Information processing device

Abstract

To improve a technique for an operation plan of an automatic driving vehicle.SOLUTION: An information processing device 20 has a control unit 23. The control unit 23 prepares a power consumption amount map indicating a power consumption amount of a plurality of links included in an operation route defined in an operation plan for vehicles 10 operated by automatic driving, calculates a first predicted value that is a predicted value of a total power consumption amount required for travel of the operation route based on the power consumption amount map, compares the total power consumption amount indicated by the first predicted value with a SOC before operation of the vehicle 10, extracts, as a candidate link to be switched to a manual driving section, a link that satisfies predetermined conditions from links that are included in a plurality of links and designated as an automatic driving section when the SOC before operation does not satisfy the total power consumption amount, and by switching at least one candidate link of the extracted candidate links to the manual driving section, changes the operation plan so that the SOC before the operation satisfies the total power consumption amount.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device. [Background technology]

[0002] Conventionally, there are known techniques related to operation planning for vehicles that are operated by autonomous driving. For example, Patent Document 1 discloses a technique for outputting information indicating the amount of power consumption of a module used for the autonomous driving of a vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-205294 Summary of the Invention [Problem to be solved by the invention]

[0004] The amount of power consumed by an autonomous driving system that controls the operation of a vehicle operated by autonomous driving (hereinafter also referred to as an "autonomous driving vehicle") varies depending on road conditions such as the state of road maintenance. Furthermore, even when road conditions are the same, the amount of power consumed by an autonomous driving system tends to vary depending on the developer of the autonomous driving system. Therefore, when formulating an operation plan for an autonomous driving vehicle, it is necessary to take into account the characteristics of each autonomous driving system, which are circumstances unique to autonomous driving vehicles. However, conventional technology does not take such characteristics into account, leaving room for improvement.

[0005] In view of the above circumstances, the purpose of the present disclosure is to improve technology related to operation planning for autonomous vehicles. [Means for solving the problem]

[0006] An information processing device according to an embodiment of the present disclosure includes: An information processing device including a control unit, The control unit For vehicles operated by autonomous driving, a power consumption map is created that shows the power consumption of multiple links included in the operation route specified in the operation plan, calculating a first predicted value that is a predicted value of total energy consumption required for traveling the travel route based on the energy consumption map; an information processing device that compares the total power consumption indicated by the first predicted value with the SOC before operation of the vehicle, and if it is determined that the SOC before operation does not satisfy the total power consumption, extracts links that satisfy predetermined conditions from links that are designated as automatic driving sections and are included in the plurality of links as candidate links for switching to manual driving sections, and changes the operation plan by switching at least one of the extracted candidate links to a manual driving section. [Effects of the Invention]

[0007] According to one embodiment of the present disclosure, techniques for planning trips for autonomous vehicles are improved. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram illustrating a schematic configuration of a system according to an embodiment of the present disclosure. [Figure 2] 1 is a block diagram showing a schematic configuration of a vehicle. [Figure 3] FIG. 1 is a block diagram showing a schematic configuration of an information processing device. [Figure 4] 4 is a flowchart showing the operation of the information processing device according to the first embodiment. [Figure 5] 10 is a flowchart showing the operation of the information processing device according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described.

[0010] (Outline of the embodiment) An overview of a system 1 according to an embodiment of the present disclosure will be described with reference to Fig. 1. The system 1 includes a vehicle 10 and an information processing device 20. The vehicle 10 and the information processing device 20 are communicably connected to a network 30 including, for example, the Internet and a mobile communication network.

[0011] The vehicle 10 may be, for example, an automobile, but is not limited to this, and may be any vehicle that runs on charged power. The automobile may be, for example, an HEV (hybrid electric vehicle), a PHEV (plug-in hybrid electric vehicle), a BEV (battery electric vehicle), or an FCEV (fuel cell electric vehicle), but is not limited to these. In this embodiment, the vehicle 10 is driven by a driver in manual driving sections and is automated at an arbitrary level in automatic driving sections. Hereinafter, a vehicle that is automated at an arbitrary level is also referred to as a "vehicle operated by automatic driving" or an "automated vehicle." The automation level is, for example, any of levels 1 to 5 in the classification of the Society of Automotive Engineers (SAE). The vehicle 10 may also be a vehicle dedicated to MaaS (Mobility as a Service). The vehicle 10 is equipped with an automatic driving system that controls automatic driving. In this embodiment, the equipped automatic driving system is an Autonomous Driving Kit (ADK), but is not limited to this. The vehicle 10 moves along a route defined in an operation plan specified before operation. If the operation plan is changed, the vehicle 10 moves along a route defined in the changed operation plan. The number of vehicles 10 included in the system 1 can be determined arbitrarily.

[0012] The information processing device 20 is, for example, a computer such as a server device. The information processing device 20 is capable of communicating with the vehicles 10 via a network 30. The information processing device 20 is capable of acquiring any information related to the vehicles 10, such as vehicle information, from each vehicle 10. "Vehicle information" refers to information indicating the status of the vehicle 10. The vehicle information includes, for example, information indicating the position, orientation, vehicle speed, acceleration, shift position, mileage, number of users getting on and off the vehicle, and power consumption of the vehicle 10.

[0013] In this embodiment, the system 1 is used for a passenger transportation service using buses as the vehicles 10 that travel along a travel route defined in an operation plan. The travel route includes a section where the vehicle 10 is driven by a driver (hereinafter also referred to as a "manually driven section") and a section where the vehicle 10 is operated by automatic driving (hereinafter also referred to as an "automatic driving section").

[0014] First, an overview of this embodiment will be described, and details will be provided later. For a vehicle operated by autonomous driving, the information processing device 20 creates a power consumption map that indicates the power consumption of multiple links included in a travel route defined in a travel plan. Based on the power consumption map, the information processing device 20 calculates a first predicted value, which is a predicted value of the total power consumption required for traveling the travel route. The information processing device 20 compares the total power consumption indicated by the first predicted value with the vehicle's pre-travel SOC (State of Charge) to determine whether the pre-travel SOC satisfies the total power consumption. If it is determined that the pre-travel SOC does not satisfy the total power consumption, the information processing device 20 extracts, from among the multiple links designated as an autonomous driving section, links that satisfy predetermined conditions as candidate links for switching to a manual driving section. The information processing device 20 changes the travel plan by switching at least one of the extracted candidate links to a manual driving section so that the pre-travel SOC satisfies the total power consumption.

[0015] As described above, according to this embodiment, the operation plan is changed based on the power consumption map. Therefore, for example, if a power consumption map is created for each autonomous driving system, it becomes possible to change the operation plan to match the characteristics of the autonomous driving system installed in the vehicle 10. Therefore, technology related to operation planning for autonomous vehicles is improved in that it becomes easier to create an operation plan that takes into account the characteristics of the autonomous driving system installed in the vehicle 10.

[0016] Next, each component of the system 1 will be described in detail.

[0017] <Vehicle configuration> As shown in FIG. 2, the vehicle 10 includes a communication unit 11, an acquisition unit 12, an ADK 13, a battery 14, a storage unit 15, and a control unit 16.

[0018] The communication unit 11 includes one or more communication interfaces connected to the network 30. The communication interfaces are compatible with mobile communication standards such as, but not limited to, 4G (4th Generation) or 5G (5th Generation). In this embodiment, the vehicle 10 communicates with the information processing device 20 via the communication unit 11 and the network 30.

[0019] The acquisition unit 12 includes one or more devices that acquire location information of the vehicle 10. Specifically, the acquisition unit 12 includes, for example, a receiver compatible with the Global Positioning System (GPS), but is not limited thereto and may include a receiver compatible with any satellite positioning system. The acquisition unit 12 also includes any sensor module capable of acquiring vehicle information of the vehicle 10. For example, the sensor module includes a vibration sensor, an infrared sensor, a speed sensor, an angular velocity sensor, an acceleration sensor, a geomagnetic sensor, a distance sensor such as a LiDAR (light detection and ranging), a temperature sensor, a power monitor, or a combination thereof. The power monitor detects the amount of power consumed by the vehicle 10 over time. The amount of power consumed by the vehicle 10 refers to the amount of power consumed to run the vehicle 10, and includes, for example, the amount of power consumed by the motor and the autonomous driving system, as well as the amount of power consumed to operate the air conditioning system and electrical components.

[0020] The ADK 13 is an ECU (Electronic Control Unit) equipped with a computer in which autonomous driving software is installed. The ADK 13 is configured to be able to execute, for example, any one of SAE Levels 1 to 5 as driving control for the vehicle 10. At least one of the sensor modules included in the acquisition unit 12 may be incorporated into the ADK 13. The vehicle 10 can perform autonomous driving in accordance with driving control that can be executed by the ADK 13 by sending a control request to the control unit 16 (described later) using the ADK 13.

[0021] The battery 14 is a secondary battery that can be repeatedly charged and discharged. The vehicle 10 is driven by supplying power from the battery 14 to a drive mechanism such as a motor. The battery 14 may be, for example, a lithium-ion battery, a nickel-metal hydride battery, or a lead-acid battery. The battery 14 is connected by wire or wirelessly to a charging device installed at a charging station such as a bus depot provided by a bus operator, and is charged.

[0022] The storage unit 15 includes one or more memories. The memories may be, for example, semiconductor memories, magnetic memories, optical memories, etc., but are not limited to these. Each memory included in the storage unit 15 may function, for example, as a main storage device, an auxiliary storage device, or a cache memory. The storage unit 15 stores any information used in the operation of the vehicle 10. For example, the storage unit 15 may store system programs, application programs, embedded software, map information, etc. In this embodiment, the map information is a digital map (including base map information and digital elevation data, etc.) provided by the Geospatial Information Authority of Japan, but is not limited to this and may include any geospatial information. The information stored in the storage unit 15 may be updatable with information obtained from the network 30 via the communication unit 11, for example.

[0023] The control unit 16 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The processor may be, for example, a general-purpose processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), or a dedicated processor specialized for a specific process, but is not limited to these. The programmable circuit may be, for example, but is not limited to, an FPGA (Field-Programmable Gate Array). The dedicated circuit may be, for example, but is not limited to, an ASIC (Application Specific Integrated Circuit). The control unit 16 controls the overall operation of the vehicle 10.

[0024] In this embodiment, the control unit 16 can perform autonomous driving of the vehicle 10 in response to a control request from the ADK 13. For example, the control unit 16 can switch the driving mode of the vehicle 10, i.e., manual driving or autonomous driving, by setting the ADK 13 to ON in an autonomous driving section and to OFF in a manual driving section.

[0025] In this embodiment, the control unit 16 can transmit the vehicle information acquired via the acquisition unit 12 to the information processing device 20 via the communication unit 11 and the network 30.

[0026] In this embodiment, the control unit 16 can acquire from the battery 14 information indicating the amount of power consumption (for example, kWh) and information indicating the SOC (for example, a continuous value from 0% to 100%).

[0027] <Configuration of information processing device> As shown in FIG. 3, the information processing device 20 includes a communication unit 21, a storage unit 22, and a control unit .

[0028] The communication unit 21 includes one or more communication interfaces connected to the network 30. The communication interfaces correspond to, for example, a mobile communication standard, a wired LAN (Local Area Network) standard, or a wireless LAN standard, but are not limited to these and may correspond to any communication standard. In this embodiment, the information processing device 20 communicates with the vehicle 10 via the communication unit 21 and the network 30.

[0029] The storage unit 22 includes one or more memories. Each memory included in the storage unit 22 may function as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores any information used in the operation of the information processing device 20. For example, the storage unit 22 may store system programs, application programs, databases, map information, etc. The information stored in the storage unit 22 may be updatable with information obtained from the network 30 via the communication unit 21, for example.

[0030] The control unit 23 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The control unit 23 controls the operation of the information processing device 20 as a whole.

[0031] In this embodiment, the control unit 23 receives vehicle information of each vehicle 10 from the communication unit 11 of the vehicle 10 via the communication unit 21 and the network 30.

[0032] <Operation flow of information processing device> The operation of the information processing device 20 according to the first embodiment will be described with reference to Fig. 4. The operation of Fig. 4 corresponds to the method according to the present embodiment. The operation of Fig. 4 can be executed at any timing, but may be executed, for example, when the information processing device 20 receives an operation plan.

[0033] Step S100: The control unit 23 of the information processing device 20 creates a power consumption map indicating the power consumption of a plurality of links included in a travel route defined in an operation plan for the vehicle 10 that is operated by automatic driving.

[0034] Specifically, the control unit 23 acquires an operation plan for the vehicle 10. Any method can be used to acquire the operation plan. For example, the control unit 23 may acquire the operation plan for the vehicle 10 by reading out the operation plan stored in the database of the storage unit 22.

[0035] The control unit 23 calculates the amount of power consumption of each link included in the operation route specified in the acquired operation plan. A link is a road section whose end point (node) is an intersection or a dead end on the road. Each link has two nodes: a start node and an end node. Each link and node has absolute position information such as two-dimensional coordinate data including latitude and longitude that indicates its position on a map.

[0036] Any method can be used to calculate the amount of power consumption for each link included in the travel route. For example, data indicating the history of the amount of power consumption required for the vehicle 10 to travel on each link during the aggregation period may be acquired for each ADK 13, and a representative value (e.g., average value) of the amount of power consumption for each link may be calculated. In this case, the control unit 23 may set the calculated representative value as the amount of power consumption for each link in the ADK 13. The history of the amount of power consumption required for the vehicle 10 to travel may be, for example, the amount of power consumption for the motor and the autonomous driving system (here, the ADK 13), as well as the amount of power consumption required to operate the air conditioning system and electrical components. The aggregation period may be, for example, one month or more immediately preceding the travel date (when focusing on trends in power consumption immediately before the travel date), one day or more including the same day of the previous year corresponding to the travel date (when focusing on seasonal fluctuations in power consumption), or both.

[0037] The control unit 23 creates a power consumption map corresponding to the ADK 13 mounted on the vehicle 10 based on the power consumption of each set link. Any method can be used to create the power consumption map. For example, the control unit 23 may create the power consumption map by associating the set power consumption amounts with each link and mapping them on a map. The control unit 23 may store the created power consumption map in the storage unit 22.

[0038] As described above, the performance of the autonomous driving systems installed in the vehicle 10 tends to differ depending on the developer. Therefore, even if the driving route is the same, the total power consumption will differ if the autonomous driving system installed in the vehicle 10 is different. Therefore, in this embodiment, the control unit 23 does not uniformly calculate the predicted power consumption value of each link regardless of the autonomous driving system installed in the vehicle 10, but calculates the predicted power consumption value of each link for each autonomous driving system (here, ADK 13) installed in the vehicle 10. As a result, the power consumption map created according to this embodiment shows the predicted power consumption value of each link specific to each ADK 13.

[0039] Step S101: The control unit 23 calculates a first predicted value, which is a predicted value of the total amount of power consumption required to travel the travel route, based on the power consumption map created in step S100.

[0040] Specifically, the control unit 23 reads the power consumption map corresponding to the ADK 13 from the storage unit 22, acquires the power consumption of each link included in the travel route, and calculates the sum of the power consumption predicted from the acquired power consumption amounts as the first predicted value. Any method can be used to calculate the first predicted value. A simple addition process of the power consumption of each link included in the travel route is also possible. However, for example, the first predicted value may be calculated using machine learning such as deep learning based on the power consumption of each link calculated in step S100. For example, each link included in the travel route may be input into a prediction model for predicting power consumption, and a prediction result may be obtained from the prediction model, thereby predicting the power consumption of each link in the ADK 13 mounted on the vehicle 10 on the day of travel. The control unit 23 may generate or update the prediction model by performing machine learning while linking data indicating the power consumption history of each link in the ADK 13 to the ADK 13 mounted on the vehicle 10. The prediction model is generated or updated for each autonomous driving system that controls the autonomous driving of the vehicle 10 (i.e., for each individual ADK 13) or for each developer of an autonomous driving system. Teacher data for machine learning can be created by linking past power consumption amounts to each link as a label. This teacher data can be used to perform machine learning using a known algorithm to generate a trained model as a prediction model. The control unit 23 can calculate the predicted value of the power consumption of each link by obtaining the prediction results obtained from the generated trained model as the predicted value of the power consumption, and can calculate the sum of the calculated predicted values ​​as the first predicted value.

[0041] Step S102: The control unit 23 compares the total power consumption indicated by the first predicted value with the SOC before the vehicle 10 is driven, and determines whether the SOC before the vehicle is driven satisfies the total power consumption. If it is determined that the SOC before the vehicle is driven satisfies the total power consumption (step S102-Yes), the process ends. On the other hand, if it is determined that the SOC before the vehicle is not driven satisfies the total power consumption (step S102-No), the process proceeds to step S103.

[0042] Specifically, the control unit 23 acquires information indicating the SOC (here, a continuous value from 0% to 100%) from the battery 14 of the vehicle 10 before operation. In this embodiment, the SOC of the battery 14 of the vehicle 10 before operation is also referred to as the "SOC before operation." The control unit 23 converts the acquired SOC before operation into the remaining capacity of the battery 14. The control unit 23 compares the total power consumption indicated by the first predicted value with the remaining capacity of the battery 14. As a first example, if the total power consumption indicated by the first predicted value is 60 kWh and the remaining capacity converted from a SOC of 80% before operation is 80 kWh, the control unit 23 determines that the SOC before operation satisfies the total power consumption. As a second example, if the total power consumption indicated by the first predicted value is 60 kWh and the remaining capacity converted from a SOC of 50% before operation is 50 kWh, the control unit 23 determines that the SOC before operation does not satisfy the total power consumption.

[0043] Step S103: If it is determined that the SOC before operation does not satisfy the total power consumption amount (step S102-No), the control unit 23 extracts a link that satisfies a predetermined condition (hereinafter also referred to as the "first condition") from the links that are included in the multiple links and are designated as automatic driving sections, as a candidate link for switching to a manual driving section.

[0044] The first condition may be set arbitrarily. For example, the first condition may be that the average amount of power consumption per unit distance exceeds a reference value. The reference value may be set arbitrarily, and may be, for example, the average amount of power consumption per unit distance (here, 1 km) of multiple links. In this case, the control unit 23 extracts, as candidate links, links whose average amount of power consumption per unit distance exceeds the reference value from among the multiple links designated as an automated driving section.

[0045] When switching at least one candidate link to a manual driving section, the control unit 23 may prioritize switching to the manual driving section a candidate link with a higher amount of power consumption per unit distance. For example, assume that the average amount of power consumption per unit distance of multiple links is 0.15 kWh / km, the average amount of power consumption per unit distance of link AB is 0.07 kWh / km, and the average amount of power consumption per unit distance of link BC is 0.20 kWh / km. In this case, link BC corresponds to a link whose average amount of power consumption per unit distance exceeds a reference value. Therefore, the control unit 23 prioritizes link BC over link AB when switching to the manual driving section. Links with a higher amount of power consumption per unit distance are typically links without guardrails or links with unclear lane markings, which tend to consume more power from the distance sensor included in the acquisition unit 12 of the vehicle 10 than other links (e.g., links with guardrails). Note that when two or more links with the same average amount of power consumption per unit distance that exceeds the reference value are identified, the control unit 23 may switch to the manual driving section in order of the link with the shortest driving distance. This makes it easier to minimize the increase in the length of the manually driven section.

[0046] Step S104: The control unit 23 changes the operation plan by switching at least one of the candidate links extracted in step S103 to a manual operation section so that the SOC before operation satisfies the total power consumption amount.

[0047] Specifically, the control unit 23 switches at least one candidate link that satisfies the first condition to a manual driving section and turns off the ADK 13 in that section. The control unit 23 adds candidate links to be switched to a manual driving section until the remaining capacity of the battery 14 corresponding to the SOC before operation becomes equal to or greater than the total power consumption amount, and turns off the ADK 13 in that section. In this way, the control unit 23 can change the operation plan so that the SOC before operation satisfies the total power consumption amount.

[0048] Here, if the sole purpose is to simply reduce power consumption, it is sufficient to prioritize switching to manual operation from links with high power consumption. However, in this case, the manual driving section tends to be longer. Therefore, in this embodiment, by prioritizing switching to manual operation from links with high power consumption per unit distance, it becomes easier to minimize the length of the manual driving section. As a result, it becomes easier to reduce the burden on the remote monitor of the vehicle 10 while suppressing an increase in the burden on the driver of the vehicle 10.

[0049] As described above, according to the first embodiment, the operation plan is changed based on the power consumption map. Therefore, for example, if a power consumption map is created for each autonomous driving system, it becomes possible to change the operation plan in accordance with the characteristics of the autonomous driving system (here, ADK 13) installed in the vehicle 10. Therefore, technology related to operation planning for autonomously driven vehicles is improved in that it becomes easier to create an operation plan that takes into account the characteristics of the autonomous driving system installed in the vehicle 10.

[0050] Next, the operation of the information processing device 20 according to the second embodiment will be described with reference to Fig. 5. The operation of Fig. 5 corresponds to a method according to a modified example of this embodiment. While the operation of Fig. 4 described above is performed before the vehicle 10 starts to operate, the operation of Fig. 5 is performed after the vehicle 10 starts to operate. The operation of Fig. 5 is repeatedly performed while the vehicle 10 is operating, for example, at a predetermined cycle. The predetermined cycle can be set arbitrarily.

[0051] Step S200: The control unit 23 calculates a second predicted value, which is a predicted value of the remaining energy consumption required for traveling the remaining part of the travel route. The remaining part of the travel route refers to the part of the travel route where the vehicle 10 is scheduled to travel (the untraveled part).

[0052] Any method can be used to calculate the second predicted value. For example, the control unit 23 may predict the remaining power consumption taking into account the occupancy rate of the vehicle 10 after the start of operation and the operation rate of the air conditioning system. The occupancy rate of the vehicle 10 may be calculated by predicting the number of passengers based on historical data counting the number of passengers getting on and off using a sensor at the entrance, for example. The operation rate of the air conditioning system may also be calculated based on the predicted temperature for the relevant time period. Typically, the higher the occupancy rate of the vehicle 10, the higher the cabin temperature and the higher the operation rate of the air conditioning system. Therefore, the predicted value of the remaining power consumption may be calculated so that the higher the occupancy rate of the vehicle 10. The control unit 23 may calculate the calculated predicted value of the remaining power consumption as the second predicted value.

[0053] Step S201: The control unit 23 compares the remaining energy consumption indicated by the second predicted value with the SOC during operation of the vehicle 10, and determines whether the SOC during operation satisfies the remaining energy consumption. If it is determined that the SOC during operation satisfies the remaining energy consumption (step S201-Yes), the process ends. On the other hand, if it is determined that the SOC during operation does not satisfy the remaining energy consumption (step S201-No), the process proceeds to step S202.

[0054] Specifically, the control unit 23 acquires information indicating the SOC from the battery 14 of the vehicle 10 while it is in operation. In this embodiment, the SOC of the battery 14 of the vehicle 10 while it is in operation is also referred to as the "SOC during operation." The control unit 23 converts the acquired SOC during operation into the remaining capacity of the battery 14. The control unit 23 compares the remaining power consumption indicated by the second predicted value with the remaining capacity of the battery 14. As a third example, if the remaining power consumption indicated by the second predicted value is 30 kWh and the remaining capacity converted from an SOC of 40% during operation is 40 kWh, the control unit 23 determines that the SOC during operation satisfies the remaining power consumption. As a fourth example, if the remaining power consumption indicated by the second predicted value is 30 kWh and the remaining capacity converted from an SOC of 20% during operation is 20 kWh, the control unit 23 determines that the SOC during operation does not satisfy the remaining power consumption.

[0055] Step S202: If it is determined that the SOC during operation does not satisfy the remaining power consumption amount (step S201-No), the control unit 23 changes the operation plan so that the SOC during operation satisfies the remaining power consumption amount by switching another candidate link among the candidate links that has not been switched to a manual driving section to a manual driving section.

[0056] Specifically, the control unit 23 extracts one or more candidate links from among the candidate links extracted in step S103 that have not been switched to a manual driving section and that satisfy a second condition different from the first condition as another candidate link.

[0057] The second condition may be arbitrarily defined. For example, the second condition may be the shortest travel distance. In this case, the control unit 23 extracts the link with the shortest travel distance as another candidate link from among the candidate links extracted in step S103 and not switched to a manual driving section in step S104. The control unit 23 extracts candidate links that satisfy the second condition in ascending order of travel distance until the remaining capacity of the battery 14 corresponding to the SOC during operation becomes equal to or greater than the remaining power consumption amount. Then, the control unit 23 may change the operation plan so that the SOC during operation satisfies the remaining power consumption amount by turning off the ADK 13 in the section of the extracted candidate link.

[0058] As described above, according to the second embodiment, an additional candidate link is switched to a manual driving section while the vehicle 10 is in operation, and the ADK 13 is turned off in that section, thereby reducing the amount of power consumed by the vehicle 10. That is, according to the second embodiment, it is easier to reduce the amount of power consumed by the vehicle 10 in accordance with the situation on the day of operation. Furthermore, even if the bus schedule is delayed due to more boarding and disembarking users than expected, according to this embodiment, it is possible to appropriately increase the number of manual driving sections in accordance with the situation on the day of operation. As a result, it is easier to resolve delays in the bus schedule by appropriately adjusting the speed through manual driving, such as by accelerating departure when arrival is delayed.

[0059] As described above, the information processing device 20 according to this embodiment creates a power consumption map showing the power consumption of multiple links included in a travel route defined in a travel plan for a vehicle 10 operated by autonomous driving. Based on the power consumption map, the information processing device 20 calculates a first predicted value, which is a predicted value of the total power consumption required for traveling the travel route. The information processing device 20 compares the total power consumption indicated by the first predicted value with the SOC of the vehicle 10 before travel and determines whether the SOC before travel satisfies the total power consumption. If it is determined that the SOC before travel does not satisfy the total power consumption, the information processing device 20 extracts, from among the multiple links designated as autonomous driving sections, links that satisfy predetermined conditions as candidate links for switching to manual driving sections. The information processing device 20 changes the travel plan by switching at least one of the extracted candidate links to a manual driving section so that the SOC before travel satisfies the total power consumption.

[0060] With this configuration, the operation plan is changed based on the power consumption map. Therefore, for example, if a power consumption map is created for each autonomous driving system, it becomes possible to change the operation plan to suit the characteristics of the autonomous driving system installed in the vehicle 10. Therefore, technology related to operation planning for autonomous vehicles is improved in that it becomes easier to create an operation plan that takes into account the characteristics of the autonomous driving system installed in the vehicle 10.

[0061] Although the present disclosure has been described based on the drawings and examples, it should be noted that those skilled in the art may make various modifications and alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included in the scope of the present disclosure. For example, the functions included in each component or step can be rearranged so as not to be logically inconsistent, and multiple components or steps can be combined or divided into one.

[0062] For example, in one variation of this embodiment, vehicle 10 may be a semi-demand bus that travels to at least one bus stop on a route in response to a user request. In this case, control unit 23 may change the operation plan so that the SOC during operation satisfies the remaining energy consumption by changing the user boarding and disembarking location to a location other than the bus stop specified by the user. For example, assume that a user specifies bus stop A as the boarding location, and the route with the shortest travel distance includes a link with high energy consumption beyond bus stop A. In this case, control unit 23 may specify bus stop B, which is located just before the link with relatively low energy consumption, as the boarding location to avoid traveling on the link with high energy consumption beyond bus stop A. In other words, control unit 23 can reduce the remaining energy consumption by changing the travel route from the route with the shortest travel distance to a route with the minimum energy consumption. As a result, control unit 23 can more easily change the operation plan so that the SOC during operation satisfies the remaining energy consumption. At this time, taking into consideration the burden on the user, a limit on the change of the boarding position (for example, within 100 m from the original boarding position) may be set. The control unit 23 can notify the user of a request to change the boarding position by transmitting and outputting bus stop B to the user's terminal device.

[0063] In addition, for example, in the above-described embodiment, the configuration and operation of the information processing device 20 may be distributed among multiple computers that can communicate with each other. Also, for example, an embodiment in which some or all of the components of the information processing device 20 are provided in the vehicle 10 is possible. For example, a navigation device installed in the vehicle 10 may include some or all of the components of the information processing device 20.

[0064] Also, for example, an embodiment is possible in which a general-purpose computer functions as the information processing device 20 according to the above-described embodiment. Specifically, a program describing the processing content for realizing each function of the information processing device 20 according to the above-described embodiment is stored in the memory of the general-purpose computer, and the program is read and executed by a processor. Therefore, the present disclosure can also be realized as a program executable by a processor, or a non-transitory computer-readable medium storing the program. [Explanation of symbols]

[0065] 1 System 10 vehicles 11 Communications Department 12 Acquisition Department 13 ADK (Autonomous Driving Kit) 14 Battery 15 Storage section 16 Control Unit 20 Information processing equipment 21 Communications Department 22 Memory section 23 Control Unit 30 Network

Claims

1. An information processing device including a control unit, The control unit For vehicles operated by autonomous driving, a power consumption map is created that shows the power consumption of multiple links included in the operation route specified in the operation plan, calculating a first predicted value that is a predicted value of total energy consumption required for traveling the travel route based on the energy consumption map; an information processing device that compares the total power consumption indicated by the first predicted value with the SOC of the vehicle before operation, and if it is determined that the SOC before operation does not satisfy the total power consumption, extracts links that satisfy predetermined conditions from links that are designated as automatic driving sections and are included in the plurality of links as candidate links for switching to manual driving sections, and switches at least one of the extracted candidate links to a manual driving section, thereby changing the operation plan so that the SOC before operation satisfies the total power consumption.

2. 2. The information processing device according to claim 1, The control unit creates the power consumption map for each autonomous driving system that controls autonomous driving of the vehicle.

3. 3. The information processing device according to claim 1, the predetermined condition is that the average value of the amount of power consumption per unit distance exceeds a reference value; The information processing device, wherein when switching the at least one candidate link to a manual driving section, the control unit preferentially switches the candidate link with the highest amount of power consumption per unit distance to the manual driving section.

4. 4. The information processing device according to claim 3, The control unit calculating a second predicted value that is a predicted value of the remaining amount of energy consumed for traveling the remaining portion of the travel route; An information processing device that compares the remaining power consumption indicated by the second predicted value with the SOC of the vehicle during operation, and if it is determined that the SOC during operation does not satisfy the remaining power consumption, changes the operation plan by switching another candidate link among the candidate links that has not been switched to a manual driving section to a manual driving section so that the SOC during operation satisfies the remaining power consumption.

5. 5. The information processing device according to claim 4, the vehicle is a semi-on-demand bus that travels to at least one stop on the route in response to a user request, The control unit changes the operation plan by changing the location where the user gets on and off to a location other than the stop specified by the user so that the SOC during operation satisfies the remaining power consumption amount.

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