Object detection device, object detection method, and program

Abstract

The present invention provides an object detection device, an object detection method, and a program that can identify the shape of a detected object based on reflected waves. [Solution] The object detection device of the present disclosure comprises a signal analysis unit that derives a frequency characteristic representing the relationship between the amplitude and frequency of a reflected wave based on the reflected wave of ultrasonic waves of multiple frequencies reflected by an object, and an identification unit that identifies whether the reflective surface of the object is a flat or curved surface based on the frequency characteristic in a predetermined frequency range.

Description

Technical Field

[0001] The present disclosure relates to an object detection device, an object detection method, and a program for detecting an object by ultrasonic waves.

Background Art

[0002] There is known an object detection device that transmits ultrasonic waves, receives reflected waves reflected by an object, and detects the object based on the reflected waves (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, detecting the shape of an object using an object detection device that performs object detection using reflected waves has been studied.

[0005] The present disclosure contributes to providing an object detection device, an object detection method, and a program that can identify the shape of a detected object based on reflected waves.

Means for Solving the Problems

[0006] An object detection device according to an aspect of the present disclosure includes a signal analysis unit that derives a frequency characteristic representing the relationship between the amplitude and frequency of the reflected wave based on the reflected wave in which ultrasonic waves of a plurality of frequencies are reflected by an object, and an identification unit that identifies whether the reflection surface of the object is flat or curved based on the frequency characteristic in a predetermined frequency range.

[0007] An object detection method according to one aspect of the present disclosure involves a computer deriving a frequency characteristic representing the relationship between the amplitude and frequency of a reflected wave based on the reflected wave of ultrasonic waves of multiple frequencies reflected by an object, and identifying whether the reflective surface of the object is a flat or curved surface based on the frequency characteristic in a predetermined frequency range.

[0008] A program according to one aspect of the present disclosure causes a computer to perform the following steps: derive a frequency characteristic representing the relationship between the amplitude and frequency of a reflected wave based on the reflected wave of ultrasonic waves of multiple frequencies reflected by an object; and identify whether the reflective surface of the object is a flat or curved surface based on the frequency characteristic in a predetermined frequency range. [Effects of the Invention]

[0009] According to this disclosure, the shape of a detected object can be identified based on reflected waves. [Brief explanation of the drawing]

[0010] [Figure 1] Block diagram illustrating the functional configuration of an object detection device. [Figure 2A] A schematic diagram illustrating the simulations performed to create baseline data. [Figure 2B] A schematic diagram illustrating the simulations performed to create baseline data. [Figure 2C] A schematic diagram illustrating the simulations performed to create baseline data. [Figure 3A] A diagram illustrating the reference data (distance of 1m) created by the simulation. [Figure 3B] A diagram illustrating the reference data (distance of 1m) created by the simulation. [Figure 3C] A diagram illustrating the reference data (distance of 1m) created by the simulation. [Figure 4A]Figure for explaining the reference data (distance 2m) created by simulation [Figure 4B] Figure for explaining the reference data (distance 2m) created by simulation [Figure 4C] Figure for explaining the reference data (distance 2m) created by simulation [Figure 5A] Figure for explaining the reference data (distance 3m) created by simulation [Figure 5B] Figure for explaining the reference data (distance 3m) created by simulation [Figure 5C] Figure for explaining the reference data (distance 3m) created by simulation [Figure 6] Flowchart for explaining the operation example of the identification unit during the first identification process [Figure 7] Flowchart for explaining the operation example of the identification unit during the second identification process [Figure 8A] Figure for explaining the method by which the identification unit makes a detailed determination as to whether an object is a curb [Figure 8B] Figure for explaining the method by which the identification unit makes a detailed determination as to whether an object is a curb [Figure 8C] Figure for explaining the method by which the identification unit makes a detailed determination as to whether an object is a curb [Figure 9] Flowchart for explaining the operation example of the object detection device [Figure 10] Figure illustrating the hardware configuration of a computer

Embodiments for Carrying Out the Invention

[0011] Hereinafter, each embodiment of the present disclosure will be described in detail with reference to the drawings. However, detailed descriptions that are not necessary, for example, detailed descriptions of well-known matters and duplicate descriptions of substantially the same configurations, may be omitted in some cases.

[0012] <Structure> The configuration of the object detection device 10 according to the embodiment of this disclosure will now be described. Figure 1 is a block diagram illustrating the functional configuration of the object detection device 10. As shown in Figure 1, the object detection device 10 includes a transmitter 11, a transmission control unit 12, a receiver 13, a signal analysis unit 14, an identification unit 15, a storage unit 16, and a distance measuring unit 17.

[0013] The object detection device 10 is mounted, for example, on a vehicle. In this case, the functional configuration of the object detection device 10 described above, excluding the transmitter 11 and receiver 13, is realized by a computer mounted on the vehicle executing a predetermined program. An example of a computer mounted on a vehicle is an ECU (Electronic Control Unit) for controlling the vehicle. However, the computer that realizes the functional configuration of the object detection device 10 is not limited to an ECU; for example, it may be a microcomputer independent of the ECU, a PC (Personal Computer), a tablet terminal, etc. Furthermore, if the computer that realizes the functional configuration of the object detection device 10 is connected to the transmitter 11 and receiver 13 via wireless communication or the like, the computer does not need to be mounted on the vehicle.

[0014] The object detection device of this disclosure does not necessarily have to be mounted on a vehicle.

[0015] The transmitter 11 transmits ultrasonic waves of multiple frequencies based on the control of the transmission control unit 12. The transmitter 11 is, for example, a speaker that transmits ultrasonic waves corresponding to the voltage of the control signal from the transmission control unit 12.

[0016] For example, the transmitter 11 transmits a chirp wave whose frequency changes over time, based on the control of the transmission control unit 12.

[0017] Alternatively, for example, the transmitter 11 may have multiple vibrators that are driven at different frequencies, thereby simultaneously transmitting ultrasound waves of multiple frequencies.

[0018] The transmission control unit 12 controls the transmitter 11 to transmit ultrasonic waves of multiple frequencies.

[0019] The receiver 13 receives the reflected waves that are formed when the ultrasonic waves transmitted by the transmitter 11 are reflected by an object. As described above, since the transmitter 11 transmits ultrasonic waves of multiple frequencies, the reflected waves received by the receiver 13 are ultrasonic waves that contain multiple frequencies.

[0020] The receiver 13 is, for example, a microphone that receives reflected waves and has a receiving element that generates a received signal corresponding to the intensity of the received reflected wave. The receiver 13 outputs the generated received signal to the signal analysis unit 14.

[0021] When the object detection device 10 is mounted on a vehicle, an object is, for example, an obstacle that exists in the direction of the vehicle's travel and obstructs the vehicle's movement. Alternatively, an object is a non-obstacle that exists in the direction of the vehicle's travel but does not obstruct the vehicle's movement. Examples of object types include walls, poles, and curbs. A wall is, for example, the wall of a parking lot. A pole is, for example, a pole that defines a parking space. A curb is a wheel stop (also called a wheel chock, parking block, or tire stopper) that defines the position of the rear wheels when a vehicle is stopped in a parking lot. Walls and poles are objects that prevent contact with the vehicle body and therefore qualify as obstacles. A curb, due to its height, is assumed to be an object that may come into contact with the wheels but is unlikely to come into contact with the vehicle body. For this reason, a curb is considered a non-obstacle.

[0022] In the following description, it is assumed that walls, poles, and curbs are pre-configured as candidate types of objects to be detected by the object detection device 10. However, the candidate types of objects to be detected by the object detection device of this disclosure are not limited to these examples, and various other obstacles or non-obstacles may be assumed.

[0023] The signal analysis unit 14 derives a frequency characteristic showing the relationship between the amplitude (received intensity) and frequency of the reflected wave based on the received signal output from the receiver 13. The signal analysis unit 14 derives the frequency characteristic of the envelope of the reflected wave by performing envelope detection on the reflected wave. The signal analysis unit 14 outputs the derived frequency characteristic data to the identification unit 15. In the following description, the data output by the signal analysis unit 14 may be referred to as measured data.

[0024] The identification unit 15 performs object identification processing based on the measured data. The identification processing by the identification unit 15 includes a first identification process that identifies whether the reflective surface of the object is flat or curved, and a second identification process that identifies the type of object. Details of the identification processing by the identification unit 15 will be described later.

[0025] The memory unit 16 stores reference data that the identification unit 15 uses to identify objects in the second identification process. The reference data is used by the identification unit 15 to identify the type of object by comparing it with measured data. The reference data is obtained by measuring or calculating in advance, through experimentation or simulation, what characteristics the frequency characteristics based on reflected waves from a particular type of object exhibit. "In advance" means, for example, before the design or shipment of the object detection device 10.

[0026] Reference data is created by conducting experiments or simulations with different types of objects. When reference data is created by experiment, for example, the transmitter 11 actually transmits ultrasonic waves to each of the pre-set candidate types of objects (walls, poles, and curbs), and the signal analysis unit 14 derives the frequency characteristics based on the received signals generated by the receiver 13 that receives the reflected waves. When reference data is created by simulation, for example, the frequency characteristics derived from the reflected waves generated when ultrasonic waves are transmitted to each of the walls, poles, and curbs are derived through simulation.

[0027] Figures 2A to 2C are schematic diagrams illustrating the simulations performed to create reference data. Figure 2A illustrates the simulation when the object is a wall with sufficient width and height. Figure 2B illustrates the simulation when the object is a pole with a diameter of 60 mm. Figure 2C illustrates the simulation when the object is a curbstone with a height of 100 mm. Note that Figures 2A to 2C show the positional relationship between the transmitter 11 and the object, and the receiver 13 is not shown.

[0028] Figure 2A shows a wall that is sufficiently long relative to the ultrasonic transmission range (e.g., the detectable range of the object detection device 10) in the direction of ultrasonic transmission. Figure 2B shows a pole with a diameter of 60 mm in the direction of ultrasonic transmission. Figures 2A and 2B show the transmitter 11 and the object as viewed from above. In the examples shown in Figures 2A and 2B, the height of the wall and the pole is infinite.

[0029] Figure 2C shows a curb 100 mm high in the direction of ultrasonic wave transmission. In the example shown in Figure 2C, it is assumed that the transmitter 11 (and receiver 13) are installed at a height of 390 mm from the ground (the height of the rear bumper of an existing vehicle). Figure 2C shows the transmitter 11 and the object viewed from the side. In the examples shown in Figures 2A and 2B, a two-dimensional simulation is performed in a plane parallel to the ground (the XY plane in the figures). In the example shown in Figure 2C, a two-dimensional simulation is performed in a plane perpendicular to the ground (the XZ plane in the figures).

[0030] The frequency characteristics derived from the reflected waves from the object vary depending on the distance (hereinafter simply referred to as distance) from the transmitter 11 and receiver 13 to the object. For this reason, reference data may be created for each predetermined distance. In the following explanation, it will be assumed that reference data will be created for distances of 1m, 2m, and 3m.

[0031] Figures 3A-3C, 4A-4C, and 5A-5C are diagrams illustrating the reference data created by the simulation in Figures 2A-2C. In Figures 3A-3C, 4A-4C, and 5A-5C, the frequency characteristics of the reflected wave are shown in graphs with the amplitude (received intensity) of the reflected wave on the vertical axis and frequency on the horizontal axis. In Figures 3A-3C, 4A-4C, and 5A-5C, the received intensity on the vertical axis is shown in the range of approximately -7dB to approximately 7dB. In Figures 3A-3C, 4A-4C, and 5A-5C, the frequency on the horizontal axis is shown in the range of approximately 40kHz to approximately 80kHz.

[0032] Figures 3A to 3C show the reference data for a distance of 1m. Figures 4A to 4C show the reference data for a distance of 2m. Figures 5A to 5C show the reference data for a distance of 3m.

[0033] Figures 3A, 4A, and 5A show the baseline data when the object is a wall surface. Figures 3B, 4B, and 5B show the baseline data when the object is a pole with a diameter of 60 mm. Figures 3C, 4C, and 5C show the baseline data when the object is a curbstone with a height of 100 mm.

[0034] As shown in Figures 3A-3C, 4A-4C, and 5A-5C, the shape of the frequency response graph obtained by the simulation differs depending on the type of object and the distance. For example, if the object is a wall, the shape of the frequency response graph is a monotonically increasing linear shape regardless of distance. For example, if the object is a pole, the shape of the frequency response graph is a curved shape with multiple poles regardless of distance. For example, if the object is a curb, the shape of the frequency response graph changes greatly with distance, being an upward-convex curve (distance 1m), a downward-convex curve (distance 2m), or a slowly monotonically decreasing curve (distance 3m).

[0035] As shown above, the shape of the frequency response graph derived from the reflected waves from an object varies greatly depending on the type of object. This is thought to be because the shape and area of ​​the reflective surface on which the object reflects ultrasound differ. For example, if the object is a pole, the reflective surface is curved, and the effective area that reflects ultrasound in the direction of transmission is significantly smaller than if the material were a wall. For example, if the object is a curbstone, the reflective surface is flat, but the area of ​​the reflective surface is smaller than if the object were a wall, but larger than if it were a pole. The differences in the reference data for each object obtained by simulation, as shown in Figures 3A-3C, 4A-4C, and 5A-5C, are thought to be caused by these differences in the shape and area of ​​the reflective surface.

[0036] When the object is a curb, the difference in the shape of the frequency response graph depending on the distance is thought to be due to the positional relationship between the transmitter 11 and the curb. As mentioned above, in the simulation for the curb (see Figure 2C), it is assumed that the curb is 100 mm high and the transmitter 11 is installed at 390 mm above the ground. Depending on the distance between the transmitter 11 and the curb, the angle of the line connecting the transmitter 11 and the reflective surface of the curb (the vertical surface closest to the transmitter 11) changes. This changes the degree of ultrasonic reflection by the reflective surface of the curb, and therefore the shape of the frequency response graph changes depending on the distance.

[0037] The storage unit 16 may store the reference data as data showing the shape of the frequency response graph, as shown in Figures 3A to 3C, 4A to 4C, and 5A to 5C, or as table-formatted data showing the frequency response. If the storage unit 16 stores the reference data in table format, when the identification unit 15 reads the reference data, it should generate data showing the shape of the frequency response graph, as shown in Figures 3A to 3C, 4A to 4C, and 5A to 5C, based on the table-formatted reference data.

[0038] The shape of the frequency response graph in this disclosure includes at least the number of poles the frequency response graph has in a given frequency range, or the slope of the frequency response graph in a given frequency range.

[0039] Returning to the explanation of Figure 1, the distance measuring unit 17 measures distance. The distance measuring unit 17 measures distance by, for example, emitting a laser or millimeter wave and measuring the flight time until it is reflected by an object and returns.

[0040] <Details of the identification process> The object identification process performed by the identification unit 15 will be described in detail below.

[0041] As described above, the identification unit 15 includes a first identification process that identifies whether an object is a flat surface or a curved surface based on measured frequency characteristic data output by the signal analysis unit 14, and a second identification process that identifies which of the preset candidate object types the object is by comparing the measured data with reference data stored in the storage unit 16. In this embodiment, the candidate object types in the second identification process are a wall, a pole, and a curb.

[0042] (First identification process) Figure 6 is a flowchart illustrating an example of the operation of the identification unit 15 during the first identification process.

[0043] In step S1, the identification unit 15 extracts poles from a graph of measured data within a predetermined frequency range. The predetermined frequency range is an arbitrarily set range within the range of frequencies that the transmitter 11 can transmit. In the flowchart shown in Figure 6, it is assumed that the predetermined frequency range is set to a range between f1 and f2. The values ​​of f1 and f2 are not limited in this disclosure, but for accurate identification processing, it is preferable to set the predetermined frequency range to a reasonably wide range. For example, the values ​​of f1 and f2 should be set to 40kHz ≤ f1 ≤ 55kHz and 70kHz ≤ f2 ≤ 80kHz, considering the graphs of reference data shown in Figures 3A to 3C, 4A to 4C, and 5A to 5C.

[0044] In step S2, the identification unit 15 determines whether the number of poles extracted in step S1 is three or more. If it determines that the number of poles is three or more (step S2: Y), the identification unit 15 proceeds to step S3; otherwise (step S2: N), the identification unit 15 proceeds to step S4. Note that the number of poles is not limited to three, and can be set to any number (a first predetermined number) taking into consideration the measurement environment.

[0045] In step S3, the identification unit 15 identifies the object that returned the reflected wave showing the frequency characteristics of the measured data as an object having a curved reflective surface.

[0046] In step S4, the identification unit 15 identifies the object that returned the reflected wave showing the frequency characteristics of the measured data as an object having a planar reflective surface.

[0047] The identification unit 15 terminates the first identification process after step S3 or step S4.

[0048] The basis for identification in this first identification process is as follows: As shown in the reference data (simulation data) in Figures 3A-3C, 4A-4C, and 5A-5C, it is known that in the case of a pole with a curved reflective surface, the graph of the frequency characteristics of the reflected wave will be a curved shape containing many poles in the frequency range from 40kHz to 80kHz. Also, as shown in the reference data in Figures 3A-3C, 4A-4C, and 5A-5C, it is known that in the case of a wall or curb with a flat reflective surface, the graph of the frequency characteristics of the reflected wave will be a curved shape containing 0 or 1 poles in the frequency range from 40kHz to 80kHz. Therefore, the identification unit 15 can identify an object that returns a reflected wave showing the frequency characteristics of the measured data as an object that includes a curved reflective surface if the graph of the frequency characteristics of the measured data includes three or more poles, and otherwise identify an object that returns a reflected wave showing the frequency characteristics of the measured data as an object that includes a flat reflective surface.

[0049] As explained above, the first identification process can appropriately identify whether an object that returns a reflected wave showing the frequency characteristics of the measured data has a flat reflective surface or a curved reflective surface.

[0050] (Second identification process) Figure 7 is a flowchart illustrating an example of the operation of the identification unit 15 during the second identification process. In the second identification process, the system identifies which of the pre-set candidate object types returned the reflected wave showing the frequency characteristics of the measured data.

[0051] In step S11, the identification unit 15 extracts poles from a graph of the measured data for a predetermined frequency range. Step S11 in the second identification process is the same as step S1 in the first identification process.

[0052] In step S12, the identification unit 15 determines whether the number of poles extracted in step S11 is three or more. If it determines that the number of poles is three or more (step S12: Y), the identification unit 15 proceeds to step S13; otherwise (step S12: N), the identification unit 15 proceeds to step S14. Step S12 in the second identification process is the same as step S2 in the first identification process.

[0053] In step S13, the identification unit 15 identifies the object that returned the reflected wave showing the frequency characteristics of the measured data as a pole having a curved reflective surface. After step S13, the identification unit 15 terminates the second identification process.

[0054] In step S14, the identification unit 15 determines whether the number of poles extracted in step S11 is one. If it determines that the number of poles is one (step S14: Y), the identification unit 15 proceeds to step S15; otherwise (step S14: N), the identification unit 15 proceeds to step S16. Note that in step S14, the number of poles is not limited to one, and can be set to a second predetermined number that is less than a first predetermined number and greater than zero, taking into consideration the measurement environment.

[0055] In step S15, the identification unit 15 determines whether or not an object is a curb by comparing the measured data with reference data related to curbs (see Figures 3C, 4C, and 5C).

[0056] In the example reference data shown in Figures 3A-3C, 4A-4C, and 5A-5C, the frequency response graph with one pole in a predetermined frequency range is caused by a curb at a distance of 1m or 2m, as shown in Figures 3C and 4C. The identification unit 15 can determine that if the number of poles in a predetermined frequency range in the frequency response of the measured data is one, there is a high probability that the object is a curb.

[0057] The identification unit 15 may more accurately identify whether an object is a curb by comparing the measured data with reference data in more detail, as described below.

[0058] Figures 8A to 8C illustrate how the identification unit 15 makes a detailed determination as to whether or not an object is a curb. In Figures 8A to 8C, the vertical axis represents the amplitude (received intensity) of the ultrasound, and the horizontal axis represents the frequency. In the example shown in Figures 8A to 8C, it is assumed that the graph of the frequency characteristics of the measured data has a downward-convex curve shape, as shown in Figure 8A. First, the identification unit 15 determines whether the graph of the reference data for a curb at a distance of 2m has a similar shape from the reference data stored in the memory unit 16.

[0059] For example, as shown in Figure 8B, the identification unit 15 determines whether the frequency f0 at the pole of the measured data is a value within the range of the frequency f0'±α at the pole of the reference data. α is a predetermined margin, for example, α = 5 kHz. If the frequency f0 at the pole of the measured data is a value within the range of the frequency f0'±α at the pole of the reference data, the identification unit 15 determines that the object is a curb. If the frequency f0 at the pole of the measured data is not within the range of the frequency f0'±α at the pole of the reference data, the identification unit 15 determines, for example, that the object that returned the reflected wave showing the frequency characteristics of the measured data is not a curb.

[0060] Alternatively, as shown in Figure 8C, the identification unit 15 identifies the difference d between the amplitude a0 at the pole and the amplitude a2 at frequency f2 in the measured data. a The difference d between the amplitude a0' at the pole and the amplitude a2' at the maximum frequency f2 within a predetermined range is calculated in the reference data. a The identification unit 15 calculates the difference d of the measured data. a The difference d from the reference data a It determines whether the value falls within the range of ±β. β is a predetermined margin, for example, β = 1 dB. The identification unit 15 determines the difference d of the measured data. a The difference d from the reference data aIf it falls within ±β, the object is determined to be a curb. The identification unit 15 uses the difference d of the measured data. a The difference d from the reference data a If the value is not within the ±β range, for example, it is determined that the object that returned the reflected wave showing the frequency characteristics of the measured data is not a curb.

[0061] In the example shown in Figure 8C, the identification unit 15 is described as taking the difference between the amplitude value at the pole and the amplitude value at the maximum frequency f2 within a predetermined range. However, in this disclosure, identification may also be performed by calculating the difference between the amplitude value at the pole and the amplitude value at the minimum frequency f1 within a predetermined range, using both the measured data and the reference data.

[0062] Returning to the explanation of Figure 7, in step S16, the identification unit 15 determines whether the number of poles extracted in step S11 is 0 or not. If it determines that the number of poles is 0 (step S16: Y), the identification unit 15 proceeds to step S17; otherwise (step S16: N), the identification unit 15 proceeds to step S18.

[0063] In step S17, the identification unit 15 determines whether the object is a wall or a curb by comparing it with reference data related to the wall surface (see Figures 3A, 4A, and 5A), reference data related to the curb (see Figures 3C, 4C, and 5C), and measured data.

[0064] In the examples of reference data shown in Figures 3A-3C, 4A-4C, and 5A-5C, the frequency response graphs with zero poles in a predetermined frequency range occur for walls at distances of 1m, 2m, and 3m, and for curbs at a distance of 3m, as shown in Figures 3A, 4A, 5A, and 5C. Here, as shown in Figures 3A, 4A, and 5A, the graph of the reference data for walls has a monotonically increasing shape. On the other hand, as shown in Figure 5C, the graph of the reference data for curbs at a distance of 3m has a monotonically decreasing shape. Therefore, the identification unit 15 identifies the object as a wall when the slope is a positive value in the frequency response of the measured data, and identifies the object as a curb when the slope is a negative value.

[0065] In step S18, the identification unit 15 determines that the object that returned the reflected wave showing the frequency characteristics of the measured data is not one of the candidate types of object (wall, pole, curb). This is because, in the example of reference data shown in Figures 3A to 3C, 4A to 4C, and 5A to 5C, there is no data showing a frequency characteristic graph where the number of poles is less than 3 in a given frequency range and is neither 1 nor 0.

[0066] As explained above, the second identification process allows for the identification of an object type by comparing the measured data of reflected waves from the object with reference data prepared in advance for each type of object.

[0067] In the example of the second identification process shown in Figure 7, the identification unit 15 performs identification by comparing the reference data stored in the storage unit 16 with the measured data and extracting reference data that has a shape similar to the shape of the graph of the measured data. For example, the identification unit 15 may perform identification by pre-extracting reference data corresponding to the distance to an object measured separately by the distance measuring unit 17 and comparing it with the measured data. In this case, the number of reference data that the identification unit 15 compares can be reduced, thus speeding up the second identification process.

[0068] The example of operation of the second identification process shown in Figure 7 is based on the premise that the reference data has the shape of the frequency characteristic graphs exemplified in Figures 3A to 3C, 4A to 4C, and 5A to 5C. In practice, the object detection device of this disclosure can appropriately adjust the judgment criteria for each judgment in the second identification process performed by the identification unit based on the shape of the frequency characteristic graph shown by the reference data. For example, if the candidate object types include things other than walls, poles, and curbs, the identification unit may make a new judgment based on the shape of the frequency characteristic graph in the reference data of those objects.

[0069] <Example of operation of object detection device 10> The following describes an example of the overall operation of the object detection device 10. Figure 9 is a flowchart illustrating the operation example of the object detection device 10.

[0070] In step S21, the transmitter 11 transmits ultrasonic waves of multiple frequencies.

[0071] In step S22, the signal analysis unit 14 derives measured frequency characteristics data based on the received signal generated by the receiver 13 due to the reflected waves from an object.

[0072] In step S23, the identification unit 15 performs identification processing based on the measured data (see Figure 6 or Figure 7).

[0073] In step S24, the identification unit 15 outputs the identification result.

[0074] For example, when the object detection device 10 is used to prevent collisions in automatic parking control of a vehicle, the object detection device 10 outputs a detection result indicating that an object has been detected, as well as an identification result, to the computer that performs the automatic parking control of the vehicle. For example, if the object identification result is a curb, the possibility of the object contacting the vehicle body is low, so the computer that performs the automatic parking control does not stop the vehicle at that point, but moves the vehicle until the wheels are close to the curb (wheel stop). For example, if the object identification result is a wall, the computer that performs the automatic parking control can stop the vehicle to avoid contact between the wall and the vehicle body. For example, if the object identification result is a pole that defines a parking space, the computer that performs the automatic parking control can slow down the vehicle and gradually move it closer to the pole in order to avoid contact between the pole and the vehicle body and bring the vehicle closer to the pole.

[0075] <Example of computer hardware configuration> The object detection device 10 described in the above embodiments is a computer, and its functional configuration is realized by the computer executing a predetermined program. Below, an example of the hardware configuration of the computer that realizes each function of the object detection device 10 will be described.

[0076] Figure 10 illustrates the hardware configuration of computer 2100. As shown in Figure 10, computer 2100 includes input devices 2101 such as input buttons and a touchpad, output devices 2102 such as a display and speakers, a CPU (Central Processing Unit) 2103, ROM (Read Only Memory) 2104, and RAM (Random Access Memory) 2105. Computer 2100 also includes storage devices 2106 such as a hard disk drive and SSD (Solid State Drive), a reader 2107 that reads information from recording media such as DVD-ROM (Digital Versatile Disk Read Only Memory) and USB (Universal Serial Bus) memory, and a transceiver 2108 that communicates via a network. The above components are connected by a bus 2109.

[0077] The reading device 2107 reads the program for realizing the functions of each of the above-mentioned parts from the recording medium on which the program is recorded and stores it in the storage device 2106. Alternatively, the transmitting / receiving device 2108 communicates with a server device connected to the network and stores the program for realizing the functions of each of the above-mentioned parts downloaded from the server device in the storage device 2106.

[0078] The CPU 2103 copies the program stored in the memory device 2106 to the RAM 2105, and then sequentially reads and executes the instructions contained in that program from the RAM 2105, thereby realizing the functions of each of the above-mentioned parts. Furthermore, when the program is executed, information obtained from the various processes described in each embodiment is stored in the RAM 2105 or the memory device 2106 and used as appropriate.

[0079] In the above description, the notation "...part" used for each component may be replaced with other notations such as "...assembly," "...circuitry," "...device," "...unit," or "...module." Furthermore, the device may be configured to be executed by a CPU using a program stored in memory. [Industrial applicability]

[0080] This disclosure is useful for object detection devices that use ultrasound to detect objects. [Explanation of symbols]

[0081] 10 Object detection device 11 Transmitter 12 Transmission Control Unit 13 Receiver 14 Signal analysis section 15 Identification unit 16 Memory section 17 Ranging section

Claims

1. A signal analysis unit that derives a frequency characteristic representing the relationship between the amplitude and frequency of ultrasonic waves of multiple frequencies, based on the reflected waves reflected by an object. An identification unit that identifies whether the reflective surface of the object is a flat or curved surface based on the frequency characteristics in a predetermined frequency range, An object detection device equipped with the following features.

2. The aforementioned identification unit is The identification is performed based on the number of extreme points in the frequency characteristic graph within the predetermined frequency range. The object detection device according to claim 1.

3. The aforementioned identification unit is If the number of poles is less than a first predetermined number, the reflecting surface is identified as a plane. If the number of poles is equal to or greater than the first predetermined number, the reflective surface is identified as a curved surface. The object detection device according to claim 2.

4. The identification unit identifies the type of object from a preset list of candidate object types based on the shape of the graph of the frequency characteristics in the predetermined frequency range. The object detection device according to any one of claims 1 to 3.

5. The identification unit identifies the object as a curb if the number of poles on the graph in the predetermined frequency range is one. The object detection device according to claim 4.

6. The identification unit identifies the object as either a wall or a curb if the number of poles is zero. The object detection device according to claim 5.

7. The identification unit, if the number of poles is 0, identifies whether the object is a wall or a curb based on the slope of the graph. The object detection device according to claim 6.

8. The aforementioned identification unit is If the graph is monotonically increasing, the object is identified as a wall. The object is identified as a curb when the graph is monotonically decreasing. The object detection device according to claim 7.

9. The system further includes a storage unit that stores reference data for the frequency characteristics, which has been created in advance for each type of object. The identification unit identifies whether the object is a wall or a curb by comparing the reference data with the shape of the graph. The object detection device according to claim 6.

10. The system further includes a distance measuring unit for measuring the distance to the aforementioned object, The storage unit stores the reference data that has been created in advance for each of the different distances between them. The identification unit identifies whether the object is a wall or a curb by comparing the reference data corresponding to the measured distance with the graph. The object detection device according to claim 9.

11. The object detection device according to claim 1, wherein the ultrasonic waves of multiple frequencies are chirp waves whose frequency changes over time.

12. The signal analysis unit derives the frequency characteristics by performing envelope detection on the reflected wave. The object detection device according to claim 11.

13. Computers Based on the reflected waves generated by an object reflecting ultrasonic waves of multiple frequencies, a frequency characteristic representing the relationship between the amplitude and frequency of the reflected waves is derived. Based on the frequency characteristics in a predetermined frequency range, it is determined whether the reflective surface of the object is a flat or curved surface. Object detection method.

14. The identification is performed based on the number of poles in the graph of the frequency characteristics in the predetermined frequency range. The object detection method according to claim 13.

15. The aforementioned reflective surface is If the number of poles is less than a first predetermined number, it is identified as a plane. If the number of poles is equal to or greater than the first predetermined number, it is identified as a curved surface. The object detection method according to claim 14.

16. The computer further identifies the type of object from a set of candidate object types based on the shape of the graph of the frequency characteristics in the predetermined frequency range. The object detection method according to any one of claims 13 to 15.

17. The computer identifies the object as a curb if the number of poles on the graph in the predetermined frequency range is one. The object detection method according to claim 16.

18. The computer identifies the object as either a wall or a curb if the number of poles is zero. The object detection method according to claim 17.

19. The computer, if the number of poles is zero, identifies whether the object is a wall or a curb based on the slope of the graph. The object detection method according to claim 18.

20. A procedure for deriving a frequency characteristic representing the relationship between the amplitude and frequency of a reflected wave, based on the reflected waves obtained by an object reflecting ultrasonic waves of multiple frequencies, A procedure for determining whether the reflective surface of an object is a flat or curved surface based on the frequency characteristics in a predetermined frequency range, A program that causes a computer to execute something.

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