Vehicle travel control device, autonomously travelling vehicle, and vehicle travel control method

Abstract

An autonomously travelling vehicle 1 has attached thereto a LiDAR 2 that detects, with respect to the autonomously travelling vehicle 1, the positions of markers M1, M2 provided at a travel destination 9 of the autonomously travelling vehicle 1. Two of the markers M1, M2 are provided at the travel destination 9. A marker position detection unit 41 detects, by means of the LiDAR 2, the positions of the two markers M1, M2 with respect to the autonomously travelling vehicle 1, and acquires the detected positions p1, p2 of the two markers M1, M2. Additionally, when the autonomously travelling vehicle 1 has traveled to the travel destination 9, a storage unit 5 respectively holds, for the two markers M1, M2, reference positions P1, P2 which are the detected positions p1, p2 of the markers M1, M2 to be acquired by the marker position detection unit 41. On the basis of the differences between the reference positions P1, P2 and the detected positions p1, p2 of the two markers M1, M2, a steering 14 and a travel motor 13 which drives the autonomously travelling vehicle 1 are controlled.

Description

Vehicle driving control device, autonomous vehicle, and vehicle driving control method

[0001] The present invention relates to a technique for controlling a vehicle to travel toward a destination.

[0002] Patent Literature 1 describes a technology in which a sensor mounted on the vehicle detects markers (first and second physical features) provided on a cart to which the vehicle is to be moved, thereby acquiring the positional relationship between the vehicle and the markers and autonomously driving the vehicle toward the cart. Such autonomous driving technology requires highly accurate control of the vehicle's position relative to the markers. Patent Literature 2 describes a technology in which the position of the vehicle is estimated by matching the position of the marker with map data, and Patent Literature 3 describes a technology in which the position and orientation of the vehicle are estimated using a marker with a moiré pattern.

[0003] US10168711 JP2021-194995A Republished No. 2018-135063A

[0004] However, in Patent Document 2, it is necessary to prepare map data, and in Patent Document 3, it is necessary to prepare a lighting environment for accurately reading the moiré pattern. Therefore, there has been a demand for a technology that can easily and accurately control the position of a vehicle relative to a marker.

[0005] The present invention has been made in consideration of the above-mentioned problems, and has as its object to enable the position of a vehicle relative to a marker to be controlled easily and with high precision.

[0006] The vehicle driving control device of the present invention includes a position sensor attached to the vehicle, a marker position detection unit that detects the positions of N (N is an integer greater than or equal to 2) reference markers relative to the vehicle using the position sensor and acquires the detected positions of each of the N reference markers, a memory unit that stores, for each of the N reference markers, the reference positions that are the detected positions of the reference markers that should be acquired by the marker position detection unit when the vehicle moves to its destination, and a drive control unit that controls a vehicle drive unit that drives the vehicle based on the difference between the reference position and the detected position of each of the N reference markers, thereby causing the vehicle to drive toward its destination.

[0007] An autonomous vehicle according to the present invention includes a vehicle body, a vehicle drive unit that drives the vehicle body, and the above-described vehicle driving control device that controls the vehicle drive unit.

[0008] The vehicle driving control method of the present invention includes the steps of: detecting, relative to the vehicle, the positions of N (N is an integer greater than or equal to 2) reference markers provided at the vehicle's destination using a position sensor attached to the vehicle, and acquiring the detected positions of each of the N reference markers using a marker position detection unit; reading out the reference positions of each of the N reference markers from a memory unit that stores, for each of the N reference markers, the reference positions that are the detected positions of the reference markers that should be acquired by the marker position detection unit when the vehicle moves to the destination; and controlling a vehicle drive unit that drives the vehicle based on the difference between the reference position and the detected position of each of the N reference markers, thereby driving the vehicle toward the destination.

[0009] In the present invention (vehicle driving control device, autonomous vehicle, and vehicle driving control method) configured as described above, a position sensor is attached to the vehicle to detect the position of a reference marker provided at a destination of the vehicle relative to the vehicle. N (N is an integer greater than or equal to 2) reference markers are provided at the destination, and a marker position detection unit detects the positions of the N reference markers relative to the vehicle using the position sensor and acquires the detected positions of each of the N reference markers. Furthermore, a memory unit stores, for each of the N reference markers, a reference position that is the detected position of the reference marker to be acquired by the marker position detection unit when the vehicle moves to the destination. A vehicle drive unit that drives the vehicle is controlled based on the difference between the reference position and the detected position of each of the N reference markers. In other words, the driving of the vehicle is controlled based on the difference between the reference position of each of the N reference markers stored in the memory unit and the detected position of each of the N reference markers detected using the position sensor. As a result, it is possible to simply and accurately control the position of the vehicle relative to the markers.

[0010] The vehicle driving control device may be configured so that an object having N reference markers attached thereto is provided at the destination, each of the N reference markers having a convex shape that protrudes from the object in a plan view, and the marker position detection unit acquires the position of an apex of the convex shape of the reference marker as the detected position of the reference marker. In this configuration, the detected position of the reference marker can be easily acquired by detecting the position of the apex of the convex shape.

[0011] The vehicle driving control device may also be configured so that the convex shape is a V. In such a configuration, the detected position of the reference marker can be easily obtained by detecting the position of the apex of the V shape.

[0012] The vehicle driving control device may also be configured to include a point cloud data acquisition unit that acquires point cloud data indicating the shapes of the object and N reference markers at the destination by scanning the destination with a position sensor; a minimum point extraction unit that extracts from the point cloud data points where the Y coordinate value is a minimum on a θ-Y plane formed by a θ coordinate corresponding to the rotational direction of the vehicle and a Y coordinate corresponding to the straight-line direction of the vehicle; and a minimum point selection unit that acquires, as the detection positions of the N reference markers, the positions of N minimum points that satisfy predetermined conditions among the M minimum points (M is a natural number greater than N) extracted by the minimum point extraction unit as the points with minimum values. With this configuration, the positions of the N reference markers attached to the object can be accurately detected and the detection positions of each of the N reference markers can be acquired. This allows the detection of reference marks from a wide range.

[0013] Various predetermined conditions may be considered for acquiring the positions of the M minimum points as the detection positions of the N reference markers. For example, the predetermined condition may include a condition that the difference between the distance to the reference position and the distance to the minimum point is less than a first threshold. The predetermined condition may include a condition that the difference between the distance between two of the N minimum points and the distance between the reference positions of two of the N reference markers is less than a second threshold. Alternatively, the predetermined condition may include a condition that the slope of a straight line indicated by a plurality of points included in a predetermined target range of the point cloud data, the end of which is the minimum point, is within a predetermined slope range.

[0014] Alternatively, N may be 2. In this configuration, the traveling of the vehicle can be easily controlled based on the difference between the reference positions of the two reference markers stored in the memory unit and the detected positions of the two reference markers detected using the position sensor.

[0015] According to the present invention, it is possible to control the position of a vehicle relative to a marker simply and with high accuracy.

[0016] 5 is a diagram schematically showing an example of an autonomous vehicle according to the present invention and an object placed at a destination of the autonomous vehicle. FIG. 1 is a block diagram showing an example of the electrical configuration of the autonomous vehicle of FIG. 1. FIG. 2 is a flowchart showing an example of cruise control of the autonomous vehicle executed by a cruise control unit. FIG. 3 is a diagram schematically showing operations executed in accordance with the cruise control of FIG. 3. FIG. 4 is a diagram schematically showing operations executed in accordance with the cruise control of FIG. 3. FIG. 5 is a diagram schematically showing operations executed in accordance with the cruise control of FIG. 3. FIG. 6 is a flowchart showing an example of processing executed in marker position detection of FIG. 3. FIG. 7 is a flowchart showing calculations using a likelihood function used in the flowchart of FIG. 5. FIG. 8 is a diagram schematically showing the contents of calculations executed on point cloud data in the flowchart of FIG. 5.

[0017] Fig. 1 is a diagram schematically illustrating an example of an autonomous vehicle according to the present invention and an object located at the destination of the autonomous vehicle, and Fig. 2 is a block diagram showing an example of the electrical configuration of the autonomous vehicle of Fig. 1. Note that dimensional relationships in Fig. 1 are depicted schematically and do not represent actual dimensional relationships. The same applies to the following figures.

[0018] The autonomous vehicle 1 is a so-called AGV (Automatic Guided Vehicle). As shown in Fig. 1, the autonomous vehicle 1 includes a vehicle body 11 and a plurality of wheels 12 that drive the vehicle body 11, and the autonomous vehicle 1 moves when the wheels 12 rotate. Note that the specific configuration for driving the vehicle body 11 is not limited to the wheels 12, and may be, for example, a caterpillar track.

[0019] This autonomous vehicle 1 travels toward a destination 9 of the autonomous vehicle 1 and stops when it reaches the destination 9. An object 91 is placed at the destination 9, and when the autonomous vehicle 1 reaches the destination 9, it stops in a state where it is docked to or in close proximity to the object 91.

[0020] The object 91 has a vertically standing wall 911, and two markers M1 and M2 are attached to the wall 911. The markers M1 and M2 are attached to the wall 911 with a horizontal gap between them and positioned at the same height. In a plan view (i.e., viewed from above in the vertical direction), the markers M1 and M2 have a convex shape, particularly a V-shape, protruding from the wall 911 and having vertices V1 and V2. In other words, the markers M1 and M2 have left slopes S11 and S12 extending from the wall 911 toward the vertices V1 and V2 on the left side of the vertices V1 and V2, and right slopes Sr1 and Sr2 extending from the wall 911 toward the vertices V1 and V2 on the right side of the vertices V1 and V2. Note that the right and left correspond to the right and left when the object 91 is viewed from the front from the side where the autonomous vehicle 1 approaches the object 91.

[0021] 2, the autonomous vehicle 1 has a traction motor 13 that drives the wheels 12 and a steering wheel 14 that changes the direction of the wheels 12. In other words, the traction motor 13 drives the wheels 12, causing the wheels 12 to rotate and driving the autonomous vehicle 1. The steering wheel 14 also changes the direction of the wheels 12, changing the direction in which the autonomous vehicle 1 drives.

[0022] The autonomous vehicle 1 is equipped with a LiDAR (Light Detection and Ranging) 2. The LiDAR 2 scans a predetermined range ahead of the autonomous vehicle 1 to detect objects located ahead and acquire point cloud data Dp indicating the three-dimensional shape of the objects. In particular, the LiDAR 2 is used to detect the positions of two markers M1 and M2 provided at the destination 9.

[0023] Furthermore, the autonomous vehicle 1 includes a driving control unit 3 that controls the driving motor 13 and the steering wheel 14 based on the point cloud data Dp acquired by the LiDAR 2. The driving control unit 3 controls the driving motor 13 and the steering wheel 14 based on the respective positions of the two markers M1 and M2 indicated by the point cloud data Dp, thereby causing the autonomous vehicle 1 to travel toward the destination 9.

[0024] The driving control unit 3 has a calculation unit 4 and a memory unit 5. The calculation unit 4 is a processor such as a CPU (Central Processing Unit), and the memory unit 5 is a storage device such as an SSD (Solid State Drive). The memory unit 5 stores reference position data Dr, which will be described later.

[0025] The calculation unit 4 has a marker position detection unit 41 that detects the positions of two markers M1 and M2 based on the point cloud data Dp acquired by the LiDAR 2. In particular, the marker position detection unit 41 detects the positions of the vertices V1 and V2 of the markers M1 and M2 as the positions of the markers M1 and M2. The marker position detection unit 41 has a point cloud data acquisition unit 411, a minimum point extraction unit 412, and a minimum point selection unit 413. The point cloud data acquisition unit 411 acquires the point cloud data Dp from the LiDAR 2. The functions of the minimum point extraction unit 412 and the minimum point selection unit 413 will be described later.

[0026] The calculation unit 4 also has a drive control unit 43. The drive control unit 43 controls the travel motor 13 and the steering 14 based on the positions of the two markers M1 and M2 detected by the marker position detection unit 41, thereby causing the autonomous vehicle 1 to travel toward the destination 9.

[0027] Fig. 3 is a flowchart showing an example of driving control of an autonomous vehicle executed by a driving control unit, and Figs. 4A to 4D are diagrams schematically showing operations executed in accordance with the driving control of Fig. 3. Figs. 4A to 4D show an XY Cartesian coordinate system fixed to LiDAR 2 (in other words, vehicle body 11), in which the Y coordinate indicates a position coordinate in the direction in which autonomous vehicle 1 travels straight (straight direction), and the X coordinate indicates a position coordinate in a direction perpendicular to the straight direction of autonomous vehicle 1 (orthogonal direction).

[0028] The LiDAR2 acquires point cloud data Dp representing the three-dimensional shape of surrounding objects by scanning in a rotational direction around a central axis parallel to the vertical direction. This point cloud data Dp is represented by polar coordinates consisting of a combination of distance and rotational position. Correspondingly, in Figures 4A to 4D, the detection positions p1 and p2 of markers M1 and M2 detected by the LiDAR2 are shown as polar coordinates (r1, θ1) and (r2, θ2). Here, the distances r1 and r2 are the distances from the LiDAR2 to the detection positions p1 and p2 of markers M1 and M2, and the angles θ1 and θ2 are the angles of the detection positions p1 and p2 of markers M1 and M2 relative to the LiDAR2.

[0029] 4A to 4D, the reference positions P1 and P2 of the markers M1 and M2 are indicated by polar coordinates (R1, Θ1) and (R2, Θ2). Here, the reference positions P1 and P2 are the detected positions p1 and p2 of the markers M1 and M2 that the marker position detection unit 41 should acquire when the autonomous vehicle 1 moves to the destination 9, and are included in the reference position data Dr. In other words, when the autonomous vehicle 1 has reached the destination 9, the detected positions p1 and p2 of the markers M1 and M2 coincide with the reference positions P1 and P2 of the markers M1 and M2, respectively. In response to this, the marker position detection unit 41 and the drive control unit 43 of the marker position detection unit 41 appropriately read the reference position data Dr from the storage unit 5 and perform the control described below.

[0030] 4A to 4C show the state before LiDAR 2 reaches destination 9, and FIG. 4D shows the state when LiDAR 2 reaches destination 9. As the driving control of FIG. 3 is executed, autonomous vehicle 1 moves in the order shown in FIG. 4A to 4D. For simplicity, the following description will be given assuming that R1·sin(Θ1)=R2·sin(Θ2).

[0031] In step S101, the point cloud data acquisition unit 411 causes the LiDAR 2 to perform scanning and acquires point cloud data Dp from the LiDAR 2. Then, the point cloud data acquisition unit 411 acquires the detection positions p1 and p2 of the markers M1 and M2 based on the point cloud data Dp.

[0032] In step S102, the drive control unit 43 determines whether the amount of deviation of the detected positions p1 and p2 of the markers M1 and M2 from the reference positions P1 and P2 of the markers M1 and M2 in the rotation direction of the polar coordinates is within an allowable range. Specifically, the determination is made based on the following condition: ΔΔy=||Y1-y1|-|Y2-y2||<ΔΔYth, where, Y1: Y-coordinate component of the reference position P1 of the marker M1 (=R1·sin(Θ1)), y1: Y-coordinate component of the detected position p1 of the marker M1 (=r1·sin(θ1)), Y2: Y-coordinate component of the reference position P2 of the marker M2 (=R2·sin(Θ2)), y2: Y-coordinate component of the detected position p2 of the marker M2 (=r2·sin(θ2)), and ΔΔYth: a predetermined threshold value corresponding to the allowable range.

[0033] 4A is less than the threshold value ΔΔYth, it is determined that the deviation in the rotational direction is within the allowable range ("YES" in step S102), and the process proceeds to step S104. On the other hand, if the distance ΔΔy shown in FIG. 4A is equal to or greater than the threshold value ΔΔYth, it is determined that the deviation in the rotational direction is outside the allowable range ("NO" in step S102), and the process proceeds to step S104 after executing step S103. However, the method for evaluating the deviation in the rotational direction is not limited to this example, and it is also possible to evaluate the deviation in the rotational direction based on whether the angle between a line passing through the reference positions P1 and P2 of the markers M1 and M2 and a line passing through the detection positions p1 and p2 of the markers M1 and M2 is less than a predetermined threshold angle.

[0034] In this example, step S102 is executed in the state shown in Fig. 4A and the result is "NO." Therefore, in step S103, the drive control unit 43 controls the steering 14 in accordance with the distance ΔΔy, thereby rotating the autonomous vehicle 1 in the rotational direction so that the distance ΔΔy decreases to less than the threshold value ΔΔYth. The result of executing step S103 from the state shown in Fig. 4A is shown in Fig. 4B. As shown in Fig. 4B, the distance ΔΔy is substantially zero.

[0035] In step S104, the drive control unit 43 determines whether the amount of deviation of the detected positions p1 and p2 of the markers M1 and M2 from the reference positions P1 and P2 of the markers M1 and M2 in the Y direction (the direction corresponding to the Y coordinate) is within an allowable range. Specifically, the determination is made based on the following conditions: Δy1=|Y1-y1|<Y1th Δy2=|Y1-y1|<Y2th. Here, Y1th: a predetermined threshold value corresponding to the allowable range Y2th: a predetermined threshold value corresponding to the allowable range, and the threshold values ​​Y1th and Y2th are equal.

[0036] Here, the distance Δy1 and the distance Δy2 are equal. Therefore, if the distance Δy1 shown in FIG. 4B is less than the threshold value Y1th, it is determined that the deviation in the Y direction is within the allowable range ("YES" in step S104), and the process proceeds to step S106. On the other hand, if the distance Δy1 shown in FIG. 4B is equal to or greater than the threshold value Y1th, it is determined that the deviation in the Y direction is outside the allowable range ("NO" in step S104), and the process executes step S105 before proceeding to step S106.

[0037] In this example, step S104 is executed in the state shown in Figure 4B, and the result is "NO." Therefore, in step S105, the drive control unit 43 controls the travel motor 13 in accordance with the distance Δy1, thereby moving the autonomous vehicle 1 in the Y direction so that the distance Δy1 decreases to less than the threshold value Y1th. The result of executing step S105 from the state shown in Figure 4B is shown in Figure 4C. As shown in Figure 4C, the distance Δy1 is substantially zero.

[0038] In step S106, the drive control unit 43 determines whether the amount of deviation of the detected positions p1 and p2 of the markers M1 and M2 from the reference positions P1 and P2 of the markers M1 and M2 in the X direction (the direction corresponding to the X coordinate) is within an allowable range. Note that in this example, the deviation in the rotational direction in polar coordinates is evaluated instead of the X direction, so that the deviation in the X coordinate is essentially evaluated. Specifically, the determination is made based on the following conditions: Δθ1=|Θ1-θ1|<Θ1th Δθ2=|Θ2-θ2|<Θ2th where Θ1th: a predetermined threshold corresponding to the allowable range Θ2th: a predetermined threshold corresponding to the allowable range, and the thresholds Θ1th and Θ2th are equal.

[0039] That is, if the angle Δθ1 shown in Fig. 4C is less than the threshold value Θ1th and the angle Δθ2 shown in Fig. 4C is less than the threshold value Θ2th, it is determined that the deviation in the X direction is within the allowable range ("YES" in step S106), and the flowchart in Fig. 3 is terminated. On the other hand, if the angle Δθ1 shown in Fig. 4C is equal to or greater than the threshold value Θ1th or the angle Δθ2 shown in Fig. 4C is equal to or greater than the threshold value Θ2th, it is determined that the deviation in the X direction is outside the allowable range ("NO" in step S106), and step S107 is executed before the flowchart in Fig. 3 is terminated.

[0040] In this example, step S106 is executed in the state shown in FIG. 4C , and the result is determined to be "NO." Therefore, in step S107, the drive control unit 43 controls the travel motor 13 and the steering wheel 14 in accordance with the angles Δθ1 and Δθ2, thereby moving the autonomous vehicle 1 to the X coordinate so that the angles Δθ1 and Δθ2 decrease to less than the thresholds Θ1th and Θ2th, respectively. The result of executing step S107 from the state shown in FIG. 4C is shown in FIG. 4D . As shown in FIG. 4D , the angles Δθ1 and Δθ2 are each substantially zero.

[0041] FIG. 5 is a flowchart showing an example of processing performed in the marker position detection of FIG. 3, FIG. 6 is a flowchart showing calculations using the likelihood function used in the flowchart of FIG. 5, and FIG. 7 is a diagram schematically showing the contents of the calculations performed on point cloud data in the flowchart of FIG. 5.

[0042] In step S201, the point cloud data acquisition unit 411 acquires the point cloud data Dp in the above-described manner from the LiDAR 2. Note that Fig. 7 schematically illustrates the point cloud data Dp composed of a plurality of dots dt each indicating a three-dimensional position.

[0043] In step S202, points (minimum points Ia, Ib, Ic) where the Y coordinate value is a minimum on a θ-Y plane formed by a θ coordinate corresponding to the rotational direction in which the autonomous vehicle 1 rotates and a Y coordinate corresponding to the straight-ahead direction in which the autonomous vehicle 1 travels straight are extracted from the point cloud data Dp by the minimum point extraction unit 412. In the example of Fig. 7, three minimum points Ia, Ib, Ic are extracted from the point cloud data Dp.

[0044] In step S203, the minimum point selection unit 413 determines whether the number M of minimum points Ia, Ib, and Ic extracted by the minimum point extraction unit 412 is greater than the number N of markers M1 and M2. In this example, the number of minimum points Ia, Ib, and Ic (3) is greater than the number N of markers M1 and M2 (2), so the determination is "YES" and the process proceeds to step S204.

[0045] In step S204, the minimum point selection unit 413 generates combinations by selecting N (two) minimum points from the M (three) minimum points Ia, Ib, and Ic. In step S205, the minimum point selection unit 413 calculates an evaluation value for one of the combinations generated in step S204 using a likelihood function. Here, the likelihood function is a function that evaluates the degree of matching between the N minimum points included in the one target combination and the markers M1 and M2, and the higher the degree, the smaller the evaluation value that is output.

[0046] The likelihood function calculation algorithm shown in FIG. 6 is executed by the minimum point selection unit 413. In step S301, of the two minimum points included in one combination that is the target of the likelihood function calculation, a distance Δr1 is calculated for the left minimum point, and a distance Δr2 is calculated for the right minimum point. Here, the distance Δr1 is the absolute value of the difference between the distance from the LiDAR2 to the left minimum point and the distance R1 from the LiDAR2 to the reference position P1, and the distance Δr2 is the absolute value of the difference between the distance from the LiDAR2 to the right minimum point and the distance R2 from the LiDAR2 to the reference position P2. For example, when a combination consisting of minimum points Ia and Ib is the target, the following equations are satisfied: Δr1 = |R1 - ra| Δr2 = |R2 - rb| When a combination of minimum points Ib and Ic is the target, Δr1=|R1-rb| Δr2=|R2-rc| When a combination of minimum points Ia and Ic is the target, Δr1=|R1-ra| Δr2=|R2-rc| Then, it is determined whether the following condition is met (step S301): Δr1<R1th AND Δr2<R2th. Here, R1th and R2th are preset thresholds. When the condition of step S301 is not met (in the case of "NO"), the evaluation value C is set to infinity in step S306.

[0047] On the other hand, if the condition in step S301 is met ("YES"), the process proceeds to step S302. Then, it is determined whether the following condition, Δd=|D-d|<ΔDth, is met between the distance d between the two minimum points included in one combination and the distance D between the two reference positions P1 and P2 (step S302). Here, ΔDth is a preset threshold. For example, if a combination of minimum points Ia and Ib is the target, then: Δd=|D-dab| dab: distance between the two minimum points Ia and Ib. If a combination of minimum points Ib and Ic is the target, then: Δd=|D-dbc| dbc: distance between the two minimum points Ib and Ic. If a combination of minimum points Ia and Ic is the target, then: Δd=|D-dac| dac: distance between the two minimum points Ia and Ic. If the condition in step S302 is not met (if "NO"), the evaluation value C is determined to be infinity in step S306.

[0048] On the other hand, if the condition of step S302 is satisfied ("YES"), the process proceeds to step S303. Then, of the two minimum points included in one combination, a slope m1 is calculated for the left minimum point, and a slope m2 is calculated for the right minimum point. Here, the slope m1 is the slope of a straight line indicated by the plurality of dots dt included in a predetermined target range (a range equal to or smaller than the width of the right slope Sr1 of the left marker M1) whose end is the left minimum point, and the slope m2 is the slope of a straight line indicated by the plurality of dots dt included in a predetermined target range (a range equal to or smaller than the width of the left slope Sl2 of the right marker M1) whose end is the right minimum point. The straight line indicated by the plurality of dots dt is, for example, a regression line for the plurality of dots dt. For example, when a combination of minimum points Ia and Ib is the target, m1 = mar (the gradient formed by the group of points included on the right side of minimum point Ia) m2 = mbl (the gradient formed by the group of points included on the left side of minimum point Ib) When a combination of minimum points Ib and Ic is the target, m1 = mbr (the gradient formed by the group of points included on the right side of minimum point Ib) m2 = mcl (the gradient formed by the group of points included on the left side of minimum point Ic) When a combination of minimum points Ia and Ic is the target, m1 = mar (the gradient formed by the group of points included on the right side of minimum point Ia) m2 = mcl (the gradient formed by the group of points included on the left side of minimum point Ic) Then, it is determined whether the following condition is met (step S303): M1thm<m1<M1thp AND M2thm<m2<M1thp. Here, M1thm, M1thp, M2thm, and M2thp are preset thresholds. If the condition of step S303 is not met ("NO"), evaluation value C is set to infinity in step S306.

[0049] On the other hand, if the condition in step S303 is met (if "YES"), the process proceeds to step S304. Then, it is determined whether or not the slope m1 of the left minimum point and the slope m2 of the right minimum point among the two minimum points included in one combination satisfy the following conditional expression: Δm=|m1-m2|<ΔMth (step S304), where ΔMth is a preset threshold value. If the condition in step S304 is not met (if "NO"), the evaluation value C is set to infinity in step S306.

[0050] On the other hand, if the condition of step S304 is met (YES), the process proceeds to step S305. Then, the evaluation value C is determined by the following relational expression: C=Δr1 / R1+Δr2 / R2+Δd / D.

[0051] Returning to FIG. 5, the explanation continues. When the calculation of the evaluation value using the likelihood function in FIG. 6 (step S205) is completed for all combinations ("YES" in step S206), the minimum point selection unit 413 selects the positions of the two minimum values ​​of the combination with the smallest evaluation value as the detection positions p1 and p2 of the two markers M1 and M2 (step S207). On the other hand, if "NO" in step S203, the minimum point selection unit 413 selects the positions of the two minimum values ​​extracted in step S202 as the detection positions p1 and p2 of the two markers M1 and M2 (step S208). As a result, in the example of FIG. 7, the positions of the minimum points I1 and I3 are selected as the detection positions p1 and p2 of the two markers M1 and M2 (steps S207 and S208).

[0052] In the embodiment described above, the autonomous vehicle 1 is equipped with a LiDAR2 (position sensor) that detects the positions of markers M1 and M2 (reference markers) provided at the destination 9 of the autonomous vehicle 1 relative to the autonomous vehicle 1. Two markers M1 and M2 are provided at the destination 9, and the marker position detection unit 41 detects the positions of the two markers M1 and M2 relative to the autonomous vehicle 1 using the LiDAR2 and acquires detected positions p1 and p2 of the two markers M1 and M2, respectively (step S102). Furthermore, the storage unit 5 stores reference positions P1 and P2 for each of the two markers M1 and M2, which are the detected positions p1 and p2 of the markers M1 and M2 that the marker position detection unit 41 should acquire when the autonomous vehicle 1 moves to the destination 9. Then, based on the difference between the reference positions P1, P2 of the two markers M1, M2 and the detected positions p1, p2, respectively, the travel motor 13 and steering 14 (vehicle drive unit) that drive the autonomous vehicle 1 are controlled. In other words, based on the difference between the reference positions P1, P2 of the two markers M1, M2 stored in the memory unit 5 and the p1, p2 of the two markers M1, M2 detected using the LiDAR 2, the travel of the autonomous vehicle 1 is controlled. As a result, it is possible to control the position of the autonomous vehicle 1 relative to the markers M1, M2 simply and with high accuracy.

[0053] Furthermore, an object 91 with two markers M1 and M2 attached thereto is provided at the destination 9, and each of the two markers M1 and M2 has a convex shape that protrudes from the object 91 in a planar view. In response to this, the marker position detection unit 41 acquires the positions of vertices V1 and V2 of the convex shapes of the markers M1 and M2 as detection positions p1 and p2 of the markers M1 and M2. With this configuration, the detection positions p1 and p2 of the markers M1 and M2 can be easily acquired by detecting the positions of the vertices V1 and V2 of the convex shapes.

[0054] Furthermore, the convex shapes of the markers M1 and M2 are V-shaped. In this configuration, the detected positions p1 and p2 of the markers M1 and M2 can be easily obtained by detecting the positions of the vertices V1 and V2 of the V-shape.

[0055] The marker position detection unit 41 also has a point cloud data acquisition unit 411, a minimum point extraction unit 412, and a minimum point selection unit 413. The point cloud data acquisition unit 411 acquires point cloud data Dp indicating the shapes of the object 91 and two markers M1 and M2 provided at the destination 9 by scanning the destination 9 with the LiDAR 2. The minimum point extraction unit 412 extracts, from the point cloud data Dp, minimum points I1, I2, and I3 whose Y coordinate values ​​are minimum on a θ-Y plane formed by a θ coordinate corresponding to the rotation direction in which the autonomous vehicle 1 rotates and a Y coordinate corresponding to the straight-ahead direction in which the autonomous vehicle 1 travels straight. Furthermore, the minimum point selection unit 413 acquires the positions of two minimum points I1 and I3 that satisfy predetermined conditions (steps S301, S302, S303, and S304) from among the three or more minimum points I1, I2, and I3 extracted by the minimum point extraction unit 412 as points with minimum values, as the detection positions p1 and p2 of the two markers M1 and M2, respectively. With this configuration, the positions of the two markers M1 and M2 provided on the object 91 can be accurately detected, and the detection positions p1 and p2 of the two markers M1 and M2 can be acquired. Therefore, the markers M1 and M2 can be detected from a wide range.

[0056] As described above, in the above embodiment, the autonomous vehicle 1 corresponds to an example of the "vehicle" and "autonomous vehicle" of the present invention, the vehicle body 11 corresponds to an example of the "vehicle body" of the present invention, the travel motor 13 and the steering 14 constitute an example of the "vehicle drive unit" of the present invention, the LiDAR 2 corresponds to an example of the "position sensor" of the present invention, the LiDAR 2 and the travel control unit 3 constitute an example of the "vehicle travel control device" of the present invention, the marker position detection unit 41 corresponds to an example of the "marker position detection unit" of the present invention, the point cloud data acquisition unit 411 corresponds to an example of the "point cloud data acquisition unit" of the present invention, the minimum point extraction unit 412 corresponds to an example of the "minimum point extraction unit" of the present invention, the minimum point selection unit 413 corresponds to an example of the "minimum point selection unit" of the present invention, and the drive control unit 43 corresponds to an example of the "minimum point selection unit" of the present invention. The reference positions P1 and P2 correspond to examples of "reference positions" of the present invention, the threshold values ​​ΔR1th and ΔR2th correspond to an example of a "first threshold value" of the present invention, the threshold value ΔDth corresponds to an example of a "second threshold value" of the present invention, and the ranges M1thm to M1thp and M2thm to M2thp correspond to examples of a "target range" of the present invention.

[0057] The present invention is not limited to the above-described embodiment, and various modifications can be made to the above-described embodiment without departing from the spirit of the present invention. For example, the marker position detection unit 41 and the storage unit 5 may be implemented in a server computer separate from the autonomous vehicle 1, and the autonomous vehicle 1 may be wirelessly controlled from the server computer.

[0058] Furthermore, the number N of markers M1 and M2 is not limited to two, but may be three or more.

[0059] Furthermore, the arrangement or shape of the markers M1 and M2 may be changed as appropriate.

[0060] REFERENCE SIGNS LIST 1...Autonomous vehicle (vehicle) 11...Vehicle body 13...Travel motor (vehicle drive unit) 14...Steering (vehicle drive unit) 2...LiDAR (position sensor, vehicle drive control device) 3...Travel control unit (vehicle drive control device) 41...Marker position detection unit 411...Point cloud data acquisition unit 412...Minimum point extraction unit 413...Minimum point selection unit 43...Drive control unit 5...Memory unit 9...Destination 91...Object Dp...Point cloud data M1, M2...Marker (reference marker) p1, p2...Detected position P1, P2...Reference position

Claims

1. A position sensor attached to a vehicle, a marker position detection unit that detects, by the position sensor, the positions of N (N is an integer of 2 or more) reference markers provided at a destination of the vehicle with respect to the vehicle, and obtains the detection positions of each of the N reference markers, a storage unit that holds, for each of the N reference markers, a reference position that is the detection position of the reference marker to be obtained by the marker position detection unit when the vehicle moves to the destination, a drive control unit that controls a vehicle drive unit that drives the vehicle to travel toward the destination by controlling the vehicle drive unit based on the difference between the reference position and the detection position of each of the N reference markers A vehicle travel control device comprising.

2. At the destination, an object to which the N reference markers are attached is provided, each of the N reference markers has a convex shape protruding from the object in a plan view, The vehicle travel control device according to claim 1, wherein the marker position detection unit obtains, as the detection position of the reference marker, the position of the apex of the convex shape of the reference marker.

3. The vehicle travel control device according to claim 2, wherein the convex shape is a V shape.

4. The marker position detection unit, a point cloud data acquisition unit that acquires point cloud data indicating the shapes of the object and the N reference markers provided at the destination by scanning the destination with the position sensor, a minimum point extraction unit that extracts, from the point cloud data, points at which the value of the Y coordinate is a minimum value on a θ-Y plane composed of a θ coordinate corresponding to a rotation direction in which the vehicle rotates and a Y coordinate corresponding to a straight-ahead direction in which the vehicle travels straight, a minimum point selection unit that obtains, as the detection positions of each of the N reference markers, the positions of N minimum points that satisfy a predetermined condition among M (M is a natural number greater than N) minimum points extracted by the minimum point extraction unit as points at which the minimum value is obtained The vehicle travel control device according to claim 2 or 3, comprising.

5. The vehicle travel control device according to claim 4, wherein the predetermined condition includes a condition that the difference between the distance to the reference position and the distance to the minimum point is less than a first threshold value.

6. The vehicle travel control device according to claim 4 or 5, wherein the predetermined condition includes a condition that a difference between a distance between two of the N local minimum points and a distance between the reference positions of two of the N reference markers is less than a second threshold value.

7. The vehicle travel control device according to any one of claims 4 to 6, wherein the predetermined condition includes a condition that an inclination of a straight line indicated by a plurality of points included in a predetermined target range having the local minimum point as an end point in the point group data is within a predetermined inclination range.

8. The vehicle travel control device according to any one of claims 1 to 7, wherein N is 2.

9. A vehicle body, A vehicle drive unit that drives the vehicle body, The vehicle travel control device according to any one of claims 1 to 8 that controls the vehicle drive unit An autonomous driving vehicle comprising.

10. A step of detecting, by a position sensor attached to the vehicle, a position of N (N is an integer of 2 or more) reference markers provided at a movement destination of the vehicle with respect to the vehicle, and acquiring detection positions of the N reference markers by a marker position detection unit; A step of reading out the reference positions of the N reference markers from a storage unit that holds the reference positions of the N reference markers, which are the detection positions of the reference markers to be acquired by the marker position detection unit, for each of the N reference markers when the vehicle moves to the movement destination; A step of causing the vehicle to travel toward the movement destination by controlling a vehicle drive unit that drives the vehicle based on a difference between the reference position and the detection position of each of the N reference markers A vehicle travel control method comprising.

11. A position sensor attached to the vehicle, A marker position detection unit that detects, by the position sensor, a position of N (N is an integer of 2 or more) reference markers provided at a movement destination of the vehicle with respect to the vehicle by performing scanning in a rotational direction around a central axis parallel to the vertical direction and acquiring point group data indicating a three-dimensional shape of an object around the vehicle, and acquires detection positions of the N reference markers; A storage unit that holds, for each of the N reference markers, a reference position that is the detection position of the reference marker to be acquired by the marker position detection unit when the vehicle moves to the movement destination; Based on the difference between the reference position and the detected position of each of the N reference markers, a drive control unit that controls a vehicle drive unit that drives the vehicle to cause the vehicle to travel toward the destination of movement. A vehicle travel control device comprising the same.