A collision avoidance warning method, device, electronic device and readable storage medium

Abstract

This disclosure proposes a collision avoidance warning method, device, electronic device, and readable storage medium. The method includes: receiving first state information sent by a target vehicle, the first state information including the target vehicle's vehicle identification, first location information, first speed information, and first direction information; determining second state information of a smart wearable device, the second state information including the smart wearable device's second location information, second speed information, and second direction information; determining a collision probability level based on the first and second state information; and generating a multi-dimensional warning reminder corresponding to the collision probability level, the multi-dimensional warning reminder including a first-dimensional display information reminder, a second-dimensional sound information reminder, and a third-dimensional tactile information reminder. The technical solution provided by one or more embodiments of this disclosure can utilize smart wearable devices to promptly remind pedestrians to avoid vehicle collision risks.

Description

Technical Field

[0001] This disclosure relates to the field of wearable smart device technology, specifically to a collision avoidance warning method, device, electronic device, and readable storage medium. Background Technology

[0002] With the acceleration of urbanization and the surge in the number of motor vehicles, the complexity of urban traffic is also increasing. This complex traffic situation not only exacerbates road congestion but also raises the incidence of traffic accidents, posing a serious challenge to road traffic safety. Furthermore, the emergence of new business models, such as the rapid development of the express delivery and food delivery industries, has led to a rise in traffic accidents involving electric bicycles, further complicating traffic safety management.

[0003] A vehicle blind spot is an area that a driver cannot directly observe from their normal driving position due to the vehicle's structural limitations. These blind spots are particularly dangerous when the vehicle is starting, changing lanes, or overtaking, as pedestrians and cyclists may be within the driver's blind spot, thus increasing the risk of traffic accidents.

[0004] Pedestrians and cyclists often face more blind spots. Vehicles suddenly appearing in these blind spots can catch them off guard, increasing the risk of property damage and physical harm. Therefore, the safety of pedestrians and cyclists on the road requires greater attention and protection. Summary of the Invention

[0005] In view of this, one or more embodiments of the present disclosure provide a collision avoidance warning method, device, electronic device and readable storage medium, which can use smart wearable devices to promptly remind pedestrians to avoid the risk of vehicle collision.

[0006] This disclosure provides a collision avoidance warning method applied to a smart wearable device. The method includes: receiving first status information sent by a target vehicle, the first status information including the target vehicle's vehicle identifier, first location information, first speed information, and first direction information; determining second status information of the smart wearable device, the second status information including the smart wearable device's second location information, second speed information, and second direction information; determining a collision probability level based on the first status information and the second status information; and generating a multi-dimensional warning reminder corresponding to the collision probability level, the multi-dimensional warning reminder including a first-dimensional display information reminder, a second-dimensional sound information reminder, and a third-dimensional tactile information reminder.

[0007] This disclosure also provides a collision avoidance warning device, which is applied to a smart wearable device. The device includes: a receiving unit for receiving first status information sent by a target vehicle, the first status information including the vehicle identifier, first location information, first speed information, and first direction information of the target vehicle; an acquiring unit for determining second status information of the smart wearable device, the second status information including the second location information, second speed information, and second direction information of the smart wearable device; an information processing unit for determining a collision probability level based on the first status information and the second status information; and a warning unit for generating a multi-dimensional warning reminder corresponding to the collision probability level, the multi-dimensional warning reminder including a first-dimensional display information reminder, a second-dimensional sound information reminder, and a third-dimensional tactile information reminder.

[0008] This disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, which, when executed by the processor, implements the above-described anti-collision warning method.

[0009] This disclosure also provides a computer-readable storage medium for storing a computer program that, when executed by a processor, implements the above-described anti-collision warning method.

[0010] The technical solutions provided by one or more embodiments of this disclosure can utilize smart wearable devices to receive vehicle status information. By combining this information with the smart wearable device's own status information, the device can determine the collision probability level and generate corresponding warning information. On one hand, the information processing capabilities of the smart wearable device can provide timely and accurate collision probability levels. On the other hand, the rich extended functions of the smart wearable device can provide various forms of warning information, increasing the effectiveness and flexibility of the warnings. Attached Figure Description

[0011] The features and advantages of the embodiments of this disclosure will be more clearly understood by referring to the accompanying drawings, which are illustrative and should not be construed as limiting the present disclosure in any way. In the drawings:

[0012] Figure 1 A schematic diagram of the steps of a collision avoidance warning method in one embodiment of this disclosure is shown;

[0013] Figure 2 A schematic diagram of an arrow display screen of a smart bracelet according to one embodiment of the present disclosure is shown;

[0014] Figure 3 A flowchart illustrating a collision avoidance warning method according to one embodiment of this disclosure is shown;

[0015] Figure 4 A schematic diagram illustrating the distance determination between a user and a vehicle is shown in one embodiment of this disclosure;

[0016] Figure 5 A schematic diagram of the functional units of a collision avoidance warning device in one embodiment of this disclosure is shown;

[0017] Figure 6 A schematic diagram of the structure of an electronic device according to one embodiment of the present disclosure is shown. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0019] Please see Figure 1 The present disclosure provides an anti-collision warning method according to one embodiment, which can be applied to smart wearable devices and may include the following steps.

[0020] S1: Receive first status information sent by the target vehicle, the first status information including the vehicle identifier, first location information, first speed information and first direction information of the target vehicle.

[0021] In this embodiment, the smart wearable device can be an electronic device with data processing capabilities. Examples include watches, glasses, wristbands, and earphones. Smart wearable devices typically have connectivity, allowing them to connect to the internet and connect to other devices such as smartphones and smart cars via Bluetooth, Wi-Fi, etc., and to receive data sent by other devices.

[0022] In this embodiment, the vehicle can actively send out first status information, which can be transmitted via broadcast, Bluetooth, or Wi-Fi. Smart wearable devices can receive this status information. The first status information may include a vehicle identifier to distinguish different vehicles. The vehicle identifier may also reflect information such as the vehicle model. The first status information may include first location information. The first location information can be the vehicle's current location or historical location data, which can be obtained by the vehicle through its onboard GPS. The first status information may include first speed information. The first speed information can be the vehicle's current speed or historical speed data. The first status information may include first direction information. The first direction information can be the vehicle's current direction of travel or its historical direction of travel.

[0023] In this embodiment, by receiving the first status information sent by the vehicle, the smart wearable device can accurately perceive the surrounding vehicle information, thereby providing some early warning information.

[0024] It should be noted that some practical application scenarios of this method are: the wearable smart device and the target vehicle have determined the communication protocol in advance, so that the smart wearable device can obtain the first state information sent by the target vehicle on the road.

[0025] In a practical application example, a smart wearable device can use Bluetooth communication to obtain initial vehicle status information. Bluetooth's propagation distance is typically around 10 meters, but in some cases, with enhanced radio frequency power, it can reach 100 meters or even further. In this application example, a Bluetooth device with enhanced radio frequency power can be selected to establish Bluetooth communication, thus ensuring the effectiveness of providing early warning of sudden vehicle appearance within a short distance. This short distance is generally within 100 meters or 50 meters, allowing pedestrians or cyclists to notice a vehicle about to suddenly appear in their vicinity.

[0026] In some implementations, the target vehicle is a vehicle equipped with a Bluetooth sub-device, which is enabled by default. The Bluetooth master in the smart wearable device can detect the Bluetooth sub-device within a preset spatial area. If the Bluetooth sub-device is detected, a Bluetooth communication connection is established between the Bluetooth master and the sub-device. Based on this Bluetooth communication connection, the smart wearable device can receive first status information sent by the target vehicle.

[0027] Specifically, considering that Bluetooth applications can support not only typical one-to-one applications but also one-to-many applications, for example, instead of using Bluetooth's fixed-point pairing function, its proximity information broadcasting function can be utilized. In this way, as long as the Bluetooth parent device can scan for a paired Bluetooth daughter device, information exchange and transmission can occur.

[0028] Bluetooth technology, through adaptive frequency hopping and small data packet design, can improve anti-interference capability and transmission efficiency, which enables it to maintain good communication quality even in complex environments, making it well-suited for the complex traffic scenarios considered in this disclosure.

[0029] In some implementations, the first state information actively transmitted by the vehicle may include its motion state, i.e., whether the vehicle is moving or stationary. For a stationary vehicle, collision avoidance warning may not be issued, or the warning level may be reduced. Determining the vehicle's motion state may not require map data or specific in-vehicle applications; it can be determined through vehicle sensor information. For example, engine heat can indicate that the vehicle is in motion. Similarly, tire rotation can also indicate that the vehicle is in motion.

[0030] S2: Determine the second state information of the smart wearable device, the second state information including the second position information, second speed information and second direction information of the smart wearable device.

[0031] In this embodiment, the smart wearable device can utilize pre-integrated sensors, such as accelerometers and gyroscopes, to determine motion speed and direction information, thereby obtaining second speed information and second direction information. The second speed information can be the current speed of the smart wearable device or its historical speed data. The second direction information can be the current direction of movement of the smart wearable device or its historical direction of movement. The smart wearable device can integrate Global Positioning System (GPS) functionality to determine its own location information, thereby obtaining second location information. The second location information can be the current location of the smart wearable device or its historical location data.

[0032] S3: Determine the collision probability level based on the first state information and the second state information.

[0033] In this embodiment, the smart wearable device can comprehensively analyze the first state information and the second state information to determine the collision probability level. The smart wearable device can connect to a network or the cloud to enhance its computing power and determine the collision probability level. The smart wearable device can determine the collision probability level based on preset rules or a pre-trained machine learning model. The smart wearable device can determine a customized and optimized collision probability level based on the user's user characteristics and the target vehicle's vehicle characteristics.

[0034] In some implementations, the vehicle trajectory of the target vehicle can be determined based on the first location information, the first speed information, and the first direction information. The device trajectory of the smart wearable device can be determined based on the second location information, the second speed information, and the second direction information. Based on the vehicle trajectory and the device trajectory, a collision probability level can be determined. For example, if there is a possibility that the two trajectories will rapidly intersect, a high collision probability level can be determined; if the two trajectories intersect at a distant time or there is no intersection point, a low collision probability level can be determined.

[0035] In a practical application example, based on the first direction information, the historical movement direction of the target vehicle within a first predetermined period (e.g., 1 second) can be determined. When the historical movement direction does not trigger a reverse driving condition (i.e., the movement direction is reversed within the first predetermined period), the historical movement direction can be determined as the predicted movement direction of the vehicle. Based on the predicted movement direction, the first position information, and the first speed information, the vehicle's trajectory can be determined.

[0036] In a practical application example, based on the second direction information, the historical movement direction of the smart wearable device within a second predetermined period (e.g., 1 second) can be determined. When the historical movement direction does not trigger a reverse movement condition (i.e., the movement direction is reversed within the first predetermined period), the historical movement direction can be determined as the predicted movement direction. Based on the predicted movement direction, the second position information, and the second velocity information, the device's trajectory can be determined.

[0037] In some implementations, the spatial straight-line distance between the target vehicle and the smart wearable device can be determined based on the first location information and the second location information. The collision probability level can be determined by comparing the spatial straight-line distance with a preset safety distance (e.g., 5 meters, 10 meters, etc.).

[0038] In some implementations, a baseline probability level can be determined based on first position information, first velocity information, first direction information, second position information, second velocity information, and second direction information. The collision avoidance coefficient of the target vehicle can be determined based on the vehicle identification. The collision probability level can be determined based on the baseline probability level and the collision avoidance coefficient.

[0039] Specifically, vehicle characteristics can be determined based on vehicle identification, such as weight, size, braking sensitivity, and whether it has an onboard collision avoidance system. Based on these characteristic parameters, a collision avoidance coefficient can be determined, which is used to adjust the baseline probability level to obtain the collision probability level. The collision probability level can be more customized to better suit the user's specific characteristics, ensuring that the generated warning information is more accurate and effective.

[0040] In some implementations, when determining the collision probability level, user-authorized user characteristic information (such as age, gender, height, physical characteristics, current heart rate, etc.) collected or stored by smart wearable devices can be used to correct the generated collision probability level, making the final collision probability level more customized and more in line with the user's specific characteristics, thereby making the generated warning information more accurate and effective.

[0041] S4: Based on the collision probability level, generate a multi-dimensional warning reminder corresponding to the collision probability level. The multi-dimensional warning reminder includes a first-dimensional display information reminder, a second-dimensional sound information reminder, and a third-dimensional tactile information reminder.

[0042] In this implementation, multi-dimensional warning information ensures the reliability of the warning messages and meets the needs of various practical application scenarios. For example, sometimes users see the displayed warning message and sometimes they don't; sound and tactile warnings can still promptly alert the user. Similarly, sometimes users are in noisy environments and may not hear the sound warning message; tactile warnings combined with visual reminders can still effectively alert the user. This multi-dimensional warning system enables smart wearable devices to provide comprehensive warnings, ensuring users are promptly informed of warning information.

[0043] In this embodiment, the smart wearable device can be equipped with a small OLED or LCD screen. When a warning message is received, the screen will light up and display the warning information, which can be in the form of text, images, symbols, etc. The displayed information can distinguish the warning information corresponding to different collision probability levels through different icons, colors, or display frequencies.

[0044] In this embodiment, the smart wearable device can have a built-in miniature speaker. When there is a warning message, the miniature speaker can emit a sound, such as a simple beep, a specific ringtone, or a voice prompt. Users can set different volume levels according to their needs and environment to ensure that they can hear the audio reminders when needed.

[0045] In this embodiment, the smart wearable device can have a built-in vibration motor. When a warning message is received, the vibration motor can vibrate to provide tactile feedback. The intensity and mode of the vibration can be adjusted according to the user's preferences to suit different usage scenarios and individual needs.

[0046] In a practical application example, a smart wearable device could be a smart bracelet. (See also...) Figure 2 The smart bracelet can be equipped with arrow display function, vibration function, information receiving module and information processing module when applying the anti-collision warning method disclosed herein.

[0047] Specifically, when a wearer is walking or cycling on the road, a car may suddenly appear from either the left or right. Using the arrow display function, a left arrow indicates a car approaching from the left, and a right arrow indicates a car approaching from the right. Different arrow display frequencies represent different levels of danger. Low-frequency arrow display indicates a slow-approaching car, signifying a low level of danger for the user. High-frequency arrow display indicates a fast-approaching car, signifying a high level of danger for the user.

[0048] Corresponding to the two modes indicated by the arrows, the vibration function can provide either rapid or slow vibration. Slow vibration corresponds to a low arrow frequency, indicating a low level of danger for the user. Rapid vibration corresponds to a high arrow frequency, indicating a high level of danger for the user. By setting both arrow display and vibration modes, users can receive warnings via vibration when they don't see the warning information indicated by the arrow.

[0049] The wristband's receiving and processing modules correspond to its communication and information processing functions, respectively. The wristband can process information sent from the vehicle to determine its direction and speed. Furthermore, it can determine the frequency of the arrow display and the speed of the vibration.

[0050] It is worth noting that the target vehicles disclosed herein can be one or more, and the corresponding multi-dimensional warning alerts can be for one or more vehicles. Depending on the actual application scenario, if the smart wearable device cannot provide multi-dimensional warning alerts for multiple vehicles simultaneously, the target vehicle closest to the smart wearable device can be identified as the warning vehicle, and the multi-dimensional warning alerts corresponding to the warning vehicle can be provided only on the smart wearable device.

[0051] This disclosure provides an anti-collision warning method according to one embodiment, which can be applied to smart bracelets.

[0052] First, the smart bracelet can receive vehicle status information sent by the vehicle itself. Specifically, the vehicle can determine whether it is moving or stationary. As long as the vehicle's speed is greater than 0, it can be considered to be moving for the next 3 seconds. Only after coming to a complete stop, with a further 3-second delay, can the vehicle be considered stationary. When the vehicle is moving, its speed range can be further determined. For example, a vehicle moving at a speed greater than 0 and less than or equal to 2 m / s is in the low-speed range. A vehicle moving at a speed greater than 2 m / s is in the high-speed range. The vehicle also needs to locate its position. The vehicle status information sent by the vehicle to the bracelet can include its speed, location coordinates, and vehicle ID (each vehicle can be uniquely numbered to prevent confusion).

[0053] Secondly, the smart bracelet can determine the user's direction of movement, specifically based on the user's movement two seconds before and after a move. Simultaneously, the smart bracelet can receive real-time vehicle status information from surrounding vehicles. Upon receiving this information, the smart bracelet can determine the distance between the user and the vehicle based on the vehicle's location data. If the distance is less than 5 meters and the vehicle is traveling at a high speed, a high-risk warning is triggered, and the smart bracelet displays a high-frequency red arrow and vibrates violently. In other cases, a low-risk warning is indicated, and the smart bracelet displays a very low-frequency green arrow and vibrates slightly.

[0054] In some practical applications, the arrow display color on a smart bracelet can be divided into green and red, with green indicating no danger and red indicating danger. The arrow display frequency on a smart bracelet can be divided into low and high frequencies, with low frequencies indicating no danger and high frequencies indicating danger. The frequency values ​​of the high and low frequencies can exhibit a distinguishable multiple relationship. For example, the low display frequency of the arrow on a smart bracelet could be 20Hz, and the high display frequency could be 1kHz. The vibration frequency on a smart bracelet can also be divided into low and high vibration frequencies, with low vibration frequencies indicating no danger and high vibration frequencies indicating danger. The frequency values ​​of the high and low vibration frequencies can also exhibit a distinguishable multiple relationship. For example, a slight vibration on a smart bracelet might be a vibration lasting less than 3 seconds, while a strong vibration might be 3 vibrations per second.

[0055] Please see Figure 3 The collision avoidance warning method provided in one embodiment of this disclosure may include the following process.

[0056] After determining its direction of travel, the vehicle sends the vehicle information to the smart wearable device. The smart wearable device then determines the user's direction of travel and, combined with the vehicle information, performs a fusion analysis to determine the current distance between the user and the vehicle. If the current distance reaches a preset distance limit, the smart wearable device can issue a corresponding warning. This allows pedestrians or cyclists to promptly detect potential collision hazards.

[0057] The direction of movement for pedestrians or cyclists can be determined by their movement direction within 1 second. Once movement begins, it is assumed that they will move in the same direction for 3 seconds, unless they trigger reverse movement (i.e., the movement direction is opposite within 1 second). The direction of movement for cars is determined similarly.

[0058] Please see Figure 4The distance between the user and the vehicle can be determined not by the intersection distance on the path, but by the straight-line distance in space. A warning message will be generated as long as the straight-line distance in space meets the warning requirements. The distance limit requirement for low risk can be 10 meters, and the distance limit requirement for high risk can be 5 meters.

[0059] Specifically, if multiple vehicles appear simultaneously, nearest-vehicle identification can be performed to find the vehicle with the closest straight-line distance in space. Based on this, it can be determined whether the distance between a person and the warning vehicle triggers the low-risk or high-risk distance restriction requirements.

[0060] The technical solutions provided by one or more embodiments of this disclosure can utilize smart wearable devices to receive vehicle status information. By combining this information with the smart wearable device's own status information, the device can determine the collision probability level and generate corresponding warning information. On one hand, the information processing capabilities of the smart wearable device can provide timely and accurate collision probability levels. On the other hand, the rich extended functions of the smart wearable device can provide various forms of warning information, increasing the effectiveness and flexibility of the warnings.

[0061] Please see Figure 5 This disclosure also provides a collision avoidance warning device that can be applied to smart wearable devices, the device comprising:

[0062] The receiving unit 100 is used to receive first status information sent by the target vehicle, the first status information including the vehicle identifier, first location information, first speed information and first direction information of the target vehicle;

[0063] The acquisition unit 200 is used to determine the second state information of the smart wearable device, the second state information including the second position information, second speed information and second direction information of the smart wearable device;

[0064] The information processing unit 300 is used to determine the collision probability level based on the first state information and the second state information;

[0065] The warning unit 400 is used to generate a multi-dimensional warning reminder corresponding to the collision probability level, wherein the multi-dimensional warning reminder includes a first-dimensional display information reminder, a second-dimensional sound information reminder, and a third-dimensional tactile information reminder.

[0066] In one embodiment, the information processing unit 300 is specifically configured to: determine the vehicle trajectory of the target vehicle based on the first location information, the first speed information, and the first direction information; determine the device trajectory of the smart wearable device based on the second location information, the second speed information, and the second direction information; and determine the collision probability level based on the vehicle trajectory and the device trajectory.

[0067] In one embodiment, the information processing unit 300 includes a first processing subunit 301, configured to determine the historical movement direction of the target vehicle within a first predetermined period based on the first direction information; when the historical movement direction of the vehicle does not trigger a reverse driving condition, determine the historical movement direction of the vehicle as the predicted movement direction of the vehicle; and determine the vehicle movement trajectory based on the predicted movement direction of the vehicle, the first position information, and the first speed information.

[0068] In one embodiment, the information processing unit 300 includes a second processing subunit 302, configured to determine the historical motion direction of the smart wearable device within a second predetermined period based on the second direction information; when the historical motion direction of the device does not trigger a reverse movement condition, determine the historical motion direction of the device as the predicted motion direction of the device; and determine the motion trajectory of the device based on the predicted motion direction of the device, the second position information, and the second speed information.

[0069] In one embodiment, the information processing unit 300 is specifically configured to determine the spatial straight-line distance between the target vehicle and the smart wearable device based on the first location information and the second location information; and to determine the collision probability level based on a comparison between the spatial straight-line distance and a preset safety distance.

[0070] In one embodiment, the information processing unit 300 is specifically configured to determine a baseline probability level based on the first location information, the first speed information, the first direction information, the second location information, the second speed information, and the second direction information; determine the collision avoidance coefficient of the target vehicle based on the vehicle identifier; and determine the collision probability level based on the baseline probability level and the collision avoidance coefficient.

[0071] In one embodiment, the target vehicle is a vehicle equipped with a Bluetooth sub-device, which is in an on-default state. The receiving unit 100 is specifically used to detect the Bluetooth sub-device within a preset spatial area based on the Bluetooth master device in the smart wearable device; if the Bluetooth sub-device is detected, a Bluetooth communication connection is established between the Bluetooth master device and the Bluetooth sub-device; and based on the Bluetooth communication connection, the first status information sent by the target vehicle is received.

[0072] The various units described in the above embodiments can be implemented by a computer chip or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0073] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0074] Please see Figure 6 This disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and when the computer program is executed by the processor, it implements the above-described anti-collision warning method.

[0075] This disclosure also provides a computer-readable storage medium for storing a computer program that, when executed by a processor, implements the above-described anti-collision warning method.

[0076] The processor can be a central processing unit (CPU). It can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof.

[0077] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods in the above-described embodiments.

[0078] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0079] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0080] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, embodiments of apparatus, devices, and storage media are basically similar to method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0081] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0082] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A collision avoidance warning method, characterized in that, The method is applied to a smart wearable device, and the method includes: Receive first status information sent by the target vehicle, the first status information including the vehicle identifier, first location information, first speed information and first direction information of the target vehicle; Determine the second state information of the smart wearable device, the second state information including the second position information, second speed information and second direction information of the smart wearable device; Based on the first state information and the second state information, the collision probability level is determined; The first state information includes the vehicle motion state, which indicates whether the target vehicle is moving or stationary. The vehicle motion state is determined by vehicle sensing information, which includes at least one of engine heating information and tire rotation information. For the target vehicle in a stationary state, no collision avoidance warning is issued, or the warning level of the warning information is reduced. Based on the collision probability level, a multi-dimensional warning reminder is generated corresponding to the collision probability level. The multi-dimensional warning reminder includes a first-dimensional display information reminder, a second-dimensional sound information reminder, and a third-dimensional tactile information reminder. The step of determining the collision probability level based on the first state information and the second state information includes: determining a baseline probability level based on the first position information, the first speed information, the first direction information, the second position information, the second speed information, and the second direction information; determining vehicle characteristic parameters based on the vehicle identifier, the vehicle characteristic parameters including vehicle weight, vehicle volume, braking sensitivity, and whether it has an on-board collision avoidance system; determining the collision avoidance coefficient of the target vehicle based on the vehicle characteristic parameters; and determining the collision probability level based on the baseline probability level and the collision avoidance coefficient.

2. The method according to claim 1, characterized in that, Determining the collision probability level based on the first state information and the second state information includes: The vehicle trajectory of the target vehicle is determined based on the first location information, the first speed information, and the first direction information. The device motion trajectory of the smart wearable device is determined based on the second position information, the second speed information, and the second direction information. The collision probability level is determined based on the vehicle's trajectory and the device's trajectory.

3. The method according to claim 2, characterized in that, Determining the vehicle trajectory of the target vehicle based on the first position information, the first speed information, and the first direction information includes: Based on the first direction information, the historical movement direction of the target vehicle within a first predetermined period is determined; When the vehicle's historical direction of motion does not trigger the reverse driving condition, the vehicle's historical direction of motion is determined as the vehicle's predicted direction of motion. The vehicle's trajectory is determined based on the predicted direction of movement, the first position information, and the first speed information.

4. The method according to claim 2, characterized in that, Determining the device motion trajectory of the smart wearable device based on the second position information, the second speed information, and the second direction information includes: Based on the second direction information, the historical movement direction of the smart wearable device within a second predetermined period is determined; When the historical movement direction of the device does not trigger the reverse movement condition, the historical movement direction of the device is determined as the predicted movement direction of the device. The device's motion trajectory is determined based on the device's predicted motion direction, the second position information, and the second speed information.

5. The method according to claim 1, characterized in that, Determining the collision probability level based on the first state information and the second state information includes: Based on the first location information and the second location information, the spatial straight-line distance between the target vehicle and the smart wearable device is determined; The collision probability level is determined based on the comparison between the straight-line distance in space and the preset safety distance.

6. The method according to claim 1, characterized in that, The target vehicle is a vehicle equipped with a Bluetooth sub-device, and the Bluetooth sub-device is in an enabled state by default. The first status information received from the target vehicle includes: Based on the Bluetooth master in the smart wearable device, the Bluetooth slave is detected within a preset spatial area; If the Bluetooth child is detected, a Bluetooth communication connection is established between the Bluetooth parent and the Bluetooth child. Based on the Bluetooth communication connection, the first status information sent by the target vehicle is received.

7. A collision avoidance warning device, characterized in that, The device is used in a smart wearable device, and the device includes: The receiving unit is configured to receive first status information sent by the target vehicle, the first status information including the vehicle identifier, first location information, first speed information and first direction information of the target vehicle; The acquisition unit is used to determine the second state information of the smart wearable device, the second state information including the second position information, second speed information and second direction information of the smart wearable device; An information processing unit is configured to determine a collision probability level based on the first state information and the second state information; wherein, the first state information includes the vehicle motion state, the vehicle motion state representing whether the target vehicle is moving or stationary, the vehicle motion state being determined by vehicle sensing information, the vehicle sensing information including at least one of engine heating information and tire rotation information; for the target vehicle in a stationary state, no collision avoidance warning is issued, or the warning level of the warning reminder information is reduced; The step of determining the collision probability level based on the first state information and the second state information includes: determining a baseline probability level based on the first position information, the first speed information, the first direction information, the second position information, the second speed information, and the second direction information; determining vehicle characteristic parameters based on the vehicle identifier, the vehicle characteristic parameters including vehicle weight, vehicle volume, braking sensitivity, and whether it has an on-board collision avoidance system; determining the collision avoidance coefficient of the target vehicle based on the vehicle characteristic parameters; and determining the collision probability level based on the baseline probability level and the collision avoidance coefficient. The warning unit is used to generate a multi-dimensional warning reminder corresponding to the collision probability level. The multi-dimensional warning reminder includes a first-dimensional display information reminder, a second-dimensional sound information reminder, and a third-dimensional tactile information reminder.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory being used to store a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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