Object detection device

Abstract

In a case where CFAR processing is performed, an absorption attenuation value is accurately estimated. An object detection device as one example of the present disclosure includes a transmission unit that transmits a transmission wave toward a road surface; a reception unit that receives a reflection wave of the transmission wave at an object as a reception wave; a CFAR processing unit that acquires a CFAR signal at a detection time point by CFAR processing, the CFAR processing being processing based on a value of a first processing target signal based on the reception wave received at a prescribed detection time point and an average value of values of second processing target signals based on the reception wave received in prescribed intervals before and after the detection time point; and an estimation unit that estimates an absorption attenuation value based on fitting processing of fitting the CFAR signal to a road surface reflection estimation formula that defines a relationship between a CFAR signal and an absorption attenuation value.

Description

Technical Field

[0001] This disclosure relates to an object detection device. Background Technology

[0002] Previously, there were techniques for detecting information related to objects based on the transmission and reception of ultrasonic waves, etc. Furthermore, in these techniques, CFAR (Constant False Alarm Rate) processing is known as a method for reducing noise, known as clutter, caused by reflections from objects that are not the object being detected. According to CFAR processing, a CFAR signal can be obtained that is equivalent to a signal from the object signal after removing clutter by using a moving average of the value (signal level) of the received wave.

[0003] Furthermore, transmitted and received waves typically experience absorption and attenuation due to factors such as temperature and humidity of the medium in which they propagate. For example, in the prior art, there exists a technique that uses temperature data from both inside and outside the vehicle (based on a temperature sensor) and humidity data from inside the vehicle (based on a humidity sensor) to infer the humidity outside the vehicle and correct for the absorption and attenuation of the transmitted and received waves. According to this technique, this correction can be performed even if a humidity sensor for detecting the humidity outside the vehicle is not present.

[0004] Patent Document 1: Japanese Patent Application Publication No. 2018-115957

[0005] However, in the aforementioned prior art, the humidity outside the vehicle is inferred based on the humidity inside the vehicle and a correction is made relative to the absorption attenuation, which sometimes results in low accuracy. Furthermore, in performing this correction or setting the threshold for the CFAR signal, it would be meaningful to be able to infer the absorption attenuation value with high accuracy even without knowing at least one of the temperature and humidity of the target environment. Summary of the Invention

[0006] Therefore, one of the objectives of this disclosure is to provide an object detection apparatus capable of inferring absorption attenuation values ​​with high accuracy while performing CFAR processing.

[0007] An object detection apparatus, as an example of this disclosure, includes: a transmitting unit that transmits a transmitted wave toward an exterior including a road surface; a receiving unit that receives a reflected wave of the transmitted wave as a received wave; a CFAR processing unit that acquires a CFAR signal at the detection time point by CFAR processing based on the average value of a first processed object signal of the received wave received at a predetermined detection time point and the average value of a second processed object signal of the received wave received within a predetermined interval before and after the detection time point; and an inference unit that infers an absorption attenuation value corresponding to the average value based on a road surface reflection inference formula that predefines the relationship between the average value and the absorption attenuation value.

[0008] Based on this structure, when performing CFAR processing, the absorption attenuation value can be inferred with high accuracy by fitting the CFAR signal to the road surface reflection inference formula.

[0009] Furthermore, in the object detection device described above, the inference unit also corrects the absorption attenuation value based on a distance characteristic formula that predefines the relationship between the absorption attenuation value and the distance.

[0010] Based on this structure, the absorption attenuation value can be corrected to a more accurate value by fitting the absorption attenuation value to the distance characteristic formula.

[0011] In addition, the object detection device described above also includes a calculation unit that calculates the humidity of the object environment based on the absorption attenuation value and the temperature data detected by a temperature sensor that detects the temperature of the object environment.

[0012] Based on this structure, as long as temperature data exists, humidity can be calculated with high accuracy using temperature data and absorption attenuation values. Attached Figure Description

[0013] Figure 1 This is an illustrative and schematic diagram showing the appearance of a vehicle equipped with the object detection system according to the embodiment, viewed from above.

[0014] Figure 2 This is an exemplary and schematic block diagram illustrating the hardware structure of the object detection system involved in the implementation.

[0015] Figure 3 This is an illustrative and schematic diagram used to explain the outline of the techniques used by the object detection system according to the embodiments for detecting the distance to an object.

[0016] Figure 4 This is an illustrative and schematic block diagram illustrating the function of the object detection device involved in the embodiment.

[0017] Figure 5 This is an illustrative and schematic diagram used to illustrate an example of CFAR processing that can be performed in an implementation.

[0018] Figure 6 This is an illustrative and schematic diagram showing an example of the processing target signal and noise involved in the implementation method.

[0019] Figure 7 It is an illustrative and schematic graph showing the relationship between amplitude and distance related to the road surface reflection inference formula involved in the implementation.

[0020] Figure 8 This is an illustrative and schematic flowchart illustrating a series of processes performed by the object detection system involved in the implementation.

[0021] Explanation of reference numerals in the attached figures

[0022] 1...Vehicle; 50...Temperature sensor; 100...ECU; 200...Object detection device; 210...Transceiver; 411...Transmitter; 421...Receiver; 423...CFAR processing unit; 424...Threshold processing unit; 425...Detection processing unit; 426...Inference unit. Detailed Implementation

[0023] Hereinafter, embodiments and modifications of the present disclosure will be described with reference to the accompanying drawings. The structures of the embodiments and modifications described below, as well as the effects and functions brought about by such structures, are merely examples and are not limited to the following description.

[0024] <Implementation Method>

[0025] Figure 1 This is an illustrative and schematic diagram showing the appearance of the vehicle 1 equipped with the object detection system according to the embodiment, viewed from above.

[0026] As explained below, the object detection system according to the embodiments detects objects, including those present in the surrounding environment (such as those described later), by transmitting and receiving sound waves (ultrasound) and obtaining the time difference between the transmission and reception. Figure 2 An onboard sensor system that detects information related to the obstacle O shown.

[0027] More specifically, such as Figure 1As shown, the object detection system according to the embodiment includes an ECU (Electronic Control Unit) 100 as an on-board control device and object detection devices 201 to 204 as on-board sonar. The ECU 100 is mounted inside a four-wheeled vehicle 1, which includes a pair of front wheels 3F and a pair of rear wheels 3R, and the object detection devices 201 to 204 are mounted on the exterior of the vehicle 1.

[0028] exist Figure 1 In the example shown, as an example, the object detection devices 201 to 204 are installed at different positions in the rear end (rear bumper) of the vehicle body 2, which is the outer casing of the vehicle 1, but the installation positions of the object detection devices 201 to 204 are not limited to... Figure 1 The example shown. For example, the object detection devices 201 to 204 may be installed at the front end (front bumper) of the vehicle body 2, or at the side of the vehicle body 2, or at two or more of the rear end, front end, and side.

[0029] It should be noted that, in the embodiments, the object detection devices 201 to 204 have the same hardware structure and function. Therefore, for the sake of simplicity, the object detection devices 201 to 204 will sometimes be collectively referred to as object detection device 200. Furthermore, in the embodiments, the number of object detection devices 200 is not limited to [specific number missing]. Figure 1 Four as shown.

[0030] Figure 2 This is an exemplary and schematic block diagram illustrating the hardware structure of the object detection system involved in the implementation.

[0031] like Figure 2 As shown, ECU 100 has the same hardware structure as a typical computer. More specifically, ECU 100 includes an input / output device 110, a storage device 120, and a processor 130.

[0032] The input / output device 110 is an interface used to transmit and receive information between the ECU 100 and external devices. For example, in Figure 2 In the example shown, the ECU 100 communicates with the object detection device 200 and the temperature sensor 50. The temperature sensor 50 is mounted on the vehicle 1 to measure the air temperature of the vehicle's surroundings (object environment).

[0033] Storage device 120 includes main storage devices such as ROM (Read Only Memory) and RAM (Random Access Memory) and / or auxiliary storage devices such as HDD (Hard Disk Drive) and SSD (Solid State Drive).

[0034] The processor 130 is responsible for various processes performed in the ECU 100. The processor 130 includes, for example, a computing device such as a CPU (Central Processing Unit). The processor 130 reads and executes computer programs stored in the storage device 120 to perform various functions, such as automatic parking.

[0035] In addition, such as Figure 2 As shown, the object detection device 200 includes a transceiver 210 and a control unit 220.

[0036] The transceiver 210 has an oscillator 211 composed of piezoelectric elements or the like, and performs ultrasonic wave transmission and reception through the oscillator 211.

[0037] More specifically, transceiver 210 transmits ultrasonic waves generated by the vibration of oscillator 211 as transmitted waves, and receives the vibration of oscillator 211 generated by the ultrasonic waves being reflected back by an external object as received waves. Figure 2 In the example shown, an obstacle O set on the road surface RS is shown as an object that reflects ultrasonic waves from transceiver 210.

[0038] It should be noted that, in Figure 2 The example shown illustrates a structure in which both the transmission of the transmitted wave and the reception of the received wave are achieved by a single transceiver 210 having a single oscillator 211. However, the technique of the embodiment can also be used in structures where the transmitting side and the receiving side are separated, such as structures in which a first oscillator for transmitting the transmitted wave and a second oscillator for receiving the received wave are respectively provided.

[0039] The control unit 220 has the same hardware structure as a typical computer. More specifically, the control unit 220 includes an input / output device 221, a storage device 222, and a processor 223.

[0040] Input / output device 221 is used to realize the connection between control unit 220 and external ( Figure 1 The example shown is an interface for sending and receiving information between ECU100 and transceiver 210.

[0041] Storage device 222 includes main storage devices such as ROM and RAM, and / or auxiliary storage devices such as HDD and SSD.

[0042] Processor 223 is responsible for various processes performed in control unit 220. Processor 223 includes arithmetic devices such as CPU. Processor 223 performs various functions by reading and executing computer programs stored in storage device 333.

[0043] Here, the object detection device 200 according to the embodiment detects the distance to an object using a technique known as the so-called TOF (Time Of Flight) method. As detailed below, the TOF method is a technique that calculates the distance to an object by taking into account the difference between the time when the transmitted wave was sent (more specifically, the start of transmission) and the time when the received wave was received (more specifically, the start of reception).

[0044] Figure 3 This is an illustrative and schematic diagram used to explain the outline of the technology used by the object detection device 200 according to the embodiment for detecting the distance to an object.

[0045] More specifically, Figure 3 This is a graph illustrating, and schematically representing, the time-varying signal level (e.g., amplitude) of the ultrasonic waves transmitted and received by the object detection device 200 according to the embodiment. Figure 3 In the graph shown, the horizontal axis corresponds to time, and the vertical axis corresponds to the signal level of the signals transmitted and received by the object detection device 200 via the transceiver 210 (oscillator 211).

[0046] exist Figure 3 In the graph shown, the solid line L11 represents an example of the envelope of the signal level of the signal transmitted and received by the object detection device 200, or in other words, the time-varying degree of the vibration of the oscillator 211. From this solid line L11, it can be read that the oscillator 211 is driven to vibrate for a time Ta from time point t0, and thus, after the transmission of the transmitted wave ends at time point t1, the vibration of the oscillator 211 continues while decaying during the time Tb from there until it reaches time point t2. Therefore, in Figure 3 In the graph shown, time Tb corresponds to the so-called reverberation time.

[0047] For the solid line L11, at time point t4, after time Tp has elapsed from the start of wave transmission (t0), the vibration intensity of oscillator 211 reaches a peak value exceeding the specified threshold Th1 (or higher) represented by the dashed line L21. This threshold Th1 is used to identify the vibration of oscillator 211 by means of an object (e.g., [object name missing]) being detected. Figure 2 The transmitted wave is reflected back by the obstacle O shown, and the received wave is generated by the reception of the transmitted wave or by an object other than the object being tested (e.g., Figure 2 The received wave is generated by the reflection of the road surface RS and the return of the transmitted wave, and the value is preset.

[0048] It should be noted that, Figure 3An example is shown where the threshold Th1 is set to a constant value that does not change over time, but in an implementation, the threshold Th1 may also be set to a value that changes over time.

[0049] Here, vibrations that can be considered to have a peak value exceeding the threshold Th1 (or higher) are generated by receiving a received wave as a transmitted wave reflected back by an object being detected. On the other hand, vibrations that can be considered to have a peak value below (or insufficient to) the threshold Th1 are generated by receiving a received wave as a transmitted wave reflected back by an object other than the object being detected.

[0050] Therefore, it is read from the solid line L11 that the vibration of the oscillator 211 at time point t4 is generated by the reception of the transmitted wave as a transmitted wave reflected back by the object being detected.

[0051] It should be noted that in solid line L11, the vibration of oscillator 211 decays after time point t4. Therefore, time point t4 corresponds to the time when the received wave, which is the transmitted wave reflected back by the object being detected, ends receiving; in other words, it corresponds to the time when the transmitted wave last transmitted at time point t1 returns as the received wave.

[0052] Furthermore, in solid line L11, the time point t3, which is the starting point of the peak value at time point t4, corresponds to the time point at which the reception of the transmitted wave reflected back by the object being detected begins—in other words, the time point at which the transmitted wave initially transmitted at time point t0 returns as a received wave. Therefore, in solid line L11, the time ΔT between time point t3 and time point t4 is equal to the time Ta, which is the transmission time of the transmitted wave.

[0053] Based on the above, in order to determine the distance to the object being detected using the Time-of-Flight (TOF) method, it is necessary to calculate the time Tf between the time point t0 when the transmitted wave begins to be transmitted and the time point t3 when the received wave begins to be received. This time Tf can be obtained by subtracting the time ΔT, which is equal to the transmission time Ta, from the time Tp, which is the difference between time point t0 and the time point t4 when the signal level of the received wave reaches a peak exceeding the threshold Th1.

[0054] The time point t0 at which the transmitted wave begins to be transmitted can be easily determined as the time point at which the object detection device 200 begins to operate, and the transmission time Ta of the transmitted wave is predetermined by settings, etc. Therefore, in order to determine the distance to the object being detected using the TOF method, it is important to ultimately determine the time point t4 at which the signal level of the received wave reaches a peak value exceeding the threshold Th1. Moreover, in order to determine this time point t4, it is important to accurately detect the correspondence between the transmitted wave and the received wave, which is the transmitted wave reflected back by the object being detected.

[0055] Furthermore, as mentioned above, transmitted and received waves typically experience absorption and attenuation due to factors such as temperature and humidity of the medium through which they propagate. Therefore, for example, in the prior art, there exists a technique that uses temperature data from both inside and outside the vehicle based on a temperature sensor, and humidity data from inside the vehicle based on a humidity sensor, to infer the humidity outside the vehicle and correct for the absorption and attenuation of the transmitted and received waves. According to this technique, this correction can be performed even if a humidity sensor for detecting the humidity outside the vehicle is not available.

[0056] However, in the aforementioned prior art, the correction for absorption attenuation is made by inferring the humidity outside the vehicle based on the humidity inside the vehicle, which sometimes results in low accuracy. Furthermore, in performing this correction or setting the threshold for the CFAR signal, it would be meaningful to be able to infer the absorption attenuation value with high accuracy even without knowing at least one of the temperature and humidity of the target environment.

[0057] Therefore, in this embodiment, when CFAR processing is performed using the object detection device 200 configured as described below, the absorption attenuation value can be inferred with high accuracy. The following is a detailed description.

[0058] Figure 4 This is an illustrative and schematic block diagram showing the detailed structure of the object detection device 200 according to the embodiment.

[0059] It should be noted that, Figure 4 The diagram illustrates a state where the transmitting and receiving structures are separated, but this illustration is for ease of explanation. Therefore, in the embodiment, as described above, both the transmission of the transmitted wave and the reception of the received wave are achieved through a single transceiver 210. However, as mentioned above, the technology of the embodiment can also be applied to structures where the transmitting and receiving structures are separated.

[0060] like Figure 4 As shown, the object detection device 200 has a transmitting unit 411 as a transmitting side structure. In addition, the object detection device 200 has a receiving unit 421, a preprocessing unit 422, a CFAR processing unit 423, a threshold processing unit 424, a detection processing unit 425, and an inference unit 426 as a receiving side structure.

[0061] It should be noted that, in the implementation method, Figure 4 At least a portion of the structure shown is implemented as a result of the cooperation of hardware and software, more specifically as a result of the processor 223 of the object detection device 200 reading from the storage device 222 and executing a computer program. However, in the embodiment, Figure 4At least a portion of the structure shown can also be implemented using dedicated hardware (circuitry). Additionally, in the implementation, Figure 4 The structures shown can operate under the control of the control unit 220 of the object detection device 200 itself, or under the control of the external ECU 100.

[0062] First, the structure of the transmitting side will be explained.

[0063] The transmitting unit 411 transmits a transmission wave toward the outside, including the road surface, by causing the aforementioned oscillator 211 to vibrate at a predetermined transmission interval. The transmission interval is the time interval from the start of transmitting a transmission wave to the next transmission wave. The transmitting unit 411 is configured, for example, using a circuit for generating a carrier wave, a circuit for generating a pulse signal corresponding to the identification information to be assigned to the carrier wave, a multiplier for modulating the carrier wave according to the pulse signal, and an amplifier for amplifying the transmission signal output from the multiplier.

[0064] Next, the structure of the receiving side will be explained.

[0065] The receiving unit 421 receives the reflected wave from the object of the transmitted wave sent by the transmitting unit 411 as the received wave, and receives the wave from the time the transmitted wave is sent until a predetermined measurement time has elapsed. The measurement time is a waiting time set after the transmitted wave is sent in order to receive the received wave, which is the reflected wave of the transmitted wave.

[0066] The preprocessing unit 422 performs preprocessing to convert the received signal corresponding to the received wave received by the receiving unit 421 into a processing target signal that should be input into the CFAR processing unit 423. Preprocessing includes, for example, amplification processing to amplify the received signal corresponding to the received wave, filtering processing to reduce noise contained in the amplified received signal, correlation processing to obtain a correlation value representing the similarity between the transmitted and received signals, and envelope processing to generate a processing target signal based on the envelope of a waveform representing the time variation of the correlation value.

[0067] The CFAR processing unit 423 performs CFAR processing relative to the processing target signal output from the preprocessing unit 422, thereby acquiring a CFAR signal. As described above, CFAR processing is the process of acquiring a CFAR signal that is equivalent to a signal from the processing target signal after removing noise by using a moving average of the value (signal level) of the processing target signal.

[0068] For example, the CFAR processing unit 423 according to the embodiment uses the following... Figure 5 The structure shown is used to acquire CFAR signals.

[0069] Figure 5This is an illustrative and schematic diagram used to illustrate an example of CFAR processing that can be performed in an implementation.

[0070] like Figure 5 As shown, in CFAR processing, firstly, the target signal 510 is sampled at predetermined time intervals. Furthermore, the arithmetic unit 511 of the CFAR processing unit 423 calculates the sum of the values ​​of the target signal corresponding to the N sampled quantities of the received wave received in the interval T51 existing before a certain detection time point t50. Additionally, the arithmetic unit 512 of the CFAR processing unit 423 calculates the sum of the values ​​of the target signal corresponding to the N sampled quantities of the received wave received in the interval T52 existing after the detection time point t50.

[0071] Then, the arithmetic unit 520 of the CFAR processing unit 423 adds the calculation results of arithmetic units 511 and 512. Furthermore, the arithmetic unit 530 of the CFAR processing unit 423 divides the calculation result of arithmetic unit 520 by the sum of the number of samples N of the target signal in interval T51 and the number of samples N of the target signal in interval T52, which is 2N, to calculate the average value of the target signal values ​​in intervals T51 and T52.

[0072] Then, the arithmetic unit 540 of the CFAR processing unit 423 subtracts the average value of the calculation result of the arithmetic unit 530 from the value of the processing target signal at the detection time point t50 to obtain the CFAR signal 550.

[0073] Thus, the CFAR processing unit 423 according to the embodiment samples the processing target signal based on the received wave, and obtains the CFAR signal based on the difference between the value of the first processing target signal based on a sample amount of the received wave (at least) received at a predetermined detection time point and the average value of the second processing target signal based on the average value of multiple sample amounts of the received wave received during predetermined intervals T51 and T52 before and after the detection time point.

[0074] It should be noted that, as an example of CFAR processing described above, a process for obtaining a CFAR signal based on the difference between the average value of a first processing target signal and the average value of a second processing target signal is illustrated. However, the CFAR processing involved in the embodiments may also be based on the ratio of the average value of the first processing target signal to the average value of the second processing target signal, or it may be based on the normalization of the difference between the average value of the first processing target signal and the average value of the second processing target signal.

[0075] Here, as described above, the CFAR signal is equivalent to a signal from the target signal after removing clutter. To explain in more detail, the CFAR signal is equivalent to a signal from the target signal after removing various noises, including clutter and steady-state noise normally generated in transceiver 210. The target signal and the noise (clutter and steady-state noise) are, for example, described below. Figure 6 The waveform shown is as shown.

[0076] Figure 6 This is an illustrative and schematic diagram showing an example of the processing target signal and noise involved in the implementation method.

[0077] exist Figure 6 In the example shown, the solid line L601 represents the time variation of the value of the processed signal, the single-dotted line L602 represents the time variation of the clutter value, and the double-dotted line L603 represents the time variation of the steady-state noise value.

[0078] like Figure 6 As shown, the magnitude relationship between clutter (refer to the single-dotted line L602) and steady-state noise (refer to the double-dotted line L603) varies for each interval. For example, in Figure 6 In the example shown, in interval T61, the value of steady-state noise is greater than the value of clutter; in the next interval T62 after interval T61, the value of clutter is greater than the value of steady-state noise; and in the next interval T63 after interval T62, the value of steady-state noise is greater than the value of clutter.

[0079] It should be noted that the time point at which the clutter value increases is roughly determined in advance based on the installation position and orientation of the transceiver 210. Furthermore, the steady-state noise value is also roughly determined in advance. Therefore, the aforementioned intervals T61 to T63 are roughly determined in advance based on the installation position and orientation of the transceiver 210. Additionally, the starting point of the aforementioned interval T61 coincides with the starting point of the aforementioned measurement time, which is the waiting time of the receiving unit 421 set for receiving the reflected wave as the transmitted wave, and the ending point of the aforementioned interval T63 coincides with the ending point of the aforementioned measurement time.

[0080] Here, in intervals T61 and T63 mentioned above, the clutter is small enough to be negligible relative to the target signal. Therefore, the CFAR signal, which is equivalent to removing clutter and steady-state noise from the target signal, becomes a signal that includes the absorption and attenuation effect of the target signal but not the absorption and attenuation effect of the clutter. On the other hand, in interval T62 mentioned above, the clutter is large enough to be non-negligible relative to the target signal. Therefore, the CFAR signal becomes a signal that does not include the absorption and attenuation effect because the absorption and attenuation effect of the clutter cancels out the absorption and attenuation effect of the target signal.

[0081] Furthermore, the detection processing unit 425 determines the detection time point when the CFAR signal value exceeds the threshold by comparing the CFAR signal value with the threshold set by the threshold processing unit 424. The detection time point when the CFAR signal value exceeds the threshold coincides with the time point when the signal level of the received wave, which is the transmitted wave reflected back by the object, reaches its peak. Therefore, if the detection time point when the CFAR signal value exceeds the threshold is determined, the distance to the object can be detected by the aforementioned TOF method.

[0082] return Figure 4 The inference unit 426 infers the absorption attenuation value based on a fitting process (e.g., using the least squares method) that fits the CFAR signal to a road surface reflection inference formula that defines the relationship between the CFAR signal and the absorption attenuation value. The road surface reflection inference formula A is expressed, for example, by the following Equation 1.

[0083] A = f(α, β) ... Equation (1)

[0084] Here, α is a parameter representing the absorption attenuation value. β is a parameter representing the magnification in the direction of the amplitude (signal level). f(α, β) is a function with the two parameters α and β, pre-prepared based on experimental results, and may include exponential or logarithmic functions.

[0085] Alternatively, the inference unit 426 may also correct the absorption attenuation value based on a fitting process that fits the absorption attenuation value to a distance characteristic formula that defines the relationship between the absorption attenuation value and the distance. The distance characteristic formula is prepared in advance based on experimental results, etc.

[0086] Furthermore, the two fitting processes based on the inference unit 426 are, for example, in Figure 6 In the example, it is preferable to perform the operation in interval T62, where the clutter in intervals T61 to T63 is large enough to be negligible relative to the signal being processed, but it is not limited to this.

[0087] Figure 7 This is an illustrative and schematic graph showing the relationship between amplitude and distance related to the road surface reflection inference formula involved in the implementation method. Figure 7 In the graph, the vertical axis represents amplitude (signal level), and the horizontal axis represents distance. Line L1 represents the case where α (absorption attenuation value) = 1. Line L2 represents the case where α = 1.2. Line L3 represents the case where α = 1.4. Line L4 represents the case where α = 1.6.

[0088] Furthermore, if the result of the fitting process performed by the inference unit 426 to fit the CFAR signal to the road surface reflection inference formula is line L11, then it can be determined (inferred) that α = 1.1.

[0089] Furthermore, the relationship between the absorption attenuation value, temperature, and humidity is known. Therefore, if two of these three values ​​are known, the remaining value can be calculated. In other words, if the absorption attenuation value is determined, processor 223 (computing unit) can, for example, calculate the humidity of the environment based on the absorption attenuation value and temperature data detected by a temperature sensor of the environment. Similarly, temperature can also be calculated based on the absorption attenuation value and humidity.

[0090] The threshold processing unit 424 sets the threshold for the CFAR signal using the absorption attenuation value inferred by the inference unit 426.

[0091] Figure 8 This is an exemplary and schematic flowchart illustrating a series of processes performed by the object detection system involved in the implementation.

[0092] like Figure 8 As shown, in the embodiment, firstly, in S801, the transmitting unit 411 of the object detection device 200 transmits a transmission wave.

[0093] Next, in S802, the receiving unit 421 of the object detection device 200 receives the received wave corresponding to the transmitted wave sent in S801.

[0094] Next, in S803, the preprocessing unit 422 of the object detection device 200 performs preprocessing for the next processing in S804, relative to the received signal corresponding to the received wave received in S802.

[0095] Next, in S804, the CFAR processing unit 423 of the object detection device 200 performs CFAR processing on the processing object signal output from the preprocessing unit 422 via the preprocessing in S803, and generates a CFAR signal.

[0096] Next, in S805, the inference unit 426 infers the absorption attenuation value based on a fitting process that fits the CFAR signal to a road surface reflection inference formula. Alternatively, the inference unit 426 may also correct the absorption attenuation value based on a fitting process that fits the absorption attenuation value to a distance characteristic formula.

[0097] Next, in S806, the threshold processing unit 424 of the object detection device 200 sets a threshold based on the absorption attenuation value deduced in S805 for the CFAR signal generated in S804.

[0098] Next, in S807, the detection processing unit 425 of the object detection device 200 detects the distance to the object based on a comparison between the value of the CFAR signal and the threshold set in S805. Then, the processing ends.

[0099] Thus, according to the object detection device 200 of this embodiment, when performing CFAR processing, the absorption attenuation value can be inferred with high accuracy based on the fitting process that fits the CFAR signal to the road surface reflection inference formula.

[0100] Furthermore, by fitting the absorption attenuation value to the distance characteristic formula, the absorption attenuation value can be corrected to a more accurate value.

[0101] In addition, if temperature data is available, humidity can be calculated with high accuracy based on the temperature data and absorption attenuation value.

[0102] Therefore, instead of using the worst-case value as the absorption attenuation value as before, it is possible to perform humidity inference, CFAR signal threshold setting, and ranging with high precision using the inferred absorption attenuation value. Furthermore, it can also be applied to absorption attenuation correction when identifying road surface steps.

[0103] <Variation Example>

[0104] It should be noted that, in the above embodiments, the technology of this disclosure is applied to a structure that detects the distance to an object by transmitting and receiving ultrasonic waves. However, the technology of this disclosure can also be applied to a structure that detects the distance to an object by transmitting and receiving waves other than ultrasonic waves, such as sound waves, millimeter waves, radar, and electromagnetic waves.

[0105] The embodiments and modifications of this disclosure have been described above. However, the above embodiments and modifications are merely examples and are not intended to limit the scope of the invention. The above-described new embodiments and modifications can be implemented in various forms, and various omissions, substitutions, and changes can be made without departing from the spirit of the invention. The above-described embodiments and modifications are included in the scope and spirit of the invention, and are included in the scope of the invention as described in the claims and its equivalents.

Claims

1. An object detection device, characterized in that, have: The transmitting unit transmits waves towards the outside, including the road surface; The receiving unit receives the reflected wave of the transmitted wave as the received wave. The CFAR processing unit acquires the CFAR signal at the detection time point by performing CFAR processing based on the average value of a first processing target signal of the received wave received at a predetermined detection time point and the value of a second processing target signal of the received wave received within a predetermined interval before and after the detection time point. The inference unit infers the absorption attenuation value corresponding to the average value based on a road surface reflection inference formula that predefines the relationship between the average value and the absorption attenuation value. The threshold processing unit sets a threshold for the CFAR signal using the absorption attenuation value inferred by the inference unit. as well as The detection processing unit determines the detection time point when the value of the CFAR signal exceeds the threshold based on a comparison between the value of the CFAR signal and the threshold set by the threshold processing unit, thereby detecting the distance to the object.

2. The object detection device according to claim 1, characterized in that, The inference unit also corrects the absorption attenuation value based on a distance characteristic formula that predefines the relationship between the absorption attenuation value and the distance.

3. The object detection device according to claim 1 or 2, characterized in that, It also includes a calculation unit that calculates the humidity of the object environment based on the absorption attenuation value and temperature data detected by a temperature sensor that detects the temperature of the object environment.

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