Living object detection method, passenger detection method, related device, vehicle and medium

Abstract

The invention relates to a living object detection method, a passenger detection method, a related device, a vehicle and a medium. The living object detection method comprises the following steps: controlling a loudspeaker to emit a first sound wave signal in a preset space; controlling the microphone to collect a second sound wave signal in a preset space, wherein the second sound wave signal is generated based on the first sound wave signal; determining channel state information corresponding to the second sound wave signal according to the first sound wave signal and the second sound wave signal; and according to the channel state information corresponding to the second sound wave signal, determining whether there is a living object in the preset space. Therefore, the detection of the living object is realized through the signals transmitted and received by the microphone and the loudspeaker, no additional equipment is needed, and the accurate detection of the living object can be realized on the basis of not increasing the hardware cost. In addition, detection of living things can be achieved only through the microphone and the loudspeaker which are low in cost, and therefore the applicability of the method is high.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle technology, and in particular to a living thing detection method, a passenger detection method, related devices, a vehicle and a medium. Background Art

[0002] With the development of intelligence, the smart cockpit of vehicles is becoming more and more intelligent. In the smart cockpit, identifying whether there are passengers in the car is crucial for intelligent decision-making in the smart cockpit. For example, when identifying the presence of passengers in the car, the smart cockpit can make decisions based on data detected by other perception systems, such as whether to turn on the air conditioning in the smart cockpit, whether to remind the driver to fasten the seat belt, etc.

[0003] At present, the related technologies mostly detect whether there is a passenger in the seat by detecting the pressure value of the pressure sensor installed on the seat. However, this method will detect the object placed on the seat as a passenger, which may lead to wrong decision-making of the smart cockpit. In order to improve the accuracy of passenger detection in the car, it is also necessary to identify whether the object in the car is alive. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides a living thing detection method, a passenger detection method, related devices, a vehicle and a medium.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for detecting living things is provided, the method comprising:

[0006] Controlling the loudspeaker to emit a first sound wave signal in a preset space;

[0007] Controlling the microphone to collect a second sound wave signal in the preset space, where the second sound wave signal is generated based on the first sound wave signal;

[0008] Determine, according to the first sound wave signal and the second sound wave signal, channel state information corresponding to the second sound wave signal;

[0009] Determine whether there is a living thing in the preset space according to the channel state information corresponding to the second sound wave signal.

[0010] According to a second aspect of an embodiment of the present disclosure, a passenger detection method is provided, the method comprising:

[0011] Determine whether the seats in the cabin are occupied;

[0012] When the seat is in the occupied state and the living thing detection result in the vehicle cabin indicates that a living thing exists, determining that a passenger exists in the vehicle cabin;

[0013] Among them, the living thing detection result in the cabin is obtained according to the living thing detection method as described in the first aspect of the present disclosure.

[0014] According to a third aspect of an embodiment of the present disclosure, a living thing detection device is provided, the living thing detection device comprising:

[0015] A first control module is configured to control the speaker to emit a first sound wave signal in a preset space;

[0016] A second control module is configured to control the microphone to collect a second sound wave signal in the preset space, where the second sound wave signal is generated based on the first sound wave signal;

[0017] A first determining module is configured to determine channel state information corresponding to the second sound wave signal according to the first sound wave signal and the second sound wave signal;

[0018] The second determination module is configured to determine whether there is a living thing in the preset space according to the channel state information corresponding to the second sound wave signal.

[0019] According to a fourth aspect of an embodiment of the present disclosure, a passenger detection device is provided, the device comprising:

[0020] A third determination module is configured to determine whether a seat in the vehicle cabin is in an occupied state;

[0021] A fourth determination module is configured to determine that there is a passenger in the cabin when the seat is in the occupied state and the living thing detection result in the cabin is that there is a living thing;

[0022] Among them, the living thing detection result in the cabin is obtained according to the living thing detection method as described in the first aspect of the present disclosure.

[0023] According to a fifth aspect of an embodiment of the present disclosure, a vehicle is provided, comprising:

[0024] A processor, a speaker, and a microphone, wherein the processor is connected to the speaker and the microphone respectively;

[0025] a memory for storing processor-executable instructions;

[0026] Wherein, the processor is configured to execute the living thing detection method as described in the first aspect of the present disclosure, or to execute the passenger detection method as described in the second aspect of the present disclosure.

[0027] According to a sixth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the living thing detection method provided in the first aspect of the present disclosure are implemented, or the steps of the passenger detection method provided in the second aspect of the present disclosure are implemented.

[0028] By adopting the above technical solution, the speaker is controlled to emit a first sound wave signal in a preset space, and the microphone is controlled to collect signals to obtain a second sound wave signal. Then, according to the first sound wave signal and the second sound wave signal, the channel state information corresponding to the second sound wave signal is determined. Finally, according to the channel state information, it is determined whether there is a living thing in the preset space. In this way, the detection of living things is achieved through the sound wave signals received and sent by the microphone and the speaker, without the need for additional equipment, and accurate detection of living things can be achieved without increasing hardware costs. In addition, only low-cost microphones and speakers are needed to detect living things, which makes the method more applicable.

[0029] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0031] Figure 1 The figure is a flow chart of a living thing detection method according to an exemplary embodiment.

[0032] Figure 2 The invention is a flowchart of a method for determining a target dynamic or static state of an object in a preset space according to an exemplary embodiment.

[0033] Figure 3 According to an exemplary embodiment Figure 2 Schematic diagram of the process of step S22 in FIG.

[0034] Figure 4 The figure is a flow chart of a living thing detection method according to an exemplary embodiment.

[0035] Figure 5 The figure is a flow chart of a passenger detection method according to an exemplary embodiment.

[0036] Figure 6 The figure is a block diagram of a living thing detection device according to an exemplary embodiment.

[0037] Figure 7 It is a block diagram of a passenger detection device according to an exemplary embodiment.

[0038] Figure 8 is a block diagram of a vehicle according to an exemplary embodiment.

[0039] Fig. 9 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0040] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0041] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device.

[0042] Common living thing detection technologies currently include: 1. Determine whether there is a living thing by extracting image change information from the video stream collected by the camera. However, this method has serious privacy issues and is affected by light, resulting in the extracted image change information not being able to accurately reflect whether the image change is caused by the movement of the object. 2. Determine whether there is a living thing by using a thermal sensor to sense whether there is a changing heat source. However, this method can only determine whether there is a target with a heat source, and the thermal sensor needs to be installed in an external manner, which will affect the beauty of the vehicle shape. 3. Use the reflection of electromagnetic waves to sense whether there is a living thing. However, it is necessary to install millimeter wave radar or UWB radar additionally, and the hardware cost is relatively high. 4. Detect whether there is a living thing by the fluctuation change of WiFi signals. However, since the extraction of channel state information currently used for perception will affect the communication capability of WiFi, the use of this technology is limited.

[0043] In view of this, the present disclosure provides a living thing detection method, a passenger detection method, related devices, a vehicle and a medium, which realize the detection of living things through the sound wave signals received and sent by a microphone and a speaker, without the need for additional equipment, and can realize accurate detection of living things without increasing hardware costs. In addition, the method has strong applicability.

[0044] Figure 1 FIG. 1 is a flow chart of a method for detecting living things according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0045] In step S11, the speaker is controlled to emit a first sound wave signal in a preset space.

[0046] In step S12, the microphone is controlled to collect a second sound wave signal in a preset space, where the second sound wave signal is generated based on the first sound wave signal.

[0047] In the present disclosure, the preset space may be any space, for example, the preset space may be a room equipped with a speaker and a microphone. For another example, the preset space may be a vehicle cabin equipped with a speaker and a microphone. In the case where the preset space is a vehicle cabin, the solution provided by the present disclosure can detect living things in the vehicle cabin. In this way, when the owner leaves the vehicle and the windows are closed, the living thing detection method provided by the present disclosure can detect whether there are passengers left in the closed vehicle cabin, thereby ensuring the safety of the passengers.

[0048] The second sound wave signal may be a signal generated after a preset space performs at least one of reflection, diffraction, and scattering operations on the first sound wave signal.

[0049] It should be understood that the number of the speaker and the microphone may be one or more, and the number of the speaker and the microphone may be the same or different, and the present disclosure does not specifically limit this.

[0050] In step S13, channel state information corresponding to the second sound wave signal is determined according to the first sound wave signal and the second sound wave signal.

[0051] In step S14, it is determined whether there is a living thing in the preset space according to the channel state information corresponding to the second sound wave signal.

[0052] The channel state information can reflect the changes in the channel for transmitting the sound wave signal. If there is a living thing, the movement of the living thing will cause the channel to change, that is, cause the change in the channel state information. Therefore, in the present disclosure, it can be determined whether there is a living thing in the preset space based on the channel state information corresponding to the second sound wave signal.

[0053] By adopting the above technical solution, the speaker is controlled to emit a first sound wave signal in a preset space, and the microphone is controlled to collect signals to obtain a second sound wave signal. Then, according to the first sound wave signal and the second sound wave signal, the channel state information corresponding to the second sound wave signal is determined. Finally, according to the channel state information, it is determined whether there is a living thing in the preset space. In this way, the detection of living things is achieved through the sound wave signals received and sent by the microphone and the speaker, without the need for additional equipment, and accurate detection of living things can be achieved without increasing hardware costs. In addition, only low-cost microphones and speakers are needed to detect living things, which makes the method more applicable.

[0054] In order to facilitate those skilled in the art to better understand the living thing detection method provided by the present disclosure, the living thing detection method is described below with a complete embodiment.

[0055] In the present disclosure, the first sound wave signal emitted by the speaker is a specific sound wave signal. In one embodiment, the first sound wave signal can be generated by:

[0056] First, a plurality of frames of first baseband signals whose autocorrelation values ​​are greater than a second preset threshold are generated.

[0057] In the present disclosure, the multiple frames of first baseband signals may be the same baseband signal.

[0058] For example, considering that the autocorrelation of the ZC (Zadoff-Chu) sequence is good, the first baseband signal can be a ZC sequence. The ZC sequence is usually represented by a complex number. Therefore, in this example, the first baseband signal is a set of complex signal values. Preferably, the length of the first baseband signal can be 85.

[0059] It should be understood that, in practical applications, the first baseband signal may also be other sequences with good autocorrelation, and the present disclosure does not make any specific limitation to this.

[0060] Afterwards, the multiple frames of first baseband signals are modulated into ultrasonic frequency band signals, and the ultrasonic frequency band signals are determined as first sound wave signals.

[0061] It should be understood that the first baseband signal is a low-frequency signal. In order to avoid interference to the user caused by the signal emitted by the speaker heard by the human ear, in the present disclosure, the low-frequency first baseband signal can be modulated into an ultrasonic frequency band signal, and the ultrasonic frequency band signal can be determined as the first sound wave signal.

[0062] For example, multiple first baseband signals can be modulated into an ultrasonic frequency range of 18kHz to 20kHz, and the human ear can hardly hear the sound waves in this frequency range emitted by the speaker. In addition, for a sound wave with a sampling rate of 48kHz, the length of the modulated signal will change from the original 85 to 2048, that is, the length of a frame of modulated data is 2048.

[0063] It should be understood that if the first baseband signal is a set of complex signal values, the modulated ultrasonic frequency band signal is also a set of complex signal values, that is, the first ultrasonic signal is a set of complex signal values. When playing, the speaker only plays the real part of the complex signal value, and accordingly, the microphone also receives a set of real part values.

[0064] Considering that the movement of the object is a continuous action, in order to improve the accuracy of the determined dynamic and static state of the object, it is necessary to determine the dynamic and static state of the object based on the channel state information within a period of time, that is, it is necessary to determine the dynamic and static state of the object based on the channel state information corresponding to multiple frames of data. Therefore, in one embodiment, the first sound wave signal and the second sound wave signal both include multiple frames of data, that is, the channel state information corresponding to the second sound wave signal includes the channel state information corresponding to the multiple frames of data.

[0065] In the present disclosure, each frame of data included in the first sound wave signal is generated based on the first baseband signal corresponding to each frame, that is, the first baseband signal is adjusted to generate a frame of data. Among them, multiple frames of data can be generated based on the same first baseband signal, that is, the first baseband signal corresponding to each frame of data is the same.

[0066] In one embodiment of the present disclosure, the specific implementation method of step S13 determining the channel state information corresponding to the second sound wave signal according to the first sound wave signal and the second sound wave signal is as follows: first, according to the cross-correlation between the first sound wave signal and the second sound wave signal, multiple frames of data included in the second sound wave signal are identified. Finally, for each frame of data, the frame data is demodulated to obtain a second baseband signal corresponding to the frame data, and the channel state information corresponding to the frame data is determined according to the second baseband signal and the first baseband signal corresponding to the frame data.

[0067] It should be understood that the second sound wave signal received by the microphone includes multiple frames of data, but it is impossible to determine which of the received data belongs to one frame of data. Therefore, in the present disclosure, it is necessary to identify the multiple frames of data included in the second sound wave signal based on the cross-correlation between the first sound wave signal and the second sound wave signal. That is, multiple frames of complete data are divided from the received data sequence. Afterwards, for each frame of data, the frame data is demodulated to obtain the second baseband signal corresponding to the frame data, and finally, the channel state information corresponding to the frame data is determined based on the second baseband signal and the first baseband signal corresponding to the frame data.

[0068] Optionally, determining the channel state information corresponding to the frame data according to the second baseband signal and the first baseband signal corresponding to the frame data may include:

[0069] Determining a frequency response of a baseband signal according to the second baseband signal and a first baseband signal corresponding to the frame data;

[0070] The frequency response of the baseband signal is determined as the channel state information corresponding to the frame data.

[0071] For example, the second baseband signal and the first baseband signal corresponding to the frame data are converted from the time domain to the frequency domain, and then the second baseband signal in the frequency domain is divided by the first baseband signal in the frequency domain to obtain the frequency response of the baseband signal.

[0072] In one possible manner, the frequency response of the baseband signal can be used to characterize the channel state information, that is, the quotient of the frequency domain of the second baseband signal corresponding to the frame data and the frequency domain of the first baseband signal corresponding to the frame data is determined as the channel state information corresponding to the frame data.

[0073] In another possible manner, considering that the channel impulse response can characterize the length of the channel, in this manner, the channel impulse response can also be used to characterize the channel state information, that is, the channel impulse response is determined according to the frequency response of the baseband signal; and the channel impulse response is determined as the channel state information corresponding to the frame data. For example, the frequency response of the baseband signal can be converted into the channel impulse response by inverse Fourier transform.

[0074] It should be understood that, in this manner, the channel impulse response is determined as the channel state information corresponding to the frame data, so that the length of the channel state information corresponding to the frame data is the same as the length or sampling point of the frame data.

[0075] In this disclosure, Figure 1 The specific implementation method of step S14 is: determine the target dynamic and static state of the object in the preset space according to the channel state information corresponding to the multi-frame data; if the target dynamic and static state of the object is a moving state, it is determined that there is a living thing in the preset space; if the target dynamic and static state of the object is a static state, it is determined that there is no living thing in the preset space.

[0076] It should be understood that if the object in the preset space is in motion, the position of the object may be different at different times, which will affect the data transmission channel, that is, the channel state information will change. Therefore, in this embodiment, the target dynamic and static state of the object in the preset space can be determined based on the channel state information corresponding to the transmitted multi-frame data.

[0077] In one embodiment, the speaker sends out multiple frames of data sequentially, that is, the speaker sends out the multiple frames of data sequentially at different times. Figure 2 FIG. 1 is a flow chart showing a method for determining the target dynamic and static state of an object in a preset space according to an exemplary embodiment. Figure 2 As shown, the method for determining the target dynamic and static state of an object in a preset space may include the following steps.

[0078] In step S21, based on the channel state information corresponding to each frame of data in the multiple frames of data, the change amount of the channel state information corresponding to each frame of data is determined.

[0079] For example, assume that the multiple frames of data are N frames of data, and the i-th frame of data is transmitted at time ti, the value range of i is [1, N], each frame of data includes M sampling points, each sampling point is a data, that is, each frame of data includes M data, preferably, M=85.

[0080] As shown in the following matrix, c1, c2...cM are sampling points, that is, the data included in each frame of data. Each element in the matrix represents the channel state information corresponding to each data transmitted. For example, s{t1,c1} represents the channel state information corresponding to the first data of the first frame of data, and s{tN,cM} represents the channel state information corresponding to the Mth data of the Nth frame of data. In the following matrix, the i-th column represents the channel state information corresponding to the i-th frame of data, and the value range of i is [1,N].

[0081]

[0082] In this embodiment, determining the change amount of the channel state information corresponding to each frame of data refers to determining the change amount of each column element in the above matrix.

[0083] In one manner of this embodiment, the change amount of the channel state information corresponding to each frame of data may be the difference between the maximum channel state information and the minimum channel state information in each column.

[0084] In another way of this embodiment, the amount of change in the channel state information corresponding to each frame of data may also be the variance of the channel state information in each column. That is, for each column in the above matrix, the variance of the channel state information of the column is calculated, and the variance is determined as the amount of change in the channel state information corresponding to the frame data of the column. For example, the variance of the channel state information of the first column is determined as the amount of change in the channel state information corresponding to the first frame of data, the variance of the channel state information of the Nth column is determined as the amount of change in the channel state information corresponding to the Nth frame of data, and so on.

[0085] In another method of this embodiment, each frame of data includes M data, and the M data are transmitted on M channels respectively, M is an integer greater than 1, and the specific method for determining the change amount of the channel state information corresponding to each frame of data is: for each channel, according to the channel state information corresponding to each frame of data on the channel and a preset step size, determine the difference in channel state information corresponding to different frame data on the channel to obtain a deviation value of the channel state information corresponding to each frame of data on each channel; for each frame of data, according to the deviation value of the channel state information corresponding to the frame of data on each channel, determine the variance of the deviation value, and determine the variance as the change amount of the channel state information corresponding to the frame of data.

[0086] For example, continuing to take the above matrix as an example, in the above matrix, each row can represent a channel. First, for each row of the above matrix, the difference of the channel state information corresponding to different frame data on the channel is determined according to a preset step size to obtain the deviation value of the channel state information corresponding to each frame data on each channel.

[0087] For example, if the preset step size is 2, the difference between s{t3,c1} and s{t1,c1} is determined as the deviation value of the channel state information corresponding to the first frame data on the first channel, the difference between s{t4,c1} and s{t2,c1} is determined as the deviation value of the channel state information corresponding to the second frame data on the first channel, ..., the difference between s{tN,c1} and s{t(N-2),c1} is determined as the deviation value of the channel state information corresponding to the N-2 frame data on the first channel, the difference between s{t1,c1} and s{t(N-1),c1} is determined as the deviation value of the channel state information corresponding to the N-1 frame data on the first channel, and the difference between s{t2,c1} and s{tN,c1} is determined as the deviation value of the channel state information corresponding to the N-1 frame data on the first channel. It should be understood that the difference described here refers to the amplitude difference of two complex numbers.

[0088] Finally, for each frame of data, the variance of the deviation value of the channel state information corresponding to the frame of data on each channel is calculated to obtain the amount of change of the channel state information corresponding to the frame of data. For example, after calculating the deviation value of the channel state information corresponding to each frame of data on each channel, it is represented in a matrix form, and the deviation value of the channel state information corresponding to each frame of data on each channel is located in a column, so that the deviation value matrix of the channel state information is obtained, and thus, the variance of each column is calculated to obtain the amount of change of the channel state information corresponding to the frame data corresponding to the column.

[0089] In step S22, for each frame of data, the first target dynamic and static state of the object when the frame of data is transmitted is determined according to the change amount of the channel state information corresponding to the frame of data.

[0090] First, it should be understood that when there are multiple microphones, the positions of the multiple microphones are different, so that the channels for transmitting the frame data received by each microphone are also different, and accordingly, the channel state information corresponding to the multiple frames of data received by each microphone is also different. Therefore, when there are multiple microphones, step S13 determines the channel state information corresponding to the second sound wave signal according to the first sound wave signal and the second sound wave signal, which may include: for each microphone, determining the channel state information corresponding to the multiple frames of data received by the microphone according to the first sound wave signal and the second sound wave signal collected by the microphone.

[0091] Secondly, it should be understood that due to the different positions of the microphones, the attenuation of the data in the second sound wave signal received by different microphones is different, that is, the channel state information corresponding to the multi-frame data received by different microphones is different. Therefore, in the case of multiple microphones, the target dynamic and static state of the object in the preset space is determined according to the channel state information corresponding to the multi-frame data received by the multiple microphones. Among them, the channel state information corresponding to the multi-frame data received by each microphone can be represented in the form of the above matrix.

[0092] Accordingly, Figure 3 According to an exemplary embodiment Figure 2 Schematic diagram of the process of step S22 in FIG. Figure 2 As shown, step S22 may further include step S221 and step S222 for each frame of data, determining the first target dynamic and static state of the object when transmitting the frame of data according to the change amount of the channel state information corresponding to the frame of data.

[0093] In step S221, for each microphone, according to the change amount of the channel state information corresponding to the multiple frames of data received by the microphone, the second target dynamic and static state of the object when transmitting each frame of data received by the microphone is determined.

[0094] For example, for each microphone, it is determined that the second target dynamic and static state of the object is a moving state when the change in the transmission channel state information is greater than the first preset threshold frame data, and it is determined that the second target dynamic and static state of the object is a stationary state when the change in the transmission channel state information is not greater than the first preset threshold frame data.

[0095] As described above, the channel state information corresponding to the multiple frames of data received by each microphone can be represented in the form of the above-mentioned matrix. That is, for each microphone, the variance of each column in the deviation value matrix can be further calculated according to the method of determining the change in the channel state information described above, and the change in the channel state information corresponding to the frame data corresponding to each column can be obtained.

[0096] It should be understood that the volatility of the channel state information corresponding to the frame data corresponding to each column can be determined based on the variance of each column. If the variance is large, it indicates that the volatility of the channel state information corresponding to the frame data is large, that is, it is determined that the second target dynamic state of the object is in motion when the frame data is transmitted. If the variance is small, it indicates that the volatility of the channel state information corresponding to the frame data is small, that is, it is determined that the second target dynamic state of the object is in a stationary state when the frame data is transmitted.

[0097] For example, assuming that the microphones include microphone 1, microphone 2, microphone 3, microphone 4 and microphone 5, and the multi-frame data includes five frames of data, and the five frames of data are transmitted in sequence at time t1, time t2, time t3, time t4 and time t5, respectively. The second target dynamic and static state of the object when each frame of data received by each microphone is transmitted is shown in Table 1. In Table 1, the second to sixth rows respectively represent the second target dynamic and static state of the object when each frame of data received by each microphone is transmitted. Among them, "dynamic" in Table 1 represents that the second target dynamic and static state is a moving state, and "static" represents that the second target dynamic and static state is a static state.

[0098] Table 1

[0099] t1 t2 t3 t4 t5 Microphone 1 move move move move move Microphone 2 move quiet quiet quiet quiet Microphone 3 move quiet move quiet move Microphone 4 quiet quiet quiet quiet move Microphone 5 quiet move move move quiet

[0100] In step S222, for each frame of data, the first target dynamic and static state of the object when transmitting the frame of data is determined according to the second target dynamic and static state of the object when transmitting the frame of data received by each microphone.

[0101] For example, taking the above Table 1 as an example, the second to sixth columns in Table 1 represent the first frame data, the second frame data, the third frame data, the fourth frame data, and the fifth frame data, respectively. For example, for the second frame data, according to the second target dynamic and static state of the object when transmitting the second frame data received by each microphone shown in the third column, the first target dynamic and static state of the object when transmitting the second frame data is determined.

[0102] In the present disclosure, a voting method can be used to determine the first target motion state of an object when transmitting the frame data. Accordingly, an implementation of step S222 is: if the number of second target motion states that are in motion is greater than the number of second target motion states that are in stillness, then the third target motion state of the object when transmitting the frame data is determined to be in motion, and, if the number of second target motion states that are in motion is not greater than the number of second target motion states that are in stillness, then the third target motion state of the object when transmitting the frame data is determined to be in stillness; based on the third target motion state of the object when transmitting the frame data, the first target motion state of the object when transmitting the frame data is determined.

[0103] Continuing with Table 1 as an example, for the first frame of data, the number of second target dynamic and static states that are in motion is greater than the number of second target dynamic and static states that are in static state, then it is determined that the third target dynamic and static state of the object is in motion when the first frame of data is transmitted. Similarly, it is determined that the third target dynamic and static state of the object is in static state when the second frame of data is transmitted, it is determined that the third target dynamic and static state of the object is in motion when the third frame of data is transmitted, it is determined that the third target dynamic and static state of the object is in static state when the fourth frame of data is transmitted, and it is determined that the third target dynamic and static state of the object is in motion when the fifth frame of data is transmitted. Among them, the third target dynamic and static state of the object when the above five frames of data are transmitted is as shown in Table 2.

[0104] Table 2

[0105] t1 t2 t3 t4 t5 The third target dynamic and static state move quiet move quiet move

[0106] Another implementation of step S222 is: if the number of second target dynamic and static states that are motion states is greater than a preset number, then it is determined that the third target dynamic and static state of the object is a motion state when transmitting the frame data; otherwise, it is determined that the third target dynamic and static state of the object is a static state when transmitting the frame data; based on the third target dynamic and static state of the object when transmitting the frame data, the first target dynamic and static state of the object when transmitting the frame data is determined.

[0107] In this embodiment, a preset number is set in advance. For each frame of data, as long as the number of motion states is greater than the preset number, it can be determined that the third target dynamic state of the object when transmitting the frame of data is a motion state, without determining the size relationship between the number of motion states and the number of static states.

[0108] In one possible manner, the specific implementation method of determining the first target dynamic and static state of the object when transmitting the frame data based on the third target dynamic and static state of the object when transmitting the frame data is as follows: the third target dynamic and static state of the object when transmitting the frame data is determined as the first target dynamic and static state of the object when transmitting the frame data.

[0109] In a possible manner, considering the detection error, the third target motion state determined above can be subjected to state filtering processing, and then the first target motion state can be determined based on the third target motion state after the state filtering processing. That is, the specific implementation method of determining the first target motion state of the object when transmitting the frame data based on the third target motion state of the object when transmitting the frame data is as follows: the third target motion state of the object when transmitting the frame data is subjected to state filtering processing, and the third target motion state after the state filtering processing is determined as the first target motion state of the object when transmitting the frame data.

[0110] In this method, the state filtering processing is specifically that when the third target dynamic and static state of the object when transmitting the frame data is inconsistent with the third target dynamic and static state of the two adjacent frame data before and after the transmission of the frame data, the third target dynamic and static state of the object when transmitting the frame data is updated with the third target dynamic and static state of the two adjacent frame data before and after the transmission of the frame data.

[0111] For example, taking Table 2 as an example, for the first frame data, the third target dynamic and static state of the object when transmitting the first frame data is different from the third target dynamic and static state of the object when transmitting the second frame data, but it is the same as the third target dynamic and static state of the object when transmitting the fifth frame data, that is, it is not updated. For the second frame data, since the third target dynamic and static state of the object when transmitting the second frame data is different from the corresponding third target dynamic and static state when transmitting the first frame data and the second frame data, it is necessary to update the third target dynamic and static state of the object when transmitting the second frame data to the motion state. Similarly, for the third frame data, since the third target dynamic and static state of the object when transmitting the second frame data is updated to the motion state, the third target dynamic and static state of the object when transmitting the third frame data is the same as the third target dynamic and static state of the object when transmitting the second frame data, and then it is not updated. For the fourth frame data, the third target dynamic and static state of the object when transmitting the third frame data is updated to the motion state. It is not updated for the fifth frame data either. In this way, the third target dynamic and static state after state filtering can be obtained, and it is determined as the first target dynamic and static state of the object when transmitting the frame data.

[0112] In another embodiment, determining the target dynamic and static state of an object in a preset space based on channel state information corresponding to multiple frames of data may also include: performing mean filtering on the change in the channel state information corresponding to each frame of data to obtain the processed change; accordingly, the above-mentioned determining, for each frame of data, the first target dynamic and static state of the object when transmitting the frame of data based on the change in the channel state information corresponding to the frame data may include: determining, for each frame of data, the first target dynamic and static state of the object when transmitting the frame of data based on the processed change in the channel state information corresponding to the frame data.

[0113] In this embodiment, after calculating the change amount of the channel state information corresponding to each frame of data, the obtained change amount is subjected to mean filtering. For example, the mean filtering process is performed with a sliding window, for example, the sliding window size is 5, then the mean of the change amount of the channel state information corresponding to the first frame of data to the second frame of data is calculated, and the mean is determined as the processed change amount of the channel state information corresponding to the first frame of data, similarly, the mean of the change amount of the channel state information corresponding to the second frame of data to the sixth frame of data is calculated, and the mean is determined as the processed change amount of the channel state information corresponding to the second frame of data, and so on, respectively, the processed change amount of the channel state information corresponding to each frame of data is calculated, and then for each frame of data, according to the processed change amount of the channel state information corresponding to the frame of data, the first target dynamic and static state of the object when the frame of data is transmitted is determined.

[0114] It should be noted that, in this embodiment, the specific implementation method of determining the first target dynamic and static state of the object when transmitting the frame data based on the processed change in the channel state information corresponding to the frame data is similar to the method described in the above step S22, and the present disclosure will not go into details about this.

[0115] return Figure 2 , Figure 2 As shown, the method for determining the target dynamic and static state of an object in a preset space may include step S23.

[0116] In step S23, the target dynamic and static state of the object in the preset space is determined according to the first target dynamic and static state of the object when each frame of data is transmitted.

[0117] In one embodiment, the target dynamic and static state of the object in the space can be determined based on all frame data included in the sound wave signal, that is, the target dynamic and static state of the object in the space can be determined based on the first target dynamic and static state of the object when each frame data in all frame data is transmitted.

[0118] For example, based on the first target dynamic and static state of the object when transmitting each frame of all frame data, if the number of motion states is greater than the number of static states, the target dynamic and static state of the object in the space is determined to be a motion state; otherwise, the target dynamic and static state of the object in the space is determined to be a static state.

[0119] Considering that in actual applications, the number of frames of data included in the sound wave signal is relatively large, if the target dynamic and static state of the object in the space is determined based on all the frame data included in the sound wave signal, the amount of calculation is relatively large. Therefore, in another embodiment, the target dynamic and static state of the object in the space can be determined based on part of the frame data in each cycle of living thing detection. The specific implementation of step S23 can be: determining a first preset number of first target frame data from multiple frames of data; determining the target dynamic and static state of the object in the preset space based on the first target dynamic and static state of the object when transmitting each first target frame data.

[0120] For example, a sliding window may be used to select a first preset number of first target frame data from all frame data, wherein the height of the sliding window is M, and the width is the value of the first preset number. For example, assuming that the first preset number is 30, then in the first cycle of live object detection, the first target motion state of the object when transmitting the first frame data to the thirtieth frame data may be used to determine the target motion state of the object in the preset space, and in the second cycle of live object detection, the first target motion state of the object when transmitting the second frame data to the thirty-first frame data may be used to determine the target motion state of the object in the preset space, and so on.

[0121] In this example, according to the first target dynamic and static state of the object when transmitting each first target frame data, if the number of motion states is greater than the number of static states, the target dynamic and static state of the object in the space is determined to be a motion state; otherwise, the target dynamic and static state of the object in the space is determined to be a static state.

[0122] In this way, the detection of living things can be achieved through the signals received and sent by the microphone and the speaker, without the need for additional equipment, and accurate detection of living things can be achieved without increasing hardware costs.

[0123] In the present disclosure, after the target dynamic and static state of the object in the preset space is determined, the target dynamic and static state of the object in the preset space may also be displayed and / or reported.

[0124] For example, the target dynamic and static state of the determined object in the preset space is displayed to achieve visualization. For another example, the target dynamic and static state of the determined object in the preset space is reported to the application layer so that the application layer combines the data detected by other sensors with the target dynamic and static state of the determined object in the preset space to perform corresponding actions.

[0125] The following is a complete example of a method for detecting living things provided by the present disclosure. Figure 4 FIG. 1 is a flow chart of a method for detecting living things according to an exemplary embodiment. Figure 4 As shown, the living thing detection method may include the following steps.

[0126] In step S41, a plurality of frames of first baseband signals having autocorrelation values ​​greater than a second preset threshold are generated.

[0127] For example, the first baseband signal may be a ZC sequence. Preferably, the length of the ZC sequence is 85, and the sequence is a set of complex signal values.

[0128] In step S42, a plurality of frames of first baseband signals are modulated into ultrasonic frequency band signals, and the ultrasonic frequency band signals are determined as first sound wave signals.

[0129] The multiple frames of the first baseband signal are modulated to the ultrasonic frequency band to obtain ultrasonic frequency band signals. For example, the multiple frames of the first baseband signal can be modulated to the 18kHz to 20kHz ultrasonic frequency band, so that the human ear cannot hear the first sound wave signal, thereby avoiding interference to the user.

[0130] In step S43, at least one speaker is controlled to emit a first sound wave signal in a preset space.

[0131] In step S44, at least one microphone is controlled to collect a second sound wave signal in a preset space, where the second sound wave signal is generated based on the first sound wave signal.

[0132] When it is necessary to detect living things in a preset space, at least one speaker and at least one microphone may be pre-arranged in the preset space.

[0133] In step S45, multiple frames of data included in the second sound wave signal are identified based on the cross-correlation between the first sound wave signal and the second sound wave signal, so that complete frame data can be obtained.

[0134] In step S46, for each frame of data received by each microphone, the frame of data received by the microphone is demodulated to obtain a second baseband signal corresponding to the frame of data.

[0135] In step S47, a frequency response of a baseband signal is determined according to the second baseband signal and the first baseband signal corresponding to the frame data.

[0136] In step S48, a channel impulse response is determined according to the frequency response of the baseband signal, and the channel impulse response is determined as the channel state information corresponding to the frame data.

[0137] In this way, channel state information corresponding to multiple frames of data received by each microphone can be obtained.

[0138] In step S49, for each channel, the difference in channel state information corresponding to different frame data on the channel is determined based on the channel state information corresponding to each frame data on the channel and a preset step size to obtain a deviation value of the channel state information corresponding to each frame data on each channel.

[0139] Each frame of data includes M data, and the M data are transmitted on M channels respectively, where M is an integer greater than 1.

[0140] In step S410, for each frame of data, the variance of the deviation value is determined according to the deviation value of the channel state information corresponding to the frame of data on each channel, and the variance is determined as the variation of the channel state information corresponding to the frame of data.

[0141] In step S411, mean filtering is performed on the variation of the channel state information corresponding to each frame of data to obtain a processed variation.

[0142] In step S412, for each microphone, a second target dynamic and static state of the object when transmitting each frame of data received by the microphone is determined according to the processed change amount of the channel state information corresponding to the multiple frames of data received by the microphone.

[0143] For example, it is determined that when the change in the transmission channel state information is greater than the frame data of the first preset threshold, the second target dynamic and static state of the object is a moving state, and it is determined that when the change in the transmission channel state information is not greater than the frame data of the first preset threshold, the second target dynamic and static state of the object is a stationary state.

[0144] In step S413, for each frame of data, the third target dynamic and static state of the object when transmitting the frame of data is determined according to the second target dynamic and static state of the object when transmitting the frame of data received by each microphone.

[0145] For example, if the number of second target motion states in motion is greater than the number of second target motion states in stillness, then it is determined that the third target motion state of the object is a motion state when the frame data is transmitted; and, if the number of second target motion states in motion is not greater than the number of second target motion states in stillness, then it is determined that the third target motion state of the object is a still state when the frame data is transmitted.

[0146] In step S414, the first target motion state of the object when transmitting the frame data is determined according to the third target motion state of the object when transmitting the frame data.

[0147] In step S415, a first preset number of first target frame data is determined from the multiple frame data using a sliding window.

[0148] In step S416, the target dynamic and static states of the objects in the preset space are determined according to the first target dynamic and static states of the objects when each first target frame data is transmitted.

[0149] In step S417, it is determined whether there is a living thing in the preset space according to the target dynamic and static state of the object in the preset space.

[0150] In step S418, the target dynamic and static status of the objects in the preset space is displayed and / or reported.

[0151] Regarding the various steps in the above embodiments, the specific methods of each step are already described in Figures 1 to 3 The invention has been described in detail in the embodiments of the present invention and will not be described in detail here.

[0152] Based on the same inventive concept, the present disclosure also provides a passenger detection method. Figure 5 FIG. 1 is a flow chart of a passenger detection method according to an exemplary embodiment. Figure 5 As shown, the passenger detection method may include the following steps.

[0153] In step S51, it is determined whether the seats in the vehicle cabin are occupied.

[0154] In step S52, when the seat is in an occupied state and the living thing detection result in the vehicle cabin is that a living thing exists, it is determined that there is a passenger in the vehicle cabin.

[0155] Among them, according to the living thing detection method provided by the present disclosure, the living thing detection result in the vehicle cabin is obtained.

[0156] In the present disclosure, a pressure sensor disposed on the seat can be used to detect whether the seat in the vehicle cabin is in an occupied state. For example, a pressure signal of the seat in the vehicle cabin is obtained; if the pressure value represented by the pressure signal is within a preset range, it is determined that the seat is in an occupied state. The preset range is determined according to the weight range of the passenger.

[0157] It should be understood that if the presence of a passenger on the seat is detected only by the pressure value detected by the pressure sensor arranged on the seat, the object placed on the seat will be mistakenly detected as a passenger, which will lead to the smart cockpit outputting an erroneous decision. Therefore, in the present disclosure, in order to improve the accuracy of passenger detection, when the seat is determined to be occupied according to the pressure signal detected by the pressure sensor, it is further determined whether the object placed on the seat is a living thing according to the living thing detection method provided by the present disclosure. If it is a living thing, it is determined that the object on the seat is a passenger, that is, it is determined that there is a passenger in the cabin, otherwise it is determined that there is no passenger in the cabin.

[0158] By adopting the above technical solution, when the seat is occupied, the presence of passengers in the cabin can be determined in combination with the results of living thing detection in the cabin, thereby effectively improving the efficiency of passenger detection in the cabin and enhancing the user experience.

[0159] In addition, after determining that there is a passenger in the cabin, the passenger detection method may also include at least one of the following steps: outputting prompt information; controlling the vehicle in which the cabin is located to power on; and starting the air conditioner in the cabin.

[0160] For example, after determining that there is a passenger in the cabin, a prompt message is output, wherein the prompt message may be a prompt message to the driver that there is a passenger left in the cabin, or a message to the passenger to fasten the seat belt, etc. This disclosure does not specifically limit this.

[0161] For another example, after determining that there are passengers in the cabin, the vehicle in which the cabin is located is controlled to be powered on. In this way, after the vehicle is powered on, the passenger can use the power on the vehicle to meet their needs, for example, turn on the air conditioner in the car, charge a mobile phone, or start the vehicle, etc.

[0162] For another example, after determining that there are passengers in the cabin, the cabin air conditioner is started. In this way, when the vehicle is in a closed state and the driver is not in the vehicle, after determining that there are passengers in the cabin, the cabin air conditioner is automatically started to prevent the passengers left in the cabin from suffocating and ensure the safety of the passengers in the cabin.

[0163] The adoption of the above technical solution further improves the intelligence level of the passenger detection method and further enhances the user experience.

[0164] Based on the same inventive concept, the present disclosure also provides a living thing detection device. Figure 6 is a block diagram of a living thing detection device according to an exemplary embodiment. Figure 6 As shown, the living thing detection device 600 may include:

[0165] The first control module 601 is configured to control the speaker to emit a first sound wave signal in a preset space;

[0166] A second control module 602 is configured to control the microphone to collect a second sound wave signal in the preset space, where the second sound wave signal is generated based on the first sound wave signal;

[0167] A first determination module 603 is configured to determine channel state information corresponding to the second sound wave signal according to the first sound wave signal and the second sound wave signal;

[0168] The second determination module 604 is configured to determine whether there is a living thing in the preset space according to the channel state information corresponding to the second sound wave signal.

[0169] Optionally, both the first sound wave signal and the second sound wave signal include multiple frames of data; accordingly, the channel state information corresponding to the second sound wave signal includes the channel state information corresponding to the multiple frames of data.

[0170] Optionally, the second determining module 604 includes:

[0171] A first determination submodule is configured to determine a target dynamic or static state of an object in the preset space according to channel state information corresponding to the multiple frames of data;

[0172] A second determination submodule is configured to determine that there is a living thing in the preset space if the target static and dynamic state of the object is a moving state;

[0173] The third determination submodule is configured to determine that there is no living thing in the preset space if the target dynamic and static state of the object is a static state.

[0174] Optionally, the speaker sends out the multiple frames of data in sequence; and the first determining submodule includes:

[0175] a change amount determination submodule, configured to determine a change amount of the channel state information corresponding to each frame of data according to the channel state information corresponding to each frame of data in the multiple frames of data;

[0176] A first target dynamic and static state determination submodule is configured to determine, for each frame of data, a first target dynamic and static state of the object when the frame of data is transmitted according to a change amount of the channel state information corresponding to the frame of data;

[0177] The target motion state determination submodule is configured to determine the target motion state of the object in the preset space according to the first target motion state of the object when each frame of data is transmitted.

[0178] Optionally, there are multiple microphones; the first determination module 603 is configured to: for each microphone, determine the channel state information corresponding to the multiple frames of data received by the microphone according to the first sound wave signal and the second sound wave signal collected by the microphone;

[0179] Correspondingly, the first target dynamic and static state determination submodule is configured as: for each of the microphones, determining the second target dynamic and static state of the object when transmitting each frame of data received by the microphone based on the change in channel state information corresponding to the multiple frames of data received by the microphone; for each frame of data, determining the first target dynamic and static state of the object when transmitting the frame data based on the second target dynamic and static state of the object when transmitting the frame data received by each of the microphones.

[0180] Optionally, the first target motion / still state determination submodule is configured as follows: if the number of the second target motion / still state being a moving state is greater than the number of the second target motion / still state being a stationary state, then determining that the third target motion / still state of the object is a moving state when transmitting the frame data; and, if the number of the second target motion / still state being a moving state is not greater than the number of the second target motion / still state being a stationary state, then determining that the third target motion / still state of the object is a stationary state when transmitting the frame data; and determining the first target motion / still state of the object when transmitting the frame data based on the third target motion / still state of the object when transmitting the frame data.

[0181] Optionally, the first target motion and still state determination submodule is configured to: determine the third target motion and still state of the object when transmitting the frame data as the first target motion and still state of the object when transmitting the frame data; or, perform state filtering processing on the third target motion and still state of the object when transmitting the frame data, and determine the third target motion and still state after the state filtering processing as the first target motion and still state of the object when transmitting the frame data.

[0182] Optionally, the first target motion / still state determination submodule is configured to: determine that the second target motion / still state of the object is a motion state when the change in the transmission channel state information is greater than the frame data of a first preset threshold, and determine that the second target motion / still state of the object is a stationary state when the change in the transmission channel state information is not greater than the frame data of the first preset threshold.

[0183] Optionally, each frame of data includes M data, and the M data are transmitted on M channels respectively, where M is an integer greater than 1; the change amount determination submodule is configured to: for each channel, determine the difference in channel state information corresponding to different frame data on the channel according to the channel state information corresponding to each frame of data on the channel and a preset step size, so as to obtain a deviation value of the channel state information corresponding to each frame of data on each channel; for each frame of data, determine the variance of the deviation value according to the deviation value of the channel state information corresponding to the frame data on each channel, and determine the variance as the change amount of the channel state information corresponding to the frame data.

[0184] Optionally, the first determining submodule further includes:

[0185] The filtering processing submodule is configured to perform mean filtering on the variation of the channel state information corresponding to each frame of data to obtain a processed variation;

[0186] The first target dynamic and static state determination submodule is configured to: for each frame of data, determine the first target dynamic and static state of the object when transmitting the frame of data according to the processed change amount of the channel state information corresponding to the frame of data.

[0187] Optionally, the target dynamic and static state determination submodule is configured to: determine a first preset number of first target frame data from multiple frames of data; and determine the target dynamic and static state of the object in the preset space based on the first target dynamic and static state of the object when transmitting each of the first target frame data.

[0188] Optionally, the living thing detection device 600 further includes:

[0189] The processing module is configured to display and / or report the target dynamic and static status of the object in the preset space.

[0190] Optionally, each frame data included in the first sound wave signal is generated based on the first baseband signal corresponding to it; the first determination module 603 is configured to: identify multiple frames of data included in the second sound wave signal according to the cross-correlation between the first sound wave signal and the second sound wave signal; for each frame data, demodulate the frame data to obtain the second baseband signal corresponding to the frame data, and determine the channel state information corresponding to the frame data according to the second baseband signal and the first baseband signal corresponding to the frame data.

[0191] Optionally, the first determination module 603 is configured to: determine a frequency response of a baseband signal according to the second baseband signal and a first baseband signal corresponding to the frame data; and determine the frequency response of the baseband signal as channel state information corresponding to the frame data.

[0192] Optionally, the first sound wave signal is generated in the following manner:

[0193] Generate a plurality of frames of first baseband signals having autocorrelation values ​​greater than a second preset threshold;

[0194] The multiple frames of first baseband signals are modulated into ultrasonic frequency band signals, and the ultrasonic frequency band signals are determined as first sound wave signals.

[0195] Based on the same inventive concept, the present disclosure also provides a passenger detection device. Figure 7 FIG. 1 is a block diagram of a passenger detection device according to an exemplary embodiment. Figure 7 As shown, the passenger detection device 700 may include:

[0196] The third determination module 701 is configured to determine whether the seat in the vehicle cabin is occupied;

[0197] A fourth determination module 702 is configured to determine that there is a passenger in the cabin when the seat is in the occupied state and the living thing detection result in the cabin is that there is a living thing;

[0198] Among them, the living thing detection result in the cabin is obtained according to the living thing detection method described in the present disclosure.

[0199] Optionally, the third determination module 701 is configured to: obtain a pressure signal of a seat in the vehicle cabin; if a pressure value represented by the pressure signal is within a preset range, determine that the seat is in an occupied state.

[0200] Optionally, the passenger detection device 700 further includes:

[0201] The execution module is configured to execute at least one of the following after determining that there is a passenger in the cabin: output a prompt message; control the vehicle where the cabin is located to power on; and start the air conditioner of the cabin.

[0202] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0203] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the living thing detection method provided by the present disclosure, or implement the steps of the living thing detection method provided by the present disclosure.

[0204] Figure 8 800 is a block diagram of a vehicle according to an exemplary embodiment. For example, vehicle 800 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. Vehicle 800 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0205] Reference Figure 8 , the vehicle 800 may include various subsystems, for example, an infotainment system 810, a perception system 820, a decision control system 830, a drive system 840, and a computing platform 850. The vehicle 800 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 800 may be interconnected by wire or wireless means.

[0206] In some embodiments, the infotainment system 810 may include a communication system, an entertainment system, and a navigation system, etc.

[0207] The perception system 820 may include several sensors for sensing information about the environment around the vehicle 800. For example, the perception system 820 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0208] The decision control system 830 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0209] The drive system 840 may include components that provide powered motion for the vehicle 800. In one embodiment, the drive system 840 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0210] Some or all functions of the vehicle 800 are controlled by a computing platform 850. The computing platform 850 may include at least one processor 851 and a memory 852, and the processor 851 may execute instructions 853 stored in the memory 852.

[0211] The processor 851 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0212] The memory 852 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0213] In addition to instructions 853 , memory 852 may also store data, such as road maps, route information, vehicle location, direction, speed, etc. The data stored in memory 852 may be used by computing platform 850 .

[0214] In the embodiment of the present disclosure, the processor 851 may execute instruction 853 to complete all or part of the steps of the above-mentioned living thing detection method or passenger detection method.

[0215] Fig. 9 900 is a block diagram of an electronic device according to an exemplary embodiment. For example, the electronic device 900 may be provided as a server. Fig. 9 , the electronic device 900 includes a processing component 922, which further includes one or more processors, and a memory resource represented by a memory 932 for storing instructions that can be executed by the processing component 922, such as an application. The application stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 922 is configured to execute instructions to perform the above-mentioned living thing detection method or the above-mentioned passenger detection method. The electronic device 900 may also include a power supply component 926 configured to perform power management of the device 900, a wired or wireless network interface 950 configured to connect the electronic device 900 to a network, and an input / output interface 958. The electronic device 900 can operate based on an operating system stored in the memory 932.

[0216] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned living thing detection method or passenger detection method when executed by the programmable device.

[0217] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0218] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for detecting living things, characterized in that: The method comprises: Controlling the loudspeaker to emit a first sound wave signal in a preset space; Controlling the microphone to collect a second sound wave signal in the preset space, where the second sound wave signal is generated based on the first sound wave signal; Determine, according to the first sound wave signal and the second sound wave signal, channel state information corresponding to the second sound wave signal; Determine whether there is a living thing in the preset space according to the channel state information corresponding to the second sound wave signal.

2. The method for detecting living things according to claim 1, characterized in that: The first sound wave signal and the second sound wave signal both include multiple frames of data; accordingly, the channel state information corresponding to the second sound wave signal includes the channel state information corresponding to the multiple frames of data.

3. The method for detecting living things according to claim 2, characterized in that: The determining, according to the channel state information corresponding to the second sound wave signal, whether there is a living thing in the preset space includes: Determining a target dynamic or static state of an object in the preset space according to channel state information corresponding to the multiple frames of data; If the target static / dynamic state of the object is a moving state, determining that there is a living thing in the preset space; If the target dynamic and static state of the object is a static state, it is determined that there is no living thing in the preset space.

4. The method for detecting living things according to claim 3, characterized in that: The speaker sends out the multiple frames of data in sequence; and determining the target dynamic and static state of the object in the preset space according to the channel state information corresponding to the multiple frames of data includes: Determine, according to the channel state information corresponding to each frame of data in the multiple frames of data, a change amount of the channel state information corresponding to each frame of data; For each frame of data, determining a first target dynamic or static state of the object when transmitting the frame of data according to a change amount of the channel state information corresponding to the frame of data; The target dynamic and static state of the object in the preset space is determined according to the first target dynamic and static state of the object when each frame of data is transmitted.

5. The method for detecting living things according to claim 4, characterized in that: The number of the microphones is multiple; and the determining, according to the first sound wave signal and the second sound wave signal, the channel state information corresponding to the second sound wave signal comprises: For each of the microphones, determining channel state information corresponding to multiple frames of data received by the microphone according to the first sound wave signal and the second sound wave signal collected by the microphone; Correspondingly, for each frame of data, determining the first target dynamic and static state of the object when transmitting the frame of data according to the change amount of the channel state information corresponding to the frame of data includes: For each of the microphones, determining a second target dynamic and static state of the object when transmitting each frame of data received by the microphone according to a change amount of the channel state information corresponding to the multiple frames of data received by the microphone; For each frame of data, the first target dynamic and static state of the object when transmitting the frame of data is determined according to the second target dynamic and static state of the object when transmitting the frame of data received by each microphone.

6. The method for detecting living things according to claim 5, characterized in that: The determining, according to the second target dynamic and static state of the object when transmitting the frame data received by each microphone, the first target dynamic and static state of the object when transmitting the frame data comprises: If the number of the second target motion-static state being the moving state is greater than the number of the second target motion-static state being the stationary state, determining that the third target motion-static state of the object is the moving state when the frame data is transmitted, and if the number of the second target motion-static state being the moving state is not greater than the number of the second target motion-static state being the stationary state, determining that the third target motion-static state of the object is the stationary state when the frame data is transmitted; The first target motion state of the object when transmitting the frame data is determined according to the third target motion state of the object when transmitting the frame data.

7. The method for detecting living things according to claim 6, characterized in that: The determining, according to the third target motion state of the object when transmitting the frame data, the first target motion state of the object when transmitting the frame data comprises: determining the third target motion / still state of the object when transmitting the frame data as the first target motion / still state of the object when transmitting the frame data; or A state filtering process is performed on the third target motion and still state of the object when the frame data is transmitted, and the third target motion and still state after the state filtering process is determined as the first target motion and still state of the object when the frame data is transmitted.

8. The method for detecting living things according to claim 5, characterized in that: The determining, according to the change amount of the channel state information corresponding to the multiple frames of data received by the microphone, the second target dynamic and static state of the object when transmitting each frame of data received by the microphone comprises: Determine that the second target dynamic and static state of the object is a moving state when the change in the transmission channel state information is greater than the frame data of the first preset threshold, and determine that the second target dynamic and static state of the object is a stationary state when the change in the transmission channel state information is not greater than the frame data of the first preset threshold.

9. The method for detecting living things according to claim 4, characterized in that: Each frame of data includes M data, and the M data are transmitted on M channels respectively, where M is an integer greater than 1; The determining, according to the channel state information corresponding to each frame of data in the multiple frames of data, a change amount of the channel state information corresponding to each frame of data, comprises: For each channel, according to the channel state information corresponding to each frame of data on the channel and a preset step size, determine the difference of the channel state information corresponding to different frames of data on the channel, so as to obtain a deviation value of the channel state information corresponding to each frame of data on each channel; For each frame of data, the variance of the deviation value is determined according to the deviation value of the channel state information corresponding to the frame of data on each channel, and the variance is determined as the change amount of the channel state information corresponding to the frame of data.

10. The method for detecting living things according to claim 4, characterized in that: The determining, according to the channel state information corresponding to the multiple frames of data, the target dynamic and static state of the object in the preset space also includes: Performing mean filtering on the variation of the channel state information corresponding to each frame of data to obtain the processed variation; The determining, for each frame of data, according to a change amount of channel state information corresponding to the frame of data, a first target dynamic and static state of the object when the frame of data is transmitted, includes: For each frame of data, a first target dynamic or static state of the object when the frame of data is transmitted is determined according to a processed change amount of the channel state information corresponding to the frame of data.

11. The method for detecting living things according to claim 4, characterized in that: The determining the target dynamic and static state of the object in the preset space according to the first target dynamic and static state of the object when transmitting each frame of data includes: Determine a first preset number of first target frame data from the multiple frame data; The target dynamic and static states of the objects in the preset space are determined according to the first target dynamic and static states of the objects when the first target frame data are transmitted.

12. The method for detecting living things according to any one of claims 3 to 11, characterized in that: The living thing detection method further comprises: Display and / or report the target dynamic and static status of the objects in the preset space.

13. The method for detecting living things according to any one of claims 2 to 11, characterized in that: Each frame of data included in the first sound wave signal is generated based on the first baseband signal corresponding to the first sound wave signal; and determining the channel state information corresponding to the second sound wave signal according to the first sound wave signal and the second sound wave signal, including: identifying, according to the cross-correlation between the first sound wave signal and the second sound wave signal, a plurality of frames of data included in the second sound wave signal; For each frame of data, the frame data is demodulated to obtain a second baseband signal corresponding to the frame data, and channel state information corresponding to the frame data is determined based on the second baseband signal and a first baseband signal corresponding to the frame data.

14. The method for detecting living things according to claim 13, characterized in that: The determining, according to the second baseband signal and the first baseband signal corresponding to the frame data, the channel state information corresponding to the frame data includes: determining a frequency response of a baseband signal according to the second baseband signal and a first baseband signal corresponding to the frame data; The frequency response of the baseband signal is determined as the channel state information corresponding to the frame data.

15. The method for detecting living things according to claim 14, characterized in that: The determining, according to the second baseband signal and the first baseband signal corresponding to the frame data, the channel state information corresponding to the frame data further includes: determining a channel impulse response according to a frequency response of the baseband signal; The channel impulse response is determined as channel state information corresponding to the frame data.

16. The method for detecting living things according to any one of claims 1 to 11, characterized in that: The first sound wave signal is generated by: Generate a plurality of frames of first baseband signals having autocorrelation values ​​greater than a second preset threshold; The multiple frames of first baseband signals are modulated into ultrasonic frequency band signals, and the ultrasonic frequency band signals are determined as first sound wave signals.

17. A passenger detection method, characterized in that: The passenger detection method comprises: Determine whether the seats in the cabin are occupied; When the seat is in the occupied state and the living thing detection result in the vehicle cabin indicates that a living thing exists, determining that a passenger exists in the vehicle cabin; Wherein, the living thing detection result in the cabin is obtained according to the living thing detection method according to any one of claims 1 to 16.

18. The passenger detection method according to claim 17, characterized in that: The determining whether a seat in the vehicle cabin is in an occupied state comprises: Obtain pressure signals of seats in the vehicle cabin; If the pressure value represented by the pressure signal is within a preset range, it is determined that the seat is in an occupied state.

19. The passenger detection method according to claim 17 or 18, characterized in that: After determining that there is a passenger in the vehicle cabin, the passenger detection method further includes at least one of the following steps: Output prompt information; Controlling the vehicle where the cabin is located to power on; The air conditioning of the cabin is activated.

20. A living thing detection device, characterized in that: The living thing detection device comprises: A first control module is configured to control the speaker to emit a first sound wave signal in a preset space; A second control module is configured to control the microphone to collect a second sound wave signal in the preset space, where the second sound wave signal is generated based on the first sound wave signal; A first determining module is configured to determine channel state information corresponding to the second sound wave signal according to the first sound wave signal and the second sound wave signal; The second determination module is configured to determine whether there is a living thing in the preset space according to the channel state information corresponding to the second sound wave signal.

21. A passenger detection device, characterized in that: The passenger detection device comprises: A third determination module is configured to determine whether the seat in the vehicle cabin is in an occupied state; A fourth determination module is configured to determine that there is a passenger in the cabin when the seat is in the occupied state and the living thing detection result in the cabin is that there is a living thing; Wherein, the living thing detection result in the cabin is obtained according to the living thing detection method according to any one of claims 1 to 16.

22. A vehicle, characterized in that: include: A processor, a speaker, and a microphone, wherein the processor is connected to the speaker and the microphone respectively; a memory for storing processor-executable instructions; The processor is configured to execute the living thing detection method according to any one of claims 1 to 16, or to execute the passenger detection method according to any one of claims 17 to 19.

23. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the living thing detection method according to any one of claims 1 to 16 is implemented, or the passenger detection method according to any one of claims 17 to 19 is implemented.

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