Automated driving system

Abstract

To solve the problem that there is a possibility that the capacity of a storage device is strained and necessary log data will not be able to be stored when the log data related to automated driving control is stored in an in-vehicle storage device in an automated driving system.SOLUTION: An automated driving system comprises one or more processors that perform automated driving control of a vehicle in accordance with a control parameter, and one or more storage devices. The one or more processors are configured to execute processing of storing log data related to the automated driving control in the one or more storage devices while performing the automated driving control, and processing of adjusting the control parameter to restrict traveling of the vehicle when a free capacity of the one or more storage devices is less than a threshold value.SELECTED DRAWING: Figure 3

Description

[Technical field]

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

[0002] There is known a technology for controlling automatic driving of a vehicle using a machine learning model. Patent Document 1 discloses a method for collecting training data that can be used for learning 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 2021-174413 A [Patent Document 3] JP 2020-046728 A Summary of the Invention [Problem to be solved by the invention]

[0004] As a method for retroactively verifying the autonomous driving control of a vehicle, it is possible to store log data related to the autonomous driving 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 situation. When the capacity becomes tight, there is a risk that a situation will occur in which it will not be possible to store necessary log data.

[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 an autonomous driving system installed in a vehicle. The autonomous driving system includes one or more processors that perform autonomous driving control of the vehicle according to a control parameter, 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 autonomous driving control in one or more storage devices during execution of the autonomous driving control, and a process of adjusting the control parameters to restrict the traveling of the vehicle when the free space in 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, the control parameters are adjusted to restrict the vehicle's travel. This makes it possible to reduce the amount of log data stored in the storage device, thereby preventing the storage device from becoming overloaded. [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. 4 is a diagram showing an example of a map for adjusting a control parameter. [Diagram 5] FIG. 1 is a diagram illustrating an example of an autonomous driving system according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0010] 1.Autonomous Driving System The autonomous driving system according to this embodiment is installed in a vehicle and performs autonomous driving control of the vehicle.

[0011] FIG. 1 is a diagram showing an example of a configuration 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 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.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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 has control parameters related to the contents of processing for each of the recognition unit 20, the planning unit 30, and the control amount calculation unit 40. Examples of the control parameters related to the recognition unit 20 include a parameter that determines the type of object to be recognized, a parameter that determines the range of recognition, and a parameter that determines the period to be predicted in the behavior prediction. Examples of the control parameters related to the planner 30 include a parameter that determines the maximum vehicle speed of the vehicle 1, a parameter that determines the inter-vehicle distance in the automatic driving control, and a parameter that determines the type of driving judgment given in the driving plan. Examples of the control parameters related to the control amount calculation unit 40 include a parameter that determines the upper limit of the acceleration, deceleration, steering angle acceleration, etc. of the vehicle 1.

[0024] The autonomous driving control unit performs autonomous driving control of the vehicle according to the set control parameters. For example, the autonomous driving control unit switches a model to be operated according to the control parameters. Also, for example, the autonomous driving control unit is configured such that the model operates by referring to the control parameters.

[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 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, control parameters 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 executing the computer program 121 and the storage device 120. In particular, the recognition unit 20, the planner 30, and the control amount calculation unit 40 may be realized. In this case, the models included in the recognition unit 20, the planner 30, and the control amount calculation unit 40 may be part of the computer program 121. Furthermore, the recognition unit 20, the planner 30, and the control amount calculation unit 40 may be realized by a single processor 110, or may be realized by separate processors 110.

[0030] As described above, the control parameters 122 are parameters related to the content of the processing of the automatic driving control unit. The processor 110 executes the computer program 121 by referring to the control parameters 122. This realizes an automatic driving control unit that performs automatic driving control of the vehicle according to the control parameters 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] 2. Adjustment of control parameters 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.

[0033] Therefore, in the autonomous driving system 100 according to this embodiment, the processor 110 adjusts the control parameters so as to restrict the traveling of the vehicle 1 when the free space in the storage device 120 is less than a predetermined threshold value.

[0034] It is known that the amount of log data LOG collected during execution of automatic driving control is relatively small when the vehicle 1 is traveling steadily, and conversely, becomes significantly large when some event occurs in the automatic driving control. This is because when an event occurs, data accompanying the execution of processing corresponding to the event is collected as log data LOG. Examples of events that occur during automatic driving control include when it is determined that an object needs to be avoided, when it is determined that a collision damage mitigation brake needs to be activated, when it is determined that a preceding vehicle needs to be overtaken, when it is determined that a lane change is necessary, and the like.

[0035] It is considered that such an event is more likely to occur as the change in the driving state or driving environment of the vehicle 1 becomes greater. For example, if the vehicle 1 suddenly starts or steers, an event associated with the sudden appearance of a recognized object is more likely to occur. Therefore, by restricting the driving of the vehicle 1, it is possible to suppress the occurrence of an event that increases the amount of data in the log data LOG. Based on the above viewpoint, the autonomous driving system 100 according to this embodiment makes it possible to suppress the tightness of the capacity of the storage device 120.

[0036] Fig. 3 is a flowchart showing an example of processing executed by the processor 110. The processor 110 may be configured to repeatedly execute the processing according to the flowchart shown in Fig. 3 at a predetermined processing cycle while executing the automatic driving control.

[0037] First, in step S 110 , the processor 110 obtains the free space of the storage device 120 .

[0038] 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 due to the occurrence of an event, for example. The processor 110 may be configured to change the threshold value depending on the expected driving time and the driving environment.

[0039] If the free space is equal to or larger than the threshold (step S120; No), processor 110 ends the current process without adjusting the control parameters. If the free space is smaller than the threshold (step S120; Yes), the process proceeds to step S130.

[0040] In step S130, the processor 110 adjusts the control parameters 122 to limit the traveling of the vehicle 1. After step S130, the processor 110 ends the current process.

[0041] Regarding the adjustment of the control parameters 122, the following embodiments can be considered.

[0042] The first embodiment is to adjust a parameter that determines an upper limit of the acceleration or steering angular acceleration of the vehicle 1. In this case, the processor 110 adjusts the control parameter 122 so as to reduce the upper limit according to the free space of the storage device 120. In particular, the processor 110 may be configured to reduce the upper limit as the free space decreases. The processor 110 can adjust the control parameter 122, for example, according to a map that gives a set value of the upper limit for the free space. FIG. 4(A) is a diagram showing an example of a map that gives a set value of the upper limit for the free space. According to the map shown in FIG. 4(A), the processor 110 continuously reduces the set value of the upper limit as the free space decreases within a certain range where the free space is less than a threshold value. However, the processor 110 may be configured to reduce the set value of the upper limit in stages.

[0043] By reducing the upper limit of the acceleration or steering angular acceleration, it is possible to restrict the vehicle 1 from suddenly starting or being suddenly steered under automatic driving control, which in turn makes it possible to effectively prevent an event from occurring.

[0044] The second embodiment is to adjust a parameter that determines the maximum vehicle speed of the vehicle 1. In this case, the processor 110 adjusts the control parameter 122 to reduce the maximum vehicle speed according to the free space of the storage device 120. In particular, the processor 110 may be configured to reduce the upper limit as the free space decreases. As in the first embodiment, the processor 110 may adjust the control parameter 122 according to, for example, a map that gives a set value of the maximum vehicle speed relative to the free space.

[0045] By reducing the maximum vehicle speed, the vehicle 1 can travel more gently under automatic driving control, which in turn can effectively prevent events from occurring.

[0046] The third embodiment is to adjust a parameter that determines the inter-vehicle distance in the automatic driving control. In this case, the processor 110 adjusts the control parameter 122 so as to increase the inter-vehicle distance according to the free space of the storage device 120. In particular, the processor 110 may be configured to increase the inter-vehicle distance as the free space decreases. The processor 110 can adjust the control parameter 122, for example, according to a map that gives a set value of the inter-vehicle distance with respect to the free space. FIG. 4(B) is a diagram showing an example of a map that gives a set value of the inter-vehicle distance with respect to the free space. According to the map shown in FIG. 4(B), the processor 110 continuously increases the inter-vehicle distance as the free space decreases within a certain range where the free space is less than a threshold value. However, the processor 110 may be configured to increase the set value of the inter-vehicle distance in stages.

[0047] By increasing the inter-vehicle distance, it is possible to provide the vehicle 1 with a margin of safety during automatic driving control, which in turn makes it possible to effectively prevent an event from occurring.

[0048] 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 control parameters are adjusted to restrict the traveling of the vehicle 1. This makes it possible to suppress the occurrence of an event that increases the amount of data in the log data LOG. In turn, it is possible to suppress the capacity of the storage device 120 from becoming tight.

[0049] FIG. 5 is a diagram showing an example of the automatic driving system 100 according to this embodiment. The horizontal axis of FIG. 5 represents time or position. In the example shown in FIG. 5, the free space is less than the threshold value at T1. When the free space is less than the threshold value, the control parameters 122 are adjusted to reduce the upper limit of the acceleration or steering angular acceleration of the vehicle 1 and to increase the inter-vehicle distance in the automatic driving control. By limiting the travel of the vehicle 1 in this way, the amount of data of the log data LOG to be stored is suppressed, and the reduction in the free space of the storage device 120 can be made gradual as shown in FIG. 5. [Explanation of symbols]

[0050] 1 vehicle, 100 autonomous driving system, 110 processor, 120 storage device, 121 computer program, 122 control parameters, LOG log data

Claims

1. One or more processors that perform automatic driving control of a vehicle according to a control parameter; one or more storage devices; Equipped with The one or more processors further include: 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; adjusting the control parameters to limit driving of the vehicle when the free space of the one or more storage devices is less than a threshold value; It is configured to run Autonomous driving system.

2. The automated driving system according to claim 1, the control parameters include a parameter that defines an upper limit of an acceleration or a steering angular acceleration of the vehicle, The process of adjusting the control parameter includes reducing the upper limit in accordance with the free space. Characterized by Autonomous driving system.

3. The automated driving system according to claim 1, the control parameters include a parameter that determines a maximum vehicle speed of the vehicle; The process of adjusting the control parameters includes reducing the maximum vehicle speed in accordance with the available space. Characterized by Autonomous driving system.

4. The automated driving system according to claim 1, The control parameters include a parameter that determines a vehicle distance in the automatic driving control, The process of adjusting the control parameters includes increasing the inter-vehicle distance in accordance with the available space. Characterized by Autonomous driving system.

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