Information processing device, vehicle, information processing system, information processing method, and information processing program

Abstract

To evaluate a driver's operation when a risk occurs according to a small-radius turn during right or left turning.SOLUTION: A brake ECU that is an information processor for calculating a feature amount concerning a driver's operation comprises a CPU for calculating the feature amount, and an input / output interface for outwardly transmitting the calculated feature amount. The CPU identifies whether or not to be a predetermined situation (collection scene) where a risk occurs according to a small-radius turn during right or left turning (in step S212, step S221), and calculates, as a feature amount, information about driving action of reducing the risk identified from information about at least one of among a handle operation, an accelerator operation and a brake operation of a driver, when it is identified to be the predetermined situation (in step S222 to step S233).SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, a vehicle, an information processing system, an information processing method, and an information processing program, and in particular to an information processing device that calculates characteristics related to a driver's driving, a vehicle including the information processing device, an information processing system including the vehicle and a server, an information processing method executed by the information processing device, and an information processing program executed by the information processing device. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there has been a driving diagnosis device that diagnoses the driving skill of a driver based on operation information related to the driving operation by the driver of a vehicle (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-012481 A Summary of the Invention [Problem to be solved by the invention]

[0004] As a problem, a new method for evaluating a driver's driving in accordance with various situations while driving a vehicle is required.

[0005] This disclosure has been made to solve such problems, and its purpose is to provide an information processing device, a vehicle, an information processing system, an information processing method, and an information processing program that are capable of evaluating a driver's driving when risks arise when making tight turns. [Means for solving the problem]

[0006] The information processing device according to the present disclosure is an information processing device that calculates a feature amount related to a driver's driving, and includes a processor that calculates the feature amount and a transmission unit that transmits the calculated feature amount to an external device. The processor identifies whether a predetermined situation is one in which a risk occurs in making a sharp turn to the right or left, and if the predetermined situation is identified, calculates, as a feature amount, information on a driving behavior that reduces the risk identified from information on at least one of the driver's steering operation, accelerator operation, and brake operation.

[0007] According to this configuration, when it is determined that a certain situation is occurring in which a risk occurs in making sharp right or left turns, information on a driving behavior that reduces the risk determined from information on at least one of the driver's steering operation, accelerator operation, and brake operation is calculated as a feature amount, and the feature amount is transmitted to the outside. As a result, it is possible to provide an information processing device that is capable of evaluating the driver's driving when a risk occurs in making sharp right or left turns.

[0008] The predetermined situation is a situation in which the vehicle must pull over to the edge of the road before making a sharp turn, and may be specified using information related to accelerator operation and steering angle.

[0009] According to this configuration, when a risk occurs when making a sharp right or left turn, the driver's driving can be evaluated in a situation where the vehicle pulls over to the edge of the road before making a sharp right or left turn.

[0010] The predetermined situation may further be identified using information related to the brake operation.

[0011] With this configuration, when a risk occurs when making a sharp turn to the right or left, the driver's driving can be more accurately evaluated in a situation where the vehicle pulls over to the edge of the road before making a sharp turn to the right or left.

[0012] The predetermined situation is a situation in which the vehicle is making a sharp right or left turn, and may be specified using information on at least one of the steering wheel angle, the yaw rate, and the left and right wheel speeds.

[0013] According to this configuration, when a risk occurs in making sharp right or left turns, the driver's driving in a situation where the vehicle is making sharp right or left turns can be evaluated.

[0014] The processor may calculate, as the feature, information regarding the frequency with which driving behavior that reduces risk is performed in a given situation.

[0015] With this configuration, the driver's driving can be appropriately evaluated when a risk occurs when making sharp turns, based on the frequency with which driving actions that reduce risk are performed in a given situation in which a risk occurs when making sharp turns.

[0016] According to another aspect of the present disclosure, a vehicle includes an information processing device that calculates a feature amount related to a driver's driving. The information processing device includes a processor that calculates the feature amount and a transmission unit that transmits the calculated feature amount to an external device. The processor identifies whether a predetermined situation occurs in which a risk occurs in making a sharp turn to the right or left, and if the processor identifies the predetermined situation, calculates, as the feature amount, information on a driving behavior that reduces the risk identified from information on at least one of the driver's steering operation, accelerator operation, and brake operation.

[0017] With this configuration, a vehicle can be provided that is capable of evaluating the driver's driving when a risk occurs when making a tight turn.

[0018] According to yet another aspect of the disclosure, an information processing system includes a vehicle including an information processing device that calculates a feature amount related to a driver's driving, and a server. The information processing device includes a processor that calculates the feature amount, and a transmission unit that transmits the calculated feature amount to an external device. The processor identifies whether a predetermined situation occurs in a tight turn, and if the predetermined situation is identified, calculates, as the feature amount, information on a driving behavior that reduces the risk identified from information on at least one of the driver's steering operation, accelerator operation, and brake operation. The server executes a predetermined process on the feature amount.

[0019] With this configuration, it is possible to provide an information processing system that is capable of evaluating the driver's driving when a risk occurs when making a tight turn.

[0020] According to yet another aspect of the present disclosure, an information processing method is executed by an information processing device that calculates a feature amount related to a driver's driving. The information processing device includes a processor that calculates the feature amount and a transmission unit that transmits the calculated feature amount to an external device. The information processing method includes a step in which the processor identifies whether a predetermined situation occurs in which a risk occurs in making a tight turn to the right or left, and a step in which, if the processor identifies the predetermined situation, the processor calculates, as a feature amount, information on a driving behavior that reduces the risk identified from information on at least one of the driver's steering operation, accelerator operation, and brake operation.

[0021] According to this configuration, it is possible to provide an information processing method capable of evaluating the driver's driving when a risk occurs when making a tight turn.

[0022] According to yet another aspect of the present disclosure, an information processing program is executed by an information processing device that calculates a feature amount related to a driver's driving. The information processing device includes a processor that calculates the feature amount and a transmission unit that transmits the calculated feature amount to an external device. The information processing program causes the processor to execute a step of identifying whether a predetermined situation occurs in which a risk occurs in making a sharp turn to the right or left, and a step of calculating, when the predetermined situation is identified, information on a driving behavior that reduces the risk identified from information on at least one of the driver's steering operation, accelerator operation, and brake operation as the feature amount.

[0023] According to this configuration, it is possible to provide an information processing program that is capable of evaluating the driver's driving when a risk occurs when making a tight turn. Effect of the Invention

[0024] According to this disclosure, it is possible to provide an information processing device, a vehicle, an information processing system, an information processing method, and an information processing program that are capable of evaluating a driver's driving when a risk occurs when making a tight turn. [Brief description of the drawings]

[0025] [Figure 1] FIG. 1 is a diagram for explaining an example of a configuration of a vehicle information management system. [Diagram 2] 1 is a diagram for explaining a configuration of an example of a vehicle information processing device according to an embodiment of the present invention; [Diagram 3] 11 is a diagram for explaining an example of a process executed in a second processing unit. FIG. [Figure 4] 11 is a diagram for explaining an example of processing executed in a third processing unit. FIG. [Diagram 5] 10 is a flowchart showing a flow of a feature amount transmission process executed by a brake ECU. [Figure 6] FIG. 13 is a diagram showing state transitions for making right and left turns with small turning radius in this embodiment. [Figure 7]11A to 11C are diagrams showing changes in various operations for turning right or left in a tight radius in this embodiment. [Figure 8] FIG. 11 is a diagram for explaining calculation of a feature amount in the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] [First embodiment] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference characters and their description will not be repeated.

[0027] 1 is a diagram for explaining an example of the configuration of a vehicle information management system 1. As shown in FIG. 1, in this embodiment, the vehicle information management system 1 includes a plurality of vehicles 2 and 3, a communication network 6, a base station 7, and a data center 100.

[0028] The vehicles 2 and 3 may be any vehicle that can communicate with the data center 100, and may be, for example, a vehicle driven by an engine, an electric vehicle driven by an electric motor, or a hybrid vehicle equipped with an engine and an electric motor and using at least one of them as a drive source. Note that, for convenience of explanation, only two vehicles 2 and 3 are shown in Fig. 1, but the number of vehicles is not particularly limited to two, and may be three or more.

[0029] The vehicle information management system 1 is configured to acquire predetermined information from vehicles 2 and 3 that are configured to be able to communicate with a data center 100, and to manage the acquired information.

[0030] The data center 100 includes a control device 110, a storage device 120, and a communication device 130. The control device 110, the storage device 120, and the communication device 130 are connected to each other via a communication bus 140 so that they can communicate with each other.

[0031] The control device 110 is configured to include a CPU (Central Processing Unit), memory (such as ROM (Read Only Memory) and RAM (Random Access Memory)), and input / output ports for inputting and outputting various signals, all of which are not shown. The various controls performed by the control device 110 are software processes, that is, executed by the CPU reading out programs stored in the memory. The various controls by the control device 110 can also be realized by a general-purpose server (not shown) executing programs stored in a storage medium. However, the various controls by the control device 110 are not limited to software processes and may be processed by dedicated hardware (electronic circuits).

[0032] The storage device 120 stores predetermined information related to a plurality of vehicles 2, 3 configured to be able to communicate with the data center 100. The predetermined information includes, for example, information related to the characteristic quantities of each of the vehicles 2, 3 described below, and information for identifying the vehicles 2, 3 (hereinafter referred to as a vehicle ID). The vehicle ID is unique information set for each vehicle. The data center 100 becomes able to identify the vehicle that is the source of the transmission by the vehicle ID.

[0033] The communication device 130 realizes two-way communication between the control device 110 and the communication network 6. The data center 100 enables communication with a plurality of vehicles including the vehicles 2 and 3 via a base station 7 provided on the communication network 6 using the communication device 130.

[0034] Next, a description will be given of the specific configuration of the vehicles 2 and 3. Since the vehicles 2 and 3 basically have a common configuration, the following description will be given of the configuration of the vehicle 2 as a representative example.

[0035] The vehicle 2 includes left and right drive wheels 50, 51 and left and right driven wheels 52, 53. The drive wheels 50, 51 are rotated by the operation of a drive source, whereby a drive force acts on the vehicle 2, causing the vehicle 2 to travel.

[0036] The vehicle 2 further includes an ADAS-ECU (Electronic Control Unit) 10, a brake ECU 20, a DCM (Data Communication Module) 30, and a central ECU 40.

[0037] The ADAS-ECU 10, the brake ECU 20 and the central ECU 40 are all computers having a processor such as a CPU 11, 21, 41 that executes a program, a memory 15, 25, 45, and an input / output interface 13, 23, 43, respectively.

[0038] The ADAS-ECU 10 includes a driving assistance system having functions related to driving assistance of the vehicle 2. The driving assistance system is configured to execute implemented applications to realize various functions for assisting driving of the vehicle 2, including at least one of steering control, drive control, and braking control of the vehicle 2. Applications implemented in the driving assistance system include, for example, an application that realizes the functions of an autonomous driving system (AD), an application that realizes the functions of an automatic parking system, and an application that realizes the functions of an advanced driver assistance system (ADAS).

[0039] Examples of ADAS applications include at least one of the following: an application that realizes a function of adaptive cruise control (ACC (Adaptive Cruise Control) etc.) that maintains a constant distance from the vehicle ahead while driving; an application that realizes an auto speed limiter (ASL) function that recognizes vehicle speed limits and maintains the upper limit of the vehicle's speed; an application that realizes a lane keeping assist (LKA (Lane Keeping Assist) or LTA (Lane Tracing Assist) etc.) that maintains the vehicle in the lane in which it is traveling; an application that realizes a collision mitigation braking function (AEB (Autonomous Emergency Braking) or PCS (Pre-Crash Safety) etc.) that automatically applies the brakes to reduce the damage of a collision; and an application that realizes a lane departure warning function (LDW (Lane Departure Warning) or LDA (Lane Departure Alert) etc.) that warns of vehicle 2 departing from the lane it is traveling in.

[0040] Each application of the driving assistance system outputs a request for an action plan that ensures the marketability (function) of the application alone to the brake ECU 20 based on information on the vehicle surroundings situation acquired (input) from multiple sensors, an assistance request from the driver, etc. The multiple sensors include, for example, a vision sensor such as a forward-facing camera 71, a millimeter wave radar 72, a LiDAR (Light Detection And Ranging), a position detection device, etc.

[0041] The forward-facing camera 71 captures an image of the area ahead of the vehicle 2, and transmits data of the captured image to the ADAS-ECU 10. The millimeter-wave radar 72 is a sensor that measures the distance, speed, and angle of surrounding objects, such as those in front of the vehicle 2, using radio waves in the millimeter-wave (30 GHz to 300 GHz) band, and transmits data of the measurement results to the ADAS-ECU 10. However, the multiple sensors connected to the ADAS-ECU 10 are not limited to being connected to the ADAS-ECU 10, and any one of the sensors may be connected to another ECU, and the data of the detection results of that sensor may be input to the ADAS-ECU 10 via a communication bus or the central ECU 40.

[0042] Each application acquires information on the vehicle's surroundings that is an integration of detection results from one or more sensors as recognition sensor information, and also acquires assistance requests from the driver via a user interface (not shown) such as a switch. Each application can recognize other vehicles, obstacles, or people around the vehicle by, for example, processing images and videos of the vehicle's surroundings acquired by multiple sensors using artificial intelligence (AI) or an image processing processor.

[0043] The action plan also includes, for example, requirements regarding the longitudinal acceleration / deceleration to be generated in the vehicle 2, requirements regarding the steering angle of the vehicle 2, requirements regarding keeping the vehicle 2 stationary, and the like.

[0044] The brake ECU 20 uses detection results from the sensors to control a brake actuator that generates a braking force on the vehicle 2. Furthermore, the brake ECU 20 sets motion requirements for the vehicle 2 to realize the requirements of the action plan from the ADAS-ECU 10. The motion requirements for the vehicle 2 set in the brake ECU 20 are realized by an actuator system (not shown) provided in the vehicle 2. The actuator system includes multiple types of actuator systems, such as a powertrain system, a brake system, and a steering system.

[0045] The brake ECU 20 is connected to, for example, a steering angle sensor 60, an accelerator pedal depression amount sensor 62, a brake pedal depression amount sensor 64, a gyro sensor 66, a left front wheel speed sensor 54, a right front wheel speed sensor 55, a left rear wheel speed sensor 56, and a right rear wheel speed sensor 57.

[0046] The steering angle sensor 60 detects the steering angle and transmits a signal indicating the detected steering angle to the brake ECU 20.

[0047] The accelerator pedal depression amount sensor 62 detects the depression amount of an accelerator pedal (not shown) and transmits to the brake ECU 20 a signal indicating the detected depression amount of the accelerator pedal.

[0048] The brake pedal depression amount sensor 64 detects the depression amount of a brake pedal (not shown) and transmits a signal indicating the detected depression amount of the brake pedal to the brake ECU 20.

[0049] The gyro sensor 66 detects the angular velocity around at least the yaw axis (which may include the roll axis and the pitch axis) of the vehicle 2. The gyro sensor 66 transmits to the brake ECU 20 a signal indicating the detected angular velocity.

[0050] The left front wheel speed sensor 54 detects the rotation speed (wheel speed) of the left front wheel (left driving wheel 50). The left front wheel speed sensor 54 transmits a signal indicating the detected rotation speed of the left driving wheel 50 to the brake ECU 20. The right front wheel speed sensor 55 detects the rotation speed (wheel speed) of the right front wheel (right driving wheel 51). The right front wheel speed sensor 55 transmits a signal indicating the detected rotation speed of the right driving wheel 51 to the brake ECU 20.

[0051] The left rear wheel speed sensor 56 detects the rotation speed of the left rear wheel (left driven wheel 52). The left rear wheel speed sensor 56 transmits a signal indicating the detected rotation speed of the left driven wheel 52 to the brake ECU 20. The right rear wheel speed sensor 57 detects the rotation speed of the right rear wheel (right driven wheel 53). The right rear wheel speed sensor 57 transmits a signal indicating the detected rotation speed of the right driven wheel 53 to the brake ECU 20.

[0052] 1 has been described as an example in which the steering angle sensor 60, accelerator pedal depression amount sensor 62, brake pedal depression amount sensor 64, gyro sensor 66, left front wheel speed sensor 54, right front wheel speed sensor 55, left rear wheel speed sensor 56, and right rear wheel speed sensor 57 are connected to the brake ECU 20 and transmit their detection results directly to the brake ECU 20. However, any of the sensors may be connected to another ECU and input to the brake ECU 20 via a communication bus or the central ECU 40.

[0053] Furthermore, the brake ECU 20 receives, for example, information regarding the operation status of various applications, information regarding other driving operations such as shift range, and information regarding the behavior of the vehicle 2, in addition to information regarding the action plan from the ADAS-ECU 10.

[0054] The DCM 30 is a communication module configured to enable two-way communication with the data center 100 .

[0055] The central ECU 40 is configured to be able to communicate with, for example, the brake ECU 20, and is also configured to be able to communicate with the data center 100 using the DCM 30. The central ECU 40 transmits information received from the brake ECU 20 to the data center 100 via the DCM 30, for example.

[0056] In this embodiment, the central ECU 40 has been described as transmitting information received from the brake ECU 20 to the data center 100 via the DCM 30, but it may also have a function such as relaying communications between various ECUs (a gateway function), or it may include a memory (not shown) whose stored contents can be updated using update information from the data center 100, and predetermined information including update information stored in the memory from the various ECUs is read out when the system of the vehicle 2 is started.

[0057] In the vehicle 2 having the above configuration, it is possible to diagnose the driving skill of the driver based on operation information related to the driving operation by the driver of the vehicle. In this case, a method for evaluating the driving skill of the driver according to various situations in driving the vehicle is desired.

[0058] Therefore, the brake ECU 20 identifies whether a specific situation exists in which a risk occurs when making sharp right or left turns, and if it identifies a specific situation, calculates, as a feature, information regarding driving behavior that reduces the risk identified from information regarding at least one of the driver's steering operation, accelerator operation, and brake operation.

[0059] A tight turn is a way of turning left onto a property or road on the left side of a road when driving on the left edge of the road (the leftmost road in the case of multiple lanes) in countries or regions where the law of driving on the left side of vehicles is applied, or a way of turning right onto a property or road on the right side of a road when driving on the right edge of a road with no oncoming traffic (see Figure 6 below). Note that in countries or regions where the law of driving on the right side of vehicles is applied, the definition of a tight turn is reversed from the above-mentioned case of driving on the left side.

[0060] In this way, when it is determined that a certain situation is occurring in which a risk occurs in making sharp right or left turns, information on driving behavior that reduces the risk determined from information on at least one of the driver's steering operation, accelerator operation, and brake operation is calculated as a feature amount, and the feature amount is transmitted to the outside. As a result, it is possible to evaluate the driver's driving when a risk occurs in making sharp right or left turns.

[0061] 2 is a diagram for explaining the configuration of an example of a vehicle information processing device according to the present embodiment. The vehicle information processing device according to the present embodiment is realized by a brake ECU 20.

[0062] The brake ECU 20 includes a first processing unit 22, a second processing unit 24, and a third processing unit 26. The first processing unit 22, the second processing unit 24, and the third processing unit are virtually configured inside the brake ECU 20 by the CPU 21, the memory 25, and the input / output interface 23 of the brake ECU 20 operating in cooperation with each other.

[0063] The first processing unit 22 receives information indicating the depression amount of the accelerator pedal, information indicating the depression amount of the brake pedal, information indicating the operation angle of the steering wheel, and the angular velocity of the yaw axis of the vehicle 2 as information regarding the driving operation of the vehicle 2. Furthermore, the first processing unit 22 receives a request for an action plan from the ADAS-ECU 10 and information indicating the operation state of the driving assistance system as information regarding the operation state of the driving assistance of the vehicle 2. Furthermore, the first processing unit 22 receives information indicating detection results from various sensors as information regarding the behavior of the vehicle 2. The first processing unit 22 outputs to the second processing unit 24 the input information received during a period in which a predetermined condition is established during the period in which the input information is received.

[0064] The second processing unit 24 calculates a feature amount related to the operation of the vehicle 2 by using the input information received during a period in which a predetermined condition is satisfied during the period in which the input information is received.

[0065] Fig. 3 is a diagram for explaining an example of processing executed in the second processing unit 24. As shown in Fig. 3, the following is input as input information to the second processing unit 24. The second processing unit 24 uses the input information to determine whether or not a predetermined condition is satisfied.

[0066] The predetermined conditions include a condition that the driving situation of the vehicle 2 is a predetermined driving situation. In this embodiment, the feature amount indicates, for example, a driving behavior that reduces risk when making a tight turn to the right or left.

[0067] For example, the feature amount indicates a personal characteristic such as the frequency of risk reduction behavior relative to the total number of collection scenes where a predetermined condition is satisfied, and is calculated by a formula of the number of risk reduction behaviors in one trip / total number of collection scenes. This feature amount can quantify the unevenness of the driver's behavior, and the driving level of the driver when driving can be estimated. In addition, the other feature amount indicates an objective characteristic such as an integrated value of the difference with the model of an experienced driver, and is calculated from the difference between the number of risk reduction behaviors in one trip / total number of collection scenes of the expectation value model of the experienced driver and the number of risk reduction behaviors in one trip / total number of collection scenes of the driver. This other feature amount can quantify the driver's proficiency, and it is possible to know how to deal with disturbances from surrounding traffic. The calculation method of the feature amount is not limited to the above-mentioned calculation method.

[0068] When it is determined that the predetermined condition is satisfied, the second processing unit 24 turns on a satisfaction flag, and outputs a signal indicating the state of the satisfaction flag as a scene identification signal.

[0069] Furthermore, when it is determined that the predetermined condition is satisfied, the second processing unit 24 calculates the above-mentioned feature amount using the input information received during the period during which the predetermined condition is satisfied. For example, when the predetermined condition is satisfied, the second processing unit 24 calculates the feature amount and stores (saves) the calculated feature amount in memory in association with time. The second processing unit 24 outputs the calculated feature amount together with the scene classification signal and time.

[0070] The third processing unit 26 executes pre-processing for transmitting information to a central ECU via a controller area network (CAN) using the information output from the second processing unit 24. As the pre-processing, the third processing unit 26 executes, for example, concealment of the feature amount (for example, statistical processing) and detection of a change in the feature amount (for example, a change from a past trip or the presence or absence of a sudden change).

[0071] Fig. 4 is a diagram for explaining an example of processing executed in the third processing unit 26. As shown in Fig. 4, the scene classification signal, the feature amount, and information indicating time are input to the third processing unit 26 from the second processing unit 24.

[0072] The third processing unit 26 outputs information required for determining whether or not the change history of the feature amount corresponds to a predetermined state at the data center 100. The third processing unit 26 outputs the generated information to the central ECU 40.

[0073] The central ECU 40 transmits the information input from the third processing unit 26 to the data center 100 via the DCM 30.

[0074] The information transmitted from the DCM 30 to the data center 100 includes, for example, a processing time, a scene identification number, and a feature amount (there are multiple sets of scene identification numbers and feature amounts). Therefore, the data center 100 stores the information input from the DCM 30 as a single piece of data in the storage device 120. This enables the data center 100 to obtain statistics regarding changes in the feature amounts of each of the vehicles 2, 3 that can communicate with the data center 100 and statistics regarding changes in the driving behavior characteristics of the drivers.

[0075] Fig. 5 is a flowchart showing the flow of the feature amount transmission process executed by the brake ECU 20. Referring to Fig. 5, this feature amount transmission process is called from a higher-level process and executed at every predetermined control cycle.

[0076] First, the brake ECU 20 acquires data for calculating feature amounts from various sensors or the ADAS-ECU 10 (step S211). Next, the brake ECU 20 identifies a scene of a tight turn from the acquired data (step S212).

[0077] Fig. 6 is a diagram showing state transitions for turning left or right in a tight turn in this embodiment. Referring to Fig. 6, first, when the driver makes a left turn while the vehicle 2 is traveling as in state A, in order to prevent bicycles, motorcycles, etc. from being caught in the vehicle 2, the driver turns the handlebars slightly to the left as shown in state B, moves the vehicle 2 to the left edge of the road, and immediately returns the handlebars to their original position. After that, the driver turns the handlebars significantly to the left at the corner as shown in state C to state E, and makes the vehicle 2 turn left.

[0078] FIG. 7 is a diagram showing changes in various operations for turning right or left in a tight turn in this embodiment. Referring to FIG. 7, the horizontal axis indicates time. When turning left, first, at time t1, the driver changes the operation of the accelerator pedal from the on state to the off state (first condition). Then, at time t2 within 10 seconds from time t1, the driver turns the steering wheel angle to α (for example, 30 degrees) or more in order to move the vehicle 2 to the left edge of the road, and then returns it to the original angle (second condition). Next, at time t3 within 2 seconds from time t2, the driver changes the operation of the brake pedal from the off state to the on state (third condition) and turns the steering wheel so that the vehicle 2 turns left. After that, at time t4, the steering wheel angle becomes β (for example, 90 degrees; 45 degrees in the case of steer-by-wire) or more (fourth condition). The threshold values ​​included in these conditions may be determined with reference to model driving operations of skilled drivers.

[0079] Then, the brake ECU 20 judges whether or not the scene of the change in inter-vehicle distance is a collection scene (in this embodiment, a scene where the first condition and the fourth condition are satisfied) (step S221). If it is judged that the scene is not a collection scene (NO in step S221), the brake ECU 20 ends this feature transmission process and returns the process to be executed to the upper process that called the process.

[0080] When it is determined that the scene is a capture scene (YES in step S221), the brake ECU 20 increments the total number of scenes ZNN, that is, adds 1 to the original ZNN to set the new ZNN (step S222).

[0081] Next, the brake ECU 20 determines whether or not a driving action that reduces risk when making a tight turn has been performed (in this embodiment, a driving action of moving the vehicle 2 close to the edge of the road before turning right or left. Specifically, an action that satisfies the second and third conditions in addition to the first and fourth conditions) (step S231). If it is determined that no action that reduces risk has been performed (NO in step S231), the brake ECU 20 ends this feature transmission process and returns the process to be executed to the higher-level process that called it.

[0082] On the other hand, when it is determined that the behavior to reduce the risk has been practiced (YES in step S231), the brake ECU 20 accumulates the number of times the behavior has been practiced OKN, that is, adds 1 to the original OKN to set the new OKN (step S232).

[0083] Next, the brake ECU 20 calculates the feature amount=OKN / ZNN (step S233).

[0084] When the current trip is ending, the brake ECU 20 performs concealment of the feature (e.g., statistical processing) as pre-processing for CAN transmission (step S235). Specifically, the brake ECU 20 calculates the maximum value, minimum value, and standard deviation of the OKN / ZNN of the feature for a predetermined number of past trips (e.g., 50 trips).

[0085] Next, the brake ECU 20 transmits the scene identification information, the maximum value, minimum value and standard deviation of the feature quantities OKN / ZNN, and the sudden change presence / absence flag to the central ECU 40 via the CAN (step S236). The central ECU 40 transmits the received information to the data center 100 via the DCM 30. The control device 110 of the data center 100 executes a predetermined process (for example, a process of storing in the storage device 120, a statistical process of the feature quantities stored in the storage device 120) on the feature quantities identified by the received information.

[0086] [Second embodiment] In the first embodiment, the feature amount is shown for the driving behavior of pulling the vehicle over to the road edge before turning right or left in a small right or left turn. In the second embodiment, the feature amount is shown for the driving behavior of turning right or left in a small right or left turn.

[0087] Fig. 8 is a diagram for explaining calculation of a feature amount in the second embodiment. With reference to Fig. 8, the vertical axis represents the vehicle speed Vx of the vehicle 2. The horizontal axis represents the turning curvature 1 / r = Vx / Yaw (Yaw: yaw rate (angular velocity of the yaw axis)).

[0088] In the case of an unskilled driver, the vehicle starts turning while decelerating the vehicle speed Vx from state B shown in Fig. 6, and reaches state C2 via route R21. After that, the vehicle continues turning while further decelerating the vehicle speed Vx, and reaches state E2 via route R22.

[0089] On the other hand, in the exemplary driving operation of an experienced driver, the vehicle speed Vx is sufficiently decelerated without starting a turn from state B shown in Fig. 6, and the vehicle reaches state C1 via route R11. After that, the vehicle speed Vx is further decelerated while the vehicle makes a sharp turn, and the vehicle reaches state E1 via route R12. In this way, compared to cases where the experienced driver does not perform the exemplary driving operation, the amount of reduction in vehicle speed before the turn is sufficient, the turning curvature is sharp, and the turning curvature is also large, so the vehicle can turn without straying too far from the corner, bulging outward, or making a wide turn.

[0090] Therefore, the scenes from when the condition that the steering wheel angle is equal to or greater than β (e.g., 90 deg., 45 deg. in the case of steer-by-wire) (11th condition), the condition that the yaw rate of the vehicle 2 exceeds a predetermined value (e.g., 20 deg / s) (12th condition), and the condition that the difference between the left and right wheel speeds exceeds 2 km / h (13th condition) are met until the condition that the steering wheel angle is less than γ (e.g., 30 deg.) (14th condition) is met are defined as the collected scenes. Then, in step S222 of FIG. 5, the total number ZNN of collected scenes is accumulated.

[0091] In this collection scene, in step S231 in Fig. 5, by determining whether the route of the driver is included in a specific range G including routes R11 and R12 of the exemplary driving operation of the skilled driver in Fig. 6, it is determined whether the driver has taken an action to reduce risk, and if it is determined that the driver has taken an action to reduce risk, in step S232, the number of times the risk-reducing action was taken is accumulated as OKN. Then, in step S233, the feature amount = OKN / ZNN is calculated.

[0092] [Other variations] (1) In the above-described embodiment, whether or not a scene is a collection scene is determined based on whether or not the first to fourth conditions are satisfied, or whether or not the eleventh to fourteenth conditions are satisfied, as shown in Figures 5 to 8. However, the present invention is not limited to this, and whether or not a scene is a collection scene may be determined by another method as long as the method is based on information regarding at least one of the driver's steering operation, accelerator operation, and brake operation.

[0093] (2) In the above-described embodiment, as shown in Fig. 5 to Fig. 8, it is determined whether or not the driving behavior that reduces risk in making a tight turn is performed depending on whether or not the second and third conditions are satisfied, or whether or not the route resulting from the driver's operation falls within the specific range G in Fig. 8. However, the present invention is not limited to this, and the feature amount may be calculated by another method as long as information regarding driving behavior that reduces risk, which is specified from information regarding at least one of the driver's steering operation, accelerator operation, and brake operation, is calculated as the feature amount.

[0094] (3) In the above-described embodiment, as shown in Fig. 5 to Fig. 8, the scene and the feature amount are identified using data detectable by the sensor of the vehicle 2. However, without being limited to this, the scene and the feature amount may be identified using information outside the vehicle 2 (for example, an image captured by a camera on a site ahead of a right or left turn, an image captured by a camera of a following vehicle or a surrounding vehicle, or information detected by a sensor).

[0095] (4) In the above-described embodiment, the feature amount is calculated as shown in step S233 in FIG. 5. However, the present invention is not limited to this, and the feature amount may be calculated by other methods as long as it indicates the driver's driving in a tight turn. For example, the feature amount may be an amount obtained by accumulating the difference between the feature amount OKN / ZNN of an experienced driver and the feature amount OKN / ZNN of the driver. In addition, when a specific good behavior is performed, points may be added to the feature amount. For example, when a driver performs a right-left turn in a tight turn when there is a following vehicle, points may be added to the feature amount.

[0096] (5) In the above-described embodiment, the statistical processing (anonymization) is performed by calculating the frequency OKN / ZNN. However, the present invention is not limited to this, and anonymization may be performed by other statistical processing, for example, by calculating an average value, a minimum value, a maximum value, or a standard deviation. Furthermore, anonymization may be performed by calculating a feature amount that changes suddenly or a feature amount that changes gradually.

[0097] (6) In the embodiment described above, the past feature amount is transmitted at the end of the trip as shown in step S231 in Fig. 5. However, this is not limited to this, and the past feature amount may be transmitted at another timing, for example, at the start of the trip or a predetermined time after the start of the trip (for example, a few minutes such as 5 minutes).

[0098] (7) In the embodiment described above, as shown in FIG. 1, the vehicle 2 includes the central ECU 40. However, the present invention is not limited to this, and the vehicle 2 may not include the central ECU 40. In this case, data to be transmitted to the data center 100 is generated in the brake ECU 20 and transmitted to the data center 100.

[0099] (8) In the embodiment described above, the process in Fig. 5 is executed by the brake ECU 20. However, this is not limiting, and the process in Fig. 5 may be executed by another information processing device, for example, another ECU of the vehicle 2 or an external information processing device (for example, the data center 100).

[0100] (9) The above-described embodiments can be understood as disclosure of an information processing device such as the brake ECU 20, as disclosure of a vehicle 2, 3 including the information processing device, as disclosure of an information processing system such as a vehicle information management system 1 including the vehicle 2, 3 and a server such as a data center 100, as disclosure of an information processing method executed by the information processing device, or as disclosure of an information processing program executed by the information processing device.

[0101] [summary] (1) As shown in Fig. 1, the brake ECU 20 is an information processing device that calculates a feature amount related to the driving of the driver, and includes a CPU 21 that calculates the feature amount, and an input / output interface 23 that transmits the calculated feature amount to the outside. As shown in Fig. 2 to Fig. 8, the CPU 21 identifies whether a predetermined situation (e.g., a sampling scene) occurs in a tight turn (e.g., step S212, step S221), and if it is identified as the predetermined situation, calculates information on a driving behavior that reduces the risk identified from information on at least one of the driver's steering operation, accelerator operation, and brake operation as a feature amount (e.g., step S222 to step S233).

[0102] In this way, when it is determined that a certain situation is occurring in which a risk occurs in making sharp right or left turns, information on driving behavior that reduces the risk determined from information on at least one of the driver's steering operation, accelerator operation, and brake operation is calculated as a feature amount, and the feature amount is transmitted to the outside. As a result, it is possible to evaluate the driver's driving when a risk occurs in making sharp right or left turns.

[0103] (2) As shown in Figures 5 to 7, the specified situation is a situation in which the vehicle 2 is driven close to the edge of the road before making a sharp turn, and may be identified using information regarding the accelerator operation and the steering wheel angle.

[0104] This makes it possible to evaluate the driver's driving in a situation where the vehicle 2 pulls over to the edge of the road before making a sharp right or left turn when a risk occurs when making a sharp right or left turn.

[0105] (3) As shown in FIG. 5 to FIG. 7, the predetermined situation may be further identified using information related to the brake operation.

[0106] This makes it possible to more accurately evaluate the driver's driving in a situation where the vehicle 2 pulls over to the edge of the road before making a sharp right or left turn when a risk occurs when making a sharp right or left turn.

[0107] (4) As shown in FIG. 8, the predetermined situation is a situation in which the vehicle is making a sharp turn to the right or left, and may be specified using information on at least one of the steering wheel angle, the yaw rate, and the left and right wheel speeds.

[0108] This makes it possible to evaluate the driver's driving in a situation where the vehicle is making sharp right and left turns when a risk occurs when making sharp right and left turns.

[0109] (5) As shown in FIG. 5, the CPU 21 may calculate, as a feature amount (for example, step S233), information on the frequency with which a driving behavior that reduces risk has been performed in a predetermined situation.

[0110] This makes it possible to appropriately evaluate the driver's driving when a risk occurs when making a sharp turn, based on the frequency with which driving behavior that reduces the risk is performed in a given situation in which a risk occurs when making a sharp turn.

[0111] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present disclosure is defined by the claims, not by the description of the embodiments described above, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0112] 1 vehicle information management system, 2, 3 vehicle, 6 communication network, 7 base station, 10 ADAS-ECU, 11, 21, 41 CPU, 13, 23, 43 input / output interface, 15, 25, 45 memory, 20 brake ECU, 22 first processing section, 24 second processing section, 26 third processing section, 30 DCM, 40 central ECU, 50, 51 drive wheel, 52, 53 driven wheel, 54 left front wheel speed sensor, 55 right front wheel speed sensor, 56 left rear wheel speed sensor, 57 right rear wheel speed sensor, 60 steering angle sensor, 62 accelerator pedal depression amount sensor, 64 brake pedal depression amount sensor, 66 gyro sensor, 71 forward-facing camera, 72 millimeter wave radar, 100 data center, 110 control device, 120 storage device, 130 communication device, 140 communication bus.

Claims

1. An information processing device for calculating a characteristic amount related to a driver's driving, A processor for calculating the feature amount; a transmission unit that transmits the calculated feature amount to an outside, The processor, Identify whether a specific situation occurs in which a risk occurs when making a right or left turn, When the predetermined situation is identified, information on a driving behavior that reduces the risk is calculated as the feature amount, the driving behavior being identified from information on at least one of a steering operation, an accelerator operation, and a brake operation of the driver. The predetermined situation is a situation in which the vehicle must pull over to the edge of the road before making a sharp turn, and is specified using information related to the accelerator operation and the steering wheel angle.

2. The information processing device according to claim 1 , wherein the predetermined situation is further identified using information related to a brake operation.

3. The information processing device according to claim 1 , wherein the predetermined situation is a situation in which the vehicle is making a sharp turn to the right or left, and is specified using information on at least one of a steering wheel angle, a yaw rate, and left and right wheel speeds.

4. The processor, The information processing device according to claim 1 , further comprising: a calculation unit configured to calculate, as the feature amount, information on a frequency at which a driving behavior that reduces the risk is performed in the predetermined situation.

5. A vehicle including an information processing device that calculates a feature amount related to a driver's driving, The information processing device includes: A processor for calculating the feature amount; a transmission unit that transmits the calculated feature amount to an outside, The processor, Identify whether a specific situation occurs in which a risk occurs when making a right or left turn, When the predetermined situation is identified, information regarding a driving behavior that reduces the risk, which is identified from information regarding at least one of a steering operation, an accelerator operation, and a brake operation of the driver, is calculated as the feature amount; The predetermined situation is a situation in which the vehicle is driven close to the edge of the road before making a sharp turn to the right or left, and is specified using the information regarding the accelerator operation and the steering wheel angle.

6. An information processing system including a vehicle including an information processing device that calculates a feature amount related to a driver's driving, and a server, The information processing device includes: A processor for calculating the feature amount; a transmission unit that transmits the calculated feature amount to an outside, The processor, Identify whether a specific situation occurs in which a risk occurs when making a right or left turn, When the predetermined situation is identified, information regarding a driving behavior that reduces the risk, which is identified from information regarding at least one of a steering operation, an accelerator operation, and a brake operation of the driver, is calculated as the feature amount; The server executes a predetermined process on the feature amount, The predetermined situation is a situation in which the vehicle must pull over to the edge of the road before making a sharp turn to the right or left, and is identified using information regarding the accelerator operation and the steering wheel angle.

7. An information processing method executed by an information processing device that calculates a feature amount related to a driver's driving, The information processing device includes: A processor for calculating the feature amount; a transmission unit that transmits the calculated feature amount to an outside, The information processing method includes the processor: A step of identifying whether a predetermined situation occurs in a tight turn; When the predetermined situation is identified, information on a driving behavior that reduces the risk is identified from information on at least one of a steering operation, an accelerator operation, and a brake operation of the driver, and the information on the driving behavior that reduces the risk is calculated as the feature amount; The predetermined situation is a situation in which the vehicle must pull over to the edge of the road before making a sharp turn to the right or left, and is identified using information regarding the accelerator operation and the steering wheel angle.

8. An information processing program executed by an information processing device that calculates a feature amount related to a driver's driving, The information processing device includes: A processor for calculating the feature amount; a transmission unit that transmits the calculated feature amount to an outside, The information processing program includes: A step of identifying whether a predetermined situation occurs in a tight turn; when it is determined that the predetermined situation exists, calculating, as the feature amount, information on a driving behavior that reduces the risk, which is determined from information on at least one of a steering operation, an accelerator operation, and a brake operation of the driver; The predetermined situation is a situation in which the vehicle must pull over to the edge of the road before making a sharp turn, and is identified using information regarding the accelerator operation and the steering wheel angle.

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