Embodiments of the information management device, the information management method, and the program according to the present invention will be described below with reference to the drawings. In the following description, a case in which the left-hand traffic regulation is applied to a road will be described, but when the right-hand traffic regulation is applied, the right and left may be reversely read.
Overall configuration
FIG. 1 is a schematic configuration diagram of an information management system 1 using the information management device of the embodiment. The information management system 1 includes, for example, one or more vehicles M1 to Mn and an information management device 200. Each of the vehicles M1 to Mn and the information management device 200 can communicate with each other via, for example, a network NW or the like. The network NW includes, for example, a cellular network, a Wi-Fi network, Bluetooth (registered trademark), the Internet, a wide area network (WAN), a local area network (LAN), a public line, a provider device, a dedicated line, a wireless base station and the like. The vehicles M1 to Mn are, for example, vehicles contracted in advance to receive a service (for example, an information providing service) provided by the information management device 200. In the following description, each of the vehicles M1 to Mn is collectively referred to simply as a “vehicle M” unless the vehicles M1 to Mn are separately described.
The vehicle M is, for example, a two-wheeled, three-wheeled, or four-wheeled vehicle, and its drive source is an internal combustion engine such as a diesel engine or a gasoline engine, an electric motor, or a combination thereof. The electric motor operates using electric power generated by a generator connected to the internal combustion engine, or electric power discharged from a secondary battery or a fuel cell. The vehicle M acquires vehicle information including position information and behavior information, and transmits the acquired vehicle information to the information management device 200 via the network NW at predetermined intervals or at a timing requested by the information management device 200. The behavior information includes, for example, information on a behavior of the vehicle M (steering, a speed, an operation of an in-vehicle device, and the like) and information on a behavior of an occupant (a driver or the like) of the vehicle M (for example, a driving operation). The vehicle information may also include information on surrounding conditions recognized by the vehicle M (external recognition information).
In addition, the vehicle M receives information on an abnormality in road conditions from the information management device 200 and presents the received information to the occupant of the vehicle M. The abnormality in the road conditions includes, for example, a traffic jam, a traffic accident, a stopped vehicle, an obstacle on a road, weather that impedes driving, road surface freezing, a cave-in, flooding, or the like, but may include other events. The information on the abnormality includes, for example, information on an influence range in which the abnormality affects traveling (hereinafter sometimes referred to as an “influence range due to the abnormality”).
The information management device 200 may be, for example, a server device or a personal computer (PC), or may also be a cloud server or the like configured by cloud computing consisting of one or more information processing devices. The information management device 200 acquires vehicle information from the plurality of vehicles M1 to Mn and determines an abnormality in road conditions and the like, or when the abnormality is determined, estimates an influence range due to the abnormality in the road conditions on the basis of the acquired vehicle information. In addition, the information management device 200 also provides information on the abnormality in the road conditions to a target vehicle determined on the basis of the estimated influence range. Next, specific configurations of the vehicle M and the information management device 200 will be described.
Vehicle
FIG. 2 is a configuration diagram of a vehicle system mounted in the vehicle M of the embodiment. The vehicle system includes, for example, a detection device 10, a communication device 20, a human machine interface (HMI) 30, a vehicle sensor 40, an in-vehicle device 50, a driving operator 60, a traveling drive force output device 70, a brake device 80, a steering device 90, and a vehicle control device 100. These devices and apparatuses are connected to each other by multiplex communication lines such as controller area network (CAN) communication lines, serial communication lines, wireless communication networks, and the like. The configuration shown in FIG. 2 is merely an example, and part of the configuration may be omitted or another configuration may be added.
The detection device 10 recognizes surrounding conditions (external world) of the vehicle M. The detection device 10 includes, for example, a camera, a radar device, a light detection and ranging (LIDAR), an object recognition device, and the like. The camera is, for example, a digital camera using a solid-state imaging device such as a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS). The camera is attached to any location on the vehicle M, and images a part (including at least a front direction) or an entirety of all directions. The camera periodically and repeatedly captures, for example, surroundings of the vehicle. The camera may be a stereo camera. The radar device emits radio waves such as millimeter waves around the vehicle M and detects radio waves (reflected waves) reflected by a surrounding object to detect at least a position (a distance and a direction) of the object. The radar device is attached to any location on the vehicle M. The radar device may detect the position and speed of an object by a frequency modulated continuous wave (FM-CW) method. LIDAR irradiates the surroundings of the vehicle M with light (or electromagnetic waves with a wavelength close to that of light) and measures scattered light. LIDAR detects the distance to an object on the basis of a time from light emission to light reception. The irradiated light is, for example, pulsed laser light. The LIDAR is attached to any location on the vehicle M.
The object recognition device performs sensor fusion processing on results of the detections by some or all of the camera, the radar device, and the LIDAR 14, and recognizes the position, type, speed, and the like of an object present around the vehicle M. The detection device 10 outputs detected information (for example, camera images) and external recognition information such as results of recognition based on the results of the detection to the vehicle control device 100.
The communication device 20 communicates with the information management device 200 and other various server devices via the network NW. In addition, the communication device 20 may use a cellular network, a Wi-Fi network, Bluetooth, dedicated short range communication (DSRC), or the like to communicate with communication terminals, such as smartphones or tablet terminals, used by the occupants of another vehicle present around the vehicle M and the vehicle M.
The HMI 30 presents various types of information to the occupant of the vehicle M and receives an input operation performed by the occupant. The HMI 30 includes a display device, a speaker, a microphone, a buzzer, a touch panel, a switch (for example, an operating switch), a key, and the like. The display device is provided, for example, at a center of an instrument panel of the vehicle M, and is a display device (so-called a multi-information display) that displays various types of information on the vehicle M such as a speedometer that represents a traveling speed of the vehicle M or a tachometer that represents the number of rotations (a rotational speed) of an internal combustion engine provided in the vehicle M. The microphone collects not only a voice of the occupant, but also sounds inside and outside a compartment of the vehicle (for example, an engine sound of the vehicle M, a sound around the vehicle, and an alarm sound).
The vehicle sensor 40 includes a vehicle speed sensor for detecting the speed of the vehicle M, an acceleration sensor (G sensor) for detecting the acceleration, and a yaw rate sensor for detecting the yaw rate (for example, a rotational angular speed around a vertical axis passing through a center of gravity of the vehicle M), an orientation sensor for detecting an orientation of the vehicle M, and the like. In addition, the vehicle sensor 40 may be provided with a position sensor for detecting the position of the vehicle M. A position sensor is, for example, a sensor that acquires position information (latitude and longitude information) from a global positioning system (GPS) device. In addition, the position sensor may also be a sensor that acquires the position information using a global navigation satellite system (GNSS) receiver of a navigation device. Moreover, the vehicle sensor 40 may be provided with a vibration sensor for detecting a vibration of the vehicle M, an operating sensor for detecting an operating state of each device included in the in-vehicle device 50, a sensor for detecting specific surrounding weather conditions such as rain, fog, and snow or illuminance, and a temperature sensor, and the like. A result of the detection by the vehicle sensor 40 is output to the vehicle control device 100.
The in-vehicle device 50 is a device that is mounted in the vehicle M and is operable according to the behavior of the vehicle M or an operation instruction from the occupant. The in-vehicle device 50 includes, for example, a wiper device 51, an airbag device 52, a lighting device 53, a navigation device 54, a driving assistance device 55, and the like. Note that the in-vehicle device 50 may include devices other than the devices described above (for example, a drive recorder, a driver monitor camera, an audio device, and the like).
The wiper device 51 operates or stops a wiper that removes raindrops and the like adhering to the windshield and the like on the basis of, for example, an operation instructed by the occupant or a result of detection of the surrounding rain by the vehicle sensor 40. The airbag device 52 inflates an airbag provided at a predetermined position inside the vehicle compartment, for example, when the behavior of the vehicle M satisfies a predetermined condition (for example, rapid deceleration) based on a result of the detection by the acceleration sensor. The lighting device 53 is, for example, a head lamp, a fog lamp, a hazard lamp, or a room lamp. The lighting device 53 causes a target lamp to be lit or to blink on eh basis of, for example, an operation instructed by the occupant or a result of the detection of the surrounding fog, illuminance, and the like by the vehicle sensor 40.
The navigation device 54 includes, for example, a GNSS receiver, a navigation HMI, and a route determiner. The navigation device 54 holds map information in a storage device such as a hard disk drive (HDD) or flash memory. A GNSS receiver identifies a position of the vehicle M on the basis of a signal received from a GNSS satellite. The position of the vehicle M may be identified or supplemented by an inertial navigation system (INS) using an output of the vehicle sensor 40. A navigation HMI includes a display device, a speaker, a touch panel, a key, and the like. The navigation HMI may be partly or entirely shared with the HMI 30 described above. A route determiner determines, for example, a route (hereinafter referred to as a route on a map) from the position of the vehicle M identified by the GNSS receiver (or any position to be input) to a destination to be input by the occupant using the navigation HMI by referring to map information. Map information is, for example, information in which a road shape is represented by links indicating roads and nodes connected by the links. Map information may include information on a curvature of a road, a point of interest (POI), and the like, and may also include information on road shapes and road structures, and the like. Road shapes include, for example, branching, merging, a tunnel (entrances and exits), a curved road, curvature of a road or a road marking line, a radius of curvature, the number of lanes, a width, a slope, and the like. The information on a road structure may include information such as a type, a position, a direction with respect to an extension direction of the road, a size, a shape, a color, and the like of the road structure. The map information may be updated at any time by the communicator 210 communicating with an external device. The navigation device 54 performs route guidance using the navigation HMI on the basis of a route on a map.
The driving assistance device 55 controls, for example, one or both of the steering and speed of the vehicle M under control of a traveling controller 120, which will be described below, to execute driving control by automated driving. The driving control includes, for example, emergency brake control, contact avoidance steering control, and the like for avoiding contact between the vehicle M and an object. In addition, the driving control may include, for example, various types of driving control such as a lane keeping assistance system (LKAS), auto lane changing (ALC), an adaptive cruise control system (ACC), and the like.
The driving operator 60 includes, for example, a brake pedal, an accelerator pedal, a steering wheel, an operating switch of a direction indicator, a shift lever, and other operators. The driving operator 60 is attached to a sensor that detects an amount of operation or the presence or absence of an operation, and a result of the detection is output to a part or all of the traveling drive force output device 70, the brake device 80, and the steering device 90, or to the vehicle control device 100.
The traveling drive force output device 70 outputs a traveling drive force (torque) for the traveling of the vehicle M to drive wheels. The traveling drive force output device 70 includes, for example, a combination of an internal combustion engine, an electric motor, a transmission, and the like mounted in the vehicle M, and an electronic control unit (ECU) for controlling these. The ECU controls the constituents described above according to information input from the driving assistance device 55 or information input from the accelerator pedal of the driving operator 60.
The brake device 80 includes, for example, a brake caliper, a cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the cylinder, and a brake ECU. The brake ECU controls the electric motor according to the information input from the driving assistance device 55 or the information input from the brake pedal of the driving operator 60 so that a brake torque corresponding to a braking operation is output to each wheel. The brake device 80 may have a mechanism that transmits the hydraulic pressure generated by an operation of the brake pedal to the cylinder via a master cylinder as a backup. The brake device 80 is not limited to the configuration described above, and may be an electronically controlled hydraulic brake device that controls the actuator according to the information input from the driving assistance device 55 and transmits the hydraulic pressure of the master cylinder to the cylinder.
The steering device 90 includes, for example, a steering ECU and an electric motor. The electric motor applies, for example, force to a rack and pinion mechanism to change a direction of the steered wheels. The steering ECU drives the electric motor according to the information input from the driving assistance device 55 or the information input from the steering wheel of the driving operator 60 to change the direction of the steered wheels.
The vehicle control device 100 includes, for example, an acquirer 110, a traveling controller 120, a manager 130, an HMI controller 140, and a storage 150. The acquirer 110, the traveling controller 120, the manager 130, and the HMI controller 140 are realized by, for example, a hardware processor such as a central processing unit (CPU) executing a program (software). Some or all of these components may be realized by hardware (a circuit unit; including circuitry) such as large scale integration (LSI), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and a graphics processing unit (GPU) or by software and hardware in cooperation. The program may be stored in advance in a storage device (a storage device having a non-transitory storage medium) such as an HDD or a flash memory of the vehicle control device 100, or may be stored in a detachable storage medium such as a DVD or a CD-ROM and may be installed in the HDD or flash memory of the vehicle control device 100 by the storage medium (the non-transitory storage medium) being equipped in a drive device.
The storage 150 is realized by various storage devices described above, a solid state drive (SSD), an electrically erasable programmable read only memory (EEPROM), a read only memory (ROM), a random access memory (RAM), or the like. The storage 150 stores, for example, a program and various types of other information. In addition, the storage 150 may store, for example, the map information described above, and may store a vehicle ID and an occupant ID.
The acquirer 110 acquires various types of information from the detection device 10, the communication device 20, the HMI 30, the vehicle sensor 40, an in-vehicle device 50, the driving operator 60, the traveling drive force output device 70, the brake device 80, the steering device 90, the traveling controller 120, and the manager 130. In addition, the acquirer 110 may also store the acquired information in the storage 150, and generate vehicle information including position information and behavior information on the basis of the acquired information and other information stored in the storage 150. Position information is acquired by a position sensor or a navigation device 54. Among pieces of the behavior information, the behavior of the vehicle M is generated on the basis of a result of the detection by the vehicle sensor 40, a working state of the in-vehicle device, and the like, and the behavior of the occupant is generated on the basis of information received by the HMI 30 and the driving operator 60. The behavior information includes, for example, at least one of a steering behavior of the vehicle M (a steering angle or an amount of operating the steering wheel), an accelerator behavior (accelerator operation amount), a brake behavior (a brake operation amount), yaw rate information, speed information, acceleration information, driving assistance information (operation information of the driving assistance device 55), wiper operation information, airbag operation information, lighting operation information, vibration information, and sound information. In addition to the position information and the behavior information, the vehicle information may include external recognition information of camera images captured by a camera of the detection device 10, sound information collected by the microphone of the HMI 30, and detection information acquired by the other sensors, time information when various types of information are acquired, a vehicle ID, an occupant ID, and the like.
Based on the information acquired from the driving operator 60 and the information acquired by the acquirer 110, the traveling controller 120 controls the traveling drive force output device 70, the brake device 80, and the steering device 90 so that the vehicle M travels according to a driving operation of the occupant. In addition, the traveling controller 120 operates the driving assistance device 55 and the like so as to execute predetermined driving control on the basis of a result of recognition of the surroundings by the detection device 10.
The manager 130 manages an operation status of the entire vehicle system. For example, the manager 130 manages traveling conditions of the vehicle M (for example, the steering behavior, the accelerator behavior, the brake behavior) and operating conditions (for example, the driving assistance information, the wiper operation information, the airbag operation information, the lighting operation information), and the like of the in-vehicle device 50. Information managed by the manager 130 may be stored in the storage 150 and may be output to the acquirer 110. In addition, the manager 130 transmits the vehicle information generated by the acquirer 110 to the information management device 200 via the communication device 20 at predetermined intervals or at a timing requested by the information management device 200.
The HMI controller 140 notifies the occupant of predetermined information through the HMI 30. The predetermined information includes, for example, information related to the traveling of the vehicle M, such as information on a state of the vehicle M and information on traveling control thereof. The information on the state of the vehicle M includes, for example, the speed, an engine speed, a shift position, and the like of the vehicle M. In addition, the predetermined information may include the information on the abnormality in the road conditions provided by the information management device 200. Moreover, the predetermined information may include, for example, a current position and a destination of the host vehicle M, information on a remaining amount of fuel, and the like.
For example, the HMI controller 140 may generate an image containing the predetermined information described above and display the generated image on a display device of the HMI 30, and may generate a sound indicating the predetermined information and cause the generated sound to be output from a speaker of the HMI 30. In addition, the HMI controller 140 may also cause the HMI 30 to output an image and a sound provided by the information management device 200. The HMI controller 140 may also output information received by the HMI 30 to the acquirer 110, the traveling controller 120, the manager 130, and the like.
Information management device
FIG. 3 is a configuration diagram of the information management device 200 of the embodiment. The information management device 200 includes, for example, a communicator 210, an acquirer 220, a manager 230, an abnormality determiner 240, an influence range estimator 250, a provider 260, and a storage 270. The acquirer 220, the abnormality determiner 240, the influence range estimator 250, and the provider 260 are realized by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (a circuit unit; including circuitry) such as LSI, an ASIC, an FPGA, a GPU, and the like, or by software and hardware in cooperation. The program may be stored in advance in a storage device (a storage device having a non-transitory storage medium) such as an HDD or a flash memory provided in the information management device 200, or may be stored in a detachable storage medium (a non-transitory storage medium) such as a DVD or a CD-ROM and installed in the HDD or the flash memory provided in the information management device 200 by the storage medium being equipped in the drive device provided in the information management device 200.
The storage 270 may be realized by the various storage devices described above, or an SSD, an EEPROM, a ROM, a RAM, or the like. The storage 270 stores, for example, a vehicle information database (DB) 272, map information 274, a program, and various types of other information. The vehicle information DB 272 stores vehicle information transmitted from each of the vehicles M1 to Mn. In this case, time-series data for a predetermined period may be stored for each vehicle ID. In addition, the map information 274 may be information similar to the map information stored in the navigation device 54 or the storage 150, or may be high-definition map information. The map information 274 may be updated at any time by the communicator 210 communicating with an external device.
The communicator 210 communicates with the vehicles M1 to Mn and other external devices via the network NW. For example, the communicator 210 receives vehicle information and the like from the vehicles M1 to Mn. In addition, the communicator 210 transmits information on an abnormality in road conditions and the like to a target vehicle corresponding to an influence range among the vehicles M1 to Mn.
The acquirer 220 acquires vehicle information from the information received by the communicator 210. In addition, the acquirer 220 may request a predetermined vehicle among a plurality of vehicles to transmit vehicle information and acquire vehicle information from the requested vehicle. The acquired information may be stored in the vehicle information DB 272 of the storage 270. In addition, the acquirer 220 may also acquire information provided from other external devices via the network NW.
The manager 230 manages all constituents of the information management device 200. For example, the manager 230 manages the operation status of the abnormality determiner 240, the influence range estimator 250, the provider 260, and the like. In addition, the manager 230 may also manage registration or the like of vehicles (contracted vehicles) to which services are provided. Moreover, the manager 230 performs authentication processing based on a vehicle ID, an occupant ID, and other types of authentication information in advance, and provides services to vehicles and occupants registered in advance.
The abnormality determiner 240 determines whether a vehicle has detected an abnormality in road conditions on the basis of the vehicle information acquired by the acquirer 220. The abnormality determiner 240 may determine an abnormality such as a failure of the vehicle M. In addition, when at least one of the plurality of vehicles has detected an abnormality in road conditions based on the behavior information, the abnormality determiner 240 determines whether the same type of abnormality is detected from a vehicle different from the vehicle that has detected the abnormality before a predetermined period of time elapses after the abnormality is detected. Details of the function of the abnormality determiner 240 will be described below.
The influence range estimator 250 estimates an influence range of the abnormality in the road conditions on the basis of the position information and the behavior information when the same type of abnormality is detected from a vehicle different from the vehicle that has detected the abnormality using the abnormality determiner 240. Details of functions of the influence range estimator 250 will be described below.
The provider 260 provides various types of information to the vehicle M. The provider 260 includes, for example, a determiner 262 and a generator 264. The determiner 262 determines a target vehicle to which information on the abnormality in the road conditions will be provided on the basis of the influence range estimated by the influence range estimator 250. For example, the determiner 262 determines a vehicle that is moving from a position outside the influence range toward the abnormality influence range as a target vehicle. Furthermore, the determiner 262 may determine, in addition to (or instead of) the conditions described above, a target vehicle according to a distance from the influence range or may also determine a target vehicle according to a traveling lane of each vehicle.
In addition, the determiner 262 determines whether to provide the influence range in a map area based on the map information 274 or based on road information, depending on a type of the abnormality in the road conditions. The type of the abnormality is determined by the abnormality determiner 240. The type of the abnormality includes, for example, at least one of a traffic jam, a traffic accident, a stopped vehicle, an obstacle on the road, weather that impedes driving (for example, heavy rain, thunderstorms, typhoons, fog, heavy snow, and the like), road surface freezing, a cave-in, and flooding. For example, the determiner 262 determines to provide the influence range in a map area based on the map information 274 when the type of the abnormality is weather that impedes driving, road surface freezing, flooding, or the like. In addition, the determiner 262 determines to provide the influence range on the basis of road information because the influence range is expected to be identified to some extent when the type of the abnormality is a traffic jam, a traffic accident, a stopped vehicle, an obstacle on a road, or a cave-in. As a result, when the type of the abnormality is a traffic accident, information on a road on which the accident has occurred can be provided, and in the case of heavy rain, information on a corresponding area can be provided regardless of a road. Therefore, it is possible to provide more appropriate information on the abnormality in the road conditions.
For the target vehicle determined by the determiner 262, the generator 264 generates, for example, provision information (information on an abnormality in road conditions) including road conditions (the type of the abnormality, and the like) and an influence range thereof. In addition, the generator 264 may generate provision information other than the information on the abnormality in the road conditions (for example, information indicating that the abnormality in the road conditions has been resolved, detour route information, answer information to inquiries from an occupant) and the like. The generator 264 transmits the generated provision information to the target vehicle and provides it to the occupant.
Abnormality determiner and influence range estimator
Next, details of abnormality determination processing performed by the abnormality determiner 240 and influence range estimation processing performed by the influence range estimator 250 will be described. FIG. 4 is a diagram for describing the abnormality determination processing and the influence range estimation processing. In the example of FIG. 4, a road RD1 is shown that includes a lane L1 that extends along an X axis in FIG. 4 and in which travel in an X axis direction is possible, and a lane (an opposing lane) L2 in which travel in an opposite direction (a - X axis direction) to the lane L1 is possible. The lane L1 is defined by road marking lines LL and CL, and the lane L2 is defined by road marking lines CL and RL. In the example of FIG. 4, vehicles M1 to M4 are traveling in the lane L1 at speeds VM1 to VM4, respectively, and vehicles M5 to M8 are traveling in the lane L2 at speeds VM5 to VM8, respectively. In addition, it is assumed that the vehicles M1 to M8 are contracted vehicles that can be provided with an information providing service provided by the information management device 200.
In the example of FIG. 4, each of the vehicles M1 to M8 transmits vehicle information including position information and behavior information to the information management device 200 at predetermined intervals. The abnormality determiner 240 refers to the map information 274 using the position information included in the vehicle information transmitted from the vehicles M1 to M8, and identifies the road RD1 (or the lanes L1 and L2) on which the vehicles M1 to M8 are traveling. In this case, the abnormality determiner 240 may acquire a shape of the road RD1, and the like from the map information 274. Then, the abnormality determiner 240 determines whether an abnormality in the identified road conditions is detected on the basis of the behavior information included in the vehicle information.
For example, when information included in the behavior information satisfies a predetermined abnormality determination condition, the abnormality determiner 240 determines that the vehicle M that has transmitted the behavior information has detected the abnormality in the road conditions, and determines that the vehicle M does not detect the abnormality when the information included in the behavior information does not satisfy the abnormality determination condition. The abnormality determination conditions may include, for example, conditions related to the speed and acceleration or deceleration of the vehicle M (in other words, an accelerator operation and a brake operation performed by the occupant), conditions related to the steering (steering behavior) of the vehicle M, and conditions related to a predetermined operation of the in-vehicle device 50.
FIG. 5 is a flowchart which shows an example of processing by the abnormality determiner 240. In the example of FIG. 5, the abnormality determiner 240 determines whether a difference between a speed VM of the vehicle M included in the behavior information and a reference speed is equal to or greater than a threshold value (step S100). The reference speed may be, for example, a speed limit or a legal speed of the road RD1, or may be a predetermined fixed speed. When it is determined that the difference between the speed VM and the reference speed is not equal to or greater than the threshold value, the abnormality determiner 240 determines whether a degree of deceleration of the vehicle M during a predetermined period of time is equal to or greater than a threshold value based on the acceleration information included in the behavior information (step S110).
When it is determined that the degree of deceleration is not equal to or greater than the threshold value, the abnormality determiner 240 determines whether a steering angle of the vehicle M is equal to or greater than a predetermined angle based on the steering behavior included in the behavior information (step S120). Note that, in the processing of step S120, the abnormality determiner 240 may determine whether a degree of change in the steering angle during a predetermined period of time is equal to or greater than a threshold value. In addition, when the road RD1 on which the vehicle is traveling is curved, the steering angle increases with the curvature. For this reason, instead of the steering angle, the abnormality determiner 240 may use a divergence angle of the steering angle with respect to an extending direction of the road RD1 obtained from the map information 274.
When it is determined that the steering angle is not equal to or greater than the predetermined angle, the abnormality determiner 240 determines whether the driving assistance device 55 has been operated on the basis of the driving assistance information included in the behavior information (step S130). In the processing of step S130, the abnormality determiner 240 may determine whether predetermined control (for example, emergency brake control or contact avoidance steering control) has been operated by the driving assistance device 55. When it is determined that the driving assistance device 55 is not operating, the abnormality determiner 240 determines that the vehicle M, which has transmitted the behavior information during the determination, has not detected the abnormality in the road conditions (step S140). When it is determined that the driving assistance device 55 is operating, the abnormality determiner 240 determines that the vehicle has detected the abnormality in the road conditions (step S150). In addition, when it is determined in the processing of step S100 that the difference between the speed VM of the vehicle M and the reference speed is greater than or equal to the threshold value, when it is determined that the degree of deceleration is equal to or greater than the threshold value in the processing of step S110, or when it is determined that the steering angle is equal to or greater than the predetermined angle in the processing of S120, it is determined that there is an abnormality in the road conditions. After the processing of step S150, the abnormality determiner 240 identifies the type of the abnormality in the road conditions (step S160). With this, the processing of this flowchart ends.
Note that the processing of steps S100 to S130 may be executed in an order different from the order shown in FIG. 5. Moreover, the abnormality determiner 240 may perform determination based on all the abnormality determination conditions and identify one or more types of abnormality, regardless of results of respective determinations in the processing of steps S100 to S130. In addition to (or instead of) conditions of steps S100 to S130 described above, the abnormality determination conditions may include whether other in-vehicle devices (for example, the wiper device 51, the airbag device 52, or the lighting device 53) have operated, whether the vibration of the vehicle M is equal to or greater than a threshold value, whether a predetermined sound (an alarm sound) has been detected, and the like.
For example, when the abnormality determiner 240 detects that the occupant has turned on a fog light of the lighting device 53 or enabled a fog sensor in the operation of the occupant included in the vehicle information, it analyzes a camera image captured by the vehicle M to confirm weather conditions. Then, it is determined that an abnormality in road conditions has been detected when fog is detected from a result of the analysis, and it is determined that an abnormality has not been detected when fog is not detected. For the detection of fog, a well-known image detection algorithm capable of detecting a state of fog may be used. In addition, the abnormality determiner 240 may also determine whether the abnormality of road surface freezing is detected on the basis of a distance from when the occupant performs a brake operation to when the vehicle M stops as another abnormality determination condition. In this case, the abnormality determiner 240 may further include the analysis result of the camera image in the abnormality determination conditions.
In addition, when the abnormality determiner 240 determines that at least one vehicle (part of the vehicle) among the vehicles M1 to M8 has detected an abnormality, it sets the abnormality to be in a provisional state and manages it. The provisional state is a state in which it is predicted that there is a possibility that an abnormality does not actually occur on the road RD1, and prediction of an abnormality in road conditions is not confirmed at this time. Then, when it is determined that the same type (a predetermined similar type may be included) of abnormality has been detected from a vehicle (another vehicle) other than the vehicle that has detected the abnormality within a predetermined period of time after the provisional state is set, the abnormality determiner 240 changes a state of the abnormality from the provisional state to a confirmed state.
When the abnormality is confirmed, the influence range estimator 250 estimates the influence range on the basis of position information of the vehicle M that has detected the abnormality. For example, the influence range estimator 250 refers to the map information 274 on the basis of the position information of the vehicle M that has detected the same type of abnormality, and estimates an area within a predetermined distance from the vehicle M that has detected the abnormality as the influence range. For example, the influence range estimator 250 estimates, as the influence range, a range within a predetermined distance from the vehicle M that has detected the abnormality in the provisional state as a center. In this case, the predetermined range includes at least the position of another vehicle M that has detected the same type of abnormality. In addition, the influence range estimator 250 may set influence ranges centered on each vehicle that has detected the same type of abnormality, and estimate a range including all of the influence ranges as a final influence range.
In addition, the influence range estimator 250 may estimate the influence range based on a road or a lane in which the vehicle M that has detected the abnormality is traveling. For example, in the situation shown in FIG. 4, when an abnormality is first detected by a vehicle M1 and the same type of abnormality is detected by a vehicle M2 within a predetermined period of time after the detection, the influence range estimator 250 estimates a range AR1 of the lane L1 from a position of the vehicle M1 to a position of the vehicle M2 as the influence range of the abnormality. Furthermore, when the same type of abnormality is detected by a vehicle M7 in addition to the vehicle M2 before the predetermined period of time elapses, the influence range estimator 250 estimates an area AR2 of the road RD1 including respective positions of the vehicles M1, M2, and M7 as the influence range of the abnormality. As a result, it is possible to estimate a more accurate influence range based on road information. Therefore, it is possible to exclude, for example, lanes that have no abnormalities such as oncoming lanes, branching lanes, lanes heading in other directions at an intersection, and the like, which are present within a predetermined distance from the vehicle that has detected the abnormality.
Further, when the abnormality set to be in the provisional state is detected, the abnormality determiner 240 may acquire the position of the vehicle M that has detected the same type of abnormality from the behavior information of the surrounding vehicles within a predetermined range from the vehicle that has detected the abnormality, and estimate the influence range of the abnormality on the basis of the acquired position information (distribution, and the like) of the vehicle M.
FIG. 6 is a flowchart which shows an example of abnormality determination and influence range estimation processing. The example of FIG. 6 shows the processing after an abnormality set to be in the provisional state is detected in the determination processing shown in FIG. 5. In the example of FIG. 6, the acquirer 220 acquires behavior information of other vehicles present within a predetermined range from a vehicle which has detected the abnormality in the provisional state (step S200). In this case, the acquirer 220 compares position information of the vehicle that has detected the abnormality with position information of other vehicles and acquires the behavior information of the other vehicles which are present within a predetermined distance from the vehicle. For example, in the situation shown in FIG. 4, when an abnormality is detected by the vehicle M1, behavior information of vehicles M2, M3, M4, M7, and M8 present within a predetermined distance from the vehicle M1 is acquired. In addition, the acquirer 220 may also refer to the map information 274 to identify the road or lane in which the vehicle M that has detected the abnormality in the provisional state travels, and acquire the behavior information of other vehicles (more specifically, following vehicles) traveling in the identified lane. For example, in the situation shown in FIG. 4, when an abnormality is detected by the vehicle M1, the behavior information of the vehicles M2 to M4 traveling in the same lane L1 as the vehicle M1 and following the vehicle M1 is acquired.
Next, the abnormality determiner 240 determines whether the acquired behavior information includes behavior indicating the same type of abnormality (step S210). When the abnormality determiner 240 determines that the behavior indicating the same type of abnormality is included, the abnormality determiner 240 manages a vehicle corresponding to the behavior information as a vehicle influenced by the same type of abnormality (step S220). Next, the influence range estimator 250 estimates the influence range on the basis of the position information (distribution) of vehicles influenced by the same type of abnormality (step S230). As a result, processing of this flowchart ends. In processing of step S210, when it is determined that the behavior information does not include behavior indicating the same type of abnormality, since the abnormality is not confirmed, the processing ends without estimating the influence range. By the processing described above, abnormality determination can be performed more efficiently and quickly than determination using behavior information of all of the vehicles M1 to M8. For this reason, it is possible to realize management of road information with higher immediacy.
After the influence range is estimated by the influence range estimator 250, a target vehicle to which information will be provided is determined by the determiner 262, and information is provided to the determined target vehicle. For example, in the situation of FIG. 4, when the influence range is the range AR1, information on the abnormality of the lane L1 is provided to the vehicles M1 and M2 traveling toward the range AR1. As a result, the vehicles M3 and M4 can provide more real-time road conditions before detecting an abnormality. In addition, the provider 260 refers to the map information 274 using the respective position information of the vehicles M3 and M4 that have not yet entered the influence range, and when there is a detour route that detours the influence range, information on the detour route may also be provided. As a result, more appropriate information can be provided for each vehicle, vehicles M3 and M4 can be caused to detour, and expansion of the influence range (for example, a traffic jam range) of the abnormality can be suppressed. The provider 260 may also provide information on an abnormality of the lane L1 to the vehicles M1 and M2 traveling in the range AR1.
Here, for the abnormality determination by the abnormality determiner 240 and the estimation of an influence range by the influence range estimator 250 described above, functions of AI (artificial intelligence) such as machine learning (a neural network) and deep learning may also be used. For example, when vehicle information (behavior information, sensor data) is input, the abnormality determiner 240 inputs the vehicle information of the vehicles M1 to M8 to a learned model that has been learned in advance to output a result of determining whether the vehicle M has detected an abnormality (in addition, an abnormality level) to acquire a result of the determination of each vehicle. In addition, when the influence range estimator 250 receives vehicle information of a vehicle that has detected an abnormality and vehicle information of other vehicles that have not been determined whether they have detected an abnormality, it inputs the vehicle information of the vehicle that has detected the abnormality in the provisional state and the vehicle information of the other vehicles to the learned model that has been learned in advance to output the influence range on the basis of a tendency (similarity) of the vehicle state, thereby acquiring the influence range. Note that the learned model described above may be stored in the storage 270 or acquired from the outside via the communicator 210.
In addition, the abnormality determiner 240 may determine whether the abnormality in the road conditions continues on the basis of the vehicle information including at least one of behavior information of the vehicle M present within the influence range and external recognition information recognized by the vehicle M. In this case, the abnormality determiner 240 requests, for example, vehicles present within the influence range to transmit vehicle information (more specifically, camera images in which the surroundings included in the external recognition information is captured), and determines whether the abnormality continues depending on whether the abnormality is detected based on the camera images obtained from the requested vehicles. Note that the abnormality determiner 240 may determine whether the abnormality continues from only the behavior information, and may more accurately determine whether the abnormality continues using both the behavior information and the external recognition information. In addition, the influence range estimator 250 may reduce or expand the influence range at predetermined intervals on the basis of a result of the determination on whether the abnormality continues. As a result, it is possible to provide the vehicle M with more real-time information. Moreover, the provider 260 may also provide the target vehicle with information on the continuation of the abnormality. As a result, it is possible to provide the occupant with a continued state of the abnormality.
In addition, the abnormality determiner 240 may change a predetermined period of time from when an abnormality set to be in the provisional state is detected to when it is determined whether the same type of abnormality is detected by other vehicles according to the degree of behavior (a degree of change, a strength) included in the behavior information. In this case, the abnormality determiner 240 shortens the predetermined period of time as the degree of behavior of the vehicle M increases, or lengthens the predetermined period of time as the degree of behavior decreases. As a result, it is possible to perform the determination of an abnormality more appropriately, and it is possible to shorten a time for changing the state of the abnormality from the provisional state to the confirmed state according to the degree of behavior.
In addition, depending on the degree of behavior described above, the number of times the same type of abnormality is detected may be adjusted to change the state of the abnormality from the provisional state to the confirmed state. In this case, the abnormality determiner 240 reduces the number of times as the degree of behavior increases, or increases the number of times as the degree of behavior decreases. When the same type of abnormality is detected for the number of times corresponding to the degree of behavior included in the behavior information before a predetermined time elapses after the abnormality is set to be in the provisional state, the influence range estimator 250 estimates the influence range of the abnormality in the road conditions on the basis of the vehicle information. As a result, it is possible to execute more accurate abnormality determination and to estimate more appropriate influence range.
In addition, the influence range estimator 250 may adjust the influence range according to the type of abnormality. For example, when the type of abnormality is road surface freezing, the influence range estimator 250 estimates the influence range on the basis of the position of the vehicle that has detected the abnormality and a distance (a freezing level) from a brake operation to a stop of each vehicle. For example, when the abnormality is road surface freezing, by setting the influence range larger than that of other types of abnormality, it is possible to provide other vehicles with a more appropriate influence range that takes into account effects of slipping.
In addition, in the continued abnormality determination by the abnormality determiner 240, when it is determined that there is no abnormality (abnormality is resolved), the provider 260 may provide the vehicle that has provided the information on the abnormality in the road conditions with information indicating that the abnormality in the road conditions is resolved. As a result, it is possible to cause the occupant of the vehicle M to ascertain more accurate current road conditions.
Processing flow
FIG. 7 is a flowchart which shows an example of processing executed by the information management device 200 of the embodiment. In the processing of FIG. 7, the acquirer 220 acquires vehicle information including position information, behavior information, and occupant operation information from a plurality of vehicles (step S300). Next, the abnormality determiner 240 determines whether an abnormality in road conditions is detected on the basis of the vehicle information (step S310). When it is determined that an abnormality in road conditions has been detected, the abnormality is set to be in the provisional state (step S320), and determines whether the same type of abnormality is detected from the vehicle information of other vehicles before a predetermined period of time elapses after the setting (step S330). When it is determined that the same kind of abnormality has been detected, a state of the abnormality is changed from the provisional state to the confirmed state, and the influence range estimator 250 estimates the influence range of the abnormality (step S340).
Next, the provider 260 determines a target vehicle to which information on the abnormality in the road conditions will be provided (step S350), generates information to be provided to each determined vehicle (step S360), and transmits the generated information to the target vehicle (step S370). As a result, the processing of this flowchart ends. In addition, when it is determined in the processing of step S310 that an abnormality in road conditions has not been detected, or it is determined in the processing of step S330 that the same type of abnormality has not been detected from other vehicles before a processing time elapses, processing of this flowchart ends.
Modified example
In the information management system 1 described above, at least part of functions of the vehicle M may be executed by the information management device 200, and part of functions of the information management device 200 may be executed by the vehicle M. For example, the abnormality determiner 240 may also execute the abnormality determination for each vehicle on a vehicle side. In this case, a result of the abnormality determination is transmitted from the vehicle M to the information management device 200, and the information management device 200 performs confirmation determination on whether an abnormality is detected or estimates an influence range based on a result of the abnormality determination for each vehicle and the position information of a vehicle. As a result, a processing load on the information management device 200 can be reduced.
According to the embodiment described above, the information management device 200 includes the acquirer 220 that acquires vehicle information including position information and behavior information from each of a plurality of vehicles, the abnormality determiner 240 that determines, when at least one of the plurality of vehicles has detected an abnormality in road conditions on the basis of the vehicle information, whether the same type of abnormality is detected from a vehicle different from the vehicle that has detected the abnormality before a predetermined period of time elapses after the abnormality has been detected, the influence range estimator 250 that estimates an influence range of the abnormality in the road conditions on the basis of the vehicle information when the same type of abnormality is detected, and the provider 260 that provides a target vehicle determined on the basis of the influence range among the plurality of vehicles with information on the abnormality in the road conditions, including the influence range, thereby more appropriately providing the target vehicle with information on the road conditions.
For example, according to the embodiment, when one or some of a plurality of vehicles has detected an abnormality in road conditions, a provisional abnormality (provisional state of abnormality) is set, and the provisional abnormality is confirmed by determining whether the same type of vehicle behavior is detected from a vehicle different from the vehicle within a predetermined time thereafter. For this reason, it is possible to estimate the influence range even in an early stage of abnormality such as a traffic jam, and to provide early the target vehicle with the abnormality in the road conditions even in a scene where immediacy is required. For example, according to the embodiment, the abnormality in the road conditions and the influence range of the abnormality are determined based on the behavior information of a vehicle and a result of the determination is transmitted to other vehicles, thereby it is possible to transmit an abnormality such as a traffic jam or weather that affects driving to other vehicles in a timely manner. In addition, according to the embodiment, it is possible to adjust the influence range of an abnormality on the basis of information between neighboring vehicles, and furthermore to notify a vehicle moving toward this range set as a target vehicle of the influence range earlier to cause it to avoid an influence of the abnormality.
The embodiment described above can be expressed as follows.
An information management device includes a storage medium that stores computer-readable instructions, and a processor connected to the storage medium, in which the processor executes the computer-readable instructions to acquire vehicle information including position information and behavior information from each of a plurality of vehicles, to determine, when at least one of the plurality of vehicles has detected an abnormality in road conditions, whether the same type of abnormality is detected from a vehicle different from the vehicle that has detected the abnormality before a predetermined period of time elapses after the abnormality is detected, on the basis of the vehicle information, to estimate an influence range of the abnormality in the road conditions on the basis of the vehicle information when the same type of abnormality is detected, and to provide information on the abnormality in the road conditions including the influence range to a target vehicle determined on the basis of the influence range among the plurality of vehicles.
As described above, a mode for carrying out the present invention has been described using the embodiments, but the present invention is not limited to such embodiments at all, and various modifications and replacements can be added without departing from the scope of the present invention.