Object recognition device and object recognition method

Abstract

To provide an object recognition device and an object recognition method that can predict positionally-related information of a peripheral vehicle with higher precision, taking into account a yaw-angle rotation and movement of the peripheral object and a yaw-angle rotation and movement of the own vehicle.SOLUTION: An object recognition device predicts an untransformed predicted value of a relative position of a peripheral object based on a position of the own vehicle at a previous target time, and an untransformed predicted value of a relative yaw-angle, in accordance with a predicted value of a relative position of the peripheral object, a predicted value of an absolute speed, a predicted value of the relative yaw-angle, and a predicted value of a yaw-angle speed at the previous target time; and the object recognition device converts the untransformed predicted value of the relative position and the untransformed predicted value of the relative yaw-angle of the peripheral object into a predicted value of the relative position and a predicted value of the relative yaw-angle of the peripheral object based on a position of the own vehicle at the current target time, in accordance with an amount of movement and a yaw-angle change of the own vehicle.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] This application relates to an object recognition device and an object recognition method.

Background Art

[0002] In the technology of Patent Document 1, for each of a plurality of objects detected by a sensor, an integrated determination distance for determining the same object is initially set, the traveling direction of the object is estimated based on the sensor detection information, the integrated determination distance is expanded along the traveling direction, and the objects are integrated based on the integrated determination distance. The traveling direction of the object is estimated from the absolute velocity of the object or the longitudinal direction of the shape of the object.

[0003] In the technology of Patent Document 2, using the vehicle speed and yaw angular velocity of the host vehicle, the relative velocity of the object is converted into an absolute velocity, the relative position of the object at the previous time is converted into the relative position of the object at the current time, and the absolute velocity of the object is converted into a relative velocity, thereby predicting the object position, velocity, and their errors at the current time from the object position, velocity, and their errors at the previous time.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the technology of Patent Document 1, the direction of the absolute velocity (ground velocity) or the longitudinal direction of the object shape is estimated as the direction (traveling direction) of the object, and the integrated determination distance is expanded along the direction of the object. However, when the object turns right or left or turns with a small turning radius and the direction of the object changes, there is a problem that the estimation accuracy of the direction of the object deteriorates and the integration fails.

[0006] The technology described in Patent Document 2 uses the vehicle speed and yaw angular velocity of the vehicle itself to convert the relative velocity of an object into absolute velocity (ground velocity), predicts the object's position at the current time from its position at the previous time, and converts the absolute velocity of the object back into relative velocity. Using this, it is possible to predict the direction of the object's absolute velocity, i.e., the object's orientation, taking into account the movement and turning of the vehicle itself during the predicted time interval. However, each time relative velocity is converted to absolute velocity, or vice versa, the error in the vehicle's ground velocity is superimposed multiple times on the error in the object's relative velocity. This leads to a problem of degraded prediction accuracy. Furthermore, while the technology in Patent Document 2 considers the yaw angular velocity of the vehicle itself, it does not consider the yaw angular velocity of the object, and, similar to Patent Document 1, there is a problem of degraded estimation accuracy when the object is turning.

[0007] Therefore, the present invention aims to provide an object recognition device and object recognition method that can accurately predict the position-related information of a surrounding vehicle by considering the rotation and movement of the yaw angle of the surrounding object and the rotation and movement of the yaw angle of the own vehicle, without converting the predicted value of the relative velocity of the surrounding object to the vehicle itself, then to the predicted value of the absolute velocity of the surrounding object, and then converting it back to the predicted value of the relative velocity of the surrounding object. [Means for solving the problem]

[0008] The object recognition device relating to this application is A vehicle information acquisition unit that acquires the detected absolute speed and yaw angular velocity of the vehicle, An object motion prediction unit predicts, based on the predicted relative positions of surrounding objects around the vehicle at the previous target time, the predicted absolute velocities of the surrounding objects, the predicted relative yaw angles of the surrounding objects with respect to the position of the vehicle, and the predicted yaw angular velocities of the surrounding objects, the object motion prediction unit predicts the pre-conversion predicted relative positions of the surrounding objects, the predicted absolute velocities of the surrounding objects, the pre-conversion predicted relative yaw angles of the surrounding objects, and the predicted yaw angular velocities of the surrounding objects at the current target time, which is different from the previous target time, with respect to the position of the vehicle at the previous target time. A vehicle motion prediction unit predicts the amount of movement of the vehicle and the amount of change in the vehicle's yaw angle from the previous target time to the current target time, based on the detected absolute speed and yaw angular velocity of the vehicle acquired at the previous target time. An object motion conversion unit converts the predicted values ​​of the relative position and relative yaw angle of the surrounding objects before conversion, based on the amount of movement of the vehicle and the amount of change in the yaw angle of the vehicle, into predicted values ​​of the relative position and relative yaw angle of the surrounding objects at the target time, with reference to the position of the vehicle at the target time. It is something that is provided.

[0009] The object recognition method relating to the present invention is an object recognition method that causes a processing unit to execute the following steps: A vehicle information acquisition step that acquires the detected absolute speed and yaw angular velocity of the vehicle, A step to predict object motion, based on the predicted relative positions of surrounding objects, the predicted absolute velocities of the surrounding objects, the predicted relative yaw angles of the surrounding objects, and the predicted yaw angular velocities of the surrounding objects at the previous target time, predicting the pre-transformation predicted relative positions of the surrounding objects, the predicted absolute velocities of the surrounding objects, the pre-transformation predicted relative yaw angles of the surrounding objects, and the predicted yaw angular velocities of the surrounding objects at the current target time, which is different from the previous target time and is based on the position of the vehicle at the previous target time; A vehicle motion prediction step that predicts the amount of movement of the vehicle and the amount of change in the vehicle's yaw angle from the previous target time to the current target time, based on the detected absolute speed and yaw angular velocity of the vehicle obtained at the previous target time. A motion transformation step that transforms the predicted values ​​of the relative position and relative yaw angle of the surrounding objects before transformation, based on the amount of movement of the vehicle and the amount of change in the yaw angle of the vehicle, into predicted values ​​of the relative position and relative yaw angle of the surrounding objects at the target time, with reference to the position of the vehicle at the target time. It is something that is provided. [Effects of the Invention]

[0010] According to the object recognition device and object recognition method of the present invention, in the object motion prediction unit, the predicted value of the absolute velocity of the surrounding object is converted to a predicted value of the relative velocity and used as is without being converted back to the predicted value of the absolute velocity. Therefore, the deterioration of accuracy due to conversion and reconversion can be suppressed. Furthermore, in the object motion prediction unit, the pre-conversion predicted value of the relative position of the surrounding object and the pre-conversion predicted value of the relative yaw angle of the surrounding object at the current target time are predicted based on the position of the vehicle at the previous target time, so that the rotation and movement of the yaw angle of the surrounding object can be accurately predicted separately from the motion of the vehicle. In the vehicle motion prediction unit, the amount of movement of the vehicle from the previous target time to the current target time and the amount of change in the yaw angle of the vehicle are predicted as the motion of the vehicle. Then, in the object motion conversion unit, based on the amount of movement and change in yaw angle of the vehicle itself, the predicted values ​​of the relative position and relative yaw angle of surrounding objects are converted into predicted values ​​of the relative position and relative yaw angle of surrounding objects at the current time, based on the position of the vehicle at the current time. Therefore, by taking into account the rotation and movement of the yaw angle of surrounding objects, as well as the rotation and movement of the yaw angle of the vehicle itself, the predicted values ​​of the relative position and relative yaw angle of surrounding vehicles can be predicted with high accuracy. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic block diagram of the object recognition device and vehicle control device according to Embodiment 1. [Figure 2] This is a schematic hardware configuration diagram of the object recognition device and vehicle control device according to Embodiment 1. [Figure 3] It is a schematic hardware configuration diagram of another example of the object recognition device and the vehicle control device according to Embodiment 1. [Figure 4] It is a flowchart for explaining the schematic processing of the object recognition device and the vehicle control device according to Embodiment 1. [Figure 5] It is a diagram for explaining the coordinate system of the host vehicle according to Embodiment 1. [Figure 6] It is a diagram for explaining the processing of the host vehicle motion prediction unit according to Embodiment 1. [Figure 7] It is a diagram for explaining the processing of the object motion prediction unit according to Embodiment 1. [Figure 8] It is a diagram for explaining the processing of the object motion conversion unit according to Embodiment 1. [Figure 9] It is a diagram for explaining the coordinate system of the host vehicle according to Embodiment 2. [Figure 10] It is a diagram for explaining prediction data including the shape information of surrounding objects according to other embodiments.

Embodiments for Carrying Out the Invention

[0012] 1. Embodiment 1 The object recognition device 1 according to Embodiment 1 will be described with reference to the drawings. In this embodiment, the object recognition device 1 is incorporated in the vehicle control device 30.

[0013] As shown in FIG. 1, the host vehicle includes a surrounding monitoring device 31, a position detection device 32, a vehicle state detection device 3, a wireless communication device, a vehicle control device 30, a drive control device 35, a power unit 8, an electric steering device 7, an electric brake device 9, and a human interface device 36, etc.

[0014] The surrounding monitoring device 31 is a device such as a camera or radar that monitors the surroundings of the vehicle. As the radar, a millimeter-wave radar, a lidar (LiDAR: Light Detection and Ranging), an ultrasonic radar, etc. are used. When a plurality of types of surrounding monitoring devices 31 are provided, the detection results may be fused.

[0015] The position detection device 32 detects the current position (latitude, longitude, altitude) of the host vehicle. As the position detection device 32, a GNSS antenna or the like that receives a signal output from an artificial satellite such as GNSS (Global Navigation Satellite System) is used. Note that, for the detection of the current position of the host vehicle, various methods such as a method using the running lane number of the host vehicle, a map matching method, a dead reckoning method, and a method using detection information around the host vehicle may be used in combination.

[0016] The wireless communication device 34 performs wireless communication with a base station or the like using a cellular wireless communication standard such as 4G or 5G. The wireless communication device 34 communicates with surrounding vehicles and roadside units existing around the host vehicle by wireless communication to acquire various types of information.

[0017] As the drive control device 35, a power control device, a brake control device, an automatic steering control device, a light control device, etc. are provided. The power control device controls the output of the power unit 8 such as an internal combustion engine or a motor. The brake control device controls the braking operation of the electric brake device 9. The automatic steering control device controls the electric steering device 7. The light control device controls the direction indicator, hazard lamp, etc.

[0018] The vehicle state detection device 33 is a detection device that detects the driving state of the vehicle. The vehicle's driving state is detected as follows: absolute velocity Ve, absolute acceleration αe, absolute direction of travel (direction of travel), yaw angular velocity ωe, roll angle Φe, roll angular velocity φe, pitch angle Γe, and pitch angular velocity γe. The absolute velocity Ve and absolute acceleration αe are the velocity and acceleration relative to the ground, respectively. The roll angle Φe and pitch angle Γe are angles relative to the horizontal plane. For example, the vehicle state detection device 33 may be equipped with a speed sensor to detect the rotational speed of the wheels, an acceleration sensor, a compass, a three-axis angular velocity sensor to detect the yaw angular velocity ωe, roll angular velocity φe, and pitch angular velocity γe acting on the vehicle, and a horizontal sensor to detect the roll angle Φe and pitch angle Γe. In this embodiment, at least the absolute direction of travel (direction of travel) and yaw angular velocity ωe need to be detected as angle-related information. The vehicle's velocity may also be detected by other methods, such as integrating the acceleration.

[0019] The human interface device 36 is a device that receives input from the driver, such as a speaker, display screen, and input device, and transmits information to the driver.

[0020] 1-1. Vehicle control device 30 The vehicle control device 30 includes functional units such as a self-vehicle information acquisition unit 51, a surrounding information acquisition unit 52, a self-vehicle motion prediction unit 53, an object motion prediction unit 54, an object motion conversion unit 55, a predicted value comparison and update unit 56, and a vehicle control unit 57. Each function of the vehicle control device 30 is realized by the processing circuits provided in the vehicle control device 30. Specifically, as shown in Figure 2, the vehicle control device 30 includes a arithmetic processing unit 90 such as a CPU (Central Processing Unit), a storage device 91, and an input / output device 92 that inputs and outputs external signals to and from the arithmetic processing unit 90.

[0021] The arithmetic processing unit 90 may include an ASIC (Application Specific Integrated Circuit), an IC (Integrated Circuit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), a GPU (Graphics Processing Unit), an AI (Artificial Intelligence) chip, various logic circuits, and various signal processing circuits. Furthermore, multiple arithmetic processing units 90 of the same or different types may be provided, with each processing unit being assigned to a specific task. The storage device 91 may include various storage devices such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), and hard disks.

[0022] The input / output device 92 is equipped with a communication device, an A / D converter, input / output ports, a drive circuit, etc. The input / output device 92 is connected to the surrounding monitoring device 31, the position detection device 32, the vehicle status detection device 33, the wireless communication device 34, the drive control device 35, and the human interface device 36, etc., and communicates with these devices.

[0023] The functions of each functional unit 51 to 57 of the vehicle control device 30 are realized by the arithmetic processing unit 90 executing software (programs) stored in the storage device 91 and cooperating with other hardware of the vehicle control device 30, such as the storage device 91 and the input / output device 92. Various setting data used by each functional unit 51 to 57 are stored in the storage device 91, such as an EEPROM.

[0024] Alternatively, the vehicle control device 30 may be equipped with dedicated hardware 93 as a processing circuit, as shown in Figure 3, such as a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, a GPU, an AI chip, or a circuit combining these. The functions of the vehicle control device 30 will be described in detail below.

[0025] Figure 4 is a schematic flowchart illustrating the processing (object recognition method and vehicle control method) of the object recognition device 1 and vehicle control device 30 according to this embodiment. The processing in Figure 4 is executed, for example, at cycles of the target time, by the arithmetic processing unit 90 executing the software (program) stored in the storage device 91. Note that the calculation cycles of each step may be different.

[0026] 1-1-1. Vehicle Information Acquisition Unit 51 In step S01 of Figure 4, the vehicle information acquisition unit 51 periodically acquires the driving status of the vehicle. Based on the detection information from the vehicle state detection device 33, the vehicle information acquisition unit 51 acquires the detected absolute speed value Ve_det and the detected yaw angular velocity value ωe_det of the vehicle. The vehicle information acquisition unit 51 also acquires the detected absolute direction of travel (direction of travel) of the vehicle based on the detection information from the vehicle state detection device 33. The absolute direction of travel of the vehicle is the direction X in the longitudinal direction of the vehicle.

[0027] The vehicle information acquisition unit 51 acquires the detected absolute acceleration value αe_det of the vehicle based on the detection information from the vehicle state detection device 33.

[0028] Furthermore, the vehicle information acquisition unit 51 acquires detected values ​​of the vehicle's position coordinates (latitude, longitude, altitude) from the position detection device 32. Based on the vehicle's position coordinates, the vehicle information acquisition unit 51 may also acquire detected values ​​of the vehicle's absolute speed Ve_det, absolute acceleration αe_det, and absolute direction of travel. If the vehicle information acquisition unit 51 cannot detect a GNSS signal, it may update the detected values ​​of the position coordinates based on the time-series data of the vehicle state detection device 33.

[0029] 1-1-2. Surrounding Information Acquisition Unit 52 In step S02 of Figure 4, the surrounding information acquisition unit 52 periodically acquires position-related information of surrounding objects present around the vehicle. The surrounding information acquisition unit 52 acquires the detected absolute velocity Vs_det and the detected yaw angular velocity ωs_det for the surrounding objects. The surrounding information acquisition unit 52 also acquires the detected relative position RPs_det and the detected relative yaw angle θs_det for the surrounding objects, relative to the position of the vehicle. The relative yaw angle θs of the surrounding object is the relative angle between the direction of travel (vertical direction) of the surrounding object (surrounding vehicle) and the longitudinal direction X of the vehicle. The following explanation will use the case where the surrounding object is a moving surrounding vehicle as an example. Note that the surrounding object may be various stationary objects. If multiple surrounding objects are detected, the object recognition device 1 performs processing for each surrounding object.

[0030] The surrounding information acquisition unit 52 acquires detected values ​​of various relative information of surrounding objects in the vehicle's coordinate system. As shown in Figure 5, the vehicle's coordinate system is a coordinate system with the vehicle's vertical X and horizontal Y as coordinate axes. In this embodiment, the origin of the vehicle's coordinate system is set to a representative point of the vehicle, such as the neutral steering point.

[0031] In this embodiment, the surrounding information acquisition unit 52 acquires the following relative position values ​​RPs_det[RPsX_det, RPsY_det], relative yaw angle values ​​θs_det, relative velocity values ​​RVs_det[RVsX_det, RVsY_det], and relative acceleration values ​​Rαs_det[RαsX_det, RαsY_det] for surrounding objects based on the detection information from the surrounding monitoring device 31, with respect to the position of the vehicle. Various known methods are used to acquire these values. Here, RPsX_det is the vertical X component of the relative position detection value, and RPsY_det is the horizontal Y component of the relative position detection value. Similarly, RVsX_det is the vertical X component of the relative velocity detection value, and RVsY_det is the horizontal Y component of the relative velocity detection value.

[0032] The surrounding information acquisition unit 52 calculates the detected absolute velocity value Vs_det of surrounding objects by adding the detected relative velocity values ​​RVs_det[RVsX_det, RVsY_det] of surrounding objects to the detected absolute velocity value Ve_det of the vehicle itself. For example, the surrounding information acquisition unit 52 calculates the magnitude of a vector consisting of the detected absolute velocity value Ve_det of the vehicle itself, the vertical X component RVsX_det of the detected relative velocity value of surrounding objects, and the horizontal Y component RVsY_det of the detected relative velocity value of surrounding objects, and uses this as the detected absolute velocity value Vs_det of surrounding objects.

[0033] The surrounding information acquisition unit 52 calculates the detected absolute acceleration value αs_det of surrounding objects by adding the detected relative acceleration values ​​Rαs_det[RαsX_det, RαsY_det] of surrounding objects to the detected absolute acceleration value αe_det of the vehicle itself. For example, the surrounding information acquisition unit 52 calculates the magnitude of a vector consisting of the detected absolute acceleration value αe_det of the vehicle itself plus the vertical X component RαsX_det of the detected relative acceleration value of the surrounding objects, and the horizontal Y component RαsY_det of the detected relative acceleration value of the surrounding objects, as the detected absolute acceleration value αs_det of the vehicle itself. Here, RαsX_det is the vertical X component of the detected relative acceleration value, and RαsY_det is the horizontal Y component of the detected relative acceleration value.

[0034] The surrounding information acquisition unit 52 may calculate the angular velocity of change of the relative yaw angle based on the time-series data of the detected relative yaw angle θs_det, and then calculate the detected yaw angular velocity ωs_det of surrounding objects by adding the detected yaw angular velocity ωe_det of the vehicle itself to the angular velocity of change of the relative yaw angle.

[0035] Alternatively, the surrounding information acquisition unit 52 may periodically acquire the detected absolute velocity Vs_det, the detected absolute acceleration αs_det, the detected absolute direction of travel ωs_det, and the detected yaw acceleration ωs_det of the surrounding object from the vehicle control device of the surrounding object via the wireless communication device 34.

[0036] 1-1-3. Vehicle motion prediction unit 53 <Displacement of the vehicle ΔLe and change in the vehicle's yaw angle Δθe> In step S03 of Figure 4, as shown in Figure 6, the vehicle motion prediction unit 53 predicts the amount of movement ΔLe of the vehicle and the amount of change in the vehicle's yaw angle Δθe from the previous target time t(k-1) to the current target time t(k), based on the detected absolute velocity value Ve_det(k-1) and the detected yaw angular velocity value ωe_det(k-1) of the vehicle acquired at the previous target time t(k-1). The current target time is set to a different time from the previous target time. In this embodiment, the current target time t(k) is set to a time later than the previous target time t(k-1). However, the current target time may be set to a time earlier than the previous target time. The time can be obtained by the timer function of the vehicle control device 30 (CPU, etc.).

[0037] In this embodiment, the vehicle motion prediction unit 53 predicts the amount of movement ΔLe of the vehicle based on the detected absolute speed of the vehicle acquired at the previous target time t(k-1), Ve_det(k-1), as shown in the following equation. Here, ΔT is the time interval from the previous target time t(k-1) to the current target time t(k), as will be described later.

number

[0038] Alternatively, the vehicle motion prediction unit 53 may predict the amount of movement ΔLe of the vehicle based on the detected absolute speed of the vehicle acquired at the previous target time t(k-1) Ve_det(k-1) and the detected absolute speed of the vehicle acquired at the previous target time t(k-1) αe_det(k-1), as shown in the following equation.

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[0039] In this embodiment, the vehicle motion prediction unit 53 predicts the change in the vehicle's yaw angle Δθe based on the detected value ωe_det(k-1) of the vehicle's yaw angular velocity acquired at the previous target time t(k-1), as shown in the following equation. In addition, yaw angular acceleration may be considered, similar to the prediction of the displacement ΔLe in equation (2).

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[0040] 1-1-4. Object motion prediction unit 54 <Predicted value of relative position before conversion RPs_tmp(k), and predicted value of relative yaw angle before conversion θs_tmp(k)> In step S04 in Figure 4, as shown in Figure 7, the object motion prediction unit 54 predicts the following values ​​for the current target time t(k), based on the position of the vehicle at the previous target time t(k): the predicted relative position of the surrounding objects RPs_est(k-1), the predicted absolute velocity of the surrounding objects Vs_est(k-1), the predicted relative yaw angle of the surrounding objects θs_est(k-1), and the predicted yaw angular velocity of the surrounding objects ωs_est(k). These values ​​are based on the position of the vehicle at the previous target time t(k-1).

[0041] With this configuration, the predicted absolute velocity Vs_est of surrounding objects is converted to a predicted relative velocity and used directly without being converted back to a predicted absolute velocity. Therefore, the deterioration of accuracy due to conversion and re-conversion can be suppressed. In addition, the pre-conversion predicted relative position RPs_tmp(k) and the pre-conversion predicted relative yaw angle θs_tmp(k) of surrounding objects at the current target time t(k) are predicted based on the position of the vehicle at the previous target time t(k-1). As a result, the rotation and movement of the yaw angle of surrounding objects can be accurately predicted, separated from the motion of the vehicle.

[0042] The target time corresponds to the detection time of each detected value. The target time is set in accordance with the detection times of the surrounding information acquisition unit 52 and the vehicle information acquisition unit 51. The previous target time t(k-1) is the target time one before the current target time t(k). The current target time t(k) does not have to be the current target time t(k), but can be any target time in the past, as long as the target time changes by one from the past to the present.

[0043] Let ΔT be the time interval between the previous target time t(k-1) and the current target time t(k). For each type of parameter, the time delay from the actual detection time to the target time may be taken into consideration when setting the time interval ΔT. If the previous target time t(k-1), the current target time t(k), and the time interval ΔT differ for each type of parameter, then the previous target time t(k-1), the current target time t(k), and the time interval ΔT may be changed for each type of parameter.

[0044] The object motion prediction unit 54 uses equations (4) and (5) to predict the pre-transformation predicted value RPs_tmp(k)[RPsX_tmp(k), RPsY_tmp(k)] of the relative position of surrounding objects at the previous target time t(k-1), based on the predicted values ​​RPs_est(k-1)[RPsX_est(k-1), RPsY_est(k-1)], the predicted value θs_est(k-1) of the relative yaw angle of surrounding objects, the predicted value Vs_est(k-1) of the absolute velocity of surrounding objects, and the predicted value ωs_est(k-1) of the yaw angular velocity of surrounding objects. Here, if the predicted value ωs_est(k-1) of the yaw angular velocity of the surrounding object at the previous target time t(k-1) is not 0, equation (4) is used, and if the predicted value ωs_est(k-1) of the yaw angular velocity of the surrounding object at the previous target time t(k-1) is 0, equation (5) is used. Equation (5) is used to prevent division by 0 using ωs_est(k-1), and is the convergence point when ωs_est(k-1)→0. Note that the predicted values ​​for the previous target time t(k-1) are the predicted values ​​for the previous target time t(k-1) after they have been updated by the predicted value comparison and update unit 56.

number

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[0045] Here, RPsX_tmp is the vertical X component of the pre-transformation predicted relative position, and RPsY_tmp is the horizontal Y component of the pre-transformation predicted relative position. RPsX_est is the vertical X component of the predicted relative position, and RPsY_est is the horizontal Y component of the predicted relative position.

[0046] The object motion prediction unit 54 uses the following equation to predict the unconverted predicted value θs_tmp(k) of the relative yaw angle of the surrounding objects relative to the position of the vehicle at the previous target time t(k-1), based on the predicted value θs_est(k-1) of the relative yaw angle of the surrounding objects and the predicted value ωs_est(k-1) of the yaw angular velocity of the surrounding objects.

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[0047] <Predicted absolute velocity of surrounding objects Vs_est(k)> In this embodiment, the object motion prediction unit 54 predicts the predicted absolute velocity of the surrounding object at the current target time t(k) based on the predicted absolute velocity of the surrounding object Vs_est(k-1) predicted at least at the previous target time t(k-1). In this embodiment, as shown in the following equation, the object motion prediction unit 54 sets the predicted absolute velocity of the surrounding object Vs_est(k-1) predicted at the previous target time t(k-1) as the predicted absolute velocity of the surrounding object Vs_est(k) at the current target time t(k).

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[0048] <Predicted yaw angular velocity of surrounding objects ωs_est(k)> In this embodiment, the object motion prediction unit 54 predicts the predicted yaw angular velocity of the surrounding object at the current target time t(k) based on the predicted yaw angular velocity of the surrounding object ωs_est(k-1) predicted at least at the previous target time t(k-1). In this embodiment, as shown in the following equation, the object motion prediction unit 54 sets the predicted yaw angular velocity of the surrounding object ωs_est(k-1) predicted at the previous target time t(k-1) as the predicted yaw angular velocity of the surrounding object ωs_est(k) at the current target time t(k).

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[0049] 1-1-5. Object motion conversion unit 55 <Predicted relative position RPs_est(k) and predicted relative yaw angle θs_est(k)> In step S05 of Figure 4, as shown in Figure 8, the object motion conversion unit 55 converts the pre-conversion predicted values ​​RPs_tmp(k) of the relative position of surrounding objects and θs_tmp(k) of the relative yaw angle of surrounding objects at the current target time t(k), based on the position of the vehicle at the previous target time t(k-1), to the predicted values ​​RPs_est(k) of the relative position of surrounding objects and θs_est(k) of the relative yaw angle of surrounding objects at the current target time t(k), based on the position of the vehicle at the current target time t(k).

[0050] With this configuration, based on the vehicle's displacement ΔLe and yaw angle change Δθe, the pre-conversion predicted values ​​of the relative positions of surrounding objects, RPs_tmp(k) and θs_tmp(k), are converted to the predicted relative positions of surrounding objects at the current time t(k), RPs_est(k) and θs_est(k), respectively, using the vehicle's position at the current time t(k) as a reference. Therefore, considering the rotation and movement of the yaw angles of surrounding objects and the vehicle's own yaw angle, the predicted relative positions of surrounding vehicles, RPs_est and θs_est can be predicted with high accuracy. The predicted relative yaw angle θs_est represents the relative direction of travel of surrounding vehicles, relative to the vehicle's direction of travel, and is therefore important information for predicting the movement of surrounding vehicles, as well as for vehicle control, which will be discussed later.

[0051] In this embodiment, the object motion transformation unit 55 uses the following equation to transform the pre-transformation predicted values ​​RPs_tmp(k)[RPsX_tmp(k), RPsY_tmp(k)] of the relative positions of surrounding objects based on the amount of movement ΔLe of the vehicle and the amount of change in the yaw angle Δθe of the vehicle, into predicted values ​​RPs_est(k)[RPsX_est(k), RPsY_est(k)] of the relative positions of surrounding objects at the current target time t(k), based on the position of the vehicle at the current target time t(k). Various other known affine transformations may also be used.

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[0052] The object motion conversion unit 55 uses the following equation to convert the pre-conversion predicted value θs_tmp(k) of the relative yaw angle of surrounding objects at the current target time t(k), based on the position of the vehicle at the previous target time t(k-1), to the predicted value θs_est(k) of the relative yaw angle of surrounding objects at the current target time t(k), based on the position of the vehicle at the current target time t(k), using the change in the yaw angle Δθe of the vehicle itself.

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[0053] <Predicted relative velocity of surrounding objects RVs_est(k)> The object motion conversion unit 55 predicts the relative velocity of the surrounding objects at the current time t(k) relative to the position of the current vehicle at the current time t(k), based on the detected absolute velocity of the current vehicle Ve_det(k), the predicted absolute velocity of the surrounding objects Vs_est(k), and the predicted relative yaw angle of the surrounding objects θs_est(k) at the current time t(k). Here, RVsX_est is the vertical X component of the predicted relative velocity, and RVsY_est is the horizontal Y component of the predicted relative velocity.

[0054] In this embodiment, the object motion conversion unit 55 makes predictions using the following equation.

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[0055] 1-1-6. Predicted Value Comparison and Update Section 56 In step S06 of Figure 4, the prediction value comparison and update unit 56 compares the prediction data for the current target time t(k), which includes the predicted relative position RPs_est(k), the predicted absolute velocity Vs_est(k), the predicted relative yaw angle θs_est(k), and the predicted yaw angular velocity ωs_est(k) of the surrounding objects at the current target time t(k), with the detection data for the current target time t(k), which includes the detected relative position RPs_det(k), the detected absolute velocity Vs_det(k), and the detected relative yaw angle θs_det(k) of the surrounding objects obtained at the current target time t(k). The unit then determines whether the prediction data for the current target time t(k) and the detection data for the current target time t(k) correspond. If it determines that they correspond, it updates the prediction data for the current target time t(k) based on the prediction data and the detection data for the current target time t(k).

[0056] With this configuration, the predicted data is updated when it is determined that the predicted data and the detected data correspond. Therefore, it is possible to suppress the updating of the predicted data due to detection data of a different surrounding object than the surrounding object of the predicted data. In addition, since the predicted data is updated based on the predicted data and the detected data, it is possible to update the predicted data while reducing the influence of error components and noise components contained in the detected data and predicted data, thereby improving the accuracy of the predicted data.

[0057] Furthermore, the detection data for the target time t(k) may include the detected yaw angular velocity of the surrounding objects, ωs_det(k). This can improve the update accuracy of predicted values ​​such as ωs_est(k) for the yaw angular velocity of the surrounding objects. Additionally, the detection data for the target time t(k) may include the detected absolute acceleration of the surrounding objects, αs_det(k). This can improve the update accuracy of predicted values ​​such as Vs_est(k) for the absolute velocity of the surrounding objects.

[0058] For example, the predicted value comparison and update unit 56 determines that the predicted data and detected data correspond if the deviation (absolute value) between the predicted data and the detected data for the current target time t(k) is less than the deviation threshold, and determines that the predicted data and detected data do not correspond if the deviation (absolute value) is greater than or equal to the deviation threshold.

[0059] The predicted value comparison and update unit 56 calculates a deviation for each type of parameter (physical quantity) included in the predicted data and the detected data, and compares the deviation for each type of parameter with the deviation threshold set for each type of parameter. For example, the predicted value comparison and update unit 56 determines that the predicted data and the detected data do not correspond if the number of types of parameters whose deviation is equal to or greater than the deviation threshold is equal to or greater than a certain threshold (e.g., 1), and determines that the predicted data and the detected data correspond if the number of types of parameters is less than a certain threshold (e.g., 1). Alternatively, the types of parameters that are compared with each other may be limited.

[0060] For example, if there is detection data of multiple surrounding objects that can be compared with the prediction data, the prediction value comparison update unit 56 sets the detection data of the surrounding object that has the smallest deviation from the prediction data as the detection data to be ultimately compared with the prediction data, and sets it as the detection data to be used to update the prediction data.

[0061] If the prediction value comparison and update unit 56 determines that the prediction data and the detected data do not correspond, it uses the prediction data for the current target time t(k) without updating it. Also, if the number of times the prediction value comparison and update unit 56 determines that there are no longer any surrounding objects corresponding to the prediction data, it deletes the prediction data for that surrounding object and stops predicting thereafter. If there is detected data for a surrounding object that does not correspond to any of the prediction data for any surrounding object, the prediction value comparison and update unit 56 determines that a new surrounding object has appeared, adds new prediction data for that surrounding object, and continues predicting thereafter. The initial value of the prediction data should be set to the detected data.

[0062] If the prediction value comparison and update unit 56 determines that the prediction data and the detected data correspond, it updates the prediction data for the current target time t(k) based on the prediction data for the current target time t(k) and the detected data for the current target time t(k). The prediction data is updated for each type of parameter (physical quantity). For each type of parameter, the prediction value comparison and update unit 56 updates the parameter values ​​of the prediction data for the current target time t(k) so that the parameter values ​​of the prediction data for the current target time t(k) approach the parameter values ​​of the detected data whose fluctuations have been smoothed.

[0063] As a simple method, the prediction value comparison and update unit 56 performs a smoothing process on the value obtained by subtracting the parameter value of the prediction data for the current target time t(k) before the update from the parameter value of the detected data for the current target time t(k) for each type of parameter, and sets the value obtained by adding the smoothed value to the parameter value of the prediction data for the current target time t(k) after the update as the parameter value of the prediction data for the current target time t(k) after the update. For example, as the smoothing process, a moving average process, a weighted average process, a low-pass filter process, or a filter process using the least squares method may be used.

[0064] For example, a smoothing processor is provided for the vertical X component RPsX_est of the predicted relative position of surrounding objects and the vertical X component RPsX_det of the detected relative position of surrounding objects. The deviation between RPsX_det(k) and the original RPsX_est(k) is input to the smoothing processor, and the smoothing process is performed. The output value of the smoothing processor is added to the original RPsX_est(k), and this value is set as the updated RPsX_est(k).

[0065] Alternatively, the predicted value comparison and update unit 56 performs filtering, such as a Kalman filter or particle filter, on the predicted data for the current target time t(k) and the detected data for the current target time t(k), and sets the filtered data as the updated predicted data for the current target time t(k).

[0066] In each smoothing and filtering process, information on previously input prediction and detection data is retained as internal variables. In each smoothing and filtering process, the degree to which the updated prediction data reflects the prediction data and detection data for the current target time is usually increased.

[0067] 1-1-7. Vehicle Control Unit 57 In step S07 of Figure 4, the vehicle control unit 57 performs either or both of the following actions: driving control and / or driving assistance of its own vehicle, based on the predicted data of surrounding objects output from the predicted value comparison and update unit 56.

[0068] For example, when performing autonomous driving, the vehicle control unit 57 determines a target driving trajectory based on predicted data of surrounding objects. The vehicle control unit 57 controls the vehicle to follow its own target driving trajectory. For example, the vehicle control unit 57 determines the target speed, target steering angle, turn signal operation commands, etc., and transmits the determined command values ​​to the drive control devices 35, such as the power control device, brake control device, automatic steering control device, and light control device.

[0069] The power control device controls the output of the power unit 8, such as an internal combustion engine or motor, so that the vehicle's speed follows the target speed. The brake control device controls the braking operation of the electric brake device 9 so that the vehicle's speed follows the target speed. The automatic steering control device controls the electric steering device 7 so that the steering angle follows the target steering angle. The light control device controls the turn signals according to the turn signal operation commands.

[0070] Alternatively, when assisting the driver, a command to assist the driver is transmitted to one or more of the power control device, brake control device, and automatic steering control device based on predictive data of surrounding objects, and one or more of the output of the power unit 8, the braking operation of the electric brake device 9, and the steering operation of the electric steering device 7 are controlled. For example, vehicle control may include lane keeping control, obstacle avoidance control, lane change control, cruise control, following distance control, and preceding vehicle following control.

[0071] Alternatively, if the driver is operating the vehicle manually, the vehicle control unit 57 provides various driving instructions to the driver via the human interface device 36 based on predictive data of surrounding objects. For example, these instructions may include route guidance, information about the surroundings, and warnings of collision risks.

[0072] 2. Embodiment 2 Next, the object recognition device 1 according to Embodiment 2 will be described. The same components as in Embodiment 1 will not be described. The basic configuration of the object recognition device 1 according to this embodiment is the same as in Embodiment 1, but it differs from Embodiment 1 in that the number of parameters detected and estimated is increased.

[0073] In this embodiment, as shown in Figure 9, the coordinate system of the vehicle is a coordinate system with the longitudinal X, lateral Y, and vertical Z directions of the vehicle as coordinate axes. The yaw angular velocity ω is the angular velocity of the longitudinal X axis around the vertical Z axis, the pitch angle Γ is the angle of the longitudinal X axis around the lateral Y axis with respect to the horizontal plane, and the roll angle Φ is the angle of the lateral Y axis around the longitudinal X axis with respect to the horizontal plane.

[0074] <Vehicle Information Acquisition Unit 51> The vehicle information acquisition unit 51 acquires the detected pitch angle Γe_det, the detected pitch angular velocity γe_det, the detected roll angle Φe_det, and the detected roll angular velocity φe_det of the vehicle based on the detection information from the vehicle state detection device 33.

[0075] <Surrounding Information Acquisition Unit 52> The surrounding information acquisition unit 52 acquires the detected pitch angle value Γs_det, the detected pitch angular velocity value γs_det, the detected roll angle value Φs_det, and the detected roll angular velocity value φs_det of the surrounding objects.

[0076] For example, the surrounding information acquisition unit 52 acquires a detected value RΓs_det for the relative pitch angle and a detected value RΦs_det for the relative roll angle of surrounding objects, based on the detection information from the surrounding monitoring device 31, with respect to the position of the vehicle. The relative pitch angle is the relative angle of the pitch angle of the surrounding object to the pitch angle of the vehicle. The relative roll angle is the relative angle of the roll angle of the surrounding object to the roll angle of the vehicle. Various known methods can be used to acquire these values.

[0077] For example, the surrounding information acquisition unit 52 calculates the detected pitch angle Γs_det of surrounding objects by adding the detected relative pitch angle RΓs_det of surrounding objects to the detected pitch angle Γe_det of the vehicle itself. The surrounding information acquisition unit 52 also calculates the detected roll angle Φs_det of surrounding objects by adding the detected relative roll angle RΦs_det of surrounding objects to the detected roll angle Φe_det of the vehicle itself. Based on the time-series data of the detected pitch angle Γs_det of surrounding objects, the surrounding information acquisition unit 52 calculates the detected pitch angular velocity γs_det of surrounding objects, and based on the time-series data of the detected roll angle Φs_det of surrounding objects, it calculates the detected roll angular velocity φs_det of surrounding objects.

[0078] The surrounding information acquisition unit 52 may periodically acquire the detected pitch angle Γs_det, the detected pitch angular velocity γs_det, the detected roll angle Φs_det, and the detected roll angular velocity φs_det of the surrounding object from the vehicle control device of the surrounding object via the wireless communication device 34.

[0079] <Vehicle motion prediction unit 53> The vehicle motion prediction unit 53 further calculates the change in the pitch angle ΔΓe and the change in the roll angle ΔΦe of the vehicle from the previous target time t(k-1) to the current target time t(k), based on the detected value γe_det(k-1) of the vehicle's pitch angular velocity and the detected value φs_det(k-1) of the vehicle's roll angular velocity acquired at the previous target time t(k-1).

[0080] In this embodiment, the vehicle motion prediction unit 53 predicts the change in the pitch angle ΔΓe of the vehicle based on the detected value γe_det(k-1) of the pitch angular velocity of the vehicle acquired at the previous target time t(k-1), as shown in the following equation. Note that pitch angular acceleration may also be taken into consideration.

number

[0081] Furthermore, the vehicle motion prediction unit 53 predicts the amount of change in the vehicle's roll angle ΔΦe based on the detected value φs_det(k-1) of the vehicle's roll angular velocity acquired at the previous target time t(k-1), as shown in the following equation. Note that roll angular acceleration may also be taken into consideration.

number

[0082] <Object motion prediction unit 54> The object motion prediction unit 54, based on the predicted values ​​of the surrounding objects at the previous target time t(k-1), the predicted relative position of the surrounding objects RPs_est(k-1), the predicted absolute velocity of the surrounding objects Vs_est(k-1), the predicted relative yaw angle of the surrounding objects θs_est(k-1), the predicted yaw angular velocity of the surrounding objects ωs_est(k-1), the predicted relative pitch angle of the surrounding objects RΓs_est(k-1), the predicted pitch angular velocity of the surrounding objects γs_est(k-1), the predicted relative roll angle of the surrounding objects RΦs_est(k-1), and the predicted roll angular velocity of the surrounding objects φs_est(k-1), performs the previous target Based on the position of the vehicle at time t(k-1), the following are predicted for the target time t(k): RPs_tmp(k) for the relative position of surrounding objects, Vs_est(k) for the absolute velocity of surrounding objects, θs_tmp(k) for the relative yaw angle of surrounding objects, ωs_est(k) for the relative yaw angular velocity of surrounding objects, RΓs_tmp(k) for the relative pitch angle of surrounding objects, γs_est(k) for the relative pitch angular velocity of surrounding objects, RΦs_tmp(k) for the relative roll angle of surrounding objects, and φs_est(k) for the roll angular velocity of surrounding objects.

[0083] The object motion prediction unit 54 uses the following equations to predict the relative position of the surrounding object at the previous target time t(k-1) as follows: RPs_est(k-1)[RPsX_est(k-1), RPsY_est(k-1), RPsZ_est(k-1)], the absolute velocity of the surrounding object as follows: Vs_est(k-1), the relative yaw angle of the surrounding object as follows: θs_est(k-1), the yaw angular velocity of the surrounding object as follows: ωs_est(k-1), and the relative pitch angle of the surrounding object as follows: RΓs_est(k -1) Based on the predicted pitch angular velocity γs_est(k-1) of the surrounding objects, the predicted relative roll angle RΦs_est(k-1) of the surrounding objects, and the predicted roll angular velocity φs_est(k-1) of the surrounding objects, the system predicts the pre-transformation predicted relative position of the surrounding objects at the current target time t(k), using the position of the vehicle at the previous target time t(k-1) as a reference, and then predicts the pre-transformation predicted position RPs_tmp(k)[RPsX_tmp(k), RPsY_tmp(k), RPsZ_tmp(k)]. Here, although the explanation is omitted, if any of the predicted values ​​of the surrounding objects at the previous target time t(k-1)—ωs_est(k-1) for the yaw angular velocity, γs_est(k-1) for the pitch angular velocity, and φs_est(k-1) for the roll angular velocity—are 0, then, in order to prevent division by zero, the calculation formula is changed to one where each value is set to 0, similar to equation (5).

number

[0084] Here, RPsZ_tmp is the vertical Z component of the predicted relative position before transformation. RPsZ_est is the vertical Z component of the predicted relative position.

[0085] The object motion prediction unit 54 uses the following equation to predict the pre-conversion predicted value RΓs_tmp(k) of the relative pitch angle of surrounding objects relative to the position of the vehicle at the previous target time t(k-1), based on the predicted value RΓs_est(k-1) of the relative pitch angle of surrounding objects and the predicted value γs_est(k-1) of the pitch angular velocity of surrounding objects at the previous target time t(k-1).

number

[0086] The object motion prediction unit 54 predicts the pre-conversion predicted value RΦs_tmp(k) of the relative roll angle of the surrounding objects relative to the position of the vehicle at the previous target time t(k-1), based on the predicted value RΦs_est(k-1) of the relative roll angle of the surrounding objects and the predicted value φs_est(k-1) of the roll angular velocity of the surrounding objects at the previous target time t(k-1), using the following equation.

number

[0087] The object motion prediction unit 54 predicts the pitch angular velocity of the surrounding object at the current target time t(k) based on the predicted value γs_est(k-1) of the pitch angular velocity of the surrounding object predicted at least at the previous target time t(k-1). In this embodiment, as shown in the following equation, the object motion prediction unit 54 sets the predicted value γs_est(k-1) of the pitch angular velocity of the surrounding object predicted at the previous target time t(k-1) as the predicted value γs_est(k) of the pitch angular velocity of the surrounding object at the current target time t(k).

number

[0088] The object motion prediction unit 54 predicts the roll angular velocity of the surrounding object at the current target time t(k) based on the predicted value φs_est(k-1) of the roll angular velocity of the surrounding object predicted at least at the previous target time t(k-1). In this embodiment, as shown in the following equation, the object motion prediction unit 54 sets the predicted value φs_est(k-1) of the roll angular velocity of the surrounding object predicted at the previous target time t(k-1) as the predicted value φs_est(k) of the roll angular velocity of the surrounding object at the current target time t(k).

number

[0089] <Object motion conversion unit 55> The object motion conversion unit 55 converts the pre-conversion predicted values ​​of the relative positions of surrounding objects at the current target time t(k), based on the vehicle's displacement ΔLe, yaw angle change Δθe, pitch angle change ΔΓe, and roll angle change ΔΦe, into the following: RPs_tmp(k), θs_tmp(k), θs_tmp(k), RΓs_tmp(k), and RΦs_tmp(k), relative yaw angle, pitch angle, and roll angle, based on the vehicle's position at the previous target time t(k-1).

[0090] In this embodiment, the object motion transformation unit 55 uses the following equation to transform the pre-transformation predicted values ​​RPs_tmp(k)[RPsX_tmp(k), RPsY_tmp(k), RPsZ_tmp(k)] of the relative positions of surrounding objects based on the amount of movement ΔLe of the vehicle, the change in yaw angle Δθe, the change in pitch angle ΔΓe, and the change in roll angle ΔΦe into predicted values ​​RPs_est(k)[RPsX_est(k), RPsY_est(k), RPsZ_est(k)] of the relative positions of surrounding objects at the current target time t(k), with the position of the vehicle at the current target time t(k) as the reference. Various other known affine transformations may also be used.

number

[0091] The object motion conversion unit 55 uses the following equation to convert the predicted values ​​of the relative yaw angle θs_tmp(k), the predicted relative pitch angle RΓs_tmp(k), and the predicted relative roll angle RΦs_tmp(k) of the surrounding objects at the current target time t(k), relative to the position of the vehicle at the previous target time t(k-1), based on the change in the yaw angle Δθe, the change in pitch angle ΔΓe, and the change in roll angle ΔΦe of the vehicle itself, into predicted values ​​of the relative yaw angle θs_est(k), the predicted relative pitch angle RΓs_est(k), and the predicted relative roll angle RΦs_est(k) of the surrounding objects at the current target time t(k), based on the position of the vehicle itself at the current target time t(k).

number

[0092] The object motion conversion unit 55 predicts the relative velocity of the surrounding objects at the target time t(k) relative to the position of the vehicle at the target time t(k), based on the predicted absolute velocity of the vehicle itself (Ve_est(k)), the predicted absolute velocity of the surrounding objects (Vs_est(k)), the predicted relative yaw angle (θs_est(k)), and the predicted relative pitch angle (RΓs_est(k)) at the target time t(k). Here, RVsZ_est is the vertical Z component of the predicted relative velocity. The predicted relative roll angle of the surrounding vehicles at the target time t(k), RΦs_est(k), may also be considered.

[0093] In this embodiment, the object motion conversion unit 55 makes predictions using the following equation.

number

[0094] <Predicted Value Comparison and Update Section 56> The prediction value comparison and update unit 56 compares and updates the prediction data for the current target time t(k), including the predicted relative position of the surrounding objects RPs_est(k), the predicted absolute velocity of the surrounding objects Vs_est(k), the predicted relative yaw angle of the surrounding objects θs_est(k), the predicted yaw angular velocity of the surrounding objects ωs_est(k), the predicted relative pitch angle of the surrounding objects RΓs_est(k), the predicted pitch angular velocity of the surrounding objects γs_est(k), the predicted relative roll angle of the surrounding objects RΦs_est(k), and the predicted roll angular velocity of the surrounding objects φs_est(k), with the prediction data for the current target time t(k) obtained for the surrounding objects The detection data for the current target time t(k), which includes the detected relative position RPs_det(k), the detected absolute velocity of the surrounding object Vs_det(k), the detected relative yaw angle of the surrounding object θs_det(k), the detected relative pitch angle of the surrounding object RΓs_det(k), and the detected relative roll angle of the surrounding object RΦs_det(k), is compared with the predicted data for the current target time t(k) to determine whether the detected data for the current target time t(k) corresponds to the predicted data for the current target time t(k). If a correspondence is determined, the predicted data for the current target time t(k) is updated based on the predicted data and the detected data for the current target time t(k). The detected data for the current target time t(k) may also include the detected yaw angular velocity ωs_det(k), the detected pitch angular velocity γs_det(k), and the detected roll angular velocity φs_det(k) of the surrounding object. This can improve the update accuracy of the predicted values ​​of the corresponding parameters. Furthermore, the detection data for the target time t(k) may include the detected absolute acceleration value αs_det(k) of the surrounding objects. This can improve the update accuracy of predicted values ​​such as Vs_est(k) of the surrounding objects' absolute velocity. Except for the addition of parameters related to pitch angle and roll angle, this is the same as Embodiment 1, so a detailed explanation is omitted.

[0095] <Other embodiments> (1) The surrounding information acquisition unit 52 may acquire shape information of surrounding objects. For example, the shape information may include one or more of the following: the vertical length L, the horizontal width W, and the vertical height of the surrounding object.

[0096] The predicted value comparison and update unit 56 may then consider the shape information of surrounding objects to determine whether the predicted data for the current target time corresponds to the detected data for the current target time, and update the predicted data for the current target time.

[0097] For example, as shown in Figure 10, the prediction value comparison update unit 56 predicts the relative position range in which surrounding objects exist relative to the position of the vehicle, based on the predicted relative position RPs_est(k) of surrounding objects, the predicted relative yaw angle θs_est(k) of surrounding objects, and the shape information of surrounding objects at the target time t(k). The prediction value comparison update unit 56 also calculates the relative position range in which surrounding objects exist relative to the position of the vehicle, based on the detected relative position RPs_det(k) of surrounding objects, the detected relative yaw angle θs_det(k) of surrounding objects, and the shape information of surrounding objects, obtained at the target time t(k). The prediction value comparison update unit 56 then compares the relative position range of surrounding objects predicted based on the prediction data and shape information with the relative position range of surrounding objects calculated based on the detected data and shape information to determine whether the prediction data and the detected data correspond. For example, it is desirable to determine the degree of overlap between the relative position ranges.

[0098] As shown in Figure 10, the predicted value comparison and update unit 56 outputs the updated predicted data for the current target time t(k), including shape information of surrounding objects, to the vehicle control unit 57, etc.

[0099] (2) In the above embodiment 2, the case in which both pitch angle-related information and roll angle-related information are predicted and detected was described as an example. However, either pitch angle-related information or roll angle-related information may be predicted and detected.

[0100] <Summary of the various aspects of this application> The various aspects of this application are summarized below as an appendix. (Note 1) A vehicle information acquisition unit that acquires the detected absolute speed and yaw angular velocity of the vehicle, An object motion prediction unit predicts, based on the predicted relative positions of surrounding objects around the vehicle at the previous target time, the predicted absolute velocities of the surrounding objects, the predicted relative yaw angles of the surrounding objects with respect to the position of the vehicle, and the predicted yaw angular velocities of the surrounding objects, the object motion prediction unit predicts the pre-conversion predicted relative positions of the surrounding objects, the predicted absolute velocities of the surrounding objects, the pre-conversion predicted relative yaw angles of the surrounding objects, and the predicted yaw angular velocities of the surrounding objects at the current target time, which is different from the previous target time, with respect to the position of the vehicle at the previous target time. A vehicle motion prediction unit predicts the amount of movement of the vehicle and the amount of change in the vehicle's yaw angle from the previous target time to the current target time, based on the detected absolute speed and yaw angular velocity of the vehicle acquired at the previous target time. An object motion conversion unit converts the predicted values ​​of the relative position and relative yaw angle of the surrounding objects before conversion, based on the amount of movement of the vehicle and the amount of change in the yaw angle of the vehicle, into predicted values ​​of the relative position and relative yaw angle of the surrounding objects at the target time, with reference to the position of the vehicle at the target time. An object recognition device equipped with the following features.

[0101] (Note 2) It further includes a surrounding information acquisition unit and a predicted value comparison and update unit, The surrounding information acquisition unit acquires the absolute velocity, the relative position with respect to the position of the vehicle, and the relative yaw angle of the surrounding objects. The object recognition device according to Appendix 1, wherein the predicted value comparison and update unit compares predicted data for the current target time, which includes predicted values ​​of the relative position of the surrounding object, predicted values ​​of the absolute velocity of the surrounding object, predicted values ​​of the relative yaw angle of the surrounding object, and predicted values ​​of the yaw angular velocity of the surrounding object, with detected data for the current target time, which includes detected values ​​of the relative position of the surrounding object, detected values ​​of the absolute velocity of the surrounding object, and detected values ​​of the relative yaw angle of the surrounding object, obtained at the current target time, to determine whether the predicted data for the current target time and the detected data for the current target time correspond, and if it is determined that they correspond, updates the predicted data for the current target time based on the predicted data for the current target time and the detected data for the current target time.

[0102] (Note 3) The object recognition device according to Appendix 1 or 2, wherein the object motion conversion unit converts the predicted absolute velocity of the surrounding objects to the predicted relative velocity of the surrounding objects at the current time, based on the detected absolute velocity of the vehicle and the predicted relative yaw angle of the surrounding objects at the current time, with respect to the position of the vehicle at the current time.

[0103] (Note 4) The surrounding information acquisition unit acquires the detected yaw angular velocity of the surrounding object, The object recognition device described in Appendix 2 includes the detected yaw angular velocity of the surrounding object, as specified in the aforementioned object recognition device for the target time.

[0104] (Note 5) The surrounding information acquisition unit acquires the detected absolute acceleration value of the surrounding object, The object recognition device described in Appendix 2 or 4 includes the detected absolute acceleration value of the surrounding object as the detection data for the target time in this case.

[0105] (Note 6) The surrounding information acquisition unit acquires shape information of the surrounding objects, The object recognition device described in Appendix 2, wherein the predicted value comparison and update unit determines whether the predicted data for the current target time corresponds to the detected data for the current target time, taking into consideration the shape information of the surrounding objects, and updates the predicted data for the current target time.

[0106] (Note 7) The vehicle information acquisition unit acquires either or both of the detected pitch angle and roll angle of the vehicle, as well as either or both of the detected pitch angular velocity and roll angular velocity of the vehicle. The object motion prediction unit predicts, based on the predicted relative position of surrounding objects present around the vehicle at the previous target time, the predicted absolute velocity of the surrounding objects, the predicted relative yaw angle of the surrounding objects, the predicted yaw angular velocity of the surrounding objects, one or both of the predicted relative pitch angle and relative roll angle of the surrounding objects, and one or both of the predicted pitch angular velocity and roll angular velocity of the surrounding objects, the following predictions for the current target time, relative to the position of the vehicle at the previous target time: The vehicle motion prediction unit further calculates, based on the detected pitch angular velocity and roll angular velocity of the vehicle, or both, from the previous target time to the current target time, one or both of the detected values ​​of the vehicle's pitch angle and roll angle from the previous target time to the current target time. The object recognition device according to any one of the appendices 1 to 6, wherein the object motion conversion unit converts the predicted value of the relative position of the surrounding object, the predicted value of the relative yaw angle, and the predicted value of the relative pitch angle and the predicted value of the relative roll angle, based on the amount of movement of the vehicle, the amount of change in the yaw angle of the vehicle, and one or both of the amount of change in the pitch angle and the roll angle of the vehicle, into one or both of the predicted values ​​of the relative position of the surrounding object, the predicted value of the relative yaw angle, and the predicted value of the relative pitch angle and the predicted value of the relative roll angle, based on the position of the vehicle at the current target time.

[0107] (Note 8) It further includes a surrounding information acquisition unit and a predicted value comparison and update unit, The surrounding information acquisition unit acquires the detected relative position of the surrounding object, the detected absolute velocity of the surrounding object, the detected relative yaw angle of the surrounding object, and one or both of the detected relative pitch angle and relative roll angle of the surrounding object. The object recognition device according to Appendix 7 compares the predicted data for the current target time, which includes the predicted value of the relative position of the surrounding object, the predicted value of the absolute velocity of the surrounding object, the predicted value of the relative yaw angle of the surrounding object, the predicted value of the yaw angular velocity of the surrounding object, one or both of the predicted value of the relative pitch angle and the predicted value of the relative roll angle, and one or both of the predicted value of the pitch angular velocity and the predicted value of the roll angular velocity of the surrounding object, with the detected data for the current target time, which includes the detected value of the relative position of the surrounding object, the detected value of the absolute velocity of the surrounding object, the detected value of the relative yaw angle of the surrounding object, and one or both of the detected value of the relative pitch angle and the detected value of the relative roll angle, obtained at the current target time, to determine whether the predicted data for the current target time and the detected data for the current target time correspond, and if it is determined that they correspond, updates the predicted data for the current target time based on the predicted data for the current target time and the detected data for the current target time.

[0108] (Note 9) The surrounding information acquisition unit acquires the detected yaw angular velocity of the surrounding object, and one or both of the detected roll angular velocity and pitch angular velocity of the surrounding object. The object recognition device described in Appendix 8 includes, for the above-mentioned target time, the detected value of the yaw angular velocity of the surrounding object, and one or both of the detected values ​​of the pitch angular velocity and roll angular velocity of the surrounding object.

[0109] While this application describes various exemplary embodiments and examples, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but are applicable individually or in various combinations to the embodiments. Accordingly, countless variations not illustrated are envisioned within the scope of the art disclosed herein. For example, these include modifying, adding, or omitting at least one component, or even extracting at least one component and combining it with a component from another embodiment. [Explanation of Symbols]

[0110] 1: Object recognition device, 51: Vehicle information acquisition unit, 52: Surroundings information acquisition unit, 53: Vehicle motion prediction unit, 54: Object motion prediction unit, 55: Object motion conversion unit, 56: Predicted value comparison and update unit, θs_det: Detected value of the relative yaw angle of the surrounding object, θs_est: Predicted value of the relative yaw angle of the surrounding object, θs_tmp: Predicted value of the relative yaw angle of the surrounding object before conversion, ωs_det: Detected value of the yaw angular velocity of the surrounding object, ωs_est: Predicted value of the yaw angular velocity of the surrounding object, RΓs _det: Detected value of the relative pitch angle of the surrounding object, RΓs_est: Predicted value of the relative pitch angle of the surrounding object, RΓs_tmp: Predicted value of the relative pitch angle of the surrounding object before conversion, γs_det: Detected value of the pitch angular velocity of the surrounding object, γs_est: Predicted value of the pitch angular velocity of the surrounding object, RΦs_det: Detected value of the relative roll angle of the surrounding object, RΦs_est: Predicted value of the relative roll angle of the surrounding object, RΦs_tmp: Predicted value of the relative roll angle of the surrounding object before conversion, φs _det: Detected value of the roll angular velocity of surrounding objects, φs_est: Predicted value of the roll angular velocity of surrounding objects, ωe_det: Detected value of the yaw angular velocity of the vehicle, Δθe: Change in the yaw angle of the vehicle, γe_det: Detected value of the pitch angular velocity of the vehicle, ΔΓe: Change in the pitch angle of the vehicle, φe_det: Detected value of the roll angular velocity of the vehicle, ΔΦe: Change in the roll angle of the vehicle, ΔLe: Displacement of the vehicle, RPs_det: Detected value of the relative position of surrounding objects, RPs_ est: Predicted relative position of surrounding objects, RPs_tmp: Predicted relative position of surrounding objects before conversion, RVs_det: Detected relative velocity of surrounding objects, RVs_est: Predicted relative velocity of surrounding objects, Ve_det: Detected absolute velocity of the vehicle itself, Vs_det: Detected absolute velocity of surrounding objects, Vs_est: Predicted absolute velocity of surrounding objects, X: Vertical direction, Y: Horizontal direction, Z: Up and down direction, t(k): Current target time, t(k-1): Previous target time

Claims

1. A vehicle information acquisition unit that acquires the detected absolute speed and yaw angular velocity of the vehicle, An object motion prediction unit predicts, based on the predicted relative positions of surrounding objects around the vehicle at the previous target time, the predicted absolute velocities of the surrounding objects, the predicted relative yaw angles of the surrounding objects with respect to the position of the vehicle, and the predicted yaw angular velocities of the surrounding objects, the object motion prediction unit predicts the pre-conversion predicted relative positions of the surrounding objects, the predicted absolute velocities of the surrounding objects, the pre-conversion predicted relative yaw angles of the surrounding objects, and the predicted yaw angular velocities of the surrounding objects at the current target time, which is different from the previous target time, with respect to the position of the vehicle at the previous target time. A vehicle motion prediction unit predicts the amount of movement of the vehicle and the amount of change in the vehicle's yaw angle from the previous target time to the current target time, based on the detected absolute speed and yaw angular velocity of the vehicle acquired at the previous target time. An object motion conversion unit converts the predicted values ​​of the relative position and relative yaw angle of the surrounding objects before conversion, based on the amount of movement of the vehicle and the amount of change in the yaw angle of the vehicle, into predicted values ​​of the relative position and relative yaw angle of the surrounding objects at the target time, with reference to the position of the vehicle at the target time. An object recognition device equipped with the following features.

2. It further includes a surrounding information acquisition unit and a predicted value comparison and update unit, The surrounding information acquisition unit acquires the absolute velocity, the relative position with respect to the position of the vehicle, and the relative yaw angle of the surrounding objects. The object recognition device according to claim 1, wherein the predicted value comparison and update unit compares predicted data for the current target time, which includes predicted values ​​of the relative position of the surrounding object, predicted values ​​of the absolute velocity of the surrounding object, predicted values ​​of the relative yaw angle of the surrounding object, and predicted values ​​of the yaw angular velocity of the surrounding object, with detected data for the current target time, which includes detected values ​​of the relative position of the surrounding object, detected values ​​of the absolute velocity of the surrounding object, and detected values ​​of the relative yaw angle of the surrounding object, obtained at the current target time, to determine whether the predicted data for the current target time and the detected data for the current target time correspond, and if it is determined that they correspond, updates the predicted data for the current target time based on the predicted data for the current target time and the detected data for the current target time.

3. The object recognition device according to claim 1 or 2, wherein the object motion conversion unit converts the predicted absolute velocity of the surrounding objects to the predicted relative velocity of the surrounding objects at the current time, based on the position of the vehicle at the current time, based on the detected absolute velocity of the vehicle and the predicted relative yaw angle of the surrounding objects at the current time.

4. The surrounding information acquisition unit acquires the detected yaw angular velocity of the surrounding object, The object recognition device according to claim 2, wherein the detection data for the target time includes the detected value of the yaw angular velocity of the surrounding object.

5. The surrounding information acquisition unit acquires the detected absolute acceleration value of the surrounding object, The object recognition device according to claim 2, wherein the detection data for the target time includes the detected absolute acceleration value of the surrounding object.

6. The surrounding information acquisition unit acquires shape information of the surrounding objects, The object recognition device according to claim 2, wherein the predicted value comparison and update unit determines whether the predicted data for the current target time corresponds to the detected data for the current target time, taking into consideration the shape information of the surrounding objects, and updates the predicted data for the current target time.

7. The vehicle information acquisition unit acquires either or both of the detected pitch angle and roll angle of the vehicle, as well as either or both of the detected pitch angular velocity and roll angular velocity of the vehicle. The object motion prediction unit predicts, based on the predicted relative position of surrounding objects present around the vehicle at the previous target time, the predicted absolute velocity of the surrounding objects, the predicted relative yaw angle of the surrounding objects, the predicted yaw angular velocity of the surrounding objects, one or both of the predicted relative pitch angle and relative roll angle of the surrounding objects, and one or both of the predicted pitch angular velocity and roll angular velocity of the surrounding objects, the following predictions for the current target time, relative to the position of the vehicle at the previous target time: The vehicle motion prediction unit further calculates, based on the detected pitch angular velocity and roll angular velocity of the vehicle, or both, from the previous target time to the current target time, one or both of the detected values ​​of the vehicle's pitch angle and roll angle from the previous target time to the current target time. The object recognition device according to claim 1, wherein the object motion conversion unit converts, based on the amount of movement of the vehicle, the amount of change in the yaw angle of the vehicle, and one or both of the amount of change in the pitch angle and the amount of change in the roll angle of the vehicle, one or both of the predicted values ​​of the relative position of the surrounding objects before conversion, the predicted value of the relative yaw angle before conversion, and one or both of the predicted values ​​of the relative pitch angle and the predicted value of the relative roll angle before conversion, to the predicted values ​​of the relative position of the surrounding objects, the predicted value of the relative yaw angle, and one or both of the predicted values ​​of the relative pitch angle and the predicted value of the relative roll angle at the target time, with reference to the position of the vehicle at the target time.

8. It further includes a surrounding information acquisition unit and a predicted value comparison and update unit, The surrounding information acquisition unit acquires the detected relative position of the surrounding object, the detected absolute velocity of the surrounding object, the detected relative yaw angle of the surrounding object, and one or both of the detected relative pitch angle and relative roll angle of the surrounding object. The object recognition device according to claim 7, wherein the predicted value comparison and update unit compares predicted data for the current target time, which includes a predicted value of the relative position of the surrounding object, a predicted value of the absolute velocity of the surrounding object, a predicted value of the relative yaw angle of the surrounding object, a predicted value of the yaw angular velocity of the surrounding object, a predicted value of the relative pitch angle and a predicted value of the relative roll angle, one or both of the predicted value of the pitch angular velocity and a predicted value of the roll angular velocity of the surrounding object, with detected data for the current target time, which includes a detected value of the relative position of the surrounding object, a detected value of the absolute velocity of the surrounding object, a detected value of the relative yaw angle of the surrounding object, and one or both of the detected value of the relative pitch angle and a detected value of the relative roll angle, to determine whether the predicted data for the current target time and the detected data for the current target time correspond, and if it is determined that they correspond, updates the predicted data for the current target time based on the predicted data for the current target time and the detected data for the current target time.

9. The surrounding information acquisition unit acquires the detected yaw angular velocity of the surrounding object, and one or both of the detected roll angular velocity and pitch angular velocity of the surrounding object. The object recognition device according to claim 8, wherein the detection data for the target time includes a detected value of the yaw angular velocity of the surrounding object, and one or both of the detected values ​​of the pitch angular velocity and roll angular velocity of the surrounding object.

10. An object recognition method that causes a processing unit to execute each of the following steps, A vehicle information acquisition step that acquires the detected absolute speed and yaw angular velocity of the vehicle, A step to predict object motion, based on the predicted relative positions of surrounding objects, the predicted absolute velocities of the surrounding objects, the predicted relative yaw angles of the surrounding objects, and the predicted yaw angular velocities of the surrounding objects at the previous target time, predicting the pre-transformation predicted relative positions of the surrounding objects, the predicted absolute velocities of the surrounding objects, the pre-transformation predicted relative yaw angles of the surrounding objects, and the predicted yaw angular velocities of the surrounding objects at the current target time, which is different from the previous target time and is based on the position of the vehicle at the previous target time; A vehicle motion prediction step that predicts the amount of movement of the vehicle and the amount of change in the vehicle's yaw angle from the previous target time to the current target time, based on the detected absolute speed and yaw angular velocity of the vehicle obtained at the previous target time. A motion transformation step that transforms the predicted values ​​of the relative position and relative yaw angle of the surrounding objects before transformation, based on the amount of movement of the vehicle and the amount of change in the yaw angle of the vehicle, into predicted values ​​of the relative position and relative yaw angle of the surrounding objects at the target time, with reference to the position of the vehicle at the target time. An object recognition method equipped with [a specific feature].

11. The system further includes steps for acquiring surrounding information and comparing and updating predicted values. In the surrounding information acquisition step, the absolute velocity, relative position with respect to the position of the vehicle, and relative yaw angle of the surrounding objects are acquired. In the predicted value comparison and update step, the predicted data for the current target time, which includes the predicted value of the relative position of the surrounding object, the predicted value of the absolute velocity of the surrounding object, the predicted value of the relative yaw angle of the surrounding object, and the predicted value of the yaw angular velocity of the surrounding object at the current target time, is compared with the detected data for the current target time, which includes the detected value of the relative position of the surrounding object, the detected value of the absolute velocity of the surrounding object, and the detected value of the relative yaw angle of the surrounding object obtained at the current target time, to determine whether the predicted data for the current target time and the detected data for the current target time correspond, and if it is determined that they correspond, the predicted data for the current target time is updated based on the predicted data for the current target time and the detected data for the current target time, as described in claim 10.

12. The object recognition device according to claim 10 or 11, wherein in the object motion conversion step, the predicted absolute velocity of the surrounding objects is converted from the predicted relative velocity of the surrounding objects at the current time, based on the position of the vehicle at the current time, based on the detected absolute velocity of the vehicle and the predicted relative yaw angle of the surrounding objects at the current time.

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