Information processing device and information processing system

Abstract

To reduce energy required for a vehicle to travel.SOLUTION: An information processing device communicable with a first vehicle determines a large vehicle traffic volume in the surroundings of the first vehicle on the basis of first data transmitted from the first vehicle, and maps information on the large vehicle traffic volume on road segments according to a result of the determination.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to controlling a vehicle. [Background technology]

[0002] There is known a system that searches for a route that can improve fuel efficiency or power efficiency before an autonomous vehicle starts operating. For example, by running vehicles in a platoon with short vehicle spacing, air resistance can be reduced and fuel efficiency and power efficiency can be improved. In this regard, Patent Document 1 discloses a device that searches for a route on which vehicles are running that is likely to achieve the slipstream effect. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-275500 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure aims to reduce the energy required to run a vehicle. [Means for solving the problem]

[0005] A first aspect of the present disclosure is an information processing device having a control unit that performs the following operations: determining a traffic volume of large vehicles in the vicinity of a first vehicle based on first data transmitted from the first vehicle; and mapping information regarding the traffic volume of large vehicles to a road segment based on the result of the determination.

[0006] A second aspect of the present disclosure is an information processing system including an in-vehicle device mounted in a first vehicle, and a server device capable of communicating with the in-vehicle device. Specifically, the vehicle-mounted device has a first control unit that detects a large vehicle in the vicinity of the first vehicle based on an image captured by an onboard camera and transmits first data including the result of the detection to the server device, and the server device has a second control unit that determines the traffic volume of the large vehicle in the vicinity of the first vehicle based on the first data and maps information regarding the traffic volume of the large vehicle to a road segment based on the result of the determination.

[0007] Another aspect of the present disclosure is a method executed by the above-described apparatus, or a computer-readable storage medium non-transitoryly storing a program for executing the method. Effect of the Invention

[0008] According to the present disclosure, the energy required for running a vehicle can be reduced. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an overview of a vehicle system. [Diagram 2] FIG. 2 shows a system configuration of an in-vehicle device 100. [Diagram 3] FIG. 2 is a schematic diagram of video data and position information data stored in a storage unit 102. [Figure 4] FIG. 2 is a diagram showing the components of the server device 200 in detail. [Diagram 5] An example of an image that has been labeled using the segmentation process. [Figure 6] 1 shows an example of data stored in a video database 202A. [Figure 7] An example of traffic data 202C. [Figure 8] 4 is a flowchart of a process executed by the in-vehicle device 100. [Figure 9] 6 is a flowchart of a process in which the server device 200 generates a traffic volume map. [Figure 10] An example of a generated traffic map. [Figure 11] 11 is a flowchart of a process in which the server device 200 generates an operation plan. [Figure 12] 4 is a flowchart of a process executed by the autonomous vehicle 300. [Figure 13] FIG. 11 is a diagram illustrating an example of a traffic volume map pattern in the second embodiment. [Figure 14] FIG. 13 is a diagram showing a system configuration of an in-vehicle device 100 according to a third embodiment. [Figure 15] 13 shows an example of detection result data according to the third embodiment. [Figure 16] 11 is a flowchart of a process executed by the in-vehicle device 100 in the third embodiment. [Figure 17] FIG. 13 is a diagram showing a system configuration of a server device 200 according to a third embodiment. [Figure 18] 13 is a flowchart of a process executed by a server device 200 in the third embodiment. [Figure 19] 13 shows an example of data used by the server device 200 in the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] There is a technology that aims to improve fuel efficiency by utilizing the slipstream effect. In particular, when the target vehicles are autonomous vehicles, forming a group of vehicles with short distances between them can reduce air resistance and save energy consumption while driving. The fuel consumption reduction effect of slipstreaming is greater as the size of the preceding vehicle increases. In relation to this, there is a technique for determining a pair of vehicles to perform slipstreaming based on, for example, operation information of large vehicles.

[0011] However, large vehicles such as buses and trucks do not always operate according to schedule, and if the schedule is delayed, the vehicles cannot meet up, which can result in the planned fuel efficiency reduction effect not being achieved. The information processing device according to the present disclosure solves such a problem.

[0012] An information processing device according to one aspect of the present disclosure is characterized in that it has a control unit that performs the following operations: determining the traffic volume of large vehicles in the vicinity of a first vehicle based on first data transmitted from the first vehicle; and mapping information regarding the traffic volume of large vehicles to a road segment based on the result of the determination.

[0013] The first data is data for detecting large vehicles, generated by a first vehicle (probe car). The first data may be, for example, an image captured by an on-board camera, or data indicating a result of detection of a large vehicle performed on the first vehicle side. When using an image, the presence of a large vehicle may be detected using machine learning, pattern matching, segmentation technology, or the like. This makes it possible to determine the number of large vehicles present around the first vehicle (probe car). The control unit also maps information about the traffic volume of large vehicles to road segments based on the result of the determination, which makes it possible to generate a map showing roads with a higher number of large vehicles, for example.

[0014] The mapping results (e.g., a map showing the traffic volume of large vehicles) can be used to control the driving of vehicles other than probe cars. For example, the generated map may be distributed to a control device mounted on an autonomous vehicle. Also, for example, when an information processing device generates a driving route for an autonomous vehicle, it can refer to the map to find a route that is easy to drive in a convoy (driving using a slipstream).

[0015] The first data may include time information and location information. With this configuration, it is possible to determine when and where a large vehicle was present, so that the traffic volume of large vehicles can be determined with greater accuracy. For example, there may be cases where the same large vehicle is captured by multiple probe cars, but by using the time information and location information, duplications can be detected. It becomes like this.

[0016] Furthermore, the speed of a large vehicle in the vicinity of the probe car may be determined based on the first data. For example, the speed of a large vehicle can be determined based on the detection results of the large vehicle acquired over time and the speed information of the probe car. If the large vehicle is moving extremely slowly or is stopped, the slipstream effect cannot be obtained, so it is preferable to exclude such vehicles from the calculation of traffic volume.

[0017] Specific embodiments of the present disclosure will be described below with reference to the drawings. Unless otherwise specified, the hardware configuration, module configuration, functional configuration, and the like described in each embodiment are not intended to limit the technical scope of the disclosure to only those.

[0018] First Embodiment An overview of a vehicle system according to a first embodiment will be described with reference to FIG. The vehicle system according to this embodiment includes a probe car 10, a server device 200, and an autonomous vehicle 300.

[0019] The probe car 10 is a vehicle for collecting data on the traffic volume of large vehicles. The probe car 10 may be an autonomous vehicle or a vehicle driven by a driver. The probe car 10 may be, for example, a general vehicle that has entered into a data provision contract with a business operator that provides a service. The autonomous vehicle 300 is an automatically driven vehicle that provides a predetermined service. The autonomous vehicle 300 may be a vehicle that transports passengers or cargo, or may be a mobile store vehicle. The autonomous vehicle 300 autonomously travels according to commands transmitted from the server device 200, and can provide a predetermined service.

[0020] The server device 200 is a device that controls the operation of the autonomous vehicle 300. The server device 200 generates a map showing the traffic volume of large vehicles based on data collected from the probe cars 10, and determines a driving route for the autonomous vehicle 300 by using the map. In the following description, a large vehicle is defined as a vehicle having a size (vehicle width, vehicle height, or frontal projection area) equal to or greater than a predetermined value. The predetermined value is preferably greater than that of a normal passenger vehicle (e.g., a passenger vehicle with a capacity of 10 or less). Examples of large vehicles include passenger buses (route buses, tourist buses, school buses, etc.), freight vehicles with van-type luggage compartments, freight vehicles that load or tow containers, tanker trucks, etc.

[0021] Each element that makes up the system will be explained. The probe car 10 is a connected car having a communication function with an external network. The probe car 10 is equipped with an in-vehicle device 100.

[0022] The in-vehicle device 100 is a computer for collecting information. In this embodiment, the in-vehicle device 100 has a camera installed facing the front of the vehicle, and transmits captured video to the server device 200 at a predetermined timing. Hereinafter, the video captured by the in-vehicle device 100 is referred to as an in-vehicle video. The in-vehicle device 100 may be a device (such as a car navigation device) that provides information to a passenger of the probe car 10, or may be an electronic control unit (ECU) included in the probe car 10. The in-vehicle device 100 may also be a data communication module (DCM) having a communication function.

[0023] The in-vehicle device 100 includes a processor such as a CPU or a GPU, a main memory such as a RAM or a ROM, It can be configured as a computer having an auxiliary storage device such as an EPROM, a hard disk drive, a removable medium, etc. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions that match a predetermined purpose, as described below, can be realized. However, some or all of the functions may be realized by hardware circuits such as ASICs and FPGAs.

[0024] FIG. 2 is a diagram showing a system configuration of the in-vehicle device 100. As shown in FIG. The in-vehicle device 100 includes a control unit 101 , a storage unit 102 , a communication unit 103 , an input / output unit 104 , a camera 105 , a position information acquisition unit 106 , and an acceleration sensor 107 .

[0025] The control unit 101 is a calculation unit that executes a predetermined program to realize various functions of the in-vehicle device 100. The control unit 101 may be realized by, for example, a CPU. The control unit 101 is configured to have, as functional modules, a video acquisition unit 1011 and a video transmission unit 1012. These functional modules may be realized by causing a CPU to execute stored programs.

[0026] The video acquisition unit 1011 captures video via the camera 105 described below, and stores the video in the storage unit 102. When the power of the device is turned on, the video acquisition unit 1011 generates a new storage area (for example, a folder, a directory, etc.).

[0027] During operation of the in-car device 100, the video acquisition unit 1011 captures video via the camera 105 and stores the obtained data (video data) in the storage unit 102. The video data is stored in file units. There is an upper limit (e.g., 1 minute or 5 minutes) on the length of a video corresponding to one file, and when the upper limit is exceeded, a new file is generated. If the storage capacity is insufficient, the video acquisition unit 1011 deletes the oldest file to secure free space and then continues shooting. Furthermore, the video acquisition unit 1011 acquires vehicle position information via the position information acquisition unit 106, which will be described later, at predetermined intervals (for example, every second), and stores the information as position information data. 3 is a schematic diagram of the video data and position information data stored in the storage unit 102. As shown in the figure, the video data and the position information data correspond one-to-one. By storing both the video data and the position information data in association with each other, it becomes possible to identify the traveling position of the vehicle after the fact.

[0028] The video transmission unit 1012 transmits the stored video data to the server device 200 at a predetermined timing. The predetermined timing may be a timing that arrives periodically. For example, the video transmission unit 1012 may transmit to the server device 200 the video data recorded in the immediately preceding file at the timing when a new file is generated.

[0029] The storage unit 102 is a memory device including a main storage device and an auxiliary storage device. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc., and by loading the programs stored therein into the main storage device and executing them, it is possible to realize various functions that meet a predetermined purpose, as described below. The primary storage device may include a random access memory (RAM) or a read only memory (ROM). The secondary storage device may include an erasable programmable read only memory (EPROM) or a hard disk drive (HDD). It may also include a disk drive (HDD, Hard Disk Drive). may include removable media, i.e., portable recording media.

[0030] The storage unit 102 stores data generated by the control unit 101, i.e., video data and and location data are stored.

[0031] The communication unit 103 is a wireless communication interface for connecting the in-vehicle device 100 to a network. The communication unit 103 is configured to be able to communicate with the server device 200 according to a communication standard such as a mobile communication network, a wireless LAN, or Bluetooth (registered trademark).

[0032] The input / output unit 104 is a unit that receives an input operation performed by a user and presents information to the user. The input / output unit 104 is configured to include, for example, a liquid crystal display, a touch panel display, and a hardware switch.

[0033] The camera 105 is an optical unit including an image sensor for acquiring images. The camera 105 is mounted facing forward of the vehicle. The location information acquisition unit 106 calculates location information based on a positioning signal transmitted from a positioning satellite (also referred to as a GNSS satellite). The location information acquisition unit 106 may include an antenna that receives radio waves transmitted from the GNSS satellite. The acceleration sensor 107 is a sensor that measures the acceleration applied to the device. The measurement result is supplied to the control unit 101, and the control unit 101 can thereby determine that an impact has been applied to the vehicle.

[0034] Next, the server device 200 will be described. The server device 200 is a device that controls the operation of the autonomous vehicle 300. The server device 200 also has a function of generating a map showing the traffic volume of large vehicles based on video data acquired from multiple probe cars 10 (in-vehicle devices 100). In the following description, a large vehicle is defined as a vehicle (e.g., a large truck, a large bus, etc.) that can obtain the slipstream effect by leading a platoon.

[0035] FIG. 4 is a diagram showing in detail components of the server device 200 included in the vehicle system according to this embodiment.

[0036] The server device 200 can be configured as a computer having a processor such as a CPU or GPU, a main storage device such as a RAM or ROM, and an auxiliary storage device such as an EPROM, a hard disk drive, or a removable medium. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc., and the programs stored therein are loaded into a working area of ​​the main storage device and executed. Each component is controlled through the execution of the programs, thereby realizing each function that matches a predetermined purpose, as described below. However, some or all of the functions may be realized by a hardware circuit such as an ASIC or an FPGA.

[0037] The server device 200 includes a control unit 201 , a storage unit 202 , and a communication unit 203 . The control unit 201 is a calculation device that manages the control performed by the server device 200. The control unit 201 can be realized by a calculation processing device such as a CPU. The control unit 201 is configured to have, as functional modules, a video management unit 2011, a map generation unit 2012, and an operation command unit 2013. Each functional module may be realized by causing a CPU to execute a stored program.

[0038] The video management unit 2011 executes a process of collecting video data transmitted from a plurality of probe cars 10 (vehicle-mounted devices 100). The video management unit 2011 also executes a process of dividing the received video data into predetermined road segments (for example, based on map data 202B described later) and storing the data in the storage unit 202 (video database 202A described later).

[0039] The map generating unit 2012 executes the following processes based on a plurality of video data collected from a plurality of probe cars 10.

[0040] (1) Detecting large vehicles from each vehicle's video and counting the number of large vehicles For example, a large vehicle detection process is performed for each of a plurality of frames constituting an in-vehicle video, and the number of detected large vehicles is counted for each road segment. The presence of a large vehicle can be recognized, for example, by segmentation technology. Segmentation technology is a technology for classifying objects included in an image into multiple classes, and can be realized mainly by a machine learning model. By performing segmentation, it is possible to assign labels such as "sky," "nature," "other vehicles," "buildings," "lane boundaries," "road," and "own vehicle" to multiple objects included in an image. In this embodiment, a class called a “large vehicle” is defined. That is, it is possible to identify an area (hereinafter, a detection area) corresponding to a large vehicle captured in an image. FIG. 5 is an example of an image (also called a labeled image) to which labels have been added by segmentation processing (classification processing).

[0041] For example, the map generating unit 2012 performs segmentation processing on an in-vehicle video corresponding to a certain road segment for each frame, and by tracking the detection area over time from when it appears to when it disappears, it is possible to determine how many large vehicles have been traveling on the corresponding road segment. For example, if N detection areas are detected between the start frame and the end frame, it can be determined that there are (at least) N large vehicles in the vicinity of the probe car 10. By such processing, it is possible to determine how many large vehicles were present in the vicinity of each probe car 10 at a certain point in time. The result of the determination is reflected in the traffic data 202C, which will be described later.

[0042] (2) A process to allocate the traffic volume of large vehicles to each road segment based on the counting results. According to the above-mentioned process, the number of large vehicles located in the vicinity of one probe car 10 can be counted. On the other hand, by combining the counting results from multiple in-vehicle videos (taken by multiple probe cars), it is possible to obtain the traffic volume of large vehicles traveling on a given road segment during a nearby time period. The traffic volume calculated here does not necessarily have to represent an accurate number of vehicles (e.g., the number of passing vehicles per hour, etc.). For example, a value (evaluation value) representing the number of large vehicles may be calculated based on the number of large vehicles captured by multiple probe cars 10. In this embodiment, an evaluation value ranging from 0 to 100 is calculated as the traffic volume of large vehicles.

[0043] In addition, the same large vehicle may be captured by multiple probe cars 10. Therefore, a process may be performed to determine whether the same large vehicle has been detected multiple times. For example, when the license plate of a large vehicle is recognized and the same license plate is detected multiple times close to each other, it may be determined that there is a duplication and one of the plates may be deleted. Also, when the time and location at which the large vehicle was detected are close to each other, it may be determined that there is a duplication and one of the plates may be deleted.

[0044] Furthermore, the map generating unit 2012 generates a map showing the number of large vehicles (hereinafter, referred to as a traffic volume map) based on the evaluation value assigned to each road segment.

[0045] The operation command unit 2013 generates an operation plan for a specific autonomous vehicle 300 and transmits the generated operation plan to the autonomous vehicle 300. An operation plan is data that instructs the autonomous vehicle 300 on tasks to be executed. If the autonomous vehicle 300 is a vehicle that transports passengers, the tasks include a task of getting passengers on and off, a task of driving to a specified point, and the like. If the autonomous vehicle 300 is a vehicle that transports luggage, the tasks include a task of picking up luggage, a task of driving to a specified point, a task of handing over luggage, and the like. If the autonomous vehicle 300 is a mobile store, the tasks include a task of driving to a specified point, a task of opening a store at the arrival point, and the like. The operation command unit 2013 generates an operation plan that combines multiple tasks, and the autonomous vehicle 300 completes the tasks in order according to the operation plan, thereby providing a predetermined service. In this embodiment, the operation plan includes a route along which the autonomous vehicle 300 will travel.

[0046] Furthermore, the traffic control unit 2013 generates a driving route for the autonomous vehicle 300 based on a traffic volume map. The driving route of the autonomous vehicle 300 is usually determined based on distance and required time. However, depending on the situation, it may be preferable to travel on a route with more large vehicles, even if it means taking a slightly longer route. One such case is when the remaining charge of the driving battery is low. When a platoon can be run with a large vehicle as the lead vehicle, improved power consumption is expected due to reduced air resistance, so that even if the driving distance is somewhat longer, the amount of energy (electricity) required for running may be reduced as a result. Under such predetermined conditions, the traffic control unit 2013 uses a traffic volume map to generate a driving route based on the "high traffic volume of large vehicles." The above is merely an example, and the conditions under which the traffic volume map is used may be changed depending on the design.

[0047] The storage unit 202 includes a main storage device and an auxiliary storage device. The main storage device is a memory in which the programs executed by the control unit 201 and data used by the control programs are expanded. The auxiliary storage device is a device in which the programs executed by the control unit 201 and data used by the control programs are stored.

[0048] The storage unit 202 also stores a video database 202A, map data 202B, and traffic data 202C. The video database 202A is a database that stores a plurality of in-vehicle videos transmitted from the in-vehicle device 100. Fig. 6 shows an example of data stored in the video database 202A. As shown in the figure, the video database 202A includes an identifier of a vehicle that transmitted the in-vehicle video, a shooting date and time, video data, a road segment identifier, and the like. The stored data may be deleted at a predetermined timing (for example, when a predetermined time has elapsed since it was received).

[0049] The map data 202B is a database that stores road maps. Road maps can be represented, for example, by a collection of nodes and links. The map data 202B includes definitions of the nodes, links, and road segments included in the links. A road segment is a unit section obtained by dividing a road link into a predetermined length. Each road segment may be associated with location information (latitude, longitude), an address, a place name, a road name, and the like.

[0050] The traffic data 202C stores information about large vehicles detected from the video data. As described above, the map generating unit 2012 records the number of large vehicles detected in a road segment in the traffic data 202C. Note that the traffic data 202C may also store other information about the detected large vehicles. Examples of such information include license plate information and feature amounts extracted from an image.

[0051] FIG. 7 is an example of the traffic data 202C. In this example, the traffic data 202C includes fields for date and time, road segment, number of vehicles, and vehicle information. The date and time field stores information about the date and time when the vehicle-mounted video was captured. The road segment field stores data for identifying the target road segment. The vehicle count information field stores a value representing the number of large vehicles detected. The vehicle information field stores other information about the detected large vehicles (e.g., license plate information, information about vehicle characteristics, etc.).

[0052] In this example, the number of large vehicles was recorded for each road segment, but if the location of a large vehicle can be accurately identified, the traffic data may be data that associates the location information of the large vehicle with the date and time when the vehicle-mounted video was shot.

[0053] The communication unit 203 is a communication interface for connecting the server device 200 to a network. The communication unit 203 includes, for example, a network interface board and a wireless communication interface for wireless communication.

[0054] 2 and 4 are merely examples, and all or part of the functions shown in the figures may be executed using specially designed circuits. Furthermore, programs may be stored or executed using a combination of a main memory device and an auxiliary memory device other than those shown in the figures.

[0055] Next, the processes executed by each device included in the vehicle system will be described in detail. 8 is a flowchart of the process executed by the in-vehicle device 100. The process shown in the figure is repeatedly executed by the control unit 101 while the in-vehicle device 100 is supplied with power.

[0056] In step S11, the video acquisition unit 1011 captures an in-vehicle video using the camera 105. In this step, the video acquisition unit 1011 records the video signal output from the camera 105 in a file as video data. As described with reference to FIG. 3, the file is divided into files of a predetermined length. If the storage area of ​​the storage unit 102 is insufficient, files are overwritten starting from the oldest file. Also, in this step, the video acquisition unit 1011 periodically acquires location information via the location information acquisition unit 106, and records the acquired location information in the location information data.

[0057] In step S12, the video acquisition unit 1011 determines whether a protection trigger has occurred. For example, a protection trigger occurs when an impact is detected by the acceleration sensor 107 or when a user presses a save button provided on the device body. In this case, the process proceeds to step S13, and the video acquisition unit 1011 moves the file currently being recorded to a protected area. The protected area is an area in which files are not automatically overwritten. This makes it possible to protect files that record important scenes.

[0058] If a protection trigger has not occurred, the process proceeds to step S14, where it is determined whether a file switch has occurred. As described above, the length of a video corresponding to one file has an upper limit (e.g., 1 minute, 5 minutes), and if the upper limit is exceeded, a new file is generated. If a switch has occurred, the process proceeds to step S15. Otherwise, the process returns to step S11. In step S15, the video transmission unit 1012 transmits the target video data to the server device 200 together with the position information data.

[0059] When the server device 200 (video management unit 2011) receives the video data, it identifies the road segment on which the probe car 10 has traveled based on the position information data, divides the video data for each road segment, and stores the divided data in the video database 202A. In the following description, one piece of video data corresponds to one road segment.

[0060] Next, the process executed by the server device 200 will be described in detail. 9 is a flowchart of a process for generating a traffic volume map based on collected video data. This process is executed by the map generating unit 2012 at a predetermined timing after the video data is accumulated.

[0061] First, in step S21, unprocessed video data is extracted from video database 202A. In this step, video data received within a recent predetermined period (for example, from 10 minutes ago to the present) may be extracted.

[0062] The processes in steps S22 to S26 are executed for each piece of extracted moving image data. First, in step S22, a segmentation process is performed on each frame of the target video data to detect large vehicles. In this step, it is determined how many large vehicles were in the vicinity of the probe car 10. For this reason, the map generating unit 2012 may determine the number of large vehicles present in the vicinity of the probe car 10 based on the time-dependent progress of the detection process performed on each frame.

[0063] In steps S23 and S24, it is determined whether the large vehicle detected in step S22 has already been detected in another vehicle-mounted video. For example, if the traffic data 202C includes license plate information of a large vehicle, this step determines whether or not there is a combination that satisfies the following condition. (1) The license plate information matches (2) The dates and times are close together If these conditions are met, it means that the newly detected large vehicle has already been detected from another vehicle-mounted video. In such a case (step S24-Yes), the process proceeds to step S26, where the overlaps are deleted and the remaining information is stored in the traffic data 202C. If the above condition is not met, the process proceeds to step S25, where information about the large vehicle is stored in the traffic data 202C. At this time, license plate information and information regarding the characteristics of the vehicle may be obtained.

[0064] In the above example, duplication is judged based on the license plate information, but duplication may be judged based on other information. For example, duplication may be judged based on the paint on the body of a large vehicle, the characters (company name) written on the body, etc.

[0065] Furthermore, if the vehicle body has no distinctive features, the determination of duplication may be made based on location information and time information. For example, if the position corresponding to each frame of a video can be identified, the driving position of a large vehicle may be estimated. In this case, two vehicles whose driving positions and dates are close to each other can be considered to be the same vehicle.

[0066] In step S27, the map generating unit 2012 calculates an evaluation value (i.e., a value representing the traffic volume of large vehicles) for each road segment based on the traffic data 202C, and generates a traffic volume map. Fig. 10 is an example of a traffic volume map generated by the map generating unit 2012. In the illustrated example, the thickness of the line represents the traffic volume of large vehicles. The generated traffic volume map is associated with the date and time when the in-vehicle video was captured, and is stored in the storage unit 202. is stored in.

[0067] Next, a process in which the server device 200 commands the autonomous vehicle 300 to operate based on the generated traffic volume map will be described. 11 is a flowchart of a process in which server device 200 commands autonomous vehicle 300 to operate. The process shown in the figure is executed by operation command unit 2013 when a trigger occurs to dispatch autonomous vehicle 300. For example, when a transportation service is provided by autonomous vehicles, the process shown in the figure is started when a vehicle dispatch request is received from a passenger.

[0068] In step S31, a vehicle to be dispatched is determined from among a plurality of autonomous vehicles 300 under the control of the system based on a vehicle dispatch request or the like. The vehicle to be dispatched can be determined based on, for example, the content of the requested service, the current location of each vehicle, the task being performed by each vehicle, and the like. For example, if the requested service is a passenger transportation service, an autonomous vehicle 300 that has a passenger transportation function and can arrive at a specified location within a predetermined time is selected. For this reason, the server device 200 may hold data regarding the status of the autonomous vehicle 300.

[0069] In step S32, it is determined whether or not the route search for the autonomous vehicle 300, which is the target vehicle, should be performed based on the number of large vehicles (in other words, whether or not the route search should be performed with priority given to fuel efficiency / electricity consumption). For example, if any of the following conditions is met, a positive determination is made in this step. (1) If the vehicle is an electric vehicle (2) If the vehicle is an electric vehicle and the remaining charge of the drive battery is below a specified value. (3) When the target vehicle is capable of automatic platooning or automatic following driving (4) When designated by the vehicle's operations manager.

[0070] If the determination in step S32 is affirmative, the process proceeds to step S33A. In step S33A, one or more route candidates are generated using a traffic volume map. The criterion for generating route candidates is the volume of large vehicle traffic. In other words, route candidates are generated by giving priority to roads on which there are more large vehicles.

[0071] If the determination in step S32 is negative, the process proceeds to step S33B. In step S33B, one or more route candidates are generated without using a traffic volume map, i.e., route candidates are generated based on distance or required time as in the conventional method.

[0072] In step S34, a route to be adopted is determined from the route candidates, for example, a route that meets a predetermined criterion, such as "priority on ordinary roads" or "priority on expressways" is selected.

[0073] In step S35, an operation plan corresponding to the selected autonomous vehicle 300 is generated. The operation plan is a set of tasks to be executed by the autonomous vehicle 300. The tasks include, for example, a task of moving to a specified point, a task of boarding and disembarking passengers, a task of loading and unloading baggage, and the like. The tasks also include a route along which the autonomous vehicle 300 will travel. The generated operation plan is transmitted to the target autonomous vehicle 300.

[0074] 12 is a flowchart of a process executed by the autonomous vehicle 300 that has received an operation plan. This process is started when the autonomous vehicle 300 receives the operation plan from the server device 200.

[0075] First, in step S41, the destination point (i.e., the server device 200).

[0076] When the autonomous vehicle 300 approaches the target point (step S42), it searches for a nearby location where the vehicle can be parked, and then executes the task (step S43).

[0077] When the task is completed, the autonomous vehicle 300 determines whether there is a next destination point according to the operation plan (step S44), and if there is a next destination point, it continues operation. If there is no next destination point (if all tasks included in the operation plan have been completed), it returns to the base.

[0078] As described above, the server device 200 according to the first embodiment generates a map showing the traffic volume of large vehicles based on the in-vehicle video transmitted from the probe car 10, and determines the driving route of the autonomous vehicles based on the map. This enables the autonomous vehicles to actively drive in a platoon or following other vehicles, thereby reducing the energy consumption required for driving.

[0079] (Modification 1 of the first embodiment) In the first embodiment, the server device 200 generates an operation plan for the autonomous vehicle 300, but the server device 200 may only generate a traffic volume map without generating an operation plan. For example, the server device 200 may be configured to distribute the generated traffic volume map to the autonomous vehicle 300. In this case, the autonomous vehicle 300 may autonomously generate an appropriate operation plan based on the received traffic volume map. Furthermore, the server device 200 may generate data other than map format data representing the traffic volume of large vehicles. For example, traffic data such as that illustrated in FIG. 9 or other data generated based on the traffic data may be distributed to multiple autonomous vehicles 300.

[0080] Also, the server device 200 may be a device that provides only a route search service. For example, the server device 200 may perform a route search that takes into account the number of large vehicles in response to a request from the autonomous vehicle 300 or the like, and return the result. In this case, it is not necessarily necessary to generate a traffic volume map.

[0081] Second Embodiment In the first embodiment, the traffic volume map is generated using video data received most recently (for example, in the past 10 minutes). In other words, the traffic volume map generated in the first embodiment reflects the latest road conditions. On the other hand, traffic volume of large vehicles can follow similar patterns depending on the day of the week and time of day. Therefore, the server device 200 may generate and store a plurality of patterns of traffic volume maps based on the accumulated video data, and control the operation of the autonomous vehicle 300 using such traffic volume maps. The second embodiment is an embodiment in which a plurality of patterns of traffic volume maps are generated for each day of the week and each time period and used.

[0082] In the second embodiment, in the process of step S21, all video data received within a predetermined period in the past (for example, the past one month) is subject to processing. In the second embodiment, traffic data is generated for each pattern, and a traffic volume map is generated for each pattern in the process of step S27. The patterns may be for each day of the week, or may be classified into weekdays, holidays, and public holidays. They may also be for each time period. FIG. 13 is a diagram illustrating a plurality of patterns in a table format. In this example, the patterns are defined by dividing the time period into 30-minute intervals.

[0083] In the second embodiment, in step S27, the traffic volume map of the target pattern is If it already exists, it is overwritten and updated. This allows the traffic map to be updated based on data generated within the past month, for example. The updated traffic volume may be calculated by weighted averaging or the like. The weights used to calculate the weighted average may be determined based on the shooting date of the in-vehicle video. For example, the smaller the number of days that have passed since the video was shot, the larger the weight may be. Conversely, the older the video was shot, the smaller the weight may be.

[0084] According to the second embodiment, a traffic volume map is generated for each pattern defined by the day of the week and the time period, so that the traffic volume of large vehicles can be determined more accurately according to the day of the week and the time period. In addition, it becomes possible to perform a route search by specifying a future date and time.

[0085] Third embodiment In the first and second embodiments, the server device 200 detects a large vehicle based on the in-vehicle video transmitted from the probe car 10. In contrast, in the third embodiment, the in-vehicle device 100 mounted on the probe car 10 executes a large vehicle detection process and transmits the result to the server device 200.

[0086] 14 is a system configuration diagram of an in-vehicle device 100A in the third embodiment. In the third embodiment, the in-vehicle device 100A (control unit 101) is configured to have a detection unit 1013 instead of the video transmission unit 1012.

[0087] The detection unit 1013 performs a process for detecting large vehicles on the video data acquired by the video acquisition unit 1011, and transmits the result to the server device 200. Specifically, the above-mentioned process (e.g., segmentation process) for detecting large vehicles is performed on each frame included in the latest video data. The process may be performed for each frame or at a predetermined interval (e.g., every second). The result of the processing is transmitted to the server device 200. Fig. 15 shows an example of data (hereinafter, detection result data) generated by the detection unit 1013 and transmitted to the server device 200.

[0088] The date and time field stores the date and time when the in-vehicle video was captured. The location information field stores location information (e.g., latitude and longitude) of the point where detection was performed. The detection result field stores data obtained as a result of detecting large vehicles. This field may store the number of detected large vehicles as a numerical value, or may store a label image obtained as a result of segmentation processing, such as that shown in FIG. 5. In addition, additional information related to large vehicles (e.g., license plate information, feature values, etc.) may be stored.

[0089] 16 is a flowchart of the process executed by the in-car device 100A in the third embodiment. The process of steps S11 to S15 is similar to that in the first embodiment. In this embodiment, in step S16, the detection unit 1013 executes a process of detecting a large vehicle and a process of generating detection result data. The generated detection result data is transmitted to the server device 200.

[0090] Next, the server device 200 in the third embodiment will be described. 17 is a system configuration diagram of a server device 200 in the third embodiment. In the third embodiment, the server device 200 does not have a video management unit 2011, and is configured to be able to store detection result data transmitted from the in-vehicle device 100A.

[0091] The map generating unit 2012A stores the detection result data received from the in-vehicle device 100A in the storage unit 202 as needed.

[0092] FIG. 18 is a flowchart of the process executed by the server device 200 (map generating unit 2012A) in the third embodiment. In step S21A, unprocessed detection result data is extracted from the storage unit 202. In the third embodiment, step S22 is omitted. In steps S23 to S26, overlap determination for large vehicles is performed using the same method as in the first embodiment, and the result is reflected in the traffic data. In step S27, similarly to the first embodiment, the map generating unit 2012 generates a traffic volume map based on the traffic data 202C.

[0093] As described above, it is possible for the in-vehicle device to perform the large vehicle detection process. In this case, it is not necessary to transmit video data every time, and the load on the network can be reduced.

[0094] (Fourth embodiment) In the first to third embodiments, all detected large vehicles are counted. However, there are also large vehicles that are not appropriate to follow, such as vehicles parked on the roadside or vehicles traveling at low speed. The fourth embodiment is an embodiment in which the presence of such large vehicles is detected and excluded from the counting.

[0095] In the fourth embodiment, speed information of the probe car 10 is added to the video data. The speed information may be included in the position information data shown in FIG. In the fourth embodiment, after execution of step S25 (S26), the map generating unit 2012 executes a process of estimating the speed of the detected large vehicle. The speed of the large vehicle can be estimated, for example, based on the speed of the probe car 10 at the time of shooting and the time that the large vehicle stays in the field of view of the camera. This is because the higher the relative speed between the probe car 10 and the large vehicle, the earlier the area (detection area) corresponding to the large vehicle disappears from the field of view of the camera. On the other hand, if the detection area continues to stay in the field of view, it can be estimated that the large vehicle is traveling at the same speed as the probe car. Such an estimation can be performed, for example, based on data (for example, FIG. 19(A)) that defines the relationship between the stay time in the detection area and the estimated speed.

[0096] The speed of the large vehicle can also be estimated based on the change over time in the size of the detection area. For example, if the large vehicle is moving away from the probe car at a higher relative speed, the size of the detection area will shrink faster, and if the large vehicle is approaching the probe car at a higher relative speed, the size of the detection area will expand faster. Thus, the speed of the large vehicle can be estimated by determining the expansion (contraction) rate of the detection area per unit time. Such an estimation can be performed, for example, based on data (e.g., FIG. 19(B)) that defines the relationship between the expansion rate of the detection area per unit time and the estimated speed.

[0097] If the speed estimated here is below a predetermined value (for example, the lower limit of the speed at which following a large vehicle is appropriate), the detected large vehicle is not counted. Other processing is the same as in the other embodiments.

[0098] According to the fourth embodiment, it is possible to detect only large vehicles traveling at a speed at which following the large vehicle is appropriate.

[0099] (Other variations) The above-described embodiment is merely an example, and the present disclosure can be modified and implemented as appropriate without departing from the spirit and scope of the present disclosure. For example, the processes and means described in this disclosure may be used without technical contradiction. , can be implemented in any combination.

[0100] In addition, in the description of the embodiment, the probe car 10 and the autonomous vehicle 300 are illustrated as separate vehicles, but the autonomous vehicle 300 may function as the probe car.

[0101] Furthermore, a process described as being performed by one device may be shared and executed by multiple devices. Alternatively, a process described as being performed by different devices may be executed by one device. In a computer system, the hardware configuration (server configuration) by which each function is realized can be flexibly changed.

[0102] The present disclosure can also be realized by supplying a computer program implementing the functions described in the above embodiments to a computer, and having one or more processors of the computer read and execute the program. Such a computer program may be provided to the computer by a non-transitory computer-readable storage medium connectable to the system bus of the computer, or may be provided to the computer via a network. Non-transitory computer-readable storage media include, for example, any type of disk, such as a magnetic disk (floppy disk, hard disk drive (HDD), etc.), an optical disk (CD-ROM, DVD disk, Blu-ray disk, etc.), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, and any type of medium suitable for storing electronic instructions. [Explanation of symbols]

[0103] 10. Probe car 100...In-vehicle equipment 101, 201...Control unit 102,202...Storage section 103,203···Communications Department 104...Input / output section 105···Camera 106...Location information acquisition unit 107 Acceleration Sensor 200 Server device 300 Autonomous Vehicles

Claims

1. determining a traffic volume of large vehicles around a first vehicle based on first data transmitted from the first vehicle; mapping information regarding the large vehicle traffic volume to road segments based on the result of the determination; generating a route for a second vehicle based on a result of the mapping, the route giving priority to a route with a higher volume of large vehicles; An information processing device having a control unit that executes the above.

2. The first data includes an image captured by an on-board camera, The control unit detects the large vehicle from the image. The information processing device according to claim 1 .

3. The first data includes a result of performing a large vehicle detection process on an image captured by an on-board camera. The information processing device according to claim 1 .

4. The control unit instructs the second vehicle to travel along the route. The information processing device according to claim 1 .

5. The control unit transmits the route to a second vehicle. The information processing device according to claim 1 .

6. The second vehicle is a vehicle capable of automatically following the large vehicle. The information processing device according to claim 1 .

7. The first data includes time information and location information, the control unit determines a location and a time when the large vehicle was present based on the first data transmitted from a plurality of the first vehicles, and determines a traffic volume of the large vehicle based on a result of the determination. The information processing device according to claim 1 .

8. The control unit determines the traffic volume while excluding the large vehicle that is detected in duplicate by a plurality of the first vehicles, based on the time information and the position information. The information processing device according to claim 7.

9. The control unit determines a speed of a large vehicle in the vicinity of the first vehicle based on the first data. The information processing device according to claim 1 .

10. The control unit determines the traffic volume while excluding the large vehicle whose speed is below a predetermined value. The information processing device according to claim 9.

11. An information processing system including an on-board device mounted in a first vehicle and a server device capable of communicating with the on-board device, The in-vehicle device includes: Detecting a large vehicle in the vicinity of the first vehicle based on an image captured by an on-board camera; transmitting first data including a result of the detection to the server device; A first control unit that executes The server device includes: determining a traffic volume of the large vehicle around the first vehicle based on the first data; mapping information regarding the large vehicle traffic volume to road segments based on the result of the determination; generating a route for a second vehicle based on a result of the mapping, the route giving priority to a route with a higher volume of large vehicles; The information processing system further comprises a second control unit that executes the above.

12. The first control unit performs object classification for the image captured by the vehicle-mounted camera. The information processing system according to claim 11.

13. The first data is a label image obtained as a result of the class classification. The information processing system according to claim 12.

14. The second control unit instructs the second vehicle to travel along the route. The information processing system according to claim 11.

15. The second control unit transmits the route to the second vehicle. The information processing system according to claim 11.

16. The second vehicle is a vehicle capable of automatically following the large vehicle.

16. An information processing system according to any one of claims 11 to 15.

17. The first data includes time information and location information, the second control unit determines a location and a time when the large vehicle was present based on the first data transmitted from a plurality of the first vehicles, and determines a traffic volume of the large vehicle based on a result of the determination; 16. An information processing system according to any one of claims 11 to 15.

18. The second control unit determines the traffic volume while excluding the large vehicle that is detected in duplicate by a plurality of the first vehicles, based on the time information and the position information.

18. The information processing system according to claim 17.

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