A vehicle early warning method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a vehicle early warning method and device, electronic equipment and storage medium, relating to the field of vehicles, the method of the present disclosure mainly comprises: distributing the first data collected by the first sensor to at least two distribution paths, and determining a first risk output, wherein the first risk output comprises the risk outputs of the at least two distribution paths; determining a second risk output according to the second data collected by the second sensor; and determining a vehicle early warning output according to the first risk output and the second risk output. The present disclosure processes sensing data through distribution, fully utilizes the information contained in the sensing data, thereby more quickly and comprehensively obtaining multiple early warning risk outputs to assist early warning, and the present disclosure also comprehensively determines the final early warning information by combining the data collected by different sensors, thereby providing more accurate vehicle early warning to meet the vehicle safety requirements.

Description

Technical Field

[0001] This disclosure relates to the field of vehicles, and more particularly to a vehicle warning method, device, electronic equipment, and storage medium. Background Technology

[0002] In the automotive field, vehicle safety is often monitored by activating surveillance while the vehicle is parked. This involves using cameras or inertial measurement units (IMUs) to monitor real-time vehicle data, providing real-time warnings when the vehicle encounters a safety threat. However, currently, when monitoring is activated while the vehicle is parked, the video data collected by the sensors can only be used by a single application, and judgments and warnings can only be made based on video data or corresponding algorithms, which is prone to misjudgments, false alarms, and missed detections. Therefore, how to utilize reasonable methods to improve the warning capabilities when monitoring is activated while the vehicle is parked, thereby meeting the vehicle's safety requirements, is a problem that needs to be solved. Summary of the Invention

[0003] This disclosure provides a vehicle warning method, device, electronic device, and storage medium to solve problems in related technologies and improve the vehicle warning effect.

[0004] A first aspect of this disclosure provides a vehicle warning method, the method comprising: distributing first data collected by a first sensor to at least two distribution paths, determining a first risk output, wherein the first risk output includes risk outputs of at least two distribution paths; determining a second risk output based on second data collected by a second sensor; and determining a vehicle warning output based on the first risk output and the second risk output.

[0005] In some embodiments of this disclosure, distributing the first data collected by the first sensor to at least two distribution paths and determining the first risk output includes: parsing the first data to obtain distribution data; distributing the distribution data to at least two distribution paths and determining the risk output of each distribution path, wherein the distribution path includes at least the first path and the second path.

[0006] In some embodiments of this disclosure, distributing data to at least two distribution paths and determining the risk output of each distribution path includes: calculating the distribution data according to a preset algorithm to determine the risk information and / or risk level of the first data, wherein the risk information of the first data is used to indicate whether the first data has a risk; and determining the risk information and / or risk level of the first data as the risk output of the first path.

[0007] In some embodiments of this disclosure, distributing data to at least two distribution paths and determining the risk output of each distribution path includes: encoding the distribution data to obtain an encoding result; and determining the encoding result as the risk output of the second path.

[0008] In some embodiments of this disclosure, determining the second risk output based on the second data collected by the second sensor includes: performing risk calculation on the second data to determine the risk information and / or risk level of the second data, wherein the risk information of the second data is used to indicate whether the second data is risky; and determining the risk information and / or risk level of the second data as the second risk output.

[0009] In some embodiments of this disclosure, determining the vehicle's warning output based on the first risk output and the second risk output includes: determining the vehicle's warning information and / or warning risk level based on the first path risk output and the second risk output; if the warning information meets a first preset condition, determining the warning risk level and the second path risk output as the vehicle's warning output, wherein the first preset condition is to issue a warning to the vehicle.

[0010] In some embodiments of this disclosure, determining the vehicle's warning information and / or warning risk level based on the first path risk output and the second risk output includes: if at least one of the risk information in the first data and the risk information in the second data indicates a risk, determining that the warning information meets the first preset condition and determining the maximum value of the risk level in the first data and the risk level in the second data as the warning risk level.

[0011] A second aspect of this disclosure provides a vehicle warning device, comprising: a first determining unit for distributing first data collected by a first sensor to at least two distribution paths and determining a first risk output, wherein the first risk output includes risk outputs from at least two distribution paths; a second determining unit for determining a second risk output based on second data collected by a second sensor; and a third determining unit for determining a vehicle warning output based on the first risk output and the second risk output.

[0012] A third aspect of this disclosure provides a vehicle including a vehicle warning device capable of performing the methods described in the first aspect of this disclosure.

[0013] A fourth aspect of this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods described in the first aspect of this disclosure.

[0014] A fifth aspect of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods described in the first aspect of this disclosure.

[0015] A sixth aspect of this disclosure provides a computer program product including a computer program that is executed by a processor using the methods described in the first aspect of this disclosure.

[0016] A seventh aspect of this disclosure provides a chip characterized by including one or more interface circuits and one or more processors; the interface circuits are configured to receive signals from the memory of an electronic device and send signals to the processors, the signals including computer instructions stored in the memory, which, when executed by the processors, cause the electronic device to perform the methods described in the first aspect of this disclosure.

[0017] The vehicle warning method, device, electronic equipment, and storage medium disclosed herein include: distributing first data collected by a first sensor to at least two distribution paths to determine a first risk output, wherein the first risk output includes risk outputs from at least two distribution paths; determining a second risk output based on second data collected by a second sensor; and determining a vehicle warning output based on the first and second risk outputs. This disclosure, by distributing and processing sensor data, fully utilizes the information contained in the sensor data, thereby acquiring multiple warning risk outputs more quickly and comprehensively to assist in warning. Furthermore, this disclosure integrates data collected by different sensors to determine the final warning information, thereby providing more accurate vehicle warnings to meet vehicle safety requirements.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0020] Figure 1 A flowchart illustrating a vehicle warning method provided in one embodiment of this disclosure;

[0021] Figure 2 A flowchart illustrating a vehicle warning method provided in one embodiment of this disclosure;

[0022] Figure 3 A flowchart illustrating a vehicle warning method provided in one embodiment of this disclosure;

[0023] Figure 4A flowchart illustrating a vehicle warning method provided in one embodiment of this disclosure;

[0024] Figure 5 A flowchart illustrating a vehicle warning method provided in one embodiment of this disclosure;

[0025] Figure 6 A flowchart illustrating a vehicle warning method provided in one embodiment of this disclosure;

[0026] Figure 7 A flowchart illustrating a vehicle warning method provided in one embodiment of this disclosure;

[0027] Figure 8 A schematic flowchart illustrating a vehicle warning method according to an embodiment of this disclosure;

[0028] Figure 9 This is a block diagram of a vehicle warning device provided in one embodiment of the present disclosure;

[0029] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure;

[0030] Figure 11 This is a schematic diagram of the structure of a chip provided in an embodiment of the present disclosure. Detailed Implementation

[0031] Embodiments of this disclosure are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0032] To address the problems existing in related technologies, this disclosure distributes and processes sensor data, making full use of the information contained in the sensor data, thereby acquiring multiple early warning risk outputs more quickly and comprehensively to assist in early warning. Furthermore, this disclosure integrates data collected by different sensors to determine the final early warning information, thereby providing more accurate vehicle early warnings to meet vehicle safety requirements.

[0033] Figure 1 This is a flowchart illustrating a vehicle warning method according to an embodiment of this disclosure. Figure 1 As shown, steps 101-103 are included.

[0034] Step 101: Distribute the first data collected by the first sensor to at least two distribution paths to determine the first risk output.

[0035] It should be noted here that the method proposed in this disclosure can be applied to vehicles, for example, to a processor in a vehicle. The first sensor can be an in-vehicle camera, and the first data can be recorded video data. The specific type of the first sensor and the first data are not limited in the embodiments of this disclosure. In addition, the first risk output includes the risk output of the at least two distribution paths. The distribution path can be an algorithm calculation path or a risk video encoding path. Specifically, during distribution, the first data can be processed first to make it available for distribution, or it can be distributed through other distribution methods. The specific distribution methods are not limited in the embodiments of this disclosure.

[0036] Step 102: Determine the second risk output based on the second data collected by the second sensor.

[0037] In this embodiment of the present disclosure, it should be noted that the second sensor may be an inertial sensor, and the second data may be data collected by the inertial sensor. When determining the second risk output, risk calculation can be performed based on the collected data, such as determining risk information and risk level. The specific risk calculation method is not limited in this embodiment of the present disclosure.

[0038] Step 103: Determine the vehicle's warning output based on the first risk output and the second risk output.

[0039] In this embodiment of the present disclosure, it should be noted that the final vehicle warning output can be determined by combining the first risk output obtained from the first sensor data and the second risk output obtained from the second sensor data. In the determination, the risk information in the two risk outputs can be used equally, or different weights can be set for different sensing devices, and the risk information in the two risk outputs can be used according to different weights. The specific way of using the risk outputs is not limited in this embodiment of the present disclosure.

[0040] In summary, the vehicle warning method provided in this disclosure includes: distributing first data collected by a first sensor to at least two distribution paths to determine a first risk output, wherein the first risk output includes risk outputs from at least two distribution paths; determining a second risk output based on second data collected by a second sensor; and determining a vehicle warning output based on the first and second risk outputs. This disclosure, by distributing and processing sensor data, fully utilizes the information contained in the sensor data, thereby acquiring multiple warning risk outputs more quickly and comprehensively to assist in warning. Furthermore, this disclosure integrates data collected by different sensors to determine the final warning information, thereby providing more accurate vehicle warnings to meet vehicle safety requirements.

[0041] Furthermore, in some embodiments of this disclosure, when performing step 101 of distributing the first data collected by the first sensor to at least two distribution paths and determining the first risk output, this can be achieved through, but is not limited to, the following methods, specifically as follows: Figure 2 As shown, it includes the following steps:

[0042] Step 201: Parse and process the first data to obtain the distribution data.

[0043] It should be noted here that when parsing and processing the first data to obtain the distribution data, different methods can be used. For example, the first data can be added to the Java Native Interface (JNI) using the Java language, thereby transferring the first data to the C language for processing. The C language can then be used to add data listeners to the first data and listen for available callbacks of the first data, thereby obtaining the first data that can be distributed. The specific method of obtaining the distribution data is not limited in the embodiments of this disclosure.

[0044] Step 202: Distribute the data to at least two distribution paths and determine the risk output for each distribution path.

[0045] In this embodiment of the present disclosure, it should be noted that the distribution path includes at least a first path and a second path. The distribution path can be an algorithm calculation path, a risk video encoding path, or other distribution paths. When determining the risk output based on sensor data in different paths, different risk outputs can be obtained, such as risk information of the sensor data, risk level, or other risk outputs obtained from the sensor data, such as risk recording video obtained by encoding video type sensor data. The specific risk output type is not limited in the embodiments of the present disclosure.

[0046] In the embodiments of this disclosure, the first data is processed to obtain distribution data, which is then distributed to different paths for processing to obtain different risk outputs, thus ensuring the accuracy of the early warning.

[0047] Furthermore, in some embodiments of this disclosure, when performing step 202 of distributing data to at least two distribution paths and determining the risk output of each distribution path, this can be achieved through, but is not limited to, the following methods, specifically as follows: Figure 3 As shown, it includes the following steps:

[0048] Step 301: Calculate the distributed data according to the preset algorithm to determine the risk information and / or risk level of the first data.

[0049] In this embodiment of the present disclosure, it should be noted that the risk information of the first data is used to indicate whether the first data is risky. The preset algorithm can be used to determine the value of the flag termination condition, such as the value to determine whether the first data is risky. For example, the preset algorithm can be the sentinel mode algorithm. The sentinel mode refers to an algorithm that can accurately identify and judge various parking risks such as people and vehicles approaching, scratching, and colliding. The preset algorithm can be predefined by itself, and the specific algorithm calculation is not limited in this embodiment of the present disclosure.

[0050] Step 302: Determine the risk information and / or risk level of the first data as the first path risk output.

[0051] In this embodiment of the present disclosure, it should be noted that the risk information may be calculated based on the first data to determine whether the current situation is risky, and the risk level may include high risk and low risk. The specific type of risk information and risk level are not limited in this embodiment of the present disclosure.

[0052] In the embodiments of this disclosure, the first data is processed according to a preset algorithm to calculate risk information and / or risk level, thereby determining the risk output of the first path and ensuring the accuracy of the early warning.

[0053] Furthermore, in some embodiments of this disclosure, when performing step 202 of distributing data to at least two distribution paths and determining the risk output of each distribution path, this can be achieved through, but is not limited to, the following methods, specifically as follows: Figure 4 As shown, it includes the following steps:

[0054] Step 401: Encode the distributed data to obtain the encoding result.

[0055] In this embodiment of the present disclosure, it should be noted that the first data to be distributed is encoded, that is, the recorded video data is video encoded to obtain the risk video, thereby assisting in subsequent risk assessment. The specific video encoding method is not limited in this embodiment of the present disclosure.

[0056] Step 402: Determine the encoding result as the second path risk output.

[0057] The points that need to be explained in this disclosure have been described above and will not be repeated here. The embodiments of this disclosure do not limit the specific types of second-path risk data.

[0058] In the embodiments of this disclosure, the first data is processed by encoding to obtain the encoded video, namely, the risk video, which serves as the risk output of the second path, thus ensuring the accuracy of the early warning.

[0059] Furthermore, in some embodiments of this disclosure, when performing step 102 to determine the second risk output based on the second data collected by the second sensor, this can be achieved through, but is not limited to, the following methods, specifically as follows: Figure 5 As shown, it includes the following steps:

[0060] Step 501: Perform risk calculation on the second data to determine the risk information and / or risk level of the second data.

[0061] It should be noted here that in the embodiments of this disclosure, the risk information of the second data is used to indicate whether the second data has a risk. The risk information of the second data can also be other types of risk information, and the risk level can include high level, low level, or other levels. The embodiments of this disclosure do not limit the specific risk information and risk level.

[0062] Step 502: Determine the risk information and / or risk level of the second data as the second risk output.

[0063] The points that need to be explained in this disclosure have been described above and will not be repeated here. The embodiments of this disclosure do not limit the specific types of second risk outputs.

[0064] In the embodiments of this disclosure, risk data is processed from the second data to obtain risk information and / or risk level, which serves as the risk output of the second data, thus ensuring the accuracy of the early warning.

[0065] Furthermore, in some embodiments of this disclosure, when performing step 103 to determine the vehicle's warning output based on the first risk output and the second risk output, this can be achieved through, but is not limited to, the following methods, specifically as follows: Figure 6 As shown, it includes the following steps:

[0066] Step 601: Determine the vehicle's warning information and / or warning risk level based on the first path risk output and the second risk output.

[0067] It should be noted in the embodiments of this disclosure that when determining the warning information and / or warning risk level of a vehicle, the risk output of the first path and the risk output of the second path can be combined, or the risk output of different paths can be used according to different path weights. The embodiments of this disclosure do not limit the specific way of using the risk output.

[0068] Step 602: If the warning information meets the first preset condition, the warning risk level and the second path risk output are determined as the warning output of the vehicle. The first preset condition is to issue a warning to the vehicle.

[0069] In this embodiment of the present disclosure, it should be noted that the warning information meets the first preset condition, which may be at least one of the risk information of the first data and the risk information of the second data indicating that there is a risk. When the warning information indicates that there is a risk, the warning risk level and the second path risk data, that is, the risk video obtained by the second path, are output. The specific warning conditions and warning output are not limited in this embodiment of the present disclosure.

[0070] The embodiments disclosed herein combine the first path risk output and the second risk output to obtain warning information and / or warning risk level, which serve as reference information for vehicle warnings, thus ensuring the accuracy of the warnings.

[0071] Furthermore, in some embodiments of this disclosure, when performing step 601 to determine the vehicle's warning information and / or warning risk level based on the first path risk output and the second risk output, this can be achieved through, but is not limited to, the following methods, specifically as follows: Figure 7 As shown, it includes the following steps:

[0072] Step 701: If at least one of the risk information in the first data and the risk information in the second data indicates that there is a risk, determine that the warning information meets the first preset condition and determine the maximum value of the risk level of the first data and the risk level of the second data as the warning risk level.

[0073] The relevant provisions of this disclosure have been explained above and will not be repeated here. The maximum value of the risk level of the first data and the risk level of the second data is determined as the warning risk level. The warning risk level may include high level and low level, or other levels. The specific warning risk level is not limited in the embodiments of this disclosure.

[0074] The embodiments disclosed herein combine the first path risk output and the second risk output to obtain warning information and / or warning risk level, which serve as reference information for vehicle warnings, thus ensuring the accuracy of the warnings.

[0075] In summary, this disclosure, by distributing and processing sensor data, makes full use of the information contained in the sensor data, thereby obtaining multiple early warning risk outputs more quickly and comprehensively to assist in early warning. Furthermore, this disclosure also integrates data collected by different sensors to determine the final early warning information, thereby providing more accurate vehicle early warnings to meet vehicle safety requirements.

[0076] The following is through Figure 8 The specific embodiments shown above relate to the above. Figure 1-7 The methods described in the text are given as examples.

[0077] 1) Obtain sensor data based on vehicle-mounted cameras and inertial measurement units (IMUs).

[0078] In this embodiment, when the vehicle warning is in sentry mode, the vehicle-mounted camera is used to obtain real-time monitoring video data (Surface data), and the inertial sensor (IMU) is used to obtain real-time monitoring IMU sensing data. The specific vehicle-mounted camera and inertial sensor (IMU) are not limited in this embodiment.

[0079] 2) The risk output is obtained by processing the sensor data from the vehicle-mounted camera and the inertial sensor (IMU) separately.

[0080] In this embodiment, video data obtained from the vehicle-mounted camera is distributed. Before distribution, the video data (Surface data) is processed to obtain distributable data. For example, the first data is added to the Java Native Interface (JNI) using Java, thereby transferring the first data to C for processing. Data listeners are added to the first data using C to monitor available callbacks, thus obtaining the first data available for distribution. The specific method of obtaining the distribution data is not limited in this embodiment. During distribution, multiple distribution paths are added as needed to obtain multiple outputs. For example, the added distribution path could be an algorithm calculation path or a risk video encoding path, thereby obtaining the risk information and risk level calculated by the algorithm, and obtaining the risk video (cached video) from the encoding path. The specific distribution process is not limited in this embodiment.

[0081] In this embodiment, risk calculation is performed on the sensing data of the inertial sensor (IMU) to obtain the calculated risk output, such as risk information and risk level. The specific calculation method is not limited in this disclosure.

[0082] 3) Assess risks by integrating risk outputs obtained from different sensor data.

[0083] In this embodiment, the risk output obtained by processing the sensing data from the vehicle-mounted camera and the inertial measurement unit (IMU) will be comprehensively considered in the final warning. For example, the risk information obtained from the sensing data of the vehicle-mounted camera and the inertial measurement unit (IMU) will be used to determine whether to issue a warning. The risk level obtained from the sensing data of the vehicle-mounted camera and the inertial measurement unit (IMU) and the risk video obtained from the vehicle-mounted camera will be used to determine the final output warning information. For example, when issuing a warning, the risk level can be reported and the risk video can be uploaded and stored in the vehicle terminal. The specific warning judgment and warning information are not limited in this embodiment.

[0084] Therefore, this solution has the following beneficial effects: By distributing and processing sensor data, this solution fully utilizes the information contained in the sensor data and integrates data collected by different sensors to determine the final warning information. Since the warning information is obtained by processing sensor information in multiple ways, it utilizes sensor information better and faster, and the integrated warning information is more accurate, thus improving the warning effect.

[0085] Figure 9 This is a block diagram illustrating the components of a vehicle warning device 900 according to an embodiment of this disclosure. Figure 9 As shown, the vehicle warning device includes:

[0086] The first determining unit 901 is configured to: distribute the first data collected by the first sensor to at least two distribution paths, and determine the first risk output, wherein the first risk output includes the risk output of at least two distribution paths.

[0087] The second determining unit 902 is configured to determine a second risk output based on the second data collected by the second sensor.

[0088] The third determining unit 903 is configured to determine the vehicle's warning output based on the first risk output and the second risk output.

[0089] In some embodiments of this disclosure, the first determining unit 901 includes:

[0090] The first determining module is configured to: distribute the first data collected by the first sensor to at least two distribution paths, and determine the first risk output, including...

[0091] The acquisition module is configured to parse and process the first data to obtain the distribution data.

[0092] The second determining module is configured to: distribute the distribution data to at least two distribution paths, and determine the risk output of each distribution path, wherein the distribution path includes at least the first path and the second path.

[0093] In some embodiments of this disclosure, the second determining module is further configured to:

[0094] The distributed data is calculated according to a preset algorithm to determine the risk information and / or risk level of the first data, wherein the risk information of the first data is used to indicate whether the first data has a risk; the risk information and / or risk level of the first data are determined as the first path risk output.

[0095] In some embodiments of this disclosure, the second determining module is further configured to:

[0096] The distributed data is encoded to obtain the encoding result; the encoding result is determined as the second path risk output.

[0097] In some embodiments of this disclosure, the second determining unit 902 is configured to:

[0098] The third determining module is configured to: perform risk calculation on the second data, determine the risk information and / or risk level of the second data, wherein the risk information of the second data is used to indicate whether the second data has a risk; and determine the risk information and / or risk level of the second data as the second risk output.

[0099] In some embodiments of this disclosure, the third determining unit 903 is configured to:

[0100] The fourth determining module is configured to: determine the vehicle's warning information and / or warning risk level based on the first path risk output and the second risk output; if the warning information meets the first preset condition, determine the warning risk level and the second path risk output as the vehicle's warning output, wherein the first preset condition is to issue a warning to the vehicle.

[0101] In some embodiments of this disclosure, the fourth determining module is further configured to:

[0102] If at least one of the risk information in the first data or the risk information in the second data indicates a risk, the warning information is determined to meet the first preset condition, and the maximum value of the risk level of the first data or the risk level of the second data is determined as the warning risk level.

[0103] In embodiments of this disclosure, when correcting an image, firstly, first data collected by a first sensor is distributed to at least two distribution paths to determine a first risk output, wherein the first risk output includes risk outputs from at least two distribution paths; secondly, based on second data collected by a second sensor, a second risk output is determined; and finally, based on the first and second risk outputs, a vehicle warning output is determined. This disclosure, by distributing and processing sensor data, fully utilizes the information contained in the sensor data, thereby acquiring multiple warning risk outputs more quickly and comprehensively to assist in warnings. Furthermore, this disclosure integrates data collected by different sensors to determine the final warning information, thereby providing more accurate vehicle warnings to meet vehicle safety requirements.

[0104] The methods and apparatus provided in the embodiments of this application have been described above. To implement the functions of the methods provided in the embodiments of this application, the electronic device may include hardware structures and software modules, and may implement the above functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. One of the above functions may be executed in the form of hardware structures, software modules, or a combination of hardware structures and software modules.

[0105] Figure 10 This is a block diagram illustrating an electronic device 1000 for implementing the above-described vehicle warning method, according to an exemplary embodiment. For example, the electronic device 1000 may be a mobile phone, computer, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0106] Reference Figure 10 The electronic device 1000 may include one or more of the following components: a processing component 1002, a memory 1004, a power supply component 1006, a multimedia component 1008, an audio component 1010, an input / output (I / O) interface 1012, a sensor component 1014, and a communication component 1016.

[0107] Processing component 1002 typically controls the overall operation of electronic device 1000, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 1002 may include one or more processors 1020 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 1002 may include one or more modules to facilitate interaction between processing component 1002 and other components. For example, processing component 1002 may include a multimedia module to facilitate interaction between multimedia component 1008 and processing component 1002.

[0108] Memory 1004 is configured to store various types of data to support the operation of electronic device 1000. Examples of this data include instructions for any application or method operating on electronic device 1000, contact data, phonebook data, messages, pictures, videos, etc. Memory 1004 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0109] Power supply component 1006 provides power to various components of electronic device 1000. Power supply component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 1000.

[0110] Multimedia component 1008 includes a screen that provides an output interface between electronic device 1000 and user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 1008 includes a front-facing camera and / or a rear-facing camera. When electronic device 1000 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0111] Audio component 1010 is configured to output and / or input audio signals. For example, audio component 1010 includes a microphone (MIC) configured to receive external audio signals when electronic device 1000 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1004 or transmitted via communication component 1016. In some embodiments, audio component 1010 also includes a speaker for outputting audio signals.

[0112] I / O interface 1012 provides an interface between processing component 1002 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0113] Sensor assembly 1014 includes one or more sensors for providing state assessments of various aspects of electronic device 1000. For example, sensor assembly 1014 may detect the on / off state of electronic device 1000, the relative positioning of components such as the display and keypad of electronic device 1000, changes in position of electronic device 1000 or a component of electronic device 1000, the presence or absence of user contact with electronic device 1000, the orientation or acceleration / deceleration of electronic device 1000, and temperature changes of electronic device 1000. Sensor assembly 1014 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1014 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 1014 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0114] Communication component 1016 is configured to facilitate wired or wireless communication between electronic device 1000 and other devices. Electronic device 1000 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G LTE, 5G NR (NewRadio), or combinations thereof. In one exemplary embodiment, communication component 1016 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1016 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0115] In an exemplary embodiment, the electronic device 1000 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0116] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1004 including instructions, which can be executed by a processor 320 of an electronic device 1000 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0117] Embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the vehicle warning method described in the above embodiments of this disclosure.

[0118] Embodiments of this disclosure also provide a computer program product, including a computer program that is executed by a processor using the vehicle warning method described in the above embodiments of this disclosure.

[0119] Embodiments of this disclosure also propose a chip, which can be found in [reference]. Figure 11 The diagram shows the structure of the chip. Figure 11The chip shown includes a processor 1101 and an interface 1102. The number of processors 1101 can be one or more, and the number of interfaces 1102 can be multiple. Optionally, the chip also includes a memory 1103 for storing computer instructions, which, when executed by the processor, cause the electronic device to perform the vehicle warning method described in the above embodiments of this disclosure.

[0120] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0121] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0122] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (control method), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic device, and portable optical disc read-only memory (CDROM). In addition, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning paper or other media, followed by editing, interpreting or otherwise processing as necessary, and then stored in computer memory.

[0124] It should be understood that various parts of the embodiments of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0126] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc.

[0127] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A vehicle early warning method, characterized in that, The methods include: The first data collected by the first sensor is distributed to at least two distribution paths to determine a first risk output, wherein the first risk output includes the risk outputs of the at least two distribution paths; the risk outputs of different distribution paths correspond to different risk output types; the distribution path includes at least a first path and a second path; the risk output corresponding to the first path is the risk information and / or risk level of the first data, and the risk output corresponding to the second path is the encoding result of the first data; Based on the second data collected by the second sensor, a second risk output is determined; Based on the first risk output and the second risk output, determine the vehicle's warning output.

2. The method according to claim 1, characterized in that, The step of distributing the first data collected by the first sensor to at least two distribution paths and determining the first risk output includes: The first data is parsed and processed to obtain the distribution data; The distribution data is distributed to the at least two distribution paths, and the risk output of each distribution path is determined.

3. The method according to claim 2, characterized in that, The step of distributing the distribution data to the at least two distribution paths and determining the risk output of each distribution path includes: The distributed data is calculated according to a preset algorithm to determine the risk information and / or risk level of the first data, wherein the risk information of the first data is used to indicate whether the first data has a risk; The risk information and / or risk level of the first data are determined as the first path risk output.

4. The method according to claim 3, characterized in that, The step of distributing the distribution data to the at least two distribution paths and determining the risk output of each distribution path includes: The distributed data is encoded to obtain the encoding result; The encoding result is determined as the second path risk output.

5. The method according to any one of claims 1-4, characterized in that, The step of determining the second risk output based on the second data collected by the second sensor includes: Perform risk calculation on the second data to determine the risk information and / or risk level of the second data, wherein the risk information of the second data is used to indicate whether the second data is risky; The risk information and / or risk level of the second data are determined as the second risk output.

6. The method according to claim 5, characterized in that, The warning output includes the vehicle's warning risk level and a second path risk output. Determining the vehicle's warning output based on the first risk output and the second risk output includes: Based on the first path risk output and the second risk output, determine the warning information and / or warning risk level of the vehicle; If the warning information meets the first preset condition, the warning risk level and the second path risk output are determined as the warning output of the vehicle, and the first preset condition is to issue a warning to the vehicle.

7. The method according to claim 6, characterized in that, The step of determining the vehicle's warning information and / or warning risk level based on the first path risk output and the second risk output includes: If at least one of the risk information in the first data and the risk information in the second data indicates a risk, the warning information is determined to meet the first preset condition, and the maximum value of the risk level of the first data and the risk level of the second data is determined as the warning risk level.

8. A vehicle warning device, characterized in that, include: A first determining unit is configured to distribute the first data collected by the first sensor to at least two distribution paths and determine a first risk output, wherein the first risk output includes the risk outputs of the at least two distribution paths; the risk outputs of different distribution paths correspond to different risk output types; the distribution paths include at least a first path and a second path; the risk output corresponding to the first path is the risk information and / or risk level of the first data, and the risk output corresponding to the second path is the encoding result of the first data. The second determining unit is used to determine the second risk output based on the second data collected by the second sensor; The third determining unit is used to determine the vehicle's warning output based on the first risk output and the second risk output.

9. A vehicle, characterized in that, Includes a vehicle warning device, which is capable of performing the method of any one of claims 1-7.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

11. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-7.

13. A chip, characterized in that, The device includes one or more interface circuits and one or more processors; the interface circuits are configured to receive signals from the memory of the electronic device and send the signals to the processors, the signals including computer instructions stored in the memory, which, when executed by the processors, cause the electronic device to perform the method of any one of claims 1-7.

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