Traffic safety guarantee method and device, terminal equipment and storage medium

Abstract

The application is suitable for the technical field of traffic safety, and provides a traffic safety guarantee method, device, terminal equipment and storage medium, wherein the traffic safety guarantee method comprises the following steps: obtaining self-motion state information and perceived-motion state information of a target person, the self-motion state information being obtained based on a self-measuring device of the target person, and the perceived-motion state information being obtained based on an external perception device; fusing the self-motion state information and the perceived-motion state information to obtain fused-motion state information of the target person; obtaining predicted-motion state information of the target person based on the fused-motion state information; performing collision analysis between a traffic tool and the target person according to the predicted-motion state information, and making a corresponding action based on an analysis result. The application can improve the guarantee of safety of the target person.

Description

Technical Field

[0001] This application belongs to the field of traffic safety technology, and in particular relates to a traffic safety assurance method, device, terminal equipment and storage medium. Background Technology

[0002] Currently, vehicle-to-everything (V2X) wireless communication technology is increasingly being applied in fields such as intelligent transportation and smart cities. Through V2X, vehicles can obtain real-time traffic information, route guidance, the latest map information, and a range of other traffic data, effectively improving traffic efficiency, saving energy, and ensuring vehicle safety. However, current V2X technology cannot facilitate interaction between vehicles and pedestrians, lacking sufficient protection for the safety of pedestrians, especially vulnerable groups such as the elderly with mobility issues, low safety awareness, and limited risk avoidance abilities. Summary of the Invention

[0003] This application provides a traffic safety assurance method, device, terminal equipment, and storage medium, which can solve the problem that the prior art lacks pedestrian safety assurance.

[0004] The first aspect of this application provides a traffic safety assurance method, including:

[0005] The system acquires the target person's own motion status information and perceived motion status information. The own motion status information is obtained based on the target person's personal measurement device, while the perceived motion status information is obtained based on external sensing devices.

[0006] By fusing one's own motion state information and perceived motion state information, the fused motion state information of the target person is obtained;

[0007] Based on the fusion of motion state information, the predicted motion state information of the target person is obtained;

[0008] Collision analysis between vehicles and target personnel is performed based on predicted motion state information, and corresponding actions are taken based on the analysis results.

[0009] Optionally, the self-motion state information and perceived motion state information are fused to obtain the fused motion state information of the target person, including:

[0010] The first weight for acquiring information about one's own motion state and the second weight for perceiving information about one's motion state;

[0011] Based on the first and second weights, the self-motion state information and the perceived motion state information are weighted and processed to obtain the fused motion state information.

[0012] Optionally, the first weight for acquiring its own motion state information and the second weight for perceiving motion state information include:

[0013] Based on the signal strength of its own motion state information, the first precision of its own motion state information is obtained;

[0014] The self-motion state information is compared with the fused motion state information to obtain the second precision of the self-motion state information;

[0015] Based on the first and second precisions of its own motion state information, the first weight of its own motion state information is obtained.

[0016] The third precision of the motion state information is obtained based on the signal strength of the perceived motion state information;

[0017] By comparing the perceived motion state information with the fused motion state information, the fourth precision of the perceived motion state information is obtained.

[0018] Based on the third and fourth precision of the perceived motion state information, the second weight of the perceived motion state information is obtained.

[0019] Optionally, based on the fused motion state information, the predicted motion state information of the target person is obtained, including:

[0020] Obtain the kinematic model of the target person; the kinematic model is used to indicate the motion state of the target person at different times.

[0021] Based on the fusion of motion state information and kinematic model, the predicted motion state information of the target person is obtained by using the Kalman filter algorithm.

[0022] Optionally, obtain the kinematic model of the target person, including:

[0023] Obtain basic information and historical movement data of the target personnel;

[0024] Based on the target personnel's basic information and historical movement data, their activity habits are obtained, and a kinematic model of the target personnel is constructed.

[0025] Optional traffic safety measures also include:

[0026] When there is a risk of collision between the vehicle and the target person, an alert message is sent to the wearable measuring device.

[0027] Optional traffic safety measures also include:

[0028] Receive emergency information sent by the wearable measuring device when it detects danger to the target person.

[0029] A second aspect of this application provides a traffic safety protection device, comprising:

[0030] The status acquisition module is used to acquire the target person's own motion status information and perceived motion status information. The self-motion status information is obtained based on the target person's personal measurement device, and the perceived motion status information is obtained based on external sensing devices.

[0031] The state fusion module is used to fuse the user's own motion state information and perceived motion state information to obtain the fused motion state information of the target person.

[0032] The state prediction module is used to predict the motion state information of the target person based on the fused motion state information.

[0033] The analysis and processing module is used to perform collision analysis between vehicles and target personnel based on predicted motion state information, and to take corresponding actions based on the analysis results.

[0034] Optionally, the state fusion module includes:

[0035] The weight acquisition unit is used to acquire the first weight of its own motion state information and the second weight of its perceived motion state information;

[0036] The state fusion unit is used to perform weighted processing on its own motion state information and perceived motion state information according to the first weight and the second weight to obtain fused motion state information.

[0037] Optionally, the weight acquisition unit includes:

[0038] The first precision unit is used to obtain the first precision of its own motion state information based on the signal strength of its own motion state information.

[0039] The second precision unit is used to compare its own motion state information with the fused motion state information to obtain the second precision of its own motion state information.

[0040] The first weighting unit is used to obtain the first weight of its own motion state information based on the first precision and the second precision of its own motion state information.

[0041] The third precision unit is used to obtain the third precision of the perceived motion state information based on the signal strength of the perceived motion state information.

[0042] The fourth precision unit is used to compare the perceived motion state information with the fused motion state information to obtain the fourth precision of the perceived motion state information;

[0043] The second weighting unit is used to obtain the second weight of the perceived motion state information based on the third and fourth precisions of the perceived motion state information.

[0044] Optionally, the state prediction module includes:

[0045] The model acquisition unit is used to acquire the kinematic model of the target person. The kinematic model is used to indicate the motion state of the target person at different times.

[0046] The state prediction unit is used to predict the motion state information of the target person based on the fusion of motion state information and kinematic model, using the Kalman filter algorithm.

[0047] Optionally, the model acquisition unit includes:

[0048] The data acquisition unit is used to acquire basic information and historical movement data of the target personnel.

[0049] The model building unit is used to obtain the target person's activity habits based on the target person's basic information and historical movement data, and to build a kinematic model of the target person.

[0050] Optional traffic safety devices may also include:

[0051] The risk alert module is used to send alert information to the wearable measuring device when there is a risk of collision between the vehicle and the target person.

[0052] Optional traffic safety devices may also include:

[0053] The emergency module is used to receive emergency information sent by the wearable measuring device when it detects that a target person is in danger.

[0054] A third aspect of this application provides a portable wearable device, including a wireless communication module for communicating with external sensing devices.

[0055] A fourth aspect of this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the traffic safety assurance method described above.

[0056] A fifth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the traffic safety assurance method described above.

[0057] The traffic safety assurance method provided in the first aspect of this application obtains and fuses the target person's own motion state information and perceived motion state information to obtain fused motion state information, predicts the motion state information based on the fused motion state information, performs collision analysis between the vehicle and the target person based on the predicted motion state information, and takes corresponding actions based on the analysis results, thereby improving the safety assurance of the target person.

[0058] It is understood that the beneficial effects of the second, third, fourth and fifth aspects mentioned above can be found in the relevant descriptions in the first aspect above, and will not be repeated here. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0060] Figure 1 A flowchart illustrating the traffic safety assurance method provided in the embodiments of this application;

[0061] Figure 2 This is a schematic diagram of the structure of a portable wearable device provided in an embodiment of this application;

[0062] Figure 3 A flowchart illustrating step S20 of the traffic safety assurance method provided in the embodiments of this application;

[0063] Figure 4 A flowchart illustrating step S21 of the traffic safety assurance method provided in this application embodiment;

[0064] Figure 5 A flowchart illustrating step S30 of the traffic safety assurance method provided in the embodiments of this application;

[0065] Figure 6 A flowchart illustrating step S31 of the traffic safety assurance method provided in this application embodiment;

[0066] Figure 7 This is a schematic diagram of the traffic safety protection device provided in the embodiments of this application;

[0067] Figure 8 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0068] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0069] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0070] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0071] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0072] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0073] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. "A plurality" means "two or more."

[0074] Example 1

[0075] Embodiment 1 of this application provides a traffic safety assurance method, which can be executed by the processor of a terminal device when running a corresponding computer program. This method is used to predict motion state information based on fused motion state information, perform collision analysis between the vehicle and a target person based on the predicted motion state information, and take corresponding actions based on the analysis results, thereby improving the safety of the target person. The vehicle can be a car, truck, etc., and the target person can be a pedestrian carrying a personal measuring device.

[0076] like Figure 1 As shown, the traffic safety assurance method provided in this embodiment includes the following steps S10 to S40:

[0077] S10. Obtain the target person's own motion state information and perceived motion state information. The own motion state information is obtained based on the target person's personal measurement device, and the perceived motion state information is obtained based on external sensing devices.

[0078] In applications, the aforementioned portable measuring device can be as follows: Figure 2 The portable wearable device 21 shown, such as a smart bracelet, can have external sensing devices such as... Figure 2 The illustrated roadside unit (RSU) 22 and onboard unit (OBU) 23 are shown. The roadside unit 22 can be any roadside V2X (Vehicle to Everything) device, and the onboard unit 23 can be any onboard V2X device. The portable wearable device includes a wireless communication module 211 and a positioning module 213. The roadside unit 22 and the onboard unit 23 can communicate wirelessly. After using the portable wearable device 21, the user can interact with the roadside unit 22 and the onboard unit 23 through the wireless communication module 211, where the wireless communication module 211 can be a V2X chip. The user's own motion state information can be obtained through the positioning module 213 of the portable wearable device 21. The roadside unit 22 and the onboard unit 23 obtain their own motion state information through the wireless communication module 211. The perceived motion state information can be directly obtained through the sensing devices within the roadside unit 22 and the onboard unit 23. The user's own motion state information and the perceived motion state information can include information such as the user's position, posture, speed, and acceleration.

[0079] S20. Integrate the self-motion state information and perceived motion state information to obtain the fused motion state information of the target person.

[0080] In application, the above-mentioned fusion of self-motion state information and perceived motion state information to obtain the fused motion state information of the target person can be achieved by weighting the self-motion state information and perceived motion state information. By fusing self-motion state information and perceived motion state information to obtain the fused motion state information of the target person, more accurate motion state information of the target person can be obtained.

[0081] S30. Based on the fusion of motion state information, predict the motion state information of the target person.

[0082] In application, the predicted motion state information of the target person, which is based on the fusion motion state information, can be predicted by using the Kalman filter algorithm based on the fusion motion state information at the current moment. The predicted motion state information can include information such as the position, attitude, velocity, and acceleration of the target person.

[0083] S40. Analyze the collision between the vehicle and the target person based on the predicted motion state information, and take corresponding actions based on the analysis results.

[0084] In applications, after obtaining the predicted motion state information, the roadside unit and the vehicle-mounted unit perform collision analysis between the vehicle and the target person, and send the analysis results to the vehicle. Based on the analysis results, the vehicle can take corresponding actions. For example, when there is a risk of collision between the vehicle and the target person, the vehicle's route can be adjusted to avoid the target person. The analysis results can also be sent to the traffic light controller to control traffic flow by adjusting the status and duration of the traffic lights. Alternatively, the analysis results can be sent to portable wearable devices to remind the target person to take evasive action.

[0085] The traffic safety assurance method provided in this application embodiment uses wireless communication technology to interact between the vehicle and the target person, obtains the target person's own motion state information and perceived motion state information, and fuses them to obtain fused motion state information. Based on the fused motion state information, predictive motion state information is obtained. Based on the predictive motion state information, collision analysis between the vehicle and the target person is performed, and corresponding actions are taken based on the analysis results, thereby improving the safety assurance of the target person.

[0086] Example 2

[0087] Embodiment 2 of this application provides a traffic safety assurance method based on Embodiment 1, which can be executed by the processor of a terminal device when running a corresponding computer program.

[0088] like Figure 3 As shown, step S20 includes steps S21 and S22:

[0089] S21, the first weight for obtaining information about one's own motion state and the second weight for perceiving information about one's motion state.

[0090] In the application, the first weight is the weight corresponding to the self-motion state information, and the second weight is the weight corresponding to the perceived motion state information.

[0091] S22. Based on the first weight and the second weight, the self-motion state information and the perceived motion state information are weighted to obtain the fused motion state information.

[0092] In application, by weighting the self-motion state information and perceived motion state information, fused motion state information is obtained, which can provide more accurate motion state information of the target person.

[0093] like Figure 4 As shown, in one embodiment, S21 includes steps S211 to S216:

[0094] S211. Based on the signal strength of its own motion state information, obtain the first precision of its own motion state information;

[0095] In applications, the stronger the signal strength of the self-motion state information, the higher the initial accuracy of the self-motion state information.

[0096] S212. Compare the self-motion state information with the fused motion state information to obtain the second precision of the self-motion state information;

[0097] In application, comparing one's own motion state information with the fused motion state information can be done by comparing the current motion state information with the current motion state inferred from the fused motion state information of the previous moment. The closer the two are, the higher the second accuracy.

[0098] S213. Based on the first precision and the second precision of its own motion state information, the first weight of its own motion state information is obtained;

[0099] In application, the first weight of the self-motion state information obtained based on the first and second precisions of the self-motion state information can be obtained by multiplying the first and second precisions by a unified evaluation standard.

[0100] S214. Based on the signal strength of the perceived motion state information, obtain the third precision of the perceived motion state information;

[0101] In applications, the stronger the signal strength for sensing motion state information, the higher the third precision of the motion state information sensing.

[0102] S215. Compare the perceived motion state information with the fused motion state information to obtain the fourth precision of the perceived motion state information;

[0103] In application, the above comparison between perceived motion state information and fused motion state information can be achieved by comparing the perceived motion state information at the current moment with the current motion state inferred from the fused motion state information at the previous moment. The closer the two are, the higher the fourth accuracy.

[0104] S216. Based on the third and fourth precisions of the perceived motion state information, the second weight of the perceived motion state information is obtained.

[0105] In application, the second weight of the perceived motion state information obtained based on the third and fourth precisions of the perceived motion state information can be obtained by multiplying the results of the third and fourth precisions using a unified evaluation standard.

[0106] like Figure 5 As shown, in one embodiment, step S30 includes steps S31 and S32:

[0107] S31. Obtain the kinematic model of the target person. The kinematic model is used to indicate the motion state of the target person at different times.

[0108] In application, the aforementioned kinematic model can be a pre-built kinematic model retrieved from a database, or a new kinematic model corresponding to the current target person can be built in real time.

[0109] S32. Based on the fusion of motion state information and kinematic model, the predicted motion state information of the target person is obtained by using the Kalman filter algorithm.

[0110] In application, the above-mentioned prediction of the target person's motion state information based on the fusion of motion state information and kinematic model, using the Kalman filter algorithm, can be achieved by using the fusion motion state information at the current moment as the parameter of the kinematic model, and then inputting the kinematic model into the Kalman filter algorithm to obtain the prediction of the motion state information at the next moment.

[0111] like Figure 6 As shown, in one embodiment, step S31 includes steps S311 and S312:

[0112] S311. Obtain basic information and historical movement data of the target personnel;

[0113] In applications, the aforementioned basic information may include the target person's gender, age, height, weight, and underlying medical conditions, such as... Figure 2 As shown, the portable wearable device can connect to the mobile terminal 24, such as a mobile phone, via Bluetooth module 214. The target person can set up basic information on the mobile terminal 24 and send it to the portable wearable device 21 for storage. The roadside unit 22 and the vehicle unit 23 can obtain the target person's basic information through the wireless communication module 211.

[0114] S312. Based on the basic information and historical movement data of the target personnel, obtain their activity habits and construct a kinematic model of the target personnel.

[0115] In application, basic information and historical movement data of target individuals can be analyzed to obtain their activity habits, such as walking speed and reaction time, and a kinematic model of the target individual can be constructed based on this. By obtaining the activity habits of target individuals based on their basic information and historical movement data, and constructing a kinematic model of them, more accurate classification and prediction can be made according to different types, such as the elderly, children, and adults. It is even possible to make customized predictions based on individual characteristics.

[0116] In one embodiment, the traffic safety assurance method further includes:

[0117] When there is a risk of collision between the vehicle and the target person, an alert message is sent to the wearable measuring device.

[0118] In application, when there is a risk of collision between the vehicle and the target person, the roadside unit 22 and the vehicle-mounted unit 23 can send alert information to the portable wearable device 21, such as... Figure 2 As shown, the portable wearable device 21 can remind the target personnel through the safety reminder module 215, further ensuring traffic safety.

[0119] In one embodiment, the traffic safety assurance method further includes:

[0120] Receive emergency information sent by the wearable measuring device when it detects danger to the target person.

[0121] In applications, such as Figure 2 As shown, the portable wearable device also includes a health detection module 212. When the portable wearable device detects that a target person is in danger or has a sudden illness through the health detection module 212, it can upload the target person's accurate location, physical condition and basic information to the cloud and other platforms through the roadside unit 22 and the vehicle-mounted unit 23. The roadside unit 22, the vehicle-mounted unit 23 and the cloud and other platforms can all perform alarm processing or report the emergency to other traffic participants.

[0122] Example 3

[0123] like Figure 7 As shown in the figure, this application embodiment also provides a traffic safety protection device, the traffic safety protection device 700 including:

[0124] The status acquisition module 701 is used to acquire the target person's own motion status information and perceived motion status information. The own motion status information is obtained based on the target person's personal measurement device, and the perceived motion status information is obtained based on external sensing devices.

[0125] The state fusion module 702 is used to fuse its own motion state information and perceived motion state information to obtain the fused motion state information of the target person.

[0126] The state prediction module 703 is used to predict the predicted motion state information of the target person based on the fused motion state information.

[0127] The analysis and processing module 704 is used to perform collision analysis between the vehicle and the target person based on the predicted motion state information, and to take corresponding actions based on the analysis results.

[0128] Optionally, the state fusion module includes:

[0129] The weight acquisition unit is used to acquire the first weight of its own motion state information and the second weight of its perceived motion state information;

[0130] The state fusion unit is used to perform weighted processing on its own motion state information and perceived motion state information according to the first weight and the second weight to obtain fused motion state information.

[0131] Optionally, the weight acquisition unit includes:

[0132] The first precision unit is used to obtain the first precision of its own motion state information based on the signal strength of its own motion state information.

[0133] The second precision unit is used to compare its own motion state information with the fused motion state information to obtain the second precision of its own motion state information.

[0134] The first weighting unit is used to obtain the first weight of its own motion state information based on the first precision and the second precision of its own motion state information.

[0135] The third precision unit is used to obtain the third precision of the perceived motion state information based on the signal strength of the perceived motion state information.

[0136] The fourth precision unit is used to compare the perceived motion state information with the fused motion state information to obtain the fourth precision of the perceived motion state information;

[0137] The second weighting unit is used to obtain the second weight of the perceived motion state information based on the third and fourth precisions of the perceived motion state information.

[0138] Optionally, the state prediction module includes:

[0139] The model acquisition unit is used to acquire the kinematic model of the target person. The kinematic model is used to indicate the motion state of the target person at different times.

[0140] The state prediction unit is used to predict the motion state information of the target person based on the fusion of motion state information and kinematic model, using the Kalman filter algorithm.

[0141] Optionally, the model acquisition unit includes:

[0142] The data acquisition unit is used to acquire basic information and historical movement data of the target personnel.

[0143] The model building unit is used to obtain the target person's activity habits based on the target person's basic information and historical movement data, and to build a kinematic model of the target person.

[0144] Optional traffic safety devices may also include:

[0145] The risk alert module is used to send alert information to the wearable measuring device when there is a risk of collision between the vehicle and the target person.

[0146] Optional traffic safety devices may also include:

[0147] The emergency module is used to receive emergency information sent by the wearable measuring device when it detects that a target person is in danger.

[0148] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0150] This application embodiment also provides a portable wearable device 21, such as... Figure 2 As shown, it includes a wireless communication module 211, which is used to communicate with external sensing devices.

[0151] In application, after using the portable wearable device 21, the target person can interact with external sensing devices such as roadside units 22 and vehicle-mounted units 23 through the wireless communication module 211. The portable wearable device 21 may also include a health detection module 212, a positioning module 213, a Bluetooth module 214, and a safety alert module 215. The portable wearable device 21 can obtain the target person's own movement status information through the positioning module 213 and send this information to the roadside unit 22 and vehicle-mounted unit 23 through the wireless communication module 211. When the portable wearable device 21 detects danger or a sudden illness in the target person through the health detection module 212, it can upload the target person's accurate location, physical condition, and basic information to cloud platforms through the roadside unit 22 and vehicle-mounted unit 23. The roadside unit 22, vehicle-mounted unit 23, and cloud platforms can then trigger alarms or report the emergency to other road users. The portable wearable device can connect to a mobile terminal 24, such as a mobile phone, via Bluetooth module 214. The target person can set up basic information on the mobile terminal 24 and send it to the portable wearable device 21 for storage. The roadside unit 22 and the vehicle-mounted unit 23 can obtain the target person's basic information via wireless communication module 211. When there is a risk of collision between the vehicle and the target person, the roadside unit 22 and the vehicle-mounted unit 23 can send a warning message to the portable wearable device 21, which can then remind the target person via safety warning module 215.

[0152] This application also provides a terminal device 800, such as... Figure 8 As shown, it includes a memory 801, a processor 802, and a computer program 803 stored in the memory 801 and executable on the processor 802. When the processor 802 executes the computer program 803, it implements the steps of the traffic safety assurance method provided in the first aspect.

[0153] In applications, terminal devices may include, but are not limited to, processors and memory. Figure 8 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than illustrated, or combinations of certain components, or different components, such as input / output devices, network access devices, etc. Input / output devices may include cameras, audio capture / playback devices, displays, etc. Network access devices may include communication modules for wireless communication with external devices.

[0154] In applications, the processor can be a Central Processing Unit (CPU), but 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, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0155] In applications, the memory may be an internal storage unit of the terminal device in some embodiments, such as the hard drive or RAM of the terminal device. In other embodiments, the memory may be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. The memory may also include both internal and external storage units of the terminal device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of computer programs. The memory can also be used to temporarily store data that has been output or will be output.

[0156] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.

[0157] This application implements all or part of the processes in the methods of the above embodiments, which can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0158] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0159] Those skilled in the art will recognize that the device and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0160] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interface, or the device may be indirectly coupled or communicated, and may be electrical, mechanical, or other forms.

[0161] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for ensuring traffic safety, characterized in that, include: The system acquires the target person's own motion state information and perceived motion state information, wherein the own motion state information is obtained based on the target person's personal measurement device, and the perceived motion state information is obtained based on external sensing devices. The self-motion state information and the perceived motion state information are fused to obtain the fused motion state information of the target person; Based on the fused motion state information, the predicted motion state information of the target person is obtained; Based on the predicted motion state information, a collision analysis is performed between the vehicle and the target person, and corresponding actions are taken based on the analysis results. The step of fusing the self-motion state information and the perceived motion state information to obtain the fused motion state information of the target person includes: The first weight of the self-motion state information and the second weight of the perceived motion state information are obtained; Based on the first weight and the second weight, the self-motion state information and the perceived motion state information are weighted to obtain the fused motion state information; The first weight for acquiring the self-motion state information and the second weight for sensing the motion state information include: Based on the signal strength of the self-motion state information, the first precision of the self-motion state information is obtained; The self-motion state information is compared with the fused motion state information to obtain the second precision of the self-motion state information; Based on the first precision and the second precision of the self-motion state information, the first weight of the self-motion state information is obtained; The third precision of the perceived motion state information is obtained based on the signal strength of the perceived motion state information; The perceived motion state information is compared with the fused motion state information to obtain the fourth precision of the perceived motion state information; Based on the third and fourth precisions of the perceived motion state information, the second weight of the perceived motion state information is obtained.

2. The traffic safety assurance method as described in claim 1, characterized in that, The step of predicting the predicted motion state information of the target person based on the fused motion state information includes: Obtain the kinematic model of the target person, the kinematic model being used to indicate the motion state of the target person at different times; Based on the fused motion state information and the kinematic model, the predicted motion state information of the target person is obtained using the Kalman filter algorithm.

3. The traffic safety assurance method as described in claim 2, characterized in that, The process of obtaining the kinematic model of the target person includes: Obtain the basic information and historical movement data of the target personnel; Based on the target person's basic information and historical movement data, the target person's activity habits are obtained, and a kinematic model of the target person is constructed.

4. The traffic safety assurance method as described in claim 1, characterized in that, The traffic safety assurance methods also include: When there is a risk of collision between the vehicle and the target person, an alert message is sent to the wearable measuring device.

5. The traffic safety assurance method as described in claim 1, characterized in that, The traffic safety assurance methods also include: Receive emergency situation information sent by the personal measuring device when it detects that the target person is in danger.

6. A traffic safety protection device, characterized in that, include: The status acquisition module is used to acquire the target person's own motion status information and perceived motion status information. The own motion status information is obtained based on the target person's personal measurement device, and the perceived motion status information is obtained based on external sensing devices. The state fusion module is used to fuse the self-motion state information and the perceived motion state information to obtain the fused motion state information of the target person; The state prediction module is used to predict the motion state information of the target person based on the fused motion state information. The analysis and processing module is used to perform collision analysis between the vehicle and the target person based on the predicted motion state information, and to take corresponding actions based on the analysis results. The state fusion module includes: A weight acquisition unit is used to acquire a first weight of the self-motion state information and a second weight of the perceived motion state information; The state fusion unit is used to perform weighted processing on the self-motion state information and the perceived motion state information according to the first weight and the second weight to obtain the fused motion state information; The weight acquisition unit includes: The first precision unit is used to obtain the first precision of the self-motion state information based on the signal strength of the self-motion state information. The second precision unit is used to compare the self-motion state information with the fused motion state information to obtain the second precision of the self-motion state information. The first weighting unit is used to obtain the first weight of the self-motion state information based on the first precision and the second precision of the self-motion state information. The third precision unit is used to obtain the third precision of the perceived motion state information based on the signal strength of the perceived motion state information. The fourth precision unit is used to compare the perceived motion state information with the fused motion state information to obtain the fourth precision of the perceived motion state information; The second weighting unit is used to obtain the second weight of the perceived motion state information based on the third precision and the fourth precision of the perceived motion state information.

7. A portable wearable device, characterized in that, It includes a wireless communication module, which is used to communicate with external sensing devices.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the traffic safety assurance method as described in any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the traffic safety assurance method as described in any one of claims 1 to 5.

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