Driving assistance device and vehicle

Abstract

A driving assistance device according to an embodiment of the present disclosure is capable of: estimating the presence of a crossing moving body, the speed of the crossing moving body, and the distance from the crossing moving body to an intersection on the basis of first data obtained when the headlights of a vehicle are on and no crossing moving body is present, and second data obtained when the vehicle is traveling toward the intersection with the headlights on; and performing travel control or notification control to avoid collision of the vehicle with the crossing moving body on the basis of the estimated speed of the crossing moving body, the estimated distance from the crossing moving body to the intersection, and third data of the position and speed of the vehicle.

Description

Driving Support Device and Vehicle

[0001] The present disclosure relates to a driving support device and a vehicle.

[0002] In recent years, various technologies have been proposed that can avoid head-on collisions with moving objects traveling in the blind spot of a vehicle driver (see, for example, Patent Documents 1 and 2).

[0003] Japanese Patent Application Laid-Open No. 2017-138766, Japanese Patent Application Laid-Open No. 2015-194835

[0004] The driving support device according to an embodiment of the present disclosure includes an acquisition unit and a control unit. The acquisition unit is capable of acquiring the data described in the following (1) to (3). (1) First data of brightness or color tone obtained when the vehicle's headlight is lit in a situation where there is no intersection moving object at an intersection in front of the vehicle. (2) Second data of brightness or color tone at the intersection obtained when the vehicle is traveling toward the intersection with the headlight lit. (3) Third data of the vehicle's position and speed. The control unit is capable of controlling the driving support of the vehicle based on the first data, second data, and third data obtained by the acquisition unit. The control unit determines whether there is an intersection moving object based on the first data and the second data. When there is an intersection moving object, the speed of the intersection moving object and the distance of the intersection moving object to the intersection are estimated based on the first data and the second data. Based on the speed of the intersection moving object obtained by the estimation, the distance of the intersection moving object to the intersection, and the third data, it is possible to perform driving control or notification control to avoid the vehicle from colliding with the intersection moving object.

[0005] The vehicle according to an embodiment of the present disclosure includes a driving support device and a controlled device controlled by the driving support device. The driving support device has the same configuration as the driving support device according to an embodiment of the present disclosure.

[0006] The accompanying drawings are provided to further understand the present disclosure, are incorporated into this specification, and constitute a part of this specification. The drawings show an embodiment and serve to explain the principle of the present disclosure together with the specification.

[0007] Figure 1 is a diagram showing an example of traffic conditions. Figure 2 is a diagram showing another example of traffic conditions. Figure 3 is a diagram showing an example of a functional block of a vehicle according to one embodiment of the present disclosure. Figure 4 is a diagram illustrating the process of acquiring base data. Figure 5 is a diagram illustrating the process of acquiring HL data. Figure 6 is a diagram illustrating the process of acquiring vehicle forward data. Figure 7 is a diagram showing an example of base data. Figure 8 is a diagram showing an example of HL data. Figure 9 is a diagram showing an example of a speed profile. Figure 10 is a diagram showing an example of a distance profile. Figure 11(A) is a diagram showing an example of vehicle forward data. Figure 11(B) is a diagram showing an example of data obtained by subtracting HL data from vehicle forward data (first other vehicle identification data). Figure 11(C) is a diagram showing an example of data obtained by subtracting base data from the first other vehicle identification data (second other vehicle identification data). Figure 12(A) is a diagram showing an example of coordinates of multiple locations of the contour position of the headlamp light of the vehicle itself (multiple contour coordinates) detected in the vehicle forward data. Figure 12(B) shows how multiple contour coordinates from Figure 12(A) are superimposed on either the first or second other vehicle identification data. Figure 12(C) shows an example of an overlapping region. Figure 13 shows an example of a driving assistance procedure in the vehicle shown in Figure 3.

[0008] <1. Background> In recent years, driver assistance systems such as AEB (Automatic Emergency Braking) have been put into practical use to reduce traffic accidents. Most of these driver assistance systems assume that the moving object to be monitored is in a position where it can be detected by sensors such as recognition cameras. Therefore, if the moving object to be monitored is in a position that is difficult to detect by sensors, for example, in a blind spot from the perspective of the driver of the vehicle equipped with the driver assistance system, these driver assistance systems often do not function.

[0009] On the other hand, experienced drivers can anticipate that pedestrians, other vehicles, or other moving objects might suddenly appear when driving through intersections with poor visibility, curves, or areas with many blind spots, and can take preventative measures such as slowing down in advance. In recent years, attempts have been made to realize this kind of "anticipatory driving" using driver assistance systems.

[0010] For example, in the invention described in Patent Document 1, it is determined whether or not another vehicle is approaching based on the time-series change in brightness of the observation area captured in the vehicle surroundings image input from the recognition camera. In the invention described in Patent Document 2, the time of approach of another vehicle is estimated based on the time-series change in brightness of the road surface, etc.

[0011] However, in the invention described in Patent Document 1, when it is unclear whether the change in brightness of the headlights illuminating the observation area is due to the approach of the own vehicle to the observation area or to the approach of another vehicle, it is difficult to isolate the cause of the change in brightness of the headlights illuminating the observation area. Similarly, in the invention described in Patent Document 2, when it is unclear whether the time-series change in brightness of the road surface, etc., is due to the approach of the own vehicle to the road surface, etc. or to the approach of another vehicle, it is difficult to isolate the cause of the change in brightness of the headlights illuminating the road surface, etc. Thus, the inventions described in Patent Documents 1 and 2 have the problem that the movement of other vehicles can only be estimated under limited conditions.

[0012] Next, we will describe a traffic situation in which it is difficult to detect moving objects traveling in blind spots, even if the time-series changes in the brightness of the road surface, etc., are tracked. Figure 1 shows an example of a traffic situation. In the traffic situation described in Figure 1, it is assumed that a vehicle (our vehicle) 100a is traveling on a road La with one lane in each direction. This road La with one lane in each direction consists of a driving lane L1 on which vehicle 100a is traveling, and an opposing lane L2 provided along the driving lane L1 via a center line. On this road La with one lane in each direction, there is an unsignaled intersection IS in front of vehicle 100a. There are no traffic lights installed at the unsignaled intersection IS. This road La with one lane in each direction is a priority road in relation to the road Lb (intersecting road) that intersects with this road La with one lane in each direction at the unsignaled intersection IS. In other words, vehicle 100a is traveling on the priority road. On the other hand, the road Lb that intersects with the priority road at the unsignaled intersection IS is a non-priority road in relation to the priority road. On a non-priority road, vehicle 100b (crossing vehicle) is traveling just before intersection IS, which is unsignalized.

[0013] From the perspective of the driver of vehicle 100b, vehicle 100b is located in the blind spot area BR of building 200a, and the driver of vehicle 100a cannot see vehicle 100b. At this time, in the image data obtained by imaging the area in front of vehicle 100a, vehicle 100b is hidden behind building 200a and is not captured in the image.

[0014] A neon sign 200c is installed near intersection IS, and because it is nighttime, neon sign light αc is emitted from the neon sign 200c. The neon sign light αc illuminates the road surface of intersection IS. Vehicle 100a emits headlamp light αa in front of vehicle 100a. The headlamp light αa also illuminates the road surface of intersection IS. Vehicle 100b emits headlamp light αb in front of vehicle 100b. The headlamp light αb also illuminates the road surface of intersection IS. Thus, on the road surface of intersection IS, the headlamp light αa, headlamp light αb, and neon sign light αc overlap. Therefore, even if the driver of vehicle 100a observes the temporal changes in the light illuminating the road surface of intersection IS, it is difficult for them to immediately recognize the presence of vehicle 100b.

[0015] As shown in Figure 1, assume that the headlamp light αa, headlamp light αb, and neon sign light αc overlap over most of the road surface at intersection IS. In this case, although vehicle 100b is approaching intersection IS, the temporal change in the headlamp light αb at intersection IS is small. Therefore, it is unclear whether the temporal change in brightness on the road surface at intersection IS is due to vehicle 100a approaching intersection IS or vehicle 100b approaching intersection IS. Consequently, in this case, it is difficult to detect the presence of vehicle 100b, which is in a blind spot, from the temporal change in brightness on the road surface at intersection IS.

[0016] Figure 2 shows another example of traffic conditions. The traffic conditions in Figure 2 are generally the same as those in Figure 1. However, in Figure 2, from the perspective of the driver of vehicle 100a, vehicle 100c is parked before intersection IS, and vehicle 100a is driving to avoid vehicle 100c.

[0017] At this time, from the perspective of the driver of vehicle 100a, a portion of the intersection IS is obscured by vehicle 100c. Therefore, the driver of vehicle 100a can only see a portion of the intersection IS. As a result, the driver of vehicle 100a will see that the headlamp light αa, headlamp light αb, and neon sign light αc are overlapping on most of the road surface of the intersection IS that is visible to the driver of vehicle 100a. Furthermore, in the image obtained by the camera mounted on vehicle 100a, the headlamp light αa, headlamp light αb, and neon sign light αc are also overlapping on most of the road surface of the intersection IS that is captured in the image.

[0018] As a result, although vehicle 100b is approaching intersection IS, the temporal change in headlamp light αb at intersection IS is small. Therefore, it is unclear whether the temporal change in brightness on the road surface at intersection IS is due to vehicle 100a approaching intersection IS or vehicle 100b approaching intersection IS. In this case, it is difficult to detect the presence of vehicle 100b, which is in a blind spot, from the temporal change in brightness on the road surface at intersection IS.

[0019] As a result of diligent research to address these problems, the applicant has conceived of a technology that can prevent head-on collisions by predicting the movements of other vehicles in various environments. The following describes a driver assistance system and vehicle that embody such technology.

[0020] 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.

[0021] <2. Embodiments> [Configuration Example] Next, a vehicle 100a equipped with a control unit 30 according to one embodiment of the present disclosure will be described. Figure 3 shows an example of the functional blocks of the vehicle 100a. The control unit 30 corresponds to one specific example of the "driving support device" according to one embodiment of the present disclosure. The vehicle 100a corresponds to one specific example of the "vehicle" according to one embodiment of the present disclosure.

[0022] Vehicle 100a is capable of moving by the drive of a prime mover 60 (engine or motor). Vehicle 100a 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 3.

[0023] The sensor unit 10 is comprised of various sensors mounted on the vehicle 100a. For example, the sensor unit 10 comprises 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.

[0024] 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.

[0025] The vehicle speed sensor is capable of detecting the speed (vehicle speed) of the vehicle 100a. 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 the vehicle 100a. 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 the vehicle 100a. 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.

[0026] The steering angle sensor is capable of detecting the steering angle of the steering wheel of the vehicle 100a. 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.

[0027] The sensor unit 10 further includes a stereo camera mounted on the vehicle 100a and a driving environment detection unit. The stereo camera is an autonomous sensor that senses the real space in front of the vehicle 100a. The stereo camera is capable of acquiring two-dimensional data of brightness or color of a specific area in front of the vehicle 100a (at least the area including the monitoring area β described later). The stereo camera is positioned, for example, symmetrically on either side of the central part in the width direction of the vehicle 100a, enabling stereo imaging of the area in front of the vehicle 100a from different viewpoints. Based on the stereo image data obtained by imaging, the stereo camera is capable of generating distance image data obtained from the amount of displacement of the corresponding object's position. The stereo camera is capable of outputting the image data obtained by imaging (stereo image data, distance image data) to the control unit 30. Hereinafter, the image data (stereo image data or distance image data) obtained by the sensor unit 10 will be referred to as image data Df.

[0028] The driving environment detection unit can determine lane markings that demarcate the road around the vehicle 100a, for example, based on stereo image data or distance image data. The driving environment detection unit can further determine the road curvature of the markings that demarcate the left and right sides of the driving lane on which the vehicle 100a is traveling, and the width between the left and right markings (vehicle width). The driving environment detection unit can further detect lanes and three-dimensional objects such as structures present around the vehicle 100a by performing predetermined pattern matching on the distance image data.

[0029] 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 100a (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 is capable of outputting driving environment information around the vehicle 100a, including the information on three-dimensional objects acquired in this way, to the control unit 30.

[0030] The communication unit 20 is capable of acquiring data to supplement data that cannot be obtained from stereo image data 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.

[0031] The communication unit 20 is capable of receiving positioning signals transmitted from multiple positioning satellites, for example, via satellite communication. The communication unit 20 is capable of acquiring road map data around the vehicle 100a, for example, via 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 constitutes traffic information. The communication unit 20 is also capable of acquiring weather information around the vehicle 100a, for example, via vehicle-to-infrastructure communication.

[0032] 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, and bus stops. "Structures surrounding roads" include, for example, various buildings and parks.

[0033] The semi-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. The semi-dynamic information that makes up traffic information consists of information that needs to be updated within a minute, such as actual congestion conditions and driving restrictions at the time of observation, temporary driving obstruction conditions such as fallen objects or obstacles, actual accident conditions, and local weather information.

[0034] 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.

[0035] The storage unit 40 is composed of, for example, non-volatile memory, and is composed of, for example, EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, resistive random-access memory, etc. The storage unit 40 stores, for example, a road map DB 41, base data 42, HL (headlamp) data 43, threshold data 44, speed profile 45, and distance profile 46, as shown in Figure 3. The HL data 43 corresponds to one specific example of the "first data" according to one embodiment of the present disclosure. The speed profile 45 corresponds to one specific example of the "speed profile" according to one embodiment of the present disclosure. The distance profile 46 corresponds to one specific example of the "distance profile" according to one embodiment of the present disclosure.

[0036] The road map DB41 is a large-capacity storage medium such as an HDD, and stores high-precision road map information (dynamic map). This high-precision road map information includes, for example, static and quasi-static information that mainly constitute road information, and quasi-dynamic and dynamic information that mainly constitute traffic information.

[0037] Base data 42 is statistical brightness or hue data obtained from image data of a predetermined monitoring area β including intersection IS, when the vehicle 100a's headlights are not illuminated at intersection IS in front of vehicle 100a, in a situation where vehicle 100b is not present, as shown in Figure 4. Vehicle 100b corresponds to one specific example of the "crossing moving object" according to one embodiment of the present disclosure. HL data 43 is statistical brightness or hue data obtained from image data of a predetermined monitoring area β including intersection IS, when the vehicle 100a's headlights are illuminated at intersection IS in front of vehicle 100a, in a situation where vehicle 100b is not present, as shown in Figure 5.

[0038] The monitoring area β at intersection IS is, for example, a rectangular area that includes the road surface of intersection IS (the road surface at the point where road La and road Lb intersect) and the road surface of road La approximately 3m before and after intersection IS as seen from vehicle 100a. The monitoring area β at intersection IS may also include, for example, the road surface of the sidewalk near intersection IS, or the wall surface of a structure such as a wall provided on the sidewalk near intersection IS.

[0039] Each pixel value of the image data Df obtained by the sensor unit 10 is, for example, brightness or color image data obtained from captured image data of a predetermined monitoring area β including intersection IS, when both the headlights of vehicle 100a and vehicle 100b are turned on, while vehicle 100b is traveling in the blind spot area BR at intersection IS in front of vehicle 100a, as shown in Figure 6. The image data Df is, for example, brightness or color image data obtained by capturing images at night without the influence of the moon, at night with the influence of moonlight, at dusk with the influence of the setting sun, or at sunrise with the influence of the rising sun.

[0040] Here, "brightness" refers to luminance, which indicates the brightness of a light source, or lightness, which indicates the brightness of an object's color. Luminance and lightness are expressed as values ​​that have a predetermined correlation with each pixel value of a color pixel consisting of R, G, and B pixels. Luminance is expressed, for example, as 0.299 × R pixel value + 0.587 × G pixel value + 0.114 × B pixel value. Lightness is expressed, for example, as the sum of (the maximum value among the R, G, and B pixel values) and (the minimum value among the R, G, and B pixel values) × 1 / 2. "Hue" refers to saturation or hue. Saturation and hue are expressed as values ​​that have a predetermined correlation with each pixel value of a color pixel consisting of R, G, and B pixels.

[0041] The base data 42 includes, for example, the position data of various structures such as intersections ((x1, y1), (x2, y2), ...) included in the road map DB 41, as shown in Figure 7, and the base data Db (Db1, Db2, ...) of the various structures. In the base data 42, each base data Db (Db1, Db2, ...) includes base data for each road surface condition (e.g., DRY, WET, SNOW). In the base data 42, each base data Db (Db1, Db2, ...) includes base data for each of the following conditions: nighttime without the influence of the moon, nighttime with the influence of moonlight, twilight with the influence of the setting sun, and sunrise with the influence of the rising sun. Instead of separate base data for nighttime with moonlight influence, twilight with sunset influence, and sunrise with sunrise influence, the base data for nighttime without moonlight influence may include a function with parameters such as solar altitude, time of day, atmospheric moisture content, atmospheric dust content, and weather.

[0042] For example, as shown in FIG. 8, the HL data 43 includes position data ((x1, y1), (x2, y2),...) of various structures such as intersections included in the road map DB 41, and HL data Dh (Dh1, Dh2,...) of the various structures. In the HL data 43, each HL data Dh (Dh1, Dh2,...) includes HL data for each road surface condition (e.g., DRY, WET, SNOW). In the HL data 43, each HL data Dh (Dh1, Dh2,...) includes, for example, HL data for each of a night without the influence of the moon, a night with the influence of moonlight, a dusk with the influence of sunset, and a dawn with the influence of sunrise. Instead of the HL data for a night with the influence of moonlight, a dusk with the influence of sunset, and a dawn with the influence of sunrise, the HL data 43 may include a function with parameters such as solar altitude, time zone, atmospheric moisture content, atmospheric dust content, and weather, etc., based on the HL data for a night without the influence of the moon.

[0043] The threshold data 44 includes a threshold th used in the process of calculating the overlapping area OA between the headlamp light αa and the headlamp light αb in the monitoring area β. The threshold th is a numerical value related to brightness or color tone.

[0044] The speed profile 45 is time-series data of the area of the overlapping area OA between the light of the headlamp of the vehicle 100a and the light of the headlamp of the vehicle 100b for each speed of the vehicle 100a and for each speed difference between the vehicle 100a and the vehicle 100b in various structures such as intersections included in the road map DB 41. The speed profile 45 includes time-series data (speed profile Dv) of the area of the overlapping area OA between the light of the headlamp of the vehicle 100a and the light of the headlamp of the vehicle 100b for each speed of the vehicle 100a and for each speed difference between the vehicle 100a and the vehicle 100b at the intersection IS.

[0045] For example, as shown in FIG. 9, the speed profile Dv is time-series data of the area of the overlapping area OA at the intersection IS for each speed Va (e.g., Va = 5 m / s, 10 m / s, 15 m / s,...) of the vehicle 100a and for each speed difference ΔV (e.g., ΔV = 0 m / s, 2 m / s,...) between the vehicle 100a and the vehicle 100b.

[0046] The distance profile 46 is time-series data of the area of the overlapping region OA between the light of the headlight of vehicle 100a and the light of the headlight of vehicle 100b in various structures such as intersections included in the road map DB41, for each distance to the structure of vehicle 100a and for each distance difference between the distance to the structure of vehicle 100a and the distance to the structure of vehicle 100b. The distance profile 46 includes time-series data (distance profile Dd) of the area of the overlapping region OA between the light of the headlight of vehicle 100a and the light of the headlight of vehicle 100b in the intersection IS, for each distance to the intersection IS of vehicle 100a and for each distance difference between the distance to the intersection IS of vehicle 100a and the distance to the intersection IS of vehicle 100b.

[0047] The distance profile Dd is, for example, as shown in FIG. 10, time-series data of the area of the overlapping region OA in the intersection IS, for each distance Da (for example, Da = 5 m, 10 m, 15 m,...) from vehicle 100a to the intersection IS and for each distance difference ΔD (for example, ΔD = 0 m, 2 m,...) between the distance from vehicle 100a to the intersection IS and the distance from vehicle 100b to the intersection IS. The distance Da and the distance difference ΔD are not limited to the above specific examples.

[0048] The control unit 30 is capable of controlling the entire vehicle 100a. The control unit 30 is, for example, a so-called ECU (Electronic Control Unit) and is configured to include, for example, one or more processors and one or more memories. The control unit 30 may be configured to include, for example, a CPU (Central Processing Unit). At this time, the control unit 30 is capable of controlling the entire vehicle 100a by executing a program stored in the storage unit.

[0049] The control unit 30 includes, for example, a locator unit. The locator unit is capable of acquiring the position coordinates of the vehicle 100a based on the positioning signal received through the communication unit 20. The locator unit is capable of estimating the vehicle's position on the road map by map matching the acquired position coordinates onto route map information. Based on the acquired position coordinates of the vehicle 100a, the locator unit acquires map information for a predetermined range including the vehicle 100a from the map information stored in the road map DB (database) 41, which will be described later.

[0050] The locator unit can switch to autonomous navigation, which estimates the vehicle's position based on vehicle speed, angular velocity, and longitudinal acceleration detected by the sensor unit 10, in environments where it cannot receive effective positioning signals from positioning satellites due to reduced sensitivity, such as when driving in a tunnel. Once the locator unit estimates the position of the vehicle 100a on the road map (vehicle position) based on the positioning signals received through the communication unit 20 or the information detected by the sensor unit 10, it can determine the type of road the vehicle 100a is traveling on based on the estimated vehicle position on the road map.

[0051] The locator unit can update the road map information stored in the road map DB 41 to the latest state using road map information acquired through external communication (vehicle-to-infrastructure communication and vehicle-to-vehicle communication) via the communication unit 20. This information update is performed not only on static information but also on quasi-static, quasi-dynamic, and dynamic information. As a result, the road map information is composed of road information and traffic information acquired through communication with the outside of the vehicle, and information on moving objects such as vehicles traveling on the road is updated in near real time.

[0052] 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 DB41 to the latest state. This information update is performed not only on static information, but also on quasi-static information, quasi-dynamic information, and dynamic information. As a result, information on moving objects such as vehicles traveling on the road, as recognized as described above, is updated in real time.

[0053] The control unit 30 further includes a driving control unit 31, as shown in Figure 3, for example. The driving control unit 31 is capable of controlling the driving of the vehicle 100a (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 100a. The driving control unit 31 includes, for example, a data acquisition unit 32, a moving object search unit 33, a state quantity estimation 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 3. The data acquisition unit 32 corresponds to one specific example of the "acquisition unit" according to one embodiment of the present disclosure. The moving object search unit 33, the state quantity estimation unit 34, the notification control unit 35, and the avoidance control unit 36 ​​correspond to one specific example of the "control unit" according to one embodiment of the present 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 100a. Hereinafter, the 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 100a will be referred to as "data obtained from the sensor unit 10, etc."

[0055] The data acquisition unit 32 is capable of acquiring time-series data of image data Df from the sensor unit 10. Image data Df corresponds to a specific example of the "second data" according to one embodiment of this disclosure. The data acquisition unit 32 is also capable of acquiring position and speed data (position and speed data Dg) of the vehicle 100a based on the data obtained from the sensor unit 10, etc. Position and speed data Dg corresponds to a specific example of the "third data" according to one embodiment of this disclosure.

[0056] Based on the position and speed data Dg, the data acquisition unit 32 can acquire base data Db of brightness or color obtained from the base data 42 of the storage unit 40 when the vehicle 100a's headlights are not illuminated at the intersection IS in front of the vehicle 100a, in a situation where vehicle 100b is not present. The data acquisition unit 32 can also acquire, for example, base data Db of road surface conditions corresponding to weather information obtained from the sensor unit 10 from the base data 42 of the storage unit 40.

[0057] The data acquisition unit 32 is further capable of acquiring brightness or color HL data Dh from the HL data 43 of the storage unit 40 based on the position speed data Dg, when the headlights of vehicle 100a are illuminated at the intersection IS in front of vehicle 100a in the absence of vehicle 100b. The data acquisition unit 32 is also capable of acquiring road surface condition HL data Dh corresponding to weather information obtained from the sensor unit 10 from the HL data 43 of the storage unit 40, for example. The HL data Dh corresponds to one specific example of the "first data" according to one embodiment of this disclosure.

[0058] The data acquisition unit 32 is capable of acquiring the speed profile Dv of the intersection IS in front of the vehicle 100a from the speed profiles 45 in the storage unit 40, based on the position of the vehicle 100a. The data acquisition unit 32 is also capable of extracting a speed profile Dv' corresponding to the speed of the vehicle 100a from the speed profiles Dv. Speed ​​profile Dv' corresponds to a specific example of the "fourth data" according to one embodiment of this disclosure.

[0059] The data acquisition unit 32 is capable of acquiring the distance profile Dd of the intersection IS in front of the vehicle 100a from the distance profiles 46 of the storage unit 40, based on the position of the vehicle 100a. The data acquisition unit 32 is also capable of extracting a distance profile Dd' from the distance profile Dd that corresponds to the distance from the vehicle 100a to the intersection IS. The distance from the vehicle 100a to the intersection can be derived from the difference between the position of the vehicle 100a and the position of the intersection IS. The distance profile Dd' corresponds to a specific example of the "fifth data" according to one embodiment of this disclosure.

[0060] The mobile object search unit 33 can determine whether or not the vehicle 100b is in the blind spot area BR based on the HL data Dh and image data Df. The mobile object search unit 33 can subtract the HL data Dh from the image data Df and determine whether or not the vehicle 100b is in the blind spot area BR based on the resulting time-series two-dimensional data.

[0061] Let's assume that the image data Df is, for example, two-dimensional data of the brightness or hue of the road surface at an intersection IS where headlight light αa, headlight light αb, and neon sign light αc overlap, as shown in Figure 11(A). In this case, the mobile object search unit 33 subtracts the HL data Dh from the image data Df, thereby obtaining image data ΔD1 from which the components of headlight αa and neon sign light αc have been removed or reduced (Figure 11(B)). The mobile object search unit 33 further subtracts the base data Db from the image data ΔD1, thereby obtaining image data ΔD2 from which ambient light noise has been removed or reduced (Figure 11(C)).

[0062] The mobile object search unit 33 can determine whether or not a vehicle 100b is present in the blind spot area BR based on the time-series two-dimensional data of image data ΔD1 or image data ΔD2. For example, the mobile object search unit 33 can determine that a vehicle 100b is present in the blind spot area BR if, in the time-series two-dimensional data obtained by subtracting the threshold th included in the threshold data 44 from the image data ΔD1 or image data ΔD2, the distribution of brightness or color is roughly trapezoidal, as shown in Figure 11(B) or Figure 11(C), and the area of ​​the trapezoidal brightness or color distribution increases over time. The shape of the brightness or color distribution may also be other than trapezoidal; for example, it may be triangular.

[0063] The mobile object search unit 33 can determine whether or not vehicle 100b actually passed through intersection IS when it determines that vehicle 100b is present. The mobile object search unit 33 can determine, for example, whether or not vehicle 100b is actually included in the image data Df obtained while vehicle 100a is passing through intersection IS. If vehicle 100b is not actually included in the image data Df, the mobile object search unit 33 can update the HL data Dh of intersection IS by replacing the HL data Dh of intersection IS contained in the HL data 43 of the storage unit 40 with the image data Df. In other words, if the mobile object search unit 33 determines that vehicle 100b is present, but then finds out from the image data Df obtained later that vehicle 100b was not actually present, it can update the HL data Dh of intersection IS by storing the image data Df in the storage unit 40.

[0064] The mobile object search unit 33 may be able to estimate the cause of the brightness or color (source light source) contained in the image data ΔD1 or image data ΔD2 based on the image data Df if the vehicle 100b is not actually included in the image data Df. The mobile object search unit 33 may be able to update the HL data Dh of the intersection IS by storing the image data Df in the storage unit 40 if it is able to estimate the cause (source light source). The mobile object search unit 33 may be able to update the HL data Dh of the intersection IS by storing the average value of the HL data Dh of the intersection IS contained in the HL data 43 of the storage unit 40 and the image data Df in the storage unit 40 as new HL data Dh of the intersection IS.

[0065] The state quantity estimation unit 34 can estimate the speed of vehicle 100b and the distance of vehicle 100b to intersection IS based on HL data Dh and image data Df, if vehicle 100b is present. The state quantity estimation unit 34 can derive the speed of vehicle 100b by performing time-series matching based on HL data Dh, image data Df and speed profile Dv. The state quantity estimation unit 34 can derive the position of vehicle 100b (distance of vehicle 100b to intersection IS) by performing time-series matching based on HL data Dh, image data Df and position profile Dd.

[0066] Let's assume that the image data Df is, for example, two-dimensional data of the brightness or hue of the road surface at an intersection IS where headlight light αa, headlight light αb, and neon sign light αc overlap each other, as shown in Figure 12(A). In this case, the state quantity estimation unit 34 can calculate the coordinates ((a1, b1), (a2, b2), (a3, b3), (a4, b4)) of each vertex P1, P2, P3, P4 of the contour of the headlight light αa included in the monitoring area β based on the brightness or hue in the image data Df, as shown in Figure 12(A). The state quantity estimation unit 34 can then superimpose each vertex P1, P2, P3, P4 onto the image data ΔD1 or image data ΔD2, as shown in Figure 12(B). The state quantity estimation unit 34 can then calculate the coordinates ((a1, b1), (a5, b5), (a6, b6), (a7, b7)) of the points (vertices P1, P5, P6, P7) where the contour of the headlight light αa formed from each vertex P1, P2, P3, P4 in the monitoring region β intersects with the contour of the headlight light αb contained in the image data ΔD1 or image data ΔD2. Then, the state quantity estimation unit 34 can use the calculated coordinates ((a1, b1), (a5, b5), (a6, b6), (a7, b7)) to calculate the area of ​​the superposition region OA of the headlight light αa and headlight light αb, for example, as shown by the shaded area in Figure 12(C). The state quantity estimation unit 34 is capable of calculating time-series data (area transition data Ds) of the area of ​​the superimposed region OA based on the time-series two-dimensional data of the image data Df.

[0067] The state quantity estimation unit 34 can derive the speed of vehicle 100b by matching the time change (area transition data) of the superimposed region OA, which is provided for each speed difference ΔV and included in the speed profile Dv', with the area transition data Ds. For example, if the area transition data of the superimposed region OA when the speed difference ΔV is 10 m / s and included in the speed profile Dv' match with the area transition data Ds, the state quantity estimation unit 34 can derive the speed of vehicle 100b based on the speed difference (10 m / s) corresponding to the matched area transition data and the speed of vehicle 100a. For example, if the area transition data of the superimposed region OA when the speed difference ΔV is 10 m / s, included in the speed profile Dv', and the area transition data Ds are the most similar profiles, the state quantity estimation unit 34 can derive the speed of vehicle 100b based on the speed difference (10 m / s) corresponding to the most similar area transition data and the speed of vehicle 100a.

[0068] The state quantity estimation unit 34 can further derive the distance of vehicle 100b to intersection IS by matching the time change (area transition data) of the superimposed region OA, which is provided for each distance difference ΔD included in the distance profile Dd', with the area transition data Ds. For example, if the area transition data of the superimposed region OA when the distance difference ΔD is 10m, included in the distance profile Dd', matches with the area transition data Ds, the state quantity estimation unit 34 can derive the distance of vehicle 100b to intersection IS based on the distance difference (10m) corresponding to the matched area transition data and the distance of vehicle 100a to intersection IS. For example, if the area transition data of the superimposed region OA when the distance difference ΔD is 10 m, included in the distance profile Dd', and the area transition data Ds are the most similar profiles, the state quantity estimation unit 34 can derive the position of vehicle 100b (the distance of vehicle 100b to the intersection IS) based on the distance difference (10 m) corresponding to the most similar area transition data and the distance of vehicle 100a to the intersection IS.

[0069] The notification control unit 35 is capable of performing notification control to inform the driver that, if vehicle 100b is present, vehicle 100b may suddenly move from the blind spot area BR into lane La. The notification control unit 35 is capable of generating a video signal to inform the driver that vehicle 100b may suddenly move from the blind spot area BR into lane La, and outputting it to the notification unit 50.

[0070] The notification unit 50 is configured, for example, to include a liquid crystal panel or an organic EL panel, and is capable of displaying video signals based on video signals input from the notification control unit 35. The notification control unit 35 is capable of generating an audio signal to inform the driver that there is a possibility that the vehicle 100b may suddenly move from the blind spot area BR into lane La, and outputting this to the notification unit 50. The notification unit 50 is configured, for example, to include a speaker, and is capable of outputting an audio message or warning sound based on the audio signal input from the notification control unit 35. The notification control unit 35 is capable of generating a vibration signal to inform the driver that there is a possibility that the vehicle 100b may suddenly move from the blind spot area BR into lane La, and outputting this to the notification unit 50. The notification unit 50 is configured, for example, to include an actuator built into the steering wheel or seat, and is capable of generating vibrations in the steering wheel or seat based on the vibration signal input from the notification control unit 35.

[0071] The avoidance control unit 36 ​​is capable of controlling the vehicle's movement based on the speeds of vehicle 100a and vehicle 100b, and the distance to intersection IS, if vehicle 100b is present. The avoidance control unit 36 ​​is capable of calculating various additional torques based on the speeds of vehicle 100a and vehicle 100b, and the distance to intersection IS, and can output the calculated additional torques to at least one of the accelerator control unit 37, brake control unit 38, and steering control unit 39.

[0072] The accelerator control unit 37 is capable of controlling the torque of the prime mover 60 based on the required torque corresponding to the amount the driver of the vehicle 100a depresses the accelerator pedal. Furthermore, the accelerator control unit 37 is capable of deriving a target torque by adding an additional torque obtained from the avoidance control unit 36 ​​to the required 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 100a and is capable of driving the steering wheels of the vehicle 100a according to the required torque or target torque input from the accelerator control unit 37.

[0073] The brake control unit 38 is capable of controlling the torque of the brake 70 based on the required torque corresponding to the amount the driver of the vehicle 100a presses the brake pedal. The brake control unit 38 is also capable of deriving a target torque by adding an additional torque obtained from the avoidance control unit 36 ​​to the required 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 100a, and is capable of braking the steering wheels of the vehicle 100a according to the required torque or target torque input from the brake control unit 37.

[0074] 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 from the avoidance control unit 36 ​​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 generates an output torque based on the input control signal and can control the steering angle of the steering wheel.

[0075] The avoidance control unit 36 ​​may be capable of performing driving control when vehicle 100b is present and either condition A or condition B below is met. Conditions A and B are conditions in which vehicle 100a and vehicle 100b come into contact with each other.

[0076] (Condition A) TTLa=D1 / V1 TTLb=D2 / V2 ΔTTL=TTLa-TTLb ΔTTL<0 Ln2 / V2<|ΔTTL|+α (Condition B) TTLa=D1 / V1 TTLb=D2 / V2 ΔTTL=TTLa-TTLb ΔTTL>0 Ln1 / V1<|ΔTTL|+α

[0077] TTLa: Time to spare for vehicle 100a to reach intersection IS TTLb: Time to spare for vehicle 100b to reach intersection IS D1: Distance of vehicle 100a to intersection IS D2: Distance of vehicle 100b to intersection IS V1: Speed ​​of vehicle 100a V2: Speed ​​of vehicle 100b Ln1: Total length of vehicle 100a Ln2: Total length of vehicle 100b α: Margin

[0078] Next, we will explain the driving assistance procedure for vehicle 100a in the traffic situation shown in Figure 1.

[0079] Figure 13 shows an example of a driving assistance procedure for vehicle 100a. The driving control unit 31 acquires position and speed data Dg of vehicle 100a (step S101). The driving control unit 31 further acquires image data Df of the area in front of vehicle 100a (step S102). Based on the map data obtained from the road map DB 41, the position and speed data Dg of vehicle 100a, and the image data Df, the driving control unit 31 determines whether or not vehicle 100a is approaching intersection IS (step S103). For example, the driving control unit 31 determines that vehicle 100a is approaching intersection IS when vehicle 100a is traveling at a distance of 100m or less from intersection IS.

[0080] When the driving control unit 31 determines that vehicle 100a is approaching intersection IS (step S103; Y), it determines whether vehicle 100b is in the blind spot area BR based on the HL data Dh and image data Df of intersection IS (step S104). If vehicle 100b is in the blind spot area BR (step S104; Y), the driving control unit 31 estimates the speed of vehicle 100b by performing time-series matching based on the HL data Dh, image data Df and speed profile Dv (step S105). The driving control unit 31 further estimates the distance of vehicle 100b to intersection IS by performing time-series matching based on the HL data Dh, image data Df and position profile Dd (step S105).

[0081] If vehicle 100b is present, the driving control unit 31 performs a notification control to inform the driver that vehicle 100b may suddenly move from the blind spot area BR into lane La (step S106). Furthermore, if vehicle 100b is present, the driving control unit 31 performs driving control based on the speeds of vehicle 100a and vehicle 100b, and the distance to intersection IS (step S106).

[0082] When the driving control unit 31 determines that vehicle 100b is present, it determines whether vehicle 100b has actually passed through intersection IS. For example, the driving control unit 31 determines whether vehicle 100b is actually included in the image data Df obtained while vehicle 100a is passing through intersection IS. If vehicle 100b is not actually included in the image data Df, the driving control unit 31 updates the HL data Dh of intersection IS by replacing the HL data Dh of intersection IS contained in the HL data 43 of the storage unit 40 with the image data Df (step S108). In other words, if the driving control unit 31 determines that vehicle 100b is present, but then finds from the image data Df obtained later that vehicle 100b was not actually present, it updates the HL data Dh of intersection IS by storing the image data Df in the storage unit 40. In this way, driving assistance is provided for vehicle 100a.

[0083] [Effects] Next, the effects of the vehicle 1 according to the first embodiment of this disclosure will be described.

[0084] In this embodiment, whether or not vehicle 100b exists is determined based on HL data Dh and image data Df. This allows the component of headlight light αb to be extracted from image data Df obtained while vehicle 100a is in motion, even if headlight light αa and headlight light αb overlap at an intersection, or if headlight light αa, headlight light αb, and neon sign light αc overlap. If the component of headlight light αb can be extracted from image data Df, it can be indirectly determined that vehicle 100b exists. On the other hand, if the component of headlight light αb cannot be extracted from image data Df, it can be indirectly determined that vehicle 100b does not exist.

[0085] Furthermore, in this embodiment, if vehicle 100b is present, the speed of vehicle 100b and the distance of vehicle 100b to intersection IS are estimated based on HL data Dh and image data Df. Then, based on the estimated speed of vehicle 100b, the distance of vehicle 100b to intersection IS, and position speed data Dg, driving control or notification control is performed to avoid vehicle 100a colliding with vehicle 100b. As a result, even in environments where headlight beams αa and αb overlap at intersection IS, or where headlight beams αa, αb, and neon sign beams αc overlap, vehicle 100a can avoid colliding with vehicle 100b by driving control or notification control that predicts the movement of vehicle 100b, which is in the driver's blind spot of vehicle 100a. Therefore, it is possible to avoid head-on collisions by predicting the movement of vehicle 100b, which is in the driver's blind spot of vehicle 100a, in various environments.

[0086] In this embodiment, the speed of vehicle 100b is estimated by performing time-series matching based on HL data Dh, image data Df, and speed profile Dv. Furthermore, in this embodiment, the distance of vehicle 100b to intersection IS is estimated by performing time-series matching based on HL data Dh, image data Df, and position profile Dd. This makes it possible for vehicle 100a to avoid colliding with vehicle 100b even in environments where headlight beams αa and αb overlap at intersection IS, or where headlight beams αa, αb, and neon sign beams αc overlap. Therefore, it becomes possible to avoid head-on collisions by predicting the movement of vehicle 100b, which is in the driver's blind spot of vehicle 100a, in various environments.

[0087] In this embodiment, the speed of vehicle 100b is derived by matching the time change (area transition data) of the superimposed region OA, which is provided for each speed difference ΔV and included in the speed profile Dv', with the area transition data Ds. Furthermore, in this embodiment, the distance of vehicle 100b to the intersection IS is derived by matching the time change (area transition data) of the superimposed region OA, which is provided for each distance difference ΔD and included in the distance profile Dd', with the area transition data Ds. As a result, even in environments where headlight beams αa and αb overlap at the intersection IS, or where headlight beams αa, αb, and neon sign beams αc overlap, vehicle 100a can avoid colliding with vehicle 100b. Therefore, it is possible to avoid head-on collisions by predicting the movement of vehicle 100b, which is in the blind spot of the driver of vehicle 100a, under various conditions.

[0088] In this embodiment, whether or not a vehicle 100b exists is determined based on the time-series two-dimensional data of image data ΔD2, which is obtained by subtracting base data Db from image data ΔD1. This enables highly accurate detection of vehicle 100b with reduced influence of ambient light noise.

[0089] In this embodiment, the HL data Dh and image data Df each include brightness or color data of the intersection IS. This allows for the selective extraction of brightness or color data of headlight light αb from image data Df by excluding brightness or color data of headlight light αa and ambient light such as neon sign light αc. As a result, highly accurate detection of vehicle 100b can be achieved while suppressing the influence of headlight light αa and ambient light noise of vehicle 100a.

[0090] In this embodiment, the HL data Dh and image data Df each include data obtained during at least one of the following time periods: nighttime, twilight, and sunrise. This enables highly accurate detection of the vehicle 100b under various environmental conditions.

[0091] In this embodiment, a storage unit 40 for storing HL data Dh is provided in the vehicle 100a. This enables highly accurate detection of the vehicle 100b in various environments, even when external communication such as vehicle-to-infrastructure communication is unavailable.

[0092] In this embodiment, if it is determined that vehicle 100b is present, but subsequent image data Df reveals that vehicle 100b was not actually present, the image data Df is stored in the storage unit 40, thereby updating the HL data Dh of the intersection IS. This enables highly accurate detection of vehicle 100b even when the environment changes.

[0093] <3. Modified Examples> Next, a modified example of the vehicle 100a according to the above embodiment will be described.

[0094] In the above embodiment, the monitoring area β at the intersection IS may be, for example, an area that includes not only the intersection IS but also a convex mirror near the intersection IS.

[0095] In this modified example, the base data Db includes statistical data of brightness or hue obtained from image data of a convex mirror when vehicle 100a's headlights are not illuminated and vehicle 100b is not present at intersection IS in front of vehicle 100a. In this modified example, the HL data Dh includes statistical data of brightness or hue obtained from image data of a convex mirror when vehicle 100a's headlights are illuminated and vehicle 100b is not present at intersection IS in front of vehicle 100a.

[0096] A convex mirror may reflect the road surface in a blind spot area BR. In such cases, it is possible to detect the headlamp light αb of a vehicle 100b traveling in the blind spot area BR based on the brightness or color data of the convex mirror. Therefore, even if the vehicle 100b is traveling at a position far enough from the intersection IS that the headlamp light αb does not illuminate the road surface at the intersection IS, the vehicle 100b can still be detected by utilizing the brightness or color data of the convex mirror.

[0097] Furthermore, the effects described herein are merely illustrative and not limiting, and other effects may also occur.

[0098] Furthermore, for example, the present disclosure can take the following configuration: (1) An acquisition unit capable of acquiring first data of brightness or color obtained when the vehicle's headlights are turned on at an intersection in front of the vehicle in a situation where no intersecting moving object is present, and second data of brightness or color at the intersection and third data of the vehicle's position and speed obtained when the vehicle is traveling toward the intersection with the headlights on; and a control unit capable of controlling the driving assistance of the vehicle based on the first data, the second data and the third data obtained by the acquisition unit, wherein the control unit determines whether or not the intersecting moving object is present based on the first data and the second data, estimates the speed of the intersecting moving object and the distance of the intersecting moving object to the intersection based on the first data and the second data, and is capable of performing driving control or notification control to avoid the vehicle colliding with the intersecting moving object based on the estimated speed of the intersecting moving object, the distance of the intersecting moving object to the intersection and the third data. (2) The acquisition unit is capable of acquiring a fourth data corresponding to the speed of the vehicle from a speed profile, which is time-series data of the area of ​​the superposition region of the light of the vehicle's headlights and the light of the headlights of the intersecting moving object at the intersection, for each vehicle speed and for each speed difference between the vehicle and the intersecting moving object, the second data is time-series two-dimensional data, and the control unit is capable of deriving the speed of the intersecting moving object by performing time-series matching based on the first data, the second data and the fourth data when the intersecting moving object is present, as described in (1).(3) The acquisition unit is capable of acquiring a fifth data corresponding to the distance of the vehicle to the intersection from distance profiles, which are time-series data of the area of ​​the superposition region of the light of the vehicle's headlights and the light of the crossing moving object's headlights at the intersection, for each distance of the vehicle to the intersection and for each distance difference between the distance of the vehicle to the intersection and the distance of the crossing moving object to the intersection, and the control unit is capable of deriving the position of the crossing moving object by performing time-series matching based on the first data, the second data and the fifth data when the crossing moving object is present, as described in (2). (4) The control unit is capable of deriving the speed of the crossing moving object by matching time-series data of the area of ​​the superposition region of the light of the vehicle's headlights and the light of the crossing moving object's headlights at the intersection, obtained based on the first data and the second data, with the fourth data, as described in (2) or (3). (5) The control unit is capable of determining the position of the intersecting moving object by matching time-series data of the area of ​​the superposition region between the light of the vehicle's headlights and the light of the intersecting moving object's headlights at the intersection, obtained based on the first data and the second data, with the fifth data. (6) The control unit is capable of determining whether or not the intersecting moving object exists based on the time-series two-dimensional data obtained by subtracting the first data from the second data. The driving assistance device according to any one of (1) to (5). (7) The first data and the second data each include brightness or color data of the intersection. The driving assistance device according to any one of (1) to (6). (8) The first data and the second data each include brightness or color data of the intersection and brightness or color data of a wall or convex mirror near the intersection. The driving assistance device according to any one of (1) to (6).(9) The driving support device according to any one of (1) to (8), wherein the first data includes data obtained during at least one of the following time periods: nighttime, twilight, and sunrise. (10) The driving support device according to any one of (1) to (9), further comprising a storage unit for storing the first data. (11) The driving support device according to (10), wherein if the control unit determines that the intersecting moving object exists, but the second data obtained thereafter reveals that the intersecting moving object did not actually exist, the control unit can update the first data by storing the second data in the storage unit. (12) A vehicle comprising a driving assistance device and a controlled device controlled by the driving assistance device, wherein the driving assistance device comprises: an acquisition unit capable of acquiring first data of brightness or color obtained when the vehicle's headlights are turned on at an intersection in front of the vehicle in a state in which no intersecting moving object is present, and second data of brightness or color at the intersection and third data of the vehicle's position and speed obtained when the vehicle is traveling toward the intersection with the headlights on, and a control unit capable of controlling the driving assistance of the vehicle based on the first data, the second data and the third data obtained by the acquisition unit, wherein the control unit determines whether or not the intersecting moving object is present based on the first data and the second data, and if the intersecting moving object is present, estimates the speed of the intersecting moving object and the distance of the intersecting moving object to the intersection based on the first data and the second data A vehicle capable of performing driving control or notification control to avoid colliding with the intersecting moving object, based on the estimated speed of the intersecting moving object, the distance of the intersecting moving object to the intersection, and the third data.

[0099] In a driving assistance system and vehicle according to one embodiment of the present disclosure, the presence or absence of a moving object is determined based on first data of brightness or color obtained when the vehicle's headlights are on at an intersection in front of the vehicle in a situation where no moving object is present, and second data of brightness or color at the intersection obtained when the vehicle is traveling toward the intersection with its headlights on. As a result, even if the headlights of the vehicle and the moving object overlap at the intersection, or if the headlights of the vehicle and the moving object overlap with light such as streetlights, neon signs, or sunsets entering the intersection, the component of the moving object's headlights can be extracted from the second data obtained while the vehicle is traveling. If the component of the moving object's headlights can be extracted from the second data, it can be indirectly determined that the moving object is present. On the other hand, if the component of the moving object's headlights cannot be extracted from the second data, it can be indirectly determined that the moving object is not present.

[0100] Furthermore, in the driver assistance system and vehicle according to one embodiment of this disclosure, if a crossing object is present, the speed of the crossing object and the distance of the crossing object to the intersection are estimated based on the first and second data. Then, based on the estimated speed of the crossing object, the distance of the crossing object to the intersection, and third data of the vehicle's position and speed, driving control or notification control is performed to avoid the vehicle colliding with the crossing object. As a result, even in environments where the headlights of the vehicle and the crossing object overlap at the intersection, or where the headlights of the vehicle and the crossing object overlap with light from streetlights, neon signs, or the setting sun entering the intersection, it is possible to avoid a collision with the crossing object by driving control or notification control that predicts the movement of the crossing object in the driver's blind spot. Therefore, it is possible to achieve avoidance of head-on collisions by predicting the movement of crossing objects in the driver's blind spot in various environments.

[0101] The control unit 30 shown in Figure 3 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 3 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 3. 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 3.

Claims

1. A driving assistance device comprising: an acquisition unit capable of acquiring first data of brightness or color obtained when the vehicle's headlights are turned on at an intersection in front of the vehicle in the absence of a crossing moving object; second data of brightness or color at the intersection and third data of the vehicle's position and speed obtained when the vehicle is traveling toward the intersection with the headlights on; and a control unit capable of controlling the driving assistance of the vehicle based on the first data, second data and third data obtained by the acquisition unit, wherein the control unit determines whether or not the crossing moving object exists based on the first data and the second data; if the crossing moving object exists, estimates the speed of the crossing moving object and the distance of the crossing moving object to the intersection based on the first data and the second data; and is capable of performing driving control or notification control to avoid the vehicle colliding with the crossing moving object based on the estimated speed of the crossing moving object, the distance of the crossing moving object to the intersection and the third data.

2. The acquisition unit is capable of acquiring a fourth data corresponding to the vehicle's speed from a speed profile, which is time-series data of the area of ​​the superposition region of the vehicle's headlights and the intersecting moving object's headlights at the intersection, for each vehicle speed and for each speed difference between the vehicle and the intersecting moving object; the second data is time-series two-dimensional data; and the control unit is capable of deriving the speed of the intersecting moving object by performing time-series matching based on the first data, the second data and the fourth data when the intersecting moving object is present, according to claim 1.

3. The acquisition unit is capable of acquiring a fifth data corresponding to the distance of the vehicle to the intersection from distance profiles, which are time-series data of the area of ​​the superposition region of the light of the vehicle's headlights and the light of the intersecting moving object's headlights at the intersection, for each distance of the vehicle to the intersection and for each distance difference between the distance of the vehicle to the intersection and the distance of the intersecting moving object to the intersection; and the control unit is capable of deriving the position of the intersecting moving object by performing time-series matching based on the first data, the second data and the fifth data when the intersecting moving object is present, according to claim 2.

4. The driving assistance device according to claim 2, wherein the control unit is capable of deriving the speed of the intersecting moving object by matching time-series data of the area of ​​the superposition region between the light of the vehicle's headlights and the light of the intersecting moving object's headlights at the intersection, obtained based on the first data and the second data, with the fourth data.

5. The driving assistance device according to claim 3, wherein the control unit is capable of deriving the position of the intersecting moving object by matching time-series data of the area of ​​the superposition region between the light of the vehicle's headlights and the light of the intersecting moving object's headlights at the intersection, obtained based on the first data and the second data, with the fifth data.

6. The driving support device according to claim 1, wherein the second data is time-series two-dimensional data, and the control unit is capable of determining whether or not the intersecting moving object exists based on the time-series two-dimensional data obtained by subtracting the first data from the second data.

7. The driving assistance device according to claim 1, wherein the first data and the second data each include data on the brightness or color of the intersection.

8. The driving assistance device according to claim 1, wherein the first data and the second data each include data on the brightness or color of the intersection and data on the brightness or color of a wall or convex mirror near the intersection, respectively.

9. The driving support device according to claim 1, wherein the first data includes data obtained during at least one of the following time periods: nighttime, twilight, and sunrise.

10. The driving support device according to claim 1, further comprising a storage unit for storing the first data.

11. The driving support device according to claim 10, wherein if the control unit determines that the intersecting moving body exists, but the second data obtained thereafter reveals that the intersecting moving body did not actually exist, it is possible to update the first data by storing the second data in the storage unit.

12. A vehicle comprising a driving assistance device and a controlled device controlled by the driving assistance device, wherein the driving assistance device comprises: an acquisition unit capable of acquiring first data of brightness or color obtained when the vehicle's headlights are turned on at an intersection in front of the vehicle in the absence of a moving object crossing; second data of brightness or color at the intersection and third data of the vehicle's position and speed obtained when the vehicle is traveling toward the intersection with the headlights on; and a control unit capable of controlling the driving assistance of the vehicle based on the first data, the second data and the third data obtained by the acquisition unit, wherein the control unit determines whether or not the moving object crossing exists based on the first data and the second data, and if the moving object crossing exists, estimates the speed of the moving object crossing and the distance of the moving object crossing to the intersection based on the first data and the second data. A vehicle capable of performing driving control or notification control to avoid colliding with the intersecting moving object, based on the estimated speed of the intersecting moving object, the distance of the intersecting moving object to the intersection, and the third data.

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