Information processing device and information processing method

Abstract

To improve the technology for diagnosing the driving conditions of vehicle drivers. [Solution] The information processing device 20 acquires predetermined data from the moving vehicle 10, identifies the starting point where the vehicle 10 began to turn left at a predetermined intersection and the ending point where the left turn was completed based on the predetermined data, estimates the driving trajectory of the vehicle 10 from the starting point to the ending point based on the predetermined data, and diagnoses the driving tendencies of the vehicle 10 driver from the difference between the estimated driving trajectory and the ideal trajectory recommended when turning left at a predetermined intersection.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus and an information processing method.

Background Art

[0002] Conventionally, there has been known a technique for diagnosing the driving tendency of a vehicle driver. For example, Patent Document 1 discloses a technique for diagnosing the driving of a vehicle by comparing an ideal trajectory corresponding to the road shape on which the vehicle travels with the actual driving trajectory during driving.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When turning left at an intersection, a driver may not know the appropriate driving trajectory and may drive in a small or large turn. However, Patent Document 1 does not disclose a diagnostic index that allows a driver to objectively view their own driving tendency when turning left. Therefore, there has been room for improvement in the technique for diagnosing the driving situation of a vehicle driver.

[0005] In view of such circumstances, an object of the present disclosure is to improve the technique for diagnosing the driving situation of a vehicle driver.

Means for Solving the Problems

[0006] An information processing device according to one embodiment of the present disclosure includes a control unit that acquires predetermined data measured by sensors mounted on a moving vehicle, identifies a starting point where the vehicle began to turn left at a predetermined intersection and an ending point where the left turn was completed based on the predetermined data, estimates the driving trajectory of the vehicle from the starting point to the ending point based on the predetermined data, and diagnoses the driving tendencies of the vehicle's driver from the difference between the estimated driving trajectory and an ideal trajectory recommended when turning left at the predetermined intersection.

[0007] An information processing device according to one embodiment of the present disclosure includes a control unit that acquires predetermined data from a moving vehicle, identifies a starting point where the vehicle began to turn left at a predetermined intersection and an ending point where the left turn was completed based on the predetermined data, estimates the driving trajectory of the vehicle from the starting point to the ending point based on the predetermined data, and diagnoses the driving tendencies of the vehicle's driver from the difference between the estimated driving trajectory and an ideal trajectory recommended when turning left at the predetermined intersection.

[0008] An information processing method according to one embodiment of the present disclosure involves using an information processing device to acquire predetermined data from a moving vehicle, to identify the starting point where the vehicle began to turn left at a predetermined intersection and the ending point where the left turn was completed based on the predetermined data, to estimate the driving trajectory of the vehicle from the starting point to the ending point based on the predetermined data, and to diagnose the driving tendencies of the vehicle's driver from the difference between the estimated driving trajectory and the ideal trajectory recommended when turning left at the predetermined intersection. [Effects of the Invention]

[0009] According to one embodiment of the present disclosure, the technology for diagnosing the driving conditions of a vehicle driver is improved. [Brief explanation of the drawing]

[0010] [Figure 1] This is a block diagram showing a schematic configuration example of a system according to one embodiment of the present disclosure. [Figure 2] This flowchart shows an example of the operation of an information processing device. [Figure 3] This diagram shows the normalized trajectory of a vehicle making a left turn at an intersection, compared to the ideal trajectory.

[0011] (Summary of the embodiment) Referring to Figure 1, an overview of System 1 according to an embodiment of this disclosure will be described. System 1 comprises a vehicle 10, an information processing device 20, and a terminal device 30. The vehicle 10, the information processing device 20, and the terminal device 30 are communicated with a network 2, which includes, for example, the Internet and a mobile communication network.

[0012] Vehicle 10 is, for example, an automobile, but is not limited to that and may be any vehicle. An automobile is, but is not limited to, a gasoline car, a BEV (Battery Electric Vehicle), a HEV (Hybrid Electric Vehicle), a PHEV (Plug-in Hybrid Electric Vehicle), or an FCEV (Fuel Cell Electric Vehicle).

[0013] The information processing device 20 is, for example, a computer such as a server. The information processing device 20 can communicate with the vehicle 10 and the terminal device 30 via the network 2.

[0014] The terminal device 30 is a smartphone owned by the driver 3 of the vehicle 10. However, the terminal device 30 is not limited to a smartphone and may be any information processing terminal. The terminal device 30 can communicate with the vehicle 10 and the information processing device 20 via the network 2.

[0015] (Vehicle configuration) As shown in Figure 1, the vehicle 10 includes a communication unit 11, a measurement unit 12, a storage unit 13, and a control unit 14.

[0016] The communication unit 11 includes a communication interface for wirelessly connecting to the network 2. The communication interface for connecting to the network 2 corresponds to, for example, mobile communication standards such as 4G (4th Generation) or 5G (5th Generation), but is not limited thereto.

[0017] The measurement unit 12 includes a sensor 12A. The sensor 12A is, for example, a front camera, 3D-LiDAR, millimeter-wave sensor, acceleration sensor, vehicle speed sensor, gyro sensor, and GPS sensor, etc., but the sensor 12A is not limited thereto.

[0018] The storage unit 13 includes one or more memories. The memory is, for example, a semiconductor memory, magnetic memory, or optical memory, etc., but is not limited thereto. Each memory included in the storage unit 13 may function as, for example, a main storage device, auxiliary storage device, or cache memory. The storage unit 13 stores any information used for the operation of the vehicle 10.

[0019] The control unit 14 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The processor is, for example, a general-purpose processor such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit), or a dedicated processor specialized for specific processing, but is not limited thereto. The programmable circuit is, for example, an FPGA (Field-Programmable Gate Array), but is not limited thereto. The dedicated circuit is, for example, an ASIC (Application Specific Integrated Circuit), but is not limited thereto. The control unit 14 controls the operation of the entire vehicle 10.

[0020] (Configuration of the information processing device) As shown in FIG. 1, the information processing device 20 includes a communication unit 21, a storage unit 22, and a control unit 23.

[0021] The communication unit 21 includes one or more communication interfaces connected to the network 2. The communication interface corresponds to, for example, a mobile communication standard, a wired LAN (Local Area Network) standard, or a wireless LAN standard, but may correspond to any communication standard.

[0022] The storage unit 22 includes one or more memories. The memory is, for example, a semiconductor memory, a magnetic memory, an optical memory, etc., but is not limited thereto. Each memory included in the storage unit 22 may function as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores any information used for the operation of the information processing device 20. For example, the storage unit 22 may include a database 22A that stores predetermined information received from the vehicle 10.

[0023] The control unit 23 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The control unit 23 controls the operation of the entire information processing device 20.

[0024] (Configuration of the terminal device) As shown in FIG. 1, the terminal device 30 includes a communication unit 31, an output unit 32 including a display 32A, a storage unit 33, and a control unit 34. Details of the configuration of the terminal device 30 will not be described.

[0025] (Operation flow of the information processing device) FIG. 2 is a sequence diagram showing an operation example of the system. FIG. 3 is a diagram in which the traveling trajectory and the ideal trajectory of the vehicle 10 at the intersection cr during a left turn are normalized. With reference to FIGS. 2 and 3, the operation of the information processing device 20 according to the present embodiment will be described.

[0026] S101: The vehicle 10 starts traveling.

[0027] S102: During the traveling of the vehicle 10, the measurement unit 12 of the vehicle 10 constantly measures predetermined data d by the sensor 12A mounted on the vehicle 10.

[0028] Sensor 12A includes, but is not limited to, a front camera, 3D-LiDAR, millimeter-wave sensor, acceleration sensor, vehicle speed sensor, gyro sensor, and GPS sensor. The predetermined data d includes, but is not limited to, the vehicle 10's GPS timestamp, vehicle speed, steering angle, yaw rate, and turn signal W (left turn signal LW and right turn signal RW). The predetermined data d may also be signals from the vehicle 10's CAN (Controller Area Network).

[0029] S103: The control unit 14 of the vehicle 10 transmits predetermined data d, which has been continuously measured, to the information processing device 20.

[0030] S104-S105: The control unit 23 of the information processing device 20 stores the predetermined data d each time it acquires the predetermined data d.

[0031] Each time the control unit 23 receives predetermined data d that is continuously transmitted from the control unit 14, it stores the acquired predetermined data d in the database 22A provided in the storage unit 22.

[0032] S106: The control unit 14 checks whether the vehicle 10 has arrived at its destination. If the vehicle 10 has arrived at its destination, it terminates information processing; otherwise, it returns to S102.

[0033] S107: Based on the acquired predetermined data d, the control unit 23 identifies the starting point sp where the vehicle 10 began to turn left at a predetermined intersection cr, and the ending point ep where the vehicle 10 completed the left turn.

[0034] The designated intersection cr is not limited to one location, but may be located in multiple places along the section from the vehicle 10's starting point to its destination. If there are multiple designated intersection cr locations, the control unit 23 performs the information processing from S107 to S109 for each of the multiple designated intersection cr locations.

[0035] The GPS timestamp included in the predetermined data d records the time and the GPS latitude and longitude coordinates. The control unit 23 detects that the vehicle 10 is approaching and entering the predetermined intersection cr based on the GPS timestamp. As shown in Figure 3(a), the vehicle 10, having entered the predetermined intersection cr, flashes its left turn signal WL, begins a left turn at the starting point sp, follows the driving trajectory shown by the dashed line, and ends the left turn at the endpoint ep.

[0036] The control unit 23 identifies the starting point where the vehicle 10 began to turn left based on the turn signal W and yaw rate included in the predetermined data d. For example, the control unit 23 may identify the starting point sp where the vehicle 10 began to turn left at the predetermined intersection cr as the point within the predetermined intersection cr where the left turn signal LW is turned ON and the absolute value of the yaw angle exceeds a predetermined angle (for example, the point where the absolute value of the yaw angle becomes > 5 degrees).

[0037] The yaw rate included in the predetermined data d is the angular velocity (deg / sec) in the direction of rotational motion of the vehicle 10, that is, the speed at which the vehicle 10 is attempting to change direction. The yaw angle is the angle (deg) indicating the direction in which the vehicle 10 is facing. The yaw angle is the integrated value of the yaw rate and is calculated by integrating the yaw rate over time.

[0038] The control unit 23 identifies the endpoint ep where the vehicle 10 has completed its left turn based on the yaw rate included in predetermined data d. The control unit 23 identifies the endpoint ep where the vehicle 10 has completed its left turn as the point where the yaw angle at the starting point sp of the vehicle 10 has changed by 90 degrees (more precisely, 80 degrees < yaw angle < 100 degrees) in the left-turn direction after the start of the left turn, and the change in yaw angle compared to a predetermined time earlier (e.g., 0.5 seconds earlier) is less than a predetermined angle (e.g., < 1 degree).

[0039] S108: The control unit 23 estimates the travel trajectory Tev of the vehicle 10 traveling through the section S from the starting point sp to the ending point ep, based on the acquired predetermined data d.

[0040] The control unit 23 estimates the driving trajectory Tev for the section S from the starting point sp to the ending point ep, based on the vehicle speed and yaw rate included in predetermined data d.

[0041] Figure 3(b) shows an example of the estimated driving trajectory Tev of vehicle 10. However, if (i) the absolute value of the steering angle is > 10 deg when the left turn signal LW is turned ON, (ii) the yaw angle, which is the cumulative value of the yaw rate from the starting point sp to the ending point ep, is not 80 deg < yaw angle < 100 deg, or (iii) the turning radius of vehicle 10 is estimated from GPS coordinate data to be < 8m or > 18m, the control unit 23 does not determine that vehicle 10 has made a left turn and does not estimate the driving trajectory Tev.

[0042] S109: The control unit 23 diagnoses the driving tendencies of the driver 3 of the vehicle 10 from the difference between the estimated driving trajectory Tev and the ideal trajectory Tid that is recommended when turning left at a predetermined intersection cr.

[0043] The ideal trajectory Tid is the average of the driving trajectories of a highly skilled test driver when making a left turn at a predetermined intersection cr. The ideal trajectory Tid is stored in the database 22A provided in the memory unit 22 for each intersection.

[0044] In the example in Figure 3(b), the estimated driving trajectory Tev is inside the ideal trajectory Tid. In this case, driver 3 is diagnosed as having a tendency to make tight turns within the intersection. The control unit 23 may further diagnose the vehicle speed of driver 3 entering the intersection cr and the timing of the left turn by comparing it with the ideal trajectory Tid.

[0045] Furthermore, the control unit 23 may derive the difference between the driving trajectory Tev and the ideal trajectory Tid as the turning radius α, and diagnose the driving tendencies of the driver 3 of the vehicle 10 from the derived turning radius α. Referring to the example in Figure 3(b), the control unit 23 extracts a first coordinate point c1(x1,y1) on the ideal trajectory Tid where the yaw rate is maximum and the vehicle speed is minimum, and a second coordinate point c2(x2,y2) on the driving trajectory Tev where the yaw rate is maximum and the vehicle speed is minimum. The control unit 23 derives the turning radius α from the distance between the extracted first coordinate point c1(x1,y1) and the second coordinate point c2(x2,y2).

[0046] Furthermore, contrary to the example in Figure 3(b), if the estimated driving trajectory Tev is outside the ideal trajectory Tid, driver 3 is diagnosed as having a "tendency to make wide turns within intersections." In such cases, the control unit 23 may derive the difference between the driving trajectory Tev and the ideal trajectory Tid as the wide turn amount β, and diagnose the driving tendency of driver 3 of vehicle 10 from the derived wide turn amount β. Also, for example, if the driving trajectory Tev of vehicle 10 is veering into the oncoming lane and making a wide turn, that is, if the driving trajectory Tev is outside the normalization range in Figure 3(b), the control unit 23 may derive the wide turn amount β as the deviation amount β′. The wide turn amount β and deviation amount β′ are derived in the same way as the tight turn amount α. Note that the tight turn amount α inside the ideal trajectory may be a positive value, and the wide turn amount β and deviation amount β′ outside the ideal trajectory may be negative values.

[0047] S110: The control unit 23 transmits the diagnostic results of driver 3's driving tendencies to the terminal device 30 held by driver 3.

[0048] S111: The control unit 34 of the terminal device 30 displays the diagnostic results of the driver 3's driving tendencies on the display 32A.

[0049] The diagnostic results of the driving tendency include one or more of the following: the graph shown in the example of Figure 3(b), the turning radius α or turning radius β, and information that encourages driver 3 to change their driving behavior.

[0050] As described above, the information processing device 20 according to this embodiment acquires predetermined data d from a moving vehicle 10, identifies a starting point sp where the vehicle 10 began to turn left at a predetermined intersection cr and an ending point ep where the vehicle 10 completed the left turn based on the predetermined data d, estimates the driving trajectory Tev of the vehicle 10 traveling through the section S from the starting point sp to the ending point ep based on the predetermined data d, and diagnoses the driving tendencies of the driver 3 of the vehicle 10 from the difference between the estimated driving trajectory Tev and the ideal trajectory Tid that is recommended when turning left at a predetermined intersection cr.

[0051] With this configuration, the vehicle's trajectory Tev is estimated based on predetermined data acquired from the vehicle 10 while it is in motion. By deriving the difference between the trajectory Tev and the ideal trajectory Tid, it becomes possible to diagnose whether the driver 3 is driving inside or outside the ideal trajectory Tid. As a result, the driver of vehicle 10 obtains an index to objectively evaluate their own driving tendencies when turning left. Therefore, the probability that driver 3 will improve their tight-turn / wide-turn driving tendencies (driving habits) is increased, thus improving the technology for diagnosing the driving situation of a vehicle driver.

[0052] While this disclosure has been described based on the drawings and embodiments, it should be noted that those skilled in the art may make various modifications and alterations based on this disclosure. Therefore, it should be noted that these modifications and alterations are within the scope of this disclosure. For example, the functions, etc., included in each component or step can be rearranged in a logically consistent manner, and multiple components or steps can be combined into one or divided into two.

[0053] For example, in the embodiment described above, it is also possible to distribute the configuration and operation of the information processing device 20 across multiple computers that can communicate with each other. For instance, the information processing device 20 may be configured as follows: a server that receives predetermined data d from the vehicle 10 and stores it in a database; and a computer that acquires the predetermined data d stored in the server, estimates the driving trajectory Tev, and diagnoses the difference between the driving trajectory Tev and the ideal trajectory Tid. This configuration makes it possible to analyze big data received from a vast number of vehicles.

[0054] Furthermore, it is also possible to implement an embodiment in which a general-purpose computer functions as the information processing device 20 according to the above embodiment. Specifically, a program describing the processing content that realizes each function of the information processing device 20 according to the above embodiment is stored in the memory of the general-purpose computer, and the processor reads and executes the program. Therefore, this disclosure can also be implemented as a program that can be executed by a processor, or as a non-temporary computer-readable medium that stores said program. [Explanation of Symbols]

[0055] 1 System 2 Network 3. Driver 10 vehicles 11, 21, 31 Communications Department 12 Measurement section 12A sensor 13,22,33 Storage section 14,23,34 Control Unit 20 Information Processing Devices 22A Database 30 Terminal devices 32 Output section 32A Display

Claims

1. An information processing device comprising: an information processing device that acquires predetermined data measured by sensors mounted on a moving vehicle; identifies the starting point where the vehicle began to turn left at a predetermined intersection and the ending point where the left turn was completed based on the predetermined data; estimates the driving trajectory of the vehicle from the starting point to the ending point based on the predetermined data; and diagnoses the driving tendencies of the vehicle's driver from the difference between the estimated driving trajectory and the ideal trajectory recommended when turning left at the predetermined intersection.

2. An information processing device comprising: an information processing device that acquires predetermined data from a moving vehicle; identifies, based on the predetermined data, the starting point where the vehicle began to turn left at a predetermined intersection and the ending point where the left turn was completed; estimates, based on the predetermined data, the driving trajectory of the vehicle from the starting point to the ending point; and diagnoses the driving tendencies of the vehicle's driver from the difference between the estimated driving trajectory and the ideal trajectory recommended when turning left at the predetermined intersection.

3. An information processing apparatus according to claim 2, The predetermined data includes the vehicle's GPS timestamp, vehicle speed, steering angle, yaw rate, and turn signal signals, as an information processing device.

4. The information processing apparatus according to claim 3, The control unit is an information processing device that identifies the starting point based on the turn signal and yaw rate included in the predetermined data, identifies the ending point based on the yaw rate included in the predetermined data, and estimates the driving trajectory based on the vehicle speed and yaw rate included in the predetermined data.

5. The information processing apparatus according to claim 3, The aforementioned difference is the amount of turning radius. The control unit extracts a first coordinate point on the ideal trajectory where the yaw rate is maximum and the vehicle speed is minimum, and a second coordinate point on the travel trajectory where the yaw rate is maximum and the vehicle speed is minimum, derives the distance from the extracted first coordinate point to the second coordinate point as the turning radius, and diagnoses the driving tendencies of the vehicle's driver from the derived turning radius.

6. Information processing equipment To acquire specific data from a moving vehicle, Based on the predetermined data, the starting point where the vehicle began to make a left turn at a predetermined intersection and the ending point where the left turn was completed are identified. Based on the predetermined data, estimate the trajectory the vehicle traveled from the starting point to the ending point, The driving tendencies of the vehicle's driver are diagnosed from the difference between the estimated driving trajectory and the ideal trajectory recommended when turning left at the predetermined intersection. An information processing method that performs [this action].

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