Locator system simulator and method for verifying locator system function

Abstract

The purpose of the present disclosure is to verify the function of a locator system. A locator system simulator (101) comprises: a computer (10) that generates a travel scenario in which a vehicle travels on a virtual road; and a real-time simulator (20) that simulates, in real-time, travel of the vehicle along the travel scenario. The computer (10) comprises a travel scenario generation unit (14) that generates a travel scenario on the basis of map data (12) and travel condition data (13). The real-time simulator (20) is provided with a model for simulating, in real time, the travel of the vehicle, and when a travel scenario is input into a model, the real-time simulator (20) acquires, as an output of the model, a position estimation signal that is a signal necessary for a locator ECU (30) to estimate the position of the vehicle, and inputs the position estimation signal into the locator ECU (30).

Description

Locator system simulator and function verification method for locator system

[0001] The present disclosure relates to functional verification of a locator system.

[0002] Patent Document 1 describes an autonomous driving simulator that is installed in an autonomous vehicle and that provides a simulated signal to an autonomous vehicle ECU (Electronic Control Unit) that controls the autonomous driving of the autonomous vehicle, causing the autonomous vehicle to run on a virtual road and verifying the function of the autonomous driving ECU.

[0003] Patent No. 6811335

[0004] The autonomous driving simulator described in Patent Document 1 is designed to verify the autonomous driving function, but not the locator system required for the autonomous driving function. Therefore, if a problem with the autonomous driving system is discovered as a result of the verification, it is difficult to determine whether the cause lies in the autonomous driving function or the locator system.

[0005] Furthermore, the autonomous driving simulator described in Patent Document 1 creates map data for autonomous driving based on driving log data obtained by driving an autonomous vehicle on a virtual road, and therefore cannot verify the functionality of a locator system consisting of map data and vehicle position information.

[0006] The technology of the present disclosure has been made to solve the above problems, and aims to verify the functionality of a locator system.

[0007] The locator system simulator disclosed herein is a locator system simulator that verifies the function of a locator system that is mounted on a vehicle and estimates the vehicle's position, and includes a computer that generates a driving scenario in which the vehicle drives a virtual road, and a real-time simulator that performs a real-time simulation of the vehicle's driving in accordance with the driving scenario. The computer includes a data storage unit that stores map data and driving condition data that represent the vehicle's driving conditions, and a driving scenario generation unit that generates a driving scenario based on the map data and the driving condition data. The real-time simulator includes a model for performing a real-time simulation of the vehicle's driving, and by inputting the driving scenario into the model, a position estimation signal, which is a signal necessary for the locator system to estimate the vehicle's position, is obtained as an output of the model, and the position estimation signal is input to the locator system.

[0008] The locator system simulator of the present disclosure allows for verification of the functionality of a locator system. Objects, features, aspects, and advantages of the present disclosure will become more apparent from the following detailed description and the accompanying drawings.

[0009] 1 is a diagram illustrating a configuration of a locator system simulator according to a first embodiment. FIG. 2 is a flowchart illustrating a driving route generation process. FIG. 3 is a flowchart illustrating a driving condition combining process. FIG. 4 is a diagram illustrating an example in which, when the first or last coordinate of an edge is the same as the coordinate of a non-node of another edge, the position is extracted as a new node. FIG. 5 is a diagram illustrating an example in which, when the coordinate of a non-node of an edge is the same as the coordinate of a non-node of another edge, the position is extracted as a new node. FIG. 6 is a diagram illustrating an example of a node group and an edge group. FIG. 7 is a diagram illustrating the weight of an edge group. FIG. 8 is a diagram illustrating the hardware configuration of a locator system simulator. FIG. 9 is a diagram illustrating the hardware configuration of a locator system simulator.

[0010] <A. First Embodiment> <A-1. Configuration> Fig. 1 shows the configuration of a locator system simulator 101 according to the first embodiment. Locator system simulator 101 will be described below with reference to Fig. 1.

[0011] The locator system simulator 101 includes a computer 10 , a real-time simulator 20 , and a locator system 30 .

[0012] The computer 10 includes a data storage unit 11 and a driving scenario generation unit 14. The data storage unit 11 includes map data 12 and driving condition data 13.

[0013] The map data 12 is data that represents a map using links. The map data 12 includes continuous position values ​​for the links (hereinafter referred to as link positions) and a traveling direction for each link (hereinafter referred to as link direction). The position values ​​here refer to values ​​that can be converted into satellite-based positions that the satellite positioning simulator 25 included in the real-time simulator 20 inputs into the locator system 30, and are expressed, for example, by latitude, longitude, and altitude. Furthermore, "continuous" here means that the coordinates between the first and last coordinates of a link are values ​​that do not deviate from the shape of the link and exist at regular intervals.

[0014] The link direction indicates the direction in which a vehicle should travel. For one-way roads, the link direction has a value indicating one-way traffic. For roads where travel is possible in both directions, such as narrow streets, the link direction has a value indicating that the road is not one-way.

[0015] The map data 12 includes link IDs in addition to link positions and link directions. Link IDs are assigned according to the shape characteristics of the links. For example, different link IDs are assigned for straight lines, curves, branches, merging points, and intersections. In lane-by-lane map data 12, detailed link IDs are assigned for each lane in addition to link IDs for each road in order to identify multiple lanes on the same road. As long as the map data 12 includes link positions, link directions, and link IDs, it may be map information actually used by the locator system 30, map information obtained by other means, or virtually created map information.

[0016] The driving condition data 13 is data indicating driving conditions assigned to each link ID. The driving conditions include a link ID indicating a target route along which the vehicle should travel among multiple lanes on the same road, a target lane change position when changing lanes on the same road, traffic regulation information including construction work, a driving direction for the link, and a target speed for the link.

[0017] The lane change target position is a value that can be acquired or calculated from a link having a corresponding link ID in the map data 12. The lane change target position may be set using coordinates such as latitude and longitude, or may be expressed as a distance from the first coordinate of the link, or as the elapsed time since passing the first coordinate of the link.

[0018] The lane change target position may be set as a pair of a lane change target start position and a lane change target end position. Alternatively, only the lane change target start position may be set, and the lane change target end position may be calculated by the operation of the vehicle motion model 23 of the vehicle motion simulator 21. There may be multiple pairs of lane change target start positions and lane change target end positions within the same link.

[0019] The traffic restriction information is information indicating construction or traffic restrictions (including time-zone restrictions), and is assigned to the corresponding link ID in the map data 12. For example, if the leftmost lane in the vehicle's travel direction is closed due to construction, a value indicating that the leftmost lane is impassable is assigned as traffic restriction information. Also, if all lanes on the same road are under traffic restriction, a value indicating that the lane is impassable is assigned as traffic restriction information to all lanes on the same road.

[0020] The driving direction is a value assigned to a corresponding link ID in the map data 12, and indicates the direction in which the vehicle travels on the link. For example, for a link ID representing a one-way road, a value indicating the forward direction is assigned as the driving direction if the vehicle's driving direction is the same as the road direction, and a value indicating the reverse direction is assigned if the vehicle's driving direction is different from the road direction.

[0021] The target speed is a value assigned to the corresponding link ID in the map data 12, and represents the speed to be followed by acceleration and deceleration in the simulation in the vehicle motion simulator.

[0022] The driving scenario generation unit 14 includes a driving route generation unit 15 and a driving condition combination unit 16 .

[0023] The driving route generation unit 15 generates a driving route based on the map data 12 .

[0024] The driving condition combining unit 16 generates a driving scenario based on the driving route and driving condition data 13 .

[0025] The real-time simulator 20 includes a vehicle motion simulator 21 and a satellite positioning simulator 25 .

[0026] The vehicle motion simulator 21 includes an environment model 22, a vehicle motion model 23, and a sensor model 24. The environment model 22 is a model that simulates the environment, which includes road conditions and the driver. The vehicle motion model 23 is a model that simulates the motion of the vehicle, which includes the operation of the engine, brakes, and steering. The sensor model 24 is a model that simulates sensors installed in the vehicle, which include a speed sensor, an acceleration sensor, and an angular velocity sensor.

[0027] Vehicle motion simulator 21 simulates the vehicle's travel in a virtual section in real time based on environment model 22, vehicle motion model 23, sensor model 24, and the travel scenario generated by travel scenario generator 14, and obtains output values ​​from environment model 22, vehicle motion model 23, and sensor model 24. These output values ​​are signals required for locator system 30 to estimate the vehicle's position, and are also referred to as position estimation signals.

[0028] The vehicle motion simulator 21 transmits the vehicle position information, which is one of the output values ​​of the environment model 22, the vehicle motion model 23, and the sensor model 24, to the satellite positioning simulator 25 in real time, and transmits the vehicle speed, acceleration, and angular velocity information, which is the output value of the sensor model 24, to the locator system 30 in real time.

[0029] The satellite positioning simulator 25 creates a satellite positioning simulation signal that simulates satellite positioning of the vehicle using an artificial satellite based on the position information received from the vehicle motion simulator 21, and transmits the satellite positioning simulation signal to the locator system 30 in real time.

[0030] Locator system 30 estimates the position of the vehicle based on the sensor information and the satellite positioning simulation signal acquired from real-time simulator 20. Locator system 30 is, for example, a locator ECU. By comparing the vehicle position information acquired by real-time simulator 20 through real-time simulation with the vehicle position information estimated by locator system 30, the function of locator system 30 can be verified.

[0031] <A-2. Operation> Figure 2 is a flowchart showing the driving route generation process by the driving route generation unit 15. The driving route generation process will be described below with reference to Figure 2. First, in step S101, the driving route generation unit 15 acquires a link position, a link direction, and a link ID from the map data 12.

[0032] Next, in step S102, the driving route generation unit 15 extracts nodes and edges in directed graph theory from the link positions, where a node is the first coordinate and the last coordinate of a link, and an edge is a link.

[0033] Then, in step S103, if the coordinates of an edge other than a node overlap with the coordinates of a node other than a node in another edge, the travel route generation unit 15 extracts the coordinates as a new node. Figure 5 shows an example of this, and the overlapping coordinates in links 1 and 2 become the new node.

[0034] Next, in step S104, if the first or last coordinate of an edge is the same as the coordinate of another edge that does not include a node, the travel path generation unit 15 extracts the corresponding position as a new node. Figure 4 shows an example of this, where the last coordinate of link 2 that overlaps with the coordinate of a non-node of link 1 is extracted as a new node.

[0035] Thereafter, in step S105, the travel path generation unit 15 divides the edges at the node positions extracted in steps S103 and S104. In the example of Fig. 4, link 1 is divided into links 1 and 3 at the newly extracted node. In the example of Fig. 5, link 2 is divided into links 2 and 4, and link 1 is divided into links 1 and 3 at the newly extracted node. Hereinafter, a set of nodes is referred to as a node group, and a set of edges is referred to as an edge group.

[0036] Next, in step S106, the travel path generation unit 15 assigns a node ID to each node in the node group and an edge ID to each edge in the edge group. FIG. 6 shows examples of a node group and an edge group. The node group includes nodes N1, N2, N3, N4, and N5, and the node IDs of the nodes N1, N2, N3, N4, and N5 are 1, 2, 3, 4, and 5, respectively. Edge E1 connects nodes N1 and N2, edge E2 connects nodes N1 and N3, edge E3 connects nodes N2 and N4, edge E4 connects nodes N2 and N5, and edge E5 connects nodes N3 and N5. Edge E4 is the only edge that connects nodes N2 and N5 in both directions, i.e., a two-way edge. The other edges E1, E2, E3, and E5 are one-way edges that go from a node with a smaller node ID to a node with a larger node ID.

[0037] Then, in step S107, the travel path generation unit 15 creates a weight array representing the weight of each edge in the edge group. Here, weight refers to the length of the link. FIG. 7 shows the weight of edge E4 between nodes N2 and N5. If edge E4 is divided into three edges E41, E42, and E43, and the lengths of edges E41, E42, and E43 are a [m], b [m], and c [m], respectively, the weight of edge E41 is (a + b + c). The travel path generation unit 15 performs this process for all edges in the edge group to create a weight array.

[0038] Next, in step S108, the travel path generation unit 15 creates a start point array and an end point array. Specifically, for an edge in the edge group, the travel path generation unit 15 adds the node ID of a node that matches the first coordinate of the edge to the last element of the start point array. Furthermore, for an edge in the edge group, the travel path generation unit 15 adds the node ID of a node that matches the last coordinate of the edge to the last element of the end point array. The node ID of a node that matches the coordinate of the end of a non-one-way edge is added to the last element of both the start point array and the end point array. The travel path generation unit 15 repeats these processes for all edges included in the edge group to create the start point array and the end point array. In the graph shown in FIG. 6, the start point array is [112253] and the end point array is [234525].

[0039] Then, in step S109, the travel route generation unit 15 selects a start node, which is the start point of the travel route, and an end node, which is the end point of the travel route, from the node group. Here, the travel route generation unit 15 may select the start node and the end node based on input from a user such as a tester.

[0040] Next, in step S110, the travel route generator 15 calculates the shortest route based on the weight array, the start point array, the end point array, the start point node, and the end point node, and generates a travel route. The shortest route may be calculated using a best-first search algorithm such as Dijkstra's algorithm.

[0041] Thereafter, in step S111, the travel route generation unit 15 selects whether or not to end the generation of the travel route.

[0042] If the answer is YES in step S111, the process ends.

[0043] If the result of step S111 is NO, the travel route generation process proceeds to step S112. In step S112, the travel route generation unit 15 sets the previous end node as the start node and selects the next end node. Here, the travel route generation unit 15 may select the next end node based on input from a user such as a tester.

[0044] After step S112, in step S113, the travel route generation unit 15 calculates the shortest route again and adds it to the end of the previously generated shortest route. Thereafter, the travel route generation unit 15 repeats the processes of steps S111 to S113.

[0045] 3 is a flowchart showing a process (driving condition combining process) in which the driving condition combining unit 16 combines a driving condition with a driving route. The driving condition combining process will be described below with reference to FIG.

[0046] First, in step S201, the driving condition combining unit 16 extracts the link ID, the lane change target position, the traffic regulation information, the driving direction, and the target speed from the driving condition data.

[0047] Next, in step S202, the driving condition combining unit 16 combines each link ID in the driving route with the lane change target position, traffic regulation information, driving direction, and target speed to generate a driving scenario.

[0048] Thereafter, in step S203, the driving condition combining unit 16 transmits the driving scenario to the vehicle motion simulator 21 in the real-time simulator 20.

[0049] <A-3. Effects> Locator system simulator 101 according to the first embodiment is a locator system simulator 101 that verifies the function of locator system 30 that is mounted on a vehicle and estimates the position of the vehicle. Locator system simulator 101 includes computer 10 that generates a driving scenario in which the vehicle drives a virtual road, and real-time simulator 20 that performs a real-time simulation of the vehicle driving in accordance with the driving scenario. Computer 10 includes data storage unit 11 that stores map data 12 and driving condition data 13 that represent the vehicle driving conditions, and driving scenario generation unit 14 that generates a driving scenario based on map data 12 and driving condition data 13. Real-time simulator 20 includes a model for performing a real-time simulation of the vehicle driving, and by inputting the driving scenario into the model, real-time simulator 20 obtains, as an output of the model, a position estimation signal that is a signal necessary for locator system 30 to estimate the position of the vehicle, and inputs the position estimation signal to locator system 30.

[0050] According to the above configuration, the real-time simulator 20 can simulate actual vehicle driving based on the driving scenarios generated by the driving scenario generation unit 14, so an actual vehicle is not required to verify the functions of the locator system, thereby reducing testing costs. Here, testing costs include the cost of equipment such as the actual vehicle, the labor costs of the driver, and the cost and time required to travel to the test location.

[0051] Furthermore, the functionality of the locator system 30 can be verified even during times when a tester is unavailable, such as at night or on holidays, in a locator system test using an actual vehicle, thereby shortening the overall test period.

[0052] Furthermore, since the driving scenario generation unit 14 generates a driving scenario based on map data 12 of any location, locator system testing can be performed at low cost in locations where testing costs would be particularly high if a locator system test were to be performed using an actual vehicle, such as in another country or a remote location, away from the location of the tester.

[0053] Furthermore, since it is possible to test the locator system even in areas where access is temporarily restricted due to social conditions or the like, the comprehensiveness of the evaluation range of the locator system 30 is improved.

[0054] Furthermore, in locator system simulator 101 according to the first embodiment, map data 12 includes link information indicating the link positions, link directions, and link IDs of a plurality of links representing roads including the virtual road, and driving conditions include lane change target positions, driving directions, and traffic regulation information linked to the link IDs. Driving scenario generation unit 14 includes driving route generation unit 15 that generates a driving route for the virtual road based on map data 12, and driving condition combination unit 16 that generates a driving scenario by combining the driving route with driving conditions linked to the same link ID.

[0055] By including the lane change target position in the driving conditions, lane changes can be applied to the driving scenario, improving the flexibility of vehicle behavior. Also, by including traffic regulation information in the driving conditions, it becomes possible to generate an appropriate driving route according to the traffic regulation information and create a driving scenario.

[0056] The driving scenario generation unit 14 may extract a group of nodes and a group of edges in a directed graph theory from the link information of the map data 12, and generate a shortest route based on a start node and an end node selected from the group of nodes. By having the driving scenario generation unit 14 create a driving scenario in this way, the time required for a tester of a locator system test to consider a driving scenario is reduced.

[0057] The multiple links may include a first link and a second link. When the coordinates of the first link overlap with coordinates in a second link that does not include the link end of the second link, the driving path generation unit 15 may extract the overlapping coordinates as a new node and update the node group, divide the first and second links that have overlapping coordinates other than the link end at the new node, update the edge group using the divided links as new edges, and generate a shortest route based on the updated node group and edge group. This allows the vehicle to drive by switching links at points where link information has common coordinates, such as intersections or T-junctions, in a vehicle driving simulation. Therefore, the locator system simulator 101 can flexibly generate driving paths that make right and left turns at intersections, T-junctions, etc.

[0058] In this embodiment, the position estimation signal includes position information indicating the position of the vehicle traveling according to the traveling scenario, and sensor information indicating the speed, acceleration, and angular velocity of the vehicle traveling according to the traveling scenario. Real-time simulator 20 also includes a satellite positioning simulator 25 that generates a satellite positioning simulation signal simulating satellite positioning of the vehicle using an artificial satellite based on the vehicle position information and transmits the satellite positioning simulation signal to locator system 30, and a vehicle motion simulator 21 that has a model and transmits sensor information to locator system 30. This configuration makes it possible to acquire vehicle sensor values ​​in real time and simulate satellite positioning.

[0059] In locator system testing using an actual vehicle, it is difficult from a safety standpoint to test a vehicle traveling the wrong way down a one-way road or exceed the speed limit set by the road. However, in this embodiment, the driving condition data includes the driving direction and target speed. By setting a driving direction that is the wrong way around or a target speed that exceeds the speed limit in the driving condition data, it is possible to test vehicle behavior that is difficult from a safety standpoint through a desktop simulation using a vehicle motion simulator. In other words, the test range of the locator system can be expanded.

[0060] <B. Hardware Configuration> Each of the computer 10, real-time simulator 20, and locator system 30 of the locator system simulator 101 described above is realized by, for example, a processing circuit 80 shown in FIG. 8 . That is, the processing circuit 80 constituting the computer 10 includes a data storage unit 11 and a driving scenario generation unit 14. The processing circuit 80 constituting the real-time simulator 20 includes a vehicle motion simulator 21 and a satellite positioning simulator 25. The processing circuit 80 also includes the locator system 30. Dedicated hardware may be applied to the processing circuit 80, or a processor that executes a program stored in memory may be applied. The processor may be, for example, a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a DSP (Digital Signal Processor), or the like.

[0061] When processing circuitry 80 is dedicated hardware, processing circuitry 80 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof.

[0062] When the processing circuit 80 is a processor, the functions of the driving scenario generation unit and the like are realized by a combination of software, etc. (software, firmware, or software and firmware). The software, etc. is written as a program and stored in a memory. As shown in FIG. 9 , a processor 81 applied to the processing circuit 80 realizes the functions of each unit by reading and executing a program stored in a memory 82. That is, the locator system simulator 101 includes a memory 82 for storing a program that, when executed by the processing circuit 80, results in the function of each unit of the locator system simulator 101 being executed. In other words, the program can be said to cause a computer to execute the procedure or method of each unit of the locator system simulator 101. Here, the memory 82 may be, for example, a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), or an EEPROM (Electrically Erasable Programmable Read Only Memory), a HDD (Hard Disk Drive), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disk) and its drive device, or any storage medium that will be used in the future.

[0063] The above describes a configuration in which the functions of each part of the locator system simulator 101 are realized either by hardware or software, etc. However, the present invention is not limited to this, and a configuration in which some parts are realized by dedicated hardware and other parts are realized by software, etc.

[0064] As described above, the processing circuit can realize each of the above functions by hardware, software, or a combination of these.

[0065] It should be noted that the embodiments can be freely combined, and each embodiment can be modified or omitted as appropriate. The above description is an example in all respects. It is understood that countless variations not illustrated can be envisioned.

[0066] 10 Computer, 11 Data storage unit, 12 Map data, 13 Driving condition data, 14 Driving scenario generation unit, 15 Driving route generation unit, 16 Driving condition combination unit, 20 Real-time simulator, 21 Vehicle motion simulator, 22 Environmental model, 23 Vehicle motion model, 24 Sensor model, 25 Satellite positioning model, 30 Locator system

Claims

1. A locator system simulator for verifying the functionality of a locator system that is mounted on a vehicle and estimates the position of the vehicle, comprising: a computer that generates driving scenarios in which the vehicle drives on virtual roads; and a real-time simulator that performs a real-time simulation of the vehicle driving in accordance with the driving scenarios, wherein the computer comprises: a data storage unit that stores map data and driving condition data that represent the driving conditions of the vehicle; and a driving scenario generation unit that generates the driving scenarios based on the map data and the driving condition data, and the real-time simulator comprises a model for performing a real-time simulation of the vehicle driving, and by inputting the driving scenarios into the model, a position estimation signal, which is a signal necessary for the locator system to estimate the position of the vehicle, is obtained as an output of the model, and the position estimation signal is input to the locator system.

2. The locator system simulator of claim 1, wherein the map data includes link information representing link positions, link directions, and link IDs of multiple links representing roads including the virtual road, the driving conditions include lane change target positions, driving directions, and traffic regulation information linked to the link IDs, and the driving scenario generation unit comprises: a driving route generation unit that generates a driving route for the virtual road based on the map data, and a driving condition combination unit that generates the driving scenario by combining the driving route with the driving conditions linked to the same link ID.

3. The locator system simulator according to claim 2, wherein the driving scenario generation unit extracts a group of nodes and a group of edges in directed graph theory from the link information of the map data, and generates a shortest route based on a start node and an end node selected from the group of nodes.

4. The locator system simulator of claim 3, wherein the plurality of links include a first link and a second link, and wherein the driving path generation unit, when a coordinate of the first link overlaps with a coordinate in the second link that does not include an end of the second link, extracts the overlapping coordinate as a new node and updates the group of nodes, divides the first link and the second link that have the overlapping coordinate other than an end of the link at the new node, updates the group of edges by using the divided link as a new edge, and generates the shortest path based on the updated group of nodes and group of edges.

5. The locator system simulator of claim 1, wherein the position estimation signal includes position information representing the position of the vehicle that has traveled according to the driving scenario, and sensor information representing the speed, acceleration, and angular velocity of the vehicle that has traveled according to the driving scenario, and the real-time simulator comprises: a satellite positioning simulator that creates a satellite positioning simulation signal that simulates satellite positioning of the vehicle using an artificial satellite based on the vehicle position information, and transmits the satellite positioning simulation signal to the locator system; and a vehicle motion simulator that has the model and transmits the sensor information to the locator system.

6. A function verification method for a locator system that is mounted on a vehicle and estimates the position of the vehicle, comprising: a computer generating a driving scenario in which the vehicle drives a virtual road; a real-time simulator performing a real-time simulation of the vehicle's driving in accordance with the driving scenario; the computer generating the driving scenario based on map data and driving condition data; and the real-time simulator inputting the driving scenario into a model for real-time simulation of the vehicle's driving, thereby obtaining a position estimation signal, which is a signal required for the locator system to estimate the position of the vehicle, as an output of the model, and inputting the position estimation signal into the locator system.

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