Information processing device

Abstract

To improve a technology related to riding comfort of an automatic driving vehicle.SOLUTION: In an information processing device 20 provided with a control part 23, the control part 23 acquires questionnaire data including a score being an evaluation index of riding comfort of each vehicle 10 for each automatic driving system mounted on each of a plurality of vehicles 10 operated along a prescribed operation route by automatic operation, identifies a vehicle 10 whose score is determined to be less than a threshold as a first vehicle from among the plurality of vehicles 10 on the basis of a tabulation data of the questionnaire data, and extracts first data to be used to analyze riding comfort from first vehicle information acquired by a first automatic driving system being an automatic driving system mounted on the first vehicle.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Conventionally, there are known technologies relating to the ride comfort of vehicles operated by automatic driving. For example, Patent Document 1 discloses a technology for receiving answers to questions about the ride comfort from passengers on board an automatically driven vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-052468 Summary of the Invention [Problem to be solved by the invention]

[0004] In passenger transport services using autonomously operated vehicles (hereinafter referred to as "autonomous vehicles"), autonomous driving systems have been continuously improved in order to dispatch vehicles with a more comfortable ride. However, with conventional technology, vehicle information analysis was carried out by each system developer, making it difficult to achieve overall optimization of the service, and there was room for improvement.

[0005] The purpose of the present disclosure, made in consideration of the above circumstances, is to improve technology related to ride comfort in 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 each of the autonomous driving systems installed in a plurality of vehicles that are operated by autonomous driving along a predetermined route, questionnaire data including a score that is an evaluation index of the ride comfort of each vehicle is acquired, Identifying a vehicle determined to have a score less than a threshold as a first vehicle from among the plurality of vehicles based on the aggregation result of the questionnaire data; First data to be used for analyzing ride comfort is extracted from first vehicle information acquired by a first automatic driving system, which is an automatic driving system installed in the first vehicle. [Effects of the Invention]

[0007] According to one embodiment of the present disclosure, technology related to ride comfort in autonomous vehicles is 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) A system 1 according to an embodiment of the present disclosure will be outlined 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 driving is automated at an arbitrary level in automated driving sections. The level of automation may be, for example, any of levels 1 to 5 in the SAE (Society of Automotive Engineers) level classification. The vehicle 10 may also be a vehicle dedicated to MaaS (Mobility as a Service). The vehicle 10 is equipped with an autonomous driving system that controls autonomous driving. In this embodiment, the autonomous driving system is, but is not limited to, an autonomous driving kit (ADK). In this embodiment, the vehicle 10 is, but is not limited to, a semi-on-demand bus that travels to at least one stop on a route in response to a user request. The vehicles 10 travel along a route defined in a travel plan. If the travel plan is changed, the vehicles 10 travel along a route defined in the changed travel 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 the 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.

[0013] "Vehicle information" is any information acquired from the vehicle 10. In this embodiment, the vehicle information includes information that changes as the vehicle 10 travels. The information that changes as the vehicle 10 travels indicates, for example, at least one of the following: the position of the vehicle 10, the driving mode, acceleration, vehicle speed, mileage, power consumption, remaining battery charge, shift position, accelerator or brake operation status, steering operation status, or operation status of a collision safety device. However, the vehicle information is not limited to these, and may include, for example, information about the status of parts or systems of the vehicle 10 (such as warning light display information or diagnostic information), the number of users getting on and off the vehicle 10, or the acquisition time of each piece of data (for example, a timestamp).

[0014] In this embodiment, the system 1 is used for a passenger transportation service using a semi-on-demand bus as the vehicle 10.

[0015] First, an overview of this embodiment will be described, and details will be provided later. An information processing device 20 acquires questionnaire data including a score, which is an evaluation index of the ride comfort of each vehicle 10, for each automatic driving system installed in each of multiple vehicles 10 that are operated by automatic driving along a predetermined route. Based on the aggregation results of the questionnaire data, the information processing device 20 identifies, from among the multiple vehicles 10, a vehicle 10 whose score is determined to be less than a threshold as a first vehicle. The information processing device 20 extracts first data to be used for analyzing the ride comfort from first vehicle information acquired by a first automatic driving system, which is an automatic driving system installed in the first vehicle.

[0016] As described above, according to this embodiment, a first vehicle is identified from among the multiple vehicles 10 based on the results of the survey data recorded for each automated driving system installed in each vehicle 10. Then, first data to be used for analyzing ride comfort is extracted from the first vehicle information acquired by the first automated driving system installed in the first vehicle. Therefore, for example, by automating the extraction of data useful for analyzing ride comfort, the burden on automated driving system developers who analyze ride comfort associated with data extraction can be reduced. As a result, automated driving system developers can easily extract data useful for analyzing ride comfort from vehicle information and identify areas for improvement in the automated driving system. Therefore, technology related to ride comfort in automated driving vehicles is improved in that it becomes easier to efficiently dispatch vehicles with better ride comfort.

[0017] Next, each component of the system 1 will be described in detail.

[0018] <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, an output unit 15, an input unit 16, a storage unit 17, and a control unit 18.

[0019] 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.

[0020] 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 to this and may include a receiver compatible with any satellite positioning system. The acquisition unit 12 also includes any sensor module that can acquire information about the vehicle 10 itself and information about the surroundings 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 temperature sensor, a power monitor, a distance sensor such as LiDAR (light detection and ranging), a camera, or a combination thereof.

[0021] 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 18, which will be described later, using the ADK 13.

[0022] 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.

[0023] The output unit 15 may include one or more output devices that output information to notify the user. The output devices are, but are not limited to, devices such as a display that outputs information as an image or video, or a speaker that outputs information as audio. The display is, for example, an LCD (liquid crystal display) or an organic EL (electro luminescence) display. The output unit 15 may include an interface for connecting an external output device.

[0024] The input unit 16 includes at least one input interface that detects user input. The input interface is, for example, a physical key, a capacitive key, a pointing device, a touch screen integrated with a display, a microphone, or a camera. The input unit 16 may also include an interface for connecting an external input device. As the connection interface, for example, an interface compatible with standards such as USB (Universal Serial Bus) or Bluetooth (registered trademark) can be used.

[0025] The storage unit 17 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 17 may function, for example, as a main storage device, an auxiliary storage device, or a cache memory. The storage unit 17 stores any information used in the operation of the vehicle 10. For example, the storage unit 17 may store system programs, application programs, embedded software, map information, etc. The map information may include any geospatial information, such as digital maps provided by the Geospatial Information Authority of Japan (including base map information and digital elevation data, etc.). The information stored in the storage unit 17 may be updatable with information obtained from the network 30 via the communication unit 11, for example.

[0026] In this embodiment, the storage unit 17 stores questionnaire data acquired via the input unit 16 of the vehicle 10. The questionnaire data can be stored in the storage unit 17 for each vehicle 10, i.e., for each autonomous driving system installed in each vehicle 10.

[0027] The control unit 18 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, but is not limited to, 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. 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 18 controls the overall operation of the vehicle 10.

[0028] In this embodiment, the control unit 18 can perform autonomous driving of the vehicle 10 in response to a control request from the ADK 13. For example, the control unit 18 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.

[0029] In this embodiment, the control unit 18 associates the vehicle information acquired via the acquisition unit 12 with the vehicle 10, i.e., the autonomous driving system, and transmits the same to the information processing device 20 via the communication unit 11 and the network 30. The control unit 18 also associates the questionnaire data accumulated in the memory unit 17 with the vehicle 10, i.e., the autonomous driving system, and transmits the same to the information processing device 20. The control unit 18 can also acquire information indicating the amount of power consumption and the remaining battery charge from the battery 14.

[0030] <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 .

[0031] 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.

[0032] 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.

[0033] 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.

[0034] In this embodiment, the control unit 23 receives vehicle information of the vehicle 10 from the communication unit 11 of the vehicle 10 via the communication unit 21 and the network 30. The control unit 23 also receives questionnaire data of the vehicle 10 from the communication unit 11 of the vehicle 10. The control unit 23 stores the received vehicle information and questionnaire data in the database of the storage unit 22 for each vehicle 10, i.e., for each autonomous driving system installed in each vehicle 10.

[0035] <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 this embodiment. The operation of Fig. 4 is repeatedly executed, for example, at a predetermined cycle. The predetermined cycle can be set arbitrarily.

[0036] Step S100: The control unit 23 of the information processing device 20 acquires questionnaire data including a score, which is an evaluation index of the ride comfort of each vehicle 10, for each autonomous driving system (here, ADK13) installed in each of multiple vehicles 10 that are operated by autonomous driving along a predetermined driving route.

[0037] Any method can be used to acquire the survey data. For example, the survey data can be acquired through a survey using a touchscreen as the input unit 16 of the vehicle 10. The survey can utilize the touchscreen of the user's terminal device. For example, when the vehicle 10 approaches a predetermined distance from a bus stop designated by the user, a question about the ride comfort of the vehicle 10 and answer buttons can be displayed on the touchscreen, and the user can tap the answer button to acquire the user's answer. In this embodiment, the user's answer is acquired once per ride, but may be acquired multiple times. The user's answer includes a score, which is an evaluation index of the ride comfort of the vehicle 10. While any index can be used for the score, in this embodiment, the score is indicated by a grade (5-point scale). While the grade can be defined arbitrarily, in this embodiment, it is defined as "score 5: very good," "score 4: good," "score 3: average," "score 2: bad," and "score 1: very bad." In other words, the higher the score, the better the ride comfort perceived by the user. The control unit 18 of the vehicle 10 transmits information including the acquired score to the information processing device 20 via the network 30 as questionnaire data for the vehicle 10.

[0038] The control unit 23 of the information processing device 20 may store the received questionnaire data for each vehicle 10 in a database of the storage unit 22. The questionnaire data may be stored in the database in association with identification information (e.g., chassis number) of the vehicle 10. The questionnaire data may be stored in the database in association with identification information (e.g., user account) of the user and identification information (e.g., chassis number) of the vehicle 10. In other words, the questionnaire data may be stored in the database in association with the user for each vehicle 10, i.e., for each autonomous driving system (here, ADK 13) installed in each vehicle 10. Note that the database is constructed in the storage unit 22 here, but may also be constructed in external storage and connected to the information processing device 20.

[0039] In this way, the control unit 23 can record the questionnaire data for each autonomous driving system by storing the questionnaire data in the memory unit 22 in association with each vehicle 10. The control unit 23 can acquire the questionnaire data by reading the questionnaire data from the database in the memory unit 22 each time the operation of Fig. 4 is performed. The control unit 23 searches the database using the identification information of the vehicle 10 as a query, and by referring to the corresponding vehicle information and questionnaire data, can ascertain how the vehicle 10, i.e., the autonomous driving system, was evaluated by the user.

[0040] Step S101: Based on the results of the survey data collected in step S100, the control unit 23 identifies, from among the multiple vehicles 10, a vehicle 10 whose score is determined to be less than the threshold as the first vehicle. Note that the autonomous driving system installed in each vehicle 10 is assumed to be the same and not replaced throughout the survey data collection period. In other words, the survey data collected for each vehicle 10 over a certain period indicates the evaluation results of one autonomous driving system installed in that vehicle 10.

[0041] Any method can be used to tally the survey data. For example, the control unit 23 may calculate a total score for a predetermined tallying period by referring to the survey data for each vehicle 10, and calculate a representative value of the scores for each vehicle 10. The representative value can be set arbitrarily, but here it is assumed to be an average value (hereinafter also referred to as an "average score"). In this case, the control unit 23 may calculate the average value of the scores given by users who rode in each vehicle 10 during a predetermined tallying period (e.g., the past year) as the average score. The control unit 23 may identify a first vehicle based on the average score. Any method can be used to identify a first vehicle. For example, the control unit 23 may identify a vehicle 10 whose average score is less than a threshold as the first vehicle. The threshold can be set arbitrarily, but here it is set to 3.0. The control unit 23 identifies a vehicle 10 determined to have a score less than 3.0 as the first vehicle from among the multiple vehicles 10.

[0042] Step S102: The control unit 23 extracts first data to be used for analyzing ride comfort from the first vehicle information acquired by the first automatic driving system, which is the automatic driving system installed in the first vehicle. The process then ends. The "first vehicle information" refers to the vehicle information acquired by the first automatic driving system.

[0043] The first data is data indicating at least one of the following, but is not limited to: the position of the first vehicle, driving mode, mileage, power consumption, remaining battery charge, acceleration, vehicle speed, shift position, accelerator or brake operation status, steering operation status, or collision safety device operation status. The control unit 23 extracts the first data by excluding data for a certain period from the vehicle information acquired by the first automated driving system during a predetermined collection period. The certain period can be determined arbitrarily, and first and second examples are shown below as specific examples.

[0044] As a first example, the partial period is a period during which the first vehicle was operated manually within a predetermined collection period. The control unit 23 may extract the first data by excluding data for a period during which the first vehicle was operated manually (hereinafter also referred to as the "first period") from the vehicle information acquired by the first automatic driving system. The first period can be identified by any method. For example, the control unit 23 may acquire information indicating the driving mode of the vehicle 10 (hereinafter also referred to as the "driving mode information") from the vehicle information acquired by the first automatic driving system during the collection period of the questionnaire data. The control unit 23 may identify, from the driving mode information, the time when the ADK 13 of the vehicle 10 was set to ON or OFF, i.e., the time when the driving mode of the vehicle 10 was set to manual driving. For example, suppose the driving mode information indicates that the ADK 13 of the vehicle 10 was set to OFF from time T1 to time T2. In this case, the control unit 23 identifies the time from T1 to T2 as the first period. The control unit 23 can extract the first data by excluding data for a first period from the vehicle information acquired by the first automatic driving system. The first data according to the first example can be said to be vehicle information for the automatic driving section. Therefore, by extracting the first data according to the first example, it becomes easier to acquire data that indicates the performance of the automatic driving system, excluding the driver's skill.

[0045] As a second example, the partial period is a period during which the first vehicle performed risk avoidance within a predetermined collection period. In this case, the control unit 23 may extract the first data by excluding data for the period during which the first vehicle performed risk avoidance (hereinafter also referred to as the "second period") from the vehicle information acquired by the first automatic driving system. The second period can be identified by any method. For example, the control unit 23 may acquire information indicating the activation status of a collision safety device (hereinafter also referred to as "PCS information"), which is included in data regarding the driving conditions of the vehicle 10, from the vehicle information acquired by the first automatic driving system during the collection period of the questionnaire data. The control unit 23 may identify the time when the collision safety device of the vehicle 10 was activated from the PCS information. For example, assume that the PCS information indicates that the collision safety device of the vehicle 10 was activated from time T3 to time T4. In this case, the control unit 23 identifies the time from T3 to T4 as the second period. The control unit 23 may extract the first data by excluding data for the second period from the vehicle information acquired by the first automatic driving system. The first data according to the second example can be considered vehicle information for a period in which sudden deceleration behavior, such as sudden braking, occurred due to unavoidable circumstances to avoid danger. From the perspective of safe operation, it is appropriate to exclude data under such circumstances from the evaluation of ride comfort. Therefore, by extracting the first data according to the second example, it becomes easier to obtain data that indicates the performance of the automated driving system, excluding data for periods in which the first vehicle avoided danger. Note that, if the time at which the questionnaire was collected is clear, questionnaires for ride periods that include periods in which the first vehicle avoided danger may be excluded from the aggregation.

[0046] In this way, according to the first embodiment, technology related to ride comfort in autonomous vehicles is improved in that it makes it easier to extract data useful for analyzing ride comfort from vehicle information and identify areas for improvement in autonomous driving systems.

[0047] 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. The operation of Fig. 5 is executed repeatedly, for example, at a predetermined cycle. The predetermined cycle can be set arbitrarily.

[0048] Step S200: The control unit 23 of the information processing device 20 acquires questionnaire data for each autonomous driving system installed in each of the multiple vehicles 10. The method for acquiring the questionnaire data is the same as that described above in step S100, and therefore, description thereof will be omitted.

[0049] Step S201: Based on the results of the collected questionnaire data recorded in step S200, the control unit 23 determines whether the score, which is an evaluation index of ride comfort, of the multiple vehicles 10 is less than a threshold value. The threshold value is the same as the threshold value described above in step S101, and is set to 3.0 here. In this embodiment, this threshold value is also referred to as the "first threshold value."

[0050] Step S202: If it is determined that the score is less than the first threshold value (step S201-Yes), the control unit 23 identifies the vehicle 10 whose score is determined to be less than the first threshold value as the first vehicle.

[0051] Specifically, the control unit 23 identifies as the first vehicle a vehicle 10 determined to have a score less than 3.0 from among the plurality of vehicles 10. After steps S201 and S202, the control unit 23 identifies as the first vehicle a vehicle 10 determined to have a score, which is an evaluation index of ride comfort, less than a first threshold value (here, 3.0), from among the plurality of vehicles 10, based on the aggregation result of the questionnaire data.

[0052] Step S203: The control unit 23 extracts first data to be used for analyzing the ride comfort from the first vehicle information. The method for extracting the first data is the same as that described above in step S102, and therefore, description thereof will be omitted.

[0053] Step S204: If it is determined that the score is equal to or greater than the first threshold (step S201-No), the control unit 23 executes a good vehicle determination. "Good vehicle determination" refers to a process of determining whether or not there is a vehicle 10 whose score is equal to or greater than the second threshold among the vehicles 10 whose score is determined to be equal to or greater than the first threshold. If it is determined as a result of the good vehicle determination that there is a vehicle 10 whose score is equal to or greater than the second threshold (step S204-Yes), the process proceeds to step S205. On the other hand, if it is determined that there is no vehicle 10 whose score is equal to or greater than the second threshold (step S204-No), the process ends.

[0054] The second threshold value is equal to or greater than the first threshold value and can be set to any value that makes it easier to extract, from a plurality of vehicles 10, a vehicle 10 that exhibits excellent performance and serves as a model for improvement activities of the autonomous driving system (here, ADK13). The second threshold value may be set to an appropriate value equal to or greater than the first threshold value. For example, if the first threshold value is set to 3.0 as in this embodiment, the second threshold value may be set to 4.0. In this case, the control unit 23 can perform the excellent vehicle determination by determining, based on the aggregation results of the questionnaire data, whether or not there is a vehicle 10 with a score of 4.0 or higher among the vehicles 10 determined to have a score of 3.0 or higher.

[0055] Step S205: The control unit 23 identifies the vehicle 10 whose score is determined to be equal to or greater than the second threshold as a good vehicle. The good vehicle corresponds to the second vehicle in this embodiment.

[0056] Specifically, the control unit 23 identifies, among the vehicles 10 whose scores are determined to be equal to or greater than the first threshold, the vehicles 10 whose scores are determined to be equal to or greater than a second threshold (e.g., 4.0) as excellent vehicles, i.e., second vehicles. After steps S201, S204, and S205, the control unit 23 identifies, from among the multiple vehicles 10, the vehicles 10 whose scores, which are an evaluation index of ride comfort, are determined to be equal to or greater than the second threshold, based on the aggregation results of the questionnaire data, as second vehicles.

[0057] Step S206: The control unit 23 extracts second data to be used for analyzing ride comfort from the second vehicle information acquired by the second automatic driving system, which is the automatic driving system installed in the second vehicle (good vehicle). The "second vehicle information" refers to the vehicle information acquired by the second automatic driving system.

[0058] The second data is data indicating at least one of the following, but is not limited to: the position of the second vehicle, driving mode, mileage, power consumption, remaining battery power, acceleration, vehicle speed, shift position, accelerator or brake operation status, steering operation status, or collision safety device activation status. In this embodiment, the second data is data of the same items as the first data extracted in step S203. For example, if the first data extracted in step S203 is data indicating the "position" and "acceleration" of the first vehicle, the second data is data indicating the "position" and "acceleration" of the second vehicle. In this case, the control unit 23 extracts data indicating the position of the second vehicle and data indicating the acceleration of the second vehicle from the vehicle information acquired by the second automatic driving system as the second data.

[0059] Step S207: The control unit 23 compares the first data extracted in step S203 with the second data extracted in step S206 to identify a deviation point.

[0060] Any method can be used to identify the deviation point. For example, the control unit 23 may compare the first data with the second data based on the magnitude of acceleration fluctuations (hereinafter also referred to as "acceleration amplitude") or the frequency of acceleration fluctuations per unit time (hereinafter also referred to as "acceleration / deceleration frequency") of the first and second vehicles. In this embodiment, each vehicle 10 travels along a predetermined travel route, so the vehicle information of the first and second vehicles includes data acquired at each position on the predetermined travel route. Therefore, the control unit 23 can compare the performance of each autonomous driving system (here, ADK 13) at the same position on the travel route by comparing the first data with the second data.

[0061] In the example described above in step S206, the control unit 23 may compare the amplitude of acceleration or the frequency of acceleration / deceleration of the first vehicle and the second vehicle at the same position on the travel route by comparing the first data with the second data. The control unit 23 may output the comparison result, for example, as a comparison result of the vehicle sway of the first vehicle and the second vehicle. For example, the control unit 23 may arbitrarily set a threshold value for the allowable deviation amount for the amplitude of acceleration or the frequency of acceleration / deceleration. In this case, the control unit 23 compares each data component point (each measurement value acquired at a predetermined sampling rate) of the data indicating the acceleration of the first vehicle and the data indicating the acceleration of the second vehicle. Based on the comparison result, the control unit 23 may identify a data component point at the same position on the travel route where the deviation is equal to or greater than a threshold as a singular point with a large deviation, i.e., a deviation point in this embodiment.

[0062] When extracting the second data in step S206, the control unit 23 may exclude data for a period corresponding to the first period or the second period, as in the first or second example described above. If this results in a period during which data comparison cannot be performed, the control unit 23 may identify the deviation point by comparing data for the period excluding that period.

[0063] Step S208: The control unit 23 acquires information that is a candidate for improvement of the first automatic driving system based on the deviation points identified in step S207.

[0064] Specifically, the control unit 23 references the database in the storage unit 22 to identify the time at which the data component point serving as the deviation point was acquired and the corresponding location on the travel route. The control unit 23 acquires information linking the identified time and location to the deviation point. For example, if a data component point is detected in which the amplitude of acceleration of the first vehicle is so large as to be considered a singular point compared to that of the second vehicle, the measurement time and corresponding location can be considered the time and location at which a sudden operation of the accelerator, brake, or the like occurred in the first vehicle without the collision safety device being activated. Furthermore, for example, if a data component point is detected in which the frequency of acceleration / deceleration per unit time of the first vehicle is so high as to be considered a singular point compared to that of the second vehicle, the measurement time and corresponding location can be considered the location and time at which the frequency of acceleration / deceleration of the first vehicle increased due to repeated short ON / OFF switching of the accelerator (i.e., the vehicle became jerky). In either case, the first vehicle (ADK13) is likely to be evaluated lower than the second vehicle (ADK13). Therefore, the control unit 23 may obtain the acquired information as information that can be used as a candidate for improvement of the first automatic driving system.

[0065] In this way, the control unit 23 can obtain information that can be used as a candidate for improvement of the first automatic driving system by comparing the first data with the second data and identifying deviation points.

[0066] Thus, according to the second embodiment, by providing the acquired information that is a candidate for improvement of the first autonomous driving system to, for example, the developer of the autonomous driving system to be installed in the first vehicle, it becomes easier to identify improvements to the autonomous driving system of the first vehicle.

[0067] As described above, the information processing device 20 according to this embodiment acquires questionnaire data including a score, which is an evaluation index of the ride comfort of each vehicle 10, for each autonomous driving system installed in each of multiple vehicles 10 that are operated by autonomous driving along a predetermined route. Based on the aggregation results of the questionnaire data, the information processing device 20 identifies, from among the multiple vehicles 10, a vehicle 10 whose score is determined to be less than a threshold value as a first vehicle. The information processing device 20 extracts first data to be used for analyzing the ride comfort from first vehicle information acquired by a first autonomous driving system, which is an autonomous driving system installed in the first vehicle.

[0068] According to this configuration, a first vehicle is identified from among the multiple vehicles 10 based on the results of the survey data recorded for each automated driving system installed in each vehicle 10. Then, first data used for analyzing ride comfort is extracted from the first vehicle information acquired by the first automated driving system installed in the first vehicle. Therefore, for example, by automating the extraction of data useful for analyzing ride comfort, the burden associated with data extraction on automated driving system developers who analyze ride comfort can be reduced. As a result, automated driving system developers can easily extract data useful for analyzing ride comfort from vehicle information and identify areas for improvement in the automated driving system. Therefore, technology related to ride comfort in automated driving vehicles is improved in that it becomes easier to efficiently dispatch vehicles 10 with better ride comfort.

[0069] 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.

[0070] For example, in the above-described embodiment, when a user reserves a semi-demand bus as the vehicle 10, the user may be able to select a ride comfort mode (here, modes 1 to 5) on a reservation application executed on the user's terminal device. For example, if "mode 5" is selected, a vehicle 10 with a high rating (e.g., a score of 4 or higher) identified from the user's personal rating history may be dispatched. This makes it easier to dispatch a vehicle based on the user's personal average rating score (hereinafter also referred to as "the user's personal past rating"), that is, to dispatch a vehicle according to the preferences of the user who made the reservation.

[0071] Furthermore, for example, in one variation of the above-described embodiment, the control unit 23 of the information processing device 20 may analyze the questionnaire data and identify as a deviation user a user whose opinion significantly deviates from the overall evaluation, which is the average score of all users. For example, a user whose response rate for an evaluation of a vehicle 10 (i.e., ADK 13) as a certain service is 50% or more and deviates from the overall evaluation by a predetermined score (e.g., 2.5) or more may be identified as a deviation user. For example, if a user is detected who has an average evaluation score of 1.0 for an evaluation of a certain vehicle 10, while the overall evaluation score is 4.0, the control unit 23 may identify the user as a deviation user. A vehicle 10 that the deviation user himself / herself has previously rated highly may be dispatched to the identified deviation user. However, even if a user once rated a vehicle 10 poorly for some reason, the overall evaluation may still be high. Therefore, a vehicle to be dispatched to a deviation user may be determined based on the overall evaluation instead of the user's personal past evaluation, and the deviation user may be given an opportunity to re-evaluate.

[0072] 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.

[0073] 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]

[0074] 1 System 10 vehicles 11 Communications Department 12 Acquisition Department 13 ADK (Autonomous Driving Kit) 14 Battery 15 Output section 16 Input section 17 Memory section 18 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 each of the autonomous driving systems installed in a plurality of vehicles that are operated by autonomous driving along a predetermined route, questionnaire data including a score that is an evaluation index of the ride comfort of each vehicle is acquired, Identifying a vehicle determined to have a score less than a threshold as a first vehicle from among the plurality of vehicles based on the aggregation result of the questionnaire data; extracting first data to be used for analyzing ride comfort from first vehicle information acquired by a first automatic driving system, which is an automatic driving system installed in the first vehicle; Here, the first vehicle information includes at least one of a position of the first vehicle, a driving mode, acceleration, vehicle speed, mileage, power consumption, remaining battery charge, shift position, accelerator or brake operation status, steering operation status, or collision safety device operation status, An information processing device wherein extracting the first data is extracting data from the first vehicle information that is useful for analyzing ride comfort to identify areas for improvement in an autonomous driving system.

2. 2. The information processing device according to claim 1, The control unit Based on the aggregation result of the questionnaire data, a vehicle is identified as a second vehicle from among the plurality of vehicles, the vehicle being determined to have a score that is an evaluation index of ride comfort equal to or greater than a second threshold value, the second threshold value being set to a value equal to or greater than a first threshold value corresponding to the threshold value; extracting second data to be used for analyzing ride comfort from second vehicle information acquired by a second automatic driving system, which is an automatic driving system installed in the second vehicle; By comparing the first data with the second data and identifying deviations, information that is a candidate for improvement of the first automated driving system is obtained; Here, the second vehicle information includes at least one of a position of the second vehicle, a driving mode, acceleration, vehicle speed, mileage, power consumption, remaining battery charge, shift position, accelerator or brake operation status, steering operation status, or collision safety device operation status, An information processing device wherein extracting the second data is extracting data from the second vehicle information that is useful for analyzing ride comfort to identify areas for improvement in an autonomous driving system.

3. 3. The information processing device according to claim 2, An information processing device, wherein the first data and the second data include information that changes as the first vehicle and the second vehicle travel, respectively.

4. 4. The information processing device according to claim 1, The control unit extracts the first data from the vehicle information acquired by the first automatic driving system by excluding data from a period during which the first vehicle was operated by manual driving.

5. 4. The information processing device according to claim 1, The control unit extracts the first data from the vehicle information acquired by the first automatic driving system by excluding data from a period during which the first vehicle performed hazard avoidance.

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