Autonomous 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 automatic driving system mounted on a vehicle.

Background Art

[0002] Techniques for performing automatic driving control of a vehicle using a machine learning model are known. Patent Document 1 discloses a method for collecting training data that can be used for training a machine learning model. In addition, as documents indicating the technical level of this technical field, there are the following Patent Documents 2 or 3.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] As a method for retrospectively verifying the automatic driving control of a vehicle, it is conceivable to store log data related to the automatic driving control in an in-vehicle storage device. However, since the capacity of the in-vehicle storage device is limited, there is a possibility that the capacity may become tight depending on the situation. If the capacity becomes tight, there is a risk that necessary log data cannot be stored.

[0005] One object of the present disclosure is in view of the above problems, and to provide a technique capable of suppressing the tightness of the capacity of the storage device.

Means for Solving the Problems

[0006] One aspect of this disclosure relates to an automated driving system installed in a vehicle. The automated driving system comprises one or more processors that perform automated driving control of the vehicle according to control parameters, and one or more storage devices. The one or more processors are further configured to perform the following: saving log data related to automated driving control to one or more storage devices while automated driving control is being performed, and adjusting the control parameters to restrict the vehicle's movement when the free capacity of one or more storage devices falls below a threshold. [Effects of the Invention]

[0007] According to this disclosure, when the free space on the storage device falls below a threshold, control parameters are adjusted to restrict the vehicle's operation. This reduces the amount of log data stored on the storage device, and consequently, prevents the storage device from becoming overloaded. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an example configuration related to the automatic driving control of a vehicle according to the embodiment. [Figure 2] This figure shows an example of the hardware configuration of the automated driving system according to the present invention. [Figure 3] This is a flowchart showing the processing performed by the processor according to the embodiment. [Figure 4] This figure shows an example of a map for adjusting control parameters. [Figure 5] This figure shows an example of an automated driving system according to the embodiment. [Modes for carrying out the invention]

[0009] The embodiments will be described below 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] Figure 1 shows an example configuration related to the automatic driving control of vehicle 1 by the automated driving system according to this embodiment. Automated driving means that at least one of the steering, acceleration, and deceleration of vehicle 1 is performed automatically without driver operation by an operator. Automated driving control is a concept that includes not only fully automated driving control but also risk avoidance control, lane keeping assist control, etc. The operator may be a driver riding in vehicle 1 or a remote operator who remotely controls vehicle 1.

[0012] Vehicle 1 includes a sensor group 10, a recognition unit 20, a planning unit 30, a control quantity calculation unit 40, and a driving device 50.

[0013] The sensor group 10 includes recognition sensors 11 used to recognize the surrounding conditions of the vehicle 1. Examples of recognition sensors 11 include cameras, LIDAR (Laser Imaging Detection and Ranging), radar, etc. The sensor group 10 may further include state sensors 12 for detecting the state of the vehicle 1, position sensors 13 for detecting the position of the vehicle 1, etc. Examples of state sensors 12 include speed sensors, acceleration sensors, yaw rate sensors, steering angle sensors, etc. An example of a position sensor 13 is a GNSS (Global Navigation Satellite System) sensor.

[0014] Sensor detection information SEN is information obtained by the sensor group 10. For example, sensor detection information SEN includes image data captured by the camera. Alternatively, sensor detection information SEN may include information (relative position, relative speed, shape, etc.) about specific objects (pedestrians, preceding vehicles, white lines, bicycles, road signs, etc.) that appear in the image. For example, sensor detection information SEN may also include point cloud data obtained by LIDAR. For example, sensor detection information SEN may also include information on the relative position and relative speed of an object detected by radar. Sensor detection information SEN may also include vehicle state information indicating the state of vehicle 1. Sensor detection information SEN may also include position information indicating the location of vehicle 1.

[0015] The recognition unit 20 receives sensor detection information SEN. Based on the information obtained by the recognition sensor 11, the recognition unit 20 recognizes the situation around vehicle 1. For example, the recognition unit 20 recognizes the positions of objects around vehicle 1 on a spatial map. 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. Furthermore, the recognition unit 20 may also predict the behavior of objects around vehicle 1. The recognition unit 20 may also generate a risk map of the area around vehicle 1. The recognition result information RES indicates the recognition result by the recognition unit 20.

[0016] The planning unit 30 receives the recognition result information RES from the recognition unit 20. Further, the planning unit 30 may receive vehicle state information, position information, and pre-generated map information. The map information may be high-precision three-dimensional map information. Based on the received information, the planning unit 30 generates a driving plan for the vehicle 1. The driving plan may be for reaching a preset destination or for avoiding risks. In the driving plan, for example, driving judgments such as maintaining the current driving lane, changing lanes, overtaking, turning right or left, steering, accelerating, decelerating, stopping, etc. are given. Further, the planning unit 30 generates a target trajectory TRJ necessary 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 necessary for the vehicle 1 to follow the target trajectory TRJ. The control amount CON can also be said to be the 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 driving 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 driving control amount include a target driving 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. Thereby, 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 predetermined set of rules. Examples of the machine learning model include a Neural Network (NN), a Support Vector Machine (SVM), a regression model, and a decision tree model. The NN may be a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a combination thereof. The type, number of layers, and number of nodes in each layer of 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 into the model. The recognition result information RES is output from the model or generated based on the output from the model.

[0020] Similarly, the planning unit 30 includes at least one of a rule-based model and a machine learning model. The planning unit 30 performs planning processing by inputting the recognition result information RES into the model. The target trajectory TRJ is output from the model or generated based on the output from the model.

[0021] Similarly, the control variable calculation unit 40 includes at least one of a rule-based model and a machine learning model. The control variable calculation unit 40 performs control variable calculation processing by inputting a target trajectory TRJ to the model. The control variable CON is output from the model or generated based on the output from the model.

[0022] Two or more of the recognition unit 20, planning unit 30, and control variable calculation unit 40 may be configured as a single unit. The recognition unit 20, planning unit 30, and control variable calculation unit 40 may all be configured as a single unit (end-to-end configuration). For example, the recognition unit 20 and planning unit 30 may be configured as a single unit by a neural network (NN) that outputs a target trajectory TRJ from sensor detection information SEN. Even in the case of a single unit configuration, intermediate products such as recognition result information RES and target trajectory TRJ may be output. For example, if the recognition unit 20 and planning unit 30 are configured as a single unit by an NN, the recognition result information RES may be the output of the intermediate layer of the NN.

[0023] The recognition unit 20, the planning unit 30, and the control variable calculation unit 40 constitute an "automatic driving control unit" that controls the automatic driving of vehicle 1. The automatic driving control unit has control parameters related to the processing content for each of the recognition unit 20, the planning unit 30, and the control variable calculation unit 40. Examples of control parameters for the recognition unit 20 include parameters that define the type of object to be recognized, parameters that define the range of recognition, and parameters that define the period to be predicted in the behavior prediction. Examples of control parameters for the planning unit 30 include parameters that define the maximum speed of vehicle 1, parameters that define the distance between vehicles in automatic driving control, and parameters that define the type of driving judgment given in the driving plan. Examples of control parameters for the control variable calculation unit 40 include parameters that define the upper limits of the acceleration, deceleration, steering angle acceleration, etc., of vehicle 1.

[0024] The automated driving control unit performs automated driving control of the vehicle according to the set control parameters. For example, the automated driving control unit switches the model to operate according to the control parameters. Alternatively, the automated driving control unit is configured so that the model operates by referring to the control parameters.

[0025] Figure 2 shows an example of the hardware configuration of the automated driving system 100 according to this embodiment. The automated driving system 100 has at least the functions of the automated driving control unit described above. The automated 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 processor 110) and one or more storage devices 120 (hereinafter simply referred to as storage devices 120).

[0027] The processor 110 performs various processes. The processor 110 can be composed of, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), etc. The storage device 120 stores various information necessary for the execution of processes by the processor 110. The storage device 120 can be composed of, for example, a recording medium such as ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), or SSD (Solid State Drive).

[0028] The storage device 120 stores the computer program 121, the control parameters 122, and the 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 through the cooperation of the processor 110 executing the computer program 121 and the storage device 120. In particular, the recognition unit 20, the planning unit 30, and the control variable calculation unit 40 may be realized. In this case, the models included in the recognition unit 20, the planning unit 30, and the control variable calculation unit 40 may be part of the computer program 121. Furthermore, the recognition unit 20, the planning unit 30, and the control variable calculation unit 40 may be realized by a single processor 110 or by separate processors 110.

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

[0031] While executing automated driving control, the processor 110 collects log data LOG related to the automated driving control. The processor 110 stores the collected log data LOG in the storage device 120. The stored log data LOG is expected to be used for verifying the automated driving control. The log data LOG may include sensor detection information SEN input to the automated driving control unit. The log data LOG may include control quantity CON output from the automated 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 planning unit 30. The log data LOG may include the reason for the decision made in the recognition process by the recognition unit 20. The log data LOG may include the reason for the decision made in the planning process by the planning unit 30. The log data LOG may include whether or not there was operator intervention in the automated driving control.

[0032] 2. Adjustment of control parameters As described above, in the autonomous driving system 100, the processor 110 stores log data LOG related to autonomous driving control in the storage device 120 for post-event verification of autonomous driving control. However, in order to properly verify autonomous driving control in the event of any incident, it is desirable that all of the stored log data LOG be retained until a single process is completed. However, the storage device 120 provided in the vehicle 1 has a limited capacity. Therefore, depending on the status of autonomous driving control, the capacity may become strained. If the capacity becomes strained, there is a risk that it may not be possible to save the necessary log data LOG.

[0033] Therefore, in the automated driving system 100 according to this embodiment, the processor 110 adjusts control parameters to restrict the driving of the vehicle 1 when the free capacity of the storage device 120 falls below a predetermined threshold.

[0034] It is known that the amount of log data LOG collected during the execution of automated driving control is relatively small when vehicle 1 is driving steadily, and conversely, it becomes significantly larger when some event occurs during automated driving control. This is because when an event occurs, data associated with the execution of processing corresponding to the event is collected as log data LOG. Examples of events that occur during automated driving control include when it is determined that avoidance of an object is necessary, when it is determined that the collision damage mitigation brake needs to be activated, when it is determined that overtaking a preceding vehicle is necessary, when it is determined that a lane change is necessary, and so on.

[0035] Such events are more likely to occur when there are significant changes in the driving state or driving environment of vehicle 1. For example, if vehicle 1 makes a sudden start or sudden steering maneuver, events associated with the sudden appearance of recognized objects are more likely to occur. Therefore, by restricting the driving of vehicle 1, it is possible to suppress the occurrence of events that increase the amount of data in the log data LOG. Based on the above viewpoint, the automated driving system 100 according to this embodiment makes it possible to suppress the strain on the capacity of the storage device 120.

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

[0037] First, in step S110, the processor 110 obtains free space in the storage device 120.

[0038] Next, in step S120, the processor 110 determines whether the free space of the storage device 120 falls below a predetermined threshold. The threshold is set to, for example, a value at which it is expected that log data LOG will no longer be able to be saved due to the occurrence of an event. The processor 110 may also be configured to change the threshold depending on the expected operating time and driving environment.

[0039] If the available space is above the threshold (step S120; No), the processor 110 terminates the current process without adjusting the control parameters. If the available space is below the threshold (step S120; Yes), the process proceeds to step S130.

[0040] In step S130, the processor 110 adjusts the control parameter 122 to restrict the movement of vehicle 1. After step S130, the processor 110 terminates the current process.

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

[0042] The first embodiment involves adjusting a parameter that determines the upper limit of the acceleration or steering angle acceleration of the vehicle 1. In this case, the processor 110 adjusts the control parameter 122 to decrease the upper limit according to the free space of the storage device 120. In particular, the processor 110 may be configured to decrease the upper limit as the free space decreases. The processor 110 can adjust the control parameter 122 according to a map that assigns upper limit settings to the free space, for example. Figure 4(A) shows an example of a map that assigns upper limit settings to the free space. According to the map shown in Figure 4(A), the processor 110 continuously decreases the upper limit setting as the free space decreases within a certain range where the free space is below a threshold. However, the processor 110 may be configured to decrease the upper limit setting in steps.

[0043] By reducing the upper limit of acceleration or steering angle acceleration, it is possible to limit sudden acceleration or sudden steering of vehicle 1 in automated driving control. Consequently, it is possible to effectively suppress the occurrence of such events.

[0044] A second embodiment involves adjusting the parameters that determine the maximum vehicle speed of vehicle 1. In this case, the processor 110 adjusts the control parameter 122 to reduce the maximum vehicle speed in accordance with the free space in the storage device 120. In particular, the processor 110 may be configured to make the upper limit smaller as the free space decreases. Similar to the first embodiment, the processor 110 can adjust the control parameter 122 according to a map that assigns a maximum vehicle speed set value to the free space, for example.

[0045] By reducing the maximum vehicle speed, the vehicle's movement can be made smoother in autonomous driving control. Consequently, the occurrence of events can be effectively suppressed.

[0046] A third embodiment involves adjusting the parameters that determine the inter-vehicle distance in automated driving control. In this case, the processor 110 adjusts the control parameter 122 to increase the inter-vehicle distance according to the available capacity of the storage device 120. In particular, the processor 110 may be configured to increase the inter-vehicle distance as the available capacity decreases. The processor 110 can adjust the control parameter 122 according to a map that assigns a set value for the inter-vehicle distance to the available capacity, for example. Figure 4(B) shows an example of a map that assigns a set value for the inter-vehicle distance to the available capacity. According to the map shown in Figure 4(B), the processor 110 continuously increases the inter-vehicle distance as the available capacity decreases within a certain range where the available capacity is below a threshold. However, the processor 110 may be configured to increase the set value for the inter-vehicle distance in steps.

[0047] By increasing the distance between vehicles, it is possible to provide more leeway for vehicle 1 during autonomous driving control. Consequently, it is possible to effectively suppress the occurrence of events.

[0048] 3. Effects As described above, according to this embodiment, when the free capacity of the storage device 120 falls below a predetermined threshold, the control parameters are adjusted to restrict the driving of the vehicle 1. This makes it possible to suppress the occurrence of events that increase the amount of log data LOG. Consequently, it is possible to prevent the storage device 120 from becoming overloaded.

[0049] Figure 5 shows an embodiment of the automated driving system 100 according to this embodiment. The horizontal axis in Figure 5 represents time or position. In the embodiment shown in Figure 5, the available capacity is below a threshold at T1. When the available capacity is below the threshold, the control parameter 122 is adjusted to reduce the upper limit of the acceleration or steering angle acceleration of the vehicle 1 and to increase the inter-vehicle distance in the automated driving control. By restricting the movement of the vehicle 1 in this way, the amount of data stored in the log data LOG is suppressed, and the decrease in the available capacity of the storage device 120 can be slowed down, as shown in Figure 5. [Explanation of symbols]

[0050] 1 vehicle, 100 autonomous driving systems, 110 processors, 120 memory devices, 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 control parameters, One or more storage devices, Equipped with, The one or more processors further include: During the execution of the automated driving control, a process is performed to save log data related to the automated driving control to one or more storage devices. When the free space of the one or more storage devices falls below a threshold, the process of adjusting the control parameters to restrict the vehicle's movement is performed. It is configured to execute Autonomous driving system.

2. An automated driving system according to claim 1, The control parameters include parameters that define the upper limit of the vehicle's acceleration or steering angle acceleration. The process of adjusting the control parameter includes reducing the upper limit as the available capacity decreases. Characterized by Autonomous driving system.

3. An automated driving system according to claim 1, The control parameters include a parameter that determines the maximum vehicle speed of the vehicle. The process of adjusting the control parameters includes reducing the maximum vehicle speed as the available capacity decreases. Characterized by Autonomous driving system.

4. An automated driving system according to claim 1, The control parameters include parameters that determine the distance between vehicles in the automatic driving control, The process of adjusting the control parameters includes increasing the distance between vehicles as the available capacity decreases. Characterized by Autonomous driving system.

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