Driving assistance device, driving assistance system, and driving assistance method

Abstract

This driving assistance device accurately detects a case in which a two-wheeled vehicle (201) takes evasive action with respect to an abnormality on a road, estimates the position and condition of the abnormality on the road, and transmits said position and condition to another vehicle. A driving assistance device (101): receives vehicle information including the position and inclination angle of a vehicle, and road information including the position and lane width of a road on which the vehicle is traveling, and calculates an inclination angular velocity from the inclination angle of the vehicle; calculates the distance of the vehicle from a lane center line, on the basis of the position and lane width of the road; determines, from the above information, whether or not the course of the vehicle has changed; if there has been a change of course, determines whether or not the vehicle has taken evasive action; and if it is determined that evasive action was taken, estimates the position and condition of an abnormality on the road. The present invention provides a driving assistance system (100) including a driving assistance device (101) and vehicle-mounted devices (102, 103), and a driving assistance method.

Description

Driving assistance device, driving assistance system, and driving assistance method

[0001] The present disclosure relates to a driving assistance device, a driving assistance system, and a driving assistance method.

[0002] Vehicles that use on-board cameras, laser measuring devices, millimeter-wave radar, and other peripheral monitoring sensors to understand the surroundings and position the vehicle using inertial navigation systems and positioning sensors such as the Global Navigation Satellite System (GNSS) are becoming more and more common. Driving assistance systems that share information from these on-board sensors via wireless communication and provide driving assistance to vehicles are being developed.

[0003] In most cases, vehicles equipped with these on-board sensors have been assumed to be four-wheeled vehicles. Due to the volume, weight, and cost of on-board sensors, application to four-wheeled vehicles has been prioritized. However, driving assistance systems that share information from on-board sensors of multiple vehicles to prepare for road abnormalities that require attention are also being developed for motorcycles.

[0004] A technology has been disclosed that identifies road areas that require caution based on information about the tilt angle in the rolling direction of a motorcycle, and also identifies road areas that require caution based on the difference between the travel path of a four-wheeled vehicle and the travel path of a motorcycle (see, for example, Patent Document 1).

[0005] Patent No. 7337251

[0006] The technology disclosed in Patent Document 1 identifies a location where the inclination angle of the rolling direction of the motorcycle temporarily changes as a cautionary location where the motorcycle has taken evasive action. However, detecting that the difference between the motorcycle's inclination angle and the appropriate inclination angle is equal to or greater than a predetermined value does not allow for sufficiently accurate detection of evasive action. Motorcycles can change their inclination angle while traveling straight. Since there can be inclination angles of motorcycles that do not involve evasive action, it is possible that evasive action by the motorcycle is incorrectly detected.

[0007] Furthermore, the technology disclosed in Patent Document 1 identifies cautionary points on the road by comparing the travel path of a four-wheeled vehicle with the travel path of a two-wheeled vehicle. However, detecting differences in the travel path of a two-wheeled vehicle based on the travel path of a four-wheeled vehicle does not provide sufficient accuracy in detecting evasive maneuvers. This is because there is a possibility that the evasive maneuvers of the two-wheeled vehicle may be erroneously detected when the four-wheeled vehicle changes lanes.

[0008] The present disclosure aims to solve these problems by providing a driving assistance device that can accurately detect when a motorcycle has taken evasive action in response to an abnormality on the road and estimate the location and state of the abnormality on the road, a driving assistance system that transmits the location and state of the abnormality on the road to other vehicles, and a driving assistance method.

[0009] The driving assistance device according to the present disclosure includes: a receiving unit that receives vehicle information including the position and tilt angle of the vehicle, and road information including the position and lane width of the road on which the vehicle is traveling; a course change determination unit that calculates a tilt angular velocity from the vehicle's tilt angle in the vehicle information received via the receiving unit, calculates the distance from the vehicle's lane center line based on the vehicle's position in the vehicle information and the road's position and lane width in the road information, and determines whether the vehicle has changed course based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line; and a road surface abnormality estimation unit that, when the course change determination unit determines that a course change has occurred, determines whether the vehicle has taken evasive action based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line, and if it determines that evasive action has been taken, estimates the position and state of an abnormality on the road.

[0010] The driving assistance system according to the present disclosure includes a driving assistance device having a transmitting unit that transmits the location and state of an abnormality on the road estimated by a road surface abnormality estimation unit to another vehicle, a first on-board device mounted on the vehicle that transmits vehicle information and road information of the vehicle to a receiving unit of the driving assistance device, and a second on-board device mounted on the other vehicle that receives the location and state of the estimated abnormality on the road from the transmitting unit of the driving assistance device.

[0011] The driving assistance method according to the present disclosure includes: a receiving step of receiving vehicle information including the position and tilt angle of the vehicle, and road information including the position and lane width of the road on which the vehicle is traveling; a course change determination step of calculating a tilt angular velocity from the vehicle's tilt angle in the vehicle information received in the receiving step, calculating the distance from the vehicle's lane center line based on the vehicle's position in the vehicle information and the road's position and lane width in the road information, and determining whether the vehicle has changed course based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line; a road abnormality estimation step of determining whether the vehicle has taken evasive action based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line when it is determined that a course change has occurred in the course change determination step, and estimating the position and state of an abnormality on the road when it is determined that the vehicle has taken evasive action; and a transmitting step of transmitting the position and state of the abnormality on the road estimated in the road abnormality estimation step to another vehicle.

[0012] The driving assistance device, driving assistance system, and driving assistance method according to the present disclosure can accurately detect when a motorcycle takes evasive action in response to an abnormality on the road and estimate the location and state of the abnormality on the road. The location and state of the abnormality on the road can then be transmitted to other vehicles. This allows appropriate driving assistance to be provided to the other vehicles.

[0013] 1 is a configuration diagram of a driving assistance system according to Embodiment 1. FIG. 1 is a configuration diagram of a driving assistance device and an on-vehicle device according to Embodiment 1. FIG. 2 is a diagram showing a hardware configuration diagram of the driving assistance device and the on-vehicle device according to Embodiment 1. FIG. 3 is a diagram showing a lean angle of a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 4 is a diagram showing a lean angle range of a scooter-type two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 5 is a diagram showing a lean angle range of a naked-type two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 6 is a diagram showing an example of an evasive maneuver of a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 7 is a time chart showing the lean angle and lean angular velocity when an evasive maneuver is performed by a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 8 is a diagram showing the distance from the lane center line when an evasive maneuver is performed by a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 9 is a diagram showing the distance from the lane center line when a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1 is normally traveling. FIG. 10 is a diagram showing an example of an evasive maneuver performed by a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1 while following a four-wheeled vehicle. FIG. 11 is a time chart showing the lean angle and lean angular velocity when an evasive maneuver is performed by a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1 while following a four-wheeled vehicle. 1 is a time chart showing an example in which the behavior of a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1 does not change in a specific section. FIG. 2 is a diagram showing the relationship between the traveling position and lean angle of a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 3 is a diagram showing the relationship between the traveling position and probability distribution of a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 4 is a diagram showing the relationship between the lean angle and traveling speed of a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 5 is a time chart showing the lean angle, lean angular velocity, and heart rate when an evasive action is taken by a two-wheeled vehicle equipped with an on-vehicle device according to Embodiment 1. FIG. 6 is a diagram showing the setting of an assistance level section for a cautionary portion of a driving assistance device according to Embodiment 1. FIG. 7 is a flowchart showing the transmission processing of an on-vehicle device according to Embodiment 1. FIG. 8 is a flowchart showing the driving assistance processing of a driving assistance device according to Embodiment 1. FIG. 9 is a flowchart showing the reception processing of an on-vehicle device according to Embodiment 1. FIG. 10 is a first flowchart showing the details of the processing of estimating a road abnormality of a driving assistance device according to Embodiment 1. FIG. 11 is a second flowchart showing the details of the processing of estimating a road abnormality of a driving assistance device according to Embodiment 1.Fig. 10 is a third flowchart showing details of the process of estimating a road abnormality in the driving assistance device according to embodiment 1. Fig. 11 is a flowchart showing details of the process of received assistance information by the in-vehicle device according to embodiment 1. Fig. 12 is a configuration diagram of the driving assistance device and the in-vehicle device according to embodiment 2. Fig. 13 is a time chart showing signal processing of the sensor of the user device when an evasive action is taken by a two-wheeled vehicle equipped with the in-vehicle device according to embodiment 2.

[0014] Hereinafter, embodiments will be described in detail with reference to the drawings. Note that the drawings are schematic, and for the sake of convenience, configurations may be omitted or simplified as appropriate. In the following description, similar components are denoted by the same reference numerals, and their names and functions are also the same. Therefore, detailed descriptions thereof may be omitted to avoid duplication.

[0015] 1. Embodiment 1 <Driving Assistance System> Fig. 1 is a configuration diagram of a driving assistance system 100 including a driving assistance device 101 according to Embodiment 1. The driving assistance system 100 is configured to include on-board devices 102, 103 of a plurality of vehicles, such as a two-wheeled vehicle 201 and a four-wheeled vehicle 202, that provide vehicle information and road information. The driving assistance system 100 is configured to include a driving assistance device 101 that receives vehicle information and road information and transmits driving assistance information to each vehicle. The vehicle information and road information may be provided from a roadside device 203, or may be provided from another device via a network.

[0016] The driving assistance device 101 may execute the processing of its storage device and processing device on other devices via a network. These processes may also be executed using cloud data 205 on a server configured as a cloud 204. The driving assistance system 100 may include, as components, the roadside device 203, the cloud 204, and other devices connected via a network.

[0017] The driving assistance device 101 may communicate with the motorcycle and four-wheeled vehicle and the server via a network, or may communicate with the server via roadside infrastructure. The driving assistance system 100 is not limited to the example configuration in which motorcycles and four-wheeled vehicles are connected as shown in Figure 1, but may also connect drivers and pedestrians carrying user devices 104, electric kick scooters, personal mobility devices, shuttle buses, large vehicles, etc.

[0018] The two-wheeled vehicle 201 may include a motorcycle, a motorized bicycle, a three-wheeled bike, and a kick scooter. The four-wheeled vehicle may include a passenger car, a commercial vehicle, a truck, a bus, a multi-wheeled vehicle, and the like.

[0019] <Driving Assistance Device> Fig. 2 is a configuration diagram of the driving assistance device 101, in-vehicle device 102, and in-vehicle device 103 according to the first embodiment. The driving assistance device 101 is configured from a communication unit 7, a database 20, and a processing device 9. The communication unit 7 is configured from a receiving unit 7a and a transmitting unit 7b. The communication unit 7 enables communication with the in-vehicle devices 102 and 103, the roadside device 203, and a server constituting the cloud 204 via a network. The driving assistance device 101 receives vehicle information and road information via the communication unit 7.

[0020] The processing device 9 is composed of a data management unit 1, a data analysis unit 22, and an information provision unit 6. The data analysis unit 22 is composed of a course change determination unit 2 and a road surface abnormality estimation unit 21. The road surface abnormality estimation unit 21 determines whether the vehicle has changed course based on vehicle information and road information. If it is determined that the vehicle has changed course, the road surface abnormality estimation unit 21 estimates whether the vehicle has taken evasive action and estimates the position and state of the road surface abnormality.

[0021] The road surface abnormality estimation unit 21 is composed of a road surface estimation unit 3, a vehicle behavior estimation unit 4, a fallen object estimation unit 5, and a behavior learning unit 8. The road surface estimation unit 3 receives the data calculated by the course change determination unit 2, as well as vehicle information and road information. The road surface estimation unit 3 extracts points at specific points where the amount of change or rate of change of each piece of data is equal to or greater than a certain level, and estimates the presence of manholes, gratings, slippery road surface conditions such as ice, ruts, and bumps on the road. The location and state of the road surface abnormality estimated by the road surface abnormality estimation unit 21 are notified to other vehicles from the transmission unit.

[0022] The on-vehicle device 102 includes an on-vehicle sensor group 36, a processing device 35, an external interface 24, and a vehicle control device 18. The on-vehicle sensor group 36 includes a behavior sensor 13, a positioning sensor 14, a surrounding sensor 15, and a biological sensor 19.

[0023] The behavior sensor 13 detects the motorcycle's speed, acceleration, yaw angle, pitch angle, roll angle, and the accelerations thereof to understand the behavior of the motorcycle. The behavior sensor 13 may include multiple sensors, such as an inertial measurement unit (IMU). The behavior sensor 13 may also include sensors that detect the vehicle's steering angle, braking status, engine RPM, shift position, and accelerator position, so that the vehicle's attitude and behavior can be recognized and predicted. The use of the behavior sensor 13 can compensate for the shortcomings of the positioning sensor 14, which cannot receive satellite signals when hidden by buildings or structures, and the surroundings sensor 15, which cannot compare signals with map information when traveling through deserts, grasslands, forests, tunnels, etc. due to the lack of surrounding landscape landmarks.

[0024] The positioning sensor 14 is composed of a receiver and a receiving antenna, such as a Global Navigation Satellite System (GNSS), and is a detector that can determine the vehicle's latitude, longitude, altitude, positional accuracy, etc. from external signals. The surroundings sensor 15 is a sensor that uses a visible light image sensor, an infrared image sensor, a millimeter-wave radar, a LiDAR (Light Detection and Ranging), an ultrasonic sensor, etc. to determine the position and shape of objects outside the vehicle. By recognizing the direction and relative distance of buildings, signs, traffic lights, etc. around the vehicle, the vehicle's position can be calculated by referring to map data. In addition, the visible light image sensor and the infrared image sensor can also read the relative position of road white lines, the number of lanes, lane width, etc. The biometric sensor 19 detects the driver's heart rate, blood pressure, body temperature, line of sight, oxygen saturation, etc. to determine the driver's condition.

[0025] The processing device 35 is composed of a behavior data calculation unit 10, a transmission data generation unit 11, and a data processing unit 12. The external interface 24 is composed of a communication unit 16 and a display unit 17. The communication unit 16 is composed of a receiving unit 16a and a transmitting unit 16b. The communication unit 16 enables communication with the driving assistance device 101, the in-vehicle device 103, the roadside device 203, and the server constituting the cloud 204 via a network.

[0026] The on-board device 102 can transmit information detected by each sensor of the on-board sensor group 36, such as the vehicle position, vehicle speed, acceleration, tilt angle (roll angle), tilt angular velocity, and predicted radius of curvature of the future driving trajectory calculated based on the vehicle's yaw rate, speed, and acceleration, as vehicle information to the driving assistance device 101. The on-board device 102 can also transmit road information, such as the distance from the lane center line of the road, to the driving assistance device 101. This information may be transmitted with limited types of data, and processed and used by the driving assistance device 101. This can reduce the amount of communication, contributing to a reduction in the communication traffic load.

[0027] The receiving unit 16a receives the driving assistance information transmitted from the driving assistance device 101 and transmits it to the data processing unit 12. The display unit 17 displays the driving assistance information received via the data processing unit 12 to the driver. The display unit 17 may be a smartphone, a smart watch, a smart helmet, a tablet, a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, or the like.

[0028] The vehicle control device 18 controls the brake, accelerator, and steering wheel based on the vehicle control information received from the data processing unit. The vehicle control unit controls the engine ECU (Electronic Control Unit), brake ECU, ECU for EPS (Electric Power Steering), etc. in accordance with the driving control information instructed by the driving assistance device.

[0029] <Hardware Configuration Diagram of Driving Assistance Device and In-Vehicle Device> Figure 3 is a hardware configuration diagram of the driving assistance device 101. In this embodiment, the driving assistance device 101 is an electronic control device that calculates the inclination angular velocity from the inclination angle of the vehicle, calculates the position of the road on which the vehicle is traveling and the distance of the vehicle from the lane center line from the lane width, and determines whether the vehicle has changed course and whether the vehicle has taken evasive action from these, estimates the position and state of an abnormality on the road, and transmits the estimated position and state to other vehicles. In addition, the in-vehicle devices 102 and 103 are electronic control devices mounted on the vehicles, and transmit information required for the driving assistance device and receive driving assistance information.

[0030] Here, the driving assistance device 101 will be described as a representative example. Each function of the driving assistance device 101 is realized by a processing circuit provided in the driving assistance device 101. Specifically, the driving assistance device 101 includes, as processing circuits, an arithmetic processing device 90 (computer) such as a CPU (Central Processing Unit), a storage device 91 that exchanges data with the arithmetic processing device 90, an input circuit 92 that inputs external signals to the arithmetic processing device 90, and an output circuit 93 that outputs signals from the arithmetic processing device 90 to the outside. Each piece of hardware, such as the arithmetic processing device 90, the storage device 91, the input circuit 92, and the output circuit 93, is connected to one another by a wired network such as a bus or a wireless network.

[0031] The arithmetic processing device 90 may be an ASIC (Application Specific Integrated Circuit), an IC (Integrated Circuit), a DSP (Digital Signal Processor), a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), various logic circuits, various signal processing circuits, etc. Furthermore, the arithmetic processing device 90 may be a plurality of the same or different types, and each process may be shared and executed. The storage device 91 may be a RAM (Random Access Memory) configured to be able to read and write data from and to the arithmetic processing device 90, a ROM (Read Only Memory) configured to be able to read data from the arithmetic processing device 90, etc.

[0032] The storage device 91 may be a non-volatile or volatile semiconductor memory such as a flash memory, an SSD (Solid State Drive), an EPROM, or an EEPROM, a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, or a DVD. The input circuit 92 is connected to various sensors, switches, and communication lines, and includes an A / D converter and a communication circuit that input output signals and communication information from these sensors and switches to the arithmetic processing device 90. The output circuit 93 includes a drive circuit and a communication circuit that output control signals from the arithmetic processing device 90. The interfaces of the input circuit 92 and the output circuit 93 may be based on specifications such as CAN (Control Area Network) (registered trademark), Ethernet (registered trademark), LTE (Long Term Evolution) (registered trademark), Bluetooth (registered trademark), a fifth-generation mobile communication system (5G), DSRC (Dedicated Short Range Communication) (registered trademark) for vehicle communication, or Cellular-V2X (Vehicle-to-x). Furthermore, a plurality of input circuits 92 and output circuits 93 may be provided, and the input circuit 92 and output circuit 93 to be used may be switched depending on the data. Separately from the input circuit 92 and output circuit 93, the arithmetic processing unit 90 may be directly connected to a communication device for communication.

[0033] Each function of the driving assistance device 101 is realized by the arithmetic processing device 90 executing software (programs) stored in a storage device 91 such as a ROM, and cooperating with other hardware of the driving assistance device 101, such as the storage device 91, an input circuit 92, and an output circuit 93. Setting data such as thresholds and judgment values ​​used by the driving assistance device 101 is stored in the storage device 91 such as a ROM as part of the software (program). Each function of the driving assistance device 101 may be configured as a software module, or may be configured as a combination of software and hardware.

[0034] <Tilt angle of motorcycle> Figure 4 is a diagram showing the tilt angle θ of a motorcycle 201 equipped with the on-vehicle device 102 according to the first embodiment. Figures 5 and 6 show the range of possible tilt angles depending on the type of motorcycle. Figure 5 shows the range of possible tilt angles θRSC of a scooter-type motorcycle. Figure 6 shows the range of possible tilt angles θRNK of a naked-type motorcycle. A naked-type motorcycle is a motorcycle whose engine is not covered by a cowl or the like, and has a wide range of possible tilt angles θRNK, making it highly maneuverable.

[0035] It can also be said that scooter-type and American-style motorcycles have a low center of gravity and therefore a narrow range of tilting, while naked and road sports motorcycles have a high center of gravity and therefore a wider range of tilting. Thus, even for the same motorcycle, the range of possible lean angles varies depending on the type. The determination threshold used in this embodiment can be switched depending on the type of motorcycle.

[0036] The lean angle θ shown in Figure 4 is the tilt angle in the rolling direction of the motorcycle, and is the angle between the center line of the motorcycle and a vertical line. This angle is also called the bank angle or lean angle. The lean angle of a motorcycle is also sometimes called the lean angle. Strictly speaking, the lean angle is the angle between the vertical line and a line that passes through the center of gravity of the vehicle body, including the rider, from the contact patch of the motorcycle's tires. Generally, the lean angle and the lean angle can be considered to be the same. The lean angle θ is treated as a positive value when the rider leans to the right, and a negative value when the rider leans to the left.

[0037] 4 shows the outline of a motorcycle traveling with its body tilted at an inclination angle θ from the vertical state. When the motorcycle is traveling upright, the inclination angle θ is zero.

[0038] The tilt angle θ of the motorcycle, together with the speed of the motorcycle, is detected by the behavior sensor 13. The behavior sensor 13 is a sensor that detects the speed, yaw angle, pitch angle, roll angle, and accelerations of the motorcycle to grasp the behavior of the motorcycle.

[0039] The in-vehicle device 102 may transmit the position, speed, and tilt angle data from the behavior sensor 13 to the driving assistance device 101. From this data, the acceleration, tilt angular velocity, predicted curvature, curvature change rate, etc. can be calculated by the course change determination unit 2 of the driving assistance device 101. The tilt angular velocity θv is also called the tilt angular velocity.

[0040] Of the behavior data such as the position, speed, acceleration, tilt angle, tilt angular velocity, predicted curvature, and curvature change rate of the motorcycle, the acceleration, tilt angular velocity, predicted curvature, and curvature change rate can also be calculated by the behavior data calculation unit 10 of the in-vehicle device 102. When the vehicle information transmitted from the in-vehicle device 102 includes behavior data such as the acceleration, tilt angular velocity, predicted curvature, and curvature change rate, the course change determination unit 2 of the driving assistance device 101 can skip the calculation process in the course change determination unit 2 of the driving assistance device 101.

[0041] The communication unit 16 of the in-vehicle device 102 transmits vehicle information and road information to the driving assistance device 101 via the network. The vehicle information includes information on the vehicle's position and tilt angle. The vehicle information may also include the vehicle's speed, direction of travel, acceleration, yaw angle, pitch angle, roll angle, steering angle, angular velocities thereof, braking status, engine RPM, shift position, accelerator opening, etc. The road information includes information on the position of the road on which the vehicle is traveling and lane width. The road information may also include map information including road shape, the number of lanes, the distance from the vehicle's lane center line, the relative position, relative angle, relative speed, obstacle type, etc. of surrounding obstacles.

[0042] The receiving unit 7a belonging to the communication unit 7 of the driving assistance device 101 receives vehicle information and road information from the in-vehicle device 102. The data management unit 1 receives the vehicle information and road information from the communication unit 7, saves and accumulates the information in the database 20, and transmits the information to the data analysis unit 22. The road information may be received from the roadside device 203 in addition to the in-vehicle device 102. Furthermore, map information included in the road information, such as the road location, number of lanes, lane width, and road shape, may be received from another device or a server on the cloud 204.

[0043] The course change determination unit 2 of the data analysis unit 22 determines whether there is a sudden inclination of the vehicle body, a change in the inclination angular velocity, a sudden change in the steering wheel, a sudden deceleration, or a sudden acceleration as a change in vehicle behavior based on changes in the yaw angle, pitch angle, roll angle, steering angle, acceleration, and speed of the received vehicle information. The course change determination unit 2 further determines whether there is a state in which there is little behavior change, such as no change in inclination for a predetermined period of time, or no acceleration or deceleration for a predetermined period of time.

[0044] The course change determination unit 2 also calculates a predicted radius of curvature of the vehicle's future travel path based on changes in yaw rate, speed, and acceleration. The course change determination unit 2 can also calculate the radius of curvature of each vehicle from the travel data of a large number of vehicles stored in the data management unit, and estimate the radius of curvature of the road by statistically processing the calculated values.

[0045] Furthermore, the course change determination unit 2 calculates the amount of change and the rate of change of each of the data. The course change determination unit 2 determines whether a vehicle has changed behavior by extracting points where each of the data, the amount of change of each of the data, or the rate of change of each of the data is equal to or greater than a certain threshold, or where the data has exceeded threshold A1 and then returned to below threshold B1.

[0046] The course change determination unit 2 detects whether the aforementioned yaw angle, pitch angle, roll angle, acceleration, speed, inclination, steering angle, and their change values, the predicted curvature radius value, the amount of change, and the rate of change have remained below a predetermined threshold C1 for a certain period of time. Here, the thresholds A1, B1, and C1 set for each type of sensor signal may be the same value for each type of sensor signal, or may be different values ​​(thresholds A1, B1, and C1 are not shown).

[0047] The lane change determination unit 2 calculates the distance between the vehicle and the lane center line. The lane change determination unit 2 may also calculate the distance between the vehicle and a lane marking (white line). The lane change determination unit 2 may also calculate a probability distribution of the distance between the vehicle and the lane. The lane change determination unit 2 notifies the road surface estimation unit 3, the fallen object estimation unit 5, and the vehicle behavior estimation unit 4 of the received vehicle information, road information, and each calculated data.

[0048] The thresholds used in the first embodiment may be fixed values. However, these thresholds may vary depending on the traveling speed. Furthermore, these thresholds may be values ​​calculated as a result of statistical processing of each data. Furthermore, these thresholds may be calculated using machine learning based on supervised learning. Furthermore, these thresholds may be calculated using machine learning based on unsupervised learning. Furthermore, these thresholds are not limited to the above-mentioned methods, and may be determined by dynamically switching the thresholds.

[0049] <Avoidance Action> Fig. 7 is a diagram showing an example of an avoidance action of the two-wheeled vehicle 201 equipped with the on-vehicle device 102 according to embodiment 1. Fig. 8 is a time chart showing the tilt angle θ and the tilt angular velocity θv when the two-wheeled vehicle 201 equipped with the on-vehicle device 102 according to embodiment 1 takes an avoidance action.

[0050] This shows an example of changes in the travel trajectory 41, tilt angle θ, and tilt angular velocity θv when the two-wheeled vehicle 201 is traveling alone and avoids a manhole 31. Here, an example is shown of avoiding a manhole 31 where the coefficient of friction between the tire and the road surface changes significantly, potentially causing slippage. The object to be avoided is not limited to the manhole 31, but may also be a fallen object 33. The object to be avoided may also be a frozen road surface, a puddle, or the like, and is not limited to these.

[0051] <Determining Avoidance Action Based on Inclination Angle> In Fig. 7, the two-wheeled vehicle 201 is traveling alone, and is therefore able to quickly discover the manhole 31, which is an event that must be avoided. The travel trajectory 41 shows that the vehicle is avoiding the manhole 31 by keeping a sufficient distance from it. For this reason, the inclination angle θ shown in Fig. 8 also changes in accordance with the avoidance action.

[0052] In FIG. 8 , the inclination angle θ changes in the positive direction (leaning to the driver's right), and the absolute value of the inclination angle θ exceeds the determination start inclination angle θths. The lane change determination unit 2 determines that a lane change has occurred in this case. Then, if the inclination angle θ increases and then becomes equal to or smaller than the determination end inclination angle θthe (if the driver has recovered from leaning to the right), it can be determined that an avoidance maneuver may have occurred. The avoidance period Pesc is the period from when the absolute value of the inclination angle θ exceeds the determination start inclination angle θths to when it becomes equal to or smaller than the determination end inclination angle θthe. If the avoidance period Pesc is equal to or smaller than the avoidance period threshold Pthesc, the road surface abnormality estimation unit 21 estimates that an avoidance maneuver has occurred. (The avoidance period threshold Pthesc is not shown.)

[0053] 8 shows a case where the determination start inclination angle θths is smaller than the determination end inclination angle θthe, but the present invention is not limited to this. The determination start inclination angle θths may be equal to the determination end inclination angle θthe.

[0054] Furthermore, hysteresis can be provided to the judgment threshold by setting the judgment start inclination angle θths to be greater than the judgment end inclination angle θthe. This prevents the road surface abnormality estimation unit 21 from repeatedly estimating the need for avoidance action in a short period of time when the inclination angle θ fluctuates due to vibrations caused by the motorcycle 201 while traveling.

[0055] Here, even if the value of the inclination angle θ suddenly changes within a certain period of time, if the inclination angle θ exceeds the determination start inclination angle θths and does not become equal to or less than the determination end inclination angle θthe, it is not determined that the event should be avoided. This applies to cases such as simple lane changes, right or left turns, etc. By further considering the illumination status of the turn signals (turn signals), it is possible to identify whether the inclination angle change is due to a lane change or right or left turn. This improves the accuracy of the road surface abnormality estimation unit 21 in determining whether an event such as a road surface abnormality should be avoided.

[0056] 8 has been described with reference to a case where the motorcycle 201 leans to the right of the driver. This is the case where the manhole 31 is avoided by taking a right-side travel path 41. In contrast, a similar determination can be made when the motorcycle leans to the left of the driver. This is the case where the manhole 31 is avoided by taking a left-side travel path 41. In this case, the tilt angle θ will be a negative value, but the relationship between the absolute value of the tilt angle θ and the determination start tilt angle θths and determination end tilt angle θthe will be the same as in FIG. 8.

[0057] <Determining Evasive Behavior Based on Tilt Angular Velocity> In Figure 8, it is possible to determine whether the motorcycle 201 has taken evasive action based on the tilt angular velocity θv. When the absolute value of the tilt angular velocity θv increases beyond the determination start angular velocity θvths, the lane change determination unit 2 determines that a lane change has occurred. In this case, the absolute value of the tilt angular velocity θv later decreases. Then, the tilt angular velocity θv passes through 0, increases in the opposite direction, and then decreases again. When the absolute value of the tilt angular velocity θv converges to a value equal to or less than the determination end angular velocity θvthe, the road surface abnormality estimation unit 21 estimates that there is a possibility that the motorcycle 201 has taken evasive action.

[0058] The absolute value of the tilt angular velocity θv exceeds the determination start angular velocity θvths, then decreases, passes through 0, increases in the opposite direction, and then after an avoidance period Pesc, the absolute value of the tilt angular velocity θv converges to a value equal to or less than the determination end angular velocity θvthe. The avoidance period Pesc is the period during which avoidance is performed. If the avoidance period Pesc is equal to or less than the avoidance period threshold Pthesc, the road surface abnormality estimation unit 21 estimates that an avoidance action has occurred.

[0059] 8 shows a case where the determination start angular velocity θvths is smaller than the determination end angular velocity θvthe, but this is not limited to this. The determination start angular velocity θvths may be equal to the determination end angular velocity θvthe. Also, the determination start angular velocity θvths may be larger than the determination end angular velocity θvthe.

[0060] FIG. 8 illustrates a case in which the motorcycle 201 leans to the right of the driver. However, a similar determination can be made when the motorcycle leans to the left of the driver. In this case, the tilt angular velocity θv initially takes a negative value. When the absolute value of the tilt angular velocity θv increases beyond the determination start angular velocity θvths, the lane change determination unit 2 determines that a lane change has occurred. The absolute value of the tilt angular velocity θv then decreases and passes 0, and the tilt angular velocity θv increases, taking a positive value. After the tilt angular velocity θv increases, it converges to or below the determination end angular velocity θvthe. The road surface abnormality estimation unit 21 estimates that an avoidance maneuver has been performed. The curve is a reversed version of the graph of the tilt angular velocity θv in FIG. 8 .

[0061] By determining whether the two-wheeled vehicle 201 is taking an evasive action based on the tilt angular velocity θv, it is possible to distinguish between a change in tilt angle due to a lane change or a right or left turn and an evasive action that is a relatively sudden change in course. In other words, by making a determination based on the tilt angular velocity θv, it is possible to quickly determine a more sudden and small change in course.

[0062] By checking that the tilt angular velocity θv increases in either the positive or negative direction, then decreases, exceeds 0, increases in the opposite direction, then decreases again and converges, it is possible to determine a change in the tilt angular velocity θv specific to evasive behavior. Even if the width of the course change in the lane of the road of the motorcycle 201 is small, detecting and determining the tilt angular velocity θv is advantageous because the road surface abnormality estimation unit 21 can accurately determine whether or not the motorcycle 201 is performing evasive behavior.

[0063] As described above, when the road surface abnormality estimation unit 21 detects an evasive action by using the inclination angle θ and the inclination angular velocity θv, the values ​​of the determination start inclination angle θths, the determination end inclination angle θthe, the determination start angular velocity θvths, and the determination end angular velocity θvthe may be fixed values ​​or may be values ​​according to the vehicle speed. This is because when the vehicle speed is high, an evasive action is possible with a smaller inclination angle θ and inclination angular velocity θv. By setting the values ​​of the determination start inclination angle θths, the determination end inclination angle θthe, the determination start angular velocity θvths, and the determination end angular velocity θvthe to smaller values ​​as the vehicle speed increases, it is possible to accurately estimate the presence or absence of an evasive action while eliminating erroneous detections.

[0064] The avoidance period threshold Pthesc may also be a fixed value or a value according to the vehicle speed. This is because it is considered that the avoidance action will be completed in a shorter time when the vehicle speed is high. By setting the avoidance period threshold Pthesc to a smaller value as the vehicle speed increases, it becomes possible to accurately estimate the presence or absence of an avoidance action while eliminating erroneous detections.

[0065] Furthermore, the road surface abnormality estimation unit 21 can estimate the magnitude of the road abnormality according to the length of the avoidance period Pesc and the vehicle speed, because the product of the avoidance period Pesc and the vehicle speed is considered to correspond to the magnitude of the road abnormality.

[0066] Regarding the avoidance period threshold Pthesc, if the avoidance period Pesc is longer than this value, it can be assumed that the vehicle's course change is not due to a temporary avoidance behavior. In other words, it can be assumed that it is simply a lane change, a right or left turn, etc. Furthermore, by further considering the lighting status of the blinker, it can be determined whether the change in lean angle or lean angular velocity is due to a lane change or a right or left turn.

[0067] In determining whether an evasive action is being taken, if the turn signal is activated, it can be estimated that the evasive action is not due to a road surface abnormality. Specifically, even if a lane change is determined based on the inclination angle θ or the inclination angular velocity θv in FIG. 7, if the turn signal is activated, it is not estimated that the evasive action is due to a road surface abnormality. In this case, the values ​​of the determination start inclination angle θths, the determination end inclination angle θthe, the determination start angular velocity θvths, the determination end angular velocity θvthe, or the avoidance period threshold Pthesc may be changed depending on whether the turn signal is activated. In this case, the setting of each threshold may be determined using machine learning or the like. Furthermore, whether a lane change is being taken may be determined based on the behavior of the vehicle in front of and behind the vehicle.

[0068] 7 and 8 show examples in which the presence or absence of an avoidance action is estimated based on the tilt angle θ and the tilt angular velocity θv, but other methods may be used to estimate the presence or absence of an avoidance action.

[0069] <Determination of Avoidance Action Based on Distance from Lane Center Line> Fig. 9 is a diagram showing the distance DLCL from the lane center line LCL when the two-wheeled vehicle 201 equipped with the on-vehicle device 102 according to embodiment 1 takes an avoidance action. Fig. 10 is a diagram showing the distance DLCL from the lane center line LCL when the two-wheeled vehicle 201 is traveling normally.

[0070] As shown in Figure 9, the distance DLCL between the motorcycle 201 and the lane center line LCL of the lane 29 temporarily increases and then returns to the original state. Figure 10 shows an example in which the distance DLCL between the motorcycle 201 and the lane center line LCL of the lane 29 does not change and the motorcycle 201 continues to travel within a certain distance. This makes it possible to determine whether or not evasive action has been taken.

[0071] The lane change determination unit 2 determines that there is a possibility of a lane change when the distance DLCL between the two-wheeled vehicle 201 and the lane center line LCL of the lane 29 has changed by a larger amount than the distance change determination amount Dth. When the lane change determination unit 2 determines that there is a possibility of a lane change, the road surface estimation unit 3 monitors the subsequent distance DLCL.

[0072] If the distance DLCL increases and the amount of change in the distance DLCL returns to be equal to or less than the distance change judgment amount Dth, the road surface abnormality estimation unit 21 determines that evasive action has been taken, and estimates a road surface abnormality. At this time, the difference between the time when the amount of change in the distance DLCL exceeds the distance change judgment amount Dth and the time when the amount of change in the distance DLCL thereafter becomes equal to or less than the distance change judgment amount Dth is the evasive period Pesc. The road surface abnormality estimation unit 21 may estimate that evasive action has been taken only if the evasive period Pesc is equal to or less than a predetermined evasive period threshold Pthesc.

[0073] Regarding the avoidance period threshold Pthesc, if the avoidance period Pesc is longer than this value, it can be assumed that the vehicle's course change is not due to a temporary avoidance behavior. In other words, it can be assumed that it is simply a lane change, a right or left turn, etc. Furthermore, by further considering the lighting status of the blinker, it can be determined whether the change in lean angle or lean angular velocity is due to a lane change or a right or left turn.

[0074] When the avoidance period Pesc is equal to or less than the avoidance period threshold Pthesc, the road surface abnormality estimation unit 21 estimates that an avoidance action has occurred. The avoidance period threshold Pthesc may be a fixed value or may be a value according to the vehicle speed. This is because when the vehicle speed is high, the avoidance action is considered to be completed in a shorter time. By setting the value of the avoidance period threshold Pthesc to a smaller value as the vehicle speed is higher, it becomes possible to accurately estimate the presence or absence of an avoidance action while eliminating erroneous detections.

[0075] As described above, by using the distance DLCL between the two-wheeled vehicle 201 and the lane center line LCL of the lane 29, the road surface abnormality estimation unit 21 can detect an avoidance behavior. In this case, the value of the distance change determination amount Dth may be a fixed value or may be a value according to the vehicle speed. This is because when the vehicle speed is high, the start of a lane change can be determined based on a smaller distance change determination amount Dth.

[0076] The avoidance period threshold Pthesc may also be a fixed value or a value according to the vehicle speed. This is because it is considered that the avoidance action will be completed in a shorter time when the vehicle speed is high. By setting the avoidance period threshold Pthesc to a smaller value as the vehicle speed increases, it becomes possible to accurately estimate the presence or absence of an avoidance action while eliminating erroneous detections.

[0077] Furthermore, the road surface abnormality estimation unit 21 can estimate the magnitude of the road abnormality according to the length of the avoidance period Pesc and the vehicle speed, because the product of the avoidance period Pesc and the vehicle speed is considered to correspond to the magnitude of the road abnormality.

[0078] As described above, examples have been shown in which the presence or absence of an evasive action is estimated based on the inclination angle θ and the inclination angular velocity θv, as shown in Figures 7 and 8. And, as shown in Figure 9, an example has been shown in which the presence or absence of an evasive action is estimated from a situation in which the distance DLCL between the motorcycle 201 and the lane center line LCL of the lane 29 temporarily increases and then returns to its original state. These methods may be used separately, or they may be performed in an AND condition, and it may be estimated that an evasive action has been taken only if all of the conditions are satisfied.

[0079] Alternatively, it is also possible to use an AND condition to estimate whether or not evasive action has been taken based on the tilt angular velocity θv and a method to estimate whether or not evasive action has been taken from a situation in which the distance DLCL between the motorcycle 201 and the lane center line LCL of the lane 29 temporarily increases and then returns to its original state, and to estimate that evasive action has been taken only when both of these conditions are met. This is advantageous because it can increase the reliability of the estimation that evasive action has been taken.

[0080] <Determining Avoidance Action Based on Lean Angle, Predicted Curvature, and Speed> When the two-wheeled vehicle 201 travels around a curve, centrifugal force acts on the outside. Therefore, the vehicle body needs to be leaned in order to travel stably. When traveling at low speeds, the centrifugal force is weak, so a small lean angle θ is sufficient, but when traveling at high speeds, the centrifugal force is strong, so a large lean angle θ is required. Specifically, if the vehicle body lean angle θ [rad], gravitational acceleration g [m / s2], speed V [m / s], and the road's predicted curvature R [m] are taken as the ideal predicted curvature R [m] and lean angle θ [rad], then tan θ = V2 If the difference between this predicted curvature and the road curvature calculated from the map information is greater than a predetermined value, it can be assumed that the vehicle was not traveling along the road, but rather that an event that should be avoided occurred on the road surface, such as a sudden turn.

[0081] <Determining Evasive Behavior Based on Change in Predicted Curvature> Other methods may be used to estimate the presence or absence of evasive behavior. The speed of change in predicted curvature and its change may be used to estimate the presence or absence of evasive behavior. The predicted road curvature R [m] is calculated by R = V / Y, where V [m / s] is the speed and Y [rad / sec] is the yaw rate. Typically, road curvature changes according to a clothoid curve, in which the rate of change of curvature is constant. Therefore, if the difference between the predicted curvature and the road curvature is greater than a predetermined value, it can be assumed that an event that should be avoided occurred on the road surface, such as a sudden turn, rather than driving along the road. Furthermore, even if the difference between the predicted curvature and the road curvature is greater than a predetermined value, it can be determined that the vehicle is changing lanes when the turn signal is on. However, if the difference is smaller than the predetermined value, it can be assumed that there is an error in the predicted curvature or that the vehicle is moving within the lane.

[0082] <Evasive Action of Two-Wheeled Vehicle Following Four-Wheeled Vehicle> Figure 11 is a diagram showing an example of a two-wheeled vehicle 201 equipped with the on-vehicle device 102 according to embodiment 1 taking evasive action while following a four-wheeled vehicle 202. Figure 12 is a time chart showing the tilt angle θ and the tilt angular velocity θv when the two-wheeled vehicle 201 takes evasive action while following the four-wheeled vehicle 202.

[0083] Here, a case where a manhole 31 is avoided is illustrated. The object to be avoided is not limited to the manhole 31, but may also be a fallen object 33. The object to be avoided may also be, but is not limited to, a frozen road surface, a puddle, etc. Figures 11 and 12 show an example in which the presence of a four-wheeled vehicle 202 ahead delays the discovery of an event to be avoided, causing a sudden change in the tilt angle.

[0084] In Figure 12, the absolute value of the tilt angle θ exceeds the determination start tilt angle θths. Then, if the tilt angle θ increases and then becomes equal to or smaller than the determination end tilt angle θthe, it can be inferred that an avoidance action may have occurred. The period from when the absolute value of the tilt angle θ exceeds the determination start tilt angle θths to when it becomes equal to or smaller than the determination end tilt angle θthe is the avoidance period Pesc. If the avoidance period Pesc is equal to or smaller than the avoidance period threshold Pthesc, it is inferred that an avoidance action has occurred.

[0085] 12, it is possible to determine whether or not the motorcycle 201 has taken evasive action based on the tilt angular velocity θv. The absolute value of the tilt angular velocity θv increases beyond the determination start angular velocity θvths and then decreases. The tilt angular velocity θv then passes through 0, increases in the opposite direction, and then decreases again. When the absolute value of the tilt angular velocity θv converges to a value equal to or less than the determination end angular velocity θvthe, it can be determined that the motorcycle 201 may have taken evasive action.

[0086] If the tilt angle changes and exceeds the threshold, and then falls below the threshold again within a certain time, it can be assumed that an event that should be avoided has occurred. Therefore, even if the value changes suddenly within a certain time, it is not determined to be an event that should be avoided unless it falls below the threshold again.

[0087] Although detailed description will be omitted here, it is also possible to determine whether or not an evasive action has been taken by the two-wheeled vehicle 201 traveling following the four-wheeled vehicle 202, based on the distance DLCL from the lane center line LCL shown in FIG. 9 . When the distance DLCL between the two-wheeled vehicle 201 and the lane center line LCL of the lane 29 exceeds the distance change determination amount Dth and then returns to less than or equal to the distance change determination amount Dth, the road surface abnormality estimation unit 21 estimates that an evasive action has been taken, and a road surface abnormality is estimated. In this case, the difference between the time when the change in the distance DLCL exceeds the distance change determination amount Dth and the time when the change in the distance DLCL thereafter becomes less than or equal to the distance change determination amount Dth is the evasive period Pesc. The road surface abnormality estimation unit 21 may estimate that an evasive action has been taken only when the evasive period Pesc is less than or equal to a predetermined evasive period threshold Pthesc.

[0088] Here, when estimating an evasive action, the determination start inclination angle θths, the determination end inclination angle θthe, the determination start angular velocity θvths, the determination end angular velocity θvthe, the distance change determination amount Dth, and the avoidance period threshold Pthesc may be changed according to the relative distance and relative speed from the four-wheeled vehicle 202 ahead. This is because the closer the distance to the four-wheeled vehicle 202 ahead, the later the driver of the two-wheeled vehicle 201 will discover a road abnormality and will be required to take sudden evasive action. Also, the higher the relative speed (approaching speed), the more sudden the driver of the two-wheeled vehicle 201 will be required to take evasive action. Therefore, by changing the thresholds according to the relative distance and relative speed from the four-wheeled vehicle 202 ahead, it is possible to accurately estimate the presence or absence of an evasive action while eliminating erroneous detections. Furthermore, the present invention is not limited to this, and an evasive action may be estimated according to the predicted curvature and its rate of change.

[0089] 13 is a time chart showing an example in which the behavior of the two-wheeled vehicle 201 equipped with the on-vehicle device 102 according to embodiment 1 does not change in a specific section. The road surface estimation unit 3 of the road surface abnormality estimation unit 21 receives the data calculated by the course change determination unit 2, as well as vehicle information and road information. The road surface estimation unit 3 extracts points at specific points where the amount of change or rate of change of each piece of data is equal to or less than a certain level, and estimates the presence of manholes, gratings, slippery road conditions such as ice, ruts, and bumps on the road.

[0090] Figure 13 shows an example in which the behavior of the two-wheeled vehicle 201 does not change when it travels over a manhole 31. Figure 13 shows the change in the inclination angle θ, the change in the speed V, and the change in the steering angle AST over time, relative to the travel position.

[0091] This shows a situation in which each parameter is kept constant so that no behavioral changes occur to avoid tipping over when road conditions require caution. It also shows that the change in each data is below a threshold when traveling through a section where a manhole 31 is present. It can also be seen that in the sections before and after the section that includes the manhole 31, the inclination angle θ changes from the driver's right to the left at a constant acceleration, and the vehicle speed V is in a constant acceleration state.

[0092] In Figure 13, when traveling through a section where a manhole 31 is present, the inclination angle θ, inclination angular velocity θv, vehicle speed V, and steering angle AST are maintained within predetermined ranges. Specifically, the amount of change in the inclination angle θ is maintained within the stable running inclination angle range θRSTB. Furthermore, the amount of change in the inclination angular velocity θv is maintained within the stable running inclination angular velocity range θvRSTB. Although not explicitly shown in Figure 13, the amount of change in the vehicle speed V and the amount of change in the steering angle AST are also maintained within predetermined thresholds. In addition, the amount of change in the accelerator pedal position APS (not shown) may also be maintained within a predetermined threshold.

[0093] Here, an example is shown in which the changes in the inclination angle θ, inclination angular velocity θv, vehicle speed V, steering angle AST, and accelerator opening APS are within a predetermined range, but the present invention is not limited to these and may also use predicted curvature, acceleration / deceleration, the driver's line of sight, etc. Furthermore, judgment may be made using multiple pieces of data simultaneously, or using only one piece of data. In this way, it is possible to estimate road conditions requiring caution from sections where no behavior changes occur.

[0094] <Estimation of rutting area> Figure 14 is a diagram showing the relationship between the traveling position and the inclination angle θ of the motorcycle 201 equipped with the on-vehicle device 102 according to the first embodiment. In Figure 14, the horizontal axis shows the relationship between the distance DLCL between the vehicle and the lane center line LCL. The left side of the lane center line LCL is treated as a negative distance, and the right side is treated as a positive distance. When the distance DLCL changes from a negative value to a positive value, or from a positive value to a negative value, the distance DLCL from the lane center line LCL is treated as having changed. Furthermore, when the distance DLCL from the lane center line LCL becomes greater than half the lane width, it is treated as having moved to an adjacent lane.

[0095] Figure 14 shows the relationship between the vehicle's traveling position and the inclination angle θ, and illustrates the distribution of the inclination angle θ of motorcycles that have previously traveled through a particular point relative to the positions of the lane center line and the lane markings. In the example of Figure 14, when the inclination angle is 0°, the motorcycles travel in a widely scattered area E centered on the lane center line. In contrast, when the inclination angle is ±α, the motorcycles travel in areas A, B, C, and D between the lane center line and the lane markings.

[0096] The road surface estimation unit 3 estimates that the two-wheeled vehicles are traveling in ruts made by the wheels of four-wheeled vehicles when there is a high probability that the two-wheeled vehicles are traveling in areas A to D between the lane center line and the lane marking when the inclination angle is ±α degrees, as shown in Figure 14. When the two-wheeled vehicles are traveling in areas E at an inclination angle of 0 degrees, it can be estimated that there are no ruts on the road.

[0097] 14 shows the relationship between the inclination angle and the traveling position. However, by using this positional relationship and the probability distribution of the transition of the traveling area, if there are many motorcycles that have transitioned from the left side of the lane center line LCL to the right side, it can be inferred that there is an event that requires attention on the left side. On the other hand, if there are many motorcycles that have transitioned from the right side of the lane center line LCL to the left side, it can be inferred that there is an event that requires attention on the right side.

[0098] <Probability Distribution of Ruts> Fig. 14 illustrates the distribution of locations where two-wheeled vehicles travel when ruts exist. In contrast, Fig. 15 illustrates the relationship between the traveling position of the two-wheeled vehicle 201 and the probability distribution.

[0099] 15 shows the probability distribution of the presence or absence of ruts versus the distance DLCL from the lane center line LCL. If there is a tendency for the probability of the traveling position of a four-wheeled vehicle being at a certain distance away from the lane center line LCL, which is the tire position of the four-wheeled vehicle, to be high (graph F), the road surface estimation unit 3 can estimate that the location where the two-wheeled vehicle is traveling has ruts caused by the traveling four-wheeled vehicle.

[0100] 15 shows the probability distribution DLCL of motorcycle drivers preferring to drive through ruts made by the wheels of four-wheeled vehicles. It can also be seen that some motorcycles avoid the ruts and drive near the center line of the lane. On the other hand, the road surface estimation unit 3 can estimate that there are no ruts in the location where the motorcycle is traveling if the probability distribution of the location where the motorcycle is traveling is widely dispersed (graph G), not limited to the tire positions of four-wheeled vehicles.

[0101] The road surface estimation unit 3 compares the probability distribution (average value, standard deviation) of the driving positions of four-wheeled vehicles with the probability distribution (average value, standard deviation) of the driving positions of two-wheeled vehicles, and if both standard deviations are small, it can be estimated that ruts have formed on the road surface because the vehicles are causing deviations in their driving positions within the lane.

[0102] <Correlation Between Motorcycle Speed ​​and Inclination Angle> Figure 16 is a diagram showing the relationship between the inclination angle θ and the vehicle speed V of the motorcycle 201 equipped with the on-vehicle device 102 according to the first embodiment. The road surface estimation unit 3 can determine whether the vehicle is traveling around a curve based on the radius of curvature and statistically calculate the distribution of the inclination angle when traveling around a curve. The road surface estimation unit 3 may also calculate the correlation and distribution between the inclination angle θ and the vehicle speed V when traveling around a curve (e.g., area H). After calculating the statistical value, the next time the vehicle travels through that point, the statistical value is compared with the calculated value. As shown in Figure 16, if the inclination angle is smaller than a predetermined value and the traveling speed is also smaller than a predetermined value (e.g., if the value is in area J), it can be estimated that the road surface is in an exceptional condition, such as wet, frozen, or snowing.

[0103] <Road Surface Reflectance and Inclination Angle> The road surface estimation unit 3 may estimate road surface conditions by taking into account changes in road surface reflectance and inclination angle θ as detected data by LiDAR. By additionally taking LiDAR data into consideration, it is expected that the accuracy of the determination can be improved. The road surface estimation unit 3 compares the curvature radius of the road estimated by the course change determination unit 2 with the expected curvature radius of the vehicle being processed. If this difference value is equal to or greater than a threshold, it can be estimated that the vehicle cannot travel along the normal road shape.

[0104] Therefore, it can be estimated that there is an abnormality in the road surface, such as an obstacle on the road surface, a hole, or a small coefficient of friction. The road surface estimation unit 3 may also calculate an optimal inclination angle for the road curvature radius and traveling speed, and compare it with the actual inclination angle of the vehicle. If the difference between the calculated values ​​is equal to or greater than a threshold, it can be estimated that there is an abnormality in the road surface.

[0105] When the road surface estimation unit 3 estimates road surface conditions that the motorcycle should avoid, such as ruts, holes, manholes, and gratings, it notifies the information providing unit 6 of road information including the type and location of the road surface abnormality.

[0106] The vehicle behavior estimation unit 4 acquires data on the two-wheeled vehicle and the four-wheeled vehicle and can determine whether there is a speed difference. Based on the speed difference with the four-wheeled vehicle and the traveling position of the two-wheeled vehicle, the vehicle behavior estimation unit 4 estimates whether the two-wheeled vehicle is passing through and overtaking the four-wheeled vehicle. If passing through is estimated, the passing information is notified to the information provision unit 6. It can be estimated that the two-wheeled vehicle is passing through a four-wheeled vehicle in a traffic jam and that a traffic jam has occurred. Furthermore, the vehicle behavior estimation unit 4 can determine that a traffic jam or a traffic light is stopped when multiple four-wheeled vehicles are traveling at low speeds.

[0107] The falling object estimation unit 5 estimates the presence or absence of a falling object that the vehicle should avoid based on the amount of change and the rate of change in the behavior information. If the amount of change in the tilt angle θ exceeds a judgment threshold and if all vehicles in the past history within a predetermined time period have avoided the point, the falling object estimation unit 5 estimates that a falling object is present.

[0108] <Heart Rate Monitoring> Figure 17 is a time chart showing the inclination angle θ, inclination angular velocity θv, and heart rate HR when the motorcycle 201 equipped with the on-vehicle device 102 according to embodiment 1 performs an avoidance action. As shown in Figure 17, the falling object estimation unit 5 compares the driver's heart rate HR with a heart rate threshold HRth in addition to detecting a sudden change in the inclination angle θ, and detects tension or impatience of the driver if the heart rate HR exceeds the heart rate threshold HRth. The driver's tension or impatience may also be detected by the rate of increase in heart rate, the rate of increase in blood pressure, or the number of blinks. From the driver's tension or impatience, it is estimated that an event requiring immediate avoidance, such as a falling object 33, has occurred.

[0109] The falling object estimation unit 5 may change the determination threshold such as the heart rate threshold HRth depending on whether or not a preceding vehicle is present. When a preceding vehicle is present, the determination thresholds for the amount of change and the rate of change may be set to be large to estimate the falling object 33. This is because if there is a falling object 33 that all vehicles, including four-wheeled vehicles, must avoid, the preceding vehicle will take evasive action, which will allow the following vehicles to know about it.

[0110] The falling object estimation unit 5 estimates the presence or absence of a fallen object 33 on the estimated road, and notifies the information provision unit 6 of obstacle information including the position of the fallen object 33. The behavior learning unit 8 statistically processes the ranges of the inclination angle θ, inclination angular velocity θv, and predicted curvature calculated by the course change determination unit 2 for each vehicle type, and calculates an average value and standard deviation. The behavior learning unit 8 uses the possible inclination angle, inclination angular velocity, and predicted curvature calculated for each vehicle type as determination criterion values. The behavior learning unit 8 notifies these data to the course change determination unit 2, road surface estimation unit 3, vehicle behavior estimation unit 4, and fallen object estimation unit 5. The behavior learning unit 8 may update the data each time it receives data and calculate the reference value. The behavior learning unit 8 may also update the reference value at regular intervals.

[0111] It is assumed that the lane change determination unit 2, road surface estimation unit 3, vehicle behavior estimation unit 4, fallen object estimation unit 5, and behavior learning unit 8 process all data for all vehicles. However, to reduce the processing load, the tilt angle θ, predicted curvature, vehicle speed V, heart rate HR, blood pressure, distance DLCL from the lane center line LCL, etc. may be compared with past history information, and only transmitted if they differ.

[0112] If the difference between the current location and the past history information is greater than or equal to a predetermined threshold, the current location may be set as the processing target, and only the area within several tens of meters before and after the current location may be processed. This is because it is possible to avoid exchanging information about other areas. For example, the trajectory of a motorcycle 201 during normal driving as shown in Figure 10 is recorded in the database 20.

[0113] When the behavior of a motorcycle 201 as shown in Figure 9 is detected, it will be treated as a processing target only if the distance DLCL from the lane center line LCL is greater than or equal to a normal threshold. Only the area 50 meters before and after the area where the distance is greater than or equal to the threshold may be extracted and processed. Furthermore, the processing area may be set with reference to the standard manhole installation interval of 100 meters, for example.

[0114] The information providing unit 6 provides the driver with support information such as road surface information, obstacle information, and narrow-passing information based on the estimated road surface abnormality, fallen objects, vehicle behavior, and traffic conditions to the on-board device 103 of another vehicle. In addition to providing the support information, the information providing unit 6 may also calculate a recommended speed, a recommended lane position, and a recommended tilt angle for traveling at the point based on the support information, and provide the information by adding it to the support information.

[0115] <Assistance Level Section> Fig. 18 is a diagram showing the setting of assistance level sections for caution points by the driving assistance device 101 according to embodiment 1. The information provision unit 6 sets a warning section M, a caution section L, an information provision section K, and a geofence 34 in front of the assistance target point as shown in Fig. 18. Then, the information provision unit 6 instructs information provision or control for each section.

[0116] In the warning section M, the vehicle control unit is instructed to decelerate and stop the vehicle by engine braking, fuel control (fuel cut), etc. In the caution section L, deceleration preparation information is provided to the vehicle control device 18. In the information provision section K, a deceleration instruction is notified to the display unit. As a result, if the vehicle is close to a point where braking is required, the control can instruct the driver to avoid the situation, and if the point is far away, the driver can be prompted to avoid the situation.

[0117] The information providing unit 6 determines the need for providing assistance information and control to the motorcycle and provides the information by including it in the assistance information. The information providing unit 6 may also calculate a recommended driving lane for avoiding an obstacle starting from the assistance target point and provide the information by including it in the assistance information. The timing for changing to the recommended driving lane may be determined based on a predetermined distance from the target point or on the arrival time.

[0118] The information providing unit 6 transmits support information and control information to the communication unit 37 of the in-vehicle device 103. The information providing unit 6 may switch the information to be provided depending on the support target. The information providing unit 6 switches the content of the support information to be generated and transmitted depending on the type of motorcycle (such as a motorcycle, three-wheeled bike, bicycle, or electric kick scooter) and the type of motorcycle (such as a scooter-type motorcycle or naked motorcycle). For example, since the risk of falling over manholes or gratings is low for three-wheeled bikes and bicycles, such information is not distributed. Support information regarding manholes and gratings may be distributed only to motorcycles and kick scooters.

[0119] The information providing unit 6 may distribute support information for a location. The in-vehicle device 103 may then take the type into consideration and perform filtering and display determination.

[0120] When the communication unit 7 of the driving assistance device 101 receives data from the in-vehicle device 102, it notifies the data management unit 1. The communication unit 7 of the driving assistance device 101 receives a data transmission request from the information provision unit 6. In this case, the communication unit 7 transmits the data directly to the in-vehicle device 103 or transmits the data to the in-vehicle device 103 via the roadside device 203. The communication unit 7 may transmit the data to the in-vehicle device 103 that is the support target by individual communication (unicast). Alternatively, the communication unit 7 may transmit the data by broadcast communication, and the in-vehicle devices 102, 103 that receive the data may determine whether the information is required.

[0121] The database 20 stores map data including road shapes, lane shapes, the number of lanes, lane widths, landmarks on the roads, etc. The database 20 also stores vehicle information and road information received from the in-vehicle device.

[0122] There are two types of map data: high-precision map data and motorcycle-specific map data. High-precision map data includes information such as lane centerlines, dividing lines, line types, road types, speed limits, number of lanes, and lane widths. Motorcycle-specific map data includes not only high-precision map data but also road surface information that motorcycles should be aware of.

[0123] Specifically, road surface information that motorcycles should be aware of includes dynamically changing information such as bumps, gratings, manhole locations, ruts, holes, frozen roads, etc. The dynamic data of the motorcycle map data is updated based on the determination results of the data analysis unit 22.

[0124] <Transmission process of vehicle information and road information of on-board device> Fig. 19 is a flowchart showing the process of the on-board device 102 according to the first embodiment. The operation of the driving assistance system according to the first embodiment will be described with reference to Figs. 19 to 25. The operation of the driving assistance system according to the first embodiment corresponds to the driving assistance system or driving assistance method according to the first embodiment. Furthermore, the operation of the driving assistance system according to the first embodiment corresponds to the processing of the driving assistance program according to the first embodiment.

[0125] The transmission process of vehicle information and road information by the on-board device 102 according to the first embodiment will be described with reference to Fig. 19 . The process shown in Fig. 19 is executed by the processing device 35 of the on-board device 102 at predetermined time intervals (for example, every 1 ms). The process shown in Fig. 19 may be executed not at predetermined time intervals but each time a predetermined event occurs, such as each time a signal is input from the on-board sensor group 36. The execution of the process shown in the flowchart of Fig. 19 by the on-board device 102 will be described below.

[0126] The process starts in step S101, where information is acquired from the on-board sensor group 36. The behavior data calculation unit 10 of the on-board device 102 may calculate the current inclination angle θ and predicted curvature from vehicle information acquired from the behavior sensor 13, the positioning sensor 14, etc. In step S102, vehicle information and road information are generated based on the information acquired from the on-board sensor group 36. At this time, the minimum information required is vehicle information including the vehicle position and inclination angle θ, and road information including the position of the road on which the vehicle is traveling and the lane width.

[0127] In step S113, the in-vehicle device 102 transmits the vehicle information and road information via the transmitter 16b. The transmission data generator 11 of the in-vehicle device 102 may additionally transmit to the driving assistance device 101 various data or processed data thereof collected from the in-vehicle sensor group 36 mounted on the in-vehicle device 102, including the behavior sensor 13, the positioning sensor 14, the surrounding sensor 15, and the biometric sensor 19. Then, the process ends.

[0128] <Driving Assistance Processing> Figure 20 is a flowchart showing the driving assistance processing of the driving assistance device 101 according to the first embodiment. The processing shown in Figure 20 is executed by the processing device 9 of the driving assistance device 101 at predetermined time intervals (for example, every 1 ms). The processing shown in Figure 20 may be executed not at predetermined time intervals but each time a predetermined event occurs, such as each time data is received from the in-vehicle device 102. Hereinafter, the execution of the processing described in the flowchart of Figure 20 by the driving assistance device 101 will be described.

[0129] The process starts, and in step S103, the vehicle information and road information transmitted from the in-vehicle device 102 are received. Specifically, it is checked whether information is stored in the receiving register, and if the latest data has not been received, the process may wait until the latest data is received.

[0130] In step S104, the received vehicle information and road information are registered in the database 20 and transmitted to the data analysis unit 22. Specifically, the data management unit 1 performs this operation.

[0131] In step S105, the data analysis unit 22 calculates the inclination angle θ, the inclination angular velocity θv, the distance DLCL from the lane center line LCL of the vehicle, the predicted curvature, etc. The data analysis unit 22 may obtain map information from the database 20 and calculate statistical values ​​such as the distance DLCL from the lane center line LCL of the driving lane relative to the position of the in-vehicle device, the probability distribution of the distance, the average value, and the standard deviation.

[0132] In step S114, it is determined whether or not a lane change has occurred. The determination of whether or not a lane change has occurred is made by the lane change determination unit 2. The lane change determination unit 2 of the data analysis unit 22 makes this determination based on information such as the inclination angle θ and the calculated inclination angular velocity θv in the vehicle information of the on-board device 102, and the distance DLCL between the lane center line LCL and the lane in which the vehicle is traveling.

[0133] In step S115, it is determined whether a course change has occurred. If a course change has occurred (determined YES), the process proceeds to step S106. If a course change has not occurred (determined NO), the process ends.

[0134] In step S106, the position and state of an abnormality on the road are estimated. Specifically, the road surface abnormality estimation unit 21 estimates whether or not there is an abnormality on the road surface, and the location and state of the abnormality.

[0135] In step S107, the presence or absence of an abnormality in the road surface, and the location and state of the abnormality are transmitted to the information providing unit 6. In step S108, the transmitting unit transmits the assistance information to other in-vehicle devices 103, etc. Then, the process ends.

[0136] <Assistance Information Reception Process of In-Vehicle Device> Fig. 21 is a flowchart showing reception processes of the in-vehicle device 103 according to the first embodiment. The processes shown in Fig. 21 are executed at predetermined time intervals (for example, every 1 ms) by the processing device of the in-vehicle device 103. The processes shown in Fig. 21 may be executed not at predetermined time intervals but each time a predetermined event occurs, such as each time data is received from the driving assistance device 101. Hereinafter, the execution of the processes shown in the flowchart in Fig. 21 by the in-vehicle device 103 will be described.

[0137] After starting the process, in step S109, data is received from the driving support device 101. Specifically, it is checked whether driving support information is stored in the receiving register, and if the latest data has not been received, the process may wait until the latest data is received.

[0138] In step S110, the received assistance information is displayed on the display unit, and if necessary, a control request is transmitted to the vehicle control device provided in the in-vehicle device 103. Then, the process ends.

[0139] <Details of the process of estimating road abnormality> Fig. 22 is a first flowchart showing details of the process of estimating road abnormality by the driving assistance device 101 according to embodiment 1. Fig. 23 is a second flowchart showing details of the process of estimating road abnormality by the driving assistance device 101. Fig. 24 is a third flowchart showing details of the process of estimating road abnormality by the driving assistance device 101. Fig. 23 shows a continuation of Fig. 22. Fig. 24 shows a continuation of Fig. 23.

[0140] 22 to 24 are flowcharts illustrating the details of step S106 in Fig. 20. The process starts, and in step S203, the distance DLCL from the vehicle's lane center line LCL is calculated. Also, the probability distribution of the distance DLCL from the vehicle's lane center line LCL is calculated.

[0141] In step S204, it is determined whether a lane change has occurred due to an excess of a threshold value. Specifically, the lane change determination unit 2 determines whether the absolute value of the inclination angle θ has exceeded a determination start inclination angle θths, or whether the absolute value of the inclination angular velocity θv has exceeded a determination start angular velocity θvths, or whether the distance DLCL from the vehicle's lane center line LCL has changed by a larger amount than the distance change determination amount Dth.

[0142] If any of the above conditions is met in step S302a, it is determined that a course change has occurred (determination is YES) and the process proceeds to step S403. If none of the above conditions is met, it is determined that a course change has not occurred (determination is NO) and the process proceeds to step S305.

[0143] In step S305, the road surface estimation unit determines whether the vehicle has been traveling steadily without change for a predetermined time. Specifically, it determines whether the state in which the change in vehicle speed V is within a predetermined speed range and the change in vehicle tilt angle θ is within a predetermined tilt angle range has continued for a predetermined determination time or more.

[0144] In step S305a, it is determined whether the vehicle is traveling at a constant speed. Specifically, if all of the conditions described in step S305 are satisfied (determination is YES), it is determined that the vehicle is traveling at a constant speed and the process proceeds to step S306. If even some of the conditions described in step S305 are not satisfied (determination is NO), it is determined that the vehicle is not traveling at a constant speed and the process proceeds to step S501.

[0145] In step S306, it is determined whether the vehicle is traveling on a low μ road or a rutted road. Specifically, if the changes in the inclination angle θ, inclination angular velocity θv, vehicle speed V, steering angle AST, and accelerator opening APS are within a predetermined range for a predetermined time, it is possible to estimate a road condition requiring caution, such as a low μ road. Furthermore, the presence or absence of ruts is estimated from the probability distribution of the traveling position and inclination angle. Furthermore, rutting can be estimated from the mean and standard deviation of the probability distribution for the distance from the lane center line.

[0146] In step S403, it is determined whether or not the motorcycle 201 is taking an evasive action based on the tilt angular velocity θv. Specifically, the absolute value of the tilt angular velocity θv exceeds the determination start angular velocity θvths, then decreases, passes through 0, increases in the opposite direction, and then after an avoidance period Pesc, the absolute value of the tilt angular velocity θv converges to a value equal to or less than the determination end angular velocity θvthe. The avoidance period Pesc is the period during which avoidance is performed. If the avoidance period Pesc is equal to or less than the avoidance period threshold Pthesc, the road surface abnormality estimation unit 21 estimates that an evasive action has occurred.

[0147] In step S403a, as described in step S403, it is determined whether the tilt angular velocity θv has converged after exceeding the limit. If the tilt angular velocity θv has converged after exceeding the limit (determination is YES), the process proceeds to step S404. If the tilt angular velocity θv has not converged after exceeding the limit (determination is NO), the process proceeds to step S412.

[0148] In step S404, it is confirmed whether all vehicles within a predetermined period have taken evasive action at the same location. In step S404a, it is determined whether all vehicles within a predetermined period have taken evasive action at the same location. If all vehicles have taken evasive action at the same location (determination is YES), the process proceeds to step S405. If there is a vehicle at the same location that has not taken evasive action (determination is NO), the process proceeds to step S407. Then, it is assumed that there is a fallen object that the motorcycle must avoid, and the process proceeds to step S501.

[0149] In step S405, it is estimated that a large falling object has occurred, and the process then proceeds to step S501.

[0150] In step S412, it is determined whether the distance DLCL from the vehicle's lane center line LCL has changed by more than a predetermined distance change determination amount Dth from the original distance and then become less than the distance change determination amount Dth from the original distance. In step S413, if the distance DLCL from the vehicle's lane center line LCL has changed by more than the predetermined distance change determination amount Dth from the original distance and then become less than the distance change determination amount Dth from the original distance (determination is YES), the process proceeds to step S404. If the distance DLCL from the vehicle's lane center line LCL has changed by more than the predetermined distance change determination amount Dth from the original distance and then does not become less than the distance change determination amount Dth from the original distance (determination is NO), the process proceeds to step S414.

[0151] In step S414, it is determined whether the change in the predicted curvature exceeds a threshold. If the change in the predicted curvature exceeds the threshold (determination is YES) in step S415, the process proceeds to step S404. If the change in the predicted curvature does not exceed the threshold (determination is NO), the process proceeds to step S407.

[0152] In step S407, it is estimated that no avoidance is necessary, and then the process proceeds to step S501.

[0153] In step S501, the vehicle information of the four-wheeled vehicle traveling ahead of the two-wheeled vehicle is acquired. In step S502, the speed V2wv of the two-wheeled vehicle is compared with the speed V4wv of the four-wheeled vehicle.

[0154] In step S502a, it is determined whether the speed V2wv of the two-wheeled vehicle - the speed V4wv of the four-wheeled vehicle > the determination threshold Vth. If the equation in step S502a is true (if the two-wheeled vehicle is catching up with the four-wheeled vehicle at a speed exceeding the predetermined determination threshold) (determination is YES), the process proceeds to step S504. If the equation in step S502a is not true (if the two-wheeled vehicle is not catching up with the four-wheeled vehicle at a speed exceeding the predetermined determination threshold) (determination is NO), the process ends.

[0155] In step S504, it is determined whether the motorcycle is traveling near a lane marking. If in step S504a the motorcycle is traveling near a lane marking (determination is YES), the process proceeds to step S505. If the motorcycle is not traveling near a lane marking (determination is NO), the process ends.

[0156] In step S505, it is assumed that the two-wheeled vehicle is passing by the four-wheeled vehicle, and the process then ends.

[0157] <Display of Assistance Information and Request to Vehicle Control Device> Fig. 25 is a flowchart showing details of processing of received assistance information by the in-vehicle device 103 according to embodiment 1. Fig. 25 is a flowchart illustrating details of step S110 in Fig. 21 .

[0158] In step S602, it is confirmed whether driving assistance information relating to the route of the vehicle in which the on-board device 103 is installed has been received. In step S602a, if driving assistance information relating to the route of the vehicle in which the on-board device 103 is installed has been received (determination is YES), the process proceeds to step S603. If driving assistance information relating to the route of the vehicle in which the on-board device 103 is installed has not been received (determination is NO), the process ends.

[0159] In step S603, it is determined whether or not a vehicle control request is included in the received assistance information. If a vehicle control request is included (determination is YES), the process proceeds to step S607. If a vehicle control request is not included (determination is NO), the process proceeds to step S604.

[0160] In step S607, if the vehicle is near a location where braking is required, preparations for deceleration by braking are made in advance, and deceleration is performed by engine braking. Then, the process ends.

[0161] In step S604, the support information is displayed on the display unit 17. In step S605, when road abnormality information is received, it is confirmed whether the vehicle is a target vehicle type. If the vehicle is a target vehicle type in step S605a (determination is YES), the process proceeds to step S606. If the vehicle is not a target vehicle type (determination is NO), the process ends.

[0162] In step S606, the presence or absence of road abnormalities is confirmed based on the data obtained from the vehicle control device, and appropriate measures are taken. After that, the process ends.

[0163] The processing of the flowchart described above describes a driving assistance method including: a receiving step of receiving vehicle information including the vehicle position and tilt angle, and road information including the position and lane width of the road on which the vehicle is traveling; a course change determination step of calculating a tilt angular velocity from the vehicle's tilt angle in the vehicle information received in the receiving step, calculating the vehicle's distance from the lane center line based on the vehicle's position in the vehicle information and the road position and lane width in the road information, and determining whether the vehicle has changed course based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line; a road abnormality estimation step of determining whether the vehicle has taken evasive action based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line when it is determined that a course change has occurred in the course change determination step, and estimating the position and state of an abnormality on the road if it is determined that the vehicle has taken evasive action; and a transmitting step of transmitting the position and state of the abnormality on the road estimated in the road abnormality estimation step to another vehicle.

[0164] The in-vehicle communication system in the first embodiment uses a threshold value to determine road surface abnormalities, but the present invention is not limited to this. The threshold value may be set based on the results of statistically processing past history. Furthermore, the determination may be made using supervised machine learning that learns the results of actual lane changes. Furthermore, other learning algorithms based on artificial intelligence may also be used.

[0165] As described above, the driving assistance device 101 described in the first embodiment uses the amount of change in the tilt angle θ, the tilt angular velocity θv, and the amount of change and rate of change of the predicted curvature, and is therefore able to estimate the presence of an event that the rider of the two-wheeled vehicle should avoid. According to the driving assistance device 101 of the first embodiment, the range of possible tilt angles is defined for each type of two-wheeled vehicle, and therefore the determination threshold can be switched and used for each type of two-wheeled vehicle.

[0166] According to the driving support device 101 of the first embodiment, the determination process is started only when each piece of data exceeds a threshold value, thereby reducing the processing load. Furthermore, since the analysis is performed for each section, a reduction in the processing load can be expected. According to the driving support device 101 of the first embodiment, the threshold value is changed at the start and end of the determination within a predetermined time, so that when a change occurs, it can be determined early.

[0167] According to the in-vehicle device 102 of the first embodiment, events requiring attention can be estimated taking into consideration information from the biosensors, and therefore obstacle estimation and road surface estimation can be performed taking into consideration the driver's impatience, tension, etc. According to the driving assistance system 100 of the first embodiment, vehicle behavior parameters can be calculated by either the behavior data calculation unit 10 of the in-vehicle device 102 or the course change determination unit 2 of the driving assistance device 101, which can contribute to distributing and reducing the processing load.

[0168] According to the driving assistance system 100 of the first embodiment, when the processing load of the processor of a device with a high priority is high, it is possible to distribute the processing load by sending the processing load to the device with the next highest priority. According to the driving assistance system 100 of the first embodiment, it is possible to estimate phenomena that require attention when driving a motorcycle, such as manholes on the road, slippery materials on the road surface such as gratings, fallen objects that could cause the motorcycle to tip over, and ruts caused by wear on the road surface, from parameters that indicate changes in vehicle behavior, such as changes in the distance DLCL from the vehicle's lane center line LCL, the probability distribution of the driving position, and the heart rate HR.

[0169] 2. Embodiment 2 Fig. 26 is a configuration diagram of a driving assistance device 101 and an in-vehicle device 102 according to embodiment 2. Fig. 27 is a time chart showing signal processing of a sensor of a user device when a two-wheeled vehicle 201 equipped with an in-vehicle device 102 according to embodiment 2 performs an avoidance action.

[0170] 26, a processing sequence of the data processing unit of the driving assistance device 101 according to the second embodiment will be described. In the driving assistance system according to the second embodiment, the same components as those in the first embodiment will be assigned the same reference numerals, and overlapping detailed descriptions will be omitted.

[0171] As shown in FIG. 26, the second embodiment differs from the first embodiment in that the driving assistance system further includes a user device 104 having a sensor.

[0172] The user device 104 may be a smartphone, smart watch, smart helmet, tablet, wearable device, etc., and acquires latitude, longitude, altitude position, speed, direction, acceleration, yaw angle, pitch angle, roll angle, driver's blood pressure, pulse rate, etc. from sensors in the user device. This information is then transmitted from the communication unit 56 to the behavior data calculation unit 10 via the communication unit 16 of the in-vehicle device 102.

[0173] FIG. 27 shows an example of vehicle behavior using sensor data from the user device 104 according to the second embodiment. FIG. 27 shows example values ​​of the gyro sensor and acceleration sensor, which are sensors of the user device 104. In the case of the user device 104, since it is installed on a motorcycle, errors are likely to occur due to vibration. Therefore, data within a predetermined error range is considered to be an error, and data is not used. Only data that exceeds the error range is considered to be a correct value and is used.

[0174] Note that, although the driving assistance system 100 in the second embodiment is illustrated as an example in which one user device 104 is included, it may be configured with two or more user devices 104. Also, the driving assistance system 100 in the second embodiment is illustrated as an example in which the in-vehicle device 102 is equipped with a behavior sensor 13, a positioning sensor 14, and the like, but if the sensors of the user device 104 can be used, the in-vehicle device 102 does not need to include the behavior sensor 13, the positioning sensor 14, or the biosensor 19.

[0175] As described above, the driving assistance system 100 described in embodiment 2 can use information from the sensors of the user device 104 instead of the behavior sensor 13, the positioning sensor 14, and the biometric sensor 19. Therefore, even if the vehicle-mounted device 102 is not equipped with a behavior sensor 13, a positioning sensor 14, etc., it is possible to estimate a warning location using vehicle behavior.

[0176] Although various exemplary embodiments and examples are described in this disclosure, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but may be applied to the embodiments alone or in various combinations. Therefore, countless variations not illustrated are anticipated within the scope of the technology disclosed in the specification. For example, this includes cases where at least one component is modified, added, or omitted, or where at least one component is extracted and combined with components of another embodiment.

[0177] 2 Lane change determination unit, 6 Information provision unit, 7, 16 Communication unit, 7a, 16a Receiving unit, 7b, 16b Transmitting unit, 15 Surrounding sensor, 17 Display unit, 18 Vehicle control device, 19 Biometric sensor, 20 Database, 21 Road surface abnormality estimation unit, 29 Lane, 31 Manhole, 33 Falling object, 100 Driving assistance system, 101 Driving assistance device, 102, 103 In-vehicle device, 104 User device, 201 Two-wheeled vehicle, 202 Four-wheeled vehicle

Claims

1. A driving assistance device comprising: a receiving unit that receives vehicle information including a vehicle position and inclination angle, and road information including a position and lane width of a road on which the vehicle is traveling; a course change determination unit that calculates an inclination angular velocity from the vehicle's inclination angle in the vehicle information received via the receiving unit, calculates the vehicle's distance from a lane center line based on the vehicle's position in the vehicle information and the road's position and lane width in the road information, and determines whether the vehicle has changed course based on the inclination angular velocity of the vehicle or the vehicle's distance from the lane center line; and a road surface abnormality estimation unit that, when the course change determination unit determines that a course change has occurred, determines whether the vehicle has taken evasive action based on the vehicle's inclination angular velocity or the vehicle's distance from the lane center line, and if it determines that the vehicle has taken evasive action, estimates the position and state of an abnormality on the road.

2. A driving assistance device according to claim 1, further comprising a transmitting unit that transmits the position and state of the abnormality on the road estimated by the road surface abnormality estimation unit to another vehicle.

3. A driving assistance device as described in claim 1 or 2, wherein the lane change determination unit determines that the vehicle has changed lane if the absolute value of the calculated inclination angular velocity of the vehicle is greater than a predetermined determination angular velocity, or if the calculated change in distance of the vehicle from the lane center line is greater than a predetermined distance change determination amount.

4. The receiving unit receives vehicle information including the position, speed, and tilt angle of the vehicle, and road information including the position and lane width of the road on which the vehicle is traveling, and the road surface abnormality estimation unit determines that the vehicle has taken evasive action when the absolute value of the tilt angular velocity of the vehicle calculated by the course change determination unit exceeds a predetermined determination start angular velocity and then decreases, the tilt angular velocity passes through 0 and increases in opposite directions in positive and negative directions, and the absolute value of the tilt angular velocity converges to or below a predetermined determination end angular velocity, and estimates the magnitude of the abnormality on the road from the time from when the absolute value of the tilt angular velocity exceeds the determination start angular velocity to when it converges and the speed of the vehicle, or estimates the magnitude of the abnormality on the road from the time from when the distance from the lane center line of the vehicle calculated by the course change determination unit changes from an amount of change in distance that exceeds a predetermined amount of change in distance with respect to the original distance to when it becomes less than the amount of change in distance with respect to the original distance, and the speed of the vehicle.

5. A driving assistance device as described in any one of claims 1 to 4, comprising a database that stores the vehicle information and the road information, wherein the receiving unit receives vehicle information including the type, position, and tilt angle of the vehicle, and the road surface abnormality estimation unit estimates whether the abnormality on the road is an abnormality that should be avoided only by two-wheeled vehicles or an abnormality that should be avoided by both two-wheeled vehicles and four-wheeled vehicles, depending on the proportion of the vehicle type in the history of avoidance actions in the database regarding the location of the abnormality on the road.

6. A driving assistance device as described in claim 5, wherein the road surface abnormality estimation unit estimates the magnitude of the abnormality on the road according to the proportion of the vehicle type in the history of avoidance actions in the database regarding the location of the abnormality on the road.

7. A driving assistance device as claimed in any one of claims 1 to 6, comprising a database that stores the vehicle information and the road information, and a road surface abnormality estimation unit that estimates, from the history of the database, an abnormal condition on the road at a location where the absolute value of the tilt angular velocity of the vehicle calculated by the course change judgment unit is greater than a predetermined judgment angular velocity, or at a location where the amount of change in distance of the vehicle from the lane center line calculated by the course change judgment unit is greater than a predetermined distance change judgment amount.

8. A driving assistance device as described in claim 7, wherein the road surface abnormality estimation unit estimates whether the abnormality on the road is a manhole, grating, rut, bump, or low-friction road surface based on the history of the database.

9. A driving assistance device as described in any one of claims 1 to 8, wherein the receiving unit receives vehicle information including the position, speed, and tilt angle of the vehicle, and the course change determination unit estimates that the vehicle is traveling on a low-friction road surface when a state in which a change in the vehicle's speed is within a predetermined speed range and a change in the vehicle's tilt angle is within a predetermined tilt angle range continues for a predetermined determination time or longer.

10. A driving assistance device as described in any one of claims 1 to 9, wherein the receiving unit receives vehicle information of the two-wheeled vehicle including the position, speed, and lean angle of the two-wheeled vehicle, vehicle information of the four-wheeled vehicle including the position, speed, and lean angle of the four-wheeled vehicle, and road information of the road on which the two-wheeled vehicle and the four-wheeled vehicle are traveling, and the course change determination unit estimates whether the two-wheeled vehicle will pass through other vehicles based on the position and speed of the four-wheeled vehicle and the position and speed of the two-wheeled vehicle.

11. A driving assistance device as described in any one of claims 1 to 10, wherein the receiving unit receives reflectance data of the road surface detected by a surrounding sensor of the vehicle, and the road surface abnormality estimation unit further uses the reflectance data of the road surface to estimate whether the abnormality on the road is a manhole, grating, rut, bump, or low-friction road surface.

12. A driving assistance device as described in any one of claims 1 to 11, wherein the receiving unit receives the pulse and blood pressure of the driver detected by a biometric sensor of the vehicle, and the road surface abnormality estimation unit further uses the pulse or blood pressure of the driver to estimate the abnormal state on the road.

13. A driving assistance device as described in any one of claims 1 to 12, comprising a database that stores the vehicle information and the road information, wherein the course change judgment unit causes the road surface abnormality estimation unit to estimate the position and state of an abnormality on the road and register it in the database when the difference between the vehicle's inclination angle and historical information in the database is greater than a predetermined judgment inclination angle difference, or when the difference between the vehicle's distance from the lane center line and historical information in the database is greater than a predetermined judgment distance difference.

14. A driving assistance device as described in any one of claims 1 to 13, comprising a transmitting unit that transmits the position and state of the abnormality on the road estimated by the road surface abnormality estimation unit to another vehicle, wherein when an abnormality on the road is estimated by the road surface abnormality estimation unit, the transmitting unit transmits to another vehicle the position and state of the abnormality on the road estimated by the road surface abnormality estimation unit, a recommended speed for traveling at the position on the road, a recommended driving distance from the lane center line, and a recommended driving inclination angle.

15. A driving assistance device as described in any one of claims 1 to 14, wherein the receiving unit receives at least one of data on position, speed, direction, acceleration, yaw angle, pitch angle, roll angle, driver's blood pressure, and pulse rate detected by a user sensor possessed by the user device, and the course change determination unit utilizes data detected by the user sensor received via the receiving unit that exceeds a predetermined dead zone.

16. A driving assistance device as described in any one of claims 1 to 15, wherein the receiving unit receives vehicle information including the type, position, and tilt angle of the vehicle, and the course change determination unit has a determination angular velocity and a distance change determination amount determined for each type of vehicle, and when the absolute value of the calculated tilt angular velocity of the vehicle is greater than the determination angular velocity corresponding to the type of vehicle, or when the calculated change in distance of the vehicle from the lane center line is greater than the distance change determination amount corresponding to the type of vehicle, the course change determination unit analyzes the behavior of the vehicle and causes the road surface abnormality estimation unit to perform determination and estimation.

17. A driving assistance device as described in any one of claims 1 to 16, comprising a transmitting unit that transmits to another vehicle the position and state of the abnormality on the road estimated by the road surface abnormality estimation unit, wherein the receiving unit receives vehicle information including the type, position, and tilt angle of the vehicle, and the transmitting unit transmits to the other vehicle the estimated position and state of the abnormality on the road if there is a risk that the other vehicle will roll over due to the abnormality on the road estimated by the road surface abnormality estimation unit, and does not transmit to the other vehicle the estimated position and state of the abnormality on the road if there is no risk that the other vehicle will roll over.

18. A driving assistance device according to any one of claims 1 to 17, wherein the receiving unit receives vehicle information of the two-wheeled vehicle, vehicle information of the four-wheeled vehicle, and road information of a road on which the two-wheeled vehicle and the four-wheeled vehicle are traveling; and the lane change determination unit, when the four-wheeled vehicle is present in front of the two-wheeled vehicle, analyzes the behavior of the two-wheeled vehicle and causes the road surface abnormality estimation unit to estimate an abnormal state on the road based on a first determination criterion if the absolute value of the calculated tilt angular velocity of the vehicle is greater than a predetermined first determination angular velocity, or if the calculated amount of change in distance of the vehicle from the lane center line is greater than a predetermined first distance change determination amount; and, when the four-wheeled vehicle is not present in front of the two-wheeled vehicle, analyzes the behavior of the two-wheeled vehicle and causes the road surface abnormality estimation unit to estimate an abnormal state on the road based on a second determination criterion if the calculated absolute value of the tilt angular velocity of the vehicle is greater than a predetermined second determination angular velocity, or if the calculated amount of change in distance of the vehicle from the lane center line is greater than a predetermined second distance change determination amount.

19. A driving assistance system comprising: a driving assistance device as described in any one of claims 1 to 18, which is equipped with a transmitting unit that transmits the position and state of the abnormality on the road estimated by the road surface abnormality estimation unit to another vehicle; a first on-board device mounted on the vehicle that transmits the vehicle information and the road information of the vehicle to the receiving unit of the driving assistance device; and a second on-board device mounted on the other vehicle that receives the estimated position and state of the abnormality on the road from the transmitting unit of the driving assistance device.

20. A driving assistance method comprising: a receiving step of receiving vehicle information including a vehicle position and tilt angle, and road information including a position and lane width of a road on which the vehicle is traveling; a course change determination step of calculating a tilt angular velocity from the vehicle's tilt angle in the vehicle information received in the receiving step, calculating the vehicle's distance from a lane center line based on the vehicle's position in the vehicle information and the road's position and lane width in the road information, and determining whether the vehicle has changed course based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line; a road abnormality estimation step of, when it is determined that a course change has occurred by the course change determination step, determining whether the vehicle has taken evasive action based on the vehicle's tilt angular velocity or the vehicle's distance from the lane center line, and if it is determined that the vehicle has taken evasive action, estimating the position and state of an abnormality on the road; and a transmission step of transmitting the position and state of the abnormality on the road estimated by the road abnormality estimation step to another vehicle.

Images