Braking Assist Control Device

Abstract

The present invention addresses the problem of obtaining a braking support control device with which, by changing an appropriate control characteristic in accordance with a future prediction accuracy, it is possible to prevent sudden braking when the future prediction accuracy is high, and to prevent erroneous activation when the future prediction accuracy is low. A braking support control device (100) according to the present invention is a device for performing braking support control for a host vehicle in accordance with the probability of a collision between the host vehicle and an obstacle, and is characterized by comprising: an index calculating unit (105) for calculating an index defining a future prediction accuracy of relative information relating to the host vehicle and the obstacle; and a threshold setting unit (106) for changing a threshold for determining whether to perform braking support control, in accordance with the index.

Description

[Technical Field]

[0001] The present invention relates to a brake assist control device. [Background technology]

[0002] Patent Document 1 discloses a technology for a vehicle driving control system that sets a first risk area when there is a small discrepancy between the recognized position of a moving object and its predicted future position, and sets a relatively large second risk area when there is a large discrepancy, determines whether there will be a collision with the vehicle, and activates AEB (Autonomous Emergency Braking). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-68013 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional technology, many cases have focused on preventing AEB from failing or malfunctioning in order to avoid causing accidents, but during autonomous driving, occupants have a high degree of freedom in their posture and behavior inside the vehicle, and no mention has been made of reducing the possibility of an accident inside the vehicle due to emergency braking.

[0005] The present invention has been made in consideration of the above points, and its purpose is to solve the problem of reducing the accident rate both inside and outside the vehicle by realizing AEB dedicated to autonomous driving (AD). [Means for solving the problem]

[0006] The braking assist control device of the present invention that solves the above problem is a braking assist control device that performs braking assist control of a vehicle in accordance with the possibility of a collision between the vehicle and an obstacle, and is characterized by having an index calculation unit that calculates an index that determines the accuracy of future predictions of relative information between the vehicle and the obstacle, and a threshold setting unit that sets a threshold value that determines whether or not to perform the braking assist control in accordance with the index.

[0007] The present invention utilizes the unique features of autonomous driving, such as the absence of driver operation and the ability to know future driving operations, to calculate an index that determines the accuracy of future predictions, and the higher the index, the more the threshold for determining braking action to avoid a collision is set to a value that corresponds to earlier action, thereby making it possible to avoid collisions with weaker braking force than when the index is low, and preventing accidents inside the vehicle due to sudden braking. [Effects of the Invention]

[0008] According to the present invention, by changing the appropriate control characteristics depending on the accuracy of the future prediction, it is possible to prevent AEB from operating and sudden braking when the accuracy of the future prediction is high, and to prevent malfunction when the accuracy of the future prediction is low.

[0009] Further features related to the present invention will become apparent from the description of the present specification and the accompanying drawings. In addition, problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram of a vehicle driving system according to a first embodiment. [Figure 2] 3 is a flowchart illustrating the operation of the braking assist control device of the first embodiment. [Figure 3] FIG. 10 is a diagram illustrating an example of derivation of driving control performance. [Figure 4] FIG. 10 is a diagram for explaining an example of derivation of sensor detection performance. [Figure 5] FIG. 10 is a diagram illustrating an example of setting a braking start determination threshold value. [Figure 6]6 is a flowchart illustrating the operation of a cruise control system according to a second embodiment. [Figure 7] 10 is a table showing an example of deriving the frequency of occurrence of driver operations. [Figure 8] FIG. 10 is a diagram illustrating an example of setting an obstacle determination threshold value. [Figure 9] FIG. 10 is a diagram illustrating an example of setting a collision determination threshold value. [Figure 10] FIG. 10 is a diagram showing a derivation formula for sensor detection performance. [Figure 11] FIG. 4 is a diagram showing a calculation formula for an index that determines the accuracy of future prediction in the first embodiment. [Figure 12] FIG. 10 is a diagram showing a calculation formula for a collision time. [Figure 13] FIG. 4 is a diagram showing a calculation formula for a braking G command Gcmd. [Figure 14] FIG. 10 is a diagram showing a calculation formula for an index that determines the accuracy of future prediction in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Next, an embodiment of the present invention will be described in detail with reference to the drawings.

[0012] [First embodiment] FIG. 1 is a block diagram of a vehicle driving system according to the first embodiment. The vehicle driving system 1 of this embodiment is a system that performs autonomous driving (AD) of a vehicle such as an automobile, and for example, has an autonomous driving level of level 3, that is, conditional autonomous driving in which there is a driver who can respond to an intervention request from the system, and also includes levels 4 and above.

[0013] The vehicle driving system 1 includes a braking assist control device 100, a VMC (Vehicle Motion Controller) 200, and a brake actuator 300. The braking assist control device 100 and the VMC 200 are configured by an on-board ECU having a CPU and memory. The VMC 200 includes an actuator control unit 201 as an internal function that performs engine control, steering control, and brake control of the vehicle. Upon receiving a braking G command from the braking assist control device 100, the actuator control unit 201 outputs a control signal to the brake actuator 300. The brake actuator 300 includes a brake device 301 that receives a control signal from the actuator control unit 201 and performs brake control based on the control signal.

[0014] The braking assist control device 100 performs braking assist control of the host vehicle in accordance with the possibility of collision between the host vehicle and an obstacle. A driver monitor device 401, a driving control device 402, an object detection sensor 411, a steering angle sensor 412, and a vehicle speed sensor 413 are connected to the input side of the braking assist control device 100.

[0015] The driver monitoring device 401 monitors the riding state of the driver in the vehicle, for example, based on information on the driver's operation of the steering wheel, accelerator pedal, brake pedal, and turn signal, and information detected by a camera or infrared sensor attached to the vehicle interior. The riding state can be detected, for example, whether the driver has his / her hands or eyes off, and more specifically, the position and movement of the driver's hands and feet in the vehicle, whether or not they are operating, whether or not they are looking aside, falling asleep, operating a mobile device or reading, etc.

[0016] The cruise control device 402 is configured by an on-board ECU and performs automatic driving control to drive the vehicle to a destination along a target trajectory planned based on, for example, map information and position information. The cruise control device 402 also performs control to smoothly hand over driving to the driver when the driver intervenes in driving in response to a request from the driver or when the driver intervenes of their own volition.

[0017] The object detection sensor 411 is a sensor that detects objects around the host vehicle, and can use at least one of a monocular camera, a stereo camera, a sonar, an infrared sensor, a radar, and a LiDAR, for example. The steering angle sensor 412 detects the steering angle of the steering wheels of the host vehicle, and the vehicle speed sensor 413 detects the vehicle speed from the number of rotations of the wheels of the host vehicle, etc.

[0018] The braking assist control device 100 is implemented in an on-board ECU (Electronic Control Unit), which is an AD controller, and controls the AEB. The braking assist control device 100 has, as internal functions, a driver operation occurrence probability deriving unit 101, a driving control performance deriving unit 102, a sensor detection performance deriving unit 103, a predicted path calculation unit 104, an index calculation unit 105, a threshold setting unit 106, a collision determination unit 107, and a braking control unit 108. The braking assist control device 100 detects an obstacle, calculates a predicted path of the host vehicle, determines the possibility of a collision between the host vehicle and the obstacle, and if it is determined that the host vehicle will collide with the obstacle, determines whether to start braking using the time to collision TTC, etc. If it is determined that braking should be started, it calculates a braking G command required to avoid the collision and outputs it to the VMC 200.

[0019] The driver operation occurrence derivation unit 101 derives the occurrence of a driver operation using information on actual operations by the driver detected by the driver monitoring device 401 or the like, or information on predicted operations that are expected to occur. However, when the autonomous driving level is level 4 or higher, driving without the driver present is possible (brain-off), so the driver monitoring device 401 and the driver operation occurrence derivation unit 101 are not required.

[0020] The driving control performance derivation unit 102 derives the driving control performance of the vehicle using information such as the current and target states of the vehicle (one or more of position, speed, acceleration, etc.) For example, if the absolute value of the difference between the actual position of the vehicle and the target position (position on the target trajectory) is smaller than a threshold, a high value is derived as the driving control performance (see FIG. 3).

[0021] The sensor detection performance derivation unit 103 derives the sensor detection performance using information on the variance of the sensor values ​​of an object detected by the object detection sensor 411. For example, when the same object is detected multiple times, the smaller the variation in the object width, the smaller the variance of the sensor values, and the higher the value derived as the sensor detection performance (see FIG. 4).

[0022] The predicted course calculation unit 104 calculates the predicted course of the host vehicle based on information on the current position and target position of the host vehicle, and information on the steering angle and vehicle speed of the host vehicle.

[0023] The index calculation unit 105 derives an index that determines the accuracy of future prediction of relative information between the host vehicle and an obstacle based on at least one of information on the occurrence of driver operation, driving control performance, and sensor detection performance. Here, the relative information is information calculated based on, for example, detection information from an object detection sensor that detects objects around the host vehicle, and includes at least one of the relative position, relative speed, and relative acceleration between the host vehicle and the object. Furthermore, the future prediction includes at least one of the position, speed, and acceleration predicted for the host vehicle and the obstacle at a predetermined time in the future, and the accuracy thereof is expressed as a degree (%) of difference from the actual position, speed, and acceleration at the predetermined time.

[0024] The threshold setting unit 106 sets a threshold for determining whether or not to perform AEB braking assist control according to an index that determines the accuracy of the future prediction. The threshold setting unit 106 can set multiple activation thresholds for activating AEB according to an index that determines the accuracy of the future prediction. The higher the accuracy of the future prediction, the more the threshold setting unit 106 sets the determination threshold for starting AEB braking in the braking assist control to a value that corresponds to earlier activation.

[0025] The collision determination unit 107 performs object determination to determine whether or not an object detected by the object detection sensor 411 is an obstacle. If it determines that the object is an obstacle, it determines the possibility of a collision based on the predicted path of the vehicle and the behavior of the detected object.

[0026] When the collision determination unit 107 determines that there is a possibility of a collision, the braking control unit 108 determines the start of AEB braking using the time until the collision, etc., and performs processing to calculate the braking G command required to avoid the collision.

[0027] Next, the operation of the braking assist control device 100 in this embodiment will be described with reference to the flowchart of FIG. This embodiment is an example of a case where a host vehicle with an automated driving level of Level 3 (Lv3) or higher needs to perform emergency avoidance using AEB while traveling on a main road with relatively heavy traffic.

[0028] FIG. 2 is a processing flow of the braking assist control by the braking assist control device of this embodiment. The braking assist control process by the braking assist control device of this embodiment is performed in a program cycle of once every 50 ms within the vehicle-mounted ECU, which is an AD controller.

[0029] First, in S101, the current and target values ​​of the host vehicle's position, speed, and acceleration are read. In S102, the host vehicle's driving control performance (%) is calculated using the current and target values ​​(one or more of the position, speed, acceleration, etc.) of the host vehicle read in S101. Information on the target values ​​is obtained from a preset driving plan.

[0030] 3 is a diagram illustrating an example of derivation of driving control performance, in which the vertical axis represents driving control performance and the horizontal axis represents the absolute value of the difference between the current position of the vehicle and the target position. The driving control performance is derived by the driving control performance derivation unit 102. As shown in Fig. 3, the driving control performance is set so that the driving control performance decreases as the absolute value of the difference between the current position and the target position of the vehicle increases, and the smaller the absolute value of the difference, the higher the value derived as driving control performance.

[0031] In the example shown in Figure 3, a difference of up to 0.4 m between the vehicle's current position and the target position is considered to be within the normal error range that occurs in autonomous driving control, so the cruise control performance is set to decrease relatively slowly as the difference increases. On the other hand, a difference of 0.4 m or more between the vehicle's current position and the target position exceeds the normal error range, so the cruise control performance is set to decrease rapidly as the difference increases. And when the difference is 0.6 m or more, the cruise control performance is set to be 0%.

[0032] In S103, detection information is read from the object detection sensor 411. Here, detection information (relative position, relative speed, width, type) of objects around the host vehicle detected by the object detection sensor 411 is read.

[0033] In S104, the sensor detection performance is derived using the detection information of the object detection sensor 411 read in S103. The sensor detection performance is derived by the sensor detection performance derivation unit 103.

[0034] FIG. 4 is a diagram illustrating an example of derivation of the sensor detection performance. As shown in the graph of FIG. 4 and equation (1) of FIG. 10, the sensor detection performance is set to decrease as the variance of the sensor values ​​of the object width increases.

[0035] In S105, an index that determines the accuracy of the future prediction is calculated. The calculation of the index that determines the accuracy of the future prediction is performed by the index calculation unit 105. The index calculation unit 105 calculates the index based on the occurrence of driver operation, driving control performance, and sensor detection performance. The index calculation unit 105 calculates a higher index as the accuracy of the future prediction increases. For example, as shown in equation (2) in FIG. 11, the index calculation unit 105 calculates the index by weighting the driving control performance derived in S102 and the sensor detection performance derived in S104.

[0036] Next, in S106, the predicted path of the host vehicle is read in. The predicted path of the host vehicle is calculated by the predicted path calculation unit 104.

[0037] Then, in S107, the braking start determination threshold Tth is set. For example, as shown in FIG. 5, the threshold setting unit 106 increases the braking start determination threshold Tth as the index determining the accuracy of future prediction increases. Increasing the braking start determination threshold Tth enables AEB to be activated proactively and earlier, thereby weakening the AEB braking force accordingly. Therefore, the host vehicle can be stopped with gentle braking with ample time to spare, thereby reducing in-vehicle accidents. On the other hand, the braking start determination threshold Tth is decreased as the index determining the accuracy of future prediction decreases. If the braking start determination threshold Tth is decreased, the AEB activation will be delayed and will activate only after the host vehicle approaches an object, thereby preventing erroneous activation of the AEB.

[0038] For example, when the vehicle speed V is 11.1 m / s, the average braking deceleration G_avg is 6.9 m / s when the index determining the accuracy of future prediction is the lowest. 2 ], the braking start threshold Tth = 0.81 seconds [s], and the average braking deceleration G_avg = 3.9 [m / s 2 ], the braking start determination threshold Tth is set to 1.42 seconds [s].

[0039] In S108, the object information (relative position, relative speed, width, type) read in S103 is used to determine whether the detected object is an obstacle (obstacle determination unit). If it is determined to be an obstacle, the process proceeds to S109, and if it is determined not to be an obstacle, the process ends. The determination of whether or not the object is an obstacle is made, for example, when the relative position is within a preset range.

[0040] In S109, it is determined whether or not the host vehicle will collide with an obstacle using the object information read in S103 and the predicted path of the host vehicle read in S106. If it is determined that a collision will occur, the process proceeds to S110, and if it is determined that a collision will not occur, this flow is terminated.

[0041] In S110, the time to collision TTC [s] is calculated. The time to collision TTC can be calculated, for example, by the formula (3) shown in FIG.

[0042] In S111, it is determined whether the collision time TTC calculated in S110 is greater than 0 and less than the braking start determination threshold Tth [s] set in S107. If it is determined that it is less than the braking start determination threshold Tth [s], the flow proceeds to S112, and if it is determined that it is greater than the braking start determination threshold Tth [s], the flow ends. In S112, the required braking G command Gcmd [m / s 2 The braking G command Gcmd can be calculated, for example, by equation (4) shown in FIG.

[0043] When the index for determining the accuracy of future prediction is high, the braking start determination threshold Tth is set to a lower value in S107, so that AEB is activated earlier and the value of the relative distance d between the host vehicle and the obstacle in the fore-and-aft direction of the host vehicle becomes larger compared to when the index is low. Therefore, the braking G command Gcmd becomes a smaller value, which means that sudden braking can be avoided.

[0044] In S113, the braking G command calculated in S112 is sent to the brake actuator 300 to activate the automatic brake.

[0045] For example, during autonomous driving, the route of the host vehicle can be known, and the index for determining the accuracy of future predictions is higher than when the driver is operating the vehicle. According to the brake assist control device 100 of this embodiment, if the index showing the accuracy of future predictions is high during autonomous driving of the host vehicle, the braking start determination threshold is set so that AEB is activated early, and therefore a braking G command Gcmd that is smaller than normal is calculated in S112, allowing the vehicle to stop with gentle braking with plenty of time to spare, making it possible to prevent in-vehicle accidents due to sudden braking.

[0046] On the other hand, since it is difficult to predict the path of the vehicle while the driver is operating the vehicle, the index for determining the accuracy of future predictions is low. According to the brake assist control device 100 of this embodiment, when the accuracy of future predictions is low due to driver operation, the threshold for determining braking start is set so that the start of AEB operation is delayed. Therefore, AEB can be activated after the vehicle approaches an object, preventing erroneous operation.

[0047] [Second embodiment] Next, a second embodiment of the present invention will be described. This embodiment is an example of a case where a vehicle with an autonomous driving level of Level 3 (Lv3) needs to perform emergency avoidance using AEB while traveling on a main road with relatively heavy traffic. In this embodiment, since the autonomous driving level of the vehicle is Level 3, there is a possibility that the driver may intervene and perform an operation by the driver during autonomous driving.

[0048] In this embodiment, in order to take into account disturbances caused by driver operations in the planning of the autonomous driving trajectory, the occurrence rate of driver operations is derived (S202). Then, the threshold setting unit 106 sets at least one of a plurality of determination thresholds set to determine whether or not to perform braking assist control according to the index. In this embodiment, the determination threshold for obstacles and the determination threshold for collision possibility are also set according to the index that determines the accuracy of future prediction (S209).

[0049] The processing of this embodiment is performed in a program cycle of once every 50 ms in an on-board ECU, which is an AD controller.

[0050] In S201, information on the driver's operation of the steering wheel, accelerator, brake, or turn signal, information on the driver's riding state detected by a camera or the like, information on whether the driver is looking away or falling asleep, etc. are read from the driver monitor device 401.

[0051] In S202, the occurrence rate of the driver operation is derived using the information read in S201. The driver operation occurrence rate derivation unit 101 receives information from the driver monitor device 401 or the like as input, determines the riding state of the corresponding driver, and derives the occurrence rate of the driver operation, for example, as shown in Table 1 of Fig. 7.

[0052] S203 to S206 are similar to S101 to S104 in the first embodiment, and therefore a description thereof will be omitted.

[0053] In S207, an index for determining the accuracy of the future prediction is calculated. The index for determining the accuracy of the future prediction is calculated by weighting the occurrence of the driver operation derived in S202, the driving control performance derived in S204, and the sensor detection performance derived in S206, for example, as shown in equation (5) in Fig. 14.

[0054] S208 is similar to S106 in the first embodiment, and therefore a description thereof will be omitted. In S209, AEB control activation thresholds are set. The AEB control activation thresholds include an obstacle determination threshold Th1, a collision determination threshold Th2, and a braking start determination threshold Tth. Setting of the braking start determination threshold Tth is similar to S107 in the first embodiment, so a description thereof will be omitted.

[0055] The obstacle determination threshold Th1 is a determination threshold for obstacle determination that is set for the relative position between the host vehicle and an object, for example, and an object is determined to be an obstacle when the relative position is shorter than the obstacle determination threshold Th1. Therefore, when the obstacle determination threshold Th1 is set to a small value, an object is determined to be an obstacle early, and when the obstacle determination threshold Th1 is set to a large value, whether or not it is an obstacle is determined carefully.

[0056] 8, the threshold setting unit 106 sets the obstacle determination threshold Th1 to a smaller value as the index that determines the accuracy of future prediction becomes higher. Therefore, for example, during fully automated driving, an object such as an oncoming vehicle can be determined to be an obstacle at an early stage.

[0057] On the other hand, the threshold setting unit 106 sets the obstacle determination threshold Th1 to a larger value as the index that determines the accuracy of future prediction becomes lower. Therefore, for example, when a driver operation is involved, the determination that an object is an obstacle can be delayed and the object can be determined to be an obstacle only after the host vehicle gets closer to the object.

[0058] The collision determination threshold Th2 is a threshold for determining the possibility of a collision that is set, for example, for the difference between the future position of the host vehicle and the future position of an object, and when the difference between the future position of the host vehicle (predicted position) and the future position of an object (predicted position) becomes smaller than the collision determination threshold Th2, it is determined that the host vehicle will collide with an obstacle. Therefore, when the collision determination threshold Th2 is set to a large value, it is more likely to be determined that a collision will occur, and when the collision determination threshold Th2 is set to a small value, it is more likely to be determined that a collision will not occur.

[0059] 9, the threshold setting unit 106 sets the collision determination threshold Th2 to a larger value as the index that determines the accuracy of future prediction becomes higher. Therefore, it is possible to prevent the AEB from becoming inoperative, and to enable safe automated driving.

[0060] On the other hand, the threshold setting unit 106 sets the collision determination threshold Th2 to a smaller value as the index that determines the accuracy of future prediction becomes lower. Therefore, it is possible to prevent AEB malfunction and prevent in-vehicle accidents caused by unnecessary sudden braking.

[0061] In S210, the detection information (relative position, relative speed, width, type) of the object detection sensor 411 read in S205 is used to determine whether the object detected by the object detection sensor 411 is an obstacle. If it is determined to be an obstacle, the process proceeds to S211 to perform collision determination, and if it is determined not to be an obstacle, this flow ends. The determination of whether or not the object is an obstacle is performed using the obstacle determination threshold Th1 set in S209, and for example, if the relative position between the host vehicle and the detected object is equal to or less than the threshold, the detected object is determined to be an obstacle.

[0062] In S211, it is determined whether the host vehicle will collide with an obstacle using the detection information of the object detection sensor 411 read in S205 and the predicted path of the host vehicle read in S208. If it is determined that a collision will occur, the process proceeds to S212, and if it is determined that a collision will not occur, the process ends.

[0063] The determination of whether or not there will be a collision with an obstacle is performed using the collision determination threshold Th2 set in S209. For example, if the difference between the future position of the host vehicle and the future position of the object is smaller than the collision determination threshold Th2, it is determined that there will be a collision.

[0064] The processing from S212 to S215 is the same as the processing from S110 to S113 in the first embodiment, and therefore a description thereof will be omitted.

[0065] In the second embodiment described above, the accuracy of the future prediction is calculated (S207) taking into account the occurrence rate of driver operation (S202), unlike in the first embodiment. Furthermore, in S209, when the index for determining the accuracy of the future prediction is high, AEB inactivation is prevented, enabling safe automated driving, compared to S107 in the first embodiment. Furthermore, when the index for determining the accuracy of the future prediction is low, AEB malfunction can be prevented.

[0066] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to the above-described embodiments, and various design modifications can be made without departing from the spirit of the present invention as defined in the claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0067] 1 Vehicle driving system, 100 Braking assist control device, 101 Driver operation occurrence rate deriving unit, 102 Driving control performance deriving unit, 103 Sensor detection performance deriving unit, 104 Predicted course calculation unit, 105 Index calculation unit, 106 Threshold setting unit, 107 Collision determination unit, 108 Braking control unit

Claims

1. A braking assist control device that performs braking assist control of a host vehicle in accordance with a possibility of collision between the host vehicle and an obstacle, an index calculation unit that calculates an index that determines the accuracy of future prediction of relative information between the host vehicle and the obstacle; a threshold setting unit that sets a threshold for determining whether or not to perform the braking assist control in accordance with the index; an obstacle determination unit that performs obstacle determination based on detection information from an object detection sensor that detects objects around the host vehicle; and The threshold setting unit changes and sets a judgment threshold for performing the obstacle judgment among a plurality of judgment thresholds set for determining whether or not to perform the braking assist control in accordance with the index, and sets the judgment threshold for the obstacle judgment to a smaller value as the index increases. A braking assist control device characterized by:

2. 2. The brake assist control device according to claim 1, wherein the threshold setting unit sets the determination threshold for starting braking to a value corresponding to an earlier activation as the index increases.

3. a collision determination unit that determines a possibility of a collision based on a difference between the predicted position of the vehicle and the predicted position of the obstacle, 2. The braking assist control device according to claim 1, wherein the threshold setting unit changes the threshold value for determining the possibility of collision in accordance with the index.

4. 4. The braking assist control device according to claim 3, wherein the threshold setting unit increases the threshold value for determining the possibility of collision as the index increases.

5. a braking control unit that determines whether to start braking based on a collision time between the host vehicle and the obstacle, 2. The brake assist control device according to claim 1, wherein the threshold setting unit sets a determination threshold for determining whether to start braking in accordance with the index.

6. 6. The braking assist control device according to claim 5, wherein the threshold setting unit increases the determination threshold for braking start determination as the index increases.

7. a driver operation occurrence rate deriving unit that derives information on the occurrence rate of a driver operation based on information on the riding state of the driver of the vehicle; a driving control performance deriving unit that derives information on driving control performance of the host vehicle from a difference between a current position of the host vehicle and a route of a preset driving plan; a sensor detection performance derivation unit that derives information about sensor detection performance based on a distribution state of the sensor values ​​of the object detection sensor, 2. The braking assist control device according to claim 1, wherein the index calculation unit calculates the index based on at least one of information on the occurrence of driver operation by the driver of the vehicle, the driving control performance of the vehicle, and the sensor detection performance of the object detection sensor.

8. 2. The braking assist control device according to claim 1, wherein the index calculation unit calculates an index that is more accurate for the future prediction when the host vehicle is being automatically driven than when the host vehicle is being operated by a driver.

Images