Vehicle early warning method, electronic device, vehicle, storage medium and computer product

Abstract

The application discloses a vehicle early warning method, an electronic device, a vehicle, a storage medium and a computer product, relates to the technical field of vehicles, and specifically comprises the following steps: controlling a wireless signal emission module to emit a plurality of wireless detection signals around the vehicle; generating a target living body image based on the plurality of wireless detection signals, and extracting image features contained in the target living body image; in the case that it is identified according to the image features that there is a risk person in the target living body image, determining a target early warning strategy corresponding to the risk person, and performing an early warning operation according to the target early warning strategy. The application achieves the technical effect of improving the identification accuracy of risk persons.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle warning method, electronic device, vehicle, storage medium, and computer program product. Background Technology

[0002] With the continuous development of the automotive industry, new energy vehicles have become the preferred mode of transportation for more and more users' daily travel. To improve the safety of new energy vehicles, technicians usually equip them with multiple fisheye cameras to better identify potential risks around the vehicle.

[0003] In related technologies, vehicles typically use fisheye cameras to capture images of their surroundings after locking, and then use these images to identify potential individuals or entities in the vicinity of the vehicle.

[0004] However, fisheye cameras are easily affected by ambient light during image data acquisition, resulting in low-quality image data. This can lead to a lower accuracy rate in identifying at-risk individuals, significantly reducing vehicle safety. Summary of the Invention

[0005] The main objective of this application is to provide a vehicle warning method, electronic device, vehicle, storage medium, and computer program product, aiming to solve the technical problem of low accuracy in identifying at-risk personnel in related technologies, which leads to reduced vehicle safety.

[0006] To achieve the above objectives, this application proposes a vehicle warning method applied to an electronic device, the electronic device including a wireless signal transmitting module, the vehicle warning method comprising:

[0007] Control the wireless signal transmitting module to transmit multiple wireless detection signals around the vehicle;

[0008] A target liveness image is generated based on multiple wireless detection signals, and image features contained in the target liveness image are extracted;

[0009] If a person at risk is identified in the target live image based on the image features, a target early warning strategy corresponding to the person at risk is determined, and an early warning operation is performed according to the target early warning strategy.

[0010] In one embodiment, the step of determining the target early warning strategy corresponding to the at-risk personnel includes:

[0011] The location information of the at-risk person is determined based on the image features, and a distance parameter is determined based on the location information, wherein the distance parameter is the distance between the at-risk person and the vehicle;

[0012] If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, the preset first warning strategy is determined as the target warning strategy.

[0013] In one embodiment, after the step of determining the distance parameter based on the personnel location information, the method further includes:

[0014] If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, the actual pose information of the at-risk person is determined based on the image features.

[0015] If the actual pose information and the preset pose information are determined to match, the preset second early warning strategy is determined as the target early warning strategy.

[0016] If it is determined that the actual pose information and the preset pose information do not match, the preset third early warning strategy is determined as the target early warning strategy.

[0017] In one embodiment, the step of generating a target liveness image based on a plurality of the wireless detection signals includes:

[0018] The initial channel state information matched by each of the multiple wireless detection signals is detected, and each initial channel state information is processed to obtain complete channel state information.

[0019] The complete channel state information is filtered to determine the target channel state information, and a target liveness image is generated based on the target channel state information, wherein the target channel state information is the channel state information corresponding to the wireless detection signal that detects the liveness detection target.

[0020] In one embodiment, the step of detecting initial channel state information matching each of the plurality of wireless detection signals includes:

[0021] Detect the wireless received signal that matches each of the multiple wireless detection signals;

[0022] Determine the signal change ratio between each of the multiple wireless detection signals and the matched wireless received signal;

[0023] Based on the change ratio of each of the signals, the initial channel state information matching each of the multiple wireless detection signals is determined.

[0024] In one embodiment, the step of processing each of the initial channel state information to obtain each complete channel state information includes:

[0025] Determine the initial amplitude parameter and initial phase parameter included in each of the initial channel state information;

[0026] Phase expansion is performed on each of the initial amplitude parameters and each of the initial phase parameters, and the phase-expanded initial amplitude parameters and each of the initial phase parameters are filtered and eliminated to obtain each target amplitude parameter and each target phase parameter.

[0027] The target amplitude parameters and their respective matching target phase parameters are fused to obtain complete channel state information.

[0028] In one embodiment, the step of filtering each of the complete channel state information to determine the target channel state information includes:

[0029] Obtain preset standard channel state information, wherein the standard channel state information is the channel state information corresponding to the detection of a wireless detection signal of a non-living detection target;

[0030] Each complete channel state information is compared with the standard channel state information, and the complete channel state information that is inconsistent with the standard channel state information is determined as the target channel state information.

[0031] In one embodiment, the step of generating a target liveness image based on the target channel state information includes:

[0032] Extract the one-dimensional vector features contained in each of the target channel state information;

[0033] The one-dimensional vector features are fused to obtain two-dimensional vector features, and a target liveness image is generated based on the two-dimensional vector features.

[0034] In addition, to achieve the above objectives, this application also proposes an electronic device, the device comprising: a wireless signal transmitting module, a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle warning method as described above.

[0035] In addition, to achieve the above objectives, this application also proposes a vehicle that includes the electronic equipment described above.

[0036] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the vehicle warning method described above.

[0037] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle warning method described above.

[0038] The vehicle warning method provided in this application is applied to an electronic device. The electronic device includes a wireless signal transmitting module. By controlling the wireless signal transmitting module, multiple wireless detection signals are transmitted to the vicinity of the vehicle. A target liveness image is generated based on the multiple wireless detection signals, and image features contained in the target liveness image are extracted. If a risky person is identified in the target liveness image based on the image features, a target warning strategy corresponding to the risky person is determined, and a warning operation is performed according to the target warning strategy.

[0039] In this embodiment, during operation, the electronic device first controls its configured wireless signal transmission module to transmit multiple wireless detection signals around the vehicle. Then, the electronic device generates a target liveness image based on the multiple wireless detection signals and extracts the image features contained in the target liveness image. Finally, the electronic device identifies the image features. If the liveness detection target contained in the target liveness image is determined to be a high-risk person based on the image features, a target warning strategy matching the high-risk person is determined. Then, a warning operation is executed according to the target warning strategy to issue a warning to the driver of the vehicle and / or the high-risk person.

[0040] Thus, this application solves the technical problem of low accuracy in identifying risky individuals in related technologies, which leads to reduced vehicle safety. Specifically, this application utilizes the characteristic that wireless detection signals generate different channel state information when they come into contact with a living body, which can accurately identify risky individuals around the vehicle. This avoids the situation where image data collected by fisheye cameras is of low quality due to the influence of ambient light, making it difficult to accurately identify risky individuals. This achieves the technical effect of improving the accuracy of identifying risky individuals and greatly increases vehicle safety. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the electronic device involved in one embodiment of the vehicle warning method of this application;

[0044] Figure 2 This is a flowchart illustrating an embodiment of the vehicle warning method of this application.

[0045] Figure 3 This is a schematic diagram of a target liveness image involved in an embodiment of the vehicle warning method of this application;

[0046] Figure 4 This is a simplified flowchart of the vehicle warning method of this application;

[0047] Figure 5 This is a schematic diagram of the module structure of the vehicle warning device according to an embodiment of this application;

[0048] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the vehicle warning method in this application embodiment.

[0049] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0051] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0052] In this embodiment, for ease of description, please refer to Figure 1 , Figure 1 This is a schematic diagram of the electronic device involved in an embodiment of the vehicle warning method of this application. The following will use it as an example. Figure 1As shown, the execution entity is an electronic device with an internal wireless signal transmission module, or a mobile terminal, data storage control terminal, PC, or other terminal connected to an electronic control unit associated with the electronic device. The wireless signal transmission module comprises two parts: a TCAM (Telematics & Connectivity Antenna Module) and a DHU (Digital Cockpit Head Unit). The TCAM includes a first Wi-Fi Service, a Core Service, and an MCU (Microcontroller Unit). Similarly, the DHU includes a second Wi-Fi Service, a QNX Service (QNX Virtual Machine), an Android Service (Android Subsystem), an Algorithm Service (Data Processing Unit), a SOC (System on Chip), and an MCU.

[0053] Based on the aforementioned electronic equipment, the overall concept of the vehicle warning method of this application is proposed.

[0054] With the continuous development of the automotive industry, new energy vehicles have become the preferred mode of transportation for an increasing number of users. To improve the safety of new energy vehicles, technicians typically equip them with multiple fisheye cameras to better identify potential threats around the vehicle. In these technologies, the fisheye cameras usually capture images of the surrounding environment after the vehicle is locked, and then use this image data to identify potential threats. However, because fisheye cameras are easily affected by ambient light during image acquisition, the quality of the captured image data can be low. This can lead to a lower accuracy rate in identifying potential threats, significantly reducing vehicle safety.

[0055] To address the above issues, this application provides a vehicle early warning method applied to an electronic device. The electronic device includes a wireless signal transmitting module. The vehicle early warning method includes: controlling the wireless signal transmitting module to transmit multiple wireless detection signals around the vehicle; generating a target liveness image based on the multiple wireless detection signals and extracting image features contained within the target liveness image; and, if a risky person is identified within the target liveness image based on the image features, determining a target early warning strategy corresponding to the risky person and executing an early warning operation according to the target early warning strategy.

[0056] Thus, this application solves the technical problem of low accuracy in identifying risky individuals in related technologies, which leads to reduced vehicle safety. Specifically, this application utilizes the characteristic that wireless detection signals generate different channel state information when they come into contact with a living body, which can accurately identify risky individuals around the vehicle. This avoids the situation where image data collected by fisheye cameras is of low quality due to the influence of ambient light, making it difficult to accurately identify risky individuals. This achieves the technical effect of improving the accuracy of identifying risky individuals and greatly increases vehicle safety.

[0057] Based on the overall concept of the vehicle warning method of this application, the embodiments of this application provide a vehicle warning method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the vehicle warning method of this application.

[0058] In this embodiment, the vehicle warning method is applied to an electronic device, which includes a wireless signal transmitting module, and the vehicle warning method includes steps S10 to S30:

[0059] Step S10: Control the wireless signal transmitting module to transmit multiple wireless detection signals around the vehicle;

[0060] Step S20: Generate a target liveness image based on multiple wireless detection signals, and extract the image features contained in the target liveness image;

[0061] It should be noted that the wireless detection signal can specifically be a Wi-Fi signal. Furthermore, the target liveness image is image data containing the detected liveness target generated after detecting a liveness target around the vehicle via a Wi-Fi signal. Additionally, the image features are image features contained within the target liveness image that can reflect information such as the position and orientation of the detected liveness target.

[0062] In this embodiment, when the electronic device detects that the vehicle has entered a locked state, it first calls its own configured wireless signal transmission module to continuously transmit multiple wireless detection signals to the surrounding environment of the vehicle. Then, the electronic device controls the wireless signal transmission module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. The wireless signal transmission module then filters and obtains multiple initial channel state information based on each wireless detection signal and each wireless reflection signal, and inputs the multiple initial channel state information into its own configured convolutional neural network model. The convolutional neural network model generates a target liveness image containing liveness detection targets existing around the vehicle based on the received initial channel state information. The convolutional neural network model performs phase separation processing on the target liveness image to obtain multiple sub-liveness images, and calls the encoder to encode the multiple sub-liveness images to identify the convolutional feature weights corresponding to each of the multiple sub-liveness images. The convolutional neural network model then fuses the convolutional feature weights to obtain pose fusion data, performs inference merging operation on the pose fusion data, and samples the inference-merged pose fusion data to extract target features.

[0063] For example, when an electronic device detects that a vehicle has entered a locked state, it first controls its own wireless transmission module to activate the TCAM within the module. This causes the first Wi-Fi Service within the TCAM to transmit multiple first Wi-Fi signals to the surrounding environment. The first Wi-Fi Service then receives the first reflected signals generated by each of the multiple first Wi-Fi signals, and determines the initial CSI data corresponding to each of the multiple first Wi-Fi signals based on the first reflected signals. The TCAM then inputs the acquired initial CSI data to the DHU within the wireless transmission module, causing the initial CSI data to enter the Algorithm Service configured within the DHU. Simultaneously, the electronic device controls the DHU within the wireless transmission module to activate, causing the second Wi-Fi Service within the DHU to transmit multiple second Wi-Fi signals to the surrounding environment. The second Wi-Fi Service then receives the second reflected signals generated by each of the multiple second Wi-Fi signals, and determines the initial CSI (Channel State Information) data corresponding to each of the multiple second Wi-Fi signals based on the second reflected signals. The DHU then inputs the acquired initial CSI data to its own Algorithm Service. After acquiring initial CSI data, the Service inputs the CSI data into the RCNN (Regions with Convolutional Neural Networks) configured on the electronic device. The RCNN generates target image data containing the liveness detection target based on the CSI data. After constructing the target liveness image, the RCNN needs to segment the target liveness image to obtain multiple sub-liveness images. The RCNN calls the encoder to encode the multiple sub-liveness images separately to identify the convolutional feature weights corresponding to each sub-liveness image. The RCNN then fuses the acquired convolutional feature weights to obtain preliminary pose fusion data. The RCNN performs inference operations through 8-bit 3-channel RGB reshape to merge the preliminary pose fusion data into 24-bit data. The RCNN then performs upsampling encoding and downsampling decoding operations on the 24-bit data in sequence to output a 3-channel image. Finally, the 3-channel image is determined as the image feature corresponding to the target liveness image.

[0064] It should be noted that, in this embodiment and another embodiment, when the electronic device detects that the vehicle has entered the locked state, it can first control the initialization of the QNX virtual machine (QNX Service) configured in the DHU, so as to start the second Wifi Service and Android subsystem (Android Service) in the DHU through the QNX virtual machine. At the same time, the electronic device also controls the TCAM and DHU to conduct Socket communication to determine that the TCAM can send the acquired initial CSI data to the Algorithm Service in the DHU.

[0065] In addition, in this embodiment and another embodiment, after the electronic device controls the TCAM to start, it also needs to control the Core Service configured in the TCAM and the QNX virtual machine configured in the DHU to detect the communication status, so as to promptly initiate a re-communication request when the heartbeat communication between the TCAM and the DHU is detected to be interrupted.

[0066] In this way, by calling the wireless transmission module to transmit multiple wireless detection signals to the cockpit, the vehicle can identify live targets around the vehicle through multiple wireless detection signals, generate a target live image containing the live target, and then extract image features that reflect the position and pose of the live target from the target live image.

[0067] In one feasible implementation, the step of "generating a target liveness image based on the plurality of wireless detection signals" in step S20 above may specifically include steps S201 to S202:

[0068] Step S201: Detect the initial channel state information matched by each of the multiple wireless detection signals, and process each initial channel state information to obtain each complete channel state information;

[0069] Step S202: Filter each of the complete channel state information to determine the target channel state information, and generate a target liveness image based on each of the target channel state information, wherein the target channel state information is the channel state information corresponding to the wireless detection signal that detects the liveness detection target.

[0070] It should be noted that the initial channel state information is unfiltered and uncleaned CSI data. Furthermore, the complete channel state information is cleaned and filtered CSI data that does not contain outliers. Additionally, the target state channel information is the CSI data corresponding to the Wi-Fi signal detecting a live target around the vehicle. This CSI data is understood to be a complex decimal sequence representing the signal-to-wave ratio between the wireless detection signal and the corresponding wireless received signal.

[0071] In this embodiment, after the electronic device controls the wireless signal transmitting module to transmit multiple wireless detection signals to the surrounding environment, it further controls the wireless signal transmitting module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. The electronic device then controls the wireless signal transmitting module to obtain the initial channel state information (without cleaning processing) matching each of the received wireless detection signals and wireless reflection signals. The electronic device then controls the wireless signal transmitting module to input each initial channel state information into the data processing unit configured within the electronic device. The data processing unit then calls a convolutional neural network model to clean and denoise each initial channel state information to obtain complete channel state information without outliers. Afterward, the convolutional neural network model filters each complete channel state information to determine the target channel state information contained in each complete channel state information that corresponds to the wireless detection signal that detected the liveness detection target. Finally, based on each target channel state information, a target liveness image containing the target liveness around the vehicle is generated.

[0072] For example, after the electronic device controls the first Wi-Fi Service configured in the TCAM to continuously transmit multiple first Wi-Fi signals to the vehicle's surrounding environment, it controls the first Wi-Fi Service to continuously receive the first reflected signals generated by each of the multiple first Wi-Fi signals. Based on each first reflected signal and each first Wi-Fi signal, it determines the initial CSI data matching each of the multiple first Wi-Fi signals. Simultaneously, after the electronic device controls the second Wi-Fi Service configured in the DHU to continuously transmit multiple second Wi-Fi signals to the surrounding environment, it controls the second Wi-Fi Service to continuously receive the second reflected signals generated by each of the multiple second Wi-Fi signals. Based on each second reflected signal and each second Wi-Fi signal, it determines the initial CSI data matching each of the multiple second Wi-Fi signals. The electronic device then controls the TCAM and DHU to input their respective initial CSI data into the data processing unit Algorithm Service configured in the DHU. The Algorithm Service performs data cleaning operations on each initial CSI data to eliminate abnormal data contained in each initial CSI data, thereby obtaining complete CSI data without abnormal data. Then, the Algorithm... The Service filters each first Wi-Fi signal and each second Wi-Fi signal based on each complete CSI data, thereby identifying each target first Wi-Fi signal and each target second Wi-Fi signal that has detected a liveness detection target. The Algorithm Service determines the complete CSI data corresponding to each target first Wi-Fi signal and each target second Wi-Fi signal as target CSI data for determining whether the liveness detection target is a human body, and calls its own configured RCNN to generate a target liveness image containing the liveness detection target around the vehicle based on each target CSI data.

[0073] In this way, by calling the wireless transmission module to transmit multiple wireless detection signals to the cockpit, the vehicle can identify live targets around the vehicle through multiple wireless detection signals, generate a target live image containing the live target, and then extract image features that reflect the position and pose of the live target from the target live image.

[0074] In one feasible implementation, the step of "detecting the initial channel state information that matches each of the plurality of wireless detection signals" in step S201 above may specifically include steps S2011 to S2013:

[0075] Step S2011: Detect the wireless received signal that matches each of the plurality of wireless detection signals;

[0076] Step S2012: Determine the signal change ratio between each of the plurality of wireless detection signals and the matched wireless received signal;

[0077] Step S2013: Based on the change ratio of each signal, determine the initial channel state information matched by each of the multiple wireless detection signals.

[0078] In this embodiment, after the electronic device controls the wireless signal transmitting module to continuously transmit multiple wireless detection signals to the surrounding environment, it further controls the wireless signal transmitting module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. Then, the wireless signal transmitting module compares the multiple wireless detection signals with their respective matched wireless received signals to determine the signal change ratio between each of the multiple wireless detection signals and the matched wireless received signals. Finally, the wireless signal transmitting module determines the signal change ratio corresponding to each of the multiple wireless detection signals as the initial channel state information matched by each of the multiple wireless detection signals, and inputs each initial channel state information to the aforementioned data processing unit.

[0079] For example, after the electronic device controls the TCAM configured with the first Wifi Service to transmit multiple first Wifi signals to the vehicle's surrounding environment, it further controls the TCAM to receive the first Wifi reflection signals formed by each first Wifi signal after it comes into contact with an obstacle. Then, the TCAM compares each first Wifi signal with its corresponding first Wifi reflection signal to determine the first signal wave ratio formed between each first Wifi signal and its corresponding first Wifi reflection signal. Finally, the TCAM determines the first signal wave ratio corresponding to each first Wifi signal as the initial CSI data corresponding to each first Wifi signal.

[0080] Similarly, after the electronic device controls the DHU to transmit multiple second Wi-Fi signals to the vehicle's surrounding environment via the second Wi-Fi Service, it further controls the DHU to receive the second Wi-Fi reflection signals formed by each second Wi-Fi signal after it comes into contact with an obstacle. Then, the DHU compares each second Wi-Fi signal with its corresponding second Wi-Fi reflection signal to determine the second signal wave ratio formed between each second Wi-Fi signal and its corresponding second Wi-Fi reflection signal. Finally, the TCAM determines the second signal wave ratio corresponding to each second Wi-Fi signal as the second initial CSI data corresponding to each second Wi-Fi signal. The electronic device controls the TCAM and DHU to input the collected initial CSI data into the data processing unit Algorithm Service configured in the DHU.

[0081] In this way, by calling the wireless transmission module to transmit multiple wireless detection signals to the environment around the vehicle, the electronic device can obtain the initial channel state information generated by each of the multiple wireless detection signals.

[0082] In one feasible implementation, the step of "processing each of the initial channel state information to obtain each complete channel state information" in step S201 above may specifically include steps S2014 to S2016:

[0083] Step S2014: Determine the initial amplitude parameter and initial phase parameter contained in each of the initial channel state information;

[0084] Step S2015: Perform phase expansion on each of the initial amplitude parameters and each of the initial phase parameters, and filter and eliminate the phase-expanded initial amplitude parameters and each of the initial phase parameters to obtain each target amplitude parameter and each target phase parameter;

[0085] Step S2016: Fuse each of the target amplitude parameters and their respective matching target phase parameters to obtain complete channel state information.

[0086] In this embodiment, after acquiring each initial channel state information, the data processing unit first reads the initial amplitude parameters and initial phase parameters contained in each initial channel state information. Then, the data processing unit performs phase expansion operation on each initial amplitude parameter and initial phase parameter. The data processing unit then performs filtering and elimination operation on each initial amplitude parameter and initial phase parameter after phase expansion to eliminate the outliers contained in each initial amplitude parameter and initial phase parameter, thereby obtaining each target amplitude parameter and each target phase parameter. Finally, the data processing unit fuses each target amplitude parameter and its matching target phase parameter to obtain each complete channel state information that does not contain outliers.

[0087] For example, after obtaining the initial CSI data input from TCAM and DHU respectively, the Algorithm Service first extracts the initial amplitude parameters and initial phase parameters contained in each initial CSI data. Then, the Algorithm Service performs phase expansion processing on each initial amplitude parameter and initial phase parameter, so that the expanded amplitude curve and phase curve are restored to continuous curves. At the same time, the Algorithm Service performs median filtering and uniform filtering processing on each expanded initial amplitude parameter and initial phase parameter to eliminate outliers in the time and frequency domains, thereby obtaining target amplitude parameters and target phase parameters with target outliers eliminated. Finally, the Algorithm Service fuses each target amplitude parameter and its matching target phase parameter to obtain complete CSI data without outliers.

[0088] It should be noted that the initial CSI data suffers from random amplitude and phase drift and inversion. Therefore, without denoising the initial CSI data, these outlier values ​​will affect the final detection results. Furthermore, while the amplitude and phase in the initial CSI data can be calculated, phases and amplitudes exceeding the preset function range will fold, resulting in discontinuities. Thus, a phase unrolling operation is required to restore the phases and amplitudes to a continuous state. The specific calculation process for phase unrolling is existing technology and will not be elaborated here. Similarly, the Algorithm Service's median and uniform filtering processes for the unrolled phase and amplitude parameters are also existing technology and will not be elaborated here.

[0089] In this way, the electronic equipment can remove the outliers contained in each initial channel state information, thereby obtaining complete channel state information without outliers, thus ensuring the accuracy of the detection results for personnel left in the cockpit.

[0090] In one feasible implementation, the step of "filtering each of the complete channel state information to determine the target channel state information" in step S202 above may specifically include steps S2021 to S2022:

[0091] Step S2021: Obtain preset standard channel state information, wherein the standard channel state information is the channel state information corresponding to the wireless detection signal that detects a non-live target;

[0092] Step S2022: Compare each of the complete channel state information with the standard channel state information, so as to determine the complete channel state information that is inconsistent with the standard channel state information as the target channel state information.

[0093] It should be noted that the standard channel state information is the CSI data corresponding to the Wi-Fi signal that detects a non-live target. It can be understood that when multiple Wi-Fi signals detect a non-live target, the signal change ratio between each Wi-Fi signal and its corresponding Wi-Fi reflection signal should be consistent. That is, when the CSI data corresponding to multiple Wi-Fi signals changes, it can be determined that the Wi-Fi signal has detected a moving live target.

[0094] In this embodiment, after obtaining each complete channel state information, the data processing unit first reads the storage module configured in the electronic device to obtain the standard channel state information corresponding to the wireless detection signal that a non-live target has been detected. Then, the data processing unit compares each complete channel state information with the standard channel state information to obtain multiple first comparison results. The data processing unit reads the multiple first comparison results and determines the complete channel state information that is inconsistent with the standard channel state information as the target channel state information corresponding to the wireless detection signal that a live target has been detected based on the multiple first comparison results.

[0095] For example, after obtaining each complete CSI data, the Algorithm Service first reads the storage module configured in the electronic device to obtain the preset standard CSI data corresponding to when the Wi-Fi signal detects a non-live target. Then, the Algorithm Service compares each complete CSI data with the standard CSI data to obtain multiple first comparison results. Finally, the Algorithm Service filters each complete CSI data according to the multiple first comparison results, thereby determining the complete CSI data that is inconsistent with the standard CSI data as the target CSI data that can generate a live image that matches the live target.

[0096] In this way, the electronic device can determine the target channel state information corresponding to the wireless detection signal that detects a live target from multiple complete channel state information transmitted, and then generate a target live image containing the live target based on the target channel state information.

[0097] In one feasible implementation, the step of "generating a target liveness image based on the target channel state information" in step S202 above may specifically include steps S2024 to S2025:

[0098] Step S2024: Extract the one-dimensional vector features contained in each of the target channel state information;

[0099] Step S2025: Fuse the one-dimensional vector features to obtain two-dimensional vector features, and generate a target liveness image based on the two-dimensional vector features.

[0100] It should be noted that the one-dimensional vector feature is a latent spatial feature contained within the target channel state information. Specifically, the one-dimensional feature vector contains an amplitude tensor and a phase tensor, where each tensor is 150×3×3 in size (5 consecutive samples, 30 frequencies, 3 transmitters and 3 receivers). Thus, RCNN (Regions with Convolutional Neural Networks) can extract the spatial features contained in the one-dimensional vector feature through the last two dimensions of each tensor.

[0101] In this embodiment, after determining multiple target channel state information, the data processing unit first inputs the multiple target channel state information into its own configured convolutional neural network model, so that the convolutional neural network model calls multiple encoders to extract the one-dimensional feature vector contained in each of the multiple target channel state information. Then, the data processing unit fuses the one-dimensional feature vectors to obtain two-dimensional fused features, and converts the two-dimensional fused features into a feature map in the spatial domain. Then, the converted two-dimensional feature map is upsampled to obtain a target liveness image containing the liveness detection target.

[0102] For example, please refer to Figure 3 , Figure 3This is a schematic diagram of a target liveness image involved in an embodiment of the vehicle warning method of this application. After identifying multiple target CSI data points, the Service first inputs these data points into its configured RCNN. The RCNN then calls two different encoders to process each target CSI data point, extracting amplitude and phase tensors of size 150×3×3 for each target CSI data point. The RCNN then defines these amplitude and phase tensors as 1D features. Next, the RCNN extracts the spatial information contained in each 1D feature based on the last two dimensions (3 transmitters and 3 receivers) of each amplitude and phase tensor. It then fuses these 1D features to obtain a fused 1D feature. The RCNN then reshapes the fused 1D feature into an initial 24×24 2D feature map and extracts the spatial information contained in the initial 2D feature map using its configured two convolutional blocks to obtain a 6×6 2D feature vector. Finally, the RCNN upsamples the 6×6 2D feature vector to adjust it to a size similar to... Figure 3 The image shown is 3×720×1280 in size and contains the target liveness image in the image domain of the liveness detection target.

[0103] In this way, the electronic device can construct a target liveness image containing the liveness detection target based on the selected target channel state information.

[0104] Step S30: If a person at risk is identified in the target live image based on the image features, determine the target early warning strategy corresponding to the person at risk, and execute the early warning operation according to the target early warning strategy;

[0105] In this embodiment, after the convolutional neural network extracts the image features, it inputs the image features into the image processing module configured in the electronic device. The image processing module first determines whether the liveness detection target is a high-risk person based on the image features. If the liveness detection target is a high-risk person, the image processing module then determines a target warning strategy that matches the high-risk person based on the image features and uploads the target warning strategy to the electronic device. The electronic device executes the warning operation according to the target warning strategy to issue a warning to the liveness detection target through the vehicle and / or to the driver through the vehicle.

[0106] For example, after RCNN extracts image features, the electronic device inputs the features extracted by RCNN into the SOC configured in the DHU. The SOC calls the preset YOLO image processing model to process the image features to determine whether the liveness detection target in the target liveness image is a human. If the liveness detection target is detected to be a human, the human is identified as a high-risk person. The SOC then determines the target warning strategy matching the high-risk person based on the image features, and uploads the target warning strategy to the MCU. The MCU executes the warning operation according to the target warning strategy to issue a warning to the liveness detection target through the vehicle / to the driver through the vehicle.

[0107] In this way, electronic devices can identify whether there are risky persons around the vehicle based on image features, and if risky persons are detected around the vehicle, they can further identify the possibility that the risky persons may cause harm to the vehicle, thereby determining an appropriate warning strategy to warn the risky persons and / or the driver through the vehicle.

[0108] In one feasible implementation, the step of "determining the target early warning strategy corresponding to the at-risk personnel" in step S30 above may specifically include steps S301 to S302:

[0109] Step S301: Determine the location information of the at-risk person based on the image features, and determine the distance parameter based on the location information, wherein the distance parameter is the distance between the at-risk person and the vehicle;

[0110] Step S302: If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, determine the preset first warning strategy as the target warning strategy.

[0111] In this embodiment, when the image processing module detects that the liveness detection target contained in the target liveness image is a person at risk, it further determines the position of the person at risk in the image based on image features and the actual position information of the person at risk in the real environment. At the same time, the image processing module reads the vehicle position information obtained from the aforementioned storage module and determines the distance parameter between the vehicle and the person at risk based on the actual position information and the vehicle position information. Finally, the image processing module reads the storage module to obtain a preset distance parameter threshold and compares the distance parameter with the distance parameter threshold. Thus, if the distance parameter is detected to be less than or equal to the distance parameter threshold, the first warning strategy that needs to issue a warning to both the driver of the vehicle and the person at risk is determined as the target warning strategy to be executed.

[0112] For example, when the SOC identifies a liveness detection target within a liveness image as a person at risk based on image features, it further extracts the location feature information contained within the image features. The SOC then determines the actual location information of the person at risk based on this location feature information. Next, the SOC reads the storage module configured within the electronic device to obtain the vehicle's location information and determines the risk distance parameter between the person at risk and the vehicle based on the actual location information and the vehicle's location information. Finally, the SOC reads the storage module to obtain a preset distance parameter threshold and compares the risk distance parameter with the distance parameter threshold. If the SOC detects that the risk distance parameter is less than or equal to the distance parameter threshold, it determines that there is a risk of the person at risk contacting the vehicle. The SOC then selects a first warning strategy from among several preset warning strategies, simultaneously issuing warnings to both the person at risk and the driver via the vehicle, as the target warning strategy. The SOC then sends the preset warning information to the MCU (Microcontroller Unit) according to the first warning strategy. The MCU converts the warning information into a CAN signal and sends the CAN signal to hardware devices such as buzzers and LED headlights connected to the DHU via the vehicle's BUS bus to control the buzzers and LED headlights to enter the operating state. At the same time, the CAN signal is sent to the TGM via the BUS bus so that the warning information can be wirelessly transmitted to the driver's mobile device, thereby alerting the driver to the risk of the vehicle through the mobile device.

[0113] In addition, in this embodiment and another embodiment, after determining the location information of the at-risk personnel, the SOC can not only obtain the vehicle location information by reading the storage module, but also determine the vehicle location information corresponding to the vehicle by using the GPS module configured on the vehicle, and determine the distance parameters between the at-risk personnel and the vehicle based on the vehicle location information and the personnel location information.

[0114] In addition, in this embodiment and another embodiment, besides determining the distance parameters between the at-risk person and the vehicle based on vehicle location information and personnel location information, the SOC can also, after determining the personnel location information of the at-risk person based on image features, further call the camera device configured on the vehicle to take pictures of the at-risk person to capture target image data, and then determine the distance parameters between the at-risk person and the vehicle based on the second image features of the target image data and the personnel location information.

[0115] Furthermore, in this embodiment and another embodiment, after extracting image features, the SOC can also calculate the distance parameters between the at-risk personnel and the vehicle based on the feature parameters such as signal reception time and signal transmission time contained in the image features. It can be understood that since the target liveness image is image data generated based on CSI data, the features of the liveness image data include the corresponding Wi-Fi signal transmission time and Wi-Fi signal reception time. Thus, the SOC can calculate the distance parameters between the at-risk personnel and the vehicle based on the Wi-Fi signal transmission time, Wi-Fi signal reception time, and the preset Wi-Fi signal propagation speed.

[0116] In this embodiment, when the electronic device detects that the vehicle has entered a locked state, it first calls its configured wireless signal transmission module to continuously transmit multiple wireless detection signals to the surrounding environment of the vehicle. Then, the electronic device controls the wireless signal transmission module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. The wireless signal transmission module then filters and obtains multiple initial channel state information based on each wireless detection signal and each wireless reflection signal, and inputs the multiple initial channel state information into its configured convolutional neural network model. The convolutional neural network model generates a target liveness image containing liveness detection targets present around the vehicle based on the received initial channel state information. The convolutional neural network model performs phase separation processing on the target liveness image to obtain multiple sub-liveness images, and calls an encoder to encode the multiple sub-liveness images for identification. The convolutional neural network model then fuses the convolutional feature weights corresponding to multiple sub-liveness images to obtain pose fusion data. This pose fusion data undergoes inference merging, and the merged pose fusion data is sampled to extract target features. Finally, the convolutional neural network inputs these image features to the image processing module configured in the electronic device. The image processing module first determines whether the liveness detection target is a high-risk individual based on the image features. If the liveness detection target is a high-risk individual, the image processing module determines a target warning strategy matching the high-risk individual based on the image features and uploads this target warning strategy to the electronic device. The electronic device then executes the warning operation according to the target warning strategy to issue a warning to the liveness detection target via the vehicle and / or to the driver via the vehicle.

[0117] Thus, this application solves the technical problem of low accuracy in identifying risky individuals in related technologies, which leads to reduced vehicle safety. Specifically, this application utilizes the characteristic that wireless detection signals generate different channel state information when they come into contact with a living body, which can accurately identify risky individuals around the vehicle. This avoids the situation where image data collected by fisheye cameras is of low quality due to the influence of ambient light, making it difficult to accurately identify risky individuals. This achieves the technical effect of improving the accuracy of identifying risky individuals and greatly increases vehicle safety.

[0118] Based on the first embodiment of this application, a second embodiment of this application is proposed herein. In this second embodiment, content that is the same as or similar to the above embodiments can be referred to the above description and will not be repeated hereafter. Furthermore, after step S302 above, the vehicle warning method of this application may further include steps A10 to A30:

[0119] Step A10: If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, determine the actual pose information of the at-risk person based on the image features;

[0120] Step A20: If the actual pose information and the preset pose information are determined to match, the preset second early warning strategy is determined as the target early warning strategy;

[0121] Step A30: If it is determined that the actual pose information and the preset pose information do not match, the preset third early warning strategy is determined as the target early warning strategy.

[0122] In this embodiment, after detecting the distance parameter between the at-risk person and the vehicle, the image processing module can compare the distance parameter with the aforementioned distance parameter threshold. If the distance parameter is less than or equal to the distance parameter threshold, the module further extracts the pose features contained in the image features and determines the actual pose information of the at-risk person based on the pose features. Then, the image processing module reads the aforementioned storage module to obtain multiple preset pose information representing dangerous contact operations, and compares the actual pose information with the multiple preset pose information to obtain multiple second comparison results. The image processing module reads the multiple second comparison results, and based on the multiple second comparison results, if it is determined that the actual pose information matches at least one preset pose information, it determines a second warning strategy that requires issuing warnings to both the driver and the at-risk person simultaneously as the target warning strategy. Similarly, if, based on the multiple second comparison results, it is determined that the actual pose information does not match any of the multiple preset standard poses, the image processing module determines a third warning strategy that only issues warnings to the at-risk person as the target warning strategy.

[0123] For example, after detecting the distance parameter between the at-risk person and the vehicle, the SOC can compare the distance parameter with the aforementioned distance parameter threshold. If the distance parameter is less than or equal to the threshold, it determines that the at-risk person may have direct contact with the vehicle. The SOC further extracts pose feature information contained in the image features and determines the actual posture information adopted by the at-risk person when contacting the vehicle based on the pose feature information. Then, the SOC reads the aforementioned storage module to obtain multiple preset pose information corresponding to risky actions such as scratching the car or stealing fuel. It compares the actual pose information with each preset pose information to obtain multiple second comparison results. The SOC reads these multiple second comparison results, and if at least one of the second comparison results shows a match between the actual pose information and the preset pose information, it determines that the at-risk person intends to harm the vehicle. The SOC then uses a second warning strategy that simultaneously warns both the driver and the at-risk person as the target warning strategy. The SOC then issues the preset warning according to the second warning strategy. The information is sent to the MCU, which converts the warning information into a CAN signal and transmits it via the vehicle's BUS bus to hardware devices connected to the DHU, such as buzzers and LED headlights, to control these devices to activate. Simultaneously, the CAN signal is also sent via the BUS bus to the TGM, which wirelessly transmits the warning information to the driver's mobile device, alerting the driver to a potential vehicle risk. Similarly, if the SOC detects multiple mismatches between the actual and preset pose information in the second comparison results, it determines that the at-risk individual has no intention of harming the vehicle. In this case, the SOC adopts a third warning strategy, issuing warnings only to the at-risk individual. The SOC then sends the preset warning information to the MCU according to this third warning strategy. The MCU converts the warning information into a CAN signal and transmits it via the vehicle's BUS bus to hardware devices connected to the DHU, such as buzzers and LED headlights, to control these devices to activate.

[0124] It should be noted that the specific content of this second early warning strategy can be consistent with the first early warning strategy mentioned above.

[0125] In this way, when electronic devices detect the possibility of contact between a person at risk and the vehicle, they can further identify the posture of the person at risk when contacting the vehicle. Based on the posture, they can determine whether the person at risk will cause harm to the vehicle, and then adopt different warning strategies based on the judgment result. This avoids the situation where a warning is directly issued to the driver simply when a person at risk is detected around the vehicle. This achieves the technical effect of ensuring that electronic devices can identify whether a person at risk intends to harm the vehicle and issue a warning to the driver when such an intention is detected.

[0126] For example, to help understand the implementation process of the vehicle warning method obtained by combining this embodiment with the above embodiments, please refer to... Figure 4 , Figure 4 This is a simplified flowchart of the vehicle warning method described in this application, specifically:

[0127] In this embodiment, when the electronic device detects that the vehicle has entered the locked state, it first activates its configured TCAM and DHU. The Core Service in the TCAM checks whether the TCAM and DHU can communicate normally. If the Core Service detects that the TCAM and DHU are communicating normally, the electronic device controls the first Wifi Service in the TCAM to transmit multiple first Wifi signals to the vehicle's surrounding environment. The electronic device also controls the first Wifi Service to receive the first Wifi reflection signals generated by each first Wifi signal. The first Wifi Service then generates multiple first initial CSI data based on each first Wifi signal and each first Wifi reflection signal. The TCAM then sends the generated first initial CSI data to the data processing unit in the DHU.

[0128] Simultaneously, the electronic device controls the second Wifi Service within the DHU to transmit multiple second Wifi signals to the vehicle's surrounding environment, and controls the second Wifi Service to receive the second Wifi reflection signals generated by each second Wifi signal. The second Wifi Service then generates multiple second initial CSI data based on each second Wifi signal and each second Wifi reflection signal, and the DHU then sends the generated second CSI data to the data processing unit.

[0129] Subsequently, the data processing unit performs noise reduction processing on the acquired first initial CSI data and second initial CSI data to obtain multiple complete CSI data. At this time, the data processing unit reads the storage module in the electronic device to obtain the preset CSI data corresponding to the Wi-Fi signal when a non-live detection target is detected, and compares each complete CSI data with each preset CSI data to obtain multiple comparison results. Based on the multiple comparison results, the data processing unit determines the target CSI data corresponding to the Wi-Fi signal that detected the live detection target in each complete CSI data.

[0130] Next, the data processing unit inputs the CSI data of each target into a preset convolutional neural network model. The convolutional neural network model extracts the one-dimensional feature vector contained in each target CSI data and fuses the one-dimensional feature vectors to generate a target detection image containing the liveness detection target. The convolutional neural network model then extracts the image features of the target detection image and sends the image features to the SOC in the electronic device. The SOC matches the image features with preset human features. If the SOC determines that the image features and human features match, it identifies the liveness detection target as a risk person in the vehicle's surrounding environment and determines the distance parameter between the risk person and the vehicle based on the image features. If the SOC detects that the distance parameter is less than or equal to a preset distance parameter threshold, it determines that the first warning strategy, which requires issuing warnings to both the driver of the vehicle and the risk person, is the target warning strategy.

[0131] Finally, the electronic device executes the warning operation according to the target warning strategy, controlling the vehicle's buzzer, headlights and other hardware devices to enter the operating state, thereby issuing a warning to the at-risk personnel. At the same time, the electronic device sends the warning information to the driver's mobile terminal according to the target warning strategy, so as to display the prompt information to the driver through the mobile terminal, thereby indicating that there is a risk to the vehicle.

[0132] Furthermore, when the SOC detects that the distance parameter is less than or equal to a preset distance parameter threshold, it can also determine the real-time pose information of the at-risk person based on image features, and determine whether the at-risk person has any intention to harm the vehicle based on the real-time pose information. Then, if the SOC determines that the at-risk person has any intention to harm the vehicle, it determines the preset second warning strategy (which is consistent with the first warning strategy mentioned above) as the target warning strategy and executes the warning operation to control the vehicle to issue a warning to both the at-risk person and the driver at the same time. Similarly, if the SOC determines that the at-risk person does not have any intention to harm the vehicle, it determines the third warning strategy that only issues a warning to the at-risk person as the target warning strategy and executes the warning operation according to the third warning strategy to control the vehicle to only issue a warning to the at-risk person.

[0133] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the vehicle warning method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0134] This application also provides a vehicle warning device, please refer to... Figure 5 The vehicle warning device is applied to an electronic device, the electronic device including a wireless signal transmitting module, the device comprising:

[0135] The signal detection module 10 is used to control the wireless signal transmitting module to transmit multiple wireless detection signals around the vehicle;

[0136] The feature extraction module 20 is used to generate a target liveness image based on multiple wireless detection signals and extract the image features contained in the target liveness image;

[0137] The decision screening module 30 is used to determine the target early warning strategy corresponding to the risky person when the risky person is identified in the target live image based on the image features, and to execute the early warning operation according to the target early warning strategy.

[0138] In one feasible implementation, the decision screening module 30 is further configured to:

[0139] The location information of the at-risk person is determined based on the image features, and a distance parameter is determined based on the location information, wherein the distance parameter is the distance between the at-risk person and the vehicle;

[0140] If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, the preset first warning strategy is determined as the target warning strategy.

[0141] In one feasible implementation, the decision screening module 30 is further configured to:

[0142] If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, the actual pose information of the at-risk person is determined based on the image features.

[0143] If the actual pose information and the preset pose information are determined to match, the preset second early warning strategy is determined as the target early warning strategy.

[0144] If it is determined that the actual pose information and the preset pose information do not match, the preset third early warning strategy is determined as the target early warning strategy.

[0145] In one feasible implementation, the feature extraction module 20 is further configured to:

[0146] The initial channel state information matched by each of the multiple wireless detection signals is detected, and each initial channel state information is processed to obtain complete channel state information.

[0147] The complete channel state information is filtered to determine the target channel state information, and a target liveness image is generated based on the target channel state information, wherein the target channel state information is the channel state information corresponding to the wireless detection signal that detects the liveness detection target.

[0148] In one feasible implementation, the feature extraction module 20 is further configured to:

[0149] Detect the wireless received signal that matches each of the multiple wireless detection signals;

[0150] Determine the signal change ratio between each of the multiple wireless detection signals and the matched wireless received signal;

[0151] Based on the change ratio of each of the signals, the initial channel state information matching each of the multiple wireless detection signals is determined.

[0152] In one feasible implementation, the feature extraction module 20 is further configured to:

[0153] Determine the initial amplitude parameter and initial phase parameter included in each of the initial channel state information;

[0154] Phase expansion is performed on each of the initial amplitude parameters and each of the initial phase parameters, and the phase-expanded initial amplitude parameters and each of the initial phase parameters are filtered and eliminated to obtain each target amplitude parameter and each target phase parameter.

[0155] The target amplitude parameters and their respective matching target phase parameters are fused to obtain complete channel state information.

[0156] In one feasible implementation, the feature extraction module 20 is further configured to:

[0157] Obtain preset standard channel state information, wherein the standard channel state information is the channel state information corresponding to the detection of a wireless detection signal of a non-living detection target;

[0158] Each complete channel state information is compared with the standard channel state information, and the complete channel state information that is inconsistent with the standard channel state information is determined as the target channel state information.

[0159] In one feasible implementation, the feature extraction module 20 is further configured to:

[0160] Extract the one-dimensional vector features contained in each of the target channel state information;

[0161] The one-dimensional vector features are fused to obtain two-dimensional vector features, and a target liveness image is generated based on the two-dimensional vector features.

[0162] The vehicle warning device provided in this application, employing the vehicle warning method in the above embodiments, can solve the problem of low accuracy in identifying at-risk individuals in related technologies. Compared with the prior art, the beneficial effects of the vehicle warning device provided in this application are the same as those of the vehicle warning method provided in the above embodiments, and other technical features in the vehicle warning device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0163] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the vehicle warning method in Embodiment 1 above.

[0164] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device suitable for implementing the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, an electronic device with an internally configured wireless signal transmitting module, or a mobile terminal, data storage control terminal, PC, or other terminal connected to an electronic control unit associated with the electronic device. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0165] like Figure 6As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. While electronic devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0166] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0167] The electronic device provided in this application, employing the vehicle warning method in the above embodiments, can solve the problem of low accuracy in identifying at-risk individuals in related technologies. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the vehicle warning method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0168] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0169] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0170] This application provides a vehicle having the electronic equipment described above, the electronic equipment being used to perform the vehicle warning method in the above embodiments.

[0171] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the vehicle warning method in the above embodiments.

[0172] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0173] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0174] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: control the wireless signal transmitting module to transmit multiple wireless detection signals around the vehicle; generate a target liveness image based on the multiple wireless detection signals and extract image features contained in the target liveness image; and, if a risky person is identified in the target liveness image based on the image features, determine a target early warning strategy corresponding to the risky person and execute an early warning operation according to the target early warning strategy.

[0175] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0177] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0178] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described vehicle warning method, which can solve the problem of low accuracy in identifying at-risk individuals in related technologies. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the vehicle warning method provided in the above embodiments, and will not be repeated here.

[0179] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle warning method described above.

[0180] The computer program product provided in this application can solve the problem of low accuracy in identifying at-risk individuals in related technologies. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle warning method provided in the above embodiments, and will not be repeated here.

[0181] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A vehicle early warning method, characterized in that, The vehicle warning method is applied to an electronic device, the electronic device including a wireless signal transmitting module, and the vehicle warning method includes: Control the wireless signal transmitting module to transmit multiple wireless detection signals around the vehicle; A target liveness image is generated based on multiple wireless detection signals, and image features contained in the target liveness image are extracted; If a person at risk is identified in the target live image based on the image features, a target early warning strategy corresponding to the person at risk is determined, and an early warning operation is performed according to the target early warning strategy. The step of generating a target liveness image based on multiple wireless detection signals includes: The initial channel state information matched by each of the multiple wireless detection signals is detected, and each initial channel state information is processed to obtain complete channel state information. The complete channel state information is filtered to determine the target channel state information, and a target liveness image is generated based on the target channel state information, wherein the target channel state information is the channel state information corresponding to the wireless detection signal that detects the liveness detection target.

2. The vehicle warning method as described in claim 1, characterized in that, The step of determining the target early warning strategy corresponding to the at-risk personnel includes: The location information of the at-risk person is determined based on the image features, and a distance parameter is determined based on the location information, wherein the distance parameter is the distance between the at-risk person and the vehicle; If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, the preset first warning strategy is determined as the target warning strategy.

3. The vehicle warning method as described in claim 2, characterized in that, After the step of determining the distance parameter based on the personnel location information, the method further includes: If the distance parameter is detected to be less than or equal to a preset distance parameter threshold, the actual pose information of the at-risk person is determined based on the image features. If the actual pose information and the preset pose information are determined to match, the preset second early warning strategy is determined as the target early warning strategy. If it is determined that the actual pose information and the preset pose information do not match, the preset third early warning strategy is determined as the target early warning strategy.

4. The vehicle warning method as described in claim 1, characterized in that, The step of detecting the initial channel state information that matches each of the plurality of wireless detection signals includes: Detect the wireless received signal that matches each of the multiple wireless detection signals; Determine the signal change ratio between each of the multiple wireless detection signals and the matched wireless received signal; Based on the change ratio of each of the signals, the initial channel state information matching each of the multiple wireless detection signals is determined.

5. The vehicle warning method as described in claim 1, characterized in that, The step of processing each of the initial channel state information to obtain each complete channel state information includes: Determine the initial amplitude parameter and initial phase parameter included in each of the initial channel state information; Phase expansion is performed on each of the initial amplitude parameters and each of the initial phase parameters, and the phase-expanded initial amplitude parameters and each of the initial phase parameters are filtered and eliminated to obtain each target amplitude parameter and each target phase parameter. The target amplitude parameters and their respective matching target phase parameters are fused to obtain complete channel state information.

6. The vehicle warning method as described in claim 1, characterized in that, The step of filtering each of the complete channel state information to determine the target channel state information includes: Obtain preset standard channel state information, wherein the standard channel state information is the channel state information corresponding to the detection of a wireless detection signal of a non-living detection target; Each complete channel state information is compared with the standard channel state information, and the complete channel state information that is inconsistent with the standard channel state information is determined as the target channel state information.

7. The vehicle warning method as described in claim 1, characterized in that, The step of generating a target liveness image based on the target channel state information includes: Extract the one-dimensional vector features contained in each of the target channel state information; The one-dimensional vector features are fused to obtain two-dimensional vector features, and a target liveness image is generated based on the two-dimensional vector features.

8. An electronic device, characterized in that, The device includes: a wireless signal transmitting module, a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle warning method as described in any one of claims 1 to 7.

9. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the vehicle warning method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the vehicle warning method as described in any one of claims 1 to 7.

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