Vehicle control device

Abstract

To provide a vehicle control device capable of preventing excessive motion of an own vehicle with respect to motion of an obstacle.SOLUTION: This vehicle control device generates a maximum fluctuation amount of a state amount in motion of an obstacle from now to the future by a maximum fluctuation amount generation unit (103), predicts motion of the obstacle to the future on the basis of a predefined motion model by a motion prediction unit (104), and controls, by a vehicle control unit (102), motion of an on vehicle on the basis of the motion of the obstacle predicted by the motion prediction unit (104). The motion prediction unit (104) is configured to predict the position of the obstacle by limiting the fluctuation amount so as to prevent the fluctuation amount of the state amount in the motion of the obstacle to the future from exceeding the maximum fluctuation amount.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a vehicle control device. [Background technology]

[0002] Conventionally, in automatic driving devices for vehicles, ADAS (Advanced Driver Assistance Systems), and the like, there are known vehicle control devices that calculate the future trajectory (including time and route) of the vehicle from the present time until a predetermined predicted time has elapsed, and control the behavior of the vehicle so that the vehicle travels based on the future trajectory.

[0003] According to the vehicle control device described above, for example, if it is expected that there will be a stop line at an intersection ahead in the direction of travel of the host vehicle, the stopping action of the other vehicle ahead that the host vehicle is following will be predicted, and the future trajectory of the host vehicle will be planned in accordance with the predicted stopping action of the other vehicle, and the driving of the host vehicle will be controlled. However, if the future trajectory of the host vehicle is planned assuming that the other vehicle ahead will move at a constant speed, when the other vehicle ahead changes from moving at a constant speed to decelerating, the distance between the other vehicle and the host vehicle will suddenly decrease, which may give the driver of the host vehicle a sense of crisis.

[0004] As an improvement to the above-mentioned vehicle control device, Patent Document 1 discloses a vehicle distance control device that, assuming that the preceding vehicle is decelerating, calculates an acceleration limit value for the subject vehicle so that the collision time required for the subject vehicle to collide with the preceding vehicle is equal to or less than a threshold, and controls the distance between the subject vehicle and the preceding vehicle by limiting the acceleration of the subject vehicle using the calculated acceleration limit value.

[0005] The conventional inter-vehicle distance control device disclosed in Patent Document 1 is configured to, when calculating the acceleration limit value, assume that the preceding vehicle decelerates at a constant deceleration rate from the start of the acceleration limit value calculation and stops, and also assume that the host vehicle travels at various acceleration rates, calculate a time function connecting the points of the time until the host vehicle collides with the preceding vehicle for various acceleration target values, and use the intersection of this time function and the aforementioned collision time threshold value as the acceleration limit value for the host vehicle.

[0006] According to the conventional inter-vehicle distance control device disclosed in Patent Document 1, when calculating the acceleration limit value, it is assumed that the preceding vehicle will decelerate and stop at a constant deceleration from the start of the calculation.Therefore, when the preceding vehicle decelerates from a start to a stop, the acceleration of the vehicle decreases at a gentle gradient to follow the deceleration of the preceding vehicle, thereby reducing the sense of crisis felt by the driver of the vehicle.Furthermore, when calculating the acceleration limit value, a time function is obtained assuming that the vehicle will travel at various accelerations, so it is possible to calculate the acceleration limit value, which is the maximum acceleration at which the collision time is below a threshold, with a smaller amount of calculation. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent No. 6702104 Summary of the Invention [Problem to be solved by the invention]

[0008] According to the conventional inter-vehicle distance control device disclosed in Patent Document 1, the movement of the other preceding vehicle is predicted on the assumption that the other preceding vehicle accelerates or decelerates at a constant acceleration / deceleration rate, so when the actual acceleration or deceleration of the other preceding vehicle is temporary in a scene such as on a highway, the predicted trajectory of the other preceding vehicle will deviate significantly from the actual trajectory. Therefore, for example, in a scene on a highway where the other preceding vehicle strongly decelerates for a short period of time, the host vehicle will erroneously predict that the other preceding vehicle will continue to strongly decelerate, resulting in a problem of excessive deceleration in the predicted trajectory of the host vehicle.

[0009] The present disclosure discloses a technique for solving the above-mentioned problems, and aims to provide a vehicle control device that can prevent excessive movement of the vehicle in response to the movement of an obstacle such as another vehicle. [Means for solving the problem]

[0010] The vehicle control device of the present disclosure includes: A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that predicts the future motion of the obstacle based on a predetermined motion model; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; Equipped with The operation prediction unit the prediction is performed by limiting the amount of fluctuation in the state quantity in the operation of the obstacle up to the future so that the amount of fluctuation does not exceed the maximum amount of fluctuation. It is characterized by:

[0011] Further, the vehicle control device according to the present disclosure includes: A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a determination unit that determines whether a current motion of the obstacle is a temporary motion based on information about the obstacle acquired by a sensor mounted on the host vehicle; a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present time to a future time after a predetermined time; a motion prediction unit that predicts the future motion of the obstacle based on a predetermined motion model; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; Equipped with The operation prediction unit when the determination unit determines that the motion of the obstacle is the temporary motion, the determination unit performs the prediction by limiting the amount of fluctuation of the state quantity in the motion of the obstacle up to the future so that the amount of fluctuation does not exceed the maximum amount of fluctuation; When the determination unit determines that the motion of the obstacle is not the temporary motion, the prediction is performed without imposing the restriction. It is configured as follows: It is characterized by:

[0012] Furthermore, the vehicle control device according to the present disclosure includes: A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that generates a virtual trajectory that does not exceed the maximum fluctuation amount and predicts a motion of the obstacle by assuming that the obstacle will travel along the virtual trajectory; a vehicle control unit that controls the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; The present invention is characterized by the following features.

[0013] Further, the vehicle control device according to the present disclosure includes: A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that predicts the motion of the obstacle based on a first motion model when the amount of change in the state quantity in the motion of the obstacle up to the future does not reach the maximum amount of change, and predicts the motion of the obstacle based on a second motion model after the amount of change in the state quantity of the obstacle reaches the maximum amount of change; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; The present invention is characterized by the following features. [Effects of the Invention]

[0014] According to the vehicle control device of the present disclosure, a vehicle control device can be obtained that can prevent excessive movement of the host vehicle in response to the movement of an obstacle such as another vehicle. [Brief explanation of the drawings]

[0015] [Figure 1A] FIG. 1 is an explanatory diagram illustrating Technology 1 that forms the basis of the present disclosure. [Figure 1B] FIG. 1 is an explanatory diagram illustrating Technology 2 that forms the basis of the present disclosure. [Figure 1C] FIG. 10 is an explanatory diagram illustrating Technology 3 that forms the basis of the present disclosure. [Figure 2] 1 is a functional block diagram showing a vehicle control device according to first to third embodiments. [Figure 3] 4 is a flowchart showing the operation of the vehicle control device according to the first and second embodiments. [Figure 4] 3 is an explanatory diagram showing state quantities of an obstacle in the vehicle control device according to the first embodiment. FIG. [Figure 5] 4 is an explanatory diagram showing the maximum fluctuation amount of the vertical speed of an obstacle generated by a maximum fluctuation amount generating unit in the vehicle control device according to the first embodiment. FIG. [Figure 6] 4 is an explanatory diagram showing the maximum fluctuation amount of the acceleration in the longitudinal direction of an obstacle generated by a maximum fluctuation amount generating unit in the vehicle control device according to the first embodiment. FIG. [Figure 7] 10 is an explanatory diagram showing state quantities of an obstacle in the vehicle control device according to the second and third embodiments. FIG. [Figure 8] FIG. 10 is another explanatory diagram showing state quantities of an obstacle in the vehicle control device according to the second and third embodiments. [Figure 9] 10 is an explanatory diagram showing the maximum fluctuation amount of the lateral velocity of an obstacle generated by a maximum fluctuation amount generating unit in the vehicle control device according to the second and third embodiments. FIG. [Figure 10] 10 is an explanatory diagram showing the maximum fluctuation amount of the lateral velocity of an obstacle generated by a maximum fluctuation amount generating unit in the vehicle control device according to the second and third embodiments. FIG. [Figure 11] FIG. 10 is an explanatory diagram showing a predicted trajectory of an obstacle in a vehicle control device according to a second embodiment. [Figure 12] 10 is a flowchart showing the operation of the vehicle control device according to the third embodiment. [Figure 13] FIG. 11 is an explanatory diagram showing the behavior of an obstacle in the vehicle control device according to the third embodiment. [Figure 14] FIG. 11 is an explanatory diagram showing the concept of generating a virtual path of an obstacle in the vehicle control device according to the third embodiment. [Figure 15] FIG. 10 is a functional block diagram showing a vehicle control device according to a fourth to sixth embodiment. [Figure 16] 10 is a flowchart showing the operation of the vehicle control device according to the fourth and fifth embodiments. [Figure 17] FIG. 13 is an explanatory diagram showing the behavior of an obstacle in the vehicle control device according to the fifth embodiment. [Figure 18]13 is a flowchart showing the operation of the vehicle control device according to the sixth embodiment. [Figure 19] 1 is a block diagram showing an example of a hardware configuration of an ECU in a vehicle control device according to a first to sixth embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0016] First, the technology underlying this disclosure will be described. Fundamental Technology 1. 1A is an explanatory diagram illustrating Technology 1, which is the basis of the present disclosure. In Fig. 1A, a host vehicle 1 equipped with an ACC follows an obstacle 2, which is another vehicle traveling ahead of the host vehicle 1. Here, the current speed of the obstacle 2 is assumed to be speed V0 [m / s].

[0017] A host vehicle 1 equipped with an ACC calculates a predicted trajectory 21 of an obstacle 2 from the current time t0 to a future time tns after a predetermined time n [sec] based on a speed V0 [m / s] and a constant velocity linear motion model, and follows the obstacle 2 while maintaining a predetermined inter-vehicle distance based on the predicted trajectory 21. According to the underlying technology 1, the host vehicle 1 predicts that the obstacle 2 will be at a position 2ns at a future time tns using the constant velocity linear motion model, and similarly predicts the position of the obstacle 2 based on the predicted trajectory 21 and follows the obstacle 2 from the time tns onwards.

[0018] At this time, if the obstacle 2 actually follows the actual trajectory 22 rather than the predicted trajectory 21 based on uniform linear motion and stops at the stop line 3 ahead at time tns, the obstacle 2 will be at the actual position 2a at the future time tns, and the distance between the preceding obstacle 2 and the vehicle 1 will decrease suddenly, causing a problem in which the driver of the vehicle 1 will feel a sense of crisis.

[0019] Fundamental Technology 2 1B is an explanatory diagram illustrating Technology 2, which is the basis of the present disclosure. In FIG. 1B, a host vehicle 1 equipped with an ACC follows an obstacle 2, which is another vehicle traveling ahead of the host vehicle 1. Here, the current speed of the obstacle 2 is assumed to be speed V0 [m / s].

[0020] The host vehicle 1 equipped with ACC calculates a predicted trajectory 21 of the obstacle 2 from the current time t0 to a future time tns after a predetermined time n [sec] based on a speed V0 [m / s] and a constant acceleration linear motion model, and follows the obstacle 2 while maintaining a predetermined inter-vehicle distance based on the predicted trajectory 21. According to the underlying technology 2, the host vehicle 1 predicts that the obstacle 2 will be at a position 2ns at the future time tns using the constant acceleration linear motion model, and similarly predicts the position of the obstacle 2 based on the predicted trajectory 21 and follows the obstacle 2 from the time tns onwards.

[0021] At this time, if the preceding obstacle 2 does not actually move in a linear motion with constant acceleration, but moves along an actual trajectory 22 with a linear motion with constant velocity after the current time point t0, the obstacle 2 at time point tns will be at an actual position 2a, which will be farther from the host vehicle 1 than the predicted position 2ns. However, since the host vehicle 1 predicts that the preceding obstacle 2 will be at the predicted position 2ns at time point tns, it will decelerate more than necessary to maintain the inter-vehicle distance, which causes a problem of moving farther away than necessary from the actual position 2a.

[0022] Underlying Technology 3. 1C is an explanatory diagram illustrating Technology 3, which is the basis of the present disclosure. In FIG. 1C, when obstacle 2, as another vehicle, is traveling in lane 42 adjacent to lane 41 in which host vehicle 1 is traveling, and stationary obstacle 5, such as a stationary object, is present ahead of obstacle 2, host vehicle 1 equipped with ACC predicts that obstacle 2 will cut in front of host vehicle 1 from lane 42, crossing lane marking 43, to avoid stationary obstacle 5, and generates predicted trajectory 23, and determines that obstacle 2 will arrive at predicted position 2ns based on the predicted trajectory 23. This causes host vehicle 1 to decelerate.

[0023] In this case, if the obstacle 2 does not actually enter the lane 41 in which the vehicle 1 is traveling from the lane 42, but travels within the range of the lane 42 while avoiding the stationary obstacle 5 as shown in the actual trajectory 24, there is a problem in that the vehicle 1 will decelerate unnecessarily.

[0024] Embodiment 1, Next, a vehicle control device according to the first embodiment will be described with reference to the drawings. Fig. 2 is a functional block diagram showing a vehicle control device according to the first to third embodiments. In Fig. 2, a host vehicle 1 is equipped with a vehicle control device 100, an external sensor 200, and an internal sensor 300. The host vehicle 1 may be any of an internal combustion engine vehicle, a hybrid vehicle, and an electric motor vehicle.

[0025] The vehicle control device 100 includes an obstacle movement prediction unit 101 and a vehicle control unit 102. The obstacle movement prediction unit 101 is configured with a maximum variation generation unit 103 and a movement prediction unit 104. The maximum variation generation unit 103 generates a maximum variation of a predetermined state quantity in the movement of an obstacle from the present to a predetermined time into the future. Here, in the vehicle control device 100 according to the first embodiment, the predetermined state quantity of the obstacle is the speed of the obstacle in the longitudinal direction, which is the traveling direction of the host vehicle 1, or the acceleration of the obstacle in the longitudinal direction.

[0026] The motion prediction unit 104 has a function of predicting the motion of the obstacle based on a predetermined motion model. More specifically, the motion prediction unit 104 is configured to predict the future motion of the obstacle by limiting the amount of variation in the state quantity of the obstacle in the motion model so that the amount of variation in the state quantity does not exceed the maximum amount of variation. Here, a constant velocity linear motion model or a constant acceleration linear motion model is used as the predetermined motion model. The vehicle control unit 102 controls the motion of the host vehicle based on the future motion of the obstacle predicted by the motion prediction unit 104.

[0027] The external sensor 200 includes an obstacle information acquisition unit 201 and a surrounding environment information acquisition unit 202. The obstacle information acquisition unit 201 includes, for example, a GPS (Global Positioning System) sensor, a millimeter wave radar, an ultrasonic sensor, etc. The surrounding environment information acquisition unit 202 is configured by, for example, a camera that captures images of the surroundings of the vehicle 1, a map information database used in an in-vehicle navigation device, etc.

[0028] The internal sensor 300 includes a host vehicle state acquisition unit 301, which is configured with, for example, a speed sensor, an acceleration sensor, an angular velocity sensor, a steering angle sensor, and the like.

[0029] Next, the operation of the vehicle control device according to embodiment 1 will be described. Fig. 3 is a flowchart showing the operation of the vehicle control device according to embodiment 1 and embodiment 2. In Fig. 3, in step S101, the vehicle control device 100 acquires various state quantities of obstacles around the host vehicle using the obstacle information acquisition unit 201 in the external sensor 200.

[0030] At this time, the state quantities of the obstacle acquired by the obstacle information acquisition unit 201 include at least the longitudinal position, longitudinal velocity, and longitudinal acceleration of the obstacle. In addition to these state quantities, the jerk of the obstacle may also be included. Here, the longitudinal direction refers to the traveling direction of the host vehicle.

[0031] 4 is an explanatory diagram showing state quantities of an obstacle in the vehicle control device according to embodiment 1. In FIG. 4, a host vehicle 1 is traveling on a lane 41 of a road 40, an obstacle 2A as another vehicle is traveling ahead of the host vehicle 1 in the traveling direction, and obstacles 2B and 2C as other vehicles are traveling on a lane 42 adjacent to the lane 41 on which the host vehicle 1 is traveling.

[0032] Here, the traveling direction of the host vehicle 1 is defined as the longitudinal direction x, and the direction perpendicular to the traveling direction of the host vehicle 1 is defined as the lateral direction y. The state quantities of the obstacle 2A acquired by the vehicle control device 100 of the host vehicle 1 from the external sensor 200 are the position X1 [m] in the longitudinal direction, the velocity V in the longitudinal direction, and the like. x1 [m / ses], longitudinal acceleration a x1 [m / sec 2 ], and the state quantities of obstacle 2B are the vertical position X2 [m] and the vertical velocity V x2 [m / sec], vertical acceleration a x2 [m / sec 2 ], and the state quantities of obstacle 2C are the vertical position X3 [m] and the vertical velocity V x3 [m / sec], vertical acceleration a x3 [m / sec 2 In the following description, the units of these state quantities will be omitted.

[0033] Next, in step S102 of Fig. 3, the maximum variation generating unit 103 generates the maximum variation of the longitudinal velocity as a state quantity of the obstacle for a predetermined time in the future from the present. Fig. 5 is an explanatory diagram showing the maximum variation of the longitudinal velocity of the obstacle generated by the maximum variation generating unit in the vehicle control device according to the first embodiment. Taking the above-mentioned obstacle 2A as an example, the longitudinal velocity V x1 and the vertical acceleration a x1 The vertical axis indicates the time tns, and the horizontal axis indicates the predicted time tns.

[0034] In FIG. 5, the vertical velocity V of the obstacle 2A at the current time t0 is x1 0 is the vertical velocity V at a future time t1 after a predetermined time from time t0. x1 The maximum variation amount generator 103 reduces the vertical velocity V x1 0 and the longitudinal velocity V x1 The difference between VMAX and MIN is set as the maximum fluctuation amount VMAX. x1 0-V x1 MIN].

[0035] Here, the future time point t1 after a predetermined time from the current time point t0 is, for example, the vertical velocity V of the obstacle 2A at the time point t0. x1 The larger the value of 0, the shorter the time from time t0.

[0036] 3, the motion prediction unit 104 predicts the motion of the obstacle 2A based on a constant velocity linear motion model as a predetermined motion model. At this time, the motion prediction unit 104 predicts the motion of the obstacle 2A based on a vertical velocity V x1 The vertical velocity V from the future time point t1 onwards is set so that the fluctuation of V does not exceed the maximum fluctuation VMAX. x1 is the vertical velocity V x1 MIN and predict the behavior of obstacle 2A.

[0037] The obstacle 2A shown in FIG. 5 moves with an acceleration a x1 0, the vertical velocity V x1 0 to vertical velocity V x1 MIN, and from time t1 onwards, the longitudinal velocity V x1 MIN and perform linear motion. Therefore, the acceleration a of obstacle 2A after time t1 is x1 0 becomes "0".

[0038] The constant velocity motion model is expressed by the following equation (1). V x1 =V x1 0+[a x1 0×(t1-t0)]·····(1)

[0039] In the uniform velocity linear motion model, [V x1 <V x1 MIN], the future movement of the obstacle 2A is predicted using the following equation (2). V x1 ={VMAX[V x1 0+a x10×(t1-t0)], V x1 MIN} ·····(2) where V x1 MIN is the current longitudinal velocity V of obstacle 2A. x1 This is a value obtained by subtracting the maximum fluctuation amount VMAX from 0. The trajectory of the obstacle 2A can be predicted by repeatedly calculating equation (2) up to the prediction time.

[0040] In addition, the maximum fluctuation amount of the state quantity is calculated in equation (2), but [V x1 >V x1 MIN], the acceleration a x1 It is also possible to limit 0 to "0" and continue the calculation by repeating the above-mentioned formula (1).

[0041] As described above, the movement prediction unit 104 is configured to limit the running speed or acceleration of the obstacle so that the amount of fluctuation in the running speed in the constant speed linear motion model does not exceed the maximum amount of fluctuation in the running speed.

[0042] Next, in step S104 of FIG. 3, the vehicle control unit 102 controls the longitudinal speed or longitudinal acceleration of the host vehicle 1 based on the future movement of the obstacle 2A predicted by the movement prediction unit 104, and controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A.

[0043] The above has been an explanation of the case where the future movement of an obstacle is predicted by limiting the amount of variation in a constant velocity linear motion model based on the maximum amount of variation in the longitudinal velocity of the obstacle as a state quantity, and the movement of the host vehicle is controlled. Next, we will explain the case where the future movement of an obstacle is predicted by limiting the amount of variation in a constant acceleration linear motion model based on the maximum amount of variation in the longitudinal acceleration of the obstacle as a state quantity, and the movement of the host vehicle is controlled.

[0044] In step S102 of Fig. 3, a maximum variation in the longitudinal acceleration of the obstacle is generated as a state quantity for the obstacle at a predetermined time in the future from the present. Fig. 6 is an explanatory diagram showing the maximum variation in the longitudinal acceleration of the obstacle generated by the maximum variation generation unit in the vehicle control device according to the first embodiment. Taking the above-mentioned obstacle 2A as an example, the longitudinal velocity V x1 and the vertical acceleration a x1 The vertical axis indicates the time tns, and the horizontal axis indicates the predicted time tns.

[0045] 6, the maximum variation generating unit 103 generates a maximum variation aMAX of the vertical acceleration as a state quantity of the obstacle at a future time t1 that is a predetermined time from the current time t0. x1 0 is the acceleration a shown by the dashed line at time t1 x1 Even if the acceleration fluctuates to 11, the fluctuation amount is limited by the maximum fluctuation amount aMAX of the vertical acceleration generated by the maximum fluctuation amount generating unit 103, and after the time point t1, the acceleration fluctuates to 11. x1 A constant acceleration linear motion model is set to 1.

[0046] As a result, as shown in Fig. 6, the trajectory of the longitudinal velocity of the obstacle 2A changes from the first constant acceleration linear motion model M1 before time t1 to the second constant acceleration linear motion model M2 after time t1, and the change in longitudinal deceleration of the obstacle 2A becomes gradual. In other words, when the amount of change in the state quantity in the obstacle's motion up to the future does not reach the maximum amount of change, the motion of the obstacle is predicted based on the first constant acceleration linear motion model M1 as the first motion model, and after the amount of change in the state quantity of the obstacle reaches the maximum amount of change, the motion of the obstacle is predicted based on the second constant acceleration linear motion model M2 as the second motion model.

[0047] Here, the future time t1 after a predetermined time from the current time t0 is, for example, the longitudinal acceleration a of the obstacle 2A at the time t0. x1The larger the value of 0, the shorter the time from time t0.

[0048] 3, the motion prediction unit 104 predicts the motion of the obstacle 2A based on the constant acceleration linear motion model calculated as described above. At this time, the motion prediction unit 104 predicts the motion of the obstacle 2A based on the vertical acceleration a x1 The vertical acceleration a from the future time t1 onwards is set so that the fluctuation amount of does not exceed the maximum fluctuation amount aMAX. x1 is the vertical acceleration a x1 1 and predict the behavior of obstacle 2A.

[0049] Next, in step S104 of FIG. 3, the vehicle control unit 102 controls the longitudinal speed or longitudinal acceleration of the host vehicle 1 based on the future movement of the obstacle 2A predicted by the movement prediction unit 104, and controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A.

[0050] According to the vehicle control device of the first embodiment, the problems in the basic techniques 1 and 2 described above can be resolved.

[0051] In the vehicle control device according to the first embodiment described above, the maximum fluctuation amount generating unit 103 may be configured to change the maximum fluctuation amount of the state quantity depending on the area that the host vehicle is traveling in. For example, when the host vehicle is traveling on an ordinary road, the maximum fluctuation amount of the state quantity may be made larger, and when the host vehicle is traveling on an expressway, the maximum fluctuation amount of the state quantity may be made smaller than that for ordinary roads, so as to avoid sudden speed fluctuations.

[0052] Furthermore, the maximum fluctuation amount of the state quantity may be a fixed value set in advance, and the fixed value may be switched depending on whether the vehicle is traveling on an ordinary road or an expressway, or may be switched depending on the curvature of the road or the legal speed limit.

[0053] Embodiment 2 Next, a vehicle control device according to embodiment 2 will be described. Fig. 2 is a functional block diagram showing a vehicle control device according to embodiment 2, which is common to the vehicle control device according to embodiment 1 described above. In the vehicle control device according to embodiment 1, the state quantity of an obstacle as another vehicle is the longitudinal speed or longitudinal acceleration, but in the vehicle control device according to embodiment 2, the state quantity of the obstacle is used as the lateral position, lateral speed, and angle between the traveling direction of the host vehicle and the traveling direction of the obstacle.

[0054] Next, a description will be given of the operation of the vehicle control device 100 according to the embodiment 2. Fig. 3 is a flowchart showing the operation of the vehicle control device according to the embodiment 2, which is common to the case of the above-mentioned embodiment 1.

[0055] In step S101 of FIG. 3, the vehicle control device 100 acquires various state quantities of obstacles around the host vehicle using the obstacle information acquisition unit 201 in the external sensor 200.

[0056] At this time, the state quantities of the obstacle acquired by the obstacle information acquisition unit 201 include at least the lateral position of the obstacle, the lateral speed, and the angle between the traveling direction of the vehicle (the direction of the road) and the traveling direction of the obstacle. In addition to these state quantities, the state quantities may also include longitudinal acceleration, lateral acceleration, longitudinal jerk, lateral jerk, and yaw rate.

[0057] Fig. 7 is an explanatory diagram showing state quantities of an obstacle in a vehicle control device according to embodiment 2. In Fig. 7, host vehicle 1 is traveling in lane 41 of road 40, and obstacles 2A and 2B as other vehicles are traveling in lane 42 adjacent to lane 41 on which host vehicle 1 is traveling. In addition, stationary obstacle 5 is present ahead of obstacle 2B. Note that 411 and 421 are the centers of the lanes.

[0058] As described above, the traveling direction of the host vehicle 1 is defined as the longitudinal direction x, and the direction perpendicular to the traveling direction of the host vehicle 1 is defined as the lateral direction y. The state quantities of the obstacle 2A acquired by the vehicle control device 100 of the host vehicle 1 from the external sensor 200 are the longitudinal position x1 [m], the lateral position y1 [m], the traveling speed V1 [m / sec], and the angle θ1 [°] between the traveling direction of the host vehicle 1 (the direction in which the road 40 extends) and the traveling direction of the obstacle 2A, and the state quantities of the obstacle 2B are the longitudinal position x2 [m], the lateral position y2 [m], the traveling speed V2 [m / sec], and the angle θ2 [°] between the traveling direction of the host vehicle 1 (the direction in which the road 40 extends) and the traveling direction of the obstacle 2B. The state quantities of the stationary obstacle 5 are the longitudinal position x3 [m], the lateral position y3 [m], the traveling speed V3 [m / sec], and the angle θ3 [°] between the traveling direction of the vehicle 1 (the direction in which the road 40 extends) and the traveling direction of the obstacle 2B. Note that in the following explanation, the units of these state quantities will be omitted.

[0059] 8 is another explanatory diagram showing the state quantities of an obstacle in the vehicle control device according to the second embodiment, and shows the lateral velocity of the obstacle. As shown in FIG. 8, the lateral velocity V y1 is calculated as V1cosθ1. The lateral velocity V y2 is calculated as V2cosθ2.

[0060] In step S102 of Fig. 3, the maximum variation generator 103 generates the maximum variation of the lateral position as a state quantity of the obstacle from the present to a predetermined time in the future. Fig. 9 is an explanatory diagram showing the maximum variation of the lateral velocity of the obstacle generated by the maximum variation generator in the vehicle control device according to the second embodiment. Taking the above-mentioned obstacle 2A as an example, the maximum variation of the lateral position Y1 and the lateral velocity V of the obstacle 2A is y1 The vertical axis indicates the time tns, and the horizontal axis indicates the predicted time tns.

[0061] In FIG. 9, the lateral position Y1 of the obstacle 2A at the current time point t0 decreases to the lateral position Y1MIN at a future time point t1 after a predetermined time from the time point t0. The maximum variation amount generation unit 103 sets the value of the difference between the lateral position Y10 and the lateral position Y1MIN as the maximum variation amount YMAX. That is, [YMAX = Y10 - Y1MIN].

[0062] Here, the future time point t1 after a predetermined time from the current time point t0 is determined such that, for example, the larger the value of the lateral position Y10 of the obstacle 2A at the time point t0, the shorter the time from the time point t0.

[0063] Next, in step S103 of FIG. 3, the motion prediction unit 104 predicts the motion of the obstacle 2A based on a constant velocity linear motion model as a predetermined motion model. At this time, the motion prediction unit 104 restricts the lateral position Y1 after the future time point t1 to the lateral position Y1MIN so that the variation amount of the lateral position Y1 as a state quantity in the constant velocity linear motion model does not exceed the maximum variation amount YMAX, and predicts the motion of the obstacle 2A.

[0064] The lateral position Y1 of the obstacle 2A from the time point t0 to the time point t1 decreases from the lateral position Y10 to the lateral position Y1MIN based on the lateral velocity V y1 0, and the lateral position Y1 of the obstacle 2A after the time point t1 is maintained at the lateral position Y1MIN because the lateral velocity V Y1 becomes "0".

[0065] The constant velocity motion model of the lateral position of the obstacle 2A is represented by the following formula (3). Y1 = Y10 + [V Y1 0×(t1 - t0)] ····· (3)

[0066] In the constant velocity linear motion model, the future motion of the obstacle 2A is predicted by the following formula (4) so that [Y1 < Y1MIN] does not occur. Y1={YMAX[Y10+V Y1 0×(t1-t0)], Y1MIN} ·····(4) Here, Y1MIN is a value obtained by subtracting the maximum fluctuation amount YMAX from the current lateral position Y10 of the obstacle 2A. The trajectory of the obstacle 2A can be predicted by repeatedly calculating equation (4) up to the prediction time.

[0067] In addition, the maximum fluctuation amount of the state quantity is calculated in equation (4), but when [Y1>Y1MIN], the lateral velocity V in equation (3) y1 It is also possible to limit 0 to "0" and continue the calculation repeatedly using the above-mentioned formula (3).

[0068] As described above, the motion prediction unit 104 is configured to limit the lateral position or lateral velocity of the obstacle so that the amount of lateral positional fluctuation in the constant velocity linear motion model does not exceed the maximum amount of lateral fluctuation.

[0069] Next, in step S104 of Figure 3, the vehicle control unit 102 controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A based on the future movement of the obstacle 2A predicted by the movement prediction unit 104.

[0070] The above has been an explanation of the case where the future movement of an obstacle is predicted by limiting the amount of variation in a constant velocity linear motion model based on the maximum amount of variation in the lateral position of the obstacle as a state quantity, and the movement of the host vehicle is controlled. Next, we will explain the case where the future movement of an obstacle is predicted by limiting the amount of variation in a constant acceleration linear motion model based on the maximum amount of variation in the lateral velocity of the obstacle as a state quantity, and the movement of the host vehicle is controlled.

[0071] In step S102 of Fig. 3, a maximum variation in the lateral velocity of the obstacle is generated as a state quantity for the obstacle at a predetermined time in the future from the present. Fig. 10 is an explanatory diagram showing the maximum variation in the lateral velocity of the obstacle generated by the maximum variation generation unit in the vehicle control device according to the first embodiment, and takes the above-mentioned obstacle 2A as an example, and calculates the maximum variation in the lateral velocity V Y1 The vertical axis indicates the time tns, and the horizontal axis indicates the predicted time tns.

[0072] 10, the maximum variation generating unit 103 generates a maximum variation VMAX of the lateral velocity as a state quantity of the obstacle at a future time t1, which is a predetermined time from the current time t0. The lateral velocity V y1 0 is the velocity V shown by the dashed line at time t1. y1 Even if the speed fluctuates to V11, the fluctuation amount is limited by the maximum fluctuation amount VMAX of the lateral speed generated by the maximum fluctuation amount generating unit 103, and the lateral speed V y1 A constant acceleration linear motion model is set to 1.

[0073] As a result, as shown in Fig. 10, the trajectory of the lateral position of the obstacle 2A changes from the first constant acceleration linear motion model M1 before time t1 to the second constant acceleration linear motion model M2 after time t1, and the change in the lateral position of the obstacle 2A becomes gradual. In other words, when the amount of change in the state quantity in the obstacle's motion up to the future does not reach the maximum amount of change, the motion of the obstacle is predicted based on the first constant acceleration linear motion model M1 as the first motion model, and after the amount of change in the state quantity of the obstacle reaches the maximum amount of change, the motion of the obstacle is predicted based on the second constant acceleration linear motion model M2 as the second motion model.

[0074] Here, the future time t1 after a predetermined time from the current time t0 is, for example, the lateral velocity V of the obstacle 2A at the time t0. y1 The larger the value of 0, the shorter the time from time t0.

[0075] 3, the motion prediction unit 104 predicts the motion of the obstacle 2A based on the constant acceleration linear motion model calculated as described above. At this time, the motion prediction unit 104 predicts the motion of the obstacle 2A based on the lateral velocity V y1 The horizontal velocity V from the future time point t1 onwards is set so that the fluctuation amount of V does not exceed the maximum fluctuation amount VMAX. y1 is the lateral velocity V y1 1 and predict the behavior of obstacle 2A.

[0076] Fig. 11 is an explanatory diagram showing a predicted trajectory of an obstacle in a vehicle control device according to embodiment 2. As shown in Fig. 11, according to the vehicle control device according to embodiment 2, a predicted trajectory 25 of an obstacle 2A does not exceed the maximum amount of variation YMAX in the lateral position.

[0077] Next, in step S104 of Figure 3, the vehicle control unit 102 controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A based on the future movement of the obstacle 2A predicted by the movement prediction unit 104.

[0078] In the above, the state quantities of an obstacle have been described as the lateral position and lateral velocity of the obstacle, but instead of these state quantities, the angle between the traveling direction of the vehicle and the traveling direction of the obstacle may be used.

[0079] In the vehicle control device according to the second embodiment described above, the maximum variation generation unit 103 may be configured to change the maximum variation of the state quantity depending on the area that the host vehicle is traveling in. For example, when the host vehicle is traveling on an ordinary road, the maximum variation of the state quantity may be increased, and when the host vehicle is traveling at high speed in muddy conditions, the maximum variation of the state quantity may be decreased compared to when the host vehicle is traveling on an ordinary road, in order to avoid sudden speed fluctuations.

[0080] Furthermore, the maximum fluctuation amount of the state quantity may be a fixed value set in advance, and the fixed value may be switched depending on whether the vehicle is traveling on an ordinary road or an expressway, or may be switched depending on the curvature of the road or the legal speed limit.

[0081] Embodiment 3 Next, a vehicle control device according to embodiment 3 will be described. Fig. 2 is a functional block diagram showing a vehicle control device according to embodiment 3, which is common to the vehicle control devices according to the above-mentioned embodiments 1 and 2. In the vehicle control device according to embodiment 3, the lateral position, lateral speed, and angle between the traveling direction of the host vehicle and the traveling direction of the obstacle are used as state quantities of the obstacle.

[0082] 2, the motion prediction unit 104 generates a virtual trajectory that does not exceed the maximum variation amount generated by the maximum variation amount generation unit 103, and predicts the motion of the obstacle by assuming that the obstacle will travel along the virtual trajectory. The other configurations are the same as those of the vehicle control device according to the second embodiment.

[0083] Next, an operation of the vehicle control device 100 according to embodiment 3 will be described. Fig. 12 is a flowchart showing the operation of the vehicle control device according to embodiment 3. In step S301 of Fig. 12, the vehicle control device 100 acquires various state quantities of obstacles around the host vehicle by the obstacle information acquisition unit 201 in the external sensor 200.

[0084] At this time, the state quantities of the obstacle acquired by the obstacle information acquisition unit 201 include at least the lateral position of the obstacle, the lateral speed, and the angle between the traveling direction of the host vehicle (the direction in which the road extends) and the traveling direction of the obstacle. In addition to these state quantities, longitudinal acceleration, lateral acceleration, longitudinal jerk, lateral jerk, and yaw rate may also be included. Here, the longitudinal direction refers to the traveling direction of the host vehicle, and the lateral direction refers to the direction perpendicular to the traveling direction of the host vehicle.

[0085] As shown in Fig. 7, a host vehicle 1 is traveling in a lane 41 of a road 40, and obstacles 2A and 2B, which are other vehicles, are traveling in a lane 42 adjacent to the lane 41 on which the host vehicle 1 is traveling. In addition, a stationary obstacle 5 is present ahead of the obstacle 2B.

[0086] As described above, the traveling direction of the vehicle 1 is defined as the longitudinal direction x, and the direction perpendicular to the traveling direction of the vehicle 1 is defined as the lateral direction y. The state quantities of the obstacle 2A acquired by the vehicle control device 100 of the host vehicle 1 from the external sensor 200 are the longitudinal position X1 [m], the lateral position y1 [m], the traveling speed V1 [m / sec], and the angle θ1 [°] between the traveling direction of the host vehicle 1 (the direction in which the road 40 extends) and the traveling direction of the obstacle 2A. The state quantities of the obstacle 2B are the longitudinal position X2 [m], the traveling speed V2 [m / sec], and the angle θ2 [°] between the traveling direction of the host vehicle 1 (the direction in which the road 40 extends) and the traveling direction of the obstacle 2B. The state quantities of the stationary obstacle 5 are the longitudinal position X3 [m], the traveling speed V3 [m / sec], and the angle θ3 [°] between the traveling direction of the host vehicle 1 (the direction in which the road 40 extends) and the traveling direction of the obstacle 2B. Note that in the following description, the units of these state quantities will be omitted.

[0087] As shown in Figure 8, the lateral velocity V y1 is calculated as V1cosθ1. The lateral velocity V y2 is calculated as V2cosθ2.

[0088] In step S302 of Fig. 12, the maximum variation generating unit 103 generates the maximum variation of the lateral velocity position as a state quantity of the obstacle for a predetermined time from the present. As shown in Fig. 9, taking the above-mentioned obstacle 2A as an example, the lateral position Y1 of the obstacle 2A and the lateral velocity V y1 The vertical axis indicates the time tns, and the horizontal axis indicates the predicted time tns.

[0089] 9, the lateral position Y1 of the obstacle 2A at the current time t0 decreases to a lateral position Y1MIN at a future time t1, a predetermined time after the time t0. The maximum fluctuation amount generator 103 sets the difference between the lateral position Y10 and the lateral position Y1MIN as the maximum fluctuation amount YMAX. That is, [YMAX=Y10-Y1MIN].

[0090] Here, the future time t1, which is a predetermined time after the current time t0, is determined so that, for example, the greater the value of the lateral position Y10 of the obstacle 2A at time t0, the shorter the time from time t0.

[0091] 12, the movement prediction unit 104 generates a virtual trajectory that does not exceed the maximum variation amount generated by the maximum variation amount generation unit 103, and predicts the movement of the obstacle by assuming that the obstacle will travel along the virtual trajectory. That is, the movement prediction unit 104 predicts the future trajectory of the obstacle 2A so that future changes in the speed of the obstacle 2A do not exceed the maximum variation amount.

[0092] 13, the prediction method in step S303 is to generate a virtual path 26 that does not exceed the maximum lateral position fluctuation amount YMAX in advance, and to make a one-dimensional prediction of the path direction as shown in the following equations (5) and (6) on the assumption that the obstacle 2A travels along the virtual path 26. Here, L represents the position of the obstacle 2A in the direction of the virtual path 26, and dt represents discrete time.

[0093]

number

[0094] Fig. 14 is an explanatory diagram showing the concept of generating a virtual path for an obstacle in a vehicle control device according to embodiment 3. In Fig. 14, initial state 2A1 indicates the current position of obstacle 2A, and final state 2A2 is the final position of obstacle 2A on virtual path 26. Virtual path 26 can be expressed, for example, by a polynomial as shown in equation (7) below, and each coefficient can be derived by solving simultaneous equations of boundary conditions in the initial and final states as shown in equations (8) to (13). In equation (7), j is the degree of the polynomial, and c is the coefficient of each degree, which is set to 5 in embodiment 3.

[0095]

number

[0096] Equation (8) represents the boundary condition regarding the position of obstacle 2A in the initial state, equation (9) represents the boundary condition regarding the position of obstacle 2A in the terminal state, equation (10) represents the boundary condition regarding the inclination in the initial state (the angle between the traveling direction of vehicle 1 and the traveling direction of obstacle 2A), equation (11) represents the boundary condition regarding the inclination in the terminal state, equation (12) represents the boundary condition regarding the curvature in the initial state, and equation (13) represents the boundary condition regarding the curvature in each passable state. For example, if the terminal conditions are [curvature = 0] and [inclination = 0], then at the end point, obstacle 2A will be traveling straight in the direction in which road 40 extends.

[0097] Next, in step S304 of FIG. 12, the vehicle control unit 102 controls the longitudinal speed or longitudinal acceleration of the host vehicle 1 based on the future movement of the obstacle 2A predicted by the movement prediction unit 104, and controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A.

[0098] According to the vehicle control device of the third embodiment described above, the drawbacks of the third basic technique can be overcome.

[0099] Next, a vehicle control device according to a fourth embodiment will be described. Embodiment 4 Next, a vehicle control device according to a fourth embodiment will be described with reference to the drawings. FIG. 15 is a functional block diagram showing a vehicle control device according to the fourth to sixth embodiments. In FIG. 15, a host vehicle 1 is equipped with a vehicle control device 100, an external sensor 200, and an internal sensor 300. The host vehicle 1 may be any of an internal combustion engine vehicle, a hybrid vehicle, and an electric motor vehicle.

[0100] The vehicle control device 100 includes an obstacle movement prediction unit 101 and a vehicle control unit 102. The obstacle movement prediction unit 101 is composed of a determination unit 105, a maximum variation amount generation unit 103, and a movement prediction unit 104. The determination unit 105 determines whether the current movement of the obstacle is a temporary movement based on information about the obstacle acquired by a sensor mounted on the host vehicle.

[0101] When the determination unit 105 determines that the current obstacle movement is a temporary movement, the maximum variation amount generation unit 103 generates a maximum variation amount of a predetermined state quantity in the movement of the obstacle from the present to a predetermined time into the future. Here, in the vehicle control device 100 according to the fourth embodiment, the predetermined state quantity of the obstacle is the speed of the obstacle in the longitudinal direction, which is the traveling direction of the host vehicle 1, or the acceleration of the obstacle in the longitudinal direction.

[0102] The motion prediction unit 104 has a function of predicting the motion of the obstacle based on a predetermined motion model. More specifically, the motion prediction unit 104 is configured to predict the future motion of the obstacle by limiting the amount of variation in the state quantity of the obstacle in the motion model so that the amount of variation in the state quantity does not exceed the maximum amount of variation. Here, a constant velocity linear motion model or a constant acceleration linear motion model is used as the predetermined motion model. The vehicle control unit 102 controls the motion of the host vehicle based on the future motion of the obstacle predicted by the motion prediction unit 104.

[0103] The external sensor 200 includes an obstacle information acquisition unit 201 and a surrounding environment information acquisition unit 202. The obstacle information acquisition unit 201 includes, for example, a GPS (Global Positioning System) sensor, a millimeter wave radar, an ultrasonic sensor, etc. The surrounding environment information acquisition unit 202 is configured by, for example, a camera that captures images of the surroundings of the vehicle 1, a map information database used in an in-vehicle navigation device, etc.

[0104] The internal sensor 300 includes a host vehicle state acquisition unit 301, which is configured with, for example, a speed sensor, an acceleration sensor, an angular velocity sensor, a steering angle sensor, and the like.

[0105] Next, the operation of the vehicle control device according to embodiment 4 will be described. Fig. 16 is a flowchart showing the operation of the vehicle control device according to embodiment 4. In Fig. 16, steps S101, S102, and S103 correspond to steps S101, S102, and S103, respectively, in the flowchart shown in Fig. 3 above, and are similar to the operations at these steps in the vehicle control device according to embodiment 1.

[0106] In step S101, various state quantities of obstacles around the host vehicle are acquired from the external sensor 200, and the process proceeds to step S401. In step S401, the determination unit 105 compares the speed of the obstacle in the longitudinal direction, which is the traveling direction of the host vehicle 1, or the acceleration of the obstacle in the longitudinal direction, among the state quantities of the multiple obstacles acquired from the external sensor 200, with a threshold value for each obstacle, and determines whether the current movement of each obstacle is a temporary movement.

[0107] Here, the threshold value for the vertical speed or acceleration of the obstacle may be a fixed value determined in advance, or may be changed depending on the area, such as an ordinary road or an expressway, or may be variable based on information such as the curvature of the road and the legal speed limit.

[0108] If the result of the determination in step S401 is that the vertical speed or vertical acceleration of the obstacle exceeds the threshold (Yes), the movement of the obstacle is determined to be temporary, and the process proceeds to step S102.

[0109] In step S102, similarly to the first embodiment, the maximum variation generation unit 103 generates the maximum variation of the vertical velocity as a state quantity of the obstacle for a predetermined time in the future from the present, and the process proceeds to step S103.

[0110] In step S103, the motion prediction unit 104 predicts the motion of the obstacle 2A based on a constant velocity linear motion model as a predetermined motion model, as in the case of embodiment 1. At this time, the motion prediction unit 104 predicts the motion of the obstacle 2A based on a vertical velocity V as a state quantity in the constant velocity linear motion model. x1 The vertical velocity V from the future time point t1 onwards is set so that the fluctuation of V does not exceed the maximum fluctuation VMAX. x1 is the vertical velocity V x1 MIN and predict the behavior of obstacle 2A.

[0111] Next, the process proceeds to step S403, where the vehicle control unit 102 controls the longitudinal speed or longitudinal acceleration of the host vehicle 1 based on the future movement of the obstacle 2A predicted by the movement prediction unit 104, thereby controlling the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A.

[0112] On the other hand, if the result of the determination in step S401 is that the magnitude of the aforementioned state quantity of the obstacle does not satisfy the threshold value (No), it is determined that the movement of the obstacle is not temporary but is a steady movement, and the process proceeds to step S402. In step S402, the current state quantity is used to predict the movement of the obstacle using a constant velocity linear motion model in the same manner as in the first embodiment, without imposing any restriction based on the aforementioned maximum fluctuation amount, and the process proceeds to step S403.

[0113] In step S403, the vehicle control unit 102 controls the longitudinal speed or longitudinal acceleration of the host vehicle 1 based on the future movement of the obstacle 2A predicted by the movement prediction unit 104, and controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A.

[0114] In the vehicle control device according to the fourth embodiment described above, the maximum fluctuation amount generating unit 103 may be configured to change the maximum fluctuation amount of the state quantity depending on the area that the host vehicle is traveling in. For example, when the host vehicle is traveling on an ordinary road, the maximum fluctuation amount of the state quantity may be increased, and when the host vehicle is traveling at high speed in muddy conditions, the maximum fluctuation amount of the state quantity may be decreased compared to when the host vehicle is traveling on an ordinary road, in order to avoid sudden speed fluctuations.

[0115] Furthermore, the maximum fluctuation amount of the state quantity may be a fixed value set in advance, and the fixed value may be switched depending on whether the vehicle is traveling on an ordinary road or an expressway, or may be switched depending on the curvature of the road or the legal speed limit.

[0116] Embodiment 5 Next, a vehicle control device according to embodiment 5 will be described. Fig. 15 is a functional block diagram showing a vehicle control device according to embodiment 5, which is common to the vehicle control device according to embodiment 4 described above. In the vehicle control device according to embodiment 4, the state quantity of an obstacle as another vehicle is the longitudinal speed or longitudinal acceleration, but in the vehicle control device according to embodiment 5, the state quantity of the obstacle is the lateral position, the lateral speed, and the angle between the traveling direction of the host vehicle and the traveling direction of the obstacle.

[0117] When the determination unit 105 determines that the current obstacle movement is a temporary movement, the maximum variation amount generation unit 103 generates a maximum variation amount of a predetermined state quantity in the obstacle movement from the present to a predetermined time into the future.

[0118] The motion prediction unit 104 has a function of predicting the motion of the obstacle based on a predetermined motion model. More specifically, the motion prediction unit 104 is configured to predict the future motion of the obstacle by limiting the amount of variation in the state quantity of the obstacle in the motion model so that the amount of variation in the state quantity does not exceed the maximum amount of variation. Here, a constant velocity linear motion model or a constant acceleration linear motion model is used as the predetermined motion model. The vehicle control unit 102 controls the motion of the host vehicle based on the future motion of the obstacle predicted by the motion prediction unit 104.

[0119] The external sensor 200 includes an obstacle information acquisition unit 201 and a surrounding environment information acquisition unit 202. The obstacle information acquisition unit 201 includes, for example, a GPS (Global Positioning System) sensor, a millimeter wave radar, an ultrasonic sensor, etc. The surrounding environment information acquisition unit 202 is configured by, for example, a camera that captures images of the surroundings of the vehicle 1, a map information database used in an in-vehicle navigation device, etc.

[0120] The internal sensor 300 includes a host vehicle state acquisition unit 301, which is configured with, for example, a speed sensor, an acceleration sensor, an angular velocity sensor, a steering angle sensor, and the like.

[0121] Next, the operation of the vehicle control device according to the fifth embodiment will be described. FIG. 16 is a flowchart showing the operation of the vehicle control device according to the fifth embodiment, which is common to the case of the fourth embodiment. In FIG. 16, when the process proceeds to step S401, the determination unit 105 compares the position of the obstacle in the lateral direction, which is a direction perpendicular to the traveling direction of the host vehicle 1, or the lateral speed of the obstacle in the lateral direction, among the state quantities of the multiple obstacles acquired from the external sensor 200, with a threshold value for each obstacle, and determines whether the current movement of each obstacle is a temporary movement. For example, in the above-mentioned FIG. 8, the lateral speed V1cosθ1 of the obstacle 2A is smaller than the threshold value Vyth, and it is determined that the movement of the obstacle 2A is steady, and the lateral speed V2cosθ2 of the obstacle 2B is greater than the threshold value Vyth, and it is determined that the movement of the obstacle 2B is not steady but temporary.

[0122] Here, the threshold value for the lateral position or speed of an obstacle may be a fixed value determined in advance, or may be changed depending on the area, such as an ordinary road or an expressway, or may be variable based on information such as the curvature of the road or the legal speed limit.

[0123] As a result of the determination in step S401, if it is determined that the lateral position of the obstacle or the lateral speed of the obstacle exceeds the threshold (Yes), the movement of the obstacle is determined to be a temporary movement, and the process proceeds to step S102.

[0124] In step S102, similarly to the second embodiment, the maximum variation generation unit 103 generates the maximum variation of the lateral position as a state quantity of the obstacle for a predetermined time in the future from the present, and the process proceeds to step S103.

[0125] In step S103, the movement prediction unit 104 predicts the movement of the obstacle 2A based on a constant velocity linear motion model as a predetermined motion model, as in the case of embodiment 2. At this time, the movement prediction unit 104 predicts the movement of the obstacle 2A by limiting the lateral position Y1 from future time point t1 onwards to a lateral position Y1MIN so that the amount of fluctuation in the lateral position Y1 as a state quantity in the constant velocity linear motion model does not exceed the maximum fluctuation amount YMAX.

[0126] Next, proceeding to step S403, the vehicle control unit 102 controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A based on the future movement of the obstacle 2A predicted by the movement prediction unit 104.

[0127] On the other hand, if the result of the determination in step S401 is that the magnitude of the aforementioned state quantity of the obstacle does not satisfy the threshold value (No), it is determined that the movement of the obstacle is not temporary but is a steady movement, and the process proceeds to step S402. In step S402, the current state quantity is used to predict the movement of the obstacle using a constant velocity linear motion model in the same manner as in the first embodiment, without imposing any restriction based on the aforementioned maximum fluctuation amount, and the process proceeds to step S403.

[0128] 17 is an explanatory diagram showing the movement of an obstacle in the vehicle control device according to embodiment 5. The movement prediction unit 104 predicts the movement of the obstacle using the following equations (14) and (15).

number

[0129] In step S403, the vehicle control unit 102 controls the longitudinal speed or longitudinal acceleration of the host vehicle 1 based on the future movement of the obstacle 2A predicted by the movement prediction unit 104, and controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A.

[0130] In the vehicle control device according to the fifth embodiment described above, the maximum fluctuation amount generating unit 103 may be configured to change the maximum fluctuation amount of the state quantity depending on the area that the host vehicle is traveling in. For example, when the host vehicle is traveling on an ordinary road, the maximum fluctuation amount of the state quantity may be increased, and when the host vehicle is traveling at high speed in muddy conditions, the maximum fluctuation amount of the state quantity may be decreased compared to when the host vehicle is traveling on an ordinary road, in order to avoid sudden speed fluctuations.

[0131] Furthermore, the maximum fluctuation amount of the state quantity may be a fixed value set in advance, and the fixed value may be switched depending on whether the vehicle is traveling on an ordinary road or an expressway, or may be switched depending on the curvature of the road or the legal speed limit.

[0132] Embodiment 6 Next, a vehicle control device according to embodiment 6 will be described. Fig. 15 is a functional block diagram of a vehicle control device according to embodiment 6, which is common to embodiments 4 and 5. In the vehicle control device according to embodiment 6, the state quantities of an obstacle are used as the lateral position, the lateral speed, and the angle between the traveling direction of the host vehicle and the traveling direction of the obstacle.

[0133] Here, the operation of the vehicle control device according to embodiment 6 will be described. Fig. 18 is a flowchart showing the operation of the vehicle control device according to embodiment 6. In Fig. 18, steps S301, S302, and S303 are the same as steps S301, S302, and S303 in the flowchart of Fig. 3 according to embodiment 3 described above.

[0134] In step S601, the judgment unit 105 compares the lateral position of the obstacle, which is a direction perpendicular to the traveling direction of the vehicle 1, or the lateral speed of the obstacle, among the state quantities of the multiple obstacles acquired from the external sensor 200, with a threshold value for each obstacle, and judges whether the current behavior of each obstacle is a temporary behavior.

[0135] Here, the threshold value for the lateral position or lateral speed of an obstacle may be a fixed value determined in advance, or may be changed depending on the area, such as an ordinary road or an expressway, or may be variable based on information such as the curvature of the road or the legal speed limit.

[0136] If it is determined in step S601 that the position of the obstacle in the lateral direction or the speed of the obstacle in the lateral direction exceeds the threshold (Yes), the movement of the obstacle is determined to be temporary, and the process proceeds to step S302. The operations in steps S302 and S303 are the same as those in the third embodiment described above.

[0137] When proceeding from step S303 to step S603, the vehicle control unit 102 controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A based on the future movement of the obstacle 2A predicted by the movement prediction unit 104.

[0138] On the other hand, if the result of the determination in step S601 is that the magnitude of the aforementioned state quantity of the obstacle does not satisfy the threshold value (No), it is determined that the movement of the obstacle is not temporary but is steady movement, and the process proceeds to step S602. In step S602, the current state quantity is used to predict the movement of the obstacle using a constant velocity linear motion model in the same manner as in the fifth embodiment, without imposing any restriction based on the aforementioned maximum fluctuation amount, and the process proceeds to step S603.

[0139] When proceeding from step S602 to step S603, the vehicle control unit 102 controls the longitudinal speed or longitudinal acceleration of the host vehicle 1 based on the future movement of the obstacle 2A predicted by the movement prediction unit 104, and controls the movement of the host vehicle 1 so that the host vehicle 1 maintains a healthy distance from the obstacle 2A.

[0140] According to the vehicle control device 2 according to the fourth to sixth embodiments described above, it is possible to appropriately control the vehicle relative to the obstacle in accordance with the magnitude of the acquired state quantity of the obstacle.

[0141] At least a part of the vehicle control device 100 according to the first to sixth embodiments is configured by an ECU (Electronic Control Unit). FIG. 19 is a block diagram showing an example of the hardware configuration of the ECU in the vehicle control device according to the first to sixth embodiments. In FIG. 19, the ECU 112 is configured by a processor 1001 and a storage device 1002. Although not shown, the storage device 1002 includes a volatile storage device such as a random access memory and a non-volatile auxiliary storage device such as a flash memory. Alternatively, a hard disk auxiliary storage device may be included instead of the flash memory. The processor 1001 executes a program input from the storage device 1002. In this case, the program is input from the auxiliary storage device to the processor 1001 via the volatile storage device. The processor 1001 may output data such as calculation results to the volatile storage device of the storage device 1002, or may store the data in the auxiliary storage device via the volatile storage device.

[0142] Although various exemplary embodiments and examples are described in this disclosure, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but may be applied to the embodiments alone or in various combinations. Therefore, countless variations not illustrated are contemplated within the scope of the technology disclosed in this application. For example, this includes cases where at least one component is modified, added, or omitted, and even cases where at least one component is extracted and combined with components of another embodiment.

[0143] Next, aspects of the vehicle control device disclosed in the present application will be described below as supplementary notes. (Appendix 1) A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that predicts the future motion of the obstacle based on a predetermined motion model; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; Equipped with The operation prediction unit the prediction is performed by limiting the amount of fluctuation in the state quantity in the operation of the obstacle up to the future so that the amount of fluctuation does not exceed the maximum amount of fluctuation. A vehicle control device characterized by: (Appendix 2) A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a determination unit that determines whether a current motion of the obstacle is a temporary motion based on information about the obstacle acquired by a sensor mounted on the host vehicle; a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present time to a future time after a predetermined time; a motion prediction unit that predicts the future motion of the obstacle based on a predetermined motion model; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; Equipped with The operation prediction unit when the determination unit determines that the motion of the obstacle is not the temporary motion, the prediction is performed by limiting the amount of fluctuation of the state quantity in the motion of the obstacle up to the future so that the amount of fluctuation does not exceed the maximum amount of fluctuation; When the determination unit determines that the motion of the obstacle is the temporary motion, the prediction is performed without imposing the restriction. It is configured as follows: A vehicle control device characterized by: (Appendix 3) The determination unit When the amount of change in the state quantity of the obstacle is equal to or greater than a predetermined threshold, it is determined that the movement of the obstacle is the temporary movement. It is configured as follows: 3. A vehicle control device according to claim 2, (Appendix 4) the state quantity of the obstacle is a speed of the obstacle in a longitudinal direction, which is a traveling direction of the host vehicle; the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the speed of the obstacle in the longitudinal direction, The motion model is a constant velocity linear motion model. 4. A vehicle control device according to any one of claims 1 to 3. (Appendix 5) the state quantity of the obstacle is an acceleration of the obstacle in a longitudinal direction which is a traveling direction of the host vehicle, the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the acceleration of the obstacle in the longitudinal direction, The motion model is a constant acceleration linear motion model. 4. A vehicle control device according to any one of claims 1 to 3. (Appendix 6) the state quantity of the obstacle is a velocity of the obstacle in a lateral direction that is a direction perpendicular to the traveling direction of the host vehicle, the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the velocity of the obstacle in the lateral direction, The motion model is a constant velocity linear motion model. 4. A vehicle control device according to any one of claims 1 to 3. (Appendix 7) the state quantity of the obstacle is a position of the obstacle in a lateral direction that is perpendicular to the traveling direction of the host vehicle, the maximum variation generated by the maximum variation generation unit is a maximum variation in the position of the obstacle in the lateral direction, The motion model is a constant acceleration linear motion model. 4. A vehicle control device according to any one of claims 1 to 3. (Appendix 8) the state quantity of the obstacle is an angle formed between a traveling direction of the host vehicle and a traveling direction of the obstacle, the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the angle; 4. A vehicle control device according to any one of claims 1 to 3. (Appendix 9) A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that generates a virtual trajectory that does not exceed the maximum fluctuation amount and predicts a motion of the obstacle by assuming that the obstacle will travel along the virtual trajectory; a vehicle control unit that controls the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; A vehicle control device comprising: (Appendix 10) A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that predicts the motion of the obstacle based on a first motion model when the amount of change in the state quantity in the motion of the obstacle up to the future does not reach the maximum amount of change, and predicts the motion of the obstacle based on a second motion model after the amount of change in the state quantity of the obstacle reaches the maximum amount of change; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; A vehicle control device comprising: (Appendix 11) the first motion model is a constant acceleration linear motion model, The second motion model is a constant velocity linear motion model. 11. A vehicle control device according to claim 10. (Appendix 12) the maximum fluctuation amount generation unit is configured to change the maximum fluctuation amount depending on an area in which the host vehicle is traveling. 12. A vehicle control device according to any one of appendices 1 to 11, [Explanation of symbols]

[0144] 1 Ego-vehicle, 2, 2A, 2B, 2C Obstacles, 21, 23, 25 Predicted trajectory, 22,24 Actual trajectory, 26 Virtual path, 2ns Predicted position, 2a Actual position, 3 stop line, 40 road, 41, 42 lane, 43 dividing line, 5 stationary obstacle, 100 vehicle control device, 101 obstacle operation prediction unit, 102 vehicle control unit, 103 maximum fluctuation amount generation unit, 104 operation prediction unit, 200 external sensor, 201 Obstacle information acquisition unit, 202 Surrounding environment information acquisition unit, 300 Internal sensor, 301 Vehicle status acquisition unit

Claims

1. A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that predicts the future motion of the obstacle based on a predetermined motion model; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; Equipped with The operation prediction unit the prediction is performed by limiting the amount of change in the state quantity in the operation of the obstacle up to the future so that the amount of change does not exceed the maximum amount of change. A vehicle control device characterized by:

2. A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a determination unit that determines whether a current motion of the obstacle is a temporary motion based on information about the obstacle acquired by a sensor mounted on the host vehicle; a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present time to a future time after a predetermined time; a motion prediction unit that predicts the future motion of the obstacle based on a predetermined motion model; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; Equipped with The operation prediction unit when the determination unit determines that the motion of the obstacle is the temporary motion, the determination unit performs the prediction by limiting the amount of fluctuation of the state quantity in the motion of the obstacle up to the future so that the amount of fluctuation does not exceed the maximum amount of fluctuation; When the determination unit determines that the motion of the obstacle is not the temporary motion, the prediction is performed without imposing the restriction. It is configured as follows: A vehicle control device characterized by:

3. The determination unit When the amount of change in the state quantity of the obstacle is equal to or greater than a predetermined threshold, it is determined that the movement of the obstacle is the temporary movement. It is configured as follows:

3. The vehicle control device according to claim 2.

4. the state quantity of the obstacle is a speed of the obstacle in a longitudinal direction, which is a traveling direction of the host vehicle; the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the speed of the obstacle in the longitudinal direction, The motion model is a constant velocity linear motion model.

4. The vehicle control device according to claim 1, wherein the vehicle control device comprises: a first control unit;

5. the maximum fluctuation amount generation unit is configured to change the maximum fluctuation amount depending on an area in which the host vehicle is traveling.

5. The vehicle control device according to claim 4.

6. the state quantity of the obstacle is an acceleration of the obstacle in a longitudinal direction which is a traveling direction of the host vehicle, the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the acceleration of the obstacle in the longitudinal direction, The motion model is a constant acceleration linear motion model.

4. The vehicle control device according to claim 1, wherein the vehicle control device comprises: a first control unit;

7. the maximum fluctuation amount generation unit is configured to change the maximum fluctuation amount depending on an area in which the host vehicle is traveling.

7. The vehicle control device according to claim 6.

8. the state quantity of the obstacle is a velocity of the obstacle in a lateral direction that is a direction perpendicular to the traveling direction of the host vehicle, the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the velocity of the obstacle in the lateral direction, The motion model is a constant velocity linear motion model.

4. The vehicle control device according to claim 1, wherein the vehicle control device comprises: a first control unit;

9. the maximum fluctuation amount generation unit is configured to change the maximum fluctuation amount depending on an area in which the host vehicle is traveling.

9. The vehicle control device according to claim 8.

10. the state quantity of the obstacle is a position of the obstacle in a lateral direction that is perpendicular to the traveling direction of the host vehicle, the maximum variation generated by the maximum variation generation unit is a maximum variation in the position of the obstacle in the lateral direction, The motion model is a constant acceleration linear motion model.

4. The vehicle control device according to claim 1, wherein the vehicle control device comprises: a first control unit;

11. the maximum fluctuation amount generation unit is configured to change the maximum fluctuation amount depending on an area in which the host vehicle is traveling. The vehicle control device according to claim 10 .

12. the state quantity of the obstacle is an angle formed between a traveling direction of the host vehicle and a traveling direction of the obstacle, the maximum fluctuation amount generated by the maximum fluctuation amount generation unit is a maximum fluctuation amount of the angle; 4. The vehicle control device according to claim 1, wherein the vehicle control device comprises: a first control unit;

13. the maximum fluctuation amount generation unit is configured to change the maximum fluctuation amount depending on an area in which the host vehicle is traveling. The vehicle control device according to claim 12 .

14. A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that generates a virtual trajectory that does not exceed the maximum fluctuation amount and predicts a motion of the obstacle by assuming that the obstacle will travel along the virtual trajectory; a vehicle control unit that controls the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; A vehicle control device comprising:

15. A vehicle control device that is mounted on a host vehicle and controls movement of the host vehicle while maintaining a distance from an obstacle present around the host vehicle, a maximum variation amount generating unit that generates a maximum variation amount of a predetermined state amount in the movement of the obstacle from the present to a predetermined time into the future; a motion prediction unit that predicts the motion of the obstacle based on a first motion model when a variation in the state quantity in the motion of the obstacle up to the future does not reach the maximum variation, and predicts the motion of the obstacle based on a second motion model after the variation in the state quantity of the obstacle reaches the maximum variation; a vehicle control unit that controls the movement of the host vehicle based on the movement of the obstacle predicted by the movement prediction unit; A vehicle control device comprising:

16. the first motion model is a constant acceleration linear motion model, the second motion model is a constant velocity linear motion model; The vehicle control device according to claim 15 .

17. the maximum fluctuation amount generation unit is configured to change the maximum fluctuation amount depending on an area in which the host vehicle is traveling.

17. The vehicle control device according to claim 15 or 16.

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