Road surface friction coefficient calculating device and vehicle

Abstract

A road surface friction coefficient calculating device according to one aspect of the present disclosure is capable of determining a road surface condition corresponding to a distance in front of a vehicle on the basis of a sensor table in which the types of sensors to be used for calculation depending on the distance in front of the vehicle are defined, road surface feature data in front of the vehicle obtained from a plurality of types of non-contact sensors provided in the vehicle, and road surface temperature data in front of the vehicle obtained from a temperature sensor provided in the vehicle. The road surface friction coefficient calculating device is further capable of reading out friction coefficient data corresponding to the road surface condition obtained by the determination from a friction coefficient table, and outputting the read friction coefficient data corresponding to the distance in front of the vehicle or first friction coefficient data corresponding to the distance in front of the vehicle obtained by performing a predetermined calculation on the read friction coefficient data.

Description

Road surface friction coefficient calculation device and vehicle

[0001] The present disclosure relates to a road surface friction coefficient calculation device and a vehicle.

[0002] In order to further improve driving safety, it may be important to accurately determine the road surface condition in the traveling direction of the vehicle in advance. In the determination of the road surface condition, for example, the road surface friction coefficient can be used. Technologies for estimating the road surface friction coefficient in a non-contact manner are disclosed in, for example, Patent Documents 1 to 3.

[0003] Japanese Unexamined Patent Application Publication No. 2023-173730, Japanese Unexamined Patent Application Publication No. 2019-189069, Japanese Unexamined Patent Application Publication No. 2010-164521

[0004] The road surface friction coefficient calculation device according to one aspect of the present disclosure includes an acquisition unit, a storage unit, and a processing unit. The acquisition unit can acquire road surface feature amount data in front of the vehicle from a plurality of types of non-contact sensors provided on the vehicle, and can acquire road surface temperature data in front of the vehicle from a temperature sensor provided on the vehicle. The storage unit stores a friction coefficient table in which friction coefficient data is associated with each road surface condition, and a sensor table in which the types of sensors used in the calculation are defined according to the front distance of the vehicle. The processing unit can perform information processing using the friction coefficient table and the sensor table read from the storage unit, and the road surface feature amount data and the road surface temperature data acquired by the acquisition unit. The processing unit can determine the road surface condition according to the front distance of the vehicle based on the sensor table, the road surface feature amount data, and the road surface temperature data. The processing unit can read the friction coefficient data corresponding to the road surface condition obtained by the determination from the friction coefficient table, and output the read friction coefficient data corresponding to the front distance of the vehicle, or the first friction coefficient data corresponding to the front distance of the vehicle obtained by performing a predetermined calculation on the read friction coefficient data.

[0005] A vehicle relating to one aspect of this disclosure includes a non-contact type of sensor and a road surface friction coefficient calculation device. The road surface friction coefficient calculation device includes an acquisition unit, a storage unit, and a processing unit. The acquisition unit is capable of acquiring road surface feature data in front of the vehicle obtained from the multiple types of sensors and road surface temperature data in front of the vehicle obtained from a temperature sensor installed on the vehicle. The storage unit stores a friction coefficient table in which friction coefficient data is associated with each road surface condition, and a sensor table in which the type of sensor to be used for calculation is defined according to the distance in front of the vehicle. The processing unit is capable of performing information processing using the friction coefficient table and sensor table read from the storage unit, and the road surface feature data and road surface temperature data acquired by the acquisition unit. Based on the sensor table, road surface feature data and road surface temperature data, the processing unit is capable of determining the road surface condition according to the distance in front of the vehicle. The processing unit can read friction coefficient data corresponding to the road surface condition obtained by the determination from the friction coefficient table, and output either friction coefficient data corresponding to the vehicle's forward distance, or first friction coefficient data corresponding to the vehicle's forward distance obtained by performing a predetermined calculation on the read friction coefficient data.

[0006] The accompanying drawings are provided for further understanding of this disclosure and are incorporated herein and constitute part of this specification. The drawings illustrate one embodiment and, together with the specification, serve to illustrate the principles of this disclosure.

[0007] Figure 1 is a diagram showing a schematic configuration example of a vehicle according to one embodiment of the present disclosure. Figure 2 is a diagram showing a schematic configuration example of the front portion of the vehicle in Figure 1. Figure 3 is a diagram showing an example of the accuracy required for the road surface friction coefficient in front of the vehicle in Figure 1. Figure 4 is a diagram conceptually representing an example of the road surface condition map in Figure 1. Figure 5 is a diagram conceptually representing an example of the friction coefficient table in Figure 1. Figure 6 is a diagram conceptually representing an example of the friction coefficient table in Figure 1. Figure 7 is a diagram conceptually representing an example of the sensor table in Figure 1. Figure 8 is a diagram conceptually representing an example of the display screen of the notification unit in Figure 1. Figure 9 is a diagram showing an example of the procedure for calculating the road surface friction coefficient in the vehicle in Figure 1.

[0008] <1. Background> In order to further improve driving safety, it may be important to accurately determine the road surface conditions in the direction of vehicle travel in advance. In determining road surface conditions, for example, the road surface friction coefficient may be used. Technologies for estimating the road surface friction coefficient using a non-contact method are disclosed, for example, in Patent Documents 1 to 3.

[0009] In the invention described in Patent Document 1, a contact-type sensor is provided to detect the road surface condition of a first road surface closer to the vehicle in a contact-type manner, and a non-contact-type sensor is provided to detect the road surface condition of a second road surface further away from the vehicle in a non-contact manner. Based on the data obtained from each sensor, the road surface friction coefficients of the first and second road surfaces are estimated. Furthermore, in the invention described in Patent Document 1, if the road surface friction coefficients of the first and second road surfaces are both greater than or less than a predetermined threshold, it is determined that the road surface conditions of the first and second road surfaces are equal, and the road surface friction coefficient of the second road surface is replaced with the road surface friction coefficient of the first road surface obtained using the contact-type sensor. The invention described in Patent Document 1 states that the accuracy of the road surface friction coefficient of the second road surface can be improved by replacing the road surface friction coefficient of the second road surface with the road surface friction coefficient of the first road surface obtained using the contact-type sensor.

[0010] In the invention described in Patent Document 1, if the road surface friction coefficient of the first road surface is greater than a predetermined threshold, and furthermore, the road surface friction coefficient of the second road surface is smaller than a predetermined threshold, the accuracy of the road surface friction coefficient of the second road surface depends on the capabilities of the non-contact sensor. Therefore, in this case, it is not possible to improve the accuracy of the road surface friction coefficient of the second road surface. Thus, in the invention described in Patent Document 1, further improvements are required in the derivation of the road surface friction coefficient of the second road surface in order to perform more precise vehicle control.

[0011] The invention described in Patent Document 2 includes a non-contact sensor (first non-contact sensor) used to calculate the road surface friction coefficient in front of the vehicle, and a non-contact sensor (second non-contact sensor) used to calculate the road surface friction coefficients on the left and right sides of the vehicle. In the invention described in Patent Document 2, the accuracy of the road surface friction coefficient in front of the vehicle depends on the capabilities of the first non-contact sensor. Therefore, the invention described in Patent Document 2 is suitable for limited control using the road surface friction coefficient of the road surface at a suitable distance for the first non-contact sensor in the travel path in front of the vehicle. However, in order to perform more precise vehicle control, the invention described in Patent Document 2 requires further improvement in the derivation of the road surface friction coefficient of the travel path in front of the vehicle.

[0012] In the invention described in Patent Document 3, the road surface condition is determined using near-infrared images obtained by the imaging unit, and the road surface condition is determined with high accuracy by also taking into account information obtained from a visible camera, temperature sensor, or μ sensor. However, in the invention described in Patent Document 3, the monitoring areas of the imaging unit and the visible camera are almost the same. Therefore, in order to perform more precise vehicle control that takes into account the road surface friction coefficient of the road surface closer than the monitoring area of ​​the imaging unit and the visible camera, and the road surface friction coefficient of the road surface further away from the monitoring area of ​​the imaging unit and the visible camera, further improvements are required in deriving the road surface friction coefficient of the road in front of the vehicle.

[0013] Thus, it is clear that conventional inventions do not easily enable more precise vehicle control using the road surface friction coefficient of the road in front of the vehicle. It is desirable to provide a road surface friction coefficient calculation device and vehicle that enable more precise vehicle control using the road surface friction coefficient of the road in front of the vehicle.

[0014] Hereinafter, several exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The following description is intended to illustrate specific examples of the present disclosure and should not be construed as limiting the disclosure. For example, elements such as numerical values, shapes, materials, parts, the location of each part, and the method of connecting each part are merely examples and should not be construed as limiting the disclosure. Furthermore, in the following exemplary embodiments, components not described in separate sections based on the highest-level concepts of the present disclosure are optional and may be provided as needed. The drawings are schematic and are not intended to be to scale. Throughout this specification and the drawings, components having substantially the same function and substantially the same configuration are denoted by the same reference numerals, and redundant descriptions are omitted. Furthermore, components not directly related to an embodiment of the present disclosure are not shown in the drawings.

[0015] <2. Embodiments> [Configuration Example] First, the configuration of a vehicle 1 equipped with a control unit 30 according to one embodiment of the present disclosure will be described. Figure 1 shows a schematic configuration example of a vehicle 1 according to one embodiment of the present disclosure. The control unit 30 corresponds to one specific example of the "road surface friction coefficient calculation device" of the present disclosure. The vehicle 1 corresponds to one specific example of the "vehicle" of the present disclosure.

[0016] Vehicle 1 is capable of moving by the drive of a prime mover 60 (engine or motor). Vehicle 1 includes, for example, a sensor unit 10, a communication unit 20, a control unit 30, a storage unit 40, a notification unit 50, a prime mover 60, a brake 70, and an EPS (Electric Power Steering) motor 80, as shown in Figure 1. The control unit 30 corresponds to one specific example of the "road surface friction coefficient calculation device" of this disclosure. The storage unit 40 corresponds to one specific example of the "storage unit" of this disclosure.

[0017] The sensor unit 10 is composed of various sensors mounted on the vehicle 1. For example, the sensor unit 10 is composed of an accelerator opening sensor, a vehicle speed sensor, an acceleration sensor, an angular velocity sensor, a steering angle sensor, and a steering torque sensor. The sensor unit 10 may also include sensors other than those listed above.

[0018] The accelerator pedal position sensor can detect the accelerator pedal position from the amount the accelerator pedal is pressed. The accelerator pedal position sensor can output time-series data (accelerator pedal position data) of the detected accelerator pedal position to the control unit 30.

[0019] The vehicle speed sensor is capable of detecting the speed of vehicle 1. The vehicle speed sensor is capable of outputting time-series data (vehicle speed data) of the detected vehicle speed to the control unit 30. The acceleration sensor is capable of detecting the acceleration applied to vehicle 1. The acceleration sensor is capable of outputting time-series data (acceleration data) of the detected acceleration in three directions to the control unit 30. The angular velocity sensor is capable of detecting the angular velocity of vehicle 1. The angular velocity sensor is capable of outputting time-series data (angular velocity data) of the detected three angular velocities (yaw angular velocity, roll angular velocity, and pitch angular velocity) to the control unit 30.

[0020] The steering angle sensor is capable of detecting the steering angle of the steering wheel of vehicle 1. The steering angle sensor is capable of outputting time-series data (steering angle data) of the detected steering angle to the control unit 30. The steering torque sensor is capable of detecting the steering torque generated by the driver's steering wheel operation. The steering torque sensor is capable of outputting time-series data (steering torque data) of the detected steering torque to the control unit 30.

[0021] The sensor unit 10 further includes, for example, a plurality of non-contact sensors capable of detecting the condition of the road surface (driving surface 100) in front of the vehicle 1 without contact. The plurality of non-contact sensors included in the sensor unit 10 are sensors used to calculate friction coefficient data Da according to the distance in front of the vehicle 1, and include a plurality of sensors with different monitoring areas. The plurality of non-contact sensors included in the sensor unit 10 corresponds to one specific example of the "multiple non-contact sensors" of this disclosure.

[0022] The monitoring area refers to the region in front of vehicle 1 where the accuracy of the road surface friction coefficient derived using detection data obtained from the sensors is equal to or greater than the required accuracy. The accuracy required for the road surface friction coefficient in controlling vehicle 1 usually changes depending on the distance from vehicle 1. High accuracy is required for the friction coefficient of the road surface close to vehicle 1. On the other hand, the same high accuracy is not required for the road surface far from vehicle 1. Therefore, the multiple types of non-contact sensors included in the sensor unit 10 include sensors that satisfy the required accuracy for the friction coefficient of the road surface close to vehicle 1 and sensors that satisfy the required accuracy for the friction coefficient of the road surface far from vehicle 1.

[0023] The friction coefficient data Da includes multiple road surface friction coefficients derived for each of several regions defined according to the distance in front of vehicle 1. The friction coefficient data Da includes multiple road surface friction coefficients derived for each of three regions defined in order of proximity to vehicle 1 (short-distance region Ra, medium-distance region Rb, and long-distance region Rc), as shown in Figure 3. The short-distance region Ra is, for example, the region in the range of 0m to 1m from vehicle 1. The medium-distance region Rb is, for example, the region in the range of 4m to 10m from vehicle 1. The long-distance region Rc is, for example, the region in the range of 10m to 20m from vehicle 1. In terms of distance from vehicle 1, the point where the tire TR of vehicle 1 is in contact with the road surface 100 is defined as the reference position (0m) of the distance from vehicle 1.

[0024] The friction coefficient data Da may include, for example, the road surface friction coefficient μ1 obtained based on detection data from the road surface unevenness sensor 12 and the road surface temperature sensor 14 described later, as the road surface friction coefficient of the short-range region Ra. The friction coefficient data Da may include, for example, the road surface friction coefficient μ2 obtained based on detection data from the road surface unevenness sensor 12, the road surface temperature sensor 14, and the stereo camera 11 described later, as the road surface friction coefficient of the medium-range region Rb. The stereo camera 11 corresponds to one specific example of the "camera" in this disclosure. The friction coefficient data Da may include, for example, the road surface friction coefficient μ3 obtained based on detection data from the road surface temperature sensor 14 and the stereo camera 11 described later, as the road surface friction coefficient of the long-range region Rc.

[0025] The sensor unit 10 includes, for example, a stereo camera 11, a road surface unevenness sensor 12, a road surface moisture content sensor 13, and a road surface temperature sensor 14, as shown in Figure 2. The stereo camera 11 is positioned, for example, near the front windshield FW in the interior CR of the vehicle 1. The road surface unevenness sensor 12, the road surface moisture content sensor 13, and the road surface temperature sensor 14 are positioned, for example, at the lower front of the vehicle 1. The arrangement of the stereo camera 11, the road surface unevenness sensor 12, the road surface moisture content sensor 13, and the road surface temperature sensor 14 is not limited to the positions shown in Figure 2.

[0026] The stereo camera 11 is a camera that captures images of the area in front of the vehicle 1, including the road surface (driving surface 100) in front of the vehicle 1. The stereo camera 11 is used for purposes other than calculating friction coefficient data Da. When the stereo camera 11 is used to calculate friction coefficient data Da, the monitoring area of ​​the stereo camera 11 is, for example, the medium-range region Rb and the long-range region Rc, as shown in Figure 3. A monocular camera may be provided instead of the stereo camera 11.

[0027] The road surface unevenness sensor 12 is a sensor capable of detecting irregularities in the road surface (traveling road surface 100) in front of the vehicle 1. The road surface unevenness sensor 12 is a near-infrared light sensor that includes, for example, a laser light source capable of emitting near-infrared light toward the traveling road surface 100, a detector that detects reflected light from the traveling road surface 100 among the near-infrared light emitted from the laser light source, and a calculation unit capable of calculating the irregularities of the traveling road surface 100 based on the light intensity image of the reflected light detected by the detector. When the road surface unevenness sensor 12 is used to calculate friction coefficient data Da, the monitoring area of ​​the road surface unevenness sensor 12 is, for example, a short-range region Ra and a medium-range region Rb.

[0028] The road surface moisture content sensor 13 is a sensor capable of measuring the water film thickness on the road surface 100. The road surface moisture content sensor 13 includes, for example, a light source capable of emitting light in a wavelength band including the absorption wavelength of water and a wavelength unaffected by water (reference wavelength) toward the road surface 100, a detector that detects reflected light from the road surface 100 from the light emitted from the light source, and a calculation unit capable of calculating the water film thickness on the road surface 100 based on the amount of light of the water absorption wavelength component and the amount of light of the reference wavelength component contained in the reflected light detected by the detector. The road surface moisture content sensor 13 is capable of measuring the water film thickness on the road surface 100 at a location 1 m away from the vehicle 1.

[0029] The road surface temperature sensor 14 is a sensor capable of measuring the surface temperature of the road surface 100. The road surface temperature sensor 14 includes, for example, an infrared temperature sensor. The road surface temperature sensor 14 measures, for example, the thermal radiation in the infrared wavelength range emitted from the road surface 100 using an infrared temperature sensor, and is capable of measuring the temperature of the road surface 100 based on the thermal radiation data obtained from the measurement. The road surface temperature sensor 14 may also include, for example, a far-infrared camera. In this case, the road surface temperature sensor 14 can, for example, image the road surface 100 with a far-infrared camera to acquire imaging data representing the distribution of thermal radiation in the infrared wavelength range emitted from the road surface 100, and is capable of measuring the temperature of the road surface 100 based on the acquired imaging data. When the road surface temperature sensor 14 is used to calculate the friction coefficient data Da, the monitoring area of ​​the road surface temperature sensor 14 is, for example, a short-range area Ra, a medium-range area Rb, and a long-range area Rc. The road surface temperature sensor 14 is capable of measuring the road surface temperature according to the distance in front of the vehicle 1. For example, the road surface temperature sensor 14 is capable of measuring the road surface temperature in the short-range region Ra, the medium-range region Rb, and the long-range region Rc.

[0030] The sensor unit 10 further includes a driving environment detection unit that processes data obtained from the stereo camera 11. The stereo camera 11 is an autonomous sensor that senses the real space around the vehicle 1. The stereo camera 11 is composed of, for example, two cameras 11a and 11b positioned symmetrically on either side of the central part of the vehicle 1 in the width direction, enabling stereo imaging of the area in front of the vehicle 1 from different viewpoints. The stereo camera 11 is capable of outputting image data Ia (a pair of stereo image data) obtained by imaging to the control unit 30.

[0031] The stereo camera 11 is capable of generating distance image data based on the amount of displacement of the corresponding object's position, using the image data Ia obtained by imaging. The driving environment detection unit is capable of determining lane markings that demarcate the road around the vehicle 1 based on the distance image data. The driving environment detection unit is also capable of determining the road curvature of the markings that demarcate the left and right sides of the road (driving lane) on which the vehicle 1 travels, and the width between the left and right markings (vehicle width). Furthermore, the driving environment detection unit is capable of detecting lanes and three-dimensional objects such as structures present around the vehicle 1 by performing predetermined pattern matching on the distance image data.

[0032] In the driving environment detection unit, the detection of three-dimensional objects includes, for example, the type of object, the distance to the object, the speed of the object, and the relative speed between the object and the vehicle (the vehicle itself). Examples of objects to be detected include traffic lights, intersections, road signs, stop lines, other vehicles, pedestrians, bicycles, and buildings. Examples of buildings include detached houses, apartment buildings, commercial facilities, factories, and signs. The driving environment detection unit can output driving environment information around the vehicle 1, including the information on three-dimensional objects acquired in this way, to the control unit 30.

[0033] The communication unit 20 is capable of acquiring data to supplement data that cannot be obtained from image data Ia and distance image data, for example, through vehicle-to-vehicle communication, vehicle-to-infrastructure communication, and satellite communication. The communication unit 20 is capable of outputting the acquired data to the control unit 30.

[0034] The communication unit 20 can acquire data obtained from other vehicles (e.g., vehicle position, vehicle speed) through vehicle-to-vehicle communication, for example. The communication unit 20 can also receive positioning signals transmitted from multiple positioning satellites through satellite communication, for example.

[0035] The communication unit 20 is capable of acquiring road map data around vehicle 1, for example, through vehicle-to-infrastructure communication. The road map data consists of, for example, high-precision road map information (dynamic map) and mainly comprises static and quasi-static information that constitutes road information, and quasi-dynamic and dynamic information that mainly constitute traffic information. The communication unit 20 is also capable of acquiring weather information around vehicle 1, for example, through vehicle-to-infrastructure communication.

[0036] The static information that constitutes road information consists of information that requires updates at a frequency of no more than one month, such as roads and structures on roads, structures surrounding roads, lane information, road surface information, and permanent regulatory information. "Roads" include, for example, the location and shape of roads, intersections, and road attributes (e.g., national roads, prefectural roads, municipal roads, private roads, priority roads, non-priority roads, general roads, expressways). "Structures on roads" include, for example, traffic signs, traffic lights, convex mirrors, pedestrian overpasses, bus stops, and garbage collection points. "Structures surrounding roads" include, for example, various buildings and parks.

[0037] The quasi-static information that makes up road information consists of information that needs to be updated within an hour, such as traffic restriction information due to road construction or events, wide-area weather information, and congestion forecasts.

[0038] The semi-dynamic information that makes up traffic information consists of information that needs to be updated within one minute, such as actual traffic congestion and traffic restrictions at the time of observation, temporary traffic obstruction situations such as fallen objects and obstacles, actual accident conditions, and local weather information.

[0039] The dynamic information that constitutes traffic information consists of information that requires updates every second, such as information transmitted and exchanged between moving objects, information on currently displayed traffic signals, information on pedestrians and cyclists at intersections, and information on vehicles traveling on the roads. This road map information is maintained and updated in cycles until the next information is received from each vehicle, and the updated road map information is transmitted to each vehicle as appropriate via the communication unit 20.

[0040] The control unit 30 is capable of controlling the entire vehicle 1. The control unit 30 is, for example, a so-called ECU (Electronic Control Unit) and is composed of, for example, one or more processors and one or more memories. The control unit 30 may also be composed of, for example, a CPU (Central Processing Unit). In this case, the control unit 30 is capable of controlling the entire vehicle 1 by, for example, executing a program stored in a memory unit.

[0041] The control unit 30 includes, for example, a locator unit. The locator unit is capable of acquiring the position coordinates of the vehicle 1 based on the positioning signal received through the communication unit 20. The locator unit is capable of estimating the vehicle's position on a road map by map matching the acquired position coordinates onto route map information. Based on the acquired position coordinates of the vehicle 1, the locator unit acquires map information for a predetermined range including the vehicle 1 from the map information stored in the road map DB (database) 41, which will be described later.

[0042] The locator unit can switch to autonomous navigation, which estimates the vehicle's position on a road map based on vehicle speed, angular velocity, and longitudinal acceleration detected by the sensor unit 10, in environments where it is not possible to receive effective positioning signals from positioning satellites due to reduced sensitivity, such as when driving in a tunnel.

[0043] As described above, the locator unit estimates the position of vehicle 1 on a road map (vehicle position) based on the positioning signal received through the communication unit 20 or the information detected by the sensor unit 10. Based on the estimated vehicle position on the road map, it is possible to determine the type of road on which vehicle 1 is traveling.

[0044] The locator unit can update the road map information stored in the road map DB 41 to the latest state by using the road map information acquired through external communication (road-vehicle communication and vehicle-vehicle communication) via the communication unit 20. This information update is performed not only for static information but also for quasi-static information, quasi-dynamic information, and dynamic information. As a result, the road map information is composed of road information and traffic information acquired by communication with the outside of the vehicle, and the information of moving objects such as vehicles traveling on the road is updated almost in real time.

[0045] The locator unit verifies the road map information based on the driving environment information recognized as described above, and updates the road map information stored in the road map DB 41 to the latest state. This information update is performed not only for static information but also for quasi-static information, quasi-dynamic information, and dynamic information. As a result, the information of moving objects such as vehicles traveling on the road recognized as described above is updated in real time.

[0046] The storage unit 40 is constituted by, for example, a non-volatile memory, and is constituted by, for example, an EEPROM (Electrically Erasable Programmable Read-Only Memory), a flash memory, a resistive change type memory, or the like. The storage unit 40 stores, for example, as shown in FIG. 1, a road map DB 41, a road surface condition map 42, a friction coefficient table 43, and a sensor table 44.

[0047] The road map DB 41 is a large-capacity storage medium such as an HDD, and stores high-precision road map data (dynamic map). This high-precision road map data has, for example, static information and quasi-static information mainly constituting road information, and quasi-dynamic information and dynamic information mainly constituting traffic information.

[0048] The road surface condition map 42 has map data in which a plurality of road surface conditions are defined based on a plurality of parameters. For example, as shown in FIG. 4, the road surface condition map 42 is map data in which four road surface conditions (DRY, WET, SNOW, ICE) are defined based on three parameters X, Y, and Z. The parameter X is, for example, the road surface unevenness. The parameter Y is, for example, the road surface moisture content. The parameter Z is, for example, the road surface temperature. The number of parameters included in the road surface condition map 42 is not limited to the above. The number and content of the road surface conditions defined in the road surface condition map 42 are not limited to the above.

[0049] The friction coefficient table 43 may have, for example, a range data table 43A in which a range of friction coefficients is defined for each road surface condition defined in the road surface condition map 42, as shown in FIG. 5(A). In the range data table 43A, for example, as shown in FIG. 5(A), ranges of the road surface friction coefficients for four road surface conditions (DRY, WET, SNOW, ICE) are defined. The range data table 43A shown in FIG. 5(A) is an example of the friction coefficient table 43. The ranges of the road surface friction coefficients shown in FIG. 5(A) are examples and can be defined based on, for example, experiments, simulations, etc.

[0050] The friction coefficient table 43 may have, for example, a data table 43B in which an average value of the friction coefficient is defined for each road surface condition defined in the road surface condition map 42, as shown in FIG. 5(B). The friction coefficient table 43 may be, for example, a range data table in which an average value and a standard deviation of the friction coefficient are defined for each road surface condition defined in the road surface condition map 42. The road surface friction coefficients shown in FIG. 5(B) are examples and can be defined based on, for example, experiments, simulations, etc.

[0051] The friction coefficient table 43 includes not only map data in which multiple road surface conditions are defined based on multiple parameters, but also map data in which road surface friction coefficients corresponding to multiple road surface conditions are defined based on multiple parameters. For example, as shown in Figure 6, the friction coefficient table 43 includes map data 43C in which road surface friction coefficients corresponding to two road surface conditions (low μ road, high μ road) are defined based on two parameters V and Z. Parameter Z is, for example, road surface temperature. Parameter V is, for example, the grayscale value (for example, RGB value) that constitutes the image data Ia. The number of parameters included in the map data 43C is not limited to the above. The number and content of road surface conditions defined in the map data 43C are not limited to the above.

[0052] The sensor table 44 is a table that defines the types of sensors used in calculations according to the distance in front of the vehicle 1. In the sensor table 44, for example, as shown in Figure 7, the types of sensors are defined for each distance region Ra, medium distance region Rb, and long distance region Rc. In the sensor table 44, for example, as shown in Figure 7, the distance region Ra defines a road surface unevenness sensor 12 and a road surface temperature sensor 14, the medium distance region Rb defines a road surface unevenness sensor 12, a road surface temperature sensor 14, and a stereo camera 11, and the long distance region Rc defines a road surface temperature sensor 14 and a stereo camera 11.

[0053] The control unit 30 further includes a driving control unit 31, as shown in Figure 1, for example. The driving control unit 31 is capable of controlling the driving of the vehicle 1 (for example, the torque of the prime mover 60, the amount of brake depression, and the steering angle of the steering wheel) and providing notifications related to the driving of the vehicle 1. The driving control unit 31 includes a data acquisition unit 32, a road surface condition determination unit 33, a friction coefficient derivation unit 34, a notification control unit 35, an avoidance control unit 36, an accelerator control unit 37, a brake control unit 38, and a steering control unit 39, as shown in Figure 4, for example. The data acquisition unit 32 corresponds to one specific example of the "acquisition unit" in this disclosure. The road surface condition determination unit 33 and the friction coefficient derivation unit 34 correspond to one specific example of the "processing unit" in this disclosure.

[0054] The data acquisition unit 32 is capable of acquiring various data obtained from the sensor unit 10, various data obtained from the outside via the communication unit 20, and various control signals for various devices of the vehicle 1 (for example, turn signals). Based on the acquired data and various control signals, the data acquisition unit 32 is capable of acquiring road surface feature data Db and road surface temperature data Dc. The road surface feature data Db includes image data Ia obtained from the stereo camera 11, unevenness data of the road surface 100 obtained from the road surface unevenness sensor 12, and water film thickness data of the road surface 100 obtained from the road surface moisture content sensor 13. The road surface temperature data Dc includes temperature data of the road surface 100 obtained from the road surface temperature sensor 14.

[0055] The road surface condition determination unit 33 and the friction coefficient derivation unit 34 are capable of performing information processing using the road surface condition map 42, friction coefficient table 43, and sensor table 44 read from the storage unit 40, and the road surface feature data Db and road surface temperature data Dc acquired by the data acquisition unit 32. The road surface condition determination unit 33 is capable of determining the road surface condition according to the distance in front of the vehicle 1 based on the road surface condition map 42, sensor table 44, road surface feature data Db, and road surface temperature data Dc.

[0056] The road surface condition determination unit 33 can use, when determining the road surface condition at a specific distance forward of the vehicle 1, data obtained from multiple sensors corresponding to the specific distance as defined in the sensor table 44, from the road surface feature data Db, and data for the specific distance from the road surface temperature data Dc. Furthermore, when determining the road surface condition at a specific distance forward of the vehicle 1, the road surface condition determination unit 33 may also use not only the above data, but also water film thickness data of the road surface 100 obtained by the road surface moisture content sensor 13.

[0057] The road surface condition determination unit 33 can, for example, use data from multiple sensors corresponding to short distances, as defined in the sensor table 44, and data from the road surface temperature data Dc, when determining the road surface condition in the short distance (short distance region Ra). The road surface condition determination unit 33 may also use, for example, the above data, as well as water film thickness data of the road surface 100 obtained by the road surface moisture content sensor 13, when determining the road surface condition in the short distance (short distance region Ra), particularly in the immediate vicinity of the vehicle 1 (for example, in the range of 0m to 1m from the vehicle 1).

[0058] The road surface condition determination unit 33 can, for example, use data obtained from multiple sensors corresponding to medium distances as defined in the sensor table 44, from the road surface feature data Db, and data corresponding to medium distances from the road surface temperature data Dc, when determining the road surface condition at medium distances (medium distance region Rb). The road surface condition determination unit 33 can, for example, use data obtained from multiple sensors corresponding to long distances as defined in the sensor table 44, from the road surface feature data Db, and data corresponding to long distances from the road surface temperature data Dc, when determining the road surface condition at long distances (long distance region Rc).

[0059] The friction coefficient derivation unit 34 reads friction coefficient data corresponding to the road surface condition obtained by the road surface condition determination unit 33 from the friction coefficient table 43, and can output to the notification control unit 35 and the avoidance control unit 36 ​​road surface friction coefficient data obtained by performing a predetermined calculation on the read road surface friction coefficient data, or road surface friction coefficient data obtained by performing a predetermined calculation on the read road surface friction coefficient data.

[0060] The friction coefficient derivation unit 34 can, for example, read the range of road surface friction coefficients corresponding to the respective road surface conditions of the short-range region Ra and the medium-range region Rb obtained by the road surface condition determination unit 33 from the range data table 43A, perform a predetermined calculation on the read range of road surface friction coefficients, and thereby derive the respective road surface friction coefficients μ1 for the short-range region Ra and the medium-range region Rb. The friction coefficient derivation unit 34 can, for example, read the friction coefficient values ​​corresponding to the respective road surface conditions of the short-range region Ra and the medium-range region Rb obtained by the road surface condition determination unit 33 from the data table 43B, and use the read friction coefficient values ​​as the respective road surface friction coefficients μ2 for the short-range region Ra and the medium-range region Rb. The friction coefficient derivation unit 34 reads, for example, the friction coefficient values ​​corresponding to the grayscale values ​​(e.g., RGB values) of the far-distance region Rc included in the image data Ia obtained by the data acquisition unit 32 and the road surface temperature of the far-distance region Rc included in the road surface temperature data Dc obtained by the data acquisition unit 32 from the map data 43C, and makes it possible to use the read friction coefficient values ​​as the road surface friction coefficient μ3 of the far-distance region Rc.

[0061] The friction coefficient derivation unit 34 may be capable of deriving the road surface friction coefficient μ1 in the short-range region Ra, particularly in the immediate vicinity of the vehicle 1 (for example, in the range of 0m to 1m from the vehicle 1), based on, for example, data obtained from multiple sensors corresponding to short distances as defined in the sensor table 44, from the road surface feature data Db, data corresponding to short distances from the road surface temperature data Dc, and water film thickness data of the road surface 100 obtained by the road surface moisture content sensor 13. The friction coefficient derivation unit 34 may be capable of directly deriving the road surface friction coefficient μ1 without going through the estimation of the road surface condition, as described above, when, for example, the data corresponding to short distances from the road surface temperature data Dc is a temperature value higher than 0°C, and furthermore, the water film thickness data of the road surface 100 obtained by the road surface moisture content sensor 13 is a value indicating the presence of a water film.

[0062] The friction coefficient derivation unit 34 is capable of outputting road surface friction coefficient data (for example, road surface friction coefficients μ1, μ2, μ3) corresponding to the distance in front of the vehicle 1, derived as described above, as friction coefficient data Da to the notification control unit 35 and the avoidance control unit 36. The notification control unit 35 is capable of performing notification control according to the friction coefficient data Da obtained from the friction coefficient derivation unit 34. The avoidance control unit 36 ​​is capable of performing driving control according to the friction coefficient data Da obtained from the friction coefficient derivation unit 34.

[0063] The notification control unit 35 is capable of generating a video signal for performing notification based on friction coefficient data Da and outputting it to the notification unit 50. The notification unit 50 is composed of, for example, a liquid crystal panel, an organic EL panel, or a HUD (Head-up Display), and is capable of displaying video based on the video signal input from the notification control unit 35. The notification control unit 35 is capable of generating composite image data in which friction coefficient data obtained for short distance (short distance region Ra), medium distance (medium distance region Rb), and long distance (long distance region Rc) are superimposed on image data Ia. The notification control unit 35 is also capable of generating a video signal for displaying an image including the generated composite image data and various data on a display screen and outputting it to the notification unit 50. The notification unit 50 is capable of displaying an image such as the one shown in Figure 8 on the display screen 50A based on the video signal generated in this way.

[0064] The avoidance control unit 36 ​​is capable of outputting a control signal (for example, data on additional torque obtained based on the friction coefficient data Da) for driving control based on friction coefficient data Da to at least one of the accelerator control unit 37, brake control unit 38, and steering control unit 39. The avoidance control unit 36 ​​is capable of outputting a control signal to at least one of the accelerator control unit 37, brake control unit 38, and steering control unit 39 to prepare and respond according to the road surface condition in front of the vehicle 1 based on the friction coefficient data Da.

[0065] For example, suppose that in friction coefficient data Da, the road surface friction coefficients at short distances (short distance region Ra) and medium distances (medium distance region Rb) are large enough to correspond to DRY, while the road surface friction coefficient at long distances (long distance region Rc) is large enough to correspond to ICE. In this case, the driving control unit 31 can output a control signal to at least one of the accelerator control unit 37, brake control unit 38, and steering control unit 39 to decelerate the vehicle to a speed that will not cause slippage on a road surface where the road surface friction coefficient is large enough to correspond to ICE, before the vehicle 1 reaches the road surface where the road surface friction coefficient is large enough to correspond to ICE.

[0066] The accelerator control unit 37 is capable of controlling the torque of the prime mover 60 based on the requested torque corresponding to the amount the driver of the vehicle 1 depresses the accelerator pedal. Furthermore, the accelerator control unit 37 is capable of deriving a target torque by adding an additional torque obtained based on friction coefficient data Da to the requested torque, and controlling the torque of the prime mover 60 based on the derived target torque. The prime mover 60 is configured to drive the steering wheels of the vehicle 1 and is capable of driving the steering wheels of the vehicle 1 according to the requested torque or target torque input from the accelerator control unit 37.

[0067] The brake control unit 38 is capable of controlling the torque of the brake 70 based on the requested torque corresponding to the amount the driver of the vehicle 1 presses the brake pedal. Furthermore, the brake control unit 38 is capable of deriving a target torque by adding an additional torque obtained based on friction coefficient data Da to the requested torque, and controlling the torque of the brake 70 based on the derived target torque. The brake 70 is configured to brake the steering wheels of the vehicle 1, and is capable of braking the steering wheels of the vehicle 1 according to the requested torque or target torque input from the brake control unit 38.

[0068] The steering control unit 39 can derive a steering assist torque to assist the steering torque generated by the driver's steering wheel operation, and set an EPS torque corresponding to the derived steering assist torque. Furthermore, the steering control unit 39 can derive a target torque by adding an additional torque obtained based on friction coefficient data Da to the steering assist torque, and set an EPS torque corresponding to the derived target torque. The steering control unit 39 can output a control signal to the EPS motor 80 so that the output torque of the EPS motor 80 becomes the set EPS torque. The EPS motor 80 can generate an output torque based on the input control signal and control the steering angle of the steering wheel.

[0069] Next, we will explain the driving assistance procedure for vehicle 1.

[0070] Figure 9 shows an example of the driving assistance procedure in vehicle 1. The driving control unit 31 acquires various data, including road surface feature data Db and road surface temperature data Dc (step S101). The driving control unit 31 also acquires various control signals as needed. Next, the driving control unit 31 determines the road surface condition according to the distance in front of vehicle 1 based on the acquired road surface feature data Db and road surface temperature data Dc, etc. (step S102). Next, the driving control unit 31 derives the road surface friction coefficient (friction coefficient data Da) according to the distance in front of vehicle 1 based on the road surface condition obtained by the road surface condition determination unit 33 (step S103). Next, the driving control unit 31 performs notification control and driving control according to the derived road surface friction coefficient (friction coefficient data Da) (step S104).

[0071] For example, suppose that in friction coefficient data Da, the road surface friction coefficients at short distances (short distance region Ra) and medium distances (medium distance region Rb) are large enough to correspond to DRY, while the road surface friction coefficient at long distances (long distance region Rc) is large enough to correspond to ICE. In this case, the driving control unit 31 performs driving control to decelerate the vehicle 1 to a speed such that slippage does not occur on the road surface where the road surface friction coefficient is large enough to correspond to ICE, before the vehicle 1 reaches the road surface where the road surface friction coefficient is large enough to correspond to ICE. The driving control unit 31 further performs notification control to output an audio message to the notification unit 50 indicating that the vehicle 1 should decelerate because there is an ICE road surface far away from the vehicle 1. In this way, driving assistance for the vehicle 1 is performed.

[0072] [Effects] Next, the effects of the vehicle 1 according to one embodiment of the present disclosure will be described.

[0073] In this embodiment, the road surface condition is determined based on the road surface condition map 42, sensor table 44, road surface feature data Db, and road surface temperature data Dc, according to the distance in front of the vehicle 1. Subsequently, friction coefficient data corresponding to the road surface condition obtained by the determination is read from the friction coefficient table 43. Then, the road surface friction coefficient data corresponding to the distance in front of the vehicle 1, either read out or obtained by performing a predetermined calculation on the read friction coefficient data, is output. This ensures the accuracy of the road surface friction coefficient required according to the distance from the vehicle 1, while enabling control using the road surface friction coefficient obtained according to the distance from the vehicle 1. Therefore, more precise control of the vehicle 1 can be performed using the road surface friction coefficient of the road surface 100 in front of the vehicle 1.

[0074] In this embodiment, when determining the road surface condition at a specific distance in front of the vehicle 1, data obtained from multiple sensors corresponding to the specific distance, as defined in the sensor table 44, from the road surface feature data Db, and data for the specific distance from the road surface temperature data Dc can be used. This ensures the accuracy of the road surface friction coefficient required according to the distance from the vehicle 1, while enabling control using the road surface friction coefficient obtained according to the distance from the vehicle 1. Therefore, more precise control of the vehicle 1 can be performed using the road surface friction coefficient of the road surface 100 in front of the vehicle 1.

[0075] In this embodiment, when determining the road surface condition at short distances (short-range region Ra), data obtained from multiple sensors corresponding to short distances, as defined in the sensor table 44, from the road surface feature data Db, and data corresponding to short distances from the road surface temperature data Dc are used. When determining the road surface condition at medium distances (medium-range region Rb), data obtained from multiple sensors corresponding to medium distances, as defined in the sensor table 44, from the road surface feature data Db, and data corresponding to medium distances from the road surface temperature data Dc are used. Furthermore, when determining the road surface condition at long distances (long-range region Rc), data obtained from multiple sensors corresponding to long distances, as defined in the sensor table 44, from the road surface feature data Db, and data corresponding to long distances from the road surface temperature data Dc are used. This ensures the accuracy of the road surface friction coefficient required according to the distance from the vehicle 1, while enabling control using the road surface friction coefficient obtained according to the distance from the vehicle 1. Therefore, more precise control of the vehicle 1 can be performed using the road surface friction coefficient of the road surface 100 in front of the vehicle 1.

[0076] In this embodiment, the road surface condition corresponding to the distance in front of the vehicle 1 is determined at least for short distance (short distance region Ra), medium distance (medium distance region Rb), and long distance (long distance region Rc). Furthermore, friction coefficient data corresponding to at least short distance (short distance region Ra), medium distance (medium distance region Rb), and long distance (long distance region Rc) is read from the friction coefficient table 43, and the read friction coefficient data for short distance (short distance region Ra), medium distance (medium distance region Rb), and long distance (long distance region Rc), or friction coefficient data obtained by performing a predetermined calculation on the read friction coefficient data, is output. This ensures the accuracy of the road surface friction coefficient required according to the distance from the vehicle 1, while enabling control using the road surface friction coefficient obtained according to the distance from the vehicle 1. Therefore, more precise control of the vehicle 1 can be performed using the road surface friction coefficient of the road surface 100 in front of the vehicle 1.

[0077] In this embodiment, the sensor table 44 specifies the type of sensor to be used for calculations according to the distance in front of the vehicle 1. For short distances (short distance region Ra), a road surface unevenness sensor 12 (e.g., a near-infrared light sensor) is specified. For medium distances (medium distance region Rb), a road surface unevenness sensor 12 (e.g., a near-infrared light sensor) and a stereo camera 11 are specified. For long distances (long distance region Rc), a stereo camera 11 is specified. As a result, by using the sensor table 44 to determine the road surface condition according to the distance in front of the vehicle 1, it is possible to perform control using the road surface friction coefficient obtained according to the distance from the vehicle 1 while ensuring the accuracy of the road surface friction coefficient required according to the distance from the vehicle 1. Therefore, it is possible to perform more precise control of the vehicle 1 using the road surface friction coefficient of the road surface 100 in front of the vehicle 1.

[0078] In this embodiment, composite image data is generated by superimposing friction coefficient data obtained for short distance (short distance region Ra), medium distance (medium distance region Rb), and long distance (long distance region Rc) onto image data Ia. The image including the generated composite image data is then displayed on the display screen 50A. This allows the driver of vehicle 1 to safely drive vehicle 1 while viewing the friction coefficient data displayed on the display screen 50A.

[0079] <3. Modifications> The present disclosure has been described above with reference to embodiments, but the present disclosure is not limited to these embodiments, and various modifications are possible.

[0080] [Modification A] In the above embodiment, the range of the friction coefficient included in the range data table 43A may be a variable of the μ value-derived parameter. In other words, in the above embodiment, the range data table 43A may include a function of the friction coefficient which is a variable of the μ value-derived parameter. The μ value-derived parameter is, for example, the unevenness data of the road surface 100 obtained by the road surface unevenness sensor 12, the water film thickness data of the road surface 100 obtained by the road surface moisture content sensor 13, or the temperature data of the road surface 100 obtained by the road surface temperature sensor 14.

[0081] In this case, the friction coefficient derivation unit 34 can read, for example, the friction coefficient functions corresponding to the road surface conditions in the short-range region Ra and the medium-range region Rb, respectively, obtained by the road surface condition determination unit 33, from the range data table 43A. The friction coefficient derivation unit 34 can further derive a road surface friction coefficient corresponding to the value of the μ-value-derived parameter by, for example, applying the value of the μ-value-derived parameter obtained by the data acquisition unit 32 to the read-out road surface friction coefficient function. This further improves the accuracy of the obtained road surface friction coefficient.

[0082] [Modification B] In the above embodiment and modification A, if the difference between the derived road surface friction coefficient μ1 of the short-distance region Ra and the derived road surface friction coefficient μ2 of the medium-distance region Rb is so large that it exceeds a predetermined threshold, the friction coefficient derivation unit 34 may use a value derived based on the road surface friction coefficient μ1 of the short-distance region Ra and the road surface friction coefficient μ2 of the medium-distance region Rb within the short-distance region Ra that is close to the medium-distance region Rb (for example, the range where the distance from the vehicle 1 is greater than 1 m and 4 m or less). For example, the friction coefficient derivation unit 34 may use (μ1 + μ2) / 2 as the road surface friction coefficient of the short-distance region Ra that is close to the medium-distance region Rb (for example, the range where the distance from the vehicle 1 is greater than 1 m and 4 m or less).

[0083] In the above embodiment and modified example A, if the difference between the derived road surface friction coefficient μ2 of the intermediate distance region Rb and the derived road surface friction coefficient μ3 of the long distance region Rc is so large that it exceeds a predetermined threshold, the friction coefficient derivation unit 34 may use a value derived based on the road surface friction coefficient μ2 of the intermediate distance region Rb and the road surface friction coefficient μ3 of the long distance region Rc for the area of ​​the long distance region Rc that is close to the intermediate distance region Rb (for example, the range where the distance from the vehicle 1 is greater than 10 m and 15 m or less). For example, the friction coefficient derivation unit 34 may use (μ2 + μ3) / 2 for the road surface friction coefficient of the area of ​​the long distance region Rc that is close to the intermediate distance region Rb (for example, the range where the distance from the vehicle 1 is greater than 10 m and 15 m or less).

[0084] In this case, large steps can be eliminated in the friction coefficient corresponding to the distance in front of the vehicle 1. As a result, more precise control of the vehicle 1 can be performed using the road surface friction coefficient of the road surface 100 in front of the vehicle 1.

[0085] In this modified example, if the road surface friction coefficient μ2 corresponds to a low-μ road, and furthermore, if the road surface friction coefficient μ3 corresponds to a high-μ road, or simply if μ2 < μ3, the friction coefficient derivation unit 34 may be able to use the road surface friction coefficient μ2 as the road surface friction coefficient for the long-distance region Rc. In this case, not only can large steps be eliminated in the friction coefficient corresponding to the distance in front of the vehicle 1, but a safety margin can also be taken in the driving control using the friction coefficient.

[0086] However, since the accuracy of the road surface friction coefficient μ2 is higher than the accuracy of the road surface friction coefficient μ3, if the road surface friction coefficient μ2 corresponds to a high-μ road, and furthermore, if the road surface friction coefficient μ3 corresponds to a low-μ road, or if μ2 > μ3, the friction coefficient derivation unit 34 may use the road surface friction coefficient μ3 directly as the road surface friction coefficient for the long-distance region Rc, instead of using the road surface friction coefficient μ2, in order to avoid a decrease in accuracy due to substitution.

[0087] [Modification C] In the above embodiment and modifications A and B, the road surface unevenness sensor 12 may further include a LiDAR (Light Detection and Ranging) and a calculation unit capable of calculating the unevenness of the road surface 100 based on the output from the LiDAR. The LiDAR transmits light and receives reflected light, and is capable of detecting an object and the distance to the object based on the time from the transmission of light to the reception of reflected light. The calculation unit is capable of calculating the unevenness of the road surface 100 based on the detection results (object and distance to the object) obtained by the LiDAR. When the LiDAR is used to calculate the friction coefficient data Da, the monitoring area of ​​the road surface unevenness sensor 12 is, for example, a medium-range region Rb and a long-range region Rc.

[0088] In the modified sensor table 44, the distance region Ra is defined with a road surface unevenness sensor 12 (the near-infrared light sensor described above) and a road surface temperature sensor 14, the medium distance region Rb is defined with a road surface unevenness sensor 12 (the near-infrared light sensor, LiDAR described above), a road surface temperature sensor 14 and a stereo camera 11, and the long distance region Rc is defined with a road surface unevenness sensor 12 (LiDAR), a road surface temperature sensor 14 and a stereo camera 11.

[0089] In this modified example, a LiDAR is used as the road surface unevenness sensor 12. In this case, it is possible to derive the road surface friction coefficient in front of the vehicle 1 with even greater accuracy.

[0090] The effects described herein are illustrative only, and the effects of this disclosure are not limited to those described herein. Therefore, other effects may be obtained with respect to this disclosure.

[0091] Furthermore, this disclosure may take the following forms: (1) A road surface friction coefficient calculation device comprising: an acquisition unit capable of acquiring road surface feature data in front of the vehicle from multiple types of non-contact sensors installed on the vehicle, and acquiring road surface temperature data in front of the vehicle from a temperature sensor installed on the vehicle; a storage unit that stores a friction coefficient table to which friction coefficient data is associated for each road surface condition, and a sensor table in which the type of sensor to be used for calculation according to the distance in front of the vehicle is defined; and a processing unit capable of performing information processing using the friction coefficient table and the sensor table read from the storage unit, and the road surface feature data and road surface temperature data acquired by the acquisition unit, wherein the processing unit is capable of determining the road surface condition according to the distance in front of the vehicle based on the sensor table, the road surface feature data and the road surface temperature data, reading the friction coefficient data corresponding to the road surface condition obtained by the determination from the friction coefficient table, and outputting the read friction coefficient data according to the distance in front of the vehicle, or a first friction coefficient data according to the distance in front of the vehicle obtained by performing a predetermined calculation on the read friction coefficient data. (2) The road surface friction coefficient calculation device according to (1), wherein the processing unit can use, when determining the road surface condition at a specific distance as the distance in front of the vehicle, the data obtained from the sensor corresponding to the specific distance as defined in the sensor table from the road surface feature data and the data for the specific distance from the road surface temperature data.(3) The sensor table specifies the types of sensors to be used according to the distance in front of the vehicle, at least for short distance, medium distance and long distance, and the processing unit can use, when determining the road surface condition at short distance, the data obtained from the sensor corresponding to the short distance as specified in the sensor table and the road surface temperature data corresponding to the short distance from the road surface feature data, when determining the road surface condition at medium distance, the data obtained from the sensor corresponding to the medium distance as specified in the sensor table and the road surface temperature data corresponding to the short distance from the road surface feature data, and when determining the road surface condition at long distance, the processing unit can use, when determining the road surface condition at long distance, the data obtained from the sensor corresponding to the long distance as specified in the sensor table and the road surface temperature data corresponding to the long distance from the road surface feature data. (2) Road surface friction coefficient calculation device. (4) The road surface friction coefficient calculation device according to (3), wherein the processing unit is capable of determining the road surface condition according to the distance in front of the vehicle, at least for the short distance, the medium distance and the long distance, and reading the friction coefficient data corresponding to the short distance, the medium distance and the long distance from the friction coefficient table, and outputting the friction coefficient data for the short distance, the medium distance and the long distance that has been read, or the first friction coefficient data obtained by performing a predetermined calculation on the read friction coefficient data. (5) The road surface friction coefficient calculation device according to (3) or (4), wherein in the sensor table, a near-infrared sensor is defined as the type of sensor for the short distance, a near-infrared sensor and a camera are defined for the medium distance, and a camera is defined for the long distance. (6) The road surface friction coefficient calculation device according to (4), wherein the acquisition unit is capable of acquiring image data of the area in front of the vehicle from a camera provided on the vehicle, and the processing unit is capable of generating and outputting composite image data in which the friction coefficient data obtained for the short distance, the medium distance, and the long distance, or the first friction coefficient data, are superimposed on the image data.(7) A vehicle equipped with multiple types of non-contact sensors and a road surface friction coefficient calculation device, wherein the road surface friction coefficient calculation device includes: an acquisition unit that acquires road surface feature data in front of the vehicle obtained by the multiple types of sensors and road surface temperature data in front of the vehicle obtained by a temperature sensor provided on the vehicle; a storage unit that stores a friction coefficient table in which friction coefficient data is associated with each road surface condition and a sensor table in which the type of sensor to be used for calculation according to the distance in front of the vehicle is defined; and a processing unit capable of performing information processing using the friction coefficient table and the sensor table read from the storage unit and the road surface feature data and road surface temperature data acquired by the acquisition unit, wherein the processing unit determines the road surface condition according to the distance in front of the vehicle based on the sensor table, the road surface feature data and the road surface temperature data, A vehicle capable of reading friction coefficient data corresponding to the road surface condition obtained by determination from the friction coefficient table, and outputting the read friction coefficient data corresponding to the distance in front of the vehicle, or a first friction coefficient data corresponding to the distance in front of the vehicle obtained by performing a predetermined calculation on the read friction coefficient data.

[0092] The control unit 30 shown in Figure 1 can be implemented by a circuit including at least one semiconductor integrated circuit, such as at least one processor (e.g., a central processing unit (CPU)), at least one application-specific integrated circuit (ASIC) and / or at least one field-programmable gate array (FPGA). The at least one processor can be configured to perform all or some of the functions of the control unit 30 shown in Figure 1 by reading instructions from at least one non-transient, tangible computer-readable medium. Such a medium can take various forms, including, but is not limited to, various magnetic media such as hard disks, various optical media such as CDs or DVDs, and various semiconductor memories (i.e., semiconductor circuits) such as volatile memory or non-volatile memory. Volatile memory may include DRAM and SRAM. Non-volatile memory may include ROM and NVRAM. The ASIC is an integrated circuit (IC) specialized to perform all or some of the functions of the control unit 30 shown in Figure 1. An FPGA is an integrated circuit designed to be configurable after manufacturing to perform all or some of the various functions of the control unit 30 shown in Figure 1.

Claims

1. A road surface friction coefficient calculation device comprising: an acquisition unit capable of acquiring road surface feature data in front of the vehicle from multiple types of non-contact sensors installed on the vehicle, and acquiring road surface temperature data in front of the vehicle from a temperature sensor installed on the vehicle; a storage unit that stores a friction coefficient table to which friction coefficient data is associated for each road surface condition, and a sensor table in which the type of sensor to be used for calculation according to the distance in front of the vehicle is defined; and a processing unit capable of performing information processing using the friction coefficient table and the sensor table read from the storage unit, and the road surface feature data and road surface temperature data acquired by the acquisition unit, wherein the processing unit is capable of determining the road surface condition according to the distance in front of the vehicle based on the sensor table, the road surface feature data and the road surface temperature data, and reading the friction coefficient data corresponding to the road surface condition obtained by the determination from the friction coefficient table, and outputting the read friction coefficient data according to the distance in front of the vehicle, or a first friction coefficient data according to the distance in front of the vehicle obtained by performing a predetermined calculation on the read friction coefficient data.

2. The road surface friction coefficient calculation device according to claim 1, wherein the processing unit can use, when determining the road surface condition at a specific distance as the distance in front of the vehicle, data obtained from the sensor corresponding to the specific distance as defined in the sensor table, and data from the road surface temperature data for the specific distance.

3. The road surface friction coefficient calculation device according to claim 2, wherein the sensor table specifies at least the types of sensors to be used according to the distance in front of the vehicle for short distance, medium distance and long distance, and the processing unit can, when determining the road surface condition at short distance, use the data obtained from the sensor corresponding to the short distance as specified in the sensor table and the road surface temperature data corresponding to the short distance from the road surface feature data, when determining the road surface condition at medium distance, use the data obtained from the sensor corresponding to the medium distance as specified in the sensor table and the road surface temperature data corresponding to the short distance from the road surface feature data, and when determining the road surface condition at long distance, use the data obtained from the sensor corresponding to the long distance as specified in the sensor table and the road surface temperature data corresponding to the long distance from the road surface feature data.

4. The road surface friction coefficient calculation device according to claim 3, wherein the processing unit is capable of determining the road surface condition according to the distance in front of the vehicle for at least the short distance, the medium distance and the long distance, and reading the friction coefficient data corresponding to at least the short distance, the medium distance and the long distance from the friction coefficient table, and outputting the reading friction coefficient data for the short distance, the medium distance and the long distance, or the first friction coefficient data obtained by performing a predetermined calculation on the reading friction coefficient data.

5. The road surface friction coefficient calculation device according to claim 3 or claim 4, wherein the sensor table specifies a near-infrared sensor as the type of sensor for the short distance, a near-infrared sensor and a camera as the type of sensor for the medium distance, and a camera as the type of sensor for the long distance.

6. The road surface friction coefficient calculation device according to claim 4, wherein the acquisition unit is capable of acquiring image data of the area in front of the vehicle from a camera provided on the vehicle, and the processing unit is capable of generating and outputting composite image data in which the friction coefficient data obtained for the short distance, the medium distance, and the long distance, or the first friction coefficient data, is superimposed on the image data.

7. A vehicle equipped with multiple types of non-contact sensors and a road surface friction coefficient calculation device, wherein the road surface friction coefficient calculation device includes: an acquisition unit that acquires road surface feature data in front of the vehicle obtained by the multiple types of sensors and road surface temperature data in front of the vehicle obtained by a temperature sensor provided on the vehicle; a storage unit that stores a friction coefficient table in which friction coefficient data is associated with each road surface condition and a sensor table in which the type of sensor to be used for calculation according to the distance in front of the vehicle is defined; and a processing unit capable of performing information processing using the friction coefficient table and the sensor table read from the storage unit and the road surface feature data and road surface temperature data acquired by the acquisition unit, wherein the processing unit determines the road surface condition according to the distance in front of the vehicle based on the sensor table, the road surface feature data and the road surface temperature data. A vehicle capable of reading friction coefficient data corresponding to the road surface condition obtained by determination from the friction coefficient table, and outputting the read friction coefficient data corresponding to the distance in front of the vehicle, or a first friction coefficient data corresponding to the distance in front of the vehicle obtained by performing a predetermined calculation on the read friction coefficient data.

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