Vehicle driving assistance system, driving assistance method, and recording medium
By predicting vehicle trajectories based on yaw rate change rate and constant yaw rate assumption, the system effectively detects collisions during turns, reducing computational load and ensuring timely collision avoidance.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- SUBARU CORP
- Filing Date
- 2022-10-12
- Publication Date
- 2026-06-03
AI Technical Summary
Existing collision determination systems are ineffective when a host vehicle is turning or traveling at a curve or intersection, as they assume straight-line travel.
The system calculates the yaw rate change rate to predict vehicle trajectories, determining a hypothetical collision point and time by assuming constant yaw rate change, and checks for collision using straight-line intersections.
Enables accurate collision detection during turns by reducing computational load and processing requirements, allowing for timely collision avoidance.
Smart Images
Figure 0007869728000001 
Figure 0007869728000002 
Figure 0007869728000003
Abstract
Description
Technical Field
[0001] The present invention relates to a driving support device, a driving support method, and a recording medium for determining the presence or absence of a vehicle collision.
Background Art
[0002] Techniques for predicting the collision position between a host vehicle and another vehicle and performing driving support are known. In the driving support device of Patent Document 1, it is assumed that the host vehicle maintains its current vehicle speed and travels straight, and that the other vehicle maintains its current speed. When it is predicted that the host vehicle and the other vehicle will collide in an intersection area where the area where the other vehicle is expected to pass overlaps with the area where the host vehicle is expected to pass, the other vehicle is specified as a crossing object. Then, the braking device of the host vehicle is controlled so that the host vehicle decelerates at a deceleration from a time point before the predicted collision time point, which is the time point when it is predicted that the host vehicle and the crossing object will collide.
Prior Art Documents
Patent Documents
[0003]
Patent Document 1
Summary of the Invention
Problems to be Solved by the Invention
[0004] The collision determination device of Patent Document 1 is effective when the host vehicle is traveling straight. However, there is a problem that it is difficult for the collision determination to function when the host vehicle is turning and traveling at a curve or an intersection.
Means for Solving the Problems
[0005] A vehicle driving assistance device in one embodiment of the present invention is characterized by calculating the yaw rate change rate from the yaw rate of the own vehicle obtained at multiple points in time, calculating the predicted trajectory of another vehicle, which is the predicted trajectory of another vehicle, and calculating the own vehicle's predicted trajectory, which is the predicted trajectory of the own vehicle, assuming that the yaw rate change rate is kept constant, calculating a hypothetical collision point from the intersection of the own vehicle's predicted trajectory and the other vehicle's predicted trajectory, calculating the hypothetical collision time when the own vehicle reaches the hypothetical collision point, and determining whether a collision has occurred based on a straight-line intersection assuming that the own vehicle and the other vehicle are traveling in a straight line at the hypothetical collision time. [Effects of the Invention]
[0006] The position of the vehicle where the yaw rate change rate is maintained is represented by a clothoid curve. Then, by using the vehicle's position as the successive vehicle position and predicting from the prediction reference point to the prediction end point, which is the point at which the vehicle turns to a set angle, it is possible to determine whether or not a collision will occur based on the presence or absence of a collision point with another vehicle. [Brief explanation of the drawing]
[0007] [Figure 1] A driver assistance system installed in the vehicle. [Figure 2] A diagram showing the predicted trajectories of the vehicle itself and other vehicles. [Figure 3] A flowchart of the driver assistance process. [Figure 4] Flowchart of the collision detection process. [Figure 5] Flowchart of the first process. [Figure 6] A diagram illustrating the calculation of the yaw rate change rate. [Figure 7] This diagram shows the predicted trajectories of your vehicle and other vehicles when your vehicle is turning right. [Figure 8] This diagram shows a predicted trajectory where the predicted trajectory of the own vehicle and the predicted trajectory of other vehicles do not intersect until the prediction is completed. [Figure 9] This diagram shows a predicted trajectory where the predicted trajectory of the own vehicle and the predicted trajectory of other vehicles do not intersect until the prediction is completed. [Figure 10] Flowchart of the second process. [Figure 11] A diagram showing the predicted trajectory after coordinate transformation. [Figure 12] A diagram showing the predicted trajectory after coordinate transformation when there are no intersection points. [Figure 13] A diagram illustrating collision detection for straight-line intersections after coordinate transformation. [Figure 14] This diagram shows the case where another vehicle is in contact with the vehicle in question at the time of the hypothetical collision. [Figure 15] This diagram illustrates the situation where, at the time of a hypothetical collision, another vehicle has passed through the path of the vehicle being driven. [Modes for carrying out the invention]
[0008] Figure 1 shows a vehicle equipped with the driver assistance device 1 according to an embodiment of the present invention. To distinguish it from other vehicles VS that are the target of collision determination, the vehicle is shown as the own vehicle VM. The driver assistance device 1 of the own vehicle VM has an ECU 11, a camera 12, a radar sensor 13, and wiring 14 connecting the ECU 11 to the camera 12 and radar sensor 13. Two cameras 12 are installed side by side in the left-right direction of the vehicle, and one radar sensor 13 is installed on each side at the front of the vehicle. The ECU 11 is also connected to the braking device 2 of the own vehicle VM via communication wiring 3.
[0009] The video footage captured by the two cameras 12 and the data detected by the two radar sensors 13 are sent to the ECU 11 via wiring 14 and analyzed by the CPU 111 using software stored in memory 112. The CPU 111 and memory 112 form a computer. When the ECU 11 determines that collision response is necessary, it takes action such as sending a braking command to the braking system 2 via communication wiring 3.
[0010] The predicted trajectories of the host vehicle VM and the other vehicle VS when determining a conflict are shown in FIG. 2. FIG. 2 is a view of the host vehicle VM, the other vehicle VS, and the predicted paths from above. The host vehicle VM and the other vehicle VS shown by solid lines indicate the positions at the prediction reference time Ts. The XY coordinate system in FIG. 2 is set such that at the current position of the host vehicle VM at the prediction reference time Ts, the Y coordinate of the front center Mfc is 0, and the X coordinate of the front center Mfc when the host vehicle VM rotates by a predetermined angle of 90° from the prediction reference time Ts is 0. At the current position of the host vehicle VM at the prediction reference time Ts, the host vehicle VM is facing the +Y direction. Also, when rotated by 90°, the host vehicle VM faces the -X direction in the case of a left turn as shown in FIG. 2. When the host vehicle VM makes a right turn, it faces the +X direction.
[0011] The curves in FIG. 2 indicate the predicted trajectories of each part of the host vehicle VM. MR, MC, and ML are the predicted trajectories of the front right end Mfr, the front center Mfc, and the front left end Mfl of the host vehicle VM, respectively. Also, SR and SL are the predicted trajectories of the front right end Sfr and the front left end Sfl of the other vehicle VS, respectively. In the present invention, it is predicted that the host vehicle VM curves at a constant yaw rate change rate Yrc and travels along a clothoid curve. The predicted trajectory MC in the host vehicle is a clothoid curve. Also, the right predicted trajectory MR and the left predicted trajectory ML of the host vehicle can be obtained from the vehicle width of the host vehicle VM and the predicted trajectory MC in the host vehicle. In FIG. 2, the host vehicle VMt and the other vehicle VSt shown by dotted lines are the positions of the host vehicle VM and the other vehicle VS at the temporary collision time Tt, which will be described later.
[0012] In FIG. 2, the curves extending from the center and both sides on the front surface of the host vehicle VM indicate the host vehicle predicted trajectories, which are the predicted trajectories of each part of the host vehicle VM. The right predicted trajectory MR of the host vehicle is the host vehicle predicted trajectory of the front right end Mfr of the host vehicle VM and is the right host vehicle predicted trajectory. Also, the predicted trajectory MC in the host vehicle is the host vehicle predicted trajectory of the front center Mfc of the host vehicle VM and is the central host vehicle predicted trajectory. Further, the left predicted trajectory ML of the host vehicle is the host vehicle predicted trajectory of the front left end Mfl of the host vehicle VM and is the left host vehicle predicted trajectory.
[0013] Next, the control until the collision response operation is performed by driving support will be described with reference to the flowcharts of FIGS. 3 to 5 and 10. All steps of the flowchart indicate the driving support process and the driving support method. Also, this control is executed by the CPU 111 processing according to a program stored in the memory 112. Further, this program can be stored in a computer-readable recording medium.
[0014] The driving support process shown in FIG. 3 starts at a predetermined time interval and is repeatedly executed. First, in steps S1 and S2, it is determined whether to start the processes after the collision determination process in step S3. In step S1, it is determined whether the other vehicle VS to be determined has been recognized. The images captured by the two cameras 12 and the data detected by the two radar sensors 13 are analyzed by the ECU 11 to determine whether the other vehicle VS to be determined for the presence or absence of a collision has been recognized. If it is determined that the target other vehicle VS has been recognized, then YES is obtained in step S1 and the process proceeds to step S2. If it is determined that it has not been recognized, then NO is obtained and the process ends, waiting for the start time of the next driving support process.
[0015] In the host vehicle VM, the yaw rate Yr is calculated based on the speed, lateral acceleration, and steering wheel rotation angle detected by various sensors. In step S2, if YES, that is, when the absolute value of the yaw rate Yr continuously exceeds the turning reference value for a predetermined period, the process proceeds to step S3 to perform the collision determination process during turning. Otherwise, if NO, the process ends and waits for the start time of the next driving support process.
[0016] The subroutine of the collision determination process in step S3 is as shown in FIG. 4. In the collision determination process, after performing the first process in step S31, the second process in step S32, and the process in step S33, the third process in step S34 is performed and then the process returns to the main routine in FIG. 3. [[ID=In the first process of step S31, assuming that the vehicle VM travels along its predicted trajectory, which is a clothoid curve, and that the other vehicle VS moves in a straight line at a constant velocity, the predicted trajectory of the vehicle VM and the predicted trajectory of the other vehicle are calculated. Then, the intersection point i of the predicted trajectory of the vehicle VM and the predicted trajectory of the other vehicle is calculated. The intersection point i becomes a candidate for the hypothetical collision point Pt, which will be described later. Multiple intersection points i may be obtained, or there may be no intersection point i. Note that the first process is a process of calculating the intersection point i of the curve and the straight line, and timing is not considered. In step S32, the hypothetical collision point Pt is determined from the candidate intersection points i, and the hypothetical collision time Tt, which is the time when the vehicle reaches the hypothetical collision point Pt, is calculated. In step S33, if there is no candidate intersection point i to determine the hypothetical collision point Pt, the time when the vehicle VM turns by 90°, which is the upper limit angle of the predicted angle range, is determined as the hypothetical collision time Tt. Then, in the third process of step S35, it is assumed that the local vehicle VMt and the other vehicle VSt are moving in a straight line at a constant velocity at the time of the hypothetical collision Tt, and a collision determination is made due to a straight intersection.
[0018] First, the subroutine for the first process of step S31, which calculates the intersection point i that is a candidate for the hypothetical collision time Tt, is explained with reference to Figure 5. In step S311, the yaw rate change rate Yrc is calculated from the yaw rate Yr of the vehicle VM at multiple time points. Figure 6 is a graph showing the yaw rate Yr at each time point. The horizontal axis represents time t, and the vertical axis represents the yaw rate Yr. In this embodiment, the equation of the straight line L is calculated by the least squares method from the yaw rates Yr at three time points indicated by black dots in Figure 6. The three time points are the same as the previous two time points t=t -2 , t -1 This is the current time point (prediction reference time Ts) where t=0. The slope of the line L is the rate of change of yaw rate Yrc.
[0019] Here, the yaw rate Yr values from four or more time points may be used to calculate the equation of the straight line L, or the yaw rate Yr values from two time points may be connected and used to calculate the equation of the straight line L. If the straight line L is sloped, that is, if the yaw rate change rate Yrc is not 0, the predicted trajectory will be a clothoid curve. Then, by using the equation of the straight line L, assuming that the yaw rate change rate Yrc is maintained after the current time point Ts (t>0), the predicted trajectory of the vehicle VM, the in-vehicle predicted trajectory MC, can be obtained.
[0020] In the next step, S312, the point at which the vehicle VM rotates to the set angle of 90° while the yaw rate change rate Yrc is maintained is calculated and set as the prediction end time Te. In this embodiment, prediction is only performed until the vehicle VM has rotated 90°. In this embodiment, the prediction angle range is set to 0° to 90°, but other angle ranges may also be used. Then, using the velocity data of the vehicle VM at the prediction reference time Ts, the in-vehicle prediction trajectory MC, which is the predicted trajectory of the front center Mfc of the vehicle VM up to the prediction end time Te, is calculated. As shown in Figure 2, the in-vehicle prediction trajectory MC is the predicted trajectory of the front center Mfc of the vehicle VM from the position at the prediction reference time Ts until it has rotated 90°. Since the yaw rate change rate Yrc is constant, the in-vehicle prediction trajectory MC is a clothoid curve.
[0021] Then, in step S313, the vehicle's right predicted trajectory MR and left predicted trajectory ML are calculated from the vehicle width data of the vehicle VM stored in memory 112 and the predicted trajectory MC within the vehicle, thereby obtaining three curved vehicle trajectories. The vehicle's right predicted trajectory MR is calculated as the trajectory at a position shifted by 1 / 2 of the vehicle width to the right, perpendicular to the direction of travel of the vehicle's in-vehicle predicted trajectory MC, at each point in time. The same applies to the vehicle's left predicted trajectory ML. As a result, the vehicle's right predicted trajectory MR, the vehicle's in-vehicle predicted trajectory MC, and the vehicle's left predicted trajectory ML shown in Figure 2 are calculated.
[0022] Next, in step S314, the position of the other vehicle VS at multiple points in time is calculated, and the speed and direction of the other vehicle VS are calculated from the positions at multiple points in time. In addition, the image obtained from the camera 12 is recognized using data detected by the radar sensor 13, and the rectangle occupied by the other vehicle VS on the horizontal plane is determined as the other vehicle rectangle. The size and shape of the other vehicle rectangle differ depending on whether the image is recognized as a small vehicle or a large vehicle. Multiple other vehicle rectangles are stored in memory 112, and the other vehicle rectangle is selected based on the recognition of the image. In this way, the other vehicle VS is determined as the other vehicle rectangle, which is the rectangular frame on the XY coordinate system. The other vehicle rectangle is stored as a rectangle connecting the front right edge Sfr, the front left edge Sfl, the rear left edge Srl, and the rear right edge Srr, as shown in Figure 2 of the other vehicle VS.
[0023] In the next step, S315, the predicted right trajectory SR and the predicted left trajectory SL of the other vehicle are calculated from the other vehicle's rectangle, position, speed, and direction, as shown in Figure 2, to obtain two straight trajectories of the other vehicle.
[0024] In step S316, the intersection points are calculated from three curved vehicle trajectories (the vehicle's right predicted trajectory MR, the vehicle's center predicted trajectory MC, and the vehicle's left predicted trajectory ML) and two straight trajectories of other vehicles (the other vehicle's right predicted trajectory SR and the other vehicle's left predicted trajectory SL). In the case shown in Figure 2, six intersection points i1 to i6 are calculated.
[0025] When the vehicle VM turns left, the vehicle's trajectory and the trajectory of other vehicles are as shown in Figure 2, but the case when turning right is shown in Figure 7. In the case of turning right, the position of the vehicle VM at the end of the prediction, Te, when it has turned 90°, will be to the right of the position of the vehicle VM at the prediction reference time Ts. Therefore, the X coordinates of the front center Mfc, the vehicle's right predicted trajectory MR, the vehicle's center predicted trajectory MC, the vehicle's left predicted trajectory ML, and the yaw rate Yr will all be negative.
[0026] Once the first process is complete, step S32, shown in Figure 4, determines the provisional collision point Pt among the calculated intersection points i that the vehicle VM reaches in the shortest time. In the calculated intersection points i1 to i6 in Figure 2, the vehicle VM reaches intersection point i1 in the shortest time, so intersection point i1 becomes the provisional collision point Pt. In addition, the provisional collision time Tt, which is the time when the vehicle VM moves and is located at the vehicle VMt, is calculated. In Figure 2, the vehicle VM moves and reaches the provisional collision point Pt at the provisional collision time Tt, becoming the vehicle VMt.
[0027] In step S33, if there is no intersection point i, the point at which the vehicle VM turns 90° is defined as the provisional collision time Tt. For example, in Figures 8 and 9, there is no intersection point i between the predicted trajectory of the vehicle VM and the straight line of the other vehicle VS. As shown in Figures 8 and 9, if there is no intersection point i, the point at which the vehicle VM turns 90°, which is the upper limit of the predicted angle range, and moves towards the vehicle VMt is defined as the provisional collision time Tt. In this way, the first half of the collision detection process is completed.
[0028] As shown in Figure 4, once step S33 is completed, step S34 of the second process is performed. The second process is the latter half of the collision detection process. In the second process, assuming that the vehicle VMt and the other vehicle VSt are moving in a straight line at the hypothetical collision time Tt, a collision is determined based on the straight-line intersection. Figure 10 shows the second process in step S34. In the second process, after performing a coordinate transformation so that the vehicle VM at the hypothetical collision time Tt is in the positive position, a collision is determined using the lateral distance Dx and relative lateral velocity Vx, and the vertical distance Dy and relative vertical velocity Vy.
[0029] In step S341, the coordinates of the front center Mfc of the self-vehicle VMt at the hypothetical collision time Tt, shown by the dotted line in Figure 2, are calculated. Also, the nearest contact point Sc that is closest to the front center Mfc of the self-vehicle VMt on the other vehicle VSt at the hypothetical collision time Tt, as shown in Figure 2, is calculated. In the case of Figure 2, the nearest contact point Sc is the front right edge Sfr of the other vehicle VSt. Then, the direction of travel and velocity are obtained along with the coordinates of the front center Mfc of the self-vehicle VMt and the nearest contact point Sc, which is the front right edge Sfr of the other vehicle VSt. It is assumed that the speed of the self-vehicle VMt and the other vehicle VSt remain unchanged from the predicted reference time Ts, and that the direction of travel of the other vehicle VSt also remains unchanged. Therefore, the velocity of the front center Mfc of the self-vehicle VMt and the direction of travel and velocity of the front right edge Sfr of the other vehicle VSt are those of the predicted reference time Ts. The direction of travel of the self-vehicle VMt is obtained from the time from the predicted reference time Ts to the hypothetical collision time Tt and the yaw rate change rate Yrc.
[0030] Then, in step S342, the coordinates and direction of travel of the calculated front center Mfc of the own vehicle VMt, and the coordinates and direction of travel of the front right edge Sfr, which is the nearest point of contact Sc of the other vehicle VSt, are transformed. In the coordinate transformation, the own vehicle VMt is rotated and moved so that at the hypothetical collision point Tt, the front center Mfc faces forward in the +Y direction and the front center Mfc is in the positive position with the origin O. The rectangle of the own vehicle VMt and the rectangle of the other vehicle VSt are also transformed in coordinates. Figure 11 shows the positions of the own vehicle VMt, etc. after the coordinate transformation is complete. The front center Mfc of the own vehicle VMt is located at the origin O, and the own vehicle VMt is in the positive position facing the +Y direction.
[0031] Figure 11 shows an example where intersection i is a candidate for the hypothetical collision point Pt, while Figure 12 shows an example after coordinate transformation at hypothetical collision time Tt when intersection i is not present. In step S33, assuming there is no intersection, hypothetical collision time Tt is defined as the point when the vehicle VM has turned 90°, which is the upper limit of the predicted angle range. Therefore, in step S342, a coordinate transformation is performed that rotates 90° along with the movement. Figure 12 shows the vehicle VMt, other vehicle VSt, the predicted trajectory of the vehicle, the predicted trajectory of the other vehicle, etc., after movement and a 90° coordinate transformation.
[0032] Next, in step S343, the lateral distance Dx, longitudinal distance Dy, relative lateral velocity Vx, and relative longitudinal velocity Vy are calculated between the own vehicle VMt and the other vehicle VSt. Figure 13 is an enlarged view of the vicinity of the own vehicle VMt after coordinate transformation. In Figure 13, assuming that the own vehicle VMt and the other vehicle VSt are moving in a straight line at the hypothetical collision time Tt, the straight lines representing the predicted trajectory MRp to the right of the own vehicle, the predicted trajectory MCp in the middle of the own vehicle, the predicted trajectory MLp to the left of the own vehicle, the predicted trajectory SR to the right of the other vehicle, and the predicted trajectory SL to the left of the other vehicle are shown. On the XY coordinate system of Figure 13, the center Mfc in front of the own vehicle VMt is located at the origin O, the front of the own vehicle VMt is in the direction of +Y, and the straight-ahead predicted trajectory MCp in the middle of the own vehicle coincides with the Y axis.
[0033] The lateral distance Dx, longitudinal distance Dy, relative lateral velocity Vx, and relative longitudinal velocity Vy shown in Figure 13 are determined between the front center Mfc of the vehicle VMt and the nearest point of contact Sc of the other vehicle VSt at the hypothetical collision time Tt. The lateral distance Dx and longitudinal distance Dy are the absolute values of the X and Y coordinates of the nearest point of contact Sc in Figure 13. Since the vehicle VMt does not move in the X direction after the coordinate transformation, the relative lateral velocity Vx is the velocity of the nearest point of contact Sc of the other vehicle VSt in the X direction. In addition, the relative longitudinal velocity Vy is calculated by subtracting the velocity of the nearest point of contact Sc of the other vehicle VSt from the velocity of the front center Mfc of the vehicle VMt in the Y direction. For the relative velocity in the Y direction, it is assumed that the velocity of the vehicle VMt at the prediction reference time Ts has not changed, and this is used as the velocity of the vehicle VMt in the Y direction at the hypothetical collision time Tt.
[0034] In the next step, S344, it is determined whether another vehicle VS will collide with the own vehicle VM in the X direction. X-direction collision time Tx = lateral distance Dx / relative lateral velocity Vx ... (1) The X-direction collision time Tx is obtained using equation (1). Then, the vehicle rectangle of the own vehicle VM and the other vehicle rectangle of the other vehicle VS are calculated from the provisional collision time Tt to the X-direction collision time Tx. If the own vehicle rectangle and the other vehicle rectangle are in contact or overlap, it is determined that a collision has occurred. If the determination is YES, proceed to step S346; if NO, proceed to step S345.
[0035] In step S345, it is determined whether vehicle VM will collide with vehicle VS in the Y direction. Y-direction collision time Ty = vertical distance Dy / relative vertical velocity Vy ... (2) The Y-direction collision time Ty is obtained using equation (2). Then, the vehicle rectangle of the own vehicle VM and the vehicle rectangle of the other vehicle VS are calculated from the hypothetical collision time Tt to the Y-direction collision time Ty. If the own vehicle rectangle and the other vehicle rectangle are in contact or overlap, a collision is determined. If the determination is YES, indicating a collision, proceed to step S346; if NO, indicating no collision, terminate step S34 of the second process and return to the main routine.
[0036] Furthermore, if, at the hypothetical collision time Tt, the other vehicle VSt is in contact with the own vehicle VMt as shown in Figure 14, a collision is determined and the answer is YES. On the other hand, if, at the hypothetical collision time Tt, the other vehicle VS has passed the range of travel of the own vehicle VM as shown in Figure 15, a collision is determined and the answer is NO. In Figure 15, at the prediction reference time Ts, the other vehicle VS (not shown) is on the negative side of X, while the other vehicle VSt is moving towards the positive side of X. At the hypothetical collision time Tt, all of the X coordinates of the other vehicle VSt are greater than the predicted trajectory MRp of the own vehicle, which is the predicted trajectory of the own vehicle VMt on the positive side of X, so it is determined that the other vehicle has passed the range of travel.
[0037] In step S346, a collision is detected and remembered, after which step S34 of the second process is completed and the process returns to the main routine. Once step S34 of the second process is completed, the collision detection process in step S3 is completed as shown in Figure 4, and the process returns to the main routine as shown in Figure 3.
[0038] In Figure 3, after step S3 of the collision detection process is completed, step S4 determines whether the stored collision detection urgency is high. If no collision is stored or if it is determined that the urgency is not high, the result is NO and the process ends. For example, if there is more than a predetermined amount of time until the collision time obtained from the X-direction collision time Tx and Y-direction collision time Ty, it is determined that the urgency is not high. On the other hand, if the above case does not apply and it is determined that the stored collision detection urgency is high, the process ends after performing collision response actions such as automatic braking in step S5. When automatic braking is activated, a braking signal is sent from the ECU 11 to the braking device 2 via the communication wiring 3, applying the brakes to the vehicle VM and avoiding a collision with the other vehicle VS.
[0039] In this embodiment, as described above, the yaw rate change rate Yrc is calculated from the yaw rate Yr of the vehicle VM obtained at multiple time points t. Then, the predicted trajectory of the other vehicle VS is calculated, and the predicted trajectory of the own vehicle is calculated assuming that the yaw rate change rate Yrc is kept constant. Furthermore, a hypothetical collision point Pt is calculated from the intersection of the predicted trajectory of the own vehicle and the predicted trajectory of the other vehicle, and the hypothetical collision time Tt is calculated when the vehicle VM reaches the hypothetical collision point Pt. Then, in the subsequent stage, assuming that the vehicle VM and the other vehicle VS are traveling in a straight line at the hypothetical collision time Tt, a collision is determined based on the straight-line intersection.
[0040] In this way, by having a first stage in which the predicted trajectory of the vehicle VM is calculated from the yaw rate change rate Yrc of the vehicle VM, and a provisional collision point Pt and provisional collision time Tt are calculated, and a second stage in which collision detection is performed assuming that the vehicle VM and the other vehicle VS are moving in a straight line after the provisional collision time Tt, collision detection can be performed with less computation and the processing load on the CPU 111 can be reduced.
[0041] In this embodiment, the prediction angle range is set to 0° to 90°, and the prediction end point is defined as the point when the vehicle VM turns 90°. However, other angle ranges may be used as the prediction angle range, such as 0° to 120° or 0° to 60°, and the prediction end point being defined as the point when the vehicle turns to 120° or 60°. In this embodiment, three vehicle prediction trajectories—the vehicle's right prediction trajectory MR, the vehicle's center prediction trajectory MC, and the vehicle's left prediction trajectory ML—are used to calculate the intersection point i which is a candidate for the provisional collision point Pt. The accuracy of the determination is slightly reduced, but it is also possible to use two vehicle prediction trajectories excluding the vehicle's center prediction trajectory MC, or to use only the vehicle's center prediction trajectory MC.
[0042] In this embodiment, the predicted trajectory MR (right), predicted trajectory MC (center), and predicted trajectory ML (left) of the vehicle are used to calculate the hypothetical collision time Tt. However, the calculation may also be performed using only the predicted trajectory MR (right) and predicted trajectory ML (left) of the vehicle, without using the predicted trajectory MC (center). Furthermore, in this embodiment, the predicted trajectory of the vehicle is the predicted trajectory of each part of the front of the vehicle VM. However, the predicted trajectory of parts other than the front may be used as the predicted trajectory of the vehicle, or the predicted trajectory may be used without specifying the part in detail.
[0043] In this embodiment, the hypothetical collision point Pt was calculated assuming that the other vehicle VS was moving in a straight line at a constant velocity, and the hypothetical collision time Tt was calculated. However, if the other vehicle VS is also traveling on a curve, the degree of the curve may be calculated using camera footage or the like, and the hypothetical collision point may be calculated as the predicted trajectory of a circle with a predetermined radius, and the hypothetical collision time may be calculated.
[0044] Furthermore, in this embodiment, the second process in the latter half after the provisional collision time Tt involves calculating the lateral distance Dx, relative lateral velocity Vx, and longitudinal distance Dy for the nearest contact point Sc, which is the part of the other vehicle VS closest to the front center Mfc of the own vehicle VM. Then, a collision determination is made based on whether the other vehicle VS is in contact with or overlapping the own vehicle VM after the X-direction collision time Tx and Y-direction collision time Ty obtained from the lateral distance Dx and relative lateral velocity Vx, and the longitudinal distance Dy and relative longitudinal velocity Vy. However, collision determination processing between other straight-moving vehicles may also be used for the latter half of the determination.
[0045] Furthermore, in this embodiment, the position of other vehicles VS is detected using information obtained from the camera 12 and the radar sensor 13, but the position of other vehicles VS may also be detected using information obtained from either the camera 12 or the radar sensor 13. The position of other vehicles VS may also be detected using information obtained from all or part of the camera 12, the radar sensor 13, and other devices.
[0046] In this embodiment, a camera 12 and a radar sensor 13 were used, but various sensors can be used as long as they can detect the relative relationship (relative position, relative speed) with other vehicles VS.
[0047] Furthermore, the specific configuration is not limited to the embodiments, and any design changes, etc., that do not depart from the spirit of the present invention are also included. [Explanation of Symbols]
[0048] VM (Virtual Vehicle) VS Other vehicles t time point Ts forecast base date Te prediction end point Tt Pre-collision point Yr yaw rate Yrc (York Rate of Change) Pt hypothetical collision point Mfr front right edge Mfc front center Mfl Front left edge MR (Mid-Rear View) Predicted Trajectory of Vehicle to the Right MC (Motorcycle Prediction Trajectory) ML (Multi-Level) Predicted Trajectory of Vehicle (Left Side) Sfr Front right edge Sfl Front left edge SR Other vehicle's predicted trajectory to the right SL Other vehicles left predicted trajectory 1. Driving assistance system 11 ECU 111 CPU 112 memory 12 cameras 13 Radar Sensors 14 Wiring 2 Braking device 3. Communication wiring
Claims
1. The rate of change of yaw rate is calculated from the yaw rate of the vehicle obtained at multiple points in time. The predicted trajectory of other vehicles is calculated, and the predicted trajectory of the own vehicle is calculated, assuming that the yaw rate change rate is kept constant. The hypothetical collision point is calculated from the intersection of the predicted trajectory of the own vehicle and the predicted trajectory of the other vehicle, and the hypothetical collision time when the own vehicle reaches the hypothetical collision point is calculated. A vehicle driving assistance device characterized by determining whether a collision has occurred based on a straight-line intersection, assuming that the vehicle itself and the other vehicle are traveling straight at the time of the hypothetical collision.
2. The vehicle driving assistance device according to claim 1, characterized in that the rate of change of the yaw rate is obtained from the yaw rate obtained at two points in time.
3. The vehicle driving assistance device according to claim 1, characterized in that the rate of change of the yaw rate is obtained by the least squares method from the yaw rate obtained at three or more time points.
4. A vehicle driving assistance device according to any one of claims 1 to 3, characterized in that the self-vehicle is a rectangle on the horizontal plane of the self-vehicle, and the other vehicle is a rectangle on the horizontal plane of the other vehicle.
5. A vehicle driving assistance device according to any one of claims 1 to 3, characterized in that it predicts the trajectory of the other vehicle assuming that the other vehicle is moving in a straight line at a constant velocity.
6. On the computer, The steps include: calculating the yaw rate change rate from the yaw rate of the vehicle obtained at multiple points in time; The steps include calculating the predicted trajectory of another vehicle, which is the predicted trajectory of another vehicle, and predicting the predicted trajectory of the own vehicle, which is the predicted trajectory of the own vehicle, assuming that the yaw rate change rate is kept constant, The steps include: calculating a hypothetical collision point from the intersection of the predicted trajectory of the own vehicle and the predicted trajectory of the other vehicle, and calculating the hypothetical collision time when the own vehicle reaches the hypothetical collision point; A vehicle driving assistance method characterized by determining whether a collision has occurred based on a straight-line intersection, assuming that the vehicle itself and the other vehicle are traveling straight at the time of the hypothetical collision.
7. The steps include: calculating the yaw rate change rate from the yaw rate of the vehicle obtained at multiple points in time; The steps include calculating the predicted trajectory of another vehicle, which is the predicted trajectory of another vehicle, and predicting the predicted trajectory of the own vehicle, which is the predicted trajectory of the own vehicle, assuming that the yaw rate change rate is kept constant, The steps include: calculating a hypothetical collision point from the intersection of the predicted trajectory of the own vehicle and the predicted trajectory of the other vehicle, and calculating the hypothetical collision time when the own vehicle reaches the hypothetical collision point; A computer-readable recording medium storing a program that causes a computer to execute a step of determining whether a collision has occurred based on a straight-line intersection, assuming that the vehicle in question and the other vehicle are traveling straight at the time of the hypothetical collision.