Information processing apparatus and system, vehicle, information processing method, and storage medium

Abstract

The present application relates to an information processing device, a vehicle, an information processing system, an information processing method, and a storage medium. The information processing device calculates a feature quantity related to driving of a driver. The information processing device has a processor that calculates the feature quantity and a transmission unit that transmits the calculated feature quantity to the outside. The processor determines whether or not a prescribed condition in which a risk occurs in relation to a preceding vehicle, and in the case where it is determined that it is the prescribed condition, calculates information related to an action of the driver to reduce the risk as the feature quantity.

Description

Technical Field

[0001] This disclosure relates to information processing apparatus, vehicles, information processing systems, information processing methods, and storage media, and particularly to an information processing apparatus for calculating driving-related characteristic quantities of a driver, a vehicle including the information processing apparatus, an information processing system including the vehicle and a server, an information processing method executed by the information processing apparatus, and a storage medium storing commands that can be executed by the information processing apparatus. Background Technology

[0002] There is a vehicle driving evaluation device that measures driving behavior indicators when a specific driving scenario is detected and calculates risk indicators based on the driving behavior indicators (for example, see Japanese Patent Application Publication No. 2009-070145).

[0003] In situations where there is a risk of collision with the vehicle in front, it is desirable to evaluate the driver's driving. Summary of the Invention

[0004] This disclosure provides an information processing device, vehicle, information processing system, information processing method, and storage medium capable of evaluating a driver's driving in the event of a risk arising in relation to a vehicle ahead.

[0005] The information processing apparatus disclosed herein is configured to calculate characteristic quantities related to a driver's driving. The information processing apparatus includes: one or more processors configured to calculate the characteristic quantities; and a transmission unit configured to transmit the calculated characteristic quantities to an external device. The one or more processors are configured to: determine whether a pre-defined situation exists in relation to a vehicle ahead, and if such a situation is determined to be present, calculate information as characteristic quantities related to the driver's actions to reduce the risk.

[0006] Based on this structure, when a predefined situation is determined to be one where a risk occurs in relation to a vehicle ahead, information related to the driver's actions to reduce the risk is calculated as a feature quantity, and this feature quantity is sent externally. As a result, an information processing device is provided that can evaluate a driver's driving in situations where a risk occurs in relation to a vehicle ahead.

[0007] The specified condition can be a situation where the time between the vehicle and the preceding vehicle, calculated by dividing the distance between the vehicle and the speed associated with the vehicle, is less than the specified time.

[0008] Based on this structure, when the time taken to travel between the vehicle and the preceding vehicle (divided by the vehicle's speed) is less than a predetermined time, information related to the driver's actions to reduce risk is calculated as a feature quantity, and this feature quantity is sent externally. As a result, the driver's driving can be evaluated in situations where a risk arises in relation to the preceding vehicle.

[0009] Information related to a driver's actions to reduce risk can be information related to braking operations.

[0010] Based on this structure, when a predefined situation is determined to be one where a risk arises in relation to a vehicle ahead, information related to braking operations is calculated as a feature quantity, and this feature quantity is sent externally. As a result, it is possible to evaluate the driver's driving in situations where a risk arises in relation to a vehicle ahead.

[0011] The information involved in braking operations can be information from sensor values ​​related to the braking operation. Based on this structure, when a specified situation is determined to be one where a risk arises in relation to a vehicle ahead, the information from the sensor values ​​related to the braking operation is calculated as a feature quantity, and this feature quantity is sent externally. As a result, the driver's driving can be evaluated in situations where a risk arises in relation to a vehicle ahead.

[0012] One or more processors can calculate information as features related to the frequency of driver actions that mitigate risk for specified conditions.

[0013] Based on this structure, when a predefined situation is identified as one where a risk arises in relation to a preceding vehicle, information related to the frequency of driver actions taken to mitigate the risk in that situation is calculated as a feature quantity, and this feature quantity is sent externally. As a result, driver performance can be evaluated in situations where a risk arises in relation to a preceding vehicle.

[0014] One or more processors can calculate information as features related to the difference between the risk-reducing driver's actions and the pre-determined model actions.

[0015] Based on this structure, when a predefined situation is identified as one where a risk occurs in relation to a preceding vehicle, information related to the difference between the driver's actions to reduce the risk and a pre-determined model action is calculated as a feature quantity, and this feature quantity is sent externally. As a result, it is possible to evaluate the driver's driving in situations where a risk occurs in relation to a preceding vehicle.

[0016] According to another aspect of this disclosure, the vehicle includes an information processing device configured to calculate characteristic quantities related to the driver's driving. The information processing device includes: one or more processors configured to calculate the characteristic quantities; and a transmission unit configured to transmit the calculated characteristic quantities to an external device. The one or more processors are configured to: determine whether a pre-defined situation exists in relation to a preceding vehicle where a risk may occur, and, if determined to be a pre-defined situation, calculate information as characteristic quantities related to the driver's actions to reduce the risk.

[0017] Based on this structure, a vehicle can be provided that allows for the evaluation of the driver's driving in the event of a risk arising in relation to the vehicle ahead.

[0018] According to another aspect of this disclosure, the information processing system includes a vehicle and a server. The vehicle includes an information processing device configured to calculate characteristic quantities related to a driver's driving. The information processing device includes: one or more processors configured to calculate the characteristic quantities; and a transmission unit configured to transmit the calculated characteristic quantities to the server. The one or more processors are configured to: determine whether a pre-defined situation exists in relation to a preceding vehicle where a risk may occur, and if such a situation is determined to be present, calculate information as characteristic quantities related to the driver's actions to reduce the risk. The server is configured to perform pre-defined processing on the characteristic quantities.

[0019] Based on this structure, an information processing system can be provided that can evaluate the driver's driving in the event of a risk arising from the relationship with the vehicle in front.

[0020] According to another aspect of this disclosure, the information processing method is performed by an information processing device that calculates characteristic quantities related to a driver's driving. The information processing device includes: one or more processors that calculate the characteristic quantities; and a transmission unit that transmits the calculated characteristic quantities to an external location. The information processing method includes: determining, by one or more processors, whether a predefined situation exists in relation to a vehicle ahead where a risk may occur; and, if the predefined situation is determined to be present, calculating, by one or more processors, information related to the driver's actions to reduce the risk as characteristic quantities.

[0021] Based on this structure, an information processing method can be provided to evaluate a driver's driving in situations where a risk arises in relation to the vehicle ahead.

[0022] According to another aspect of this disclosure, a non-transitory storage medium stores commands executable by one or more processors of an information processing device capable of calculating characteristic quantities related to a driver's driving. The information processing device includes: one or more processors for calculating characteristic quantities; and a transmission unit for transmitting the calculated characteristic quantities to an external location. The commands cause one or more processors to perform the following functions: determining whether a pre-defined situation exists in relation to a vehicle ahead, and, if determined to be a pre-defined situation, calculating information as characteristic quantities related to the driver's actions to reduce the risk.

[0023] Based on this structure, a storage medium can be provided that allows for the evaluation of a driver's driving in the event of a risk arising from a relationship with a vehicle ahead.

[0024] According to this disclosure, an information processing device, vehicle, information processing system, information processing method, and storage medium are available that can evaluate a driver's driving in the event of a risk arising in relation to a vehicle ahead. Attached Figure Description

[0025] Hereinafter, the features, advantages, technical and industrial importance of exemplary embodiments of the present invention will be described with reference to the accompanying drawings, in which the same reference numerals denote the same constituent elements, wherein:

[0026] Figure 1 This is a diagram used to illustrate an example of the structure of a vehicle information management system.

[0027] Figure 2 This is a diagram illustrating the configuration of an example of the vehicle information processing device according to this embodiment.

[0028] Figure 3 This is a diagram used to illustrate an example of the processing performed in the second processing unit.

[0029] Figure 4 This is a diagram used to illustrate an example of the 6 processes performed in the 3rd processing unit.

[0030] Figure 5 It is a flowchart representing the process of sending characteristic quantities performed by the braking ECU. Detailed Implementation

[0031] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this description, identical or equivalent parts in the drawings will be labeled with the same reference numerals, and the description will not be repeated.

[0032] Figure 1 This is a diagram illustrating an example of the structure of a vehicle information management system 1. For example... Figure 1 As shown, in this embodiment, the vehicle information management system 1 includes multiple vehicles 2 and 3, a communication network 6, a base station 7, and a data center 100.

[0033] Vehicles 2 and 3 only need to be able to communicate with data center 100. For example, they could be vehicles powered by an engine, electric vehicles powered by an electric motor, or hybrid vehicles equipped with both an engine and an electric motor, using at least one as the driving force. Furthermore, in Figure 1 For ease of explanation, only two vehicles, 2 and 3, are shown, but the number of vehicles is not specifically limited to two; there can also be three or more.

[0034] The vehicle information management system 1 is configured to obtain predetermined information from vehicles 2 and 3, which are configured to communicate with the data center 100, and to manage the obtained information.

[0035] Data center 100 includes a control device 11, a storage device 12, and a communication device 13. The control device 11, the storage device 12, and the communication device 13 are connected to each other via a communication bus 14 and can communicate with each other.

[0036] Although not shown in the figures, the control device 11 includes a CPU (Central Processing Unit), memory (ROM (Read Only Memory) and RAM (Random Access Memory), etc.), and input / output ports for inputting and outputting various signals. The various controls executed by the control device 11 are processed by software, i.e., the CPU reads and executes programs stored in memory. The various controls involved in the control device 11 can also be implemented by executing programs stored on a storage medium using a general-purpose server (not shown). However, the various controls involved in the control device 11 are not limited to software processing; they can also be processed by dedicated hardware (electronic circuits).

[0037] Storage device 12 stores pre-determined information related to multiple vehicles 2 and 3 configured to communicate with data center 100. The pre-determined information includes, for example, information related to the respective characteristic quantities of vehicles 2 and 3, as described later, and information for identifying vehicles 2 and 3 (hereinafter referred to as vehicle IDs). The vehicle ID is unique information set for each vehicle. Data center 100 can determine the vehicle from which the transmission originated based on the vehicle ID.

[0038] The communication device 13 enables bidirectional communication between the control device 11 and the communication network 6. The data center 100 can use the communication device 13 to communicate with multiple vehicles, including vehicles 2 and 3, via the base station 7 set up on the communication network 6.

[0039] Next, the specific configurations of vehicles 2 and 3 will be explained. Since vehicles 2 and 3 have essentially the same configuration, the configuration of vehicle 2 will be explained representatively below.

[0040] Vehicle 2 includes drive wheels 50 and driven wheels 52. By utilizing the action of the drive source to rotate the drive wheels 50, a driving force is applied to vehicle 2, and vehicle 2 moves.

[0041] Vehicle 2 also includes ADAS-ECU (Electronic Control Unit) 10, brake ECU 20, DCM (Data Communication Module) 30 and central ECU 40.

[0042] ADAS-ECU10, Braking ECU20, and Central ECU40 are all computers with processors such as CPUs, memory, and input / output interfaces for executing programs.

[0043] ADAS-ECU10 includes a driver assistance system with functions related to driving assistance of vehicle 2. The driver assistance system is configured to perform various functions to assist driving vehicle 2 by executing installed applications, including at least one of steering control, drive control, and braking control of vehicle 2. Examples of applications installed in the driver assistance system include applications that implement the functions of an autonomous driving system (AD), an automatic parking system, and an advanced driver assistance system (ADAS).

[0044] As an ADAS application, it includes, for example, applications that achieve the following driving (ACC (Adaptive Cruise Control) etc.) while maintaining a constant distance from the vehicle in front and keeping close to the vehicle in front; applications that achieve the ASL (Auto Speed ​​Limiter) function, which identifies and maintains the speed limit of the vehicle; applications that achieve the lane keeping assist (LKA (Lane Keeping Assist) or LTA (Lane Tracing Assist) etc.) function, which maintains the vehicle in the lane it is traveling in; applications that achieve the collision damage mitigation braking (AEB (Autonomous Emergency Braking) or PCS (Pre-Crash Safety) etc.) function, which automatically brakes to reduce the damage of a collision; and applications that achieve the lane departure warning (LDW (Lane Departure Warning) or LDA (Lane Departure Alert) function, which warns the vehicle 2 of leaving its lane.

[0045] The various applications of this driver assistance system, based on information about the vehicle's surroundings obtained from multiple sensors and the driver's assistance requests, output to the braking ECU 20 to ensure the application's own action plan. These multiple sensors include, for example, a vision sensor such as a forward-facing camera 71, a millimeter-wave radar 72, LiDAR (Light Detection and Ranging), or a position detection device.

[0046] A forward-facing camera 71 captures an image of the area in front of vehicle 2 and sends the image data to the ADAS-ECU 10. A millimeter-wave radar 72 is a sensor that uses millimeter-wave (30GHz to 300GHz) radio waves to determine the distance, speed, and angle of objects in front of vehicle 2 and its surroundings, and sends the measurement results to the ADAS-ECU 10. Multiple sensors connected to the ADAS-ECU 10 are not limited to being connected to the ADAS-ECU 10. Any sensor can be connected to other ECUs, and the detection data from that sensor can be input to the ADAS-ECU 10 via the communication bus and the central ECU 40.

[0047] Each application acquires information about the vehicle's surroundings from one sensor or by integrating the detection results from multiple sensors, using this information as identification sensor information, and obtains driver assistance requests via a user interface (not shown), such as a switch. For example, each application can identify other vehicles, obstacles, or people around the vehicle using artificial intelligence (AI) on images and videos of the vehicle's surroundings acquired from multiple sensors, or image processing using an image processing processor. For instance, it calculates the time of travel using data from the forward-facing camera 71 and millimeter-wave radar 72, based on the inter-vehicle distance and relative speed between vehicle 2 and the vehicle ahead, using the formula: inter-vehicle distance / relative speed = inter-vehicle time.

[0048] Action plans may include requests related to the front-to-rear acceleration / deceleration of vehicle 2, requests related to the steering angle of vehicle 2, and requests related to stopping and maintaining vehicle 2.

[0049] The brake ECU 20 uses detection results from sensors to control the brake actuators that generate braking force in the vehicle 2. Furthermore, the brake ECU 20 sets the vehicle 2's motion request to fulfill the action plan requests from the ADAS-ECU 10. The vehicle 2's motion request set in the brake ECU 20 is realized through an actuator system (not shown) installed in the vehicle 2. The actuator system includes various types of actuator systems, such as powertrain systems, braking systems, and steering systems.

[0050] The braking ECU 20 is connected to, for example, a steering angle sensor 60, an accelerator pedal pressure sensor 62, a brake pedal pressure sensor 64, a first wheel speed sensor 54, and a second wheel speed sensor 56.

[0051] Steering angle sensor 60 detects the steering angle. Steering angle sensor 60 sends a signal indicating the detected steering angle to brake ECU 20.

[0052] Accelerator pedal press amount sensor 62 detects the amount of time the accelerator pedal (not shown) is depressed. Accelerator pedal press amount sensor 62 sends a signal indicating the detected amount of time the accelerator pedal is depressed to brake ECU 20.

[0053] Brake pedal depressor sensor 64 detects the amount of time the brake pedal (not shown) is depressed. Brake pedal depressor sensor 64 sends a signal indicating the detected amount of time the brake pedal is depressed to brake ECU 20.

[0054] The first wheel speed sensor 54 detects the rotational speed (wheel speed) of the drive wheel 50. The first wheel speed sensor 54 sends a signal indicating the detected rotational speed of the drive wheel 50 to the brake ECU 20.

[0055] The second wheel speed sensor 56 detects the rotational speed of the driven wheel 52. The second wheel speed sensor 56 sends a signal indicating the detected rotational speed of the driven wheel 52 to the brake ECU 20.

[0056] Among them, Figure 1 The example described uses a configuration where the steering angle sensor 60, accelerator pedal pressure sensor 62, brake pedal pressure sensor 64, first wheel speed sensor 54, and second wheel speed sensor 56 are connected to the brake ECU 20 and send detection results directly to the brake ECU 20. However, it is also possible for any sensor to be connected to other ECUs and input to the brake ECU 20 via a communication bus and central ECU 40.

[0057] Furthermore, the braking ECU 20, for example, receives information from the ADAS-ECU 10 not only related to the action plan, but also information related to the working status of various applications, information related to other driving operations such as gear shifting, and information related to the movement of the vehicle 2.

[0058] DCM30 is a communication module configured to enable bidirectional communication with data center 100.

[0059] The central ECU 40 is configured, for example, to communicate with the brake ECU 20, and is configured to communicate with the data center 100 using the DCM 30. The central ECU 40, for example, transmits information received from the brake ECU 20 to the data center 100 via the DCM 30.

[0060] In this embodiment, it is assumed that the central ECU 40 transmits information received from the brake ECU 20 to the data center 100 via the DCM 30. The central ECU 40 may, for example, have a function such as relaying communication between various ECUs (gateway function). Alternatively, the central ECU 40 may include a memory (not shown) capable of updating stored content using update information from the data center 100, and read prescribed information, including update information, stored in the memory from various ECUs when the vehicle 2 system is started.

[0061] In a vehicle 2 with the above-described structure, the braking ECU 20 can, for example, use information obtained from sensors installed in the vehicle 2 to infer changes in the vehicle 2's state (e.g., the deterioration state of vehicle 2's components). However, since the state of the vehicle 2 sometimes changes significantly due to disturbances or driving methods, there are cases where the inferred changes in the vehicle's state include noise caused by disturbances or driving methods. As a result, it is sometimes impossible to infer the changes in the vehicle's state with high accuracy.

[0062] Therefore, in this embodiment, the brake ECU 20 receives input information including at least one of the following: information related to driving operations of the vehicle 2, information related to the operating status of the vehicle 2's driving assistance, and information related to the vehicle 2's actions. The brake ECU 20 uses the input information received during a period when predetermined conditions are met to calculate characteristic quantities related to the vehicle's actions. The predetermined conditions include the condition that the vehicle's driving condition is a predetermined driving condition corresponding to the characteristic quantity.

[0063] In this way, compared to using input information from the entire period to calculate feature quantities, at least a portion of the input information, including noise caused by interference and driving methods, can be excluded when calculating feature quantities. Therefore, feature quantities related to the actions of vehicle 2 can be calculated with high accuracy.

[0064] Figure 2 This diagram illustrates the configuration of an example of the vehicle information processing device according to this embodiment. The vehicle information processing device according to this embodiment is implemented by a brake ECU 20.

[0065] The braking ECU 20 includes a first processing unit 22, a second processing unit 24, and a third processing unit 26. The first processing unit 22 receives information indicating the amount of pressure applied to the accelerator pedal and the amount of pressure applied to the brake pedal as information related to driving operations of the vehicle 2. Furthermore, the first processing unit 22 receives requests for action plans from the ADAS-ECU 10 and information indicating the operating status of the driver assistance system (e.g., time interval) as information related to the operating status of the driver assistance system of the vehicle 2. Additionally, the first processing unit 22 receives information indicating detection results from various sensors as information related to the actions of the vehicle 2. The first processing unit 22 outputs input information received during a predetermined period of time to the second processing unit 24 when such conditions are met.

[0066] The second processing unit 24 uses the input information received during the period when a predetermined condition is met during the period of receiving the input information to calculate the characteristic quantity related to the movement of the vehicle 2.

[0067] Figure 3 This is a diagram used to illustrate an example of the processing performed in the second processing unit 24. (See diagram for example.) Figure 3 As shown, the workshop time is input as input information to the second processing unit 24. The second processing unit 24 uses this input information to determine whether predetermined conditions are met.

[0068] The predetermined conditions include the condition that the driving condition of vehicle 2 is a predetermined driving condition. In this embodiment, the feature quantity represents, for example, an action that reduces the risk of a scenario with a narrow gap between the vehicle and the preceding vehicle. As a scenario, the gap time is divided into four stages using 2 bits. In cases where 0b00 = gap time is 2.5 seconds or more (meaning a sufficiently long gap time), 0b01 = gap time is 2.0 seconds or more but less than 2.5 seconds, 0b10 = gap time is 1.5 seconds or more but less than 2.0 seconds, and 0b11 = gap time is less than 1.5 seconds (meaning a high risk of rear-end collision), there are scenarios that change from 0b00 to 0b01, from 0b01 to 0b10, and from 0b10 to 0b11. In this embodiment, the scenario that changes from 0b00 to 0b01 is taken as the sampling scenario for collecting the feature quantity. That is, the condition that the driving condition of vehicle 2 is a predetermined driving condition is the condition that the driving condition of vehicle 2 changes from 0b00 to 0b01 in a sampling scenario.

[0069] The expected value of the reduction action (= standard model) in a scenario changing from 0b00 to 0b01 is the foot brake preparation (the action of placing the foot on the brake pedal), which can be determined based on whether a small amount of pedal pressure is detected by the brake pedal pressure sensor 64. The expected value of the reduction action (= standard model) in a scenario changing from 0b01 to 0b10 or from 0b10 to 0b11 can be determined based on whether the action of adjusting the workshop by the foot brake is detected (the hydraulic pressure of the brake master cylinder is either a first specified value or a second specified value or higher, where the first specified value is less than the second specified value).

[0070] For example, the first feature represents a personal characteristic such as the frequency of reducing actions relative to the total number of sampled scenarios. Specifically, it is calculated using the formula: the number of times a driver reduces actions in one trip / the total number of sampled scenarios. This first feature allows for the quantification of deviations in the driver's actions and enables the inference of the driver's emotions while driving. Furthermore, the second feature represents an objective characteristic such as the cumulative value of the difference from the model of a skilled driver. Specifically, it is calculated based on the difference between the number of times a driver reduces actions in one trip / the total number of sampled scenarios in the expected value model of a skilled driver and the number of times a driver reduces actions in one trip / the total number of sampled scenarios in this driver's case. This second feature allows for the quantification of the driver's proficiency and enables the understanding of how to cope with disturbances from surrounding traffic. Moreover, the calculation method for the features is not limited to the methods described above.

[0071] If the second processing unit 24 determines that the predetermined condition is met, it activates the condition flag (ON). The second processing unit 24 outputs a signal indicating the state of the condition flag as a scene recognition signal.

[0072] Furthermore, if the predetermined conditions are determined to be met, the second processing unit 24 uses the input information received during the period when the predetermined conditions are met to calculate the first feature or the second feature. For example, if the predetermined conditions are met, the second processing unit 24 calculates the first feature or the second feature and stores (saves) the calculated feature in memory by associating it with the time. The second processing unit 24 outputs the calculated first feature or the second feature along with the scene recognition signal and the time.

[0073] The third processing unit 26 uses the information output from the second processing unit 24 to perform preprocessing for sending information to the central ECU via CAN (Controller Area Network). The third processing unit 26 performs, for example, feature concealment (e.g., statistical processing) and detection of changes in feature quantities (e.g., changes relative to past journeys or the presence or absence of sudden changes) as preprocessing.

[0074] Figure 4This is a diagram used to illustrate an example of the processing performed in the third processing unit 26. (See diagram for example.) Figure 4 As shown, information representing scene recognition signals, feature quantities, and time is input from the second processing unit 24 to the third processing unit 26.

[0075] The third processing unit 26 outputs information needed to determine whether the historical change history of the characteristic quantity corresponds to a predetermined state in the data center 100. The third processing unit 26 outputs the generated information to the central ECU 40.

[0076] The central ECU40 will send the information input from the third processing unit 26 to the data center 100 via the DCM30.

[0077] The information sent from DCM30 to data center 100 includes, for example, processing time, scene identification number, and feature values ​​(multiple scene identification numbers and feature values ​​exist as a group). Therefore, data center 100 stores the information input from DCM30 as a set of data in storage device 12. As a result, data center 100 can obtain statistics on changes in the feature values ​​of vehicles 2 and 3 that can communicate with data center 100, and statistics on changes in the driving behavior characteristics of the driver.

[0078] Figure 5 This is a flowchart illustrating the process of feature quantity transmission processing performed by the brake ECU 20. (Refer to...) Figure 5 Every specified control cycle, the feature value is retrieved from the higher-level processing unit, sent for processing, and executed.

[0079] First, the braking ECU 20 obtains the inter-vehicle time from the ADAS-ECU 10 (step S211). Next, the braking ECU 20 determines the scenario of changing inter-vehicle distance based on the inter-vehicle time (step S212).

[0080] Then, the braking ECU 20 determines whether the scenario of the change in inter-vehicle distance is a sampling scenario (the scenario of the change from 0b00 to 0b01 mentioned above) (step S221). If it is determined that it is not a sampling scenario ("No" in step S221), the braking ECU 20 ends the feature quantity transmission process and returns the process to be executed to the upper-level process of the calling source.

[0081] If it is determined to be a sampling scenario ("yes" in step S221), the braking ECU20 accumulates the total number of scenarios Ns, and the value obtained by adding 1 to the original Ns is used as the new Ns (step S222).

[0082] Next, the braking ECU 20 determines whether the driver has taken any risk-reducing actions (in this embodiment, placing their foot on the brake pedal) (step S231). If it is determined that no risk-reducing action has been taken ("No" in step S231), the braking ECU 20 ends the feature quantity transmission process and returns the process to be executed to the higher-level process of the calling source.

[0083] On the other hand, if it is determined that a risk reduction action has been taken ("yes" in step S231), the braking ECU20 accumulates the number of risk reduction actions Nb, and the value obtained by adding 1 to the original Nb is used as the new Nb (step S232).

[0084] Next, the braking ECU20 calculates characteristic quantity 1 = Nb / Ns as the first characteristic quantity (step S233). Additionally, it calculates characteristic quantity 2 = characteristic quantity 1 - skilled worker characteristic quantity as the second characteristic quantity (step S234).

[0085] If the current journey is at its end, the braking ECU 20 performs feature concealment (e.g., statistical processing) as CAN transmission preprocessing (step S235). Specifically, for the first feature, the maximum, minimum, and standard deviation of the Nb / Ns ratio of the journey amount over a predetermined number of past journeys (e.g., 50) are calculated. If the current Nb / Ns exceeds the preset standard deviation, a flag indicating the degree of change is set (0b00 for small population, 0b01 for little change, 0b10 for a sudden increase, and 0b11 for a sudden decrease). The second feature is processed in the same way as the first feature.

[0086] Next, the braking ECU 20 sends the scene recognition information, the maximum value, minimum value, and standard deviation of the first feature quantity Nb / Ns and the second feature quantity, as well as the presence or absence of a sudden change flag, to the central ECU 40 via CAN (step S236). The central ECU 40 sends the received information to the data center 100 via the DCM 30. The control device 11 of the data center 100 performs prescribed processing on the first and second feature quantities determined from the received information (e.g., processing for accumulating to the storage device 12, and statistical processing of the first and second feature quantities accumulated in the storage device 12).

[0087] [Variation Example]

[0088] (1) In the above-described embodiments, such as Figure 5As shown in steps S235 and S236, CAN transmission preprocessing and CAN transmission are performed at the end of the journey. However, this is not a limitation; CAN transmission preprocessing and CAN transmission may also be performed at other times, such as at the beginning of the next journey or at a predetermined time during the next journey (e.g., after a predetermined time since the ignition switch was turned on).

[0089] (2) In the above-described embodiments, such as Figure 5 As shown in steps S233 to S236, both the first and second feature values ​​are calculated and transmitted via CAN. However, this is not a limitation; other third feature values ​​may also be calculated and transmitted via CAN, or any one or more of these feature values ​​may be calculated and transmitted via CAN.

[0090] (3) In the above-described embodiments, such as Figure 5 As shown in step S221, the sampling scenario is one that changes from 0b00 to 0b01. However, it is not limited to this; the sampling scenario can also be other scenarios, and can be any one or more of multiple scenarios that include other scenarios.

[0091] (4) In the above embodiment, the braking ECU20 performs the operation. Figure 5 However, it is not limited to this; it can also be performed by other information processing devices, such as other ECUs of vehicle 2 or external information processing devices (such as data center 100). Figure 5 The processing.

[0092] (5) The above-described embodiments can be understood as disclosure of an information processing device such as brake ECU20, or as disclosure of vehicles 2 and 3 including the information processing device, or as disclosure of an information processing system such as vehicle information management system 1 including the vehicle 2 and 3 and a server such as data center 100, or 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.

[0093] [Summarize]

[0094] (1) As Figure 1 As shown, the braking ECU20 is an information processing device that calculates driving-related characteristic quantities. It has a CPU for calculating these characteristic quantities and an input / output interface for sending the calculated characteristic quantities to the outside. Figures 2-5As shown, the CPU of the braking ECU20 determines whether it is a pre-defined situation (e.g., sampling scenario) where a risk occurs in relation to the vehicle ahead (e.g., steps S212, S221). If it is determined to be a pre-defined situation, it calculates information related to the driver's actions to reduce the risk as a feature quantity (e.g., steps S222 to S234).

[0095] Therefore, when a predefined situation is determined to be one in which a risk occurs in relation to a vehicle ahead, information related to the driver's actions to reduce the risk is calculated as a feature quantity, and this feature quantity is sent externally. As a result, it is possible to evaluate the driver's driving in situations where a risk occurs in relation to a vehicle ahead.

[0096] (2) Figure 5 As shown in step S221, the specified situation may be a situation where the time between the vehicle and the preceding vehicle is less than the specified time (e.g., 2.5 seconds) obtained by dividing the vehicle-to-vehicle distance by the speed associated with the vehicle (e.g., the relative speed between the vehicle and the preceding vehicle or the speed of the vehicle itself) (e.g., the vehicle-to-vehicle speed). (e.g., a scenario where the vehicle changes from 0b00 to 0b01).

[0097] Therefore, when it is determined that the time between the vehicle and the preceding vehicle (divided by the vehicle's speed) is less than a predetermined time, information related to the driver's actions to reduce risk is calculated as a feature quantity, and this feature quantity is sent externally. As a result, the driver's driving can be evaluated in situations where a risk arises in relation to the preceding vehicle.

[0098] (3) Figure 3 as well as Figure 5 As shown, information related to the driver's actions to reduce risk can be information related to braking operations (e.g., the first feature obtained by dividing the number of times Nb was placed on the brake pedal by the total number of sampled scenarios Ns, and the second feature being the difference between the first feature and the skilled driver feature).

[0099] Therefore, when a predefined situation is determined to be one in which a risk occurs in relation to the vehicle ahead, information related to braking operations is calculated as a characteristic quantity, and this characteristic quantity is sent externally. As a result, it is possible to evaluate the driver's driving in situations where a risk occurs in relation to the vehicle ahead.

[0100] (4) Figure 3 as well as Figure 5 As shown, information related to braking operation can be information related to sensor values ​​associated with braking operation.

[0101] Therefore, when a predefined situation is determined to be one in which a risk arises in relation to a vehicle ahead, information related to sensor values ​​associated with braking operations is calculated as a feature quantity, and this feature quantity is sent externally. As a result, it is possible to evaluate the driver's driving in situations where a risk arises in relation to a vehicle ahead.

[0102] (5) Figure 3 as well as Figure 5 As shown, the CPU of the braking ECU20 can calculate information related to the frequency of the driver's actions that reduce the risk for the specified situation (e.g., a first feature quantity, which is the number of times the driver puts his foot on the brake pedal Nb / the total number of sampled scenarios Ns, and a second feature quantity, which is the difference between the first feature quantity and the skilled driver feature quantity) as a feature quantity.

[0103] Therefore, when a predefined situation is identified as one where a risk arises in relation to a vehicle ahead, information related to the frequency of driver actions that mitigate the risk in relation to that situation is calculated as a feature quantity, and this feature quantity is sent externally. As a result, driver performance can be evaluated when a risk arises in relation to a vehicle ahead.

[0104] (6) Figure 3 as well as Figure 5 As shown, the CPU of the braking ECU20 can calculate information related to the difference between the driver's actions that reduce risk and the pre-determined model actions (e.g., a second feature quantity as the difference between the first feature quantity, Nb / total number of sampled scenarios Ns, and the skilled driver feature quantity) as a feature quantity.

[0105] Therefore, under the given condition of risk arising in relation to the vehicle ahead, information related to the difference between the driver's actions to reduce risk and the pre-determined model actions is calculated as a feature quantity, and this feature quantity is sent externally. As a result, it is possible to evaluate the driver's performance under the condition of risk arising in relation to the vehicle ahead.

[0106] The embodiments disclosed herein are illustrative and not restrictive in all respects. The scope of this disclosure is not a description of the above embodiments, but rather is shown by the scope of protection claimed in this application, including all modifications within the scope equivalent to that claimed in this application.

Claims

1. An information processing apparatus configured to calculate driving-related characteristic quantities of a driver, characterized in that, include: One or more processors are configured to compute the aforementioned feature quantity; The receiving unit is configured to receive input information, said input information including at least one of information related to driving operations of the vehicle, information related to the operating status of the vehicle's driving assistance systems, and information related to the vehicle's movements; and The transmitting unit is configured to transmit the calculated feature quantity to an external location. The one or more processors are configured as follows: The system monitors the time taken to travel between the vehicle and the preceding vehicle, calculated by dividing the distance between the vehicle and the preceding vehicle by the speed associated with the vehicle. It detects changes in this time, from being within a first predetermined range to being within a second predetermined range shorter than the first. Based on the detected changes, it identifies a predetermined condition indicating a risk in relation to the preceding vehicle. This predetermined condition occurs when the time decreases to the second range and persists only while the time remains within the second range. Furthermore, when the specified condition is determined to be present, information related to the driver's actions for reducing the risk under the specified condition is calculated as the feature quantity. This calculation is performed using only the input information received after the change in the workshop time to the second range is detected, and during the period when the workshop time remains within the second range.

2. The information processing device according to claim 1, characterized in that, Information related to the driver's actions in mitigating the risk is information related to braking operations.

3. The information processing device according to claim 2, characterized in that, The information related to the braking operation is the information related to the sensor values ​​associated with the braking operation.

4. The information processing apparatus according to any one of claims 1 to 3, characterized in that, The one or more processors are configured to calculate information related to the frequency of the driver's actions that reduce the risk in the specified situation as the feature quantity.

5. The information processing apparatus according to any one of claims 1 to 3, characterized in that, The one or more processors are configured to calculate information related to the difference between the driver's actions and the pre-determined model actions to reduce the risk, as the feature quantity.

6. A vehicle, comprising an information processing device configured to calculate driving-related characteristic quantities of a driver, characterized in that, The information processing device includes: One or more processors are configured to compute the aforementioned feature quantity; The receiving unit is configured to receive input information, said input information including at least one of information related to driving operations of the vehicle, information related to the operating status of the vehicle's driving assistance systems, and information related to the vehicle's movements; and The transmitting unit is configured to transmit the calculated feature quantity to an external location. The configuration of one or more processors is as follows: The system monitors the time taken to travel between the vehicle and the preceding vehicle, calculated by dividing the distance between the vehicle and the preceding vehicle by the speed associated with the vehicle. It detects changes in this time, from being within a first predetermined range to being within a second predetermined range shorter than the first. Based on the detected changes, it identifies a predetermined condition indicating a risk in relation to the preceding vehicle. This predetermined condition occurs when the time decreases to the second range and persists only while the time remains within the second range. Furthermore, when the specified condition is determined to be present, information related to the driver's actions for reducing the risk under the specified condition is calculated as the feature quantity. This calculation is performed using only the input information received after the change in the workshop time to the second range is detected, and during the period when the workshop time remains within the second range.

7. An information processing system comprising a vehicle and a server, the vehicle including information processing means configured to calculate driving characteristics related to a driver. The information processing system is characterized in that... The information processing device includes: One or more processors are configured to compute the aforementioned feature quantity; The receiving unit is configured to receive input information, said input information including at least one of information related to driving operations of the vehicle, information related to the operating status of the vehicle's driving assistance systems, and information related to the vehicle's movements; and The sending unit is configured to send the calculated feature quantity to the server. The configuration of one or more processors is as follows: The system monitors the time taken to travel between the vehicle and the preceding vehicle, calculated by dividing the distance between the vehicle and the preceding vehicle by the speed associated with the vehicle. It detects changes in this time, from being within a first predetermined range to being within a second predetermined range shorter than the first. Based on the detected changes, it identifies a predetermined condition indicating a risk in relation to the preceding vehicle. This predetermined condition occurs when the time decreases to the second range and persists only while the time remains within the second range. Furthermore, if the specified condition is determined to be present, information related to the driver's actions to reduce the risk under the specified condition is calculated as the feature quantity. This calculation is performed only using the input information received after the detection of the change in the workshop time to the second range, and during the period when the workshop time remains within the second range. The server is configured to perform specified processing on the feature quantity.

8. An information processing method, executed by an information processing device that calculates characteristic quantities related to a driver's driving, The information processing device includes: one or more processors for calculating the feature quantity; and a receiving unit configured to receive input information, the input information including at least one of information related to driving operations of the vehicle, information related to the working status of driving assistance of the vehicle, and information related to the movement of the vehicle. And the transmitting unit sends the calculated feature quantity to the outside. The information processing method is characterized by including: The processor monitors the time between the vehicle and the preceding vehicle, which is obtained by dividing the distance between the vehicle and the preceding vehicle by the speed associated with the vehicle. It detects a change in the time between the preceding and the preceding vehicle from being within a first specified range to being within a second specified range shorter than the first specified range. Based on the detected change, it identifies a specified condition in relation to the preceding vehicle that poses a risk. The specified condition occurs when the time between the preceding and the preceding vehicle decreases to the second specified range and persists only while the time between the preceding and the preceding vehicle remains within the second specified range. and If the specified condition is determined to be true, the one or more processors calculate information related to the driver's actions for reducing the risk under the specified condition as the feature quantity, the calculation being performed only using the input information received after the detection that the workshop time has changed to the second range and during the period when the workshop time remains within the second range.

9. A non-transitory storage medium storing commands executable by one or more processors of an information processing device that calculates driving-related characteristic quantities. The information processing device includes: one or more processors for calculating the feature quantity; a receiving unit configured to receive input information, the input information including at least one of information related to driving operations of the vehicle, information related to the operating status of the vehicle's driving assistance, and information related to the vehicle's movements; and a transmitting unit for transmitting the calculated feature quantity to an external location, wherein... The command causes one or more processors to perform functions. The non-transitory storage medium is characterized in that, The functions include: The system monitors the time taken to travel between the vehicle and the preceding vehicle, calculated by dividing the distance between the vehicle and the preceding vehicle by the speed associated with the vehicle. It detects changes in this time from being within a first predetermined range to being within a second predetermined range shorter than the first predetermined range. Based on the detected changes, it identifies a predetermined condition indicating a risk in relation to the preceding vehicle. This predetermined condition occurs when the time taken to travel decreases to the second predetermined range and persists only while the time taken to travel remains within the second predetermined range. If the specified condition is determined to be true, information related to the driver's actions to reduce the risk under the specified condition is calculated as the feature quantity. The calculation is performed only using the input information received after the change in the workshop time to the second range is detected and during the period when the workshop time remains within the second range.

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