JP2021030881A - Processing device and evaluation method - Google Patents

Abstract

To provide a processing device and an evaluation method capable of appropriately evaluating a driving skill of a rider of a saddle-riding vehicle.SOLUTION: The processing device evaluates a driving skill of a rider of a saddle-riding vehicle, and comprises an evaluation part that evaluates the driving skills of the rider of the saddle-riding vehicle on the basis of a degree of turning in traveling of the saddle-riding vehicle and a relative angle of a direction of a rider's face relative to a traveling direction of the saddle-riding vehicle. The evaluation method evaluates the driving skills of the rider of the saddle-riding vehicle, in which the evaluation part of the processing device evaluates the driving skills of the rider of the saddle-riding vehicle on the basis of the degree of turning in travelling of the saddle-riding vehicle and the relative angle of the direction of the rider's face relative to the traveling direction of the saddle-riding vehicle.SELECTED DRAWING: Figure 5

Description

This disclosure relates to a processing device and an evaluation method capable of appropriately evaluating the driving skill of a rider of a saddle-type vehicle.

Conventionally, there is a technique for evaluating the driving skill of a driver of a vehicle such as an automobile. Further, in recent years, as a technique for evaluating a driving skill, a technique for evaluating a rider's driving skill of a saddle-riding vehicle such as a motorcycle has been proposed (see, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2018-172090

By the way, in the field of evaluation of the driving skill of a rider of a saddle-riding vehicle, it is considered desirable to evaluate the driving skill of the rider more appropriately. For example, in the driving operation by the driver, what points differ depending on the difference in driving skill differs between a vehicle other than the saddle-riding vehicle (for example, an automobile) and a saddle-riding vehicle. However, the conventional technique has not sufficiently evaluated the driving skill focusing on the peculiar point of the saddle-riding vehicle as a difference in the driving operation according to the difference in the driving skill.

The present invention has been made against the background of the above-mentioned problems, and obtains a processing device and an evaluation method capable of appropriately evaluating the driving skill of a rider of a saddle-riding vehicle.

The processing device according to the present invention is a processing device that evaluates the driving skill of a rider of a saddle-riding vehicle, and is a processing device that evaluates the degree of turning of the saddle-riding vehicle and the traveling direction of the saddle-riding vehicle. An evaluation unit for evaluating the driving skill of the rider of the saddle-riding vehicle based on the relative angle in the face direction is provided.

The evaluation method according to the present invention is a method for evaluating the driving skill of a rider of a saddle-riding vehicle, and an evaluation unit of a processing device determines the degree of turning of the saddle-riding vehicle and the progress of the saddle-riding vehicle. The driving skill of the rider is evaluated based on the relative angle of the rider's face direction with respect to the direction.

In the processing device and the evaluation method according to the present invention, the rider driving of the saddle-riding vehicle is based on the turning degree of the saddle-riding vehicle and the relative angle of the rider's face to the traveling direction of the saddle-riding vehicle. Skills are evaluated. Thereby, the driving skill can be evaluated by paying attention to the relationship between the turning degree and the relative angle, which is a unique point in the saddle-riding vehicle as a difference in the driving operation according to the difference in the driving skill. Therefore, the driving skill of the rider of the saddle-riding vehicle can be appropriately evaluated.

It is a schematic diagram which shows the schematic structure of the motorcycle which mounts the processing apparatus which concerns on embodiment of this invention. It is a block diagram which shows an example of the functional structure of the processing apparatus which concerns on embodiment of this invention. It is a flowchart which shows an example of the flow of processing performed by the processing apparatus which concerns on embodiment of this invention. It is a figure which shows an example of the vehicle position at each detection time and the traveling direction vector and the face direction vector at each vehicle position which concerns on embodiment of this invention. It is a figure which shows an example of the relationship between the curvature and the inner product when a high skill rider runs and when a low skill rider runs.

Hereinafter, the processing apparatus according to the present invention will be described with reference to the drawings.

In the following, a processing device used for a two-wheeled motorcycle will be described, but the processing device according to the present invention is a saddle-riding vehicle other than a two-wheeled motorcycle (for example, a three-wheeled motorcycle, a bicycle, etc.). It may be the one used for. The saddle-riding type vehicle means a vehicle on which a rider straddles and rides, and includes a scooter and the like.

Further, although the case where the processing device 15 is mounted on the motorcycle 1 will be described below, the processing device according to the present invention may be mounted on a vehicle other than the saddle-riding vehicle, for example, the helmet 3. It may be mounted on. Further, the processing device according to the present invention may be a processing device different from the processing device that controls the operation of the display device 13.

Further, a case where the inertial measurement unit 14 is used as a device for detecting the vehicle position, which is the position of the motorcycle 1, will be described below, but a device other than the inertial measurement unit 14 (for example, as a device for detecting the vehicle position) will be described. A device for detecting the position using a signal received from a GPS (Global Positioning System) satellite such as a smartphone possessed by the rider 2) may be used.

Further, the configuration and operation described below are examples, and the processing apparatus and evaluation method according to the present invention are not limited to such configurations and operations.

Further, in the following, the same or similar description is appropriately simplified or omitted. Further, in each figure, the same or similar members or parts are omitted or given the same reference numerals. Further, for the detailed structure, the illustration is simplified or omitted as appropriate.

<Motorcycle configuration>
The configuration of the motorcycle 1 on which the processing device 15 according to the embodiment of the present invention is mounted will be described with reference to FIGS. 1 and 2.

FIG. 1 is a schematic view showing a schematic configuration of a motorcycle 1 on which a processing device 15 is mounted. Specifically, FIG. 1 shows a rider 2 wearing a helmet 3 riding on a motorcycle 1.

As shown in FIG. 1, the motorcycle 1 includes an engine 11, a hydraulic pressure control unit 12, a display device 13, an inertial measurement unit (IMU) 14, and a processing device 15. The motorcycle 1 travels using the power output from the engine 11. The motorcycle 1 may travel by using the power output from the motor.

The engine 11 corresponds to an example of the drive source of the motorcycle 1 and can output the power for driving the wheels. For example, the engine 11 is provided with one or a plurality of cylinders having a combustion chamber formed therein, a fuel injection valve for injecting fuel toward the combustion chamber, and a spark plug. When fuel is injected from the fuel injection valve, an air-fuel mixture containing air and fuel is formed in the combustion chamber, and the air-fuel mixture is ignited by a spark plug and burned. As a result, the piston provided in the cylinder reciprocates and the crankshaft rotates. Further, the intake pipe of the engine 11 is provided with a throttle valve, and the amount of intake air to the combustion chamber changes according to the throttle opening degree which is the opening degree of the throttle valve.

The hydraulic pressure control unit 12 is a unit that has a function of controlling the braking force generated on the wheels. For example, the hydraulic pressure control unit 12 is provided on an oil passage connecting the master cylinder and the wheel cylinder, and includes components (for example, a control valve and a pump) for controlling the brake hydraulic pressure of the wheel cylinder. By controlling the operation of the components of the hydraulic pressure control unit 12, the braking force generated on the wheels is controlled. The hydraulic pressure control unit 12 may control the braking force generated on both the front wheels and the rear wheels, or may control only the braking force generated on one of the front wheels and the rear wheels. ..

The display device 13 is a device for displaying an image, and is provided, for example, in the vicinity of a steering wheel in the body of the motorcycle 1. The display device 13 displays various information such as the vehicle speed of the motorcycle 1, for example.

The inertial measurement unit 14 includes a 3-axis gyro sensor and a 3-direction acceleration sensor, and detects the position of the motorcycle 1 (hereinafter, also referred to as the vehicle position). For example, the inertial measurement unit 14 detects the xy coordinates of the motorcycle 1 in the xy coordinate system set on the horizontal plane as the vehicle position, and outputs the detection result. The inertial measurement unit 14 may detect another physical quantity that can be substantially converted into the xy coordinates of the motorcycle 1. Further, the xy coordinates of the motorcycle 1 may be specified by the processing device 15 using the detection result of the inertial measurement unit 14.

Here, apart from the inertial measurement unit 14 on the vehicle body side, the inertial measurement unit 31 is also provided on the helmet 3 of the rider 2. Like the inertial measurement unit 14, the inertial measurement unit 31 includes a three-axis gyro sensor and a three-direction acceleration sensor. The inertial measurement unit 31 detects the face direction of the rider 2 (that is, the direction in which the face of the rider 2 faces) by detecting the posture of the helmet 3. For example, the inertial measurement unit 31 detects a unit vector (hereinafter, face direction vector) indicating the face direction of the rider 2 in the above xy coordinate system. That is, the face direction vector is a vector on the above xy coordinate system, and the length of the face direction vector is 1. The face direction vector may be specified by the processing device 15 using the detection result of the inertial measurement unit 31.

The processing device 15 controls the operation of each device mounted on the motorcycle 1.

For example, a part or all of the processing device 15 is composed of a microcomputer, a microprocessor unit, and the like. Further, for example, a part or all of the processing device 15 may be configured by an updatable device such as firmware, or may be a program module or the like executed by a command from a CPU or the like. The processing device 15 may be, for example, one or may be divided into a plurality of processing devices 15.

FIG. 2 is a block diagram showing an example of the functional configuration of the processing device 15.

As shown in FIG. 2, the processing device 15 includes, for example, an acquisition unit 151 and a control unit 152.

The acquisition unit 151 acquires information output from each device mounted on the motorcycle 1 and outputs the information to the control unit 152. For example, the acquisition unit 151 acquires information output from the inertial measurement unit 14 on the vehicle body side and the inertial measurement unit 31 on the helmet 3 side.

The control unit 152 controls the operation of each device of the engine 11, the hydraulic pressure control unit 12, and the display device 13 mounted on the motorcycle 1 by outputting an operation command to each device.

The control unit 152 includes, for example, an engine control unit 152a, a brake control unit 152b, a display control unit 152c, and an evaluation unit 152d that function in cooperation with a program.

The engine control unit 152a controls the operation of each device of the engine 11 (for example, a throttle valve, a fuel injection valve, a spark plug, etc.). As a result, the driving force transmitted from the engine 11 to the wheels of the motorcycle 1 is controlled, and the acceleration of the motorcycle 1 is controlled.

The brake control unit 152b controls the operation of each device (for example, a control valve, a pump, etc.) of the hydraulic pressure control unit 12. As a result, the braking force generated on the wheels of the motorcycle 1 is controlled, and the deceleration of the motorcycle 1 is controlled.

The display control unit 152c controls the display of the image by the display device 13 by outputting a control command to the display device 13.

The evaluation unit 152d evaluates the driving skill of the rider 2 of the motorcycle 1. The evaluation result of the driving skill by the evaluation unit 152d is used, for example, in each control by the engine control unit 152a, the brake control unit 152b, and the display control unit 152c. The details of using the evaluation result of the evaluation unit 152d for each control will be described later.

Here, the evaluation unit 152d evaluates the driving skill of the rider 2 of the motorcycle 1 based on the turning degree of the running of the motorcycle 1 and the relative angle of the face direction of the rider 2 with respect to the traveling direction of the motorcycle 1. .. As a result, it is possible to appropriately evaluate the driving skill of the rider 2 of the motorcycle 1. The details of the processing related to the evaluation of the driving skill performed by the evaluation unit 152d of the processing device 15 will be described later.

<Operation of processing device>
The operation of the processing device 15 according to the embodiment of the present invention will be described with reference to FIGS. 3 to 5.

FIG. 3 is a flowchart showing an example of the flow of processing performed by the processing device 15. Specifically, the control flow shown in FIG. 3 is an example of a processing flow related to the evaluation of driving skill performed by the evaluation unit 152d of the processing device 15, and is repeatedly executed. Further, steps S510 and S590 in FIG. 3 correspond to the start and end of the control flow shown in FIG. 3, respectively.

The detection of the vehicle position by the inertial measurement unit 14 on the vehicle body side and the detection of the face direction vector by the inertial measurement unit 31 on the helmet 3 side are repeatedly performed at preset time intervals. The detection times (that is, the times when the detections are performed) of the inertial measurement unit 14 and the inertial measurement unit 31 are substantially the same as each other. Then, the evaluation unit 152d repeatedly executes the control flow shown in FIG. 3 every time the inertial measurement units 14 and 31 detect it. In the following, this detection time (i.e., detection time immediately before the control flow shown in FIG. 3 are executed) and detection time t _i, and the previous detection time and detection time t _i-1, detection time before last Will be described as the detection time ti _-2.

FIG. 4 is a diagram showing an example of the vehicle position p at each detection time, the traveling direction vector a and the face direction vector f at each vehicle position p. Specifically, in FIG. 4, the vehicle position at the respective detection time in the case where the motorcycle 1 is traveling on the travel route 9 p (e.g., vehicle position p _i, the previous detection time at the present detection time t _i t vehicle position at the vehicle position _{p i-1} and the detection time _{t i-2} before the previous in _{i-1 p i-2,} etc.) is indicated by dots. In the following, as described above, _{the vehicle position at the specific detection time t j} is referred to as the vehicle position p _j, and the vehicle position at the arbitrary detection time is referred to as the vehicle position p.

Traveling direction vector a (e.g., the traveling direction vector a _i-1 and the like in the vehicle position p _i-1 at the previous detection time t _i-1) is a unit vector indicating the traveling direction of the motorcycle 1, FIG. 4 Is indicated by a thick solid arrow. The direction of the traveling direction vector a corresponds to the direction of the tangent line of the traveling path 9 at the vehicle position p corresponding to the traveling direction vector a. Face direction vector f (e.g., face direction vector f _i-1 and the like in the vehicle position p _i-1 at the previous detection time t _i-1) is a unit vector indicating the face direction of the rider 2, FIG. 4 , Indicated by a fine solid arrow. In the following, as described above, is referred to the traveling direction vector and the face the traveling direction vector direction vector, respectively a _j and the face direction vector f _j at a particular vehicle position p _j, and the traveling direction vector at an arbitrary vehicle position p The face direction vector is called a traveling direction vector a and a face direction vector f, respectively.

When the control flow shown in FIG. 3 is started, in step S511, the evaluation unit 152d identifies the _{curvature κ i-1} of the traveling path 9 at the vehicle position p _{i-1 at the previous detection time t i-1.} To do. In the following description, it referred to the curvature at a particular vehicle position p _j of curvature kappa _j, called the curvature at any vehicle position p of curvature kappa.

For example, the evaluation unit 152d of the vehicle and the vehicle position _{p i} at the current detection time _{t i,} the vehicle position _{p i-1} at the previous detection time _{t i-1,} with detection time _{t i-2} before last on the basis of the position _{p i-2,} to identify the curvature kappa _i-1 of the travel path 9 at the vehicle position _{p i-1.} As described above, each vehicle position p is the xy coordinate of the motorcycle 1 at each detection time detected by the inertial measurement unit 14 on the vehicle body side.

Evaluation unit 152d, for example, using Equation (1) below, and the vehicle position _{p i,} the vehicle position _{p i-1,} based on the vehicle position _{p i-2,} to identify the curvature kappa _i-1 .. For example, the initial vehicle position p ₀ is on a straight road, and in this case, the curvature κ _-1 is 0.

In the equation (1), _Ri-1 corresponds to the radius of curvature of the traveling path 9 at the vehicle position _{pi-1, and det corresponds to the determinant.}

Next, in step S513, the evaluation unit 152d identifies the traveling direction vector _{a i-1} at the vehicle position _{p i-1} at the previous detection time _{t i-1.}

For example, the evaluation unit 152d includes a vehicle position _{p i} at the current detection time _{t i,} based on the vehicle position _{p i-1} at the previous detection time _{t i-1,} traveling at the vehicle position _{p i-1} The direction vector _ai-1 is specified.

The evaluation unit 152d specifies the traveling direction vector _{ai-1 based on the vehicle position pi} and the vehicle position _pi-1 by using, for example, the following equation (2).

Next, in step S515, the evaluation unit 152d identifies the face direction vector _{f i-1} of the rider 2 at the vehicle position _{p i-1} at the previous detection time _{t i-1.}

Specifically, the evaluation unit 152d specifies the face direction vector f detected by the inertial measurement unit 31 on the helmet 3 side at the _{previous detection time ti-1 as the face direction vector fi-1.}

Next, in step S517, the evaluation unit 152d calculates the inner product _{I i-1} of the traveling direction vector _{a i-1} and the face direction vector _{f i-1.} In the following, the inner product corresponding to the specific traveling direction vector a _j and the face direction vector f _j is referred to as an inner product I _j, and the inner product corresponding to the arbitrary traveling direction vector a and the face direction vector f is referred to as an inner product I.

As described above, the traveling direction vector a and the face direction vector f are both unit vectors (that is, the magnitude of each vector is 1). Therefore, the inner product I corresponds to the cosine of θ, which is the relative angle of the face direction vector f with respect to the traveling direction vector a (that is, the relative angle of the rider 2 in the face direction with respect to the traveling direction of the motorcycle 1). That is, when the inner product I is 1, it corresponds to the case where the face direction of the rider 2 coincides with the traveling direction of the motorcycle 1. Further, when the inner product I is 0, it corresponds to the case where the face direction of the rider 2 is orthogonal to the traveling direction of the motorcycle 1. Further, when the inner product I is -1, it corresponds to the case where the face direction of the rider 2 is opposite to the traveling direction of the motorcycle 1. In the following, a relative angle corresponding to a particular moving direction vector a _j and the face direction vector f _j is referred to as a relative angle theta _j, the relative angle a relative angle corresponding to an arbitrary traveling direction vector a and the face direction vector f Called θ.

Next, in step S519, the evaluation unit 152d evaluates the driving skill of the rider 2, and the control flow shown in FIG. 3 ends.

As described above, the evaluation unit 152d determines the driving skill of the rider 2 of the motorcycle 1 based on the turning degree of the running of the motorcycle 1 and the relative angle of the face direction of the rider 2 with respect to the traveling direction of the motorcycle 1. evaluate. The degree of turning corresponds to the degree of bending of the traveling path 9, and for example, the smaller the turning radius, the higher the degree of turning.

Specifically, in the control flow shown in FIG. 3, the evaluation unit 152d adds the curvature κ of the traveling path 9 of the motorcycle 1 as the turning degree. Specifically, the evaluation unit 152d assumes that the larger the curvature κ, the higher the degree of turning. Further, the evaluation unit 152d adds the inner product I of the traveling direction vector a and the face direction vector f as the relative angle θ of the face direction with respect to the traveling direction. Specifically, the evaluation unit 152d assumes that the smaller the inner product I, the larger the relative angle θ. For example, the evaluation unit 152d evaluates the driving skill using the _{curvature κ i-1} and the inner product I _i-1.

FIG. 5 is a diagram showing an example of the relationship between the curvature κ and the inner product I when the high-skilled rider runs and when the low-skilled rider runs. High-skilled riders have higher driving skills than low-skilled riders.

In FIG. 5, a region D1 in which a plurality of pairs of curvature κ and inner product I when a high-skilled rider runs is distributed, and a region D2 in which a plurality of pairs of curvature κ and inner product I when a low-skilled rider runs are distributed. Is shown. Further, in FIG. 5, an approximate straight line L1 showing the relationship between the curvature κ and the inner product I when the high-skilled rider runs and an approximate straight line L2 showing the relationship between the curvature κ and the inner product I when the low-skilled rider runs. Is shown. The approximate straight lines L1 and L2 are derived based on the distribution of pairs of curvature κ and inner product I in regions D1 and D2, respectively.

By the way, in the traveling path 9 shown in FIG. 4, sections Sec1, Sec2, Sec3, Sec4, Sec5, Sec6 and Sec7 having different curvatures κ are connected in this order. The curvature κ increases in the order of section Sec1, Sec7, section Sec2, Sec6, section Sec3, Sec5, and section Sec4. Specifically, the sections Sec1 and 7 are straight roads, and the curvature κ of the sections Sec1 and 7 is 0. On the other hand, the sections Sec2, Sec3, Sec4, Sec5, and Sec6 are curved roads, and the curvature κ of these sections is larger than 0.

Further, the relative angle of the face direction vector f with respect to the traveling direction vector a (that is, the relative angle of the rider 2's face direction with respect to the traveling direction of the motorcycle 1) θ is the section Sec1, Sec7, the section Sec2, Sec6, the section Sec3, Sec5. , In the order of section Vector4. That is, as the curvature κ increases, the relative angle θ increases, so that the inner product I of the traveling direction vector a and the face direction vector f decreases.

In the approximate straight line L1 corresponding to the high-skilled rider and the approximate straight line L2 corresponding to the low-skilled rider shown in FIG. 5, the inner product I becomes smaller as the curvature κ increases, as in the example shown in FIG. There is. When traveling on a straight road having a curvature κ of 0, the face direction of the rider 2 coincides with the traveling direction of the motorcycle 1 regardless of the driving skill. Therefore, in both the approximate straight line L1 corresponding to the high-skilled rider and the approximate straight line L2 corresponding to the low-skilled rider, the inner product I is 1 when the curvature κ is 0.

Here, as shown in FIG. 5, in the approximate straight line L1 corresponding to the high-skilled rider, the ratio of the decrease in the inner product I to the increase in the curvature κ is higher than that in the approximate straight line L2 corresponding to the low-skilled rider. It's getting bigger. Therefore, in the approximate straight line L1 corresponding to the high-skilled rider, the inner product I is smaller (that is, the relative angle θ is larger) at each curvature κ as compared with the approximate straight line L2 corresponding to the low-skilled rider. .. Further, in the approximate straight line L1 corresponding to the high-skilled rider, the curvature κ is smaller at each inner product I (that is, each relative angle θ) as compared with the approximate straight line L2 corresponding to the low-skilled rider.

As described above, the tendency of the face direction to be greatly tilted with respect to the traveling direction (that is, the inner product I becomes smaller) as the curvature κ increases is a tendency common to high-skilled riders and low-skilled riders. On the other hand, focusing on the same curvature κ, the high-skilled rider tends to tilt the face direction more than the low-skilled rider with respect to the traveling direction (that is, the inner product I is smaller than the low-skilled rider). In other words, high-skilled riders tend to tilt their faces significantly toward the exit of the curved road at a relatively early stage after entering the curved road. Thereby, it is possible to appropriately realize that the motorcycle 1 is turned according to the intention.

Therefore, the evaluation unit 152d evaluates the driving skill of the rider 2 so that the smaller the inner product I at each curvature κ, the higher the driving skill, and the smaller the curvature κ at each inner product I, the higher the driving skill. _{For example, in the evaluation unit 152d, the point corresponding to the pair of the curvature κ i-1} and the inner product I _i-1 on the κ-I plane shown in FIG. 5 passes between the approximate straight line L1 and the approximate straight line L2. When the inner product I at each curvature κ is closer to the approximate straight line L1 than the straight line L0 (for example, a straight line that takes the average value of the value on the approximate straight line L1 and the value on the approximate straight line L2), the driving skill of the rider 2 is applied. The driving skill of the rider 2 can be evaluated as low skill when it is evaluated as high skill and is closer to the approximate straight line L2 than the reference straight line L0.

In this way, from the viewpoint of more appropriately evaluating the driving skill of the rider 2, the evaluation unit 152d increases the driving skill of the rider 2 as the relative angle θ increases in each degree of turning, and at each of the relative angles θ. It is preferable to evaluate so that the lower the degree of turning, the higher the degree of turning.

Here, from the viewpoint of improving the accuracy of the evaluation of the driving skill, it is preferable that the evaluation unit 152d evaluates the driving skill by using the evaluation model learned in advance for evaluating the driving skill. Specifically, the evaluation model is generated using the relationship between the degree of turning and the relative angle θ when a reference rider (for example, a high-skilled rider or a low-skilled rider) runs. The evaluation model may be generated by the processing device 15, or may be generated by another device different from the processing device 15.

For example, the evaluation model learned in advance is a function that outputs a driving skill with a pair of curvature κ and inner product I as a variable. As such an evaluation model, for example, a plurality of pairs of curvature κ and inner product I when a high-skilled rider in the region D1 shown in FIG. 5 travels, and a low-skilled rider in the region D2 shown in FIG. A model generated by preparing a plurality of pairs of the curvature κ and the inner product I when traveling is prepared in advance and using these data can be used. The pair of curvature κ and inner product I prepared in advance and the driving skill corresponding to the pair (that is, the driving skill of a high-skilled rider or the driving skill of a low-skilled rider) correspond to the teacher data in supervised learning. .. Then, for example, according to an existing algorithm such as a support vector machine, the operation is performed from a pair of curvature κ and inner product I (for example, a pair of curvature κ _i-1 and inner product I _i-1 obtained by the control flow shown in FIG. 3). An evaluation model for evaluating skills is constructed. It is more preferable to further improve the accuracy of evaluation of driving skill using the evaluation model by constructing an evaluation model using ensemble learning.

From the viewpoint of more appropriately improving the accuracy of the evaluation of the driving skill, the evaluation unit 152d determines the likelihood of the rider 2 as the driving skill of the rider (for example, a high-skilled rider or a low-skilled rider) as the above-mentioned reference. It is preferable to evaluate. For example, when the data of the pair of the curvature κ and the inner product I in the regions D1 and D2 shown in FIG. 5 is prepared in advance as described above and the evaluation model is generated using these data, the evaluation unit 152d , As the driving skill of the rider 2, the likelihood of the driving skill of the high-skilled rider or the low-skilled rider (that is, the plausibility that matches the driving skill of the high-skilled rider or the plausibility that matches the driving skill of the low-skilled rider) is evaluated. You may. _{For example, a pair of curvature κ i-1} and inner product I _i-1 obtained by the control flow shown in FIG. 3 and an evaluation model are used (specifically, of the curvature κ _i-1 and inner product I _i-1 ). By substituting the pair into an evaluation model that is a function), the likelihood of the high-skilled rider or the low-skilled rider for the driving skill can be evaluated as the driving skill of the rider 2. Specifically, a support vector machine is used for binary classification (that is, classification of a high-skilled rider or a low-skilled rider). For example, if the classification result is a high-skilled rider, the probability in the binary classification is The estimated value can be used as an evaluation result corresponding to the likelihood of a highly skilled rider for driving skill. On the other hand, when the classification result by the binary classification is a low-skilled rider, the probability estimation value in the binary classification can be used as an evaluation result corresponding to the likelihood of the low-skilled rider for the driving skill. The evaluation unit 152d may specify the likelihood of the high-skilled rider or the low-skilled rider with respect to the driving skill as a percentage, or may specify the level of likelihood by dividing the level into several stages.

From the viewpoint of further appropriately improving the accuracy of the evaluation of the driving skill, the evaluation unit 152d of the reference rider (for example, a high-skilled rider or a low-skilled rider) is based on a plurality of pairs of the degree of turning and the relative angle θ. It is preferable to evaluate the likelihood of driving skill. For example, by using a pair of curvature κ and inner product I at each vehicle position p obtained by repeatedly executing the control flow shown in FIG. 3 and an evaluation model, the driving skill of the rider 2 can be a high-skilled rider or a high-skilled rider. The likelihood of low-skilled riders for driving skills can be evaluated. In this case, specifically, each time the control flow shown in FIG. 3 is repeated, the pair of curvature κ and inner product I newly specified in the control flow is added as data used for evaluating the driving skill of the rider 2. Therefore, the evaluation result can be updated at any time.

The processing related to the evaluation of the driving skill of the rider 2 performed by the evaluation unit 152d has been described above. Here, the details of using the evaluation result of the evaluation unit 152d for each control will be described. The evaluation result of the evaluation unit 152d can be used for each control by the control unit 152.

For example, the control unit 152 performs adaptive cruise control (specifically, control for driving the motorcycle 1 according to the distance from the motorcycle 1 to the vehicle in front, the movement of the motorcycle 1 and the instruction of the rider 2). In adaptive cruise control, the acceleration / deceleration of the motorcycle 1 may be controlled based on the evaluation result of the evaluation unit 152d. Specifically, the lower the evaluation result of the driving skill of the rider 2, the less abrupt changes in the acceleration / deceleration of the motorcycle 1 occur in the adaptive cruise control, so that the engine control unit 152a and the brake control of the control unit 152 are less likely to occur. It is preferable that the unit 152b controls the acceleration / deceleration of the motorcycle 1.

Further, for example, when the control unit 152 can execute emergency brake control (specifically, control for stopping the motorcycle 1 before an obstacle in front of the obstacle without acceleration / deceleration operation by the rider 2). In the emergency brake control, the deceleration of the motorcycle 1 may be controlled based on the evaluation result of the evaluation unit 152d. Specifically, the lower the evaluation result of the driving skill of the rider 2, the less likely the sudden change of the deceleration of the motorcycle 1 occurs in the emergency brake control. It is preferable to control the deceleration in cycle 1.

Further, for example, the display control unit 152c of the control unit 152 may display the evaluation result of the evaluation unit 152d on the display device 13. Specifically, the display control unit 152c may display the likelihood of the high-skilled rider or the low-skilled rider with respect to the driving skill as a percentage on the display device 13 as the evaluation result of the driving skill of the rider 2. The level of may be displayed on the display device 13.

In the above description, an example in which the curvature κ of the traveling path 9 of the motorcycle 1 is added as the turning degree of the traveling of the motorcycle 1 has been described, but the evaluation unit 152d may add other parameters as the turning degree. Good.

For example, the evaluation unit 152d may take into account the lean angle of the motorcycle 1 (that is, the angle indicating the inclination of the motorcycle 1 in the roll direction with respect to the vertical direction) as the degree of turning. In this case, specifically, the evaluation unit 152d assumes that the larger the lean angle, the higher the degree of turning. The lean angle of motorcycle 1 can be detected by, for example, the inertial measurement unit 14. The inertial measurement unit 14 may detect another physical quantity that can be substantially converted into the lean angle of the motorcycle 1.

Further, for example, the evaluation unit 152d may add the lateral acceleration of the motorcycle 1 (that is, the component in the vehicle width direction of the motorcycle 1 in the acceleration generated in the motorcycle 1) as the turning degree. In this case, specifically, the evaluation unit 152d assumes that the degree of turning increases as the lateral acceleration increases. The lateral acceleration of the motorcycle 1 can be detected by, for example, a lateral acceleration sensor (not shown) provided on the body or the like of the motorcycle 1. The lateral acceleration sensor may detect another physical quantity that can be substantially converted into the lateral acceleration of the motorcycle 1.

<Effect of processing device>
The effect of the processing apparatus 15 according to the embodiment of the present invention will be described.

In the processing device 15, the evaluation unit 152d determines the driving skill of the rider 2 of the motorcycle 1 based on the turning degree of the running of the motorcycle 1 and the relative angle θ of the face direction of the rider 2 with respect to the traveling direction of the motorcycle 1. To evaluate. As a result, the driving skill of the rider 2 can be evaluated by paying attention to the relationship between the degree of turning and the relative angle θ, which is a unique point in the motorcycle 1, which is different depending on the difference in driving skill in the driving operation. .. Therefore, the driving skill of the rider 2 of the motorcycle 1 can be appropriately evaluated.

Preferably, in the processing device 15, the evaluation unit 152d increases the driving skill of the rider 2 as the relative angle θ increases at each degree of turning, and increases as the degree of turning decreases at each of the relative angles θ. evaluate. As a result, it is possible to more appropriately evaluate the driving skill of the rider 2 focusing on the relationship between the degree of turning and the relative angle θ. Therefore, the driving skill of the rider 2 can be evaluated more appropriately.

Preferably, in the processing device 15, the evaluation unit 152d evaluates the driving skill of the rider 2 by using a pre-learned evaluation model for evaluating the driving skill of the rider 2. Thereby, the driving skill of the rider 2 can be accurately evaluated based on the data showing the relationship between the turning degree and the relative angle θ prepared in advance. Therefore, the accuracy of evaluation of the driving skill of the rider 2 can be improved.

Preferably, in the processing device 15, the evaluation model is generated using the relationship between the degree of turning when the reference rider travels and the relative angle θ. Thereby, the evaluation model can be appropriately constructed as a model for evaluating the driving skill of the rider 2. Therefore, it is possible to appropriately improve the accuracy of the evaluation of the driving skill of the rider 2.

Preferably, in the processing device 15, the evaluation unit 152d evaluates the likelihood of the rider's driving skill, which is the above-mentioned reference, with respect to the driving skill of the rider 2. Thereby, for example, as compared with the case where the driving skill of the rider 2 is evaluated in two stages of high skill and low skill, it is possible to evaluate in more stages. Therefore, the accuracy of evaluation of the driving skill of the rider 2 can be improved more appropriately.

Preferably, in the processing device 15, the evaluation unit 152d evaluates the likelihood of the rider's driving skill, which is the above-mentioned reference, based on a plurality of pairs of the degree of turning and the relative angle θ. Thereby, the likelihood can be evaluated using more data showing the relationship between the degree of turning and the relative angle θ in the driving operation of the motorcycle 1 by the rider 2. Therefore, the accuracy of evaluation of the driving skill of the rider 2 can be improved more appropriately.

Preferably, in the processing device 15, the evaluation unit 152d adds the curvature κ of the traveling path 9 of the motorcycle 1 as the turning degree. Thereby, the driving skill of the rider 2 can be appropriately evaluated by paying attention to the relationship between the curvature κ of the traveling path 9 of the motorcycle 1 and the relative angle θ.

Preferably, in the processing device 15, the evaluation unit 152d takes into account the lean angle of the motorcycle 1 as the degree of turning. Thereby, the driving skill of the rider 2 can be appropriately evaluated by paying attention to the relationship between the lean angle of the motorcycle 1 and the relative angle θ.

Preferably, in the processing device 15, the evaluation unit 152d adds the lateral acceleration of the motorcycle 1 as the turning degree. Thereby, the driving skill of the rider 2 can be appropriately evaluated by paying attention to the relationship between the lateral acceleration of the motorcycle 1 and the relative angle θ.

The present invention is not limited to the description of embodiments. For example, only some of the embodiments may be implemented.

1 Motorcycle, 2 Rider, 3 Helmet, 11 Engine, 12 Hydraulic Control Unit, 13 Display Device, 14 Inertial Measurement Unit, 15 Processing Device, 31 Inertial Measurement Unit, 151 Acquisition Unit, 152 Control Unit, 152a Engine Control Unit, 152b Brake control unit, 152c display control unit, 152d evaluation unit.

Claims (10)

It is a processing device (15) that evaluates the driving skill of the rider (2) of the saddle-riding vehicle (1).
The saddle-riding vehicle (1) is based on the degree of turning of the saddle-riding vehicle (1) and the relative angle of the rider (2) in the face direction with respect to the traveling direction of the saddle-riding vehicle (1). ) Is equipped with an evaluation unit (152d) that evaluates the driving skills of the rider (2).
Processing equipment. The evaluation unit (152d) evaluates the driving skill so that the larger the relative angle is, the higher the driving skill is, and the lower the relative angle is, the higher the driving skill is.
The processing apparatus according to claim 1. The evaluation unit (152d) evaluates the driving skill by using a pre-learned evaluation model for evaluating the driving skill.
The processing apparatus according to claim 1 or 2. The evaluation model is generated by using the relationship between the turning degree and the relative angle when the reference rider travels.
The processing apparatus according to claim 3. The evaluation unit (152d) evaluates the likelihood of the rider as the driving skill with respect to the driving skill of the reference.
The processing apparatus according to claim 4. The evaluation unit (152d) evaluates the likelihood based on a plurality of pairs of the degree of turning and the relative angle.
The processing apparatus according to claim 5. The evaluation unit (152d) considers the curvature of the traveling path of the saddle-riding vehicle (1) as the degree of turning.
The processing apparatus according to any one of claims 1 to 6. The evaluation unit (152d) takes into account the lean angle of the saddle-riding vehicle (1) as the degree of turning.
The processing apparatus according to any one of claims 1 to 7. The evaluation unit (152d) considers the lateral acceleration of the saddle-riding vehicle (1) as the degree of turning.
The processing apparatus according to any one of claims 1 to 8. It is a method of evaluating the driving skill of the rider (2) of the saddle-riding vehicle (1).
The evaluation unit (152d) of the processing device (15) determines that the degree of turning of the saddle-riding vehicle (1) is relative to the traveling direction of the saddle-riding vehicle (1) in the face direction of the rider (2). Evaluate the driving skill of the rider (2) based on the angle.
Evaluation methods.

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