Automatic operation system

Abstract

To prevent an available space of a vehicle's storage device, which stores log data related to automatic operation control, from becoming insufficient in a vehicle provided with automatic operation control using machine learning model.SOLUTION: An automatic operation system performs processing to: execute automatic operation control for a vehicle; store log data related to the automatic operation control in one or more storage devices while the automatic operation control is in progress; and predict a vehicle's cruising distance. During the processing to predict the cruising distance, an available free space in one or more storage devices where the log data is stored is determined, and the cruising distance is set according to the available free space determined. The smaller the available free space determined the shorter the cruising distance is set.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present disclosure relates to a system for performing automatic driving control of a vehicle. [Background technology]

[0002] In recent years, there has been progress in the development of autonomous driving technology that uses models generated through machine learning (machine learning models) to control vehicles based on data from sensors mounted on the vehicles, etc. International Publication No. WO 2019 / 116423 proposes a method for collecting training data to be used in generating machine learning models.

[0003] In addition to International Publication No. 2019 / 116423, examples of documents showing the technical state of the technical field related to the present disclosure include JP 2011-113494 A, JP 2022-44235 A, and Japanese Patent No. 6761002 A. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2019 / 116423 [Patent Document 2] JP 2011-113494 A [Patent Document 3] Patent Publication No. 2022-44235 [Patent Document 4] Patent No. 6761002 Summary of the Invention [Problem to be solved by the invention]

[0005] It is desirable to verify the automatic driving control of a vehicle performed using a machine learning model after the fact. As a method for enabling this verification, it is conceivable to store log data related to the automatic driving control of the vehicle in a storage device of the vehicle. However, the capacity of the storage device of the vehicle is limited. Therefore, when the amount of data of this log data reaches a certain amount, it is conceivable to transmit it to the outside and delete it from the storage device. However, during the execution of the automatic driving control, log data related to this is generated every moment. Therefore, when the free space of the storage device becomes tight due to the log data before being transmitted to the outside and the log data generated during the automatic driving control, it may become impossible to store the latter log data from the middle. Therefore, technological development is desired to prevent such a tight situation from occurring.

[0006] One objective of the present disclosure is to provide a technology that can prevent a situation in which the free space of a storage device of a vehicle in which log data related to autonomous driving control using a machine learning model is stored becomes limited, in advance. [Means for solving the problem]

[0007] The present disclosure relates to an autonomous driving system installed in a vehicle and has the following features. The autonomous driving system includes one or more processors and one or more memory devices. The one or more processors are configured to perform processing for autonomous driving control of the vehicle, processing for storing log data related to the autonomous driving control in the one or more storage devices while the autonomous driving control is being executed, and processing for predicting a range of the vehicle. The one or more processors are configured to, in the process of predicting the cruising distance, obtain free space in the one or more storage devices in which the log data is stored, and set the cruising distance according to the free space. The less the free space, the shorter the cruising distance is set. Effect of the Invention

[0008] According to the present disclosure, the vehicle's cruising range is predicted according to the free space in one or more storage devices. In addition, the less free space there is, the shorter the cruising range is set. This makes it possible to prevent the free space from becoming tight. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an example of a configuration related to automatic driving control of a vehicle according to an embodiment. FIG. [Diagram 2] FIG. 1 is a conceptual diagram illustrating a configuration example of an autonomous driving system according to an embodiment. [Diagram 3] FIG. 1 is a diagram illustrating a concept of a mobility service. [Figure 4] FIG. 11 is a diagram showing an example of the relationship between the duration of execution of automatic driving control and the free space of a recording device. [Diagram 5] 11A and 11B are diagrams illustrating a process for increasing the free space in a recording device. [Figure 6] FIG. 2 is a diagram illustrating the total amount of log data generated while a vehicle is traveling along a candidate route. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] 1.Autonomous driving of vehicles 1 is a block diagram showing an example of a configuration related to the automatic driving control of a vehicle 1 according to the present embodiment. Automatic driving refers to automatically performing at least one of steering, acceleration, and deceleration of the vehicle 1 without the driver's operation. Automatic driving control is a concept that includes not only fully automatic driving control, but also risk avoidance control, lane keep assist control, and the like. The operator may be a driver who rides in the vehicle 1, or a remote operator who remotely operates the vehicle 1.

[0011] The vehicle 1 includes a sensor group 10, a recognition unit 20, a planner 30, a control amount calculation unit 40, and a traveling device 50.

[0012] The sensor group 10 includes a recognition sensor 11 used to recognize the situation around the vehicle 1. Examples of the recognition sensor 11 include a camera, a LIDAR (Laser Imaging Detection and Ranging), a radar, and the like. The sensor group 10 may further include a status sensor 12 that detects the status of the vehicle 1, a position sensor 13 that detects the position of the vehicle 1, and the like. Examples of the status sensor 12 include a speed sensor, an acceleration sensor, a yaw rate sensor, a steering angle sensor, and the like. Examples of the position sensor 13 include a GNSS (Global Navigation Satellite System) sensor.

[0013] The sensor detection information SEN is information obtained by the sensor group 10. For example, the sensor detection information SEN includes images captured by a camera. As another example, the sensor detection information SEN may include point cloud information obtained by a LIDAR. The sensor detection information SEN may include vehicle state information indicating the state of the vehicle 1. The sensor detection information SEN may include position information indicating the position of the vehicle 1.

[0014] The recognition unit 20 receives the sensor detection information SEN. The recognition unit 20 recognizes the situation around the vehicle 1 based on the information obtained by the recognition sensor 11. For example, the recognition unit 20 recognizes objects around the vehicle 1. Examples of objects include pedestrians, other vehicles (preceding vehicles, parked vehicles, etc.), white lines, road structures (e.g., guardrails, curbs), fallen objects, traffic lights, intersections, signs, etc. The recognition result information RES indicates the recognition result by the recognition unit 20. For example, the recognition result information RES includes object information indicating the relative position and relative speed of the object with respect to the vehicle 1.

[0015] The planner 30 receives the recognition result information RES from the recognition unit 20. The planner 30 may also receive vehicle state information, position information, and pre-generated map information. The map information may be high-precision three-dimensional map information. The planner 30 generates a driving plan for the vehicle 1 based on the received information. The driving plan may be for reaching a pre-set destination, or for avoiding risks. Examples of the driving plan include maintaining the current driving lane, changing lanes, overtaking, turning right or left, steering, accelerating, decelerating, and stopping. Furthermore, the planner 30 generates a target trajectory TRJ required for the vehicle 1 to travel according to the driving plan. The target trajectory TRJ includes a target position and a target speed.

[0016] The control amount calculation unit 40 receives the target trajectory TRJ from the planning unit 30. The control amount calculation unit 40 calculates a control amount CON required for the vehicle 1 to follow the target trajectory TRJ. It can also be said that the control amount CON is a control amount required to reduce the deviation between the vehicle 1 and the target trajectory TRJ. The control amount CON includes at least one of a steering control amount, a drive control amount, and a braking control amount. Examples of the steering control amount include a target steering angle, a target torque, a target motor angle, a target motor drive current, etc. Examples of the drive control amount include a target speed, a target acceleration, etc. Examples of the braking control amount include a target speed, a target deceleration, etc.

[0017] The traveling device 50 includes a steering device 51, a driving device 52, and a braking device 53. The steering device 51 steers the wheels. For example, the steering device 51 includes an electric power steering (EPS) device. The driving device 52 is a power source that generates a driving force. Examples of the driving device 52 include an engine, an electric motor, and an in-wheel motor. The braking device 53 generates a braking force. The traveling device 50 receives a control amount CON from the control amount calculation unit 40. The traveling device 50 operates the steering device 51, the driving device 52, and the braking device 53 according to the steering control amount, the driving control amount, and the braking control amount, respectively. As a result, the vehicle 1 travels so as to follow the target trajectory TRJ.

[0018] The recognition unit 20 includes at least one of a rule-based model and a machine learning model. The rule-based model performs recognition processing based on a group of predetermined rules. Examples of the machine learning model include a Neural Network (NN), a Support Vector Machine (SVM), a regression model, a decision tree model, and the like. The NN may be a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a combination thereof. The type of each layer, the number of layers, and the number of nodes in the NN are arbitrary. The machine learning model is generated in advance through machine learning. The recognition unit 20 performs recognition processing by inputting the sensor detection information SEN to the model. The recognition result information RES is output from the model, or is generated based on the output from the model.

[0019] Similarly, the planner 30 includes at least one of a rule-based model and a machine learning model. The planner 30 performs planning processing by inputting the recognition result information RES to the model. The target trajectory TRJ is output from the model or is generated based on the output from the model.

[0020] Similarly, the control amount calculation unit 40 includes at least one of a rule-based model and a machine learning model. The control amount calculation unit 40 performs a control amount calculation process by inputting a target trajectory TRJ to the model. The control amount CON is output from the model or is generated based on the output from the model.

[0021] Two or more of the recognition unit 20, the planner 30, and the control amount calculation unit 40 may be integrally configured. All of the recognition unit 20, the planner 30, and the control amount calculation unit 40 may be integrally configured (end-to-end configuration). For example, the recognition unit 20 and the planner 30 may be integrally configured by a NN that outputs a target trajectory TRJ from the sensor detection information SEN. Even in the case of an integral configuration, intermediate products such as recognition result information RES and target trajectory TRJ may be output. For example, when the recognition unit 20 and the planner 30 are integrally configured by a NN, the recognition result information RES may be the output of an intermediate layer of the NN.

[0022] The recognition unit 20, the planning unit 30, and the control amount calculation unit 40 constitute an "automatic driving control unit" that controls the automatic driving of the vehicle 1. In this embodiment, a machine learning model is used in at least a part of the automatic driving control unit. That is, at least one of the recognition unit 20, the planning unit 30, and the control amount calculation unit 40 includes a machine learning model. The automatic driving control unit performs at least a part of the automatic driving control of the vehicle 1 by using the machine learning model.

[0023] 2.Autonomous Driving System 2 is a conceptual diagram showing a configuration example of an autonomous driving system 100 according to this embodiment. The autonomous driving system 100 is mounted on a vehicle 1 and controls autonomous driving of the vehicle 1. The autonomous driving system 100 has at least the functions of the autonomous driving control unit described above. The autonomous driving system 100 may further include a sensor group 10 and a traveling device 50.

[0024] The autonomous driving system 100 includes one or more processors 110 (hereinafter simply referred to as "processors" 110) and one or more storage devices 120 (hereinafter simply referred to as "storage devices 120"). The processors 110 execute various processes. Examples of the processors 110 include a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), and a field-programmable gate array (FPGA). The recognition unit 20, the planning unit 30, and the control amount calculation unit 40 may be realized by a single processor 110, or may be realized by separate processors 110. The storage device 120 stores various information. Examples of the storage device 120 include a hard disk drive (HDD), a solid state drive (SSD), a volatile memory, and a non-volatile memory.

[0025] The program 130 is a computer program for controlling the vehicle 1, and is executed by the processor 110. Various processes by the autonomous driving system 100 may be realized by cooperation between the processor 110, which executes the program 130, and the storage device 120. The program 130 is stored in the storage device 120. The program 130 may be recorded on a computer-readable recording medium.

[0026] The model data 140 is data of a model included in the recognition unit 20, the planning unit 30, and the control amount calculation unit 40. As described above, in this embodiment, at least one of the recognition unit 20, the planning unit 30, and the control amount calculation unit 40 includes a "machine learning model." The model data 140 is stored in the storage device 120 and is used for automatic driving control.

[0027] During the autonomous driving control, the processor 110 acquires "log data LOG" related to the autonomous driving control. The log data LOG may include sensor detection information SEN input to the autonomous driving control unit. The log data LOG may include control amount CON output from the autonomous driving control unit. The log data LOG may include recognition result information RES output from the recognition unit 20. The log data LOG may include target trajectory TRJ output from the planner 30. The log data LOG may include reasons for judgments made in the recognition process by the recognition unit 20. The log data LOG may include reasons for judgments made in the planning process by the planner 30. The log data LOG may include the presence or absence of operator intervention in the autonomous driving control.

[0028] The processor 110 stores the log data LOG acquired during the automatic driving control in the storage device 120. The processor 110 may temporarily store the log data LOG in the storage device 120 for only a certain period of time.

[0029] The management server 200 is an external device that exists outside the vehicle 1. The management server 200 communicates with one or more vehicles 1 via a communication network. During the autonomous driving control or after the autonomous driving control is completed, the processor 110 of the vehicle 1 may upload at least a part of the log data LOG stored in the local storage device 120 to the management server 200. The log data LOG uploaded to the management server 200 is used for verifying the autonomous driving control using a machine learning model, etc. The processor 110 may erase the log data LOG uploaded to the management server 200 from the local storage device 120.

[0030] 3. Driving range In this embodiment, a case is considered in which one or more vehicles 1 are applied to a mobility service. A mobility service is a service that transports people using a vehicle. Examples of mobility services include a taxi service, a bus service, and a ride-sharing service. The management of the mobility service is performed, for example, by a management server 200. The management of the mobility service includes management of the vehicles 1, such as storage and selection (dispatch) of the vehicles 1 applied to the mobility service, and management of users US of the mobility service. The management of the mobility service also includes verification of the automatic driving control using the log data LOG described above.

[0031] Fig. 3 is a diagram showing a concept of a mobility service. Fig. 3 illustrates one of one or more vehicles 1. In response to a request from a mobility service user US1, the vehicle 1 transports the user US1 from a point P1 to a point P2.

[0032] When vehicle 1 transports user US1, vehicle 1 departs from storage location ST and heads to point P1. Storage location ST is, for example, a mobility service base. Vehicle 1 arrives at point P1 and picks up user US1 at point P1. Vehicle 1 that has picked up user US1 heads from point P1 to point P2. Vehicle 1 arrives at point P2 and drops off user US1 at point P2. Vehicle 1 that has dropped off user US1 heads from point P2 to storage location ST. While heading from point P2 to storage location ST, vehicle 1 may transport user US2 at the request of user US2, who is different from user US1.

[0033] Consider a route RT1 that starts from a storage location ST, passes through points P1 and P2, and returns to the storage location ST. While the vehicle 1 is traveling along a part or all of this route RT1, automatic driving control is performed. When the automatic driving control is executed, log data LOG is generated. The generated log data LOG is stored in the storage device 120 of the vehicle 1.

[0034] Here, log data LOG continues to be generated during execution of automatic driving control. Therefore, the total amount TQst of log data LOG in storage device 120 continues to increase during execution of automatic driving control. Fig. 4 is a diagram showing an example of the relationship between the duration of execution of automatic driving control and free space QC. The horizontal axis of Fig. 4 shows the duration of execution of automatic driving control. Also, the vertical axis on the right side of Fig. 4 shows the free space QC.

[0035] The vertical axis on the left side of Fig. 4 further shows the amount of log data LOG generated per unit time ΔQt_RT1. If the execution of the automatic driving control continues, the total amount TQst, which is the integrated value of the amount of generated ΔQt_RT1, increases. Then, the free space QC of the storage device 120 continues to decrease. As a result, if the free space QC becomes scarce, it may become impossible to save the log data LOG from the middle of the route RT1.

[0036] In view of this problem, in this embodiment, a cruising range (Milage) ML of the vehicle 1 is set. The cruising range ML is set according to the available capacity QC. For example, the relationship between the distance traveled by executing the automatic driving control and the total amount TQgn of log data LOG generated during this travel is calculated in advance. This relationship may be calculated focusing on the travel history of one vehicle 1, or may be calculated focusing on the travel history of multiple vehicles 1. Using this relationship, the amount ΔQl of log data LOG generated per unit distance (for example, 1 km) can be obtained. The cruising range ML can be calculated based on the following formula (1). ML = QC / ΔQl (1)

[0037] A concept close to the cruising distance ML is the cruising distance DTE (Distance To Empty). The cruising distance DTE is set based on the remaining charge of the battery that supplies power to the driving source of the vehicle and the remaining amount of fuel. Therefore, the calculated value of the cruising distance DTE and the cruising distance ML are fundamentally different. However, the vehicle 1 cannot travel a distance that exceeds the cruising distance DTE. Therefore, if the cruising distance ML calculated based on the formula (1) exceeds the cruising distance DTE, the cruising distance ML may be changed. In this case, the value of the cruising distance DTE is set as the cruising distance ML.

[0038] The cruising distance ML is set, for example, while the vehicle 1 is waiting for the provision of mobility services at the storage location ST. If the cruising distance ML is set while waiting for the provision of mobility services, it is possible to prevent the free capacity QC from becoming tight. For example, a vehicle 1 having a cruising distance ML shorter than the distance L_RTn of a route RTn including transportation of a mobility service user USn (n≧1) is excluded from candidates for transportation of this user USn in the selection by the management server 200.

[0039] In another example, the cruising range ML is set while a mobility service is being provided (for example, while the vehicle 1 is traveling along the route RTn). Even if the cruising range ML is set while a mobility service is being provided, it is possible to prevent the available capacity QC from becoming tight. In this case, however, the available capacity QC decreases with a decrease in the available capacity QC, and therefore the execution of the automatic driving control is restricted or stopped. While the execution of the automatic driving control is restricted or stopped, for example, the operator of the vehicle 1 performs manual driving. The switch from automatic driving control to manual driving is triggered by a request generated by the processor 110 when the available capacity ML decreases to a predetermined distance THml (described later), for example.

[0040] 4. Free space expansion process 4-1. First Example In this embodiment, when the cruising range ML set according to the available capacity QC is equal to or less than a predetermined distance THml, the available capacity QC may be expanded. FIG. 5 is a diagram for explaining the expansion process of the available capacity QC. As described with reference to FIG. 4, when the execution of the automatic driving control is continued, the available capacity QC continues to decrease. Here, the generation amount ΔQt_RT1 shown in FIG. 4 may vary depending on the position, time, surrounding environment, scene, etc. Therefore, if the situation where the decrease speed of the available capacity QC is high continues, the available capacity QC becomes tight.

[0041] Therefore, in a first example, the cruising distance ML is compared with the predetermined distance THml. This comparison may be performed while the mobility service is being provided, or may be performed while the service is being waited for. If the comparison result indicates that the cruising distance ML is equal to or less than the predetermined distance THml, an expansion process is performed. FIG. 5 shows an example of the transition of the free space QC when the expansion process is performed while the mobility service is being provided. In the expansion process, for example, the log data LOG in the storage device 120 that has already been transmitted to the management server 200 is compressed and stored in the storage device 120. In another example, the log data LOG that has already been transmitted to the management server 200 is deleted from the storage device 120.

[0042] The predetermined distance THml may be a fixed value or may be set based on the route RTn. In the case of setting based on the route RTn, for example, an average value of the total amount TQgn of log data LOG generated during traveling of the route RTn in the past is used. For example, the average value of the total amount TQgn is calculated in the management server 200 by referring to the traveling history of one or more vehicles 1 and extracting routes that are the same as or similar to the route RTn. Then, an average value is calculated based on the total amount TQgn of log data LOG generated during traveling of the extracted route. The predetermined distance THml is set by adding a margin to this average value. Information on the predetermined distance THml set in this manner is transmitted to the vehicle 1 that provides the mobility service for the user USn.

[0043] 4-2. Second Example In a second example of the expansion process, the total distance L_AD of the sections S_AD where automatic driving control can be executed among the sections S_RTk constituting the route RTk (1≦k≦n) from the current location of the vehicle 1 to the destination may be calculated. At least one of the processes of setting the route RTk and identifying the sections S_AD is performed by the management server 200 or the processor 110 of the vehicle 1 based on the position information of the current location of the vehicle 1 and the destination. The current location of the vehicle 1 includes the storage location ST (when waiting for the provision of mobility services) and points along the route RTn (when the mobility service is being provided). The destination of the vehicle 1 includes the points P1, P2, and the storage location ST shown in FIG. 3. The position information of the current location and the destination is included in various information stored in the storage device 120.

[0044] Examples of the section S_AD include a section of a highway and a section dedicated to autonomous driving. If the section S_AD has been specified, the total distance L_AD can be calculated. The calculation of the total distance L_AD is performed by the processor 110 of the vehicle 1. In a second example of the enlargement process, the total distance L_AD is compared with the cruising range ML. If it is determined as a result of this comparison that the total distance L_AD is equal to or greater than the cruising range ML, the enlargement process is performed. A specific example of the enlargement process is the same as that in the first example of the enlargement process described above.

[0045] 5. Route setting process In this embodiment, a process for setting a route RTk is performed. This route RTk is a route from the current location of the vehicle 1 described in the second example of the enlargement process to the destination. The process for setting the route RTk is performed by the management server 200 or the processor 110 of the vehicle 1 based on the position information of the current location of the vehicle 1 and the destination. The current location of the vehicle 1 includes the storage location ST (when waiting for the provision of mobility services) and points along the route RTn (when mobility services are being provided). The destination of the vehicle 1 includes points P1, P2, and the storage location ST shown in FIG. 3.

[0046] When the process of setting the route RTk is performed by the processor 110 of the vehicle 1, at least two candidates Ck of the route RTk may be set. In this case, the total amount TQgn_Ck of the log data LOG generated during the traveling of the vehicle 1 along these candidates Ck is predicted. FIG. 6 is a diagram for explaining the total amount TQgn_Ck. In FIG. 6, candidates Ck1, Ck2, and Ck3 are depicted as candidates Ck of the route RTk. These candidates are all candidates of three routes RTk leading from the current location CL of the vehicle 1 to the destination DS. The time Tcl shown on the horizontal axis of FIG. 6 represents the current time. In addition, times T1, T2, and T3 represent the times at which the vehicle 1 arrives at the destination DS when the vehicle 1 travels along the candidates Ck1, Ck2, and Ck3, respectively.

[0047] The vertical axis of FIG. 6 shows the amount ΔQt_Ck of log data LOG generated per unit time. As shown in FIG. 6, the amount ΔQt_Ck of generation differs between candidates Ck1 to Ck3. This is because at least a portion of the routes differ between candidates Ck1 to Ck3. If at least a portion of the routes differ, there will be differences in the frequency and difficulty of events that generate log data LOG (object recognition events, driving plan generation events, control amount calculation events, etc.). In general, the higher the frequency of events per unit time, the higher the amount ΔQt_Ck of generation. Also, the higher the difficulty of the events, the higher the amount ΔQt_Ck of generation.

[0048] The total amount TQgn_Ck1 of log data LOG generated while vehicle 1 is traveling along candidate Ck1 is represented by the value ∫ΔQt_Ck1dt obtained by accumulating the amount ΔQt_Ck1 generated from time Tcl to time T1. The total amount TQgn_Ck2 is represented by the value ∫ΔQt_Ck2dt obtained by accumulating the amount ΔQt_Ck2 generated from time Tcl to time T2. The total amount TQgn_Ck3 is represented by the value ∫ΔQt_Ck3dt obtained by accumulating the amount ΔQt_Ck3 generated from time Tcl to time T3.

[0049] In the route RTk setting process, the future value QC_Ck(Tds) of the free capacity QC at the time of arrival at the destination DS (arrival time Tds) is further calculated based on the total amount TQgn_Ck. The future value QC_Ck(Tds) can be expressed by the following formula (2) using the value of the free capacity QC at the time Tcl (i.e., the current value) QC(Tcl). QC_Ck(Tds)=QC(Tcl)-∫ΔQt_Ckdt···(2)

[0050] In the process of setting the route RTk, the candidate Ck with the maximum future value QC_Ck(Tds) calculated by formula (2) is selected as the route RTk. If the candidate Ck has the maximum future value QC_Ck(Tds), it is possible to prevent the free capacity QC from becoming tight on the way to the destination DS. In the example of FIG. 6, the total amount QC_Ck2 is the smallest. Therefore, the candidate Ck with the maximum future value QC_Ck(Tds) shown in formula (2) is the candidate Ck2, which is selected as the route RTk. [Explanation of symbols]

[0051] 1...vehicle, 10...sensor group, 20...recognition unit, 30...planning unit, 40...control amount calculation unit, 50...traveling device, 100...automatic driving system, 110...processor, 120...storage device, 200...management server, 220...database, CL...current location, DS...destination, QC...available capacity, RT, RT1 to RT3, RTk, RTn...route, US, US1, US2, USn...user, Ck...route candidate, CON...control amount, LOG...log data, RES...recognition result information, SEN...sensor detection information, TRJ...target trajectory, ΔQt...amount of log data LOG generated per unit time, TQst...total amount of log data LOG in storage device 120, TQgn...total amount of log data LOG generated while vehicle 1 is traveling, QC(Tcl)...current value of available capacity QC, QC_Ck(Tds)...future value of available capacity QC

Claims

1. An automated driving system installed in a vehicle, one or more processors; one or more storage devices; Equipped with The one or more processors: A process for automatic driving control of the vehicle; A process of storing log data related to the automatic driving control in the one or more storage devices during execution of the automatic driving control; A process of predicting a driving range of the vehicle; [0023] In the process of predicting the cruising range, the one or more processors Obtaining free space in the one or more storage devices in which the log data is stored; Setting the cruising range according to the available capacity It is configured as follows: The smaller the free space is, the shorter the cruising distance is set. An automated driving system characterized by

2. The automated driving system according to claim 1, The one or more processors further include: When the cruising range set according to the available capacity is equal to or shorter than a predetermined distance, a process of expanding the available capacity is performed. This is an automated driving system that is characterized by the following.

3. The automated driving system according to claim 1, The one or more storage devices store location information of the current location and destination of the vehicle, The one or more processors further include: A process of calculating a total distance of sections in which the autonomous driving control can be executed among sections constituting a route from the current location to the destination based on position information of the current location and the destination; When the total distance is equal to or greater than the range that is set according to the available capacity, a process of expanding the available capacity is performed. An automated driving system characterized by

4. The automated driving system according to claim 1, The one or more storage devices store location information of the current location and destination of the vehicle, The one or more processors further include: A process of setting a route from the current location to the destination based on the position information of the current location and the destination; In the process of setting the route, the one or more processors Setting at least two candidates for the route; predicting a total amount of the logged data that will be generated during travel of the vehicle along each of the candidate routes; predicting a future value of free space in the one or more storage devices upon arrival at the destination of the route candidate based on the total amount of log data predicted for each of the route candidates; From among the route candidates, a route candidate that has the maximum future value of the available capacity is selected as the route. An automated driving system characterized by

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