Control system

Abstract

To solve the problem that when log data related to vehicle control is stored in an on-vehicle memory device in a vehicle control system, a situation where a capacity of the memory device is tight and necessary log data cannot be stored is generated.SOLUTION: A control system includes: one or a plurality of processors for performing vehicle control including a plurality of control functions; and one or a plurality of memory devices. The one or the plurality of processors execute processing of storing log data related to vehicle control in the one or the plurality of memory devices during executing vehicle control, and processing of limiting the actuation of one or a plurality of object control functions selected from a plurality of control functions when a vacant capacity of the one or the plurality of memory devices is less than a threshold value.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to a control system installed in a vehicle. [Background technology]

[0002] In recent years, technology for controlling a vehicle using a machine learning model has been considered. Patent Document 1 discloses a method for collecting training data that can be used for learning a machine learning model with respect to autonomous driving control using a machine learning model. Other documents that show the technical level of this technical field include the following Patent Documents 2 and 3. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2019 / 116423 [Patent Document 2] JP 2018-124823 A [Patent Document 3] JP 2019-040364 A Summary of the Invention [Problem to be solved by the invention]

[0004] In a control system that controls a vehicle, it may be necessary to verify the contents of the vehicle control after the fact. As a method for verifying the vehicle control after the fact, it has been considered to store log data related to the vehicle control in an on-board storage device. However, since the capacity of the on-board storage device is limited, the capacity may become tight depending on the vehicle control situation. In particular, when a machine learning model is used for vehicle control, it is expected that the amount of log data to be stored will be relatively large. If the capacity of the storage device becomes tight, a situation may occur in which necessary log data cannot be stored.

[0005] In view of the above-mentioned problems, one object of the present disclosure is to provide a technique capable of alleviating the shortage of capacity of a storage device. [Means for solving the problem]

[0006] One aspect of the present disclosure relates to a control system mounted on a vehicle. The control system includes one or more processors that perform vehicle control including a plurality of control functions, and one or more storage devices. The one or more processors are further configured to execute a process of storing log data related to the vehicle control in the one or more storage devices during execution of the vehicle control, and a process of limiting the operation of one or more target control functions selected from the plurality of control functions when the free space of the one or more storage devices is less than a threshold value. Effect of the Invention

[0007] According to the present disclosure, when the free space of the storage device is less than a threshold value, the operation of one or more target control functions selected from a plurality of control functions is restricted. This makes it possible to reduce the amount of log data to be stored. As a result, it is possible to prevent the storage device from becoming full. [Brief description of the drawings]

[0008] [Figure 1] FIG. 2 is a diagram illustrating an example of a configuration related to automatic driving control of a vehicle according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of the autonomous driving system according to the embodiment. [Diagram 3] 4 is a flowchart showing a process executed by a processor according to the embodiment. [Figure 4] FIG. 11 is a diagram illustrating an example of an amount of generated data. [Diagram 5] 11 is a diagram showing an embodiment in which the operation of one or more target control functions is restricted in stages according to available capacity. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment will be described with reference to the drawings.

[0010] 1.Autonomous Driving System This embodiment relates to an automatic driving system that performs automatic driving control of a vehicle. FIG. 1 is a diagram showing a configuration example related to automatic driving control of a vehicle 1 by an automatic driving system according to this embodiment. Automatic driving refers to automatically performing at least one of steering, acceleration, and deceleration of the vehicle 1 without the driver's driving 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 image data captured by a camera. Alternatively, the sensor detection information SEN may include information (relative position, relative speed, shape, etc.) regarding a specific object (pedestrian, preceding vehicle, white line, bicycle, road sign, etc.) captured in the image. For example, the sensor detection information SEN may include point cloud data obtained by LIDAR. For example, the sensor detection information SEN may include information regarding the relative position and relative speed of an object detected by radar. 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 the position of the object around the vehicle 1 on the spatial map. Examples of the object include pedestrians, other vehicles (preceding vehicles, parked vehicles, etc.), white lines, road structures (e.g., guard rails, curbs), fallen objects, traffic lights, intersections, signs, etc. Furthermore, the recognition unit 20 may predict the behavior of the object around the vehicle 1. The recognition unit 20 may also generate a risk map around the vehicle 1. The recognition result information RES indicates the recognition result by the recognition unit 20.

[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. In the driving plan, for example, driving decisions such as maintaining the current driving lane, changing lanes, overtaking, turning right or left, steering, accelerating, decelerating, and stopping are given. 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 drive force, a target engine torque, etc. Examples of the braking control amount include a target braking force, a target braking torque, 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. The automatic driving control unit controls the automatic driving of the vehicle 1 so as to achieve a plurality of control functions. In other words, the automatic driving control by the automatic driving control unit includes a plurality of control functions. For example, the plurality of control functions include cruise control, lane keeping, lane change, pre-crash safety, risk avoidance control, overtaking low-speed vehicles, recognition of road signs and control according to the recognition results, recognition of traffic lights and control according to the recognition results, etc.

[0023] The autonomous driving control by the autonomous driving control unit can be considered in various patterns depending on the control functions to be implemented. For example, it is possible to consider an autonomous driving control unit that implements all of the control functions in the above examples, or an autonomous driving control unit that implements only cruise control and lane keeping.

[0024] In the autonomous driving control unit, each of the recognition unit 20, the planning unit 30, and the control amount calculation unit 40 has a configuration corresponding to the control function to be implemented. For example, the rule-based model is given a conditional statement or a function formula corresponding to the control function to be implemented. Also, for example, the machine learning model performs machine learning corresponding to the control function to be implemented.

[0025] 2 is a diagram showing an example of a hardware configuration of the autonomous driving system 100 according to this embodiment. 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 driving device 50.

[0026] The autonomous driving system 100 includes one or more processors 110 (hereinafter simply referred to as processors 110) and one or more memory devices 120 (hereinafter simply referred to as memory devices 120).

[0027] The processor 110 executes various processes. The processor 110 can be configured, for example, by a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), etc. 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 required for the execution of processes by the processor 110. The storage device 120 can be configured, for example, by a recording medium such as a read only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), or a solid state drive (SSD).

[0028] The storage device 120 stores a computer program 121, model data 122, and log data LOG.

[0029] The computer program 121 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 computer program 121, and the storage device 120. The computer program 121 may be recorded in a computer-readable recording medium.

[0030] The model data 122 is data of models included in the recognition unit 20, the planning unit 30, and the control amount calculation unit 40. When executing processing related to autonomous driving control, the processor 110 configures the recognition unit 20, the planning unit 30, and the control amount calculation unit 40 by selecting and using a model from the model data 122.

[0031] During execution of the autonomous driving control, the processor 110 collects log data LOG related to the autonomous driving control. The processor 110 stores the collected log data LOG in the storage device 120. It is assumed that the stored log data LOG is used for verifying 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 a 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 a target trajectory TRJ output from the planner 30. The log data LOG may include a reason for a determination in the recognition process by the recognition unit 20. The log data LOG may include a reason for a determination in the planning process by the planner 30. The log data LOG may include the presence or absence of an operator's intervention in the autonomous driving control.

[0032] The autonomous driving system 100 according to this embodiment is configured to be able to change the contents of a plurality of control functions implemented in the autonomous driving control unit. In particular, the autonomous driving system 100 is configured to be able to limit the operation of each control function. The limiting here includes not only turning off the operation of a control function, but also reducing the operation frequency or operation range of the control function, or reducing the contents of the control function.

[0033] For example, when an autonomous driving control unit is configured to implement cruise control, lane keeping, and lane change, the autonomous driving system 100 is configured to be able to turn off lane change operation so that the autonomous driving control unit does not perform lane change.

[0034] The processor 110 changes the content of the multiple control functions implemented in the autonomous driving control unit, for example, by switching a model selected from the model data 122. Alternatively, the processor 110 may change the content of the multiple control functions implemented in the autonomous driving control unit by changing parameters included in the model.

[0035] 2. Restrictions on the operation of control functions As described above, in the autonomous driving system 100, the processor 110 stores the log data LOG related to the autonomous driving control in the storage device 120 in order to verify the autonomous driving control after the fact. However, in order to properly verify the autonomous driving control when some kind of incident occurs, it is desirable that all the stored log data LOG is retained until one process is completed. However, the capacity of the storage device 120 provided in the vehicle 1 is limited. Therefore, the capacity may become tight depending on the situation of the autonomous driving control. If the capacity becomes tight, there is a risk that a situation will occur in which it will not be possible to store the necessary log data LOG.

[0036] Therefore, in the autonomous driving system 100 of this embodiment, when the free space in the memory device 120 is less than a predetermined threshold value, the processor 110 restricts the operation of one or more control functions (hereinafter referred to as "one or more target control functions") among the multiple control functions implemented in the autonomous driving control unit.

[0037] The amount of data of the log data LOG collected during the execution of the automatic driving control correlates with the contents of the control functions being operated. This is because data accompanying the execution of the processes related to each control function is collected as the log data LOG. In particular, the amount of data of the collected log data LOG is considered to be larger the greater the number of control functions being operated and the more complex the contents of the control functions being operated. Therefore, by restricting the operation of some of the control functions implemented in the automatic driving control unit, the amount of data of the collected and stored log data LOG can be reduced. Based on the above viewpoint, the automatic driving system 100 according to this embodiment makes it possible to suppress the tightness of the capacity of the storage device 120.

[0038] 2-1. Processing 3 is a flowchart showing an example of a process for limiting the operation of one or more target control functions, which is executed by the processor 110. The process according to the flowchart shown in FIG 3 is executed repeatedly at a predetermined processing cycle, for example, while the autonomous driving control is being executed.

[0039] In step S110, the processor 110 obtains the free space of the storage device 120.

[0040] Next, in step S120, the processor 110 determines whether the free space of the storage device 120 is less than a predetermined threshold value. The threshold value is set to a value that is expected to make it impossible to store the log data LOG during the drive to the destination. The processor 110 may be configured to change the threshold value according to the expected driving time, the driving environment, etc.

[0041] If the free space is equal to or greater than the threshold (step S120; No), processor 110 ends the current process without restricting the operation of the control function. If the free space is less than the threshold (step S120; Yes), the process proceeds to step S130.

[0042] In step S130, the processor 110 selects one or more target control functions from among the multiple control functions. Several embodiments can be considered from various perspectives regarding how to select one or more target control functions. An embodiment regarding the selection of one or more target control functions will be described later.

[0043] Next, in step S140, processor 110 restricts the selected one or more target control functions. For example, if lane change is selected as one or more target control functions, processor 110 turns off lane change operation. Alternatively, processor 110 reduces the frequency of lane change operation. Alternatively, processor 110 reduces functions related to lane change determination so that control is started only when the operator determines to perform a lane change (for example, the operator operates a turn lamp). Processor 110 may be further configured to notify the operator that one or more target control functions have been restricted.

[0044] By limiting the operation of one or more target control functions in this manner, the amount of log data LOG accompanying the execution of processing related to one or more target control functions can be reduced. For example, by limiting the operation of one or more target control functions, the sensor detection information SEN and the recognition result information RES used in the autonomous driving control unit can be reduced. Also, for example, information regarding the reasons for judgments in the recognition process by the recognition unit 20 and the planning process by the planning unit 30 can be reduced.

[0045] 2-2.Selection of target control function In the following, embodiments relating to the selection of one or more target controlled functions are described.

[0046] In the first embodiment, one or more target control functions are selected using as an index the amount of log data LOG (hereinafter referred to as "amount of generated data") generated per unit time by each of a plurality of control functions implemented in an autonomous driving control unit. Typically, in the first embodiment, the processor 110 selects a control function that generates a large amount of data among the plurality of control functions as one or more target control functions. The number of one or more target control functions may be determined based on, for example, the desired amount of reduction in the amount of generated data and the amount of reduction in the amount of generated data expected by limiting operation.

[0047] FIG. 4 is a diagram showing an example of the amount of generated data. In FIG. 4, a list of a plurality of control functions implemented in the automatic driving control unit and a bar graph showing the amount of generated data for each control function are shown. According to the first embodiment, for example, the processor 110 selects risk avoidance control, overtaking a low-speed vehicle, and lane keeping, which have a large amount of generated data, as one or more target control functions. Here, in order to ensure the function of the automatic driving control at a certain level, a control function that is not selected as one or more target control functions may be set regardless of the amount of generated data. For example, cruise control, lane keeping, and pre-crash safety are set as control functions that are not selected as one or more target control functions. In this case, for example, the processor 110 selects risk avoidance control, overtaking a low-speed vehicle, and lane change as one or more target control functions.

[0048] According to the first embodiment, the amount of stored log data LOG can be effectively reduced.

[0049] The second embodiment is to select one or more target control functions using as an index the necessity for the driving environment of the vehicle 1. The driving environment of the vehicle 1 may include the type of road on which the vehicle 1 is traveling (whether it is a general road or a motorway), the number of lanes on the road on which the vehicle 1 is traveling, the number of objects around the vehicle 1, and the like.

[0050] In the second embodiment, the processor 110 selects, as one or more target control functions, a control function that is less necessary for the driving environment of the vehicle 1 among the multiple control functions. For example, when the vehicle 1 is driving on a highway, risk avoidance control, recognition of signals, and control according to the recognition result are considered to be less necessary. Therefore, in this case, for example, the processor 110 selects risk avoidance control and recognition and control according to the recognition result as one or more target control functions. Also, for example, when the vehicle 1 is driving on a single-lane road, lane changing and overtaking of low-speed vehicles are considered to be less necessary. Therefore, in this case, for example, the processor 110 selects lane changing and overtaking of low-speed vehicles as one or more target control functions. The processor 110 can be configured to select one or more target control functions using an array that defines a control function according to the necessity for the driving environment of the vehicle 1, for example.

[0051] According to the second embodiment, it is possible to reduce the amount of log data LOG while maintaining the functionality of the autonomous driving control unit as much as possible.

[0052] In the third embodiment, one or more target control functions are selected using the necessity for the driving characteristics of the operator of the vehicle 1 as an index. Examples of the operator's driving characteristics include a tendency to be aware of the surroundings of the vehicle 1 (high awareness of the left and right, low awareness of the rear, etc.), driving operation preferences (dislike of overtaking or lane changing, etc.), etc. The processor 110 may be configured to acquire the operator's driving characteristics from information obtained from a monitor camera (such as the operator's line of sight and face direction). Alternatively, data for managing the operator's driving characteristics may be stored in the storage device 120.

[0053] In a third embodiment, the processor 110 selects, as one or more target control functions, a control function that is less necessary for the driving characteristics of the operator among the multiple control functions. For example, if the operator does not like overtaking, it is considered that overtaking a low-speed vehicle is less necessary. Therefore, in this case, for example, the processor 110 selects overtaking a low-speed vehicle as one or more target control functions. The processor 110 can be configured to select one or more target control functions, for example, by using an array that defines a control function according to necessity for the driving characteristics of the operator.

[0054] According to the third embodiment, it is possible to reduce the amount of log data LOG while suppressing an increase in the burden on the operator.

[0055] The above-described embodiments may be combined. For example, the processor 110 may select one or more target control functions using both the amount of generated data and the necessity for the driving environment of the vehicle 1 as indicators.

[0056] 2-3. Limiting the operation of target control functions according to available capacity Processor 110 may be configured to restrict the operation of the selected one or more target control functions in a stepwise manner according to the free space of storage device 120. In particular, processor 110 may be configured to restrict the operation of the one or more target control functions in descending order of the amount of generated data as the free space decreases.

[0057] Fig. 5 is a diagram showing an embodiment in which the operation of a control function is restricted in order of the amount of generated data. Fig. 5 shows a case in which lane change and overtaking a low-speed vehicle are selected as one or more target control functions. The amount of generated data relating to overtaking a low-speed vehicle is greater than the amount of generated data relating to lane change. In this case, as shown in Fig. 5, the processor 110 may be configured to restrict the operation of overtaking a low-speed vehicle at time T1 when the available capacity falls below a threshold, and further restrict the operation of lane change at time T2 when the available capacity has further decreased.

[0058] In this way, by gradually restricting the operation of one or more target control functions according to the available capacity, it is possible to maintain the control functions within a range that can prevent the available capacity from becoming constrained.

[0059] Processor 110 may gradually limit the operation of one target control function. For example, when lane change is selected as one or more target control functions, processor 110 may be configured to first reduce the frequency of lane change operation when the available capacity falls below a threshold, and then turn off lane change operation when the available capacity is further reduced.

[0060] 3.Effects As described above, according to this embodiment, when the free space of the storage device 120 is less than a predetermined threshold, the operation of one or more target control functions selected from the multiple control functions is restricted. This makes it possible to reduce the amount of log data LOG collected and stored. As a result, it is possible to prevent the storage device 120 from becoming full.

[0061] 4.Other control systems This embodiment can also be applied to other control systems mounted on the vehicle 1. For example, a driving assistance system can be cited as another control system that performs vehicle control including a plurality of control functions. The driving assistance system is a system that performs vehicle control including control functions such as collision damage mitigation braking, vehicle distance control, lane keeping, and road sign recognition. Similarly, when applied to a driving assistance system, when the free space of the storage device 120 is less than a predetermined threshold, it is sufficient to restrict the operation of one or more target control functions selected from the plurality of control functions. This makes it possible to achieve the same effect as that described above. [Explanation of symbols]

[0062] 1 vehicle, 100 autonomous driving system, 110 processor, 120 storage device, LOG Log data

Claims

1. One or more processors for performing vehicle control including a plurality of control functions; one or more storage devices; Equipped with The one or more processors further include: storing log data related to the vehicle control in the one or more storage devices during execution of the vehicle control; a process of restricting operation of one or more target control functions selected from the plurality of control functions when the free space of the one or more storage devices is less than a threshold value; It is configured to run Control system.

2. 2. The control system of claim 1, In the process of limiting the operation of the one or more target control functions, the one or more processors acquiring a generated data amount, which is a data amount of the log data generated per unit time, for each of the plurality of control functions; Selecting the one or more target control functions based on the amount of generated data Characterized by Control system.

3. 3. The control system of claim 2, In the process of limiting the operation of the one or more target control functions, the one or more processors The operation of the one or more target control functions is restricted in order of the control functions with the largest amount of generated data as the free space decreases. Characterized by Control system.

4. 2. The control system of claim 1, In the process of limiting the operation of the one or more target control functions, the one or more processors Acquire the vehicle's driving environment, Selecting one or more target control functions from among the plurality of control functions, the control functions that are less necessary for the driving environment. Characterized by Control system.

5. 2. The control system of claim 1, In the process of limiting the operation of the one or more target control functions, the one or more processors Acquire driving characteristics of a vehicle operator; A control function that is less necessary for the driving characteristic is selected from the plurality of control functions as the one or more target control functions. Characterized by Control system.

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