor data and / or event information associated with a vehicle accident to one or more event type detection modules, and the one or more event type detection modules may determine (e.g., based on keywords included in the sensor data, etc.) that the vehicle accident involves an injury / medical-related event, a criminal event, and a fire / rescue-related event.

[0108] Upon determining the type of the event, the one or more processors may obtain information of available authorities (e.g., authorities in system records, authorities with available labor, etc.) from one or more storage media (e.g., memory 930, storage component 940, etc.). The information of each available authority may be pre-stored in one or more storage media, for example, in the form of a record file.

[0109] 12, an example of a record file 1200 containing available authority information is shown, according to one or more embodiments. The record file 1200 may be retrieved by a server and / or provided by the respective authorities continuously, periodically, or in response to a trigger event.

[0110] As shown in FIG. 12, available authority information may include the type or category of authority (e.g., medical, rescue, crime, etc.), the location of each authority, the name of the authority, authority contact information (e.g., hotline contact information, online contact information, etc.), authority type, and the like.

[0111] It may be understood that the record file may contain more / less information than that shown in Figure 12 and / or the information may be arranged in a different manner. For example, multiple record files may be generated and stored, e.g., a first record file may be generated that records authority information associated with a type of "medical," "injury," or "illness," a second record file may be generated that records authority information associated with a type of "crime" or "investigation," and the like. It may also be understood that the information contained in the record file may be continuously (or periodically) updated by the server and / or the associated authority to include the most current information (e.g., latest availability, latest contact information, etc.).

[0112] To this end, upon determining the event type, one or more processors of the server may retrieve available authority information (e.g., in the form of record files) from one or more storage media and determine the most appropriate or preferred authority to handle the case related to the event.

[0113] As an example, based on a determination that the received sensor data (received in operation S1010) indicates a motor vehicle accident involving an injury-related case, a crime-related case, and a fire / rescue-related case, one or more processors of the server may retrieve a record file (e.g., record file 1200) containing available authority information from one or more storage media, determine (e.g., based on a keyword search, based on a predefined mapping, etc.) which authority is most appropriate or preferred to handle the injury-related case, the crime-related case, and / or the fire / rescue-related case, and then determine how to contact them.

[0114] According to an embodiment, the one or more processors of the server may compare the event type with the authority type to determine the relevance level therebetween, and may select the authority with the highest relevance level as the most appropriate or preferred authority. For example, the one or more processors may determine that a car accident involves an injury-related case, and may determine that "XX Medical Center" has a higher relevance level to the car accident (compared to "YY National Hospital"). This is because the authority type of "XX Medical Center" has the keyword "injury" included therein, indicating that "XX Medical Center" is available to handle injury-related cases of the car accident. Thus, the one or more processors may select "XX Medical Center" as one of the authorities to contact to report the car accident.

[0115] According to an embodiment in which multiple authorities are available to handle the same type of case, the one or more processors may determine the most appropriate authority from the multiple available authorities based on the event information. For example, in the example shown in FIG. 12, two authorities, "XY State Fire Department" and "YX State Fire Department", are available to handle fire / rescue related cases of automobile accidents. In this regard, the one or more processors may identify an event location (e.g., a location where fire / rescue is needed) and select the authority closest to the event location as one of the authorities to contact to report the automobile accident.

[0116] 11 , upon determining the associated authority to contact, in act S1120, the server may provide event information to the determined authority. The event information may include the location of the event, an identification of the event, an urgency level of the event, a case related to the event, and any other suitable information based thereon to enable the authority to prepare responsive action.

[0117] For example, one or more processors of the server may generate a text report and send the text report to one or more devices of the associated authority, generate a broadcast audio file and provide the broadcast audio file to a broadcast system of the associated authority, generate a trigger signal to trigger an alert system of the associated authority, and the like.

[0118] According to embodiments in which the server is managed or operated by an associated authority, one or more processors of the server may provide the event information directly via an output component of the server (such as, for example, output component 960). For example, the output component may include a display screen on which the event information provided by the one or more processors may be displayed, may include an emergency LED that may be triggered by the one or more processors to flash in a particular manner according to a particular event type (e.g., an emergency event), may include a siren buzzer that may be triggered by the one or more processors to generate a sound in a particular manner according to a particular event type, and the like.

[0119] 13, a flow diagram of a method 1300 for further processing received sensor data according to one or more embodiments is shown. One or more operations of the method 1300 may be part of optional operation S1030 in FIG. 10 and may be performed by one or more processors of the server.

[0120] 13, at operation S1310, the server may obtain sensor data from multiple devices. For example, in addition to the vehicle from which the sensor data is received (which may be referred to herein as a “source vehicle”), the server (or one or more processors associated therewith) may obtain sensor data from one or more additional vehicles and / or one or more additional servers corresponding to events associated with the sensor data provided by the source vehicle.

[0121] That is, the server may utilize multiple sensor data from multiple devices (e.g., vehicles, servers, etc.) to verify, analyze, understand, or decipher a particular event. For example, if a given event (e.g., high G-forces, or a pedestrian walking in the middle of the road, etc.) is detected by a single vehicle, this may not be a sufficient indication of the event (e.g., an earthquake or a very intoxicated person, etc.) or may not indicate the event with sufficient reliability. However, if a certain number of devices detect the event (e.g., if a certain number of devices capture high G-forces) or the event is detected for a certain period of time (e.g., a pedestrian walking in the middle of the road for more than one minute), the event may be verified or confirmed by the server.

[0122] An exemplary embodiment of a server for acquiring sensor data from multiple devices is described below with reference to FIGS.

[0123] 14, a block diagram of an operation for acquiring sensor data from multiple vehicles is shown, according to one or more embodiments. As shown in FIG. 14, a server 1 may be communicatively connected to multiple vehicles (vehicle A and vehicle B) and may be configured to acquire sensor data associated with an event 1 from the multiple vehicles.

[0124] For example, server 1 may first receive sensor data from vehicle A, where the sensor data indicates that a car accident is occurring. In this regard, before initiating contact with one or more associated authorities, server 1 may determine one or more associated vehicles (e.g., vehicles located at or near the event location at the event time) that can provide associated sensor data, and may obtain associated sensor data, such as image data, audio data, temperature data, and the like, from the one or more associated vehicles. In the example shown in FIG. 14, server 1 may determine that vehicle B can provide associated sensor data, and may obtain associated sensor data from vehicle B for further processing.

[0125] 15, a block diagram of an operation for acquiring sensor data from a vehicle and another server is shown, according to one or more embodiments. As shown in FIG. 15, Server 1 may be communicatively connected to Vehicle A and Server 2 and may be configured to acquire sensor data associated with Event 1 from Vehicle A and Server 2.

[0126] Similar to the example use case described above with reference to Figure 14, Server 1 may first receive sensor data from Vehicle A, where the sensor data indicates that a car accident is occurring. In this regard, instead of or in addition to obtaining the associated sensor data from one or more other vehicles as described above with reference to Figure 14, Server 1 may obtain the associated sensor data from Server 2. Server 2 may be another server utilized to obtain the sensor data from one or more vehicles and to manage the sensor data (e.g., for further processing the sensor data, reporting event information to authorities, etc.).

[0127] 13 , upon obtaining sensor data from the multiple devices at operation S1310, the server may verify the event at operation S1320. For example, one or more processors of the server may determine whether the event indicated in the sensor data and / or event information provided by the source vehicle is accurate (e.g., whether the event actually occurred, whether any instances were missed / detected by the source vehicle, etc.) based on the sensor data provided by the multiple devices. The server may perform any suitable operation to verify the event, such as comparing the sensor data obtained from the multiple devices with the sensor data obtained from the source vehicle, and the like.

[0128] Thus, based on a determination that an event is accurate (e.g., the event actually occurred, the event does not include any instances of inaccuracy, has no instances of undetected, etc.), the server may initiate contact with an associated authority, as described above with reference to Figures 10-12. Conversely, based on a determination that an event is inaccurate, the server may make corrections to the event information before initiating contact with an associated authority, or may simply not initiate contact with an associated authority.

[0129] Still referring to FIG. 13, once sensor data is acquired from multiple devices in operation S1310, the server may perform an optional operation S1330 to enhance the acquired sensor data before validating the event in operation S1320.

[0130] For example, the one or more processors may fill in any missing sensor data and / or event information, may perform corrections and / or adjustments to the sensor data, may perform noise canceling operations on low quality sensor data, and the like.

[0131] In view of the above, exemplary embodiments of the present disclosure provide a method and system that utilizes an on-board device in a vehicle to automatically detect an event that may require action from authorities. Furthermore, upon detecting an event, the method and system of the exemplary embodiments may automatically detect an associated authority and initiate contact with the associated authority to report the event. Furthermore, the method and system of the exemplary embodiments may verify whether the event is accurately detected before reporting the event to the authorities.

[0132] Finally, exemplary embodiments of the present disclosure provide efficient and effective identification and reporting of events. For example, identification and reporting of events may occur with less delay than identification and reporting of a person. Furthermore, events that are not detected or detectable by a person and / or a single vehicle may be effectively and automatically detected. Furthermore, events may be confirmed or detected with greater accuracy, thereby reducing the rate of false alarms or false reports, thereby preserving agency resources.

[0133] It is understood that the specific order or hierarchy of blocks in the processes / flowcharts disclosed herein is an example of an example approach. Based on design preferences, it is understood that the specific order or hierarchy of blocks in the processes / flowcharts may be rearranged. Further, some blocks may be combined or omitted. The accompanying methods claim elements of the various blocks in a sample order, and are not meant to be limited to the specific order or hierarchy presented.

[0134] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible level of technical detail of integration. Furthermore, one or more of the above components described above may be implemented as instructions stored in a computer-readable medium and executable by at least one processor (and / or may include at least one processor). The computer-readable medium may include a computer-readable non-transitory storage medium (or media) having computer-readable program instructions for causing a processor to perform operations.

[0135] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer diskettes, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disk read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or ridge structures in grooves with instructions recorded on them, and any suitable combination of the above. As used herein, computer-readable storage media should not be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted over wires.

[0136] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium into each computing / processing device, or may be downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may comprise copper transmission cables, optical transmission fiber, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing / processing device.

[0137] The computer readable program code / instructions for performing the operations may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or object oriented programming languages ​​such as Smalltalk, C++, or the like, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or a connection to an external computer may be made (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry to perform an aspect or operation.

[0138] The computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus produce means for implementing the function / act specified in the block or blocks of the flowcharts and / or block diagrams. The computer-readable program instructions may also be stored in a computer-readable storage medium that may direct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium having instructions stored therein comprises an article of manufacture including instructions that implement an aspect of the function / act specified in the block or blocks of the flowcharts and / or block diagrams.

[0139] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or another device to cause the computer, other programmable apparatus, or other device to perform a series of operational steps to generate a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device implement the function / act specified in the block or blocks of the flowcharts and / or block diagrams.

[0140] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions comprising one or more executable instructions that implement a specified logical function. The methods, computer systems, and computer-readable media may include additional, fewer, different, or differently arranged blocks compared to those depicted in the figures. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed simultaneously or substantially simultaneously, or the blocks may be executed in the reverse order depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart illustrations, as well as combinations of blocks and / or flowchart illustrations of the block diagrams, may be implemented by a dedicated hardware-based system that performs the specified functions or acts or executes a combination of dedicated hardware and computer instructions.

[0141] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement the systems and / or methods is not a limitation of the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

Claims

1. 1. A method implemented by one or more programmed processors in a vehicle, comprising: acquiring sensor data from at least one sensor device in the vehicle; processing the acquired sensor data to determine whether a predetermined event is occurring outside of the vehicle; based on determining that the predetermined event has occurred, transmitting information corresponding to the predetermined event to a server; Including, the acquired sensor data comprises infrared image data; The method, wherein the processing of the acquired sensor data includes determining whether the thermal image data includes a vehicle-shaped object having a temperature above a predetermined threshold.

2. The method of claim 1 , wherein the transmitted information comprises the acquired sensor data.

3. 3. The method of claim 1 or 2, wherein the processing of the acquired sensor data comprises inputting a predetermined set of the acquired sensor data into a machine learning model that is trained to detect the predetermined event.

4. The method of claim 1 or 2, wherein the acquired sensor data comprises at least one of image data, LiDAR sensor data, accelerometer data, audio data, and infrared image data.

5. the acquired sensor data comprises audio data; The method of claim 1 or 2, wherein the processing of the acquired sensor data includes determining whether the audio data contains a predefined keyword or is louder than a predefined threshold.

6. the acquired sensor data comprises LiDAR sensor data; The method of claim 1 or 2, wherein the processing of the acquired sensor data includes determining whether a collision between a vehicle and a pedestrian is occurring based on the LiDAR sensor data.

7. 3. The method of claim 1 or 2, wherein the transmitting of the information includes transmitting the information to the server configured to determine or confirm whether the event is occurring based on sensor data received from a plurality of vehicles.

8. 3. The method of claim 1 or 2, wherein the transmitting of the information includes transmitting other sensor data to the server apart from the acquired sensor data that is processed to determine the predetermined event based on the determination that the predetermined event has occurred.

9. The method of claim 1 or 2, wherein the processing of the acquired sensor data comprises processing the acquired sensor data from a plurality of sensor devices to determine whether the predetermined event is occurring.

10. 3. The method of claim 1 or 2, wherein the processing of the acquired sensor data comprises inputting the acquired sensor data to a plurality of event detection modules to determine whether the predetermined event is occurring.

11. The method of claim 9 , wherein the plurality of sensor devices comprises a sensor device for detecting data outside the vehicle and a sensor device for detecting data inside the vehicle.

12. A vehicle, At least one sensor device; A memory for storing instructions; at least one programmed processor executing the instructions to: acquiring sensor data from the at least one sensor device; Processing the acquired sensor data to determine whether a predetermined event is occurring outside of the vehicle; at least one programmed processor configured to, based on determining that the predetermined event has occurred, transmit information corresponding to the predetermined event to a server; Equipped with the acquired sensor data comprises infrared image data; The at least one programmed processor is configured to execute the instructions to process the acquired sensor data by determining whether the thermal image data includes a vehicle-shaped object having a temperature above a predetermined threshold.

13. The vehicle of claim 12 , wherein the transmitted information comprises the acquired sensor data.

14. 14. The vehicle of claim 12 or 13, wherein the at least one programmed processor is configured to execute the instructions to process the acquired sensor data by inputting a predetermined set of the acquired sensor data into a machine learning model that is trained to detect the predetermined event.

15. 14. The vehicle of claim 12 or 13, wherein the acquired sensor data comprises at least one of image data, LiDAR sensor data, accelerometer data, audio data, and infrared image data.

16. the acquired sensor data comprises audio data; 14. The vehicle of claim 12 or 13, wherein the at least one programmed processor is configured to execute the instructions to process the acquired sensor data by determining whether the voice data contains a predetermined keyword or is louder than a predetermined threshold.

17. the acquired sensor data comprises LiDAR sensor data; 14. The vehicle of claim 12 or 13, wherein the at least one programmed processor is configured to execute the instructions to process the acquired sensor data by determining whether a collision between a vehicle and a pedestrian is occurring based on the LiDAR sensor data.

18. 14. The vehicle of claim 12 or 13, wherein the at least one programmed processor is configured to execute the instructions to transmit the information by transmitting the information to the server configured to determine or confirm whether the event is occurring based on sensor data received from a plurality of vehicles.

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