Predictive course estimation device and predictive course estimation method

Abstract

Predictive course estimation device (20) for estimating a predicted course of one's own vehicle, comprising: a data acquisition device for obtaining rotation data that indicates the direction of rotation of one's own vehicle; a filtering device for removing a high-frequency component contained in the rotation data; a course prediction device for calculating an estimated value for predicting the course of the own vehicle based on the rotation data that have been filtered and a speed of the own vehicle; an investigative device to determine whether one's own vehicle is moving on a part of the road where the shape changes; and a property modification device for changing the extent of the removal of the high-frequency component by the filtering device when it is determined that the vehicle is moving on a part of the road where the shape changes, wherein The property-changing device gradually changes the degree of removal of the high-frequency component when filter properties of the filtering device are changed to properties with a high degree of removal of the high-frequency component, compared to when filter properties of the filtering device are changed to properties with a low degree of removal of the high-frequency component.

Description

Technical field

[0001] The invention relates to prediction estimation techniques for estimating a predicted vehicle course. Technical background

[0002] Vehicle control systems with an ACC (adaptive cruise control) function control the driving and braking forces of the vehicle to maintain a predetermined distance between vehicles ahead. In such systems, to improve the detection of the vehicle ahead, a predicted vehicle course is estimated based on the vehicle's speed and yaw rate. When a predicted course is estimated, filtering is performed to remove a noise component (radio frequency component) contained in the detected yaw rate value (compare JP 2009-9209A).

[0003] From DE 102 54 394 A1, a system is also known that evaluates the behavior of other objects moving in the space in front of the vehicle in order to deduce how many lanes the road has and in which of these lanes the vehicle is traveling. Based on this, a target vehicle ahead is identified in order to regulate the distance to that vehicle.

[0004] German patent DE 10 2014 103 577 A1 discloses a vehicle device for predicting the behavior of the vehicle itself based on measurement results to which a low-pass filter is applied. Based on these prediction results, the vehicle device executes processes for driving assistance. Technical problem

[0005] However, if the vehicle is moving along a section of road where the shape changes, a response delay occurs due to the filtering, and this response delay can affect the accuracy of the estimation of the predicted course of the vehicle.

[0006] The invention is based on the objective of providing a technique for estimating a predicted course which is capable of improving the accuracy of estimating a predicted course of one's own vehicle.

[0007] This problem is solved by the predictive course estimation device and the predictive course estimation method according to the independent claims. Advantageous embodiments are the subject of the dependent claims.

[0008] One aspect concerns a predictive course estimation device for estimating the predicted course of a friendly vehicle. The device includes a data acquisition device for obtaining rotation data indicating the direction of rotation of the friendly vehicle; a filtering device for removing a high-frequency component contained in the rotation data; a course prediction device for calculating an estimated value for predicting the friendly vehicle's course based on the filtered rotation data and the friendly vehicle's speed; a detection device for determining whether the friendly vehicle is moving on a portion of the road where the shape changes; and a property modification device for changing the degree of high-frequency component removal by the filtering device when it is determined that the friendly vehicle is moving on a portion of the road where the shape changes.

[0009] According to the invention, the extent of the removal of a high-frequency component (noise component) by filtering is changed when the vehicle travels through a section of the road where the shape changes. This enables the predictive course estimation device according to the invention to perform suitable filtering in accordance with the shape of the road the vehicle is traveling on. Consequently, the accuracy of estimating a predicted course of the vehicle using rotation data (data on the direction of rotation of the vehicle) that has been filtered can be increased. Brief description of the drawings Fig. Figure 1 is a block diagram representing a vehicle system. Fig. Figure 2 is a functional block diagram representing an inter-vehicle distance control ECU. Fig. 3 is a set of diagrams that represent filter property settings. Fig. Figure 4 is a flowchart that illustrates a change in filter properties during the forecast price estimation. Fig. Figure 5 provides an example of changing filter properties in the forecast price estimation. Fig. Figure 6 is a diagram that illustrates an example of alternating filtering. Description of the exemplary implementations

[0010] Exemplary embodiments of the invention are described below based on the drawings.

[0011] Fig. Figure 1 shows a block diagram of a vehicle system 10 of the present embodiment. In a vehicle in which the vehicle system 10 is installed, the driving force of a motor, which serves as a power source, is transmitted to the wheels via a transmission, thereby driving the vehicle.

[0012] As in Fig. As shown in Figure 1, the vehicle system 10 includes a vehicle speed sensor 11, a yaw rate sensor 12, a steering angle sensor 13, a radar device 15, an electronic control unit for inter-vehicle distance control or inter-vehicle distance control ECU 20, an electronic engine control unit or engine ECU 30, an electronic brake control unit or brake ECU 40 and various actuators or controllers.

[0013] The vehicle speed sensor 11 determines the speed of the vehicle itself by actual measurement or by estimation. For example, the vehicle speed sensor 11 includes several wheel speed sensors or wheel rotation sensors to detect the speed or rotational speed of the respective wheels and estimates the speed of the vehicle itself using output signals from the multiple wheel rotation sensors.

[0014] The yaw rate sensor 12 detects the yaw rate (angular velocity around the center of gravity of a vehicle) actually generated in the vehicle itself. The yaw rate sensor 12 incorporates an oscillator, such as a tuning fork, and detects the vehicle's yaw rate by identifying a detuning in the oscillator based on the vehicle's yaw moment.

[0015] The steering angle sensor 13 detects the rotation angle by which the steering wheel of the vehicle is turned, as the steering angle.

[0016] The radar device 15 detects an object in the forward direction of the vehicle. The radar device 15 performs reciprocal scanning with an electromagnetic beam or carrier, including a laser beam, millimeter wave, sound wave, or the like, in a direction orthogonal to the direction of travel of the vehicle, at a predetermined scanning angle. When the radar device 15 receives an electromagnetic wave reflected by an object within the scanning area, the radar device determines the distance, relative velocity, direction, and other parameters between the object that reflected the electromagnetic wave and the vehicle.The radar device then uses 15 parameters, such as the detected distance, relative speed, direction, and the like, to identify a vehicle ahead based on the object, and detects the inter-vehicle distance, which is the distance between the vehicle ahead and the vehicle ahead, and the relative azimuth of the vehicle ahead in relation to the vehicle ahead.

[0017] The inter-vehicle distance control ECU 20 serves as a predictive course estimation device for estimating the predicted course of the vehicle and controls the inter-vehicle distance between the vehicle and the vehicle ahead so that it equals a target distance. The function of the inter-vehicle distance control ECU 20 as a predictive course estimation device will be described in detail later.

[0018] The engine ECU 30 determines a throttle position in accordance with a command signal received from the inter-vehicle distance control ECU 20 (hereinafter referred to as "inter-vehicle control") and the current vehicle speed, and controls a throttle actuator 31 while monitoring the throttle position. Additionally, based on an upshift line and a downshift line predetermined for the throttle position and the vehicle speed, the engine ECU 30 determines whether a gear ratio needs to be changed and, if necessary, issues a command to a transmission 32 to change the gear ratio. The transmission 32 can be any mechanism, such as an AT (automatic transmission), a CVT (continuously variable transmission), and the like.

[0019] The brake ECU 40 applies braking to its own vehicle by controlling the opening / closing and position of a valve of a brake actuator 41 based on a command signal for controlling an inter-vehicle distance, received from the inter-vehicle distance control ECU 20. The brake actuator 41 controls the deceleration (or acceleration) of its own vehicle by increasing, maintaining, or decreasing the wheel cylinder pressure of each wheel using pressure generated in the working fluid (e.g., oil or the like) by means of a pump.

[0020] Next, the inter-vehicle distance control ECU 20 will be described in detail. Fig. Figure 2 is a functional block diagram of the inter-vehicle distance control ECU 20. The inter-vehicle distance control ECU 20 includes a radar signal processing unit 21, an inter-vehicle distance control unit 22, and a predictive course estimation unit 200.

[0021] The radar signal processing unit 21 is connected to the radar device 15 and obtains information relating to the vehicle ahead that is detected by the radar device 15.

[0022] The inter-vehicle distance control unit 22 uses an output signal from the radar device 15 and a predicted course of the vehicle, predicted by the predictive estimation unit 200, to issue a command signal to the engine ECU 30 and the brake ECU 40 via the radar signal processing unit 21. This command signal controls the inter-vehicle distance between the vehicle and the vehicle ahead, bringing it closer to a target distance. In other words, the inter-vehicle distance control unit 22 controls the brake actuation force by issuing a command signal to the brake ECU 40 when the vehicle decelerates. When the vehicle accelerates, the inter-vehicle distance control unit 22 controls the throttle position and the gear ratio by issuing a command signal to the engine ECU 30.

[0023] The predictive course segment unit 200 is connected to the vehicle speed sensor 11 and the yaw rate sensor 12 and functions as a filtering unit 201, a property modification unit 202, and a computation unit 203. The filtering unit 201 removes the noise component (hereinafter referred to as the "radio frequency component") contained in the vehicle's direction of rotation data (hereinafter referred to as "rotation data"). The property modification unit 202 modifies the properties of the filter that removes the radio frequency component. The computation unit 203 calculates an estimated value for the predicted course of the vehicle.

[0024] The filtering unit 201 uses the value of the yaw rate detected by the yaw rate sensor 12 as rotation data to remove the high-frequency component contained in the yaw rate using an analog filter as given in Equation 1. Y0=Y×2πf

[0025] In Equation 1, Y is the value of the yaw rate detected by the yaw rate sensor 12 (hereinafter referred to as the "actual yaw rate"). 2πf is an analog filter (low-pass filter) and cuts (removes) the high-frequency component equal to or greater than a cutoff frequency f. Y0 is a filtered yaw rate (hereinafter referred to as the "calculated yaw rate").

[0026] Situations where the vehicle is traveling on a section of road with changing shape include, for example, entering a curve (or clothoid curve) from a straight line, or exiting a curve (or clothoid curve) onto a straight road. These situations also include driving through an S-shaped curve. In such situations, the rate of change ΔY (change rate) in the actual yaw rate Y (change rate in the rotation data) becomes large and can lead to a response delay in the filtering. Therefore, there are concerns that this could impair the control of the distance between the vehicle and the vehicle ahead.

[0027] In the prediction course estimation unit 200 of the present embodiment, the property change unit 202 modifies filter characteristics in the filtering process in accordance with the results of the investigations, specifically whether the vehicle is about to travel on a section of the road where the shape changes. The following is described with reference to a set of diagrams of Fig. 3. The relationship between the change in yaw rate and the filter property settings is described in the case where the vehicle travels on a straight section (straight road) and a curved section (winding road). As in Fig. As shown in Figure 3, when the vehicle travels on a section of the road other than a straight section, the change ΔY in the actual yaw rate Y of the vehicle becomes less than a predetermined first threshold Th1. In this case, the filter's cutoff frequency f is set to a low frequency f1. Conversely, when the vehicle approaches a curved section of the road, the change ΔY in the actual yaw rate Y (= dY / dt) becomes equal to or greater than the predetermined first threshold Th1. In this case, the filter's cutoff frequency f is set to a high frequency f2 (f2 > f1).

[0028] As described above, in the vehicle system 10 of the present embodiment, when the vehicle travels on a section of the road that differs from a section where the shape changes, more of the high-frequency component of the actual yaw rate Y is removed, thereby improving the stability of the calculated yaw rate. Conversely, when the vehicle travels on a section of the road where the shape changes, less of the high-frequency component of the actual yaw rate Y is removed, thus reducing the response delay due to filtering. Accordingly, the responsiveness of the predictive course estimation (the vehicle's ability to follow) is improved.

[0029] When the vehicle travels through a section of road where the shape changes, it is preferred to immediately adjust the degree of removal of the high-frequency component (distance ratio) by the filtering to instantly improve the responsiveness of the predictive course estimation. In this respect, if the actual yaw rate Y becomes equal to or greater than the first threshold when traveling through a section of road where the shape changes, the predictive course estimation unit 200 of this embodiment uses the property change unit 202 to immediately switch the filter from the filter in which the cutoff frequency is set to f1 to the filter in which the cutoff frequency is set to f2.

[0030] However, if the filter's cutoff frequency f is set to the high frequency f2, the extent of high-frequency component removal by filtering is reduced. Therefore, when switching from the filter with the cutoff frequency set to f2 to the filter with the cutoff frequency set to f1 (where the extent of high-frequency component removal by filtering is increased), concerns arise that overshoot will occur and it will be difficult to eliminate the influence of the remaining high-frequency component on the actual yaw rate Y during filtering with the cutoff frequency set to f2 (filtering performed when the extent of high-frequency component removal is low).

[0031] Therefore, when the filter is switched from the filter in which the cutoff frequency is set to f2 to the filter in which the cutoff frequency is set to f1, the property change unit 202 of the present embodiment changes the cutoff frequency f stepwise or gradually. For example, as in Fig. 3(c) shows that the property change unit 202, in accordance with the predetermined change amount Δf, which expresses a curve-like change per unit time, changes the cutoff frequency from f2 to f3 and then from f3 to f1. Alternatively, as shown in Fig. Figure 3(d) shows the property change unit 202 changing the cutoff frequency from f2 to f3 and then from f3 to f1 in accordance with the predetermined change amount Δf, which expresses a stepwise change per unit time. Accordingly, the vehicle system 10 of the present embodiment reduces the occurrence of the aforementioned overshoot when the filter properties are changed from properties that remove less of the high-frequency component (low-distance properties) to properties that remove more of the high-frequency component (high-distance properties).

[0032] Furthermore, in the present embodiment, when the vehicle is not traveling on a section of road where the shape changes, the extent of the removal of the high-frequency component contained in the rotation data is determined in accordance with the shape of the road the vehicle is then traveling on. In other words, the actual yaw rate Y differs between the case where the vehicle is traveling on a straight section of a road and the case where the vehicle is traveling on a curved section. Therefore, when the vehicle is not traveling on a section of road where the shape changes, the prediction estimation unit 200 of the present embodiment determines the cutoff frequency f of the filter in accordance with the actual yaw rate Y using the property change unit 202.More specifically, the property change unit 202 of the present embodiment sets the higher cutoff frequency f of the filter in the case where the vehicle travels over a curved section, compared to the case where the vehicle travels over a straight section. Consequently, the vehicle system 10 of the present embodiment can ensure both stability and responsiveness of the predicted course of the vehicle when the vehicle does not travel through a section of the road where the shape changes.

[0033] On Fig. 2 Referring back, the computation unit 203 is connected to the vehicle speed sensor 11 and the filtering unit 201 and calculates an estimated value R for the predicted course of its own vehicle by performing a calculation of equation (2) using the vehicle speed V and the calculated yaw rate Y0. R=V / Y0

[0034] The calculated estimated value R of the predicted course is used for inter-vehicle distance control by the inter-vehicle distance control unit 22.

[0035] Next, the procedure for the filter property change process in the predictive course estimation of the vehicle is described. The in Fig. The process shown in section 4 is repeated by the prediction estimation unit 200 under the condition that inter-vehicle distance control is performed by the inter-vehicle distance control ECU 20. The following description shows the relationship between the filter cutoff frequencies f1 to f3: f2 > f3 > f1.

[0036] The predictive course estimation unit 200 of the present embodiment causes the property change unit 202 to determine whether the amount of change ΔY in the actual yaw rate Y is equal to or greater than the first threshold Th1 (S11). The processing of S11 corresponds to a process of determining whether the vehicle is traveling on a portion of the road where the shape is changing (determination unit). If it is determined that the amount of change ΔY is less than the first threshold Th1 (ΔY < Th1; NO at S11), the predictive course estimation unit 200 determines whether the actual yaw rate Y is less than the second threshold Th2 (S14). In other words, the processing of S14 determines whether the actual yaw rate Y is less than the second threshold Th2 if it is determined that the vehicle is not traveling on a portion of the road where the shape is changing.If it is determined that the actual yaw rate Y is equal to or greater than the second threshold Th2 (Y ≥ Th2; NO at S14), the predictive course estimator 200 instructs the property change unit 202 to set the filter's cutoff frequency f to f3 (S16). However, if it is determined that the actual yaw rate Y is less than the second threshold Th2 (Y < Th2; YES at S14), the predictive estimator 200 instructs the property change unit 202 to set the filter's cutoff frequency f to f1 (S15). Furthermore, if it is determined that the change magnitude ΔY is equal to or greater than the first threshold Th1 (ΔY ≥ Th1; YES at S11), the predictive estimator 200 determines whether the own vehicle is performing a lane change (whether there is a lane change) (S12).In other words, the S12 processing unit determines whether a lane change is occurring when it detects that the vehicle is traveling on a section of the road where the shape changes. For example, if an action or manipulation of the turn signal is detected, and it is consequently determined that a lane change is required (YES for S12), the Predictive Estimator Unit 200 terminates its processing. It should be noted that detecting the manipulation of the turn signal is equivalent to a process of detecting whether the vehicle is performing a lane change (lane change detection unit or lane change detection unit). However, if it is determined that no lane change is required (NO for S12), the Predictive Estimator Unit 200 instructs the Property Change Unit 202 to set the filter's cutoff frequency f to f2 (S13).

[0037] Next, with reference to Fig. Section 5 describes an example of the execution of the process outlined above. In the following description, neither the vehicle itself nor the vehicle in front performs a lane change. Furthermore, the Fig. The road shown in Figure 5 has a first straight section R1, a curved section R2, and a second straight section R3. Of these, the curved section R2 has a first curvature change section R21, which causes a change in curvature (clothoid curve change) at the entrance of the curve, a constant curvature section R22 with constant curvature, and a second curvature change section R23, which causes a change in curvature (clothoid curve change) at the exit of the curve.

[0038] If the vehicle is located at position A in the first straight section R1 during its movement, the magnitude of change ΔY in the actual yaw rate Y will be less than the first threshold Th1, and the actual yaw rate will be less than the second threshold Th2, since position A is not in a section of the road where the shape changes. Consequently, the filter's cutoff frequency f is set to f1. In this case, the degree of removal of the high-frequency component from the actual yaw rate Y is increased. Then, if the vehicle is located at position B in the first section of curvature R21 at the entrance to the curve, the magnitude of change ΔY in the actual yaw rate Y will be equal to or greater than the first threshold Th1, since position B is in a section of the road where the shape changes. Consequently, the cutoff frequency f of the actual yaw rate Y switches immediately from f1 to f2.At this time, the switching of the cutoff frequency f is carried out immediately at the beginning of the entry into the first curvature change section R21.

[0039] If the vehicle is at a location C in the constant curvature section R 22 during its movement, the change ΔY in the actual yaw rate Y becomes less than the first threshold Th1, and the actual yaw rate Y becomes equal to or greater than the second threshold Th2. Accordingly, the cutoff frequency f of the filter changes from f2 to f3. At this time, as in Fig. 3(c) or Fig. 3(d) shows that the cutoff frequency f is gradually changed from the cutoff frequency f2 to f3.

[0040] When the vehicle is moving through the second curvature change section R23 at the exit of the curve, the change in the actual yaw rate Y, ΔY, becomes equal to or greater than the first threshold Th1. Accordingly, the filter's cutoff frequency f is immediately changed from f3 to f2. Subsequently, when the vehicle is at a location D in the second straight section R3, the change in the actual yaw rate Y, ΔY, becomes less than the first threshold Th1. Consequently, the filter's cutoff frequency f is gradually changed from f2 to f1, and then held at f1.

[0041] In accordance with the foregoing description, the vehicle system 10 of the present embodiment provides the following advantageous effects.

[0042] When estimating a predicted course of the own vehicle based on the vehicle speed V and the rotation data (the actual yaw rate Y) of the own vehicle, filtering is performed to remove the high-frequency component contained in the rotation data. However, when the own vehicle travels through a section of the road where the shape changes, a response delay occurs due to the filtering, as the magnitude of the change in the rotation data (change ΔY in the actual yaw rate Y) becomes large. There are concerns that this response delay could impair the estimation of the predicted course. Therefore, in the vehicle system 10 of the present embodiment, when the own vehicle travels through a section of the road where the shape changes, the extent of the high-frequency component removal by the filter (cutoff frequency f) is modified.Accordingly, the vehicle system 10 of the present embodiment is able to perform suitable filtering in accordance with the shape of the road that the vehicle travels on, thereby improving the accuracy of estimating a predicted course of the vehicle using rotation data that has been filtered.

[0043] The vehicle system 10 of the present embodiment gradually or stepwise changes the extent of the removal of the high-frequency component (distance ratio) when the filter properties are changed to properties that remove more of the high-frequency component (properties with a high distance ratio), compared to when the filter properties are changed to properties that remove less of the high-frequency component (properties with a low distance ratio). Accordingly, the vehicle system 10 of the present embodiment can reduce problems that arise in the rotation data when the filter properties are changed to properties that make the high-frequency component easily removed.

[0044] The vehicle system 10 of the present embodiment is able to perform filtering and thereby distinguish between a change of direction, such as a lane change, and a change in the shape of the road, such as a straight road or a curved road.

[0045] When the vehicle enters a curve in the road (first curve change section R21) or exits it (second curvature change section R23), the change ΔY in the rotational data becomes large. Therefore, the vehicle system 10 of this embodiment modifies the filter properties to properties that remove less of the high-frequency component. Consequently, the vehicle system 10 of this embodiment can reduce the response delay of the filtering due to the large change ΔY in the rotational data.

[0046] When the vehicle travels through a curve (curved section R2) that includes the first curvature-changing section R21 and the second curvature-changing section R23, which cause a change in curvature, and a constant curvature section R22 with a constant curvature, the change ΔY in the rotational data becomes greater in the first and second curvature-changing sections R21 and R23 than in the constant curvature section R22. Therefore, the vehicle system 10 of the present embodiment modifies the filter characteristics to properties that remove less of the high-frequency component. Consequently, the vehicle system 10 of the present embodiment can increase the effect of reducing the response delay due to the filtering performed in the first and second curvature-changing sections R21 and R23.

[0047] The effect of the high-frequency component contained in the rotation data on the predicted course depends on the vehicle's speed V. Therefore, the vehicle system 10 of the present embodiment changes the degree of removal of the high-frequency component in accordance with the vehicle speed V. This allows the vehicle system 10 of the present embodiment to appropriately remove the high-frequency component contained in the rotation data in accordance with the vehicle speed V. Consequently, the accuracy of estimating a predicted course can be improved.

[0048] The present embodiment is not limited to the preceding embodiment and can be implemented as follows.

[0049] In the preceding embodiment, the predictive course estimation unit 200 uses the actual yaw rate Y as rotation data. However, the embodiment is not limited to this. The rotation data can also be the value of the steering angle detected by the steering angle sensor 13 or the values ​​of the torque detected by a torque sensor.

[0050] In the preceding embodiment, the portion of the road where the shape changes is determined based on the change amount ΔY in the rotational data. However, the embodiment is not limited to this. The method for determining the portion of the road where the shape changes also includes a method in which the portion of the road where the shape changes is determined using information from outside the vehicle, such as the results of detecting white lines (or a guardrail shape) by processing images taken by a camera mounted on the front of the vehicle, or navigational information. In this case as well, the vehicle system 10 modifies the extent of the removal of the radio frequency component from the rotational data based on the results of the determinations as to whether the portion of the road is a portion where the shape changes.

[0051] In the preceding embodiment, the extent of the removal of the high-frequency component (cutoff frequency f) is changed in accordance with the rotation data. However, the embodiment is not limited to this. The extent of the removal of the high-frequency component can be changed in accordance with the vehicle speed V. In other words, and as shown in equation (2) in the preceding embodiment, the influence of the actual yaw rate Y on the estimated value R for the predicted course decreases as the vehicle speed V increases (the speed increases). Therefore, as shown in Fig.As shown in Figure 6, when the vehicle speed V is low (the speed is slow), the filter's cutoff frequency f is set low to increase the effect of reducing the high-frequency component contained in the actual yaw rate Y. Conversely, when the vehicle speed V is high (the speed is fast), the filter's cutoff frequency f is set high to minimize the effect of reducing the high-frequency component contained in the actual yaw rate Y and to improve the responsiveness of the predictive course estimation.

[0052] In the preceding embodiment, the degree of removal of the high-frequency component is changed by altering the filter's cutoff frequency f. However, the embodiment is not limited to this. The degree of removal of the high-frequency component can also be changed by altering the filter's time constant τ. In other words, when the vehicle is not moving along a section of the road where its shape is changing, the filter's time constant τ is set relatively large to increase the degree of removal of the high-frequency component contained in the actual yaw rate Y. Conversely, when the vehicle is moving along a section of the road where its shape is changing, the filter's time constant τ is set small to decrease the degree of removal of the high-frequency component from the actual yaw rate and thus improve the predictive accuracy of the heading estimation.

[0053] In the preceding embodiment, if the change ΔY in the rotational data of the vehicle is equal to or greater than a predetermined first threshold Th1, the filter's cutoff frequency f is set high. However, the embodiment is not limited to this. The filter's cutoff frequency f can also be set low in this case.

[0054] The method of changing the filter properties can be the same both in cases where the filter properties are changed to properties that remove more of the high-frequency component, and in cases where the filter properties are changed to properties that remove less of the high-frequency component. Reference symbol list

[0055] 10...Vehicle system, 11...Vehicle speed sensor, 12...Yaw rate sensor, 13...Steering angle sensor, 15...Radar device, 20...Inter-vehicle distance control ECU, 200...Predictive course estimation unit, 201...Filtering unit, 202...Property change unit, 203...Calculation unit, A...Location, B...Location, C...Location, D...Location, R21...First curvature change part, R22...Constant curvature part, R23...Second curvature change part, V...Vehicle speed

Claims

[1] Predictive course estimation device (20) for estimating a predicted course of one's own vehicle, comprising: a data acquisition device for obtaining rotation data that indicates the direction of rotation of one's own vehicle; a filtering device for removing a high-frequency component contained in the rotation data; a course prediction device for calculating an estimated value for predicting the course of the own vehicle based on the rotation data that have been filtered and a speed of the own vehicle; an investigative device to determine whether one's own vehicle is moving on a part of the road where the shape changes; and a property modification device for changing the extent of the removal of the high-frequency component by the filtering device when it is determined that the vehicle is moving on a part of the road where the shape changes, wherein The property-changing device gradually changes the degree of removal of the high-frequency component when filter properties of the filtering device are changed to properties with a high degree of removal of the high-frequency component, compared to when filter properties of the filtering device are changed to properties with a low degree of removal of the high-frequency component. [2] Predictive course estimation device according to claim 1, comprising: a lane change detection device to detect whether a lane change of the vehicle is required; wherein The property-changing device does not change the extent of the removal of the high-frequency component if the lane change of the own vehicle must also be carried out when the own vehicle is moving on a part of the road where the shape changes. [3] Predictive course estimation device (20) for estimating a predicted course of one's own vehicle, comprising: a data acquisition device for obtaining rotation data that indicates the direction of rotation of one's own vehicle; a filtering device for removing a high-frequency component contained in the rotation data; a course prediction device for calculating an estimated value for predicting the course of the own vehicle based on the rotation data that have been filtered and a speed of the own vehicle; an investigative device to determine whether one's own vehicle is moving on a part of the road where the shape changes; a property-changing device for modifying the extent of the removal of the high-frequency component by the filtering device when it is determined that the vehicle is moving on a section of the road where the shape changes; and a lane change detection device to detect whether a lane change of the vehicle is required; wherein The property-changing device does not change the extent of the removal of the high-frequency component if the lane change must also be carried out when the vehicle is moving on a part of the road where the shape changes. [4] Predictive course estimation device according to one of claims 1 to 3, wherein the property change device changes the filter properties of the filtering device to properties with a low degree of removal of the high-frequency component when the vehicle enters a curve of a road. [5] Predictive course estimation device according to one of claims 1 to 3, wherein the property change device changes the filter properties of the filtering device to properties with a low degree of removal of the high-frequency component when the vehicle enters an exit part of a curve of a road. [6] Predictive course estimation device according to one of claims 1 to 5, wherein, when the vehicle travels over a curve of a road with a curvature change section, which causes a change in curvature, and a constant curvature section, in which the curvature is constant, the property change device changes the filter properties of the filtering device, which are applied to travel over the curvature change section, to filter properties with a lower degree of removal of the high-frequency component compared to the filter properties applied to travel over the constant curvature section. [7] Predictive course estimation device according to any one of claims 1 to 6, wherein the property change device changes the extent of the removal of the high-frequency component in accordance with the vehicle speed. [8] Predictive course estimation device according to any one of claims 1 to 7, wherein the property change device changes the extent of the removal of the high-frequency component in accordance with a curvature of the road traveled by the vehicle. [9] Predictive course estimation device according to any one of claims 1 to 8, wherein the rotation data are a yaw rate of the vehicle itself or a detected value of a steering angle. [10] Predictive course estimation device according to any one of claims 1 to 8, comprising: an object detection device provided in the vehicle to detect an object located in the forward direction of the vehicle; and a following motion control device which, when the object is a vehicle ahead, controls the acceleration and deceleration of the own vehicle in order to follow the vehicle ahead, while maintaining an intermediate vehicle distance between the vehicle ahead and the own vehicle. [11] Predictive course estimation methods for estimating a predicted course of one's own vehicle, comprising: a step in obtaining rotation data that specifies the direction of rotation of one's own vehicle; a step of performing a filter to remove a high-frequency component contained in the rotation data; a step of calculating an estimated value for predicting the course of one's own vehicle based on the rotation data that has been filtered and a speed of the own vehicle; a step in determining whether one's own vehicle is moving on a part of the road where the shape changes; and a step of changing the extent of the removal of the high-frequency component by filtering, when it is determined that the vehicle is moving on a part of the road where the shape changes, whereby In the step of changing a magnitude, the degree of removal of the high-frequency component is gradually changed when filter properties of the filtering device are changed to properties with a high degree of removal of the high-frequency component, compared to when filter properties of the filtering device are changed to properties with a low degree of removal of the high-frequency component. [12] Predictive course estimation methods for estimating a predicted course of one's own vehicle, comprising: a step in obtaining rotation data that specifies the direction of rotation of one's own vehicle; a step of performing a filter to remove a high-frequency component contained in the rotation data; a step of calculating an estimated value for predicting the course of one's own vehicle based on the rotation data that has been filtered and a speed of the own vehicle; a step in determining whether the vehicle is moving on a part of the road where the shape changes; a step of changing the extent of the removal of the high-frequency component by filtering, when it is determined that the vehicle is moving on a part of the road where the shape changes; and a step in determining whether a lane change of one's own vehicle is necessary; whereby In the step of changing a dimension, the dimension of removing the high-frequency component is not changed if the lane change is to be carried out even when the vehicle is moving on a part of the road where the shape changes.

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