Control system, control device, autonomous travel device, control method, and control program

Abstract

In the present invention, a processor of a control system for controlling an autonomous travel device, which autonomously travels by switching between a plurality of point cloud maps (Mp) used for estimating a self-position, is configured so as to execute: planning a plurality of route candidates (Rc) that can be assumed for autonomous travel in an overlap area (Ado) in which a point cloud map (Mp) of a switching source and a point cloud map (Mp) of a switching destination overlap; selecting, for an observation surface area (Os) observed by the autonomous travel device for each travel point (Pd) on the route candidates (Rc) in the overlap area (Ado), as a following route (Rcf) that the autonomous travel device is caused to follow, the route candidate (Rc) having the largest correlated evaluation index (Io); and performing, on the autonomous travel device, following control such that the following route (Rcf) follows a self-position estimated by map matching between point cloud data (Dp) and the point cloud map (Mp) observed from the autonomous travel device.

Description

Control system, control device, autonomous driving device, control method, control program Cross-reference to related applications

[0001] This application is based on Japanese Patent Application No. 2024-165513 filed in Japan on September 24, 2024, and the contents of the base application are incorporated herein by reference in their entirety.

[0002] This disclosure relates to a control technique for controlling an autonomous driving device.

[0003] Patent Document 1 discloses a control technique for controlling the autonomous driving of a robot as an autonomous driving device by switching a three-dimensional map used for estimating its own position for each driving area.

[0004] Japanese Unexamined Patent Application Publication No. 2021-196488

[0005] However, in the case of a point cloud map in which a three-dimensional map is composed of point cloud data, the number of point clouds at the edge of the target driving area tends to be small as information. Therefore, at the edge of the driving area targeted by the point cloud map before switching in the disclosed technique of Patent Document 1, the robot is controlled to follow a planned path with a deteriorated estimated accuracy of its own position. This means a concern of deteriorating the control accuracy for the autonomous driving of the robot.

[0006] An object of the present disclosure is to provide a control system that ensures the control accuracy for the autonomous driving of an autonomous driving device. Another object of the present disclosure is to provide a control device that ensures the control accuracy for the autonomous driving of an autonomous driving device, and an autonomous driving device configured to include the same. Still another object of the present disclosure is to provide a control method that ensures the control accuracy for the autonomous driving of an autonomous driving device. Yet another object of the present disclosure is to provide a control program that ensures the control accuracy for the autonomous driving of an autonomous driving device.

[0007] Hereinafter, the technical means of the present disclosure for solving the problems will be described.

[0008] A first aspect of this disclosure is a control system for controlling an autonomous driving device that has a processor and switches between a plurality of point cloud maps used for estimating its own position, wherein the processor is configured to: plan a plurality of possible paths for autonomous driving in an overlap area where the source point cloud map and the destination point cloud map overlap; select the path candidate that maximizes the correlated evaluation index with respect to the observation area observed by the autonomous driving device for each driving point on the path candidate in the overlap area as a follow path to be followed by the autonomous driving device; and perform follow control on the autonomous driving device to make its own position, estimated by map matching of point cloud data observed by the autonomous driving device and the point cloud map, follow the follow path.

[0009] A second aspect of this disclosure is a control device that can be mounted on an autonomous driving device for controlling the autonomous driving device, which autonomously drives by switching between a plurality of point cloud maps used for estimating its own position, wherein the processor is configured to perform the following actions: plan a plurality of possible paths for autonomous driving in the overlap area where the source point cloud map and the destination point cloud map overlap; select the path candidate that maximizes the correlated evaluation index with respect to the observation area observed by the autonomous driving device for each driving point on the path candidate in the overlap area, as the follow path to be followed by the autonomous driving device; and perform follow control on the autonomous driving device to make its own position, estimated by map matching of point cloud data observed from the autonomous driving device and the point cloud map, follow the follow path.

[0010] A third aspect of this disclosure comprises a driving system for autonomous driving by switching point cloud maps used for self-position estimation, and a control device according to the second aspect.

[0011] A fourth aspect of this disclosure is a control method executed by a processor to control an autonomous driving device that autonomously drives by switching between a plurality of point cloud maps used for estimating its own position, the method comprising: planning a plurality of possible paths for autonomous driving in an overlap area where the source point cloud map and the destination point cloud map overlap; selecting a path candidate that maximizes the correlated evaluation index with respect to the observation area observed by the autonomous driving device for each driving point on the path candidate in the overlap area, as a follow path to be followed by the autonomous driving device; and performing follow control on the autonomous driving device to make its own position, estimated by map matching between point cloud data observed by the autonomous driving device and the point cloud map, follow the follow path.

[0012] A fifth aspect of this disclosure is a control program stored in a storage medium for controlling an autonomous driving device that autonomously drives by switching between a plurality of point cloud maps used for estimating its own position, and which includes instructions for causing a processor to execute said control, the program including instructions for executing: planning a plurality of possible routes for autonomous driving in an overlap area where the source point cloud map and the destination point cloud map overlap; selecting the route candidate that maximizes the correlated evaluation index with respect to the observation area observed by the autonomous driving device for each driving point on the route candidate in the overlap area as a follow route to be followed by the autonomous driving device; and performing follow control on the autonomous driving device to make its own position, estimated by map matching of point cloud data observed by the autonomous driving device and the point cloud map, follow the follow route.

[0013] In these first to fifth embodiments, among the multiple point cloud maps, multiple possible routes for autonomous driving are planned in the overlap area where the source point cloud map and the target point cloud map overlap. Then, for each driving point on the route candidates in the overlap area, the route candidate that maximizes the evaluation index correlated with the observation area observed by the autonomous driving device is selected as the follow route for the autonomous driving device to follow. With this, the self-position can be estimated with high accuracy on the follow route where the evaluation index correlated with the observation area is maximized by map matching between the point cloud data observed by the autonomous driving device and the point cloud map. Therefore, in follow control that makes the self-position follow the follow route, it is possible to ensure the control accuracy of the autonomous driving device for autonomous driving.

[0014] This is a block diagram showing the overall configuration of the control system according to the first embodiment. This is a block diagram showing the functional configuration of the control system according to the first embodiment. This is a diagram illustrating the point cloud map according to the first embodiment. This is a flowchart showing the control flow according to the first embodiment. This is a schematic diagram illustrating the control flow according to the first embodiment. This is a schematic diagram illustrating the control flow according to the first embodiment. This is a schematic diagram illustrating the control flow according to the first embodiment. This is a flowchart showing the control flow according to the second embodiment. This is a schematic diagram illustrating the control flow according to the second embodiment. This is a schematic diagram illustrating the control flow according to the second embodiment.

[0015] Hereinafter, several embodiments of this disclosure will be described with reference to the drawings. In each embodiment, the same reference numerals will be used for corresponding components, and redundant explanations may be omitted. Furthermore, if only a part of the configuration is described in each embodiment, the configuration of other embodiments described earlier may be applied to the other parts of that configuration. Moreover, not only the combinations of configurations explicitly stated in the description of each embodiment, but also the configurations of multiple embodiments can be partially combined even if not explicitly stated, as long as there are no particular problems with the combination.

[0016] (First Embodiment) As shown in Figure 1, the control system 1 according to the first embodiment controls the autonomous driving device 3 that drives autonomously. The autonomous driving device 3 is an autonomous driving vehicle or autonomous driving robot that can drive autonomously indoors and / or outdoors as the driving area Ad (see Figure 3, which will be described in detail later). As shown in Figures 1 and 2, the autonomous driving device 3 is configured to include a driving drive system 4, a sensor system 5, a communication system 6, and an information presentation system 7 together with at least a part of the control system 1.

[0017] Figures 1 and 2 specifically show a representative example where the entire control system 1 is configured to be mounted on the autonomous driving device 3, as an example of implementation in the form of a control device such as a processing circuit (e.g., a processing ECU) or a semiconductor device (e.g., a semiconductor chip). However, the control system 1 may be constructed across the autonomous driving device 3 to, for example, an external center that can communicate with the device 3. Furthermore, such a control system 1 is preferably connected to the driving drive system 4, sensor system 5, communication system 6, and information display system 7 via at least one of the following: a LAN (Local Area Network) line, a wire harness, an internal bus, and a wireless communication line.

[0018] Specifically, the driving system 4 comprises wheels 40 and electric actuators 41. The multiple wheels 40 are each supported so as to be able to rotate independently. Of these wheels 40, a pair of drive wheels 400, one on each side of the autonomous driving device 3, are each independently driven by individual electric actuators 41. Each electric actuator 41 is mainly composed of an electric motor capable of outputting a driving torque to rotate its corresponding drive wheel 400. In the autonomous driving device 3, the driving state of the autonomous driving device 3 switches between straight-line driving and turning driving according to the difference in rotational speed (i.e., the difference in rotations per unit time) that occurs between the drive wheels 400 due to the difference in driving torque from each electric actuator 41. Note that the multiple wheels 40 may include at least one driven wheel that rotates in conjunction with the drive wheels 400.

[0019] Sensor system 5 acquires sensing information by sensing the external and internal environments of the autonomous driving device 3. Sensor system 5 includes an external sensor 50 and an internal sensor 51. The external sensor 50 acquires external information as sensing information from the external environment that surrounds the autonomous driving device 3. The external sensor 50 acquires external information by observing objects present in the external environment of the autonomous driving device 3. The object observation type external sensor 50 is at least one of the following: a camera, LiDAR (Light Detection And Ranging / Laser imaging Detection And Ranging), radar, and sonar. In particular, the autonomous driving device 3 is equipped with at least a LiDAR 50a as an external sensor 50 that can observe objects in the external environment by optical scanning. Here, the LiDAR 50a is configured to output point cloud data Dp (see Figure 8, which will be described in detail later), in which at least the position information of the position information and intensity information are represented as point cloud information.

[0020] The internal environment sensor 51 acquires internal environment information as sensing information from the internal environment, which is the internal environment of the autonomous driving device 3. The internal environment sensor 51 may be a physical quantity detection type that acquires internal environment information by detecting specific kinetic physical quantities in the internal environment of the autonomous driving device 3. The physical quantity detection type internal environment sensor 51 is, for example, at least one of the following: a velocity sensor, an acceleration sensor, a yaw rate sensor, and an inertia sensor.

[0021] The communication system 6 transmits and receives communication information usable by the control system 1. The communication system 6 may be a positioning type that acquires communication information by receiving positioning signals from GNSS (Global Navigation Satellite System) satellites located outside the autonomous driving device 3. A positioning type communication system 6 is, for example, a GNSS receiver. The communication system 6 may also be a V2X type that transmits and receives communication information with a V2X system located outside the autonomous driving device 3. A V2X type communication system 6 is, for example, at least one of a DSRC (Dedicated Short Range Communications) communication device and a cellular V2X (C-V2X) communication device. The communication system 6 may also be a terminal communication type that transmits and receives communication information with a mobile terminal located outside the autonomous driving device 3. A terminal communication type communication system 6 is, for example, at least one of a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, and an infrared communication device.

[0022] The information display system 7 displays notification information toward the surrounding area of ​​the autonomous driving device 3. The information display system 7 may be a monitor unit that displays notification information by displaying images. The information display system 7 may be a light-emitting unit that displays notification information by emitting a lamp. The information display system 7 may be a speaker or buzzer that displays notification information by sound.

[0023] As shown in Figure 1, the control system 1 that controls the autonomous driving device 3 is constructed to include at least one dedicated computer. The dedicated computer constituting the control system 1 has at least one memory 10 and one processor 12. The memory 10 is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, magnetic medium, and optical medium, which non-temporarily stores programs and data that can be read by the computer.

[0024] At least one memory 10 in the control system 1 constructs a map database 10a in which data of a point cloud map Mp (see Figure 3, which will be described in detail later) is stored as map information representing the driving environment of the autonomous driving device 3. In the map database 10a, the stored data is updated with the latest point cloud map Mp obtained, for example, through communication with an external center. The point cloud map Mp is generated as point cloud information for multiple observation points (i.e., for each scan point in the case of LiDAR) by a LiDAR mounted on and / or not mounted on the autonomous driving device 3, and mainly consists of point cloud data Dp that represents at least the position information among position information and intensity information. Such a point cloud map Mp may be either two-dimensional data or three-dimensional data.

[0025] As shown in Figure 3, the point cloud map Mp is stored for each of the multiple driving areas Ad that divide the driving environment of the autonomous driving device 3. This is because the autonomous driving device 3 estimates its own position using the point cloud map Mp which is switched for each driving area Ad, and autonomous driving is controlled based on this estimated self-position. In particular, in the control system 1, an overlapping area Ado is set between the driving area Ad represented by the source point cloud map Mp and the driving area Ad represented by the destination point cloud map Mp. Identification information for identifying this overlapping area Ado is added to the source point cloud map Mp and the destination point cloud map Mp. In the following explanation, when distinguishing between the source point cloud map Mp and the destination point cloud map Mp, they will be referred to as source map Mps and destination map Mpt, respectively.

[0026] Here, the driving area Ad represented by the source map Mps and the driving area Ad represented by the destination map Mpt may be either outdoors or indoors, respectively. In particular, Figure 3 is a representative example where the source map Mps represents an outdoor driving area Ad and the destination map Mpt represents an indoor driving area Ad. However, the relationship between outdoors and indoors may be reversed between the source and destination maps from the example in Figure 3, or it may be reversed between either the source or the destination map from the example in Figure 3.

[0027] The processor 12 in the control system 1 shown in Figure 1 includes at least one type as a core, such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a RISC (Reduced Instruction Set Computer)-CPU. The processor 12 executes multiple instructions included in the control program stored in the memory 10 in order to control the autonomous driving device 3, which drives autonomously by switching the point cloud map Mp used for self-position estimation. As a result, multiple functional blocks for controlling the autonomous driving device 3, which drives autonomously by switching the point cloud map Mp used for self-position estimation, are constructed. The multiple functional blocks include a planning block 100, a selection block 110, and a control block 120, as shown in Figure 2.

[0028] The control method for controlling the autonomous driving device 3, which autonomously drives by switching the point cloud map Mp used for self-position estimation, through the combined action of these blocks 100, 110, and 120, is executed according to the control flow shown in Figure 4. This control flow is executed repeatedly while the autonomous driving device 3 is running. In this control flow, each "S" represents a step executed by multiple instructions included in the control program.

[0029] In S10, the planning block 100 determines whether the autonomous driving device 3 has reached a specific driving point Pds, which is a set distance in front of the overlap area Ado. At this time, the autonomous driving device 3's position is estimated by map matching between the stored data of the point cloud map Mp in the map database 10a and the point cloud data Dp observed from the LiDAR 50a of the autonomous driving device 3. In particular, the point cloud map Mp used for estimating the autonomous driving device's position is the source map Mps, out of the source map Mps and destination map Mpt that are switched via the overlap area Ado. Furthermore, at least one additional type of sensing information other than the point cloud map Mp from the LiDAR 50a and communication information may also be used for estimating the autonomous driving device's position.

[0030] If a negative determination is made in S10, the current execution of the control flow ends. On the other hand, if a positive determination is made in S10, the control flow moves to S20. In S20, the planning block 100 plans multiple possible route candidates Rc for autonomous driving of the autonomous driving device 3 in the overlap area Ado where the source map Mps and the destination map Mpt overlap. At this time, in the overlap area Ado shown in Figure 5, one route candidate Rc with the shortest possible route length (see the dashed line in Figure 5) is planned, along with at least one route candidate Rc with a route length longer than the shortest (see the dashed and double-dotted lines in Figure 5).

[0031] Here, the planable route length in S20 is determined by comparing the entry point Pdi into the driving area Ad of the destination map Mpt in the overlap area Ado shown in Figure 5, with the exit point Pdo from the source map Mps in the same area Ado. Therefore, for each route candidate Rc planned in S20, multiple driving points Pd are assumed, as shown by the black circles in Figure 5, so as to divide the distance from the entry point Pdi to the exit point Pdo into fixed driving distances or fixed driving times. Of these entry points Pdi and exit points Pdo, it is preferable that driving points Pd that coincide with the exit point Pdo be added to the point cloud information in any point cloud map Mp, for example, as node information of a topological graph.

[0032] In the control flow shown in Figure 4, in S30 following S20, the selection block 110 selects the route candidate Rc that has the maximum evaluation index Io as the follow route Rcf that the autonomous driving device 3 will follow. At this time, the evaluation index Io is defined as an index that correlates with the observed area Os observed by the autonomous driving device 3 on the road surface at each driving point Pd on each route candidate Rc in the overlap area Ado, as shown in Figure 6. In particular, as the evaluation index Io in the first embodiment, a value that follows the cumulative sum of the observed area Os at each driving point Pd is adopted, as shown in the following equation 1. Thus, in S30, the follow route Rcf that gives the maximum evaluation index Io is selected from among the multiple route candidate Rc planned in S20, as illustrated in Figure 7.

[0033] In the control flow shown in Figure 4, in S40 following S30, the control block 120 performs follow control on the device 3, causing it to follow the follow path Rcf set up in S30, based on its own position estimated by map matching of point cloud data Dp and point cloud map Mp. At this time, as shown in Figure 8, the point cloud data Dp observed and used for map matching is generated sequentially by observations of each scanning point Pd from the LiDAR 50a in the autonomous driving device 3. At the same time, the point cloud map Mp used for map matching with the point cloud data Dp is the source map Mps, which includes the section from the specific driving point Pds to the entry point Pdi and exit point Pdo on the follow path Rcf, in accordance with the self-position estimation in S10. As a result, when the autonomous driving of the autonomous driving device 3 is controlled up to the exit point Pdo on the follow path Rcf, the execution of the control flow ends. Furthermore, in subsequent control flows, until it is confirmed by S10 that the autonomous driving device 3 has reached a specific driving point Pds, the self-position estimation and route planning by the planning block 100 and the route following control by the control block 120 should be performed for the autonomous driving device 3 using the switching destination map Mpt in the usual manner.

[0034] (Effects) The effects of the first embodiment described above are explained below.

[0035] According to the first embodiment, in the overlap area Ado where the source map Mps and the destination map Mpt overlap among multiple point cloud maps Mp, multiple possible route candidates Rc for autonomous driving are planned. Then, in the overlap area Ado, for each driving point Pd on the route candidate Rc, the route candidate Rc that maximizes the correlated evaluation index Io with respect to the observation area Os observed by the autonomous driving device 3 is selected as the follow route Rcf that the autonomous driving device 3 will follow. With this, the self-position can be estimated with high accuracy on the follow route Rcf where the evaluation index Io correlated with the observation area Os is maximized by map matching between the point cloud data Dp observed from the autonomous driving device 3 and the point cloud map Mp. Therefore, in follow control that makes the self-position follow the follow route Rcf, it is possible to ensure the control accuracy of the autonomous driving device 3 for autonomous driving.

[0036] According to the first embodiment, in the overlap area Ado, along with the shortest possible route candidate Rc between the entry point Pdi to the destination map Mpt and the exit point Pdo from the source map Mps, a route candidate Rc with a longer route length than the shortest possible route length is planned. As a result, even a long route candidate Rc, which is generally not given priority, can be preferentially selected as a follow route Rcf with high accuracy in estimating the self-position if the evaluation index Io, which correlates with the observed area Os at each travel point Pd, is maximized. Therefore, it becomes possible to ensure the control accuracy of the autonomous driving device 3 for autonomous driving in the control of following the self-position to the follow route Rcf.

[0037] According to the first embodiment, in the overlap area Ado where the outdoor and indoor point cloud maps Mp, which require switching, overlap, the tracking path Rcf that maximizes the evaluation index Io, which correlates with the observed area Os at each driving point Pd, can be selected. Therefore, in tracking control from one of the outdoor and indoor areas, which is the source of the switching, to the other, which is the destination, the control accuracy for the autonomous driving of the autonomous driving device 3 can be ensured by basing the control on the self-position estimated with high accuracy on the selected tracking path Rcf, thereby enabling smooth switching of point cloud maps Mp between indoors and outdoors.

[0038] According to the first embodiment, the candidate route Rc that maximizes the evaluation index Io, which follows the cumulative sum of the observed area Os at each travel point Pd, is selected as the follow route Rcf. With this, the self-position determined by map matching can be estimated with high accuracy on the follow route Rcf where the simple evaluation index Io, which follows the cumulative sum of the observed area Os, is maximized. Therefore, in follow control of the follow route Rcf, it becomes possible to quickly ensure the control accuracy of the autonomous driving device 3 for autonomous driving with a low processing load.

[0039] (Second Embodiment) The second embodiment is a modification of the first embodiment. As shown in Figure 9, in the control flow of the second embodiment, S230 is executed instead of S30 in order for the selection block 110 to select the route candidate Rc that maximizes the evaluation index Io correlated with the observed area Os at each travel point Pd as the follow route Rcf. The evaluation index Io of the second embodiment adopted in S230 is a value that follows the weighted sum of the observed area Os at each travel point Pd, as shown in the following equation 2.

[0040] Specifically, in the weighted sum that assigns the evaluation index Io according to Equation 2, the weight Wx of the observed area Os in Equation 2 decreases as the number of dynamic obstacles 9x within the observed area Os, such as people as shown in Figure 10, increases in the overlap area Ado, for example. At the same time, in the weighted sum that assigns the evaluation index Io according to Equation 2, the weight Wy of the observed area Os in Equation 2 increases as the number of static obstacles 9y within the observed area Os, such as structures as shown in Figure 11, increases in the overlap area Ado, for example. Furthermore, in the weighted sum that assigns the evaluation index Io according to Equation 2, the weight Wz of the observed area Os in Equation 2 increases as the driving point Pd approaches the exit point Pdo from the source map Mps (see Figure 5 of the first embodiment) in the overlap area Ado.

[0041] Thus, in S40 following S230, the autonomous driving device 3 performs follow-up control to make the vehicle follow the follow-up path Rcf where the evaluation index Io according to the weighted sum of the observation areas Os for each driving point Pd is maximized. As described above, in the second embodiment as well, when the autonomous driving of the autonomous driving device 3 is controlled up to the escape point Pdo on the follow-up path Rcf, the current execution of the control flow ends.

[0042] According to the second embodiment described so far, the path candidate Rc where the evaluation index Io according to the weighted sum with the weight Wx of the observation area Os reduced is maximized for the driving point Pd where the number of dynamic obstacles 9x within the observation area Os increases in the overlap area Ado is selected as the follow-up path Rcf. According to this, due to the dynamic obstacle 9x being observed within the observation area Os, the selection of the path candidate Rc passing through the driving point Pd where the estimation accuracy of the self-position by map matching is likely to decrease can be suppressed as the follow-up path Rcf according to the decrease in the evaluation index Io. In other words, it is possible to select the path candidate Rc where the dynamic obstacle 9x is difficult to be observed within the observation area Os for each driving point Pd as the follow-up path Rcf and increase the estimation accuracy of the self-position on the follow-up path Rcf. Therefore, it becomes possible to increase the control accuracy of the autonomous driving of the autonomous driving device 3 in the follow-up control to the follow-up path Rcf according to the estimation accuracy of the self-position.

[0043] According to the second embodiment, the path candidate Rc where the evaluation index Io according to the weighted sum with the weight Wy of the observation area Os enlarged is maximized for the driving point Pd where the number of static obstacles 9y within the observation area Os increases in the overlap area Ado is selected as the follow-up path Rcf. According to this, due to the static obstacle 9y being observed within the observation area Os, the path candidate Rc passing through the driving point Pd where the estimation accuracy of the self-position by map matching can be increased is more likely to be selected as the follow-up path Rcf according to the increase in the evaluation index Io. Therefore, it becomes possible to increase the control accuracy of the autonomous driving of the autonomous driving device 3 in the follow-up control to the follow-up path Rcf according to the estimation accuracy of the self-position.

[0044] According to the second embodiment, the closer the driving point Pd approaches the escape point Pdo from the source map Mps in the overlap area Ado, the path candidate Rc with the maximum evaluation index Io according to the weighted sum that expands the weight Wy of the observation area Os is selected as the following path Rcf. According to this, the closer it approaches the escape point Pdo immediately before switching from the source map Mps to the destination map Mpt, the estimation accuracy of the self-position by map matching can be improved. Therefore, in the follow-up control of the self-position to the following path Rcf, it is possible to improve the control accuracy for the autonomous driving of the autonomous driving device 3, especially in accordance with the switching timing of the point cloud map Mp where the influence of the self-position estimation accuracy is likely to occur.

[0045] (Other Embodiments) Although the above-described plurality of embodiments have been explained, the present disclosure is not construed as being limited to those embodiments, and can be applied to various embodiments and combinations within the scope not departing from the gist of the present disclosure.

[0046] In the modification examples of the first and second embodiments, the dedicated computer constituting the control system 1 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is, for example, at least one type among ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), SOC (System on a Chip), PGA (Programmable Gate Array), and CPLD (Complex Programmable Logic Device). Further, in the modification examples of the first and second embodiments, such a digital circuit may have a memory storing a program.

[0047] In the modified versions of the first and second embodiments, the point cloud map Mp used for map matching in S40 may be the destination map Mpt instead of the source map Mps. In this case, in S10 preceding S40, a specific travel point Pds may be set at the entry point Pdi in the overlap area Ado or at the boundary position on the destination map Mpt side.

[0048] In the modified versions of the first and second embodiments, the point cloud map Mp used for map matching in S40 may include not only the source map Mps but also the destination map Mpt. In this case, in S40, the point cloud information of the source map Mps and the destination map Mpt is merged and used for map matching with the point cloud data Dp.

[0049] In the modified versions of the first and second embodiments, during the autonomous driving control in S40, the replanning of the route candidate Rc by S20 and the reselection of the follow route Rcf by S30 or S230 may be performed at least once, thereby realizing follow control that follows the reselected follow route Rcf. In the modified version of the second embodiment, in the weighted sum that gives the evaluation index Io, one or two of the three types of weights Wx, Wy, and Wz in equation 2 may be set to a value of 0.

[0050] (Addendum) This specification discloses several technical ideas and several combinations thereof, as listed below. The symbols in parentheses in this addendum indicate the correspondence with the specific means described in the embodiments detailed above, and do not limit the technical scope of this disclosure.

[0051] (Technical Concept 1) A control system for controlling an autonomous driving device (3) having a processor (12) that autonomously drives by switching between a plurality of point cloud maps (Mp) used for estimating its own position, wherein the processor is configured to: plan a plurality of possible route candidates (Rc) for autonomous driving in an overlap area (Ado) where the source point cloud map and the destination point cloud map overlap; select the route candidate that has the maximum correlated evaluation index (Io) with respect to the driving area (Os) observed by the autonomous driving device for each driving point (Pd) on the route candidate in the overlap area as a follow route (Rcf) to be followed by the autonomous driving device; and perform follow control on the autonomous driving device to make it follow the follow route, using the self-position estimated by map matching between point cloud data (Dp) observed from the autonomous driving device and the point cloud map.

[0052] (Technical Concept 2) The control system according to Technical Concept 1, wherein the selection of the following path includes selecting the path candidate that maximizes the evaluation index according to the cumulative sum of the observed areas at each of the travel points as the following path.

[0053] (Technical Idea 3) The control system according to Technical Idea 1, wherein the selection of the following path includes selecting the path candidate that maximizes the evaluation index, which follows a weighted sum in which the weight of the observation area is reduced, as the number of dynamic obstacles (9x) within the observation area increases at the travel point in the overlap area.

[0054] (Technical Idea 4) The control system according to Technical Idea 1 or 3, wherein the selection of the following path is to select as the following path candidate the evaluation index that is maximized, which follows a weighted sum in which the weight of the observation area is increased as the number of static obstacles (9y) within the observation area in the overlap area increases at the travel point.

[0055] (Technical Idea 5) The control system according to any one of Technical Ideas 1, 3, or 4, wherein the selection of the following path is to select as the following path candidate the path that has the maximum evaluation index, which follows a weighted sum in which the weight of the observation area is increased as the travel point approaches the exit point (Pdo) from the switching source in the overlap area.

[0056] (Technical Idea 6) A control system according to any one of Technical Ideas 1 to 5, wherein the planning of the candidate paths includes planning the shortest possible path length between the entry point to the switching destination (Pdi) and the exit point from the switching source (Pdo) in the overlap area, as well as planning the candidate paths with a path length longer than the shortest possible path length.

[0057] (Technical Idea 7) A control system according to any one of Technical Ideas 1 to 6, wherein the planning of the candidate paths includes planning the candidate paths in the overlapping area where the outdoor point cloud map, which is one of the switching source and the switching destination, and the indoor point cloud map, which is the other of the switching source and the switching destination, overlap.

[0058] (Technical Concept 8) A control device configured to be mounted on an autonomous driving device (3) that autonomously drives by switching between multiple point cloud maps (Mp) used for estimating its own position, wherein the processor (12) controls the autonomous driving device, the processor is configured to: plan multiple possible paths (Rc) for autonomous driving in an overlap area (Ado) where the source point cloud map and the destination point cloud map overlap; select the path candidate that has the maximum correlated evaluation index (Io) with respect to the driving area (Os) observed by the autonomous driving device for each driving point (Pd) on the path candidate in the overlap area, as a follow path (Rcf) to be followed by the autonomous driving device; and perform follow control on the autonomous driving device to make the autonomous driving device follow the follow path with respect to the self-position estimated by map matching between point cloud data (Dp) observed from the autonomous driving device and the point cloud map.

[0059] (Technical Concept 9) An autonomous driving device comprising a driving drive system (4) for autonomous driving by switching point cloud maps (Mp) used for self-position estimation, and a control device as described in Technical Concept 8.

[0060] Furthermore, the technical concepts 1 to 7 described above may also be understood within the respective technical concepts of the methods and programs.

Claims

1. A control system for controlling an autonomous driving device (3) having a processor (12) that autonomously drives by switching between a plurality of point cloud maps (Mp) used for estimating its own position, wherein the processor is configured to: plan a plurality of possible route candidates (Rc) for autonomous driving in an overlap area (Ado) where the source point cloud map and the destination point cloud map overlap; select the route candidate that has the maximum correlated evaluation index (Io) with respect to the driving area (Os) observed by the autonomous driving device for each driving point (Pd) on the route candidate in the overlap area as a follow route (Rcf) to be followed by the autonomous driving device; and perform follow control on the autonomous driving device to make it follow the follow route, using the self-position estimated by map matching between point cloud data (Dp) observed from the autonomous driving device and the point cloud map.

2. The control system according to claim 1, wherein the selection of the following path includes selecting the path candidate that maximizes the evaluation index according to the cumulative sum of the observed areas at each of the travel points as the following path.

3. The control system according to claim 1, wherein the selection of the following path includes selecting the path candidate that maximizes the evaluation index, which follows a weighted sum in which the weight of the observation area is reduced as the number of dynamic obstacles (9x) within the observation area increases at the travel point in the overlap area.

4. The control system according to claim 1, wherein the selection of the following path includes selecting the path candidate that maximizes the evaluation index, which follows a weighted sum in which the weight of the observed area is increased as the number of static obstacles (9y) within the observed area increases at each travel point in the overlap area.

5. The control system according to claim 1, wherein the selection of the following path includes selecting the path candidate that maximizes the evaluation index, which follows a weighted sum in which the weight of the observation area is increased as the driving point approaches the exit point (Pdo) from the switching source in the overlap area.

6. The control system according to any one of claims 1 to 5, wherein the planning of the candidate paths includes planning the shortest possible path length between the entry point to the switching destination (Pdi) and the exit point from the switching source (Pdo) in the overlap area, as well as planning a path that is longer than the shortest possible path length.

7. The control system according to any one of claims 1 to 5, wherein the planning of the candidate route includes planning the candidate route in the overlapping area where the outdoor point cloud map, as one of the switching source and the switching destination, and the indoor point cloud map, as the other of the switching source and the switching destination, overlap.

8. A control device configured to be mounted on an autonomous driving device (3) for controlling the autonomous driving device (3) which autonomously drives by switching between multiple point cloud maps (Mp) used for estimating its own position, wherein the processor (12) is configured to perform the following actions: plan multiple possible paths (Rc) for autonomous driving in an overlap area (Ado) where the source point cloud map and the destination point cloud map overlap; select the path candidate that has the maximum correlated evaluation index (Io) with respect to the driving area (Os) observed by the autonomous driving device for each driving point (Pd) on the path candidate in the overlap area, as a follow path (Rcf) to be followed by the autonomous driving device; and perform follow control on the autonomous driving device to make the autonomous driving device follow the follow path using the self-position estimated by map matching between point cloud data (Dp) observed from the autonomous driving device and the point cloud map.

9. An autonomous driving device comprising a driving drive system (4) for autonomous driving by switching point cloud maps (Mp) used for self-position estimation, and the control device described in claim 8.

10. A control method executed by a processor (12) to control an autonomous driving device (3) that autonomously drives by switching between multiple point cloud maps (Mp) used for estimating its own position, the control method comprising: planning multiple possible paths (Rc) for autonomous driving in an overlap area (Ado) where the source point cloud map and the destination point cloud map overlap; selecting the path candidate that has the maximum correlated evaluation index (Io) with respect to the observation area (Os) observed by the autonomous driving device for each driving point (Pd) on the path candidate in the overlap area as a follow path (Rcf) to be followed by the autonomous driving device; and performing follow control on the autonomous driving device to make it follow the follow path with respect to its own position estimated by map matching between point cloud data (Dp) observed from the autonomous driving device and the point cloud map.

11. A control program stored in a storage medium (10) for controlling an autonomous driving device (3) that autonomously drives by switching between multiple point cloud maps (Mp) used for estimating its own position, and including instructions for causing a processor (12) to execute said control, the control program including instructions for executing: planning multiple possible paths (Rc) for autonomous driving in an overlap area (Ado) where the source point cloud map and the destination point cloud map overlap; selecting the path candidate that has the maximum correlated evaluation index (Io) with respect to the driving area (Os) observed by the autonomous driving device for each driving point (Pd) on the path candidate in the overlap area as a follow path (Rcf) to be followed by the autonomous driving device; and performing follow control on the autonomous driving device to make the self-position estimated by map matching between point cloud data (Dp) observed from the autonomous driving device and the point cloud map follow the follow path.

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