Autonomous travel control device, autonomous travel control system, and autonomous travel control method

Abstract

The present invention aims at achieving an autonomous travel control device which generates a safety-priority path for avoiding passing through or approaching a dangerous region where contact with another moving object is likely, and travels along the safety-priority path. The present invention has a travel path determination unit for generating a safety-priority path for avoiding passing through or approaching a dangerous region where contact with another moving object is likely, and a travel control unit for performing control to cause the present device to travel according to the safety-priority path generated by the travel path determination unit. The travel path determination unit generates a cost-priority path in a metric map based on a minimum cost path in a topological map, and generates the safety-priority path by modifying the cost-priority path in the metric map to bypass the dangerous region.

Description

Technical Field

[0001] The present disclosure relates to an autonomous driving control device, an autonomous driving control system, and an autonomous driving control method. More specifically, the present disclosure relates to an autonomous driving control device, an autonomous driving control system, and an autonomous driving control method for achieving safe driving of autonomous driving robots and autonomous driving vehicles. Background Art

[0002] The development and use of autonomous robots and vehicles has been rapidly expanding in recent years. For example, unmanned robots transporting goods in warehouses and offices, and autonomous cars driving on roads have been developed and used.

[0003] Autonomous robots and vehicles need to travel safely while avoiding collisions with other robots or vehicles, pedestrians, etc.

[0004] Note that, for example, PTL 1 (JP 2010-055498 A) is known as a conventional technology disclosing a safe traveling technology for an autonomous traveling robot.

[0005] This document discloses a configuration that detects obstacles such as people by using sensors or the like, and travels along a path thus defined to avoid the obstacles based on the detection information, thereby achieving safe travel while preventing collisions with the obstacles.

[0006] However, there is a possibility that a pedestrian may run out from a shadow or a side path, for example. In such a case, the autonomous driving robot's sensors often have difficulty detecting the person in the shadow or side path, making it difficult to prevent a collision in such a case.

[0007] [Citation List]

[0008] [Patent Document]

[0009] [PTL 1]

[0010] JP 2010-055498 A Summary of the Invention

[0011] [Technical Issues]

[0012] For example, the present disclosure has been developed in consideration of the above-mentioned problems. The present disclosure aims to provide an autonomous driving control device, an autonomous driving control system, and an autonomous driving method for achieving safe driving while reducing the possibility of collision with a moving object (such as a person running out of an area that is difficult for the sensors of an autonomous driving robot or vehicle to detect).

[0013] [Solution to the problem]

[0014] A first aspect of the present disclosure is directed to an autonomous travel control device including a travel path determination unit that generates a safety-first path for avoiding passing through or approaching a dangerous region in which contact with another moving object is likely, and a travel control unit that performs control to cause the self device to travel along the safety-first path generated by the travel path determination unit.

[0015] Further, a second aspect of the present disclosure is directed to an autonomous travel control system including an autonomous travel device and a server that transmits a safety-first path to the autonomous travel device, wherein the server generates a safety-first path for avoiding passing through or approaching a dangerous region in which contact between the autonomous travel device and another moving object is likely, and transmits the generated safety-first path information to the autonomous travel device, and the autonomous travel device receives the safety-first path information from the server, and performs control to cause the self device to travel along the received safety-first path.

[0016] Furthermore, a third aspect of the present disclosure is directed to an autonomous travel control method performed by an autonomous travel control device, the autonomous travel control method including: a travel route determination step of generating, by a travel path determination unit, a safety-first path for avoiding passing through or approaching a dangerous region in which contact with another moving object is likely, and a travel control step of performing control, by a travel control unit, to cause the self device to travel along the safety-first path generated by the travel path determination unit.

[0017] Further objects, features and advantages of the present disclosure will become clear from the following more detailed description of embodiments or drawings of the present disclosure. Note that the system in the present description is a logical configuration set of a plurality of devices, and the devices of the respective configurations do not need to be contained in the same housing.

[0018] The configuration according to one embodiment of the present disclosure provides an autonomous travel control device that generates a safety-first path for avoiding passing through or approaching a dangerous region in which contact with another moving object is likely, and travels along the safety-first path.

[0019] Specifically, for example, the autonomous travel control device includes a travel path determination unit that generates a safety-first path for avoiding passing through or approaching a dangerous region in which contact with another moving object is likely, and a travel control unit that performs control to cause the self device to travel along the safety-first path generated by the travel path determination unit. The travel path determination unit generates a cost-first path in a metric map based on a minimum cost path in a topological map, and generates a safety-first path that bypasses the dangerous region by correcting the cost-first path in the metric map.

[0020] The present configuration realizes an autonomous driving control device that generates a safety priority path for avoiding passing through or approaching a dangerous area where there is a possibility of contact with another moving object, and drives along the safety priority path.

[0021] Note that the beneficial effects to be produced are not limited to those described in this description which are presented only by way of example, and additional advantageous effects may be produced. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a diagram explaining the problems caused by autonomous driving control.

[0023] Figure 2 This is a diagram explaining the problems caused by autonomous driving control.

[0024] Figure 3 It is a diagram for explaining the outline of the autonomous driving control of the present disclosure.

[0025] Figure 4 is a diagram depicting a configuration example of the autonomous driving control apparatus of the present disclosure.

[0026] Figure 5 is a diagram explaining a configuration example of an autonomous driving control system.

[0027] Figure 6 is a diagram presenting a flowchart for explaining a processing sequence executed by the autonomous driving control apparatus of the present disclosure.

[0028] Figure 7 It is a diagram explaining the topological map.

[0029] Figure 8 is a diagram explaining a specific example of a metric map.

[0030] Figure 9 It is a diagram explaining a specific example of a generation process for generating a safety-prioritized path.

[0031] Figure 10 is a diagram explaining a processing sequence performed by the autonomous driving control apparatus of the present disclosure and usage data.

[0032] Figure 11 This is a diagram explaining the dynamic map (DM).

[0033] Figure 12 It is a diagram explaining a specific example of a generation process for generating a safety-prioritized path.

[0034] Figure 13 It is a diagram explaining a specific example of a generation process for generating a safety-prioritized path.

[0035] Figure 14is a diagram that explains a specific example of a generation process for generating a safety priority path.

[0036] Figure 15 is a diagram that explains a setting example of a safety priority path corresponding to various dangerous areas.

[0037] Figure 16 is a diagram that explains a setting example of a safety priority path corresponding to various dangerous areas.

[0038] Figure 17 is a diagram that explains a setting example of a safety priority path corresponding to various dangerous areas.

[0039] Figure 18 is a diagram that explains a setting example of a safety priority path corresponding to a dangerous area in a parking lot.

[0040] Figure 19 is a diagram that explains a setting example of a safety priority path corresponding to a dangerous area in a parking lot.

[0041] Figure 20 is a diagram that explains a setting example of a safety priority path corresponding to a dangerous area in a parking lot.

[0042] Figure 21 is a diagram that explains a setting example of a safety priority path corresponding to a dangerous area in a parking lot.

[0043] Figure 22 is a diagram that explains a configuration example of the autonomous travel control device of the present disclosure.

[0044] Figure 23 is a block diagram that depicts a configuration example of a vehicle control system.

[0045] Figure 24 is a diagram that depicts an example of a sensing area.

[0046] Figure 25 is a diagram that depicts an example of display data in a display unit.

[0047] Figure 26 is a diagram that explains a hardware configuration example of the autonomous travel control device and the server of the present disclosure. DETAILED DESCRIPTION

[0048] Details of the autonomous travel control device, the autonomous travel control system, and the autonomous travel control method of the present disclosure will be described below with reference to the accompanying drawings. Note that the description will be given in accordance with the following items.

[0049] 1. Problems that arise in autonomous travel control, and overview of the autonomous travel control processing of the present disclosure

[0050] 2. Configuration example of the autonomous travel control device and autonomous travel control system of the present disclosure

[0051] 3. Process executed by the autonomous travel control device of the present disclosure

[0052] 4. Specific example of the generation process of the safety priority path

[0053] 5. Setting example of the safety priority path corresponding to various dangerous areas

[0054] 6. Setting example of the safety priority path corresponding to dangerous areas in a parking lot

[0055] 7. Configuration example of the autonomous travel control device

[0056] 8. Configuration example of the vehicle control system, and example of the sensing area of the vehicle

[0057] 9. Display data example of the display unit

[0058] 10. Hardware configuration example of the respective devices

[0059] 11. Summary of the configuration of the present disclosure

[0060] [1. Problem occurring in autonomous travel control, and summary of the autonomous travel control process of the present disclosure]

[0061] First, a summary of the problem occurring in autonomous travel control, and the autonomous travel control process of the present disclosure will be described.

[0062] Figure 1 is a drawing depicting an example of travel of the autonomous travel robot 10. A T-shaped intersection is formed in the travel direction (arrow direction) of the autonomous travel robot 10. A person is running from the right.

[0063] In a case where the person continues to run in the state where the autonomous travel robot 10 is continuously traveling, a collision can occur at the intersection part of the T-shaped intersection.

[0064] In many cases, the autonomous travel robot 10 is equipped with sensors such as a camera and a radar. In a case where the sensor detects an obstacle in the travel direction, a control such as emergency stop is executed.

[0065] However, it is difficult to detect a person or an obstacle present in an area blocked by a wall or the like using the sensors such as a camera and a radar equipped on the autonomous travel robot 10.

[0066] Thus, the autonomous travel robot 10 can detect a person running from the road side of the T-shaped intersection by using the sensor only at a position just before the autonomous travel robot 10 enters the T-shaped intersection, as Figure 2depicted in the figure. Even when the autonomous travel robot 10 suddenly stops in response to a person running from the road side of the T-shaped intersection being detected by using a sensor at a position depicted in the figure Figure 2 When the autonomous travel robot 10 suddenly stops in response to a person running from the road side of the T-shaped intersection being detected by using a sensor at a position depicted in the figure

[0067] The present disclosure is configured to prevent the above situation.

[0068] An example of travel control processing performed by the autonomous travel control apparatus of the present disclosure will be described with reference to Figure 3 An example of travel control processing performed by the autonomous travel control apparatus of the present disclosure will be described with reference to

[0069] Figure 3 The autonomous travel robot 20 depicted in the figure is an example of the autonomous travel control apparatus of the present disclosure.

[0070] Based on the map information acquired in advance, the autonomous travel robot 20 acquires in advance information associated with a place (dangerous area) where a danger of collision with a moving object such as a person and another robot or vehicle is highly likely to occur, such as the intersection 21 depicted in the figure, and travels along an optimal travel path, which reduces the possibility of collision with the moving object within this dangerous area.

[0071] Figure 3 The safety-first path 32 is depicted in the figure.

[0072] Figure 3 The safety-first path 32 depicted in the figure is a travel path set at a position away from the intersection 31 corresponding to a place (dangerous area) where a danger of collision between the autonomous travel robot 20 and a moving object such as a person is highly likely to occur.

[0073] Figure 3 The safety-first path 32 depicted in the figure is a safety-first type travel path set so that a central portion of the passage is designated as a travel path at a position away from the intersection 31 corresponding to the dangerous area, and a position away from the intersection 31 is designated as a travel path at a portion near the intersection 31 corresponding to the dangerous area.

[0074] The determination processing for determining this safety-first path is performed in advance. For example, the determination processing is performed before the autonomous travel robot 20 starts traveling. Alternatively, the determination processing is performed before the autonomous travel robot 20 approaches a dangerous area such as the intersection 31, at least immediately before a time when the autonomous travel robot 20 enters a state where it cannot stop at the intersection 31 of the T-shaped intersection.

[0075] The autonomous travel control device of the present disclosure determines a safety-first path in advance, which is a safety-first type travel path for avoiding a dangerous area in which collision with a moving object is highly likely to occur, such as an intersection of a road or a travel path, and performs travel along the determined safety-first path. This processing can reduce the likelihood of collision with a moving object, such as a person and another robot or a car, even in a case where it is difficult to achieve obstacle detection using a sensor.

[0076] Note that the autonomous travel control device of the present disclosure includes not only the autonomous travel robot depicted in Figure 3 FIG. 1, but also all devices that perform autonomous travel processing without requiring a person to perform direct travel control, such as an autonomous driving vehicle.

[0077] [2. Configuration example of autonomous travel control device and autonomous travel control system of the present disclosure]

[0078] Next, a configuration example of the autonomous travel control device and the autonomous travel control system of the present disclosure will be described.

[0079] Figure 4 is a diagram depicting a configuration example of the autonomous travel control device 100 of the present disclosure. Note that Figure 4 the configuration of the autonomous travel control device 100 depicted in FIG. 1 indicates a partial configuration of the autonomous travel control device 100. For example, Figure 4 the autonomous travel control device 100 depicted in FIG. 1 corresponds to a part of the configuration of the autonomous travel robot 20 depicted in Figure 3 FIG. 2.

[0080] As Figure 4 depicted in FIG. 1, the autonomous travel control device 100 includes a travel path determination unit 101 and a travel control unit 102. The travel path determination unit 101 receives input of map data 111, determines a travel path along which the autonomous travel control device 100 travels, and generates travel path data 112.

[0081] For example, the travel path data 112 generated by the travel path determination unit 101 is a safety-first path that avoids a dangerous area, such as the intersection 31 described above with reference to Figure 3 FIG. 3.

[0082] Note that the travel path determination unit 101 can be configured to perform travel path determination processing that takes into account the travel path of another autonomous travel control device. The travel path information associated with the other autonomous travel control device is acquired from an external server or acquired through communication between travel devices.

[0083] The travel control unit 102 receives input of the travel route data 112 generated by the travel route determination unit 101, and performs travel control that causes the autonomous travel control device 100 to travel in accordance with the travel route data 112.

[0084] The travel control unit 102 also performs travel control on the basis of sensor detection information obtained by a camera, a radar, or the like provided on the autonomous travel control device 100.

[0085] In a case where an obstacle is detected on the travel path by sensor detection, travel is implemented not necessarily in accordance with the travel path data 112 generated by the travel route determination unit 101, but by setting an emergency path that includes emergency stop or avoidance of the obstacle.

[0086] Note that a configuration can be adopted in which the map data 111 used to generate the travel path data 112 by the travel route determination unit 101 is stored in a storage unit in the autonomous travel control device 100, or a configuration in which the map data 111 is input from an external server.

[0087] Note that, Figure 1 The autonomous travel control device 100 including the travel route determination unit 101 and the travel control unit 102 depicted in FIG. 1 can be configured so that the travel route determination unit 101 is not provided within the autonomous travel control device 100, but is provided within an external server.

[0088] A configuration example of the autonomous travel control system 120 having such a configuration will be described with reference to Figure 5 FIG. 2.

[0089] Figure 5 Autonomous travel control devices (autonomous travel robots) 100a and 100b, a robot management server 121, and a map information providing server 122 are depicted. These constituent elements are configured to communicate with each other via a network.

[0090] The robot management server 121 performs processing performed by the travel route determination unit 101 described above with reference to Figure 4 FIG. 1, that is, processing for determining a safe travel route that avoids a dangerous area, to generate travel route data.

[0091] The robot management server 121 determines a travel route for each of the autonomous travel control devices (autonomous travel robots) 100a and 100b that can communicate with the robot management server 121 via a network, and transmits travel route data constituted by the determined route to each of the autonomous travel control devices (autonomous travel robots) 100a and 100b.

[0092] Each of the autonomous driving control devices (autonomous driving robots) 100 a , 100 b performs driving processing according to the driving path data received from the robot management server 121 .

[0093] Note that the robot management server 121 retains robot information 125. The robot information 125 includes records of identification information associated with the respective autonomous driving control devices (autonomous driving robots) 100a and 100b, current position information, information indicating determined driving routes, and the like.

[0094] The map information providing server 122 holds map data 127 and provides the map data 127 to the robot management server 121 and the autonomous driving control devices (autonomous driving robots) 100 a and 100 b .

[0095] Incidentally, although the robot management server 121 and the map information providing server 122 are Figure 5 In the network configuration depicted in , the robot management server 121 and the map information providing server 122 are presented as separate servers, but a configuration in which these servers are unified into one server may be adopted. Moreover, each of the robot management server 121 and the map information providing server 122 may be composed of a plurality of servers.

[0096] [3. Processing Executed by the Autonomous Driving Control Device of the Present Disclosure]

[0097] Next, a description will be given of processing performed by the autonomous driving control apparatus of the present disclosure.

[0098] Note that the travel route determination process may be performed by an autonomous travel control device such as an autonomous travel robot, or may be performed by a reference Figure 4 and 5 Describes the external server implementation.

[0099] Hereinafter, an embodiment in which an autonomous driving control device such as an autonomous driving robot performs driving path determination processing will be described as a typical example.

[0100] Figure 6 is a diagram presenting a flowchart explaining a processing sequence executed by the autonomous driving control device 100 of the present disclosure.

[0101] Note that, for example, according to Figure 6 The processing performed by the flowchart presented in FIG. 1 may be performed by a control unit (data processing unit) of the autonomous driving control device 100 (such as an autonomous driving robot) (specifically, refer to FIG. Figure 4The described travel path determination unit 101 or travel control unit 102) is executed in accordance with a program stored in a storage unit of the autonomous travel control device 100. For example, this processing can be executed as program execution processing by a processor (such as a CPU) having a program execution function.

[0102] Note that, Figure 6 Some of the steps in the flow presented in FIG. 10 can also be executed as processing by a server that can communicate with the autonomous travel control device 100.

[0103] The processing of the respective steps in the flow presented in FIG. 10 will be described below. Figure 6

[0104] (Step S101)

[0105] The processing in steps S101 to S104 is processing executed by the travel path determination unit 101 included in the configuration depicted in FIG. 9. Figure 4

[0106] In step S101, the travel path determination unit 101 of the autonomous travel control device 100 first executes determination processing for determining a destination.

[0107] For example, this destination determination processing in step S101 can be executed by input processing input from a user via an input unit of the autonomous travel control device 100. Alternatively, this processing can be processing that causes a communication unit of the autonomous travel control device 100 to receive destination setting information transmitted from an external device or an external server. Thus, this processing can be executed in various processing modes.

[0108] (Step S102)

[0109] In step S102, a path in a topological map to the destination determined in step S101 is then generated.

[0110] A topological map is map information composed of typical points that are nodes (for example, intersections) and edges that are connecting lines between the nodes.

[0111] Figure 7 An example of a topological map is depicted.

[0112] In step S102, a travel path composed of these nodes and edges in the topological map, that is, a path from the current position to the destination, is generated.

[0113] Specifically, path settings of which edges to follow and which nodes to pass through in the range from the node closest to the current position to the node closest to the destination are generated.

[0114] ​​Note that the edge configuration between the acquisition nodes only needs to determine "whether a range extending from the current position to the destination is drivable." Information associated with the coordinates of the nodes and the distance (edge length) between the nodes is unnecessary for determining "whether a range extending from the current position to the destination is drivable."

[0115] Note that in many cases, a plurality of "drivable routes" from the current position to the destination can be set. In other words, a plurality of routes can be set under the assumption that any detour is allowed.

[0116] Generally, a travel route determined using a topological map is a shortest route. The concept of "cost" is set for each node and edge of the topological map to determine this shortest path. To select the shortest path, it is enough to designate a path that results in the minimum sum of the costs of the nodes and edges as the finally determined route.

[0117] Note that various methods can be employed to set the cost. The simplest example is a method of setting the cost based on distance and inclination information. Also, additional elements, such as an element that increases the cost given to a point to be avoided as much as possible, can be used.

[0118] In step S102, Figure 4 The travel path determination unit 101 depicted in FIG. 1 refers to the cost in the topological map to determine a path that reaches the destination earliest from the current position, and generates path information composed of the determined route.

[0119] (Step S103)

[0120] In step S103, the travel path determination unit 101 then determines a cost-priority path in the metric map.

[0121] The travel path determination unit 101 determines the cost-priority path in the metric map while reflecting the path determined in the topological map and generated in step S102 (i.e., the path determined based on the cost).

[0122] For example, the travel path determination unit 101 determines a cost-priority path that results in the minimum cost and corresponds to the shortest path.

[0123] A metric map is a map that reflects actual distances and dimensions. Note that the metric map has various formats depending on the purpose of use.

[0124] For example, a metric map used in the case of a wheeled mobile body is generated by defining a path indicating a travel manner in a drivable region using a two-dimensional map (e.g., a sketch or an aerial view) having information indicating whether the region is drivable.

[0125] Note that the nodes and edges in the topological map are associated with the positions in the metric map and the paths (such as roads and passages).

[0126] For each node in the topological map, coordinates are not necessary. The actual position (coordinates) of the corresponding node can be acquired based on the position in the metric map that corresponds to the position of the node in the topological map.

[0127] Note that the edges in the topological map can be data composed of only the costs and the connection relationship between the nodes. Each edge is associated with a path in the metric map. One edge is a narrow and curved road in the metric map, while another edge corresponds to a wide straight road. These edges are similarly expressed as "edges" in the topological map. However, the costs are set so that, for example, a higher cost is given to a narrow and curved road.

[0128] Various methods can be employed as the path generation method in the metric map. For example, in the case of a metric map used by an autonomous driving vehicle, a plurality of vehicle drivable paths are created in the metric map. For example, drivable paths, such as tracks, are generated.

[0129] Also, the edges in the topological map are set to be associated with the corresponding paths in the metric map for which the corresponding paths have been set.

[0130] An example of a path determination process for determining a path in a metric map will be described with reference to Figure 8 An example of a path determination process for determining a path in a metric map will be described with reference to

[0131] Figure 8 An example of a metric map is depicted. Figure 8 The metric map depicted in FIG. 1 includes a drivable path A, a drivable path B, and a drivable path C. Each drivable path includes a route indicated by a dotted line as a route that an autonomous driving control device 100 (such as an autonomous driving robot) can drive.

[0132] The drivable path A includes a route Ra1 and a route Ra2 as two drivable routes.

[0133] The drivable path B includes a route Rb1 and a route Rb2 as two drivable routes.

[0134] The drivable path C includes a route Rc1, a route Rc2, and a route Rc3 as three drivable routes.

[0135] For example, an example of a process performed in a case where a drivable route from a current position corresponding to a node to a destination corresponding to a node is determined in a topological map will be described.

[0136] In a case where the drivable path B, the drivable path A, and the drivable path C are used for driving from the current position corresponding to the node to the destination corresponding to the node.

[0137] The route Rbl or the route Rb2 can be selected from the travel path B.

[0138] The route Rail or the route Ra2 can be selected from the travel path A.

[0139] The route Rcl, the route Rc2, or the route Rc3 can be selected from the travel path C.

[0140] Each of these routes is associated with a corresponding edge in the topological map. The intersection portions of each travel path are associated with corresponding nodes of the topological map. As described above, a cost is set for each edge and node in the topological map. In step S102, the route that results in the minimum total cost is selected in the topological map.

[0141] In step S103, a route (travel path) corresponding to the route that results in the minimum total cost in the topological map is selected from the metric map.

[0142] For example, the travel path composed of the routes (i.e., the route Rbl of the travel path B, the route Ra2 of the travel path A, and the route Rc2 of the travel path C) depicted in the following Figure 8 is determined as the cost-priority path in the metric map. Specifically, for example, the path having the shortest path is determined as the cost-priority path that results in the minimum cost.

[0143] (Step S104)

[0144] In step S104, the travel path determination unit 101 subsequently performs correction processing for correcting the cost-priority path in the metric map generated in step S103 to a path that avoids passing through or approaching a dangerous area, to generate a safety-priority path in the metric map.

[0145] The cost-priority path generated in step S103 in the metric map is a cost-priority path generated without considering avoiding dangerous areas in the path (i.e., dangerous areas such as the intersection 31 described above with reference to Figure 3 ).

[0146] In step S104, the position information associated with the dangerous area in this cost-priority path is acquired, and the path is corrected to a path that avoids passing through or approaching the dangerous area in the cost-priority path, to generate a safety-priority path.

[0147] A specific processing example of step S104 will be described with reference to Figure 9 .

[0148] Figure 9 The following two path setting examples are depicted.

[0149] (1) Before path correction

[0150] (2) After path correction

[0151] Figure 9 The cost priority path 131 before path correction depicted in (1) is generated so that the path in the topological map is reflected in the path in the metric map in step S103.

[0152] This path is a path that produces the minimum cost.

[0153] However, the cost priority path 131 is a path that passes through a position very close to an intersection corresponding to a dangerous region such as the aforementioned intersection. Figure 9 Thus, traveling using this path can cause a collision with a moving object such as a person or another vehicle at the intersection.

[0154] In step S104, information indicating a dangerous region such as the aforementioned intersection is acquired from the map data 111, and path correction is performed based on the acquired dangerous region information. Specifically, in a case where a dangerous region exists in or near the cost priority path, the path is corrected to a path that is away from the dangerous region.

[0155] Note that the distance between the cost priority path corresponding to the path correction target and the dangerous region is specified in advance.

[0156] For example, path correction is performed in a case where the distance between the cost priority path and the dangerous region is equal to or less than a threshold value specified in advance.

[0157] The travel path determination unit 101 generates, for example, the safety priority path 132 depicted in (2) by the path correction performed in step S104. Figure 9 (2) After path correction

[0158] Figure 9 The safety priority path 132 depicted in (2) is a path that passes around a dangerous region (intersection) in a direction away from the intersection corresponding to the dangerous region.

[0159] The autonomous travel robot or the like travels along the safety priority path 132 that passes around the dangerous region (intersection) in a direction away from the dangerous region (intersection). In this way, it is possible to reduce the possibility of a collision or contact with another moving object at the intersection.

[0160] As described above, in step S104, the travel path determination unit 101 performs correction processing for correcting the cost priority path generated in the metric map in step S103 to a path that avoids passing through or approaching a dangerous region to generate a safety priority path in the metric map.

[0161] In other words, the path is corrected in a manner to avoid a dangerous region in the cost priority path to generate a safety priority path.

[0162] (Step S105)

[0163] The subsequent step S106 is processing executed by Figure 4 the travel control unit 102 depicted in

[0164] In step S105, the travel control unit 102 executes travel control to cause the autonomous travel control device (autonomous travel robot) 100 to travel along the safety priority path determined in step S104 in the metric map by the travel path determination unit 101.

[0165] This travel control allows the autonomous travel control device (autonomous travel robot) 100 to achieve safe travel along a path that avoids dangerous regions such as intersections.

[0166] As described above, the autonomous travel control device 100 of the present disclosure first determines a minimum cost path from the current position to the destination in the topological map, and then generates a cost priority path in the metric map so that the determined minimum cost path is reflected in the metric map.

[0167] Moreover, the autonomous travel control device 100 generates a safety priority path by correcting the cost priority path to a path away from the dangerous region in a case where the dangerous region is located in the cost priority path in the metric map or is in a position close to the cost priority path, and then executes travel control to allow the autonomous travel control device (autonomous travel robot) 100 to travel along the safety priority path.

[0168] Note that, in a case where processing is executed in accordance with the flow presented in Figure 6 Various types of map information are necessary.

[0169] Data used when executing the processing presented in Figure 10 will be described. Figure 6

[0170] The flowchart presented in Figure 10 is a flow similar to the flow presented in Figure 6 . Figure 10 is a chart that also includes information for processing respective steps in the flow.

[0171] In step S102, the travel path determination unit 101 included in the configuration depicted in Figure 4 generates a travel path in the topological map composed of nodes and edges, that is, a cost priority path from the current position to the destination as described above.

[0172] In this processing, the topological map data 111a included in the map data 111 is used.

[0173] As described above, the topological map is map information composed of typical points as nodes (e.g., intersections) and edges as connecting lines between the nodes, and is map data in which cost information is set for each node and edge. The topological map is composed of the data described above with reference to Figure 7

[0174] In step S102, a cost-priority path from the current position to the destination is generated using the topological map data 111a included in the map data 111.

[0175] In subsequent step S103, the travel path determination unit 101 generates a cost-priority path in the metric map as described above.

[0176] Specifically, the travel path determination unit 101 determines a cost-priority path in the metric map so that the determined path (i.e., the path determined based on the cost) in the topological map generated in step S102 is reflected in the metric map.

[0177] In this processing, the metric map data 111b included in the map data 111 is used.

[0178] As described above, the metric map is a map that reflects actual distances and dimensions. The metric map is map data in which reference Figure 8 The map data in which the travelable path is set as described above.

[0179] In step S103, a cost-priority path from the current position to the destination is generated using the metric map data 111b included in the map data 111.

[0180] In subsequent step S104, the travel path determination unit 101 generates a safety-priority path as described above.

[0181] Specifically, the travel path determination unit 101 corrects the cost-priority path in the metric map generated by the travel path determination unit 101 in step S103 to a safety-priority path.

[0182] In this processing, the dangerous area map data 111c included in the map data 111 is used.

[0183] The dangerous area map data 111c is map data in which position information associated with a dangerous area or a dangerous area representative point is recorded in a metric map that reflects actual distances and dimensions.

[0184] ​In step S104, a safety-first path is generated using the hazard area map data 111c contained in the map data 111.

[0185] Note that the hazard area map data 111c can be generated in advance, or, for example, after detecting a travel plan (i.e., a cost-first path in the metric map from the current position to the destination), the hazard area map data 111c can be generated based on the detection results of the hazard areas contained in the travel plan of the autonomous travel control device 100.

[0186] This process is executed by the travel path determination unit 101 of the autonomous travel control device 100. Alternatively, this process can be configured to be executed by the robot management server 121 or the map information providing server 122.

[0187] In this way, various types of map data are used until the safety-first path is generated.

[0188] Note that, for example, these map data can be acquired from the dynamic map provided by the map information providing server 122.

[0189] Reference will be made to Figure 11 A dynamic map (DM) will be described.

[0190] As Figure 11 depicted in

[0191] Type 1 = Static data

[0192] Type 2 = Semi-static data

[0193] Type 3 = Semi-dynamic data

[0194] Type 4 = Dynamic data

[0195] Type 1 = Static data is composed of, for example, data such as map information based on map generation by the Japan Geospatial Information Authority, which is updated for each medium-long term.

[0196] Type 2 = Semi-static data is composed of, for example, data that does not change much in the short term but changes in the long term, such as building structures of buildings, trees, and traffic signs, etc.

[0197] Type 3 = Semi-dynamic data is composed of data that can change for each fixed time unit, such as traffic lights, traffic congestion, and accidents.

[0198] Type 4 = Dynamic data is composed of data that is sequentially variable, such as information associated with the coming and going of vehicles, people, etc.

[0199] For example, a dynamic map (DM) composed of these data is transmitted from the map information providing server 122 to the corresponding autonomous driving control device 100 and the robot management server 121. The corresponding autonomous driving control device 100 and the robot management server 121 are thus able to analyze the dynamic map (DM) and use the dynamic map (DM) to perform autonomous driving control, such as setting a driving path and controlling driving speed and lane.

[0200] Note that the map information providing server 122 continuously performs a dynamic map (DM) update process based on the latest information. When using DM, the corresponding autonomous driving control device 100 and the robot management server 121 acquire and use the latest information from the map information providing server 122.

[0201] [4. Specific Example of Safety Priority Path Generation Process]

[0202] Next, we will describe the method for generating Figure 4 A specific example of the generation process of the safety priority path generated by the driving path determination unit 101 of the autonomous driving control device 100 is depicted in FIG.

[0203] Figure 12 It is a diagram explaining a specific example of a generation process for generating a safety-prioritized path.

[0204] As depicted in the figure, it is assumed that the autonomous driving control device 100 travels on a driving path having a width L.

[0205] There is a T-junction in the middle of the driving path. People or vehicles may come out from the left side of the T-junction.

[0206] Assuming that the above Figure 6 The processing in step S103 in sets the cost priority path 131.

[0207] The travel route determination unit 101 performs a correction process of correcting the cost-prioritized path 131 , which has been set as a path that avoids passing through or approaching a dangerous area, to generate a safety-prioritized path 132 .

[0208] A generation sequence for generating the safety priority path 132 will be described.

[0209] For example, the generation process for generating the safety priority path 132 is based on Figure 12 Steps S01 to S05 presented on the right are executed.

[0210] These processing steps will be described.

[0211] (Step S201)

[0212] Acquisition or setting of a danger zone representative point A

[0213] In step S201, the travel path determination unit 101 first executes acquisition or setting processing for acquiring or setting a danger zone representative point A. Position information associated with this danger zone representative point can be acquired from the danger zone map data 111c included in the map data 111 described above with reference to FIG. 1. Figure 10

[0214] The danger zone representative point A is determined in accordance with a pattern of the danger zone, such as a center position of the danger zone, and a center point of an intersection position of two travel paths.

[0215] Further, as described above, the danger zone map data 111c can be generated in advance, or for example, after a travel plan (i.e., a cost-priority path in the metric map from the current position to the destination) is determined, the danger zone map data 111c can be generated based on a detection result of the danger zone included in the travel plan of the autonomous travel control device 100.

[0216] In this case, the travel path determination unit 101 executes generation processing for generating the danger zone map data 111c in step S201, and then executes setting processing for setting the danger zone representative point A for each danger zone included in the generated danger zone map data 111c.

[0217] The danger zone representative point A is set in accordance with a pre-specified algorithm, such as a center position of the danger zone, and a center point of an intersection position of two travel paths.

[0218] (Step S202)

[0219] In step S202, the travel path determination unit 101 then determines two vectors AB and AC that form an opening angle φ that expands in a travel direction of the autonomous travel control device 100 from the danger zone representative point A.

[0220] (Step S203)

[0221] In step S203, the travel path determination unit 101 then calculates a distance d between the travel path end and a path that is separated by a maximum length from the danger zone representative point A and that is travelable by the autonomous travel control device 100.

[0222] The path that is separated by the maximum length from the danger zone representative point A corresponds to a path indicated by a line from B1 to C1.

[0223] The distance d needs to be determined in a manner that does not protrude from the travel path having a width R.

[0224] ​For example, assume that the autonomous travel control device 100 has a width W, and the distance d is determined so as to satisfy the following formula.

[0225] d ≥ (W / 2)

[0226] For example, preferably, the distance d is determined so as to satisfy the following formula in a state in which a predetermined margin a has been provided.

[0227] d = (W / 2) + a

[0228] Various other types of patterns can be employed as the pattern of the determination processing for determining the distance d from the end of the travel path. For example, a configuration for determining the distance d so as to satisfy the following formula based on the road width L.

[0229] d < (L / 2)

[0230] Further, in a case where attributes of a moving object that can run into the travel path of the autonomous travel control device 100, such as attributes of another mobile device, a vehicle, a human, and a child, are obtainable, for example, the separation distance from the danger zone representative point A to the safety priority path can be controlled in a manner that is changeable in accordance with the attributes of the moving object.

[0231] For example, in a case where the moving object that can run into the travel path of the autonomous travel control device 100 is likely to be a child, the separation distance from the danger zone representative point A to the safety priority path is set to be longer.

[0232] (Step S204)

[0233] In step S204, the travel path determination unit 101 then determines that the intersection of the vector AB with the line at a distance d from the end of the travel path is B1, and the intersection of the vector AC with the line at a distance d from the end of the travel path is C1.

[0234] In this way, each of the point B1 on the vector AB and the point C1 on the vector AC is determined as depicted in the figure.

[0235] (Step S205)

[0236] In step S205, the travel path determination unit 101 finally generates the safety priority path by smoothly connecting the straight line B1-C1 with the original cost priority path.

[0237] Further, as described above, the travel path determination unit 101 performs, in step S202, the processing of determining the two vectors AB, AC that expand the opening angle φ from the danger zone representative point A in the travel direction of the autonomous travel control device 100.

[0238] The angle φ formed by the two vectors AB and AC at this time can be set using a pre-designated value, or can be set to be variable according to the degree of danger of each danger zone.

[0239] Note that this angle data can be recorded in the map data (i.e., the reference Figure 10 described danger zone map data) in association with the danger zone.

[0240] For example, in a danger zone in which a child is highly likely to run out, the angle φ is set to a large angle.

[0241] Also, the setting of the angle φ can be changed according to the road width. For example, in the case of a small road width, the angle φ is set to a large value.

[0242] Furthermore, a configuration can be employed in which the angle φ (°) is calculated using a pre-designated angle calculation formula according to the danger level x, the road width L, the distance y to the next danger zone, and the like. For example, the following formula can be used.

[0243] φ = 90° + x + (α / L) + (β x y)

[0244] Note that in the above equation, the following is assumed.

[0245] x: danger level index value

[0246] L: road width

[0247] y: distance to next danger zone

[0248] Note that, Figure 12 The generation sequence for generating the safety-first path in the

[0249] Figure 13 An example of the configuration of the safety-first path in the case in which a plurality of T-shaped intersections are deployed in succession is depicted.

[0250] As Figure 13 depicted in the

[0251] According to the example depicted in the Figure 13 , a complete travel path is formed for linearly moving while maintaining the distance d from the right end region of the travel path until passing through both T-shaped intersections without returning to the original cost-first path.

[0252] Moreover, in the case of an L-shaped intersection instead of a T-shaped intersection, the safety travel path generation processing depicted in Figure 14

[0253] Figure 14 An example of an L-shaped intersection that curves to the left in the travel direction of the autonomous travel control device 100 is depicted. The autonomous travel control device (autonomous travel robot) 100 advances in the direction indicated by the black arrow depicted in the figure, and turns to the left at the L-shaped intersection.

[0254] Figure 14 The following three graphs are presented.

[0255] (1) Safety-first path before left turn

[0256] (2) Safety-first path after left turn

[0257] (3) Safety-first path before and after left turn

[0258] Initially, the travel path determination unit 101 generates the "(1) safety-first path before left turn" and the "(2) safety-first path after left turn", respectively.

[0259] In generating the "(1) safety-first path before left turn", the dangerous region representative point A is acquired from the map data, or determined based on the map data in the entry center portion of the travel path before left turn. Moreover, two vectors AB and AC that form an opening angle φ on the travel path side before left turn are set. Furthermore, the intersection points B1 and C1 of the two vectors AB and AC with a line at a distance d from the right end surface of the travel path before left turn are set, respectively.

[0260] A straight line connecting the intersection points B1 and C1 is set. The intersection point B1 is smoothly connected to the cost-first path 131 set in advance, to generate the safety-first path 132-1.

[0261] In generating the "(2) safety-first path after left turn", the dangerous region representative point A is acquired from the map data, or determined based on the map data in the entry center portion of the travel path before left turn. Moreover, two vectors AB and AC that form an opening angle φ on the travel path side after left turn are set. Furthermore, the intersection points B1 and C1 of the two vectors AB and AC with a line at a distance d from the right end surface of the travel path after left turn are set, respectively.

[0262] A straight line connecting the intersection points B1 and C1 is set. The intersection point B1 is smoothly connected to the cost-first path 131 set in advance, to generate the safety-first path 132-2.

[0263] ​"(3) the safe-priority path before and after the left turn" is generated by connecting the safe-priority path before the left turn 132-1 and the safe-priority path after the left turn 132-2.

[0264] The safe-priority path before and after the left turn 132-3 depicted in the lower part is generated by this connection processing. Figure 14

[0265] The autonomous travel control device 100 travels along the safe-priority path before and after the left turn 132-3 thus generated as depicted in the lower part of FIG. 13. Figure 14 This travel processing can reduce the possibility of collision with an oncoming vehicle from the travel path after the left turn, which is difficult to be detected by the sensor before the left turn.

[0266] Further, this travel processing can also reduce the possibility of collision with an overtaking vehicle from the travel path before the left turn, which is difficult to be detected by the sensor after the left turn.

[0267] Note that, Figure 14 The connecting portion of the safe-priority path before and after the left turn 132-3 depicted in the lower part of FIG. 13 can have a right-angle configuration as depicted in the figure, or can have a smoothly curved configuration.

[0268] [5. Setting example of safe-priority path corresponding to various dangerous regions]

[0269] Setting examples of safe-priority paths corresponding to various dangerous regions will be described below.

[0270] Dangerous regions exist not only in the travel path of the autonomous travel control device 100, but also in various other locations. Described below are setting examples of safe-priority paths corresponding to various different types of dangerous regions.

[0271] Figure 15 One example is a case where the entrance of a park is designated as a dangerous region. In Figure 15 A dangerous region representative point A is set at the entrance of the park in FIG. 14.

[0272] Further, as described above, the position information associated with the dangerous region and the dangerous region representative point A is recorded in the dangerous region map data 111c corresponding to the configuration information included in the map data 111.

[0273] The dangerous region map data 111c is map data in which the position information associated with the dangerous region or the dangerous region representative point A is recorded in a metric map reflecting actual distances and dimensions.

[0274] ​The travel path determination unit 101 acquires a dangerous region representative point A of a center portion of an entrance of a park from map data, or determines the dangerous region representative point A based on the map data. Also, two vectors AB and AC that form an opening angle φ on the side of the travel path are set. Further, in this example, a travel allowable range of the travel path is set respectively, or intersection points B1 and C1 of the two vectors AB and AC and a line at a distance d from the median strip.

[0275] A straight line connecting the intersection points B1 and C1 is set. The straight line B1-C1 is smoothly connected to the cost priority path 131 set in advance to generate the safety priority path 132.

[0276] For example, the autonomous travel control apparatus (autonomous driving vehicle) 100 depicted in the figure travels along the safety priority path 132 depicted in the figure. Such travel control allows the autonomous travel control apparatus (autonomous driving vehicle) 100 to achieve travel that reduces the possibility of collision with a child or the like running out from the park. Figure 15

[0277] Figure 16 An example in which an entrance of a building (such as an office building) is designated as a dangerous region is described. A dangerous region representative point A is set at the entrance of the building in the figure. Figure 16

[0278] The travel path determination unit 101 acquires a dangerous region representative point A of a center portion of an entrance of a building from map data, or determines the dangerous region representative point A based on the map data. Also, two vectors AB and AC that form an opening angle φ on the side of the travel path are set. Further, in this example, a travel allowable range of the travel path is set respectively, or intersection points B1 and C1 of the two vectors AB and AC and a line at a distance d from the median strip.

[0279] A straight line connecting the intersection points B1 and C1 is set. The straight line B1-C1 is smoothly connected to the cost priority path 131 set in advance to generate the safety priority path 132.

[0280] For example, the autonomous travel control apparatus (autonomous driving vehicle) 100 depicted in the figure travels along the safety priority path 132 depicted in the figure. Such travel control allows the autonomous travel control apparatus (autonomous driving vehicle) 100 to achieve travel that reduces the possibility of collision with a child or the like running out from the park. Figure 16

[0281] For example, Figure 17 An example in which an autonomous travel robot travels autonomously in an office building is described.

[0282] The autonomous travel control apparatus (autonomous travel robot) 100 travels on a corridor in the office building as a travel path. ​​​

[0283] In Figure 17 A dangerous area representative point A is set at the entrance of an office in an office building.

[0284] The travel path determination unit 101 acquires the dangerous area representative point A of the center portion of the entrance of the office from the map data, or determines the dangerous area representative point A based on the map data. Also, two vectors AB and AC that form an opening angle φ on the travel path (corridor) side are set. Further, in this example, the travel allowable range of the travel path is set respectively, or the intersection points B1 and C1 of the two vectors AB and AC and a line at a distance d from the right wall of the corridor.

[0285] A straight line connecting the intersection points B1 and C1 is set. The straight line B1-C1 is smoothly connected to the cost priority path 131 set in advance to generate the safety priority path 132.

[0286] For example, the autonomous travel control device (autonomous travel robot) 100 depicted in the figure travels along Figure 17 the safety priority path 132 depicted in the figure. This travel control allows the autonomous travel control device (autonomous travel robot) 100 to achieve travel that reduces the possibility of collision with a plurality of people, etc. who run from the office to the corridor.

[0287] Note that the map data associated with the path of the office building, etc. is generated in advance.

[0288] The map data 111 containing the topological map data 111a, the metric map data 111b, and the dangerous area map data 111c described above with reference to Figure 10 is generated before the execution of the process.

[0289] A configuration in which these data are stored in a storage unit inside the autonomous travel control device (autonomous travel robot) 100, or a configuration in which these data are held in the robot management server 121 or the map information providing server 122 described above with reference to Figure 5 may be employed.

[0290] Alternatively, a configuration in which the corresponding data are held in another building management server and the corresponding data are provided to the autonomous travel control device (autonomous travel robot) 100 can also be employed.

[0291] Instead, the building management server can determine the safety priority path based on the map data, and provide the determined safety priority path to the autonomous travel control device (autonomous travel robot) 100.

[0292] [6. Example of setting of safety priority path corresponding to dangerous area in parking lot]

[0293] Next, an example of setting of a safety priority path corresponding to a dangerous area in a parking lot will be described.

[0294] For example, many people and vehicles enter and exit a parking lot of a shopping center or the like. Thus, there are many dangerous points in such a parking lot. In the case where an autonomous traveling vehicle travels in such a parking lot, by setting a safety priority path that avoids passing through and approaching the above-described dangerous area and allowing the autonomous traveling vehicle to travel on this path, contact with a person, another vehicle, or other things, or the like can be effectively avoided.

[0295] Next, an example of setting of a safety priority path corresponding to a dangerous area set in a parking lot will be described.

[0296] Figure 18 is a diagram that depicts a configuration of a typical parking lot and an example of a cost priority path 131 in the parking lot.

[0297] For example, the autonomous traveling control device (autonomous driving vehicle) 100 enters a parking lot through an entrance, travels along a cost priority path 131 in the parking lot, and parks the vehicle in an empty parking space that allows parking.

[0298] However, a parking lot includes an entrance for vehicles, an entrance for people, a building entrance door, a disabled parking space, and the like, as depicted in the diagram. The blocks near these areas correspond to dangerous areas where contact with a person and a vehicle is likely to occur.

[0299] Thus, it is preferable for the autonomous traveling control device (autonomous driving vehicle) 100 to travel along a safety priority path that avoids passing through and approaching these dangerous areas.

[0300] An example of setting of a safety priority path will be described with reference to Figure 19 and the following diagrams.

[0301] Figure 19 In the example in FIG. 1, a dangerous area representative point A is set near the entrance for vehicles.

[0302] Further, as described above, the position information associated with the dangerous area and the dangerous area representative point A is recorded in the dangerous area map data 111c corresponding to the configuration information included in the map data 111.

[0303] In the case of this example, a parking lot management server that manages the parking lot can be configured to retain the map data 111.

[0304] The map data 111 contains the topological map data 111a, the metric map data 111b, and the dangerous area map data 111c described above with reference to Figure 10 FIG. 1.

[0305] The travel path determination unit 101 of the autonomous travel control device (autonomous vehicle) 100 acquires or determines a dangerous area representative point A near an entrance for a vehicle from map data. Also, two vectors AB and AC that form an opening angle φ on the travel path side are set. In this example, the intersection points Bl and Cl of the travel permission range of the travel path or the two vectors AB and AC and a line at a distance d from the flower bed are set, respectively.

[0306] A straight line connecting the intersection points Bl and Cl is set. The straight line Bl-C1 is smoothly connected to the cost priority path 131 set in advance to generate the safety priority path 132.

[0307] For example, the autonomous travel control device (autonomous vehicle) 100 depicted in the figure travels along the safety priority path 132 depicted in the figure. This travel processing allows the autonomous travel control device (autonomous vehicle) 100 to achieve safe travel, which reduces the possibility of contact or collision with another vehicle near the entrance. Figure 19

[0308] Figure 20 An example is depicted in which dangerous area representative points Al to A5 are set near a passage for people between the flower bed and the entrance door of the building.

[0309] The safety priority path 132 is generated by performing processing such as vector setting on each point.

[0310] The autonomous travel control device (autonomous vehicle) 100 travels along the safety priority path 132 depicted in the figure. This travel processing allows the autonomous travel control device (autonomous vehicle) 100 to achieve travel that reduces the possibility of contact or collision with a person near the passage for people between the flower bed and the entrance door of the building. Figure 20

[0311] An example is depicted in which dangerous area representative points Al to A2 are set near a disabled person parking space. Figure 21 The safety priority path 132 is generated by performing processing such as vector setting on each point.

[0312] The autonomous travel control device (autonomous vehicle) 100 travels along the safety priority path 132 depicted in the figure. This travel processing allows the autonomous travel control device (autonomous vehicle) 100 to achieve travel that reduces the possibility of contact or collision with a vehicle leaving the disabled person parking space.

[0313] Figure 21 [7. Configuration example of autonomous travel control device]

[0314] [7. Configuration example of autonomous travel control device] ​​

[0315] Next, a configuration example of the autonomous travel control apparatus of the present disclosure will be described.

[0316] Figure 22 is a block diagram depicting a configuration example of the autonomous travel control apparatus 100 of the present disclosure, such as an autonomous travel robot and an autonomous driving vehicle.

[0317] As Figure 22 depicted in

[0318] The control unit 151 controls processing performed by the autonomous travel control apparatus 100. For example, the control unit 151 executes processing in accordance with a control program stored in the storage unit 157. The control unit 151 includes a processor having a program execution function.

[0319] Note that each of the travel path determination unit 101 and the travel control unit 102 described with reference to Figure 4 corresponds to a constituent element of the control unit 151. For example, processing performed by the travel path determination unit 101 and the travel control unit 102 can be executed by the control unit 151 in accordance with a program stored in the storage unit 157.

[0320] The input unit 152 is an interface that allows various data to be input from a user, and is constituted by a touch panel, a code reading unit, various switches, and the like.

[0321] The output unit 153 is an output unit constituted by a speaker for outputting an alarm and a voice, a display for outputting an image, and a unit for outputting light and the like.

[0322] The sensor group 154 is constituted by various types of sensors such as a camera, a microphone, a radar, and a distance sensor.

[0323] The drive unit 155 is constituted by a wheel drive unit for moving the autonomous travel control apparatus, a direction control mechanism, and the like.

[0324] For example, the communication unit 156 performs communication processing for communicating with external devices and the like such as a robot management server, a map information providing server, and a building management server.

[0325] The storage unit 157 stores a program executed by the control unit 151 and others such as robot information and transportation equipment information.

[0326] [8. Configuration example of vehicle control system, and example of sensing area of vehicle]

[0327] Figure 23 is a block diagram depicting a configuration example of a vehicle control system 211 that is an example of a mobile device control system to which the present technology is applied.

[0328] The vehicle control system 211 is provided on the vehicle 200, and performs processing associated with travel assistance and autonomous driving of the vehicle 200.

[0329] The vehicle control system 211 includes a vehicle control ECU (Electronic Control Unit) 221, a communication unit 222, a map information accumulation unit 223, a position information acquisition unit 224, an external recognition sensor 225, a vehicle interior sensor 226, a vehicle sensor 227, a recording unit 228, a travel assistance and autonomous driving control unit 229, a DMS (Driver Monitoring System) 230, an HMI (Human Machine Interface) 231, and a control unit 232.

[0330] The vehicle control ECU 221, the communication unit 222, the map information accumulation unit 223, the position information acquisition unit 224, the external recognition sensor 225, the vehicle interior sensor 226, the vehicle sensor 227, the recording unit 228, the travel assistance and autonomous driving control unit 229, the driver monitoring system (DMS) 230, the human machine interface (HMI) 231, and the control unit 232 are communicatively connected to each other via a communication network 241. For example, the communication network 241 is constituted by an in-vehicle communication network, a bus, or the like that conforms to a digital bidirectional communication standard, such as CAN (Controller Area Network), LIN (Local Interconnect Network), LAN (Local Area Network), FlexRay (registered trademark), and Ethernet (registered trademark). The communication network 241 to be used can be selected in accordance with the type of data handled by communication. For example, CAN is suitable for communication of data associated with vehicle control, while Ethernet is suitable for communication of large-volume data. Further, there are also cases in which the respective units of the vehicle control system 211 are directly connected to each other by wireless communication without using the communication network 241 when assuming relatively close-distance communication, such as Near Field Communication (NFC) and Bluetooth (registered trademark).

[0331] Also, in cases in which communication is performed between the respective units of the vehicle control system 211 via the communication network 241, the description of the communication network 241 will be omitted hereinafter. For example, in cases in which communication is performed between the vehicle control ECU 221 and the communication unit 222 via the communication network 241, only the communication between the processor and the communication unit 222 will be described.

[0332] For example, the vehicle control ECU 221 is constituted by any one of various types of processors such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit). The vehicle control ECU 221 controls all or part of the functions of the vehicle control system 211.

[0333] The communication unit 222 communicates with various types of devices inside or outside the vehicle, other vehicles, servers, base stations, and the like, and transmits and receives various types of data. For such transmission and reception, the communication unit 222 is allowed to communicate using a plurality of communication methods.

[0334] A summary of the communication that the communication unit 222 can perform for communication with the outside of the vehicle will be described. For example, the communication unit 222 communicates with a server (hereinafter referred to as an external server) existing in an external network via a base station or an access point using a wireless communication method such as 5G (5th Generation mobile communication system), LTE (Long Term Evolution), and DSRC (Dedicated Short Range Communications). For example, the external network with which the communication unit 222 communicates is the Internet, a cloud network, or a network unique to a provider. The communication method used by the communication unit 222 for communication with the external network is not limited to a particular method, but can be any wireless communication method capable of achieving digital bidirectional communication at a predetermined communication speed or higher and at a predetermined distance or longer.

[0335] Also, for example, the communication unit 222 is capable of communicating with a terminal existing in the vicinity of the own vehicle using a P2P (Point to Point) technique. For example, the terminal existing in the vicinity of the host vehicle is a terminal attached to a moving body (such as a pedestrian and a bicycle) moving at a relatively low speed, a terminal installed at a fixed position (such as a store), or an MTC (Machine Type Communications) terminal. Also, the communication unit 222 also achieves V2X communication. For example, V2X communication refers to communication of the host vehicle with other things, such as Vehicle to Vehicle communication with other vehicles, Vehicle to Infrastructure communication with roadside devices, Vehicle to Home communication with a home, and Vehicle to Pedestrian communication with a terminal carried by a pedestrian, and the like.

[0336] For example, the communication unit 222 can receive a program (over the air) for updating software that controls the operation of the vehicle control system 211 from the outside. The communication unit 222 can also receive map information, traffic information, information associated with the surroundings of the vehicle 200, and the like from the outside. Also, for example, the communication unit 222 can transmit information associated with the vehicle 200, information associated with the surroundings of the vehicle 200, and the like to the outside. For example, the information associated with the vehicle 200 and transmitted from the communication unit 222 to the outside includes data indicating the state of the vehicle 200 and the recognition result obtained by the recognition unit 273. Also, for example, the communication unit 222 establishes communication corresponding to a vehicle emergency system such as an electronic call.

[0337] A summary of communication that the communication unit 222 can perform for communication within the vehicle will be described. For example, the communication unit 222 can communicate with a corresponding device in the vehicle using wireless communication. The communication unit 222 can communicate with a device in the vehicle by wireless communication such as wireless LAN, Bluetooth, NFC, WUSB (Wireless USB) at a predetermined communication speed or higher communication speed by using a communication method capable of realizing digital bidirectional communication. The communication of the communication unit 222 is not limited to this type of communication. The communication unit 222 can communicate with a corresponding device in the vehicle using wired communication. For example, the communication unit 222 can communicate with a corresponding device in the vehicle by wired communication using a cable connected to a connection terminal not shown. For example, the communication unit 222 can communicate with a corresponding device in the vehicle using a communication method capable of realizing digital bidirectional communication at a predetermined communication speed or higher communication speed by wired communication such as USB (Universal Serial Bus), HDMI (registered trademark) (High-Definition Multimedia Interface), and MHL (Mobile High-definition Link).

[0338] For example, the device in the vehicle herein refers to a device that is not connected to the communication network 241 in the vehicle. For example, it is assumed that the device in the vehicle includes a mobile device or a wearable device carried by an occupant of the vehicle such as a driver, and an information device loaded into the vehicle and temporarily installed.

[0339] For example, the communication unit 222 receives an electromagnetic wave such as a radio beacon, an optical beacon, and an FM multiplex broadcast transmitted from a vehicle information and communication system (VICS (registered trademark) (Vehicle Information and Communication System)).

[0340] The map information accumulation unit 223 accumulates either or both of a map acquired from the outside and a map created by the vehicle 200. For example, the map information accumulation unit 223 accumulates a three-dimensional high-accuracy map and a global map that is less accurate than the high-accuracy map and covers a wide area.

[0341] For example, the high-accuracy map is a dynamic map, a point cloud map, or a vector map. For example, the dynamic map is a map including a four-layer map composed of dynamic information, semi-dynamic information, semi-static information, and static information, and is provided to the vehicle 200 from an external server or the like. The point cloud map is a map composed of a point cloud (point cloud data). It is assumed herein that the vector map herein refers to a map including traffic information or the like (for example, the positions of lanes and traffic lights) associated with the point cloud map, and matches the ADAS (Advanced Driver Assistance System).

[0342] For example, the point cloud map and the vector map can be provided from an external server or the like, or can be created by the vehicle 200 based on sensing results obtained by the radar 252, the LiDAR (Light Detection and Ranging) 253, or the like as a map for matching with a local map described below, and then accumulated in the map information accumulation unit 223. Also, in a case where the high-accuracy map is provided from an external server or the like, map data of, for example, several hundred meters around associated with a planned path on which the vehicle 200 will travel from now on is acquired from the external server or the like to reduce the communication amount.

[0343] The position information acquisition unit 224 receives a GNSS signal from a GNSS satellite and acquires position information associated with the vehicle 200. The received GNSS signal is supplied to the travel assistance and autonomous driving control unit 229. Note that the position information acquisition unit 224 need not employ a method using a GNSS signal, but can acquire position information using, for example, a beacon.

[0344] The external recognition sensor 225 includes various types of sensors for recognizing situations outside the vehicle 200, and supplies sensor data obtained from the respective sensors to the respective units of the vehicle control system 211. Any type and any number of sensors can be included in the external recognition sensor 225.

[0345] For example, the external recognition sensor 225 includes the camera 251, the radar 252, the LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) 253, and the ultrasonic sensor 254. The external recognition sensor 225 need not include these sensors, but can have a configuration including one or more types of sensors selected from among the camera 251, the radar 252, the LiDAR 253, and the ultrasonic sensor 254. The number of each of the camera 251, the radar 252, the LiDAR 253, and the ultrasonic sensor 254 is not limited to a specific number, but can be any number that is actually mountable on the vehicle 200. Also, the types of sensors included in the external recognition sensor 225 are not limited to these examples. The external recognition sensor 225 can include other types of sensors. Examples of sensing regions of the respective sensors included in the external recognition sensor 225 will be described below.

[0346] Note that the imaging method used by the camera 251 is not limited to a particular method, but can be any method capable of measuring a distance. For example, any one of various types of cameras such as a ToF (Time of Flight) camera, a stereo camera, a monocular camera, and an infrared camera can be employed as the camera 251 as needed. The camera 251 is not limited to a distance measuring camera, but can be a type for simply acquiring a captured image.

[0347] Also, for example, the external recognition sensor 225 can include an environment sensor for detecting an environment of the vehicle 200. The environment sensor is a sensor for detecting an environment such as weather, climate, and brightness, and can include various types of sensors such as a raindrop sensor, a fog sensor, a sunlight sensor, a snow sensor, and a brightness sensor.

[0348] Further, for example, the external recognition sensor 225 includes a microphone for detecting a position of a sound and a sound source around the vehicle 200 or for other purposes.

[0349] The vehicle interior sensor 226 includes various types of sensors for detecting information associated with the vehicle interior, and supplies sensor data obtained by the respective sensors to the respective units of the vehicle control system 211. The types and the number of the various types of sensors included in the vehicle interior sensor 226 are not limited to particular types and numbers, but can be any types and numbers that are actually mountable on the vehicle 200.

[0350] For example, the vehicle interior sensor 226 can include one or more types of sensors selected from a camera, a radar, a seat sensor, a steering wheel sensor, a microphone, and a biological sensor. For example, any camera capable of measuring a distance and using any one of various types of imaging methods such as a ToF camera, a stereo camera, a monocular camera, and an infrared camera can be employed as the camera included in the vehicle interior sensor 226. The camera included in the vehicle interior sensor 226 is not limited to a distance measuring camera, but can be a type for simply acquiring a captured image. For example, the biological sensor included in the vehicle interior sensor 226 is provided on a seat, a steering wheel, or the like, and detects various types of biological information associated with an occupant of the vehicle such as a driver.

[0351] The vehicle sensor 227 includes various types of sensors for detecting a state of the vehicle 200 and supplies sensor data obtained by the respective sensors to the respective units of the vehicle control system 211. The types and the number of the various types of sensors included in the vehicle sensor 227 are not limited to particular types and numbers, but can be any types and numbers that are actually mountable on the vehicle 200.

[0352] The vehicle sensors 227 include, for example, a speed sensor, an acceleration sensor, an angular velocity sensor (gyro sensor), and an inertial measurement unit (IMU) that integrates these. The vehicle sensors 227 include, for example, a steering angle sensor that detects a steering angle of a steering wheel, a yaw rate sensor, an acceleration sensor that detects an operation amount of an accelerator pedal, and a brake sensor that detects an operation amount of a brake pedal. The vehicle sensors 227 include, for example, a rotation sensor that detects an engine speed and a motor speed, a tire air pressure sensor that detects a tire air pressure, a slip rate sensor that detects a tire slip rate, and a wheel speed sensor that detects a wheel rotation speed. The vehicle sensors 227 include, for example, a battery sensor that detects a remaining amount and a temperature of a battery, and an impact sensor that detects an impact received from the outside.

[0353] The recording unit 228 includes at least a nonvolatile storage medium or a volatile storage medium, and stores data and programs. The recording unit 228 is used as, for example, an EEPROM (Electrically Erasable Programmable Read Only Memory) and a RAM (Random Access Memory). A magnetic storage device such as an HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, and a magneto-optical storage device can be applied to the storage medium. The recording unit 228 records various types of programs and data used by the corresponding units of the vehicle control system 211. The recording unit 228 includes, for example, an EDR (Event Data Recorder) and a DSSAD (Driver Status and Safety Alert Data), and records information associated with the vehicle 200 before and after an event such as an accident, and biological information acquired by the vehicle interior sensor 226.

[0354] The travel assistance and autonomous driving control unit 229 controls travel assistance and autonomous driving of the vehicle 200. The travel assistance and autonomous driving control unit 229 includes, for example, an analysis unit 261, a behavior planning unit 262, and an action control unit 263.

[0355] The analysis unit 261 performs analysis processing for analyzing the situation of the vehicle 200 and the surroundings. The analysis unit 261 includes a self-position estimation unit 271, a sensor fusion unit 272, and an identification unit 273.

[0356] The self-position estimation unit 271 estimates the self-position of the vehicle 200 on the basis of sensor data received from the external identification sensor 225 and a high-accuracy map accumulated in the map information accumulation unit 223. The self-position estimation unit 271 generates a local map on the basis of sensor data received from the external identification sensor 225, for example, and performs matching between the local map and the high-accuracy map to estimate the self-position of the vehicle 200. The position of the vehicle 200 is defined on the basis of the center of a pair of rear wheel shafts, for example.

[0357] For example, the local map is a three-dimensional high-accuracy map or an occupancy grid map created using a technique such as SLAM (Simultaneous Localization and Mapping). The three-dimensional high-accuracy map is, for example, the point cloud map described above or the like. The occupancy grid map is a map that indicates the occupancy state of an object in units of a grid having a predetermined size by dividing a three-dimensional or two-dimensional space around the vehicle 200. For example, the occupancy state of the object is indicated by the presence or absence of the object or a probability of presence. The local map is also used for detection processing and recognition processing by which the recognition unit 273 detects and recognizes situations outside the vehicle 200.

[0358] Note that the self-position estimation unit 271 can estimate the self-position of the vehicle 200 on the basis of a GNSS signal and sensor data received from the vehicle sensor 227.

[0359] The sensor fusion unit 272 performs sensor fusion processing for obtaining new information by combining a plurality of different types of sensor data, for example, image data supplied from the camera 251 and sensor data provided from the radar 252. Examples of a method for combining different types of sensor data include integration, fusion, unification, and the like.

[0360] The recognition unit 273 performs detection processing for detecting situations outside the vehicle 200 and recognition processing for recognizing situations outside the vehicle 200.

[0361] For example, the recognition unit 273 performs the detection processing and the recognition processing for detecting and recognizing situations outside the vehicle 200 on the basis of information received from the external recognition sensor 225, information received from the self-position estimation unit 271, information received from the sensor fusion unit 272, and the like.

[0362] Specifically, for example, the recognition unit 273 performs detection processing, recognition processing, and the like for detecting and recognizing objects around the vehicle 200. For example, the detection processing for detecting an object is processing for detecting the presence or absence, size, shape, position, movement, and the like of the object. For example, the recognition processing for recognizing an object is processing for recognizing the attribute (such as the type) of the object or for recognizing a specific object. However, it is not necessarily required to explicitly distinguish the detection processing from the recognition processing. In some cases, these processes are overlapping.

[0363] For example, the recognition unit 273 detects the presence or absence, size, shape, and position of an object around the vehicle 200 by executing clustering of point clouds classified into point cloud blocks based on sensor data obtained by the LiDAR 253, the radar 252, or the like. In this way, the speed and travel direction (movement vector) of an object around the vehicle 200 are detected.

[0364] For example, the recognition unit 273 detects the movement of an object around the vehicle 200 by executing tracking of movement following of point cloud blocks classified by clustering. In this way, the speed and travel direction (movement vector) of an object around the vehicle 200 are detected.

[0365] For example, the recognition unit 273 detects or recognizes a vehicle, a person, a bicycle, an obstacle, a building, a road, a traffic light, a traffic sign, a road sign, or the like from image data supplied from the camera 251. Also, the recognition unit 273 can recognize the type of an object around the vehicle 200 by executing recognition processing such as semantic segmentation.

[0366] For example, the recognition unit 273 can execute recognition processing for recognizing a traffic rule around the vehicle 200 based on a map accumulated in the map information accumulation unit 223, a self-position estimation result obtained by the self-position estimation unit 271, and an object recognition result around the vehicle 200 obtained by the recognition unit 273. The recognition unit 273 that executes this processing is able to recognize the position and state of a traffic light, the content of a traffic sign and a road sign, the content of a traffic rule, a drivable lane, and the like.

[0367] For example, the recognition unit 273 is able to execute recognition processing for recognizing an environment around the vehicle 200. Examples of a surrounding environment corresponding to a recognition target of the recognition unit 273 include weather, temperature, humidity, brightness, and a road surface state.

[0368] The behavior planning unit 262 creates a behavior plan of the vehicle 200. For example, the behavior planning unit 262 creates a behavior plan by executing processing for path planning and path following.

[0369] Note that path planning (global path planning) is processing for planning a rough path from a start point to a target. This path planning is referred to as orbit planning, and also contains orbit generation (local path planning) processing that allows safe and smooth travel in the vicinity of the vehicle 200 by considering the movement characteristics of the vehicle 200 in a path planned by path planning. This path planning can be distinguished from long-term path planning, and start generation can be distinguished from short-term path planning or local path planning. A safety-first path indicates a concept similar to start generation, short-term path planning, or local path planning.

[0370] Path following is a process of planning an action so as to travel safely and correctly on a path planned by a path plan within a planned time. For example, the behavior planning unit 262 can calculate a target speed and a target angular speed of the vehicle 200 based on a result of this path following process.

[0371] The action control unit 263 controls an action of the vehicle 200 to realize a behavior plan created by the behavior planning unit 262.

[0372] For example, the action control unit 263 controls a steering control unit 281, a brake control unit 282, and a drive control unit 283 included in the vehicle control unit 232 described below to perform acceleration / deceleration control and direction control so that the vehicle 200 travels on a track calculated by the track planning. For example, the action control unit 263 performs cooperative control for the purpose of realizing an ADAS function such as collision avoidance or mitigation, following travel, constant vehicle speed travel, collision warning to the own vehicle, and lane departure warning to the own vehicle. For example, the action control unit 263 performs cooperative control for the purpose of autonomous driving of autonomous travel that does not require an operation of the driver or for other purposes.

[0373] The DMS 230 performs an authentication process for authenticating a driver, an identification process for identifying a state of the driver, and the like based on sensor data received from the vehicle interior sensor 226, input data input to the HMI 231 described below, and the like. Examples of the state of the driver corresponding to an identification target in this case include a physical condition, a degree of alertness, a degree of attention, a degree of fatigue, a line-of-sight direction, a drunkenness level, a driving operation, and a posture, assuming the case.

[0374] Note that the DMS 230 can perform an authentication process for authenticating a vehicle occupant other than the driver and an identification process for identifying a state of the vehicle occupant. Also, for example, the DMS 230 can perform an identification process for identifying a situation inside the vehicle based on sensor data received from the vehicle interior sensor 226. Examples of the situation inside the vehicle corresponding to an identification target include a temperature, a humidity, a brightness, an odor, and the like, assuming the case.

[0375] The HMI 231 inputs various types of data, instructions, and the like, and presents various types of data to the driver or the like.

[0376] A summary of data input of the HMI 231 will be described. The HMI 231 includes an input device through which a person inputs data. The HMI 231 generates an input signal based on data, instructions, and the like input through the input device, and supplies the generated input signal to a corresponding unit of the vehicle control system 211. The HMI 231 includes an operator such as a touch panel, a button, a switch, and a joystick as the input device. The HMI 231 does not require to include these examples, but can also include an input device through which information can be input by a method other than manual operation, such as a method using voice or a gesture. Also, for example, the HMI 231 can employ a remote control device using infrared light or radio waves or an external connection device such as a mobile device or a wearable device that handles the operation of the vehicle control system 211 as the input device.

[0377] A summary of data presentation of the HMI 231 will be described. The HMI 231 generates visual information, auditory information, and tactile information given to a person on board or outside the vehicle. Also, the HMI 231 performs output control for controlling the output, output content, output timing, output method, and the like of these generated information. For example, the HMI 231 generates and outputs information indicated by an image or light as visual information such as an operation screen, a status display of the vehicle 200, a warning display, and a monitoring image indicating a situation around the vehicle 200. Also, for example, the HMI 231 generates and outputs information indicated by sound as auditory information such as voice guidance, a warning sound, and a warning message. Further, for example, the HMI 231 generates and outputs information giving a tactile sensation to an on-board person as tactile information such as force, vibration, and movement.

[0378] Examples of output devices suitable for outputting visual information from the HMI 231 include a display device that presents visual information by displaying an image itself, and a projector device that presents visual information by projecting an image. Note that the display device can be a device that displays visual information within a field of view of an on-board person such as a head-up display, a transmissive display, a wearable device with an AR (Augmented Reality) function, and a display including a general display. Further, the HMI 231 can also employ a display device included in a navigation device provided on the vehicle 200, an instrument panel, a CMS (Camera Monitoring System), an electronic mirror, a lamp, and the like as an output device for outputting visual information.

[0379] Examples of output devices suitable for the HMI 231 to output auditory information include an audio speaker, a headphone, and an earplug.

[0380] Examples of output devices suitable for the HMI 231 to output haptic information therefrom include haptic elements using haptic technology. For example, haptic elements are provided at portions in contact with a person on the vehicle 200, such as a steering wheel and a seat.

[0381] The vehicle control unit 232 controls respective units of the vehicle 200. The vehicle control unit 232 includes a steering control unit 281, a brake control unit 282, a drive control unit 283, a body control unit 284, a lamp control unit 285, and a horn control unit 286.

[0382] The steering control unit 281 performs detection, control, and the like of a state of a steering system of the vehicle 200. For example, the steering system includes a steering mechanism equipped with a steering wheel or the like and an electric power steering. For example, the steering control unit 281 includes a control unit such as an ECU for controlling the steering system, and an actuator for driving the steering system.

[0383] The brake control unit 282 performs detection, control, and the like of a state of a brake system of the vehicle 200. For example, the brake system includes a brake mechanism equipped with a brake pedal or the like, an ABS (anti-lock brake system), and a regenerative brake mechanism. For example, the brake control unit 282 includes a control unit such as an ECU for controlling the brake system.

[0384] The drive control unit 283 performs detection, control, and the like of a state of a drive system of the vehicle 200. For example, the drive system includes an accelerator pedal, a drive power generation device such as an internal combustion engine and a drive motor for generating a drive power, and a drive power transmission mechanism for transmitting the drive power to a wheel. For example, the drive control unit 283 includes a control unit such as an ECU for controlling the drive system.

[0385] The body control unit 284 performs detection, control, and the like of a state of a body system of the vehicle 200. For example, the body system includes a keyless entry system, a smart key system, a power window device, a power seat, an air conditioner, an airbag, a seat belt, and a shift lever. For example, the body control unit 284 includes a control unit such as an ECU for controlling the body system.

[0386] The lamp control unit 285 performs detection, control, and the like of a state of various types of lamps of the vehicle 200. Examples of control targets are assumed to include a headlamp, a rear lamp, a fog lamp, a turn signal, a brake lamp, a projection, and a display of a bumper. The lamp control unit 285 includes a control unit such as an ECU for controlling the lamps.

[0387] The horn control unit 286 performs detection, control, and the like of a state of an automobile horn of the vehicle 200. For example, the horn control unit 286 includes a control unit such as an ECU for controlling the automobile horn.

[0388] Figure 24 is a diagram depicting an example of a sensing region defined by a camera 251, a radar 252, a LiDAR 253, an ultrasonic sensor 254, or the like of the exterior recognition sensor 225 of Figure 23 Figure 24 The situation of the vehicle 200 viewed from above is schematically depicted. The left end side corresponds to the front end (front) side of the vehicle 200, and the right end side corresponds to the rear end (rear) side of the vehicle 200.

[0389] Each of the sensing region 291F and the sensing region 291B indicates an example of a sensing region of the ultrasonic sensor 254. The sensing region 291F covers the surroundings of the front end of the vehicle 200 using a plurality of ultrasonic sensors 254. The sensing region 291B covers the surroundings of the rear end of the vehicle 200 using a plurality of ultrasonic sensors 254.

[0390] For example, the sensing results obtained at the sensing region 291F and the sensing region 291B are used for parking assistance or the like of the vehicle 200.

[0391] Each of the sensing region 292F to the sensing region 292B indicates an example of a sensing region of the radar 252 for a short distance or a middle distance. The sensing region 292F covers a block up to a position farther than the block of the sensing region 291F in front of the vehicle 200. The sensing region 292B covers a block up to a position farther than the block of the sensing region 291B behind the vehicle 200. The sensing region 292L covers the surroundings of the left side surface behind the vehicle 200. The sensing region 292R covers the surroundings of the right side surface behind the vehicle 200.

[0392] For example, the sensing results obtained at the sensing region 292F are used to detect a vehicle, a pedestrian, or the like present in front of the vehicle 200. For example, the sensing results obtained at the sensing region 292B are used for a collision avoidance function or the like behind the vehicle 200. For example, the sensing results obtained at the sensing region 292L and the sensing region 292R are used to detect an object present at a blind spot beside the vehicle 200.

[0393] Each of the sensing region 293F to the sensing region 293B indicates an example of a sensing region of the camera 251. The sensing region 293F covers a block up to a position farther than the block of the sensing region 292F in front of the vehicle 200. The sensing region 293B covers a block up to a position farther than the block of the sensing region 292B behind the vehicle 200. The sensing region 293L covers the surroundings of the left side surface of the vehicle 200. The sensing region 293R covers the surroundings of the right side surface of the vehicle 200.

[0394] ​For example, the sensing result obtained at the sensing region 293F can be used for the recognition of traffic lights and traffic signs, a lane departure prevention assist system, and an automatic headlight control system. For example, the sensing result obtained at the sensing region 293B can be used for a parking assist and a surround view system. For example, the sensing result obtained at the sensing region 293L and the sensing region 293R can be used for a surround view system.

[0395] The sensing region 294 indicates an example of a sensing region of the LiDAR 253. The sensing region 294 covers blocks up to a position farther than the blocks of the sensing region 293F in front of the vehicle 200. On the other hand, the sensing region 294 is narrower in the lateral direction than the sensing region 293F.

[0396] For example, the sensing result obtained at the sensing region 294 is used for the detection of an object such as a surrounding vehicle, and the like.

[0397] The sensing region 295 indicates an example of a sensing region of the radar 252 for long distances.

[0398] The sensing region 295 covers blocks up to a position farther than the blocks of the sensing region 294 in front of the vehicle 200. On the other hand, the sensing region 295 is narrower in the lateral direction than the sensing region 294.

[0399] For example, the sensing result obtained at the sensing region 295 is used for ACC (adaptive cruise control), emergency braking, collision avoidance, and the like.

[0400] Note that each of the sensing regions of the respective sensors including the camera 251, the radar 252, the LiDAR 253, and the ultrasonic sensor 254 included in the exterior recognition sensor 225 can have various types of configurations other than the configurations depicted in the middle. Figure 24 The mounting positions of the respective sensors are not limited to the respective examples described above. Furthermore, the number of each type of sensor can be one or more than one.

[0401] [9. Presentation of data examples of display devices]

[0402] Next, a data display example using a display device included in a vehicle will be described.

[0403] It is difficult for a user (driver) of an autonomous driving vehicle to clearly recognize which determination to make, which situation to consider when making the determination, and which control the autonomous driving function of the vehicle is to execute. In this case, the user feels uneasy.

[0404] To solve this problem, for example, it is effective to display the process and result of identifying and determining the autonomous driving vehicle as a UI on a display device provided at the driver's seat or the rear seat.

[0405] The user viewing the UI can visually intuitively recognize the determination and control made by the autonomous driving vehicle.

[0406] The user can understand the determination made by the autonomous driving vehicle and determine in advance which control to execute by checking the UI. Thus, the user can feel at ease. Also, it can be made clear whether a different recognition or determination that is appropriate for the actual situation has been made. Thus, it can be easily determined whether a malfunction or abnormality has occurred other than such a case.

[0407] Figure 25 Examples of specific display data displayed on the display unit of the vehicle are depicted.

[0408] As depicted in Figure 25 The path plan and the safety-first path set by the autonomous driving vehicle are displayed on the display unit.

[0409] Approximately 360 degrees of objects are drawn in three dimensions on the digital map of the display unit. The lanes, pedestrian crossings, and signs are displayed.

[0410] Also, information indicating the execution / stop state of the autonomous driving function, the travel speed, the yaw rate, the traffic light recognition result, and the like are displayed in the upper portion.

[0411] The icon of the displayed traffic light does not necessarily represent the actual traffic light. For example, the color of the traffic light icon becomes red in the case where braking control is necessary, such as braking of a vehicle ahead.

[0412] In the case where the actual traffic light is recognized, this traffic light is displayed in the 3D digital map located in the lower portion.

[0413] Also, in the 3D digital map in the lower portion, the vehicle entry prohibited space and the vehicle entry allowed area are displayed separately.

[0414] Furthermore, the recognized objects are displayed in different colors for each type of object.

[0415] The travel path of the vehicle is displayed with the identification of the long-term path plan to the destination using a band in a specific color (e.g., blue).

[0416] Also, the local short-term plan path (safety-first path) is displayed with the identification of the band displayed intermittently using a different color (e.g., white).

[0417] Note that the short-term planned path (safety-first path) is displayed to change according to the distance from the obstacle (curvature of the safety-first path) based on the danger level of the obstacle. For example, the danger level changes according to the type of the obstacle (pedestrian or automobile), whether the obstacle is movable, and the travel speed of the host vehicle.

[0418] In this way, the number and length of the bands indicating the short-term planned path (safety-first path) are variable.

[0419] Also, the tracking targets among the vehicles traveling around, the safety vehicle, and the like are distinguished and displayed with the marks using colors different from those of the other objects, such as red and green.

[0420] For example, among the vehicles traveling around, the dangerous vehicle is displayed in a color different from that of the other objects, such as red.

[0421] Further, the vehicle parked on the side, and the obstacle that can be dangerous or movable among the movable obstacles, such as a pedestrian, is displayed with a mark, such as a Δ mark or an! mark, over the corresponding obstacle.

[0422] The display color can be displayed in different hues for each danger level, such as in red for an especially dangerous obstacle.

[0423] Also, at the time of stopping the autonomous driving and switching to the manual driving, a notification is displayed that the control of the display is transferred to the driver.

[0424] In a situation where it is recognized that it is necessary to bend the short-term path (to make the path away from the obstacle), such as a road where many vehicles are parked and an intersection, such a situation can be reflected in advance in the long-term path planning. With reference to the information indicated by the dynamic map, the information provided by VICS (registered trademark), and the like, information such as information associated with whether the road is a road where many vehicles are parked is displayed.

[0425] As described above, in order to recognize the situation in advance, the video of the bird's-eye view obtained by a drone or the like, a satellite picture, and the like, and the dynamic map already described above are used. These can be acquired in real time.

[0426] Further, for a place where another vehicle ran out or approached closer than expected during the previous travel, captured by a camera, LiDAR, millimeter wave radar, ultrasonic wave, and the like, a configuration can be employed that displays information reflecting this place on the map as a dangerous place, according to a person's prior inspection or automatic determination under a program based on the recognition result of the same place during the current driving.

[0427] Further, in a case where the situation obtained in advance by the map or the like is different from the actual situation, it is preferable to display information reflecting these different points on the map.

[0428] In addition, the recognition results of one or more vehicles ahead can also be used to pre-recognize the situation.

[0429] The dynamic map information described above, information obtained by VICS (registered trademark), bird's-eye views obtained by drones, satellite images, recognition results obtained by the vehicle at the same location as the previous location, recognition results of the preceding vehicle, etc. can be combined.

[0430] [10. Hardware configuration examples of corresponding devices]

[0431] Next, we will refer to Figure 26 A hardware configuration example of an information processing device constituting the autonomous driving control apparatus 100 , the robot management server 121 , the map information providing server 122 , or other servers such as a building management server and a parking lot management server is described.

[0432] Figure 26 The hardware configurations depicted in present examples of hardware configurations suitable for these devices.

[0433] The CPU (Central Processing Unit) 301 functions as a data processing unit that executes various types of processing according to a program stored in the ROM (Read Only Memory) 302 or the storage unit 308. For example, the CPU 301 executes processing according to the sequence described in the above embodiments. The RAM (Random Access Memory) 303 stores programs and data executed by the CPU 301. The CPU 301, ROM 302, and RAM 303 described herein are connected to each other via a bus 304.

[0434] The CPU 301 is connected to an input / output interface 305 via a bus 304. An input unit 306 composed of various types of switches, a keyboard, a touch screen, a mouse, a microphone, etc. and an output unit composed of a display, a speaker, etc. are connected to the input / output interface 305.

[0435] The storage unit 308 connected to the input / output interface 305 is composed of a hard disk or the like, and stores programs executed by the CPU 301 and various types of data. The communication unit 309 functions as a transmission and reception unit that performs data communication via a network such as the Internet and a local area network, and communicates with external devices.

[0436] The drive 310 connected to the input / output interface 305 drives a removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, and a semiconductor memory such as a memory card, and performs data recording or data reading.

[0437] [11. Summary of the configuration of the present disclosure]

[0438] Embodiments of the present disclosure have been described in detail with reference to specific embodiments thereof. However, it would be apparent to those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the subject matter disclosed. Accordingly, the present disclosure is to be construed as only being limited by the appended claims. The parts of the claims should be considered to determine the subject matter of the present disclosure.

[0439] Note that the technology disclosed in this description can have the following configurations. (1)

[0441] An autonomous travel control device includes:

[0442] a travel path determination unit that generates a safety-first path for avoiding passing through or approaching a dangerous region that can come into contact with another moving object; and

[0443] a travel control unit that performs control to cause the self device to travel along the safety-first path generated by the travel path determination unit. (2)

[0445] The autonomous travel control device according to (1), wherein the travel path determination unit generates the safety-first path for bypassing the dangerous region. (3)

[0447] The autonomous travel control device according to (1) or (2), wherein the travel path determination unit generates the safety-first path by correcting a minimum cost path corresponding to a shortest path in a metric map. (4)

[0449] The autonomous travel control device according to any one of (1) to (3), wherein

[0450] the travel path determination unit:

[0451] generates a minimum cost path in a topological map,

[0452] generates a cost-first path in a metric map based on the minimum cost path in the topological map, and

[0453] generates a safety-first path in the metric map by correcting the cost-first path in the metric map. (5)

[0455] The autonomous travel control device according to any one of (1) to (4), wherein

[0456] the travel path determination unit:

[0457] acquires or detects position information associated with a dangerous region in a travel path, and

[0458] generating a safety priority path for avoiding passing through or approaching a dangerous area. (6)

[0460] The autonomous travel control device according to any one of (1) to (5), wherein the travel path determination unit generates the safety priority path with reference to dangerous area map data that records position information associated with the dangerous area in the travel path. (7)

[0462] The autonomous travel control device according to any one of (1) to (6), wherein

[0463] the travel path determination unit:

[0464] generates two vectors AB and AC that form an opening angle φ from a dangerous area representative point A,

[0465] detects a point B1 at which the vector AB of the generated two vectors intersects a line at a distance d from an end portion at which the own device is travelable, and a point C1 at which the vector AC intersects the line at the distance d, and

[0466] generates a safety priority path that includes a connecting line connecting the points B1 and C1. (8)

[0468] The autonomous travel control device according to (7), wherein the travel path determination unit generates the safety priority path by connecting the connecting line connecting the points B1 and C1 to a cost priority path in a metric map. (9)

[0470] The autonomous travel control device according to any one of (1) to (8), wherein the travel path determination unit generates the safety priority path by changing a distance from the dangerous area in accordance with an attribute of a moving object that is likely to make contact in the dangerous area. (10)

[0472] The autonomous travel control device according to any one of (1) to (9), wherein the travel path determination unit performs travel path determination processing that takes into account a travel path of another autonomous travel control device. (11)

[0474] The autonomous travel control device according to any one of (1) to (10), wherein the travel control unit performs control to cause the own device to travel along the safety priority path generated by the travel path determination unit, and performs travel control based on sensor detection information. (12)

[0476] An autonomous travel control system including:

[0477] autonomous travel device; and

[0478] a server that transmits a safety priority path to the autonomous travel device, wherein

[0479] the server generates a safety priority path for avoiding a dangerous area through or near which contact between the autonomous travel device and another moving object is likely, and transmits information of the generated safety priority path to the autonomous travel device, and

[0480] the autonomous travel device receives the safety priority path information from the server, and performs control to cause the self device to travel along the received safety priority path. (13)

[0482] An autonomous travel control method executed by an autonomous travel control device, the autonomous travel control method comprising:

[0483] a travel route determination step of generating, by a travel path determination unit, a safety priority path for avoiding a dangerous area through or near which contact with another moving object is likely; and

[0484] a travel control step of performing, by a travel control unit, control to cause the self device to travel along the safety priority path generated by the travel path determination unit.

[0485] Note that a series of processes described in the specification can be executed by a configuration of hardware, software, or a combination of both. In the case where the processes are executed by software, a program in which the process sequence has been recorded can be installed in a memory in a computer incorporated in a dedicated hardware and executed in this form, or can be installed in a general-purpose computer capable of executing various types of processes and executed in this form. For example, the program can be recorded in a recording medium in advance. The program can be installed in the computer from the recording medium, or can be received via a network such as a LAN (Local Area Network) and the Internet, and installed in a recording medium such as a built-in hard disk.

[0486] Also, the respective types of processes described in the present description can not only be executed in the time sequence described above, but also can be executed in parallel or individually according to the processing capacity of the device that executes the processes or as needed. Further, the system in the present description is a set of logical configurations constituted by a plurality of devices, and the devices of the respective configurations do not need to be contained in the same housing.

[0487] [Industrial applicability]

[0488] As described above, the configuration according to one embodiment of the present disclosure realizes an autonomous travel control device that generates a safety priority path for avoiding a dangerous area through or near which contact with another moving object is likely, and travels on the generated safety priority path.

[0489] Specifically, for example, the autonomous travel control device includes a travel path determination unit that generates a safety-first path for avoiding passing through or approaching a dangerous region that is likely to come into contact with another moving object, and a travel control unit that performs control to cause the self device to travel along the safety-first path generated by the travel path determination unit. The travel path determination unit generates a cost-first path in a metric map based on a minimum cost path in a topological map, and generates a safety-first path that bypasses the dangerous region by correcting the cost-first path in the metric map.

[0490] The present configuration realizes an autonomous travel control device that generates a safety-first path for avoiding passing through or approaching a dangerous region that is likely to come into contact with another moving object, and travels on the generated safety-first path.

[0491] [LIST OF SYMBOLS]

[0492] 10, 20: Autonomous travel robot

[0493] 31: Intersection

[0494] 100: Autonomous travel control device

[0495] 101: Travel path determination unit

[0496] 102: Travel control unit

[0497] 111: Map data

[0498] 111a: Topological map data

[0499] 111b: Metric map data

[0500] 111c: Dangerous region map data

[0501] 112: Travel path data

[0502] 121: Robot management server

[0503] 122: Map information providing server

[0504] 125: Robot information

[0505] 127: Map data

[0506] 131: Cost-first path

[0507] 132: Safety-first path

[0508] 151: Control unit

[0509] 152: Input unit

[0510] 153: output unit

[0511] 154: sensor group

[0512] 155: drive unit

[0513] 156: communication unit

[0514] 157: storage unit

[0515] 301: central processing unit

[0516] 302: ROM

[0517] 303: RAM

[0518] 304: bus

[0519] 305: input / output interface

[0520] 306: input unit

[0521] 307: output unit

[0522] 308: storage unit

[0523] 309: communication unit

[0524] 310: drive

[0525] 311: removable medium

Claims

1. An autonomous travel control apparatus comprising: a travel path determination unit that generates a safety-first path for avoiding passing through or approaching a dangerous region in which contact with another moving object is possible; and a travel control unit that performs control to cause the autonomous travel control apparatus to travel along the safety-first path generated by the travel path determination unit, wherein the travel path determination unit: generates two vectors AB and AC that form an opening angle φ from a dangerous region representative point A, detects a point Bl at which the vector AB of the generated two vectors intersects a line at a distance d from an end of a travel path, and a point Cl at which the vector AC intersects the line at the distance d, where the end of the travel path is an end of a travel path in which the autonomous travel control apparatus is travelable, and the distance d is a distance between the end of the travel path and a path that is separated from the dangerous region representative point A by a maximum length and in which the autonomous travel control apparatus is travelable, and generates a safety-first path that includes a connecting line connecting the points Bl and Cl. 2.The autonomous travel control apparatus according to claim 1, wherein the travel path determination unit generates a safety-first path for bypassing a dangerous region. 3.The autonomous travel control apparatus according to claim 1, wherein the travel path determination unit generates a safety-first path by correcting a minimum cost path corresponding to a shortest path in a metric map. 4.The autonomous travel control apparatus according to claim 1, wherein: the travel path determination unit: generates a minimum cost path in a topological map, generates a cost-first path in a metric map based on the minimum cost path in the topological map, and generates a safety-first path in the metric map by correcting the cost-first path in the metric map. 5.The autonomous travel control apparatus according to claim 1, wherein: the travel path determination unit: acquires or detects position information associated with a dangerous region in a travel path, and generates a safety-first path for avoiding passing through or approaching the dangerous region. 6.The autonomous travel control apparatus according to claim 1, wherein the travel path determination unit generates a safety-first path with reference to dangerous region map data that records position information associated with a dangerous region in a travel path. 7.The autonomous travel control apparatus according to claim 1, wherein the travel path determination unit generates a safety-first path by connecting a connecting line connecting the points Bl and Cl to a cost-first path in a metric map. 8.The autonomous travel control apparatus according to claim 1, wherein the travel path determination unit generates a safety-first path by changing a distance from a dangerous region in accordance with attributes of a moving object for which the possibility of contact occurring in the dangerous region is high. 9.The autonomous travel control apparatus according to claim 1, wherein the travel path determination unit performs travel path determination processing that takes into account a travel path of another autonomous travel control apparatus.

10. The autonomous travel control apparatus according to claim 1, wherein the travel control unit executes control to cause the autonomous travel control apparatus to travel along the safety-first path generated by the travel path determination unit, and executes travel control based on sensor detection information.

11. An autonomous travel control system comprising: an autonomous travel apparatus; and a server that transmits a safety-first path to the autonomous travel apparatus, wherein the server generates a safety-first path for avoiding a dangerous region where contact between the autonomous travel apparatus and another moving object is possible by or near the autonomous travel apparatus, and transmits the generated safety-first path information to the autonomous travel apparatus, and the autonomous travel apparatus receives the safety-first path information from the server, and executes control to cause the autonomous travel apparatus to travel along the received safety-first path, wherein the server: generates two vectors AB and AC that form an opening angle φ from a dangerous region representative point A, detects a point Bl where the vector AB of the generated two vectors intersects a line at a distance d from an end of a travel path, and a point Cl where the vector AC intersects the line at the distance d, where the end of the travel path is an end of a travel path where the autonomous travel apparatus is travelable, and the distance d is a distance between the end of the travel path and a path that is separated from the dangerous region representative point A by a maximum length and where the autonomous travel apparatus is travelable, and generates a safety-first path that includes a connecting line connecting the points Bl and Cl.

12. An autonomous travel control method executed by an autonomous travel control apparatus, the autonomous travel control method comprising: a travel route determination step of generating, by a travel path determination unit, a safety-first path for avoiding a dangerous region where contact with another moving object is possible; and a travel control step of executing control, by a travel control unit, to cause the autonomous travel control apparatus to travel along the safety-first path generated by the travel path determination unit, wherein in the travel route determination step: two vectors AB and AC that form an opening angle φ from a dangerous region representative point A are generated, a point Bl where the vector AB of the generated two vectors intersects a line at a distance d from an end of a travel path, and a point Cl where the vector AC intersects the line at the distance d are detected, where the end of the travel path is an end of a travel path where the autonomous travel control apparatus is travelable, and the distance d is a distance between the end of the travel path and a path that is separated from the dangerous region representative point A by a maximum length and where the autonomous travel control apparatus is travelable, and a safety-first path that includes a connecting line connecting the points Bl and Cl is generated. ​

Images