Driving assistance device, vehicle, and driving assistance method
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Filing Date
- 2023-08-23
- Publication Date
- 2026-03-26
Abstract
Description
Driving assistance device, vehicle, and driving assistance method
[0001] The present disclosure relates to a driving assistance device mounted on a vehicle, the vehicle, and a driving assistance method.
[0002] In recent years, development of automatic driving control technologies for vehicles such as automobiles that allow the vehicle to travel automatically without the need for driver operation has been progressing. Furthermore, various driving assistance devices that utilize this type of automatic driving control technology to perform various controls to assist the driver in driving have been proposed and are becoming generally put into practical use. Technologies related to such driving assistance devices are disclosed, for example, in Patent Documents 1 to 4.
[0003] Japanese Patent No. 7171808 Japanese Patent No. 2969174 Japanese Patent Application Laid-Open No. 2020-101986 Japanese Patent No. 5776838
[0004] A driving assistance device according to a first aspect of the present disclosure includes a control unit capable of predicting the behavior of a target vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction is present ahead of a first vehicle, and when a target vehicle is parked at a waiting point on the non-priority road or traveling toward a waiting point. The control unit is capable of performing the following (A1), (A2), and (A3). (A1) Obtaining first data indicating that a vehicle to be predicted exists, that multiple second vehicles exist in at least one lane ahead of the first vehicle, and that an entry space into which the vehicle to be predicted can enter exists within one or more spaces formed by two second vehicles adjacent to each other in a common lane; (A2) When the vehicle to be predicted is waiting at a waiting point, estimating the waiting time of the vehicle to be predicted at the waiting point based on the obtained first data, and when the vehicle to be predicted is traveling toward the waiting point, estimating the margin time when the vehicle to be predicted enters the entry space based on the obtained first data; (A3) Predicting the possibility that the vehicle to be predicted will enter the entry space based on the waiting time or margin time.
[0005] A vehicle according to a second aspect of the present disclosure includes a control unit capable of predicting the behavior of a target vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction is present ahead of a first vehicle, and when a target vehicle is present that is stopped at a waiting point on the non-priority road or traveling toward a waiting point. The control unit is capable of performing the following (B1), (B2), and (B3). (B1) Obtaining first data indicating that a vehicle to be predicted exists, that a plurality of second vehicles exist in at least one lane ahead of the first vehicle, and that an entry space into which the vehicle to be predicted can enter exists within one or more spaces formed by two second vehicles adjacent to each other in a common lane; (B2) When the vehicle to be predicted is waiting at a waiting point, estimating the waiting time of the vehicle to be predicted at the waiting point based on the obtained first data, and when the vehicle to be predicted is traveling toward the waiting point, estimating the margin time when the vehicle to be predicted enters the entry space based on the obtained first data; (B3) Predicting the possibility that the vehicle to be predicted will enter the entry space based on the waiting time or margin time.
[0006] A driving assistance method according to a third aspect of the present disclosure is a method capable of predicting the behavior of a target vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction is present ahead of a first vehicle, and further when a target vehicle is present that is stopped at a waiting point on the non-priority road or traveling toward a waiting point. This method includes the following (C1), (C2), and (C3). (C1) Obtaining first data indicating that a vehicle to be predicted exists, that a plurality of second vehicles exist in at least one lane ahead of the first vehicle, and that an entry space into which the vehicle to be predicted can enter exists within one or more spaces formed by two second vehicles adjacent to each other in a common lane; (C2) When the vehicle to be predicted is waiting at a waiting point, estimating the waiting time of the vehicle to be predicted at the waiting point based on the obtained first data, and when the vehicle to be predicted is traveling toward the waiting point, estimating the margin time when the vehicle to be predicted enters the entry space based on the obtained first data; (C3) Predicting the possibility that the vehicle to be predicted will enter the entry space based on the waiting time or margin time.
[0007] A driving assistance device according to a fourth aspect of the present disclosure includes a control unit capable of predicting the behavior of a target vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction is present ahead of a first vehicle, and when a target vehicle is parked at a waiting point on the non-priority road or traveling toward a waiting point. The control unit is capable of performing the following (D1), (D2), and (D3). (D1) Obtaining data indicating that a vehicle to be predicted exists, that multiple second vehicles exist in at least one lane ahead of the first vehicle, and that an entry space into which the vehicle to be predicted can enter exists within one or more spaces formed by two second vehicles adjacent to each other in a common lane; (D2) Based on the obtained data, estimating a first congestion degree in the vicinity of the entry space on the priority road and a second congestion degree in an evaluation target area on the priority road extending from the position of the first vehicle to the vicinity of the entry space; (D3) Predicting the possibility that the vehicle to be predicted will enter the entry space based on the first congestion degree and the second congestion degree.
[0008] A driving assistance device according to a fifth aspect of the present disclosure includes a control unit capable of predicting the behavior of a target vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction is present ahead of a first vehicle, and when a target vehicle is present that is stopped at a waiting point on the non-priority road or traveling toward a waiting point. The control unit is capable of performing the following (E1), (E2), and (E3). (E1) Obtaining data indicating that a vehicle to be predicted exists, that multiple second vehicles exist in at least one lane ahead of the first vehicle, and that an entry space into which the vehicle to be predicted can enter exists within one or more spaces formed by two second vehicles adjacent to each other in a common lane; (E2) Based on the obtained data, estimating a first congestion degree in the vicinity of the entry space on the priority road and a third congestion degree in an evaluation target area in the same lane as the first vehicle, extending from the position of the first vehicle to the vicinity of the entry space; (E3) Predicting the possibility that the vehicle to be predicted will enter the entry space based on the first congestion degree and the third congestion degree.
[0009] FIG. 1 is a diagram illustrating an example of a schematic configuration of a cruise control system according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating an example of a driving assistance procedure in the cruise control system of FIG. 1. FIG. 3 is a diagram illustrating an example of a driving assistance procedure subsequent to FIG. 2. FIG. 4 is a diagram illustrating an example of a passing condition at an intersection. FIG. 5 is a diagram illustrating an example of a region for counting the number of vehicles near an intersection. FIG. 6 is a diagram illustrating a modified example of a region for counting the number of vehicles near an intersection. FIG. 7 is a diagram illustrating a modified example of a driving assistance procedure subsequent to FIG. 2. FIG. 8 is a diagram illustrating an example of a traffic situation near an intersection. FIG. 9 is a diagram illustrating an example of a traffic situation near an intersection. FIG. 10 is a diagram illustrating an example of a traffic situation near an intersection. FIG. 11 is a diagram illustrating an example of a traffic situation near an intersection. FIG. 12 is a diagram illustrating an example of a traffic situation near an intersection. FIG. 13 is a diagram illustrating an example of a traffic situation near an intersection.
[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.
[0011] <1. Background> In recent years, development of automatic driving control technologies for vehicles such as automobiles, which allow vehicles to travel automatically without the need for driver operation, has been progressing. Furthermore, various driving assistance devices that utilize this type of automatic driving control technology to perform various controls to assist the driver in driving operations have been proposed and are becoming generally put into practical use. Technologies related to such driving assistance devices are disclosed, for example, in Patent Documents 1 to 4.
[0012] The invention described in Patent Document 1 discloses a technology that predicts the driving intention of the driver of a vehicle (surrounding vehicle) traveling around the vehicle from the positions and traveling parameters (speed, etc.) of the surrounding vehicles, and estimates whether or not any of the surrounding vehicles is likely to merge into the lane the vehicle is traveling in. The invention described in Patent Document 2 discloses a technology that identifies a vehicle to be merged that will be a following vehicle to merge on a priority road, and if the inter-vehicle distance from the to-be-merged vehicle is equal to or less than the safe inter-vehicle distance for merging, determines the traffic conditions before and after the to-be-merged vehicle and determines whether the vehicle should merge.
[0013] The invention described in Patent Document 3 discloses a technology for predicting the behavior of a moving object based on dynamic information of the moving object generated based on sensor data collected from a plurality of sensors, and determining a combination of actions that may result in a collision between the moving objects based on the predicted behavior.The invention described in Patent Document 4 discloses a technology for predicting a moving object that may appear from a blind spot, and calculating a speed range in which the host vehicle may come into contact with the moving object based on the estimated speed of the predicted moving object.
[0014] However, the inventions described in Patent Documents 1 to 4 only estimate the movement of the other vehicle based on whether or not there is a possibility of a collision using parameters such as the vehicle speed and inter-vehicle distance of the other vehicle, and do not take into account parameters that have a high correlation with the psychological state of the driver of the other vehicle. Therefore, the inventions described in Patent Documents 1 to 4 are unable to predict the possibility that the other vehicle will merge or change lanes due to the psychological influence of the driver of the other vehicle, even if the possibility of the other vehicle merging or changing lanes is theoretically very low. As a result, the inventions described in Patent Documents 1 to 4 only respond after the other vehicle has begun to merge or change lanes, which increases the possibility of an accident in which the vehicle collides with the other vehicle.
[0015] As described above, conventional inventions have a problem in that they are unable to predict the possibility that the other vehicle will merge, change lanes, etc. due to the psychological influence of the driver of the other vehicle. Therefore, the inventors of the present application have conducted extensive research and have come up with a technology that makes it possible to predict the possibility that the other vehicle will merge, change lanes, etc. due to the psychological influence of the driver of the other vehicle. Below, the background of this newly conceived technology will be explained using hypothetical examples of traffic situations.
[0016] 8, 9, and 10 illustrate hypothetical traffic situation examples. In FIGS. 8, 9, and 10, a vehicle (host vehicle) 100a is traveling on a road with one lane in each direction. This one-lane road is composed of a driving lane L1 in which the vehicle 100a is traveling and an oncoming lane L2 that runs parallel to the driving lane L1 via a center line. An intersection CL is located ahead of the vehicle 100a on this one-lane road. This one-lane road is a priority road Lm in relation to a road that intersects with the one-lane road at the intersection CL. In other words, the vehicle 100a is traveling on the priority road Lm. Meanwhile, the road that intersects with the priority road Lm at the intersection CL is a non-priority road Ls in relation to the priority road Lm. On the non-priority road Ls, a vehicle (target vehicle) 100b is stopped at a stop line SL (waiting point) ( FIG. 8 , time ta). There is no traffic light at intersection CL.
[0017] The driver of vehicle 100a recognizes that vehicle 100a is traveling on the priority road Lm. Therefore, vehicle 100a is about to enter intersection CL without decelerating. At this time, vehicle 100b is stopped at stop line SL (waiting point) on the non-priority road Ls. The driver of vehicle 100b intends to pass through (intersect / cross) the intersection CL or turn left at the intersection CL (merge into oncoming lane L2). While vehicle 100b is stopped at stop line SL (waiting point) on the non-priority road Ls, the driver of vehicle 100b is searching for the right timing to pass through the intersection CL or turn left at the intersection CL. At this time, the driver of vehicle 100b finds a wide space SP between vehicles 100c and 100d in the lane traveling to the left (oncoming lane L2) on the priority road Ls. The driver of vehicle 100b decides to use this space SP to pass through the intersection CL or to turn left at the intersection CL, and starts to drive vehicle 100b into the intersection CL ( FIG. 9 , time tb). At this time, the driver of vehicle 100b is so focused on using the found space SP to pass through the intersection CL or to turn left at the intersection CL that he inadvertently overlooks the presence of vehicle 100a.
[0018] The longer the vehicle 100b is stopped (waiting) at the stop line SL, the more frustrated the driver of the vehicle 100b becomes at being unable to depart. As a result, the driver of the vehicle 100b, who would normally be able to recognize the presence of the vehicle 100a, inadvertently overlooks the presence of the vehicle 100a due to his frustrated feelings. As a result, the driver of the vehicle 100b starts the vehicle 100b without recognizing the presence of the vehicle 100a. Under such traffic conditions, there is a high possibility that the vehicles 100a and 100b will collide head-on at the intersection CL ( FIG. 10 , time tc).
[0019] 11 , 12 , and 13 illustrate other hypothetical traffic situation examples. In FIGS. 11 , 12 , and 13 , a vehicle (host vehicle) 100a is traveling on a road with one lane in each direction. This one-lane road is composed of a driving lane L1 in which the vehicle 100a is traveling and an oncoming lane L2 that runs parallel to the driving lane L1 via a center line. An intersection CL is located ahead of the vehicle 100a on this one-lane road. This one-lane road is a priority road Lm in relation to a road that intersects with the one-lane road at the intersection CL. In other words, the vehicle 100a is traveling on the priority road Lm. Meanwhile, the road that intersects with the priority road Lm at the intersection CL is a non-priority road Ls in relation to the priority road Lm. On the non-priority road Ls, a vehicle (target vehicle) 100b is traveling far ahead of the stop line SL (waiting point) ( FIG. 11 , time ta). There is no traffic light at intersection CL.
[0020] The driver of vehicle 100a recognizes that vehicle 100a is traveling on the priority road Lm. Therefore, vehicle 100a is about to enter intersection CL without slowing down. At this time, vehicle 100b is traveling on the non-priority road Ls, far ahead of the stop line SL (waiting point). The driver of vehicle 100b intends to pass through intersection CL a little further ahead or to make a left turn at intersection CL. While driving vehicle 100b on the non-priority road Ls, the driver of vehicle 100b is searching for the right timing to pass through intersection CL or to turn left at intersection CL. At this time, the driver of vehicle 100b finds space SP in the lane going left (oncoming lane L2) on the priority road Ls. The driver of vehicle 100b decides to use this space SP to pass through the intersection CL or to turn left at the intersection CL, and starts to drive vehicle 100b into the intersection CL without stopping at the stop line SL ( FIG. 12 , time tb). At this time, the driver of vehicle 100b is so focused on using the found space SP to pass through the intersection CL or to turn left at the intersection CL that he inadvertently overlooks the presence of vehicle 100a.
[0021] The shorter the time (leeway time) between finding the space SP and entering the intersection CL, the more the driver of vehicle 100b feels impatient, feeling the need to enter the intersection CL immediately. In particular, when vehicle 100b can enter the space SP by entering the intersection CL without needing to slow down or with only a small amount of deceleration, the driver of vehicle 100b is likely to make a hasty decision. As a result, the driver of vehicle 100b may inadvertently overlook the presence of vehicle 100a due to feelings of impatience, even though he or she would normally be able to recognize the presence of vehicle 100a. As a result, the driver of vehicle 100b enters the intersection CL without recognizing the presence of vehicle 100a. Under such traffic conditions, there is a high possibility that vehicles 100a and 100b will collide head-on at the intersection CL (Figure 13, time tc).
[0022] Therefore, the inventors of the present application came up with the idea of predicting the behavior of vehicle 100b by using parameters that have a high correlation with the psychological state of the driver of vehicle 100b, such as the waiting time and margin time of vehicle 100b, as a measure to reduce the risk of collision between vehicle 100a and vehicle 100b under specific traffic conditions when vehicle 100a and vehicle 100b are about to enter intersection CL where priority road Lm and non-priority road Ls intersect. A cruise control system for realizing this will be described in detail below.
[0023] 2. Embodiments> [Configuration Example] Fig. 1 shows a schematic configuration example of a cruise control system 1 according to an embodiment of the present disclosure. As shown in Fig. 1, the cruise control system 1 includes cruise control devices 10 mounted on a plurality of vehicles, respectively, and a control device 200 provided in a network environment NW to which the plurality of cruise control devices 10 are connected via wireless communication. The cruise control device 10 corresponds to a specific example of a "driving assistance device" according to an embodiment of the present disclosure.
[0024] The control device 200 sequentially integrates and updates the road map information transmitted from the cruise control devices 10 of the vehicles, and transmits the updated road map information to the vehicles. The control device 200 includes, for example, a road map information integration ECU 201 and a transceiver 202.
[0025] The road map boundary information integration ECU 201 integrates road map information collected from multiple vehicles via the transceiver 202, and sequentially updates road map information surrounding the vehicle on the road. The road map information may be, for example, a dynamic map, and includes static information and quasi-static information that mainly constitute road information, and quasi-dynamic information and dynamic information that mainly constitute traffic information.
[0026] The static information that makes up road information is composed of information that requires updates within one month, such as roads, structures on roads, structures around roads, lane information, road surface information, and permanent regulation information. "Roads" include, for example, road locations and shapes, intersections, and road attributes (e.g., national roads, prefectural roads, city roads, private roads, priority roads, non-priority roads, general roads, and expressways). "Structures on roads" include, for example, traffic signs, traffic lights, convex mirrors, and pedestrian bridges. "Structures around roads" include, for example, various buildings and parks.
[0027] The quasi-static information that constitutes the road information is made up of information that needs to be updated every hour, such as traffic regulation information due to road construction or events, wide-area weather information, and congestion forecasts.
[0028] The semi-dynamic information that constitutes traffic information is composed of information that requires updating within one minute, such as the actual traffic congestion situation at the time of observation, driving restrictions, temporary driving obstructions such as fallen objects and obstacles, actual accident conditions, and narrow-area weather information.
[0029] The dynamic information that makes up the traffic information is made up of information that needs to be updated every second, such as information sent and exchanged between mobile bodies, information on currently displayed traffic signals, information on pedestrians and bicycles at intersections, information on vehicles traveling on roads, etc. Such road map information is maintained and updated periodically until the next information is received from each vehicle, and the updated road map information is transmitted to each vehicle as appropriate via the transceiver 202.
[0030] The cruise control device 10 has a cruise environment recognition unit 11 and a locator unit 12 as units for recognizing the cruise environment around the vehicle. The cruise control device 10 also has a cruise control unit (hereinafter referred to as "cruise_ECU") 21, an engine control unit (hereinafter referred to as "E / G_ECU") 22, a power steering control unit (hereinafter referred to as "PS_ECU") 23, and a brake control unit (hereinafter referred to as "BK_ECU") 24. These control units 21 to 24 are connected to the cruise environment recognition unit 11 and the locator unit 12 via an in-vehicle communication line such as a Controller Area Network (CAN).
[0031] The travel_ECU 21 controls the vehicle according to, for example, a driving mode. Examples of the driving modes include a manual driving mode and a driving control mode. The manual driving mode requires the driver to maintain steering, and the host vehicle is driven by the driver's driving operations, such as steering, accelerating, and braking. The driving control mode supports the driver in driving operations to increase the safety of pedestrians and other vehicles around the vehicle (host vehicle). In the driving control mode, for example, when the vehicle (host vehicle) approaches an intersection, the travel_ECU 21 predicts the behavior of a traveling or stopped vehicle (hereinafter referred to as a "target vehicle") on a road intersecting the intersection. If the prediction indicates that the target vehicle is likely to enter the intersection, the travel_ECU 21 can, for example, alert or warn the driver, and even perform risk avoidance control such as braking. Detailed processing in the driving control mode will be described later.
[0032] A throttle actuator 25 is connected to the output side of the E / G_ECU 22. This throttle actuator 25 opens and closes a throttle valve of an electronically controlled throttle provided in a throttle body of the engine. The E / G_ECU 22 controls the operation of the throttle actuator 25 by outputting a drive signal to the throttle actuator 25. The throttle actuator 25 opens and closes the throttle valve based on the drive signal from the E / G_ECU 22 to adjust the intake air flow rate, thereby generating a desired engine output.
[0033] An electric power steering motor 26 is connected to the output side of the PS_ECU 23. This electric power steering motor 26 applies steering torque to the steering mechanism by the rotational force of the motor. The PS_ECU 23 controls the operation of the electric power steering motor 26 by outputting a drive signal to the electric power steering motor 26. During autonomous driving, the electric power steering motor 26 performs lane keeping control, which keeps the vehicle traveling in the current lane, and lane change control, which moves the vehicle to an adjacent lane (lane change control for overtaking control, etc.), based on the drive signal from the PS_ECU 23.
[0034] A brake actuator 27 is connected to the output side of the BK_ECU 24. This brake actuator 27 adjusts the brake hydraulic pressure supplied to the brake wheel cylinders provided on each wheel. The BK_ECU 24 controls the operation of the brake actuator 27 by outputting a drive signal to the brake actuator 27. Based on the drive signal from the BK_ECU 24, the brake actuator 27 generates a braking force on each wheel using the brake wheel cylinders, forcibly decelerating the wheel.
[0035] The driving environment recognition unit 11 is fixed, for example, to the center of the upper part of the front interior of the vehicle. The driving environment recognition unit 11 has an in-vehicle camera (stereo camera) consisting of a main camera 11a and a sub-camera 11b, an image processing unit (IPU) 11c, and a driving environment detection unit 11d.
[0036] The main camera 11a and the sub-camera 11b are autonomous sensors that sense the real space around the vehicle. The main camera 11a and the sub-camera 11b are, for example, arranged at symmetrical positions on either side of the center of the vehicle in the width direction, and are capable of capturing stereo images of the area in front of the vehicle from different viewpoints.
[0037] The IPU 11c is capable of generating a distance image calculated from the amount of deviation in the positions of corresponding objects based on a pair of stereo images of the area in front of the vehicle obtained by capturing images with the main camera 11a and the sub-camera 11b.
[0038] The driving environment detection unit 11d can, for example, determine lane markings that divide the road around the vehicle based on the distance image received from the IPU 11c. The driving environment detection unit 11d can also, for example, determine the road curvature [1 / m] of the markings that divide the left and right sides of the road (driving lane) on which the vehicle is traveling, and the width between the left and right markings (vehicle width). The driving environment detection unit 11d can also, for example, perform predetermined pattern matching on the distance image to detect lanes and three-dimensional objects such as structures that exist around the vehicle.
[0039] Here, the detection of a three-dimensional object by the driving environment detection unit 11d includes, for example, detecting the type of the three-dimensional object, the distance to the three-dimensional object, the speed of the three-dimensional object, and the relative speed between the three-dimensional object and the vehicle (host vehicle). Examples of three-dimensional objects to be detected include traffic lights, intersections, road signs, stop lines, other vehicles, pedestrians, and various buildings. The driving environment detection unit 11d is capable of outputting information about the detected three-dimensional objects to the driving_ECU 21, for example.
[0040] The locator unit 12 estimates the vehicle's position (host vehicle position) on a road map and includes a locator calculation unit 13 that estimates the host vehicle position. Sensors required for estimating the vehicle's position (host vehicle position) are connected to the input side of the locator calculation unit 13. Examples of such sensors include an acceleration sensor 14, a vehicle speed sensor 15, a gyro sensor 16, and a GNSS receiver 17. The acceleration sensor 14 is capable of detecting the longitudinal acceleration of the vehicle. The vehicle speed sensor 15 is capable of detecting the vehicle's speed. The gyro sensor 16 is capable of detecting the vehicle's angular velocity or angular acceleration. The GNSS receiver 17 is capable of receiving positioning signals transmitted from multiple positioning satellites. The locator calculation unit 13 is also connected to a transceiver 18 that transmits and receives information to and from the control device 200 and other vehicles.
[0041] A high-precision road map database 19 is also connected to the locator calculation unit 13. The high-precision road map database 19 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, like the road map information included in the road map information integration_ECU 201, mainly includes static information and quasi-static information constituting road information, and quasi-dynamic information and dynamic information constituting traffic information.
[0042] The locator calculation unit 13 includes, for example, a map information acquisition unit 13a, a vehicle position estimation unit 13b, and a driving environment recognition unit 13c.
[0043] The vehicle position estimation unit 13b is capable of acquiring the position coordinates of the vehicle (host vehicle) based on the positioning signal received by the GNSS receiver 17. The vehicle position estimation unit 13b is also capable of estimating the host vehicle's position on a road map by map-matching the acquired position coordinates with route map information. The map information acquisition unit 13a is capable of acquiring map information of a predetermined range including the vehicle (host vehicle) from map information stored in the high-precision road map database 19, based on the position coordinates of the vehicle (host vehicle) acquired by the vehicle position estimation unit 13b.
[0044] In an environment where valid positioning signals from positioning satellites cannot be received due to reduced sensitivity of the GNSS receiver 17, such as when driving inside a tunnel, the vehicle position estimation unit 13b can switch to autonomous navigation, which estimates the vehicle's position based on the vehicle speed detected by the vehicle speed sensor 15, the angular velocity detected by the gyro sensor 16, and the longitudinal acceleration detected by the acceleration sensor 14, and estimate the vehicle's position on a road map.
[0045] As described above, the vehicle position estimation unit 13b estimates the position of the vehicle (host vehicle position) on a road map based on the positioning signal received by the GNSS receiver 17 or information detected by the gyro sensor 16, etc., and is then able to determine the road type, etc. of the road on which the vehicle (host vehicle) is traveling based on the estimated host vehicle position on the road map.
[0046] The driving environment recognition unit 13c is capable of updating the road map information stored in the high-precision road map database 19 to the latest state by using road map information acquired through external communication (roadside-to-vehicle communication and vehicle-to-vehicle communication) via the transceiver 18. 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 through communication with outside the vehicle, and information on moving bodies such as vehicles traveling on roads is updated in approximately real time.
[0047] The driving environment recognition unit 13c verifies road map information based on the driving environment information recognized by the driving environment recognition unit 11, and is capable of updating the road map information stored in the high-precision road map database 19 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 roads recognized by the driving environment recognition unit 11 is updated in real time.
[0048] The road map information thus updated is then transmitted to the control device 200 and vehicles around the vehicle (host vehicle) by road-to-vehicle communication and vehicle-to-vehicle communication via the transceiver 18. Furthermore, the driving environment recognition unit 13c is capable of outputting, from the updated road map information, map information of a predetermined range including the host vehicle position estimated by the vehicle position estimation unit 13b, together with the host vehicle position (vehicle position information), to the driving_ECU 21.
[0049] Next, the travel_ECU 21 will be described in detail.
[0050] 2 and 3 show an example of a driving assistance procedure in the cruise control system 1. FIG. 4 shows an example of traffic conditions in steps S101 to S108 of FIG. 2. FIG. 5 shows an example of two regions (neighborhood region Ra, evaluation target region Rb) defined for calculating the passage probability P in steps S109 to S111 of FIG. 3. FIG. 4 illustrates conditions (passage conditions) under which the vehicle 100b (target vehicle) passes through the space SP. The passage probability P refers to the possibility that the vehicle 100b will enter the space SP.
[0051] In FIG. 4 , vehicle (host vehicle) 100a is assumed to be traveling on a road with one lane in each direction. Vehicle 100a corresponds to a specific example of a "first vehicle" according to an embodiment of the present disclosure. This one-lane road is composed of a driving lane L1 in which vehicle 100a is traveling and an oncoming lane L2 that runs along driving lane L1 via a center line. An intersection CL is provided ahead of vehicle 100a on this one-lane road. This one-lane road is a priority road Lm in relation to the road that intersects with this one-lane road at intersection CL. In other words, vehicle 100a is traveling on the priority road Lm.
[0052] On the other hand, a road that intersects with the priority road Lm at the intersection CL is a non-priority road Ls in relation to the priority road Lm. On the non-priority road Ls, a vehicle (target vehicle) 100b is stopped at a stop line SL (waiting point) or is traveling toward the intersection CL. The vehicle 100b corresponds to a specific example of a "second vehicle" according to an embodiment of the present disclosure. There are no traffic lights installed at the intersection CL.
[0053] The driver of vehicle 100a recognizes that vehicle 100a is traveling on the priority road Lm. Therefore, vehicle 100a is about to enter intersection CL without decelerating. At this time, vehicle 100b is stopped at a stop line SL (waiting point) on the non-priority road Ls or is traveling toward the intersection CL. The driver of vehicle 100b intends to pass through the intersection CL or make a left turn at the intersection CL. While vehicle 100b is stopped at the stop line SL (waiting point) on the non-priority road Ls or traveling toward the intersection CL, the driver of vehicle 100b is searching for the right timing to pass through the intersection CL or to turn left at the intersection CL. At this time, the driver of vehicle 100b finds a wide space SP between vehicles 100c and 100d in the lane traveling to the left (oncoming lane L2) on the priority road Ls. The driver of vehicle 100b decides to use this space SP to pass through intersection CL or to turn left at intersection CL, and causes vehicle 100b to enter intersection CL. However, the driver of vehicle 100b is so focused on using the found space SP to pass through intersection CL or to turn left at intersection CL that he inadvertently overlooks the presence of vehicle 100a.
[0054] Now, suppose vehicle 100b is stopped at stop line SL. At this time, the longer the vehicle 100b is stopped (waiting) at stop line SL, the more frustrated the driver of vehicle 100b becomes at not being able to depart. As a result, although the driver of vehicle 100b would normally be able to recognize the presence of vehicle 100a, due to his frustrated feelings, he inadvertently overlooks the presence of vehicle 100a. As a result, the driver of vehicle 100b starts vehicle 100b without recognizing the presence of vehicle 100a. Under such traffic conditions, there is a high possibility that vehicles 100a and 100b will collide head-on at intersection CL.
[0055] Also, suppose vehicle 100b is traveling just before stop line SL. At this time, the shorter the time (leeway time) between finding space SP and entering intersection CL, the more anxious the driver of vehicle 100b becomes, feeling that he or she must enter intersection CL immediately. In particular, when vehicle 100b does not need to decelerate or can enter space SP by entering intersection CL with only a small amount of deceleration, the driver of vehicle 100b is likely to make a hasty decision. As a result, the driver of vehicle 100b may inadvertently overlook the presence of vehicle 100a due to feelings of anxiety or hasty judgment, even though he or she would normally be able to recognize the presence of vehicle 100a. As a result, the driver of vehicle 100b enters intersection CL without recognizing the presence of vehicle 100a. Under such traffic conditions, there is a high possibility that the vehicles 100a and 100b will collide head-on at the intersection CL.
[0056] Therefore, the traveling_ECU 21 is capable of performing calculations that take such events into consideration. Specifically, the traveling_ECU 21 is capable of determining whether or not a specific traffic situation exists in which the vehicles 100a and 100b are attempting to enter an intersection CL where the priority road Lm and the non-priority road Ls intersect. After determining that the specific traffic situation exists, the traveling_ECU 21 performs calculations regarding the existence of a space SP (entry space) into which the vehicle 100b can enter, and the waiting time Tw or margin time Ts of the vehicle 100b, and is capable of predicting the possibility (passing probability P) that the vehicle 100b will enter the space SP based on the results of the calculations.
[0057] (Space SP) The space SP refers to a space formed by two adjacent vehicles in a common lane (e.g., the oncoming lane L2). The "space SP (entry space) into which vehicle 100b can enter" refers to a space that has a width (length) that vehicle 100b can theoretically enter when vehicle 100b is stopped at stop line SL or traveling toward intersection CL. The "entry space" must exist at least within a range that can be recognized by the driver of vehicle 100b. Therefore, the "entry space" must exist within an area with a radius of approximately 50 meters centered on vehicle 100b, for example.
[0058] For example, the traveling_ECU 21 is capable of determining whether or not a space SP that satisfies the following passing conditions (1) and (2) exists among one or more spaces SP formed by two adjacent vehicles in the oncoming lane L2 of the priority road Ls. The passing conditions (1) and (2) are expressed as equations as shown in the following paragraph. As a result, if a space that satisfies the equations of the following passing conditions exists, the traveling_ECU 21 can recognize the space SP as a space SP (entry space) into which the vehicle 100b can enter. (1) After the vehicle 100b passes through the intersection CL without coming into contact with the vehicle 100d, the vehicle 100b passes through the space SP or merges into the space SP. (2) After the vehicle 100b passes through the space SP or merges into the space SP without coming into contact with the vehicle 100c, the vehicle 100c passes through the intersection CL.
[0059] (Passing conditions) (Wr / 2 + Ls1) / Vy > (Lx2 + Wb / 2) / Vx2 Ly / Vy < (Lx1 - Wb / 2) / Vx1 Vx1: speed of vehicle 100c [m / s] Vx2: speed of vehicle 100d [m / s] Vy: speed of vehicle 100b [m / s] Lx1: distance [m] between the rear end of space SP and the point (intersection point α) where vehicles 100c and 100b intersect within intersection CL Lx2: distance [m] between the front end of space SP and the point (intersection point α) where vehicles 100c and 100b intersect within intersection CL Ly: length [m] obtained by adding the width [m] of priority road Lm at intersection CL and the total length [m] of vehicle 100b Wr: width [m] of priority road Lm Wb: 1 / 2 the width [m] of the non-priority road Ls Wd: 1 / 2 the width [m] of the priority road Lm Ls1: Distance [m] from the stop line SL to the priority road Lm within the intersection SL (Wr / 2 + Ls1) / Vy: Time [s] required for vehicle 100b to travel from the stop line SL to the oncoming lane L2 within the intersection CL (Lx2 + Wb / 2) / Vx2: Time [s] required for vehicle 100d to pass through the intersection CL from its current position Ly / Vy: Time [s] required for vehicle 100b to move from the position of the stop line SL to a position where it passes through the intersection CL (the position of the vehicle indicated by the dashed line in FIG. 4 ) (Lx1 - Wb / 2) / Vx1: Time [s] required for vehicle 100c to travel from its current position to the oncoming lane L2 within the intersection CL
[0060] The following traffic situations are examples of traffic situations in which it can be said that "there is no entry space": When there is no space SP on the priority road Lm within a range that can be recognized by the driver of the vehicle 100b (for example, within an area with a radius of about 50 m centered on the vehicle 100b).
[0061] The traveling_ECU 21 can estimate whether the traffic conditions ahead of the vehicle 100a are as described above, for example, from data obtained from a sensor (e.g., the traveling environment recognition unit 11) of the vehicle 100a, data obtained from a road surface sensor through road-to-vehicle communication by the receiver-transmitter 18, or data obtained from another vehicle through vehicle-to-vehicle communication by the receiver-transmitter 18. For example, if these data include data indicating that multiple vehicles are traveling continuously in the oncoming lane L2, the traveling_ECU 21 can determine that the traffic conditions ahead of the vehicle 100a are as described above.
[0062] (Waiting time Tw) The waiting time Tw refers to the time that the vehicle 100b is stopped at the stop line SL. This time refers to the time (predicted time) that the vehicle 100b stopped at the stop line SL is predicted to spend from the time when the vehicle 100b is stopped at the stop line SL until it departs from the stop line SL, or an actual time that has a predetermined correlation with the predicted time.
[0063] The start timing of the predicted time and the actual measurement time may include various timings, for example, as shown below. The start timing of the predicted time and the actual measurement time may be, for example, the timing when the vehicle 100b stops at the stop line SL, or the timing when measurement of the predicted time and the actual measurement time starts while the vehicle 100b is stopped at the stop line SL. The start timing of the predicted time and the actual measurement time may be, for example, the timing when an "entry space" is detected while the vehicle 100b is stopped at the stop line SL. The start timing of the predicted time and the actual measurement time may be, for example, the timing when it is detected that the vehicle 100b is stopping at the stop line SL, or the timing when it is detected that the vehicle 100b is stopped at the stop line SL.
[0064] The timing at which the actual measurement time ends may be, for example, the timing at which the travel_ECU 21 starts calculating the waiting time Tw (the timing at which step S110, described later, starts). The timing at which the travel_ECU 21 starts calculating the waiting time Tw is a predetermined period before the timing at which the vehicle 100b actually departs from the stop line SL. The timing at which the actual measurement time ends is not limited to the timing at which step S110, described later, starts.
[0065] The driving_ECU 21 is capable of calculating the waiting time Tw (predicted time or actual measured time) based on, for example, data obtained from a sensor of the vehicle 100a (e.g., the driving environment recognition unit 11), data obtained from a road surface sensor through road-to-vehicle communication by the receiver-transmitter 18, or data obtained from another vehicle through vehicle-to-vehicle communication by the receiver-transmitter 18.
[0066] (Margin Time Ts) The margin time Ts refers to the margin time when the vehicle 100b enters the "entry space". The margin time Ts is, for example, the difference between the time when the vehicle 100b is predicted to arrive at the "entry space" and the current time. The margin time Ts may be, for example, a time that has a predetermined correlation with the difference between the time when the vehicle 100b is predicted to arrive at the "entry space" and the current time. The margin time Ts may be, for example, a time that has a predetermined correlation with the difference between the time when the vehicle 100b is predicted to arrive at the stop line SL and the current time.
[0067] The driving_ECU 21 is capable of calculating the waiting time Tw (predicted time or actual measured time) based on, for example, data obtained from a sensor of the vehicle 100a (e.g., the driving environment recognition unit 11), data obtained from a road surface sensor through road-to-vehicle communication by the receiver-transmitter 18, or data obtained from another vehicle through vehicle-to-vehicle communication by the receiver-transmitter 18.
[0068] (Passing Probability P) The passing probability P refers to the possibility that the vehicle 100b will enter the space SP. The passing probability P can be derived, for example, by the following formula (1) or formula (2). Formula (1) is a formula for deriving the passing probability P when the vehicle 100b is stopped at the stop line SL. Formula (2) is a formula for deriving the passing probability P when the vehicle 100b is traveling on the non-priority road Ls.
[0069] P = exp(α × (N1 - N2) / N2) × exp(-β / Tw) (1) P = exp(α × (N1 - N2) / N2) × exp(-γTs) (2) α, β, γ: positive constants N1: number of vehicles in the nearby area Ra (number of partial recognition loads) N2: number of vehicles in the evaluation target area Rb (number of overall recognition loads)
[0070] FIG. 5 shows an example of a counting area for the number of vehicles near an intersection CL. FIG. 5 illustrates a nearby area Ra and an evaluation area Rb as examples of the counting area. The nearby area Ra is an area on the priority road Lm near a space SP (entry space) into which vehicle 100b can enter. In FIG. 5, the nearby area Ra includes two vehicles (e.g., vehicles 100c and 100d) that make up the entry space and a vehicle (e.g., vehicle 100e) traveling in the area between the entry space and vehicle 100b in the lane (traveling lane L1) between the entry space and vehicle 100b. Therefore, the number of vehicles N1 in FIG. 5 is three. The evaluation area Rb is an area on the priority road Lm ahead of vehicle 100a, and includes vehicle 100a and the nearby area Ra. 5, the evaluation target area Rb includes vehicles 100c, 100d, 100e, 100a, and a vehicle 100f traveling beside vehicle 100a. Therefore, the number of vehicles N2 in FIG. 5 is five.
[0071] The travel_ECU 21 is capable of calculating the passage probability P based on, for example, data obtained from a sensor (e.g., the travel environment recognition unit 11) of the vehicle 100a, data obtained from a road surface sensor through road-to-vehicle communication by the receiver-transmitter 18, or data obtained from another vehicle through vehicle-to-vehicle communication by the receiver-transmitter 18. The timing for calculating the number of vehicles N1 and the number of vehicles N2 is, for example, the timing when it is determined that there is a space SP (entry space) into which the vehicle 100b can enter, that is, the timing when step S108 described below is executed.
[0072] (Driving Assistance Procedure) Next, the driving assistance procedure in the cruise control system 1 will be described with reference to Figures 2 and 3. First, a stereo camera provided on the vehicle 100a captures images of the area ahead of the vehicle 100a and outputs the resulting stereo images to the IPU 11c. The IPU 11c generates a distance image based on the stereo images acquired by the stereo camera and outputs the image to the driving environment detection unit 11d. The driving environment detection unit 11d performs predetermined pattern matching on the distance image generated by the IPU 11c to detect the priority road Lm, the driving lane L1, the oncoming lane L2, the non-priority road Ls, the intersection CL, vehicles on the priority road Lm (e.g., vehicles 100a, 100c to 100f), and vehicles on the non-priority road Ls (e.g., vehicle 100b).
[0073] Next, the driving environment recognition unit 13c uses road map information acquired via external communication to detect the priority road Lm, the driving lane L1, the oncoming lane L2, the non-priority road Ls, the intersection CL, vehicles on the priority road Lm (e.g., vehicles 100a, 100c to 100f), and vehicles on the non-priority road Ls (e.g., vehicle 100b). Here, it is assumed that the road map information acquired via external communication includes information on vehicles on the priority road Lm (e.g., vehicles 100a, 100c to 100f) and vehicles on the non-priority road Ls (e.g., vehicle 100b). In this case, the driving environment recognition unit 13c can use the road map information acquired via external communication to detect vehicles on the priority road Lm (e.g., vehicles 100a, 100c to 100f) and vehicles on the non-priority road Ls (e.g., vehicle 100b).
[0074] The vehicle position estimation unit 13b acquires the position coordinates of the vehicle 100a based on the positioning signal received by the GNSS receiver 17. The vehicle position estimation unit 13b further acquires the vehicle speed (speed of the vehicle 100a) detected by the vehicle speed sensor 15.
[0075] Next, the travel_ECU 21 acquires road information Da and vehicle information Db based on various information obtained from the travel environment detection unit 11d, the vehicle position estimation unit 13b, and the travel environment recognition unit 13c (step S101). Here, the road information Da includes information about the priority road Lm, the driving lane L1, the oncoming lane L2, the non-priority road Ls, and the intersection CL detected by the travel environment detection unit 11d or the travel environment recognition unit 13c. The vehicle information Db includes information about the speed (vehicle speed) of the vehicle 100a acquired from the vehicle position estimation unit 13b, and information about vehicles on the priority road Lm (e.g., vehicles 100a, 100c to 100f) and vehicles on the non-priority road Ls (e.g., vehicle 100b) acquired from the travel environment detection unit 11d or the travel environment recognition unit 13c.
[0076] Next, the traveling_ECU 21 determines whether an intersection CL exists ahead of the vehicle 100a (step S102). If the road information Da includes information about the intersection CL (step S102; Y), the traveling_ECU 21 determines whether the lane on which the vehicle 100a is traveling (traveling lane L1) is a priority road Lm (step S103). If the road information Da includes information about the priority road Lm (step S103; Y), the traveling_ECU 21 determines whether a vehicle (target vehicle) 100b traveling on a non-priority road Ls exists (step S104). If the vehicle information Db includes information about the vehicle 100b (step S104; Y), the traveling_ECU 21 calculates the inter-vehicle space ΔL formed by multiple vehicles traveling on the oncoming lane L2 of the priority road Lm (step S105). If the calculated inter-vehicle space ΔL is equal to or greater than a predetermined threshold value ΔLth (step S106; Y), the travel_ECU 21 recognizes the space having the inter-vehicle space ΔL equal to or greater than the threshold value ΔLth as the above-mentioned space SP.
[0077] The travel_ECU 21 then calculates the passing conditions for the space SP (step S107). If the space SP satisfies the passing conditions (step S108; Y), the travel_ECU 21 calculates the number of vehicles N1, N2, the waiting time Tw or the margin time Ts, and the passing probability P (steps S109, S110, S111).
[0078] The travel_ECU 21 executes step S101 if any of the following conditions is met in each of the above steps: - The road information Da does not include information on the intersection CL (step S102; N) - The road information Da does not include information on the priority road Lm (step S103; N) - The vehicle information Db does not include information on the vehicle 100b (step S104; N) - The inter-vehicle space ΔL is less than the threshold value ΔLth (step S106; N) - The space SP does not satisfy the passing condition (step S108; N)
[0079] Next, the traveling_ECU 21 executes driving assistance according to the passing probability P (step S112), where α=1 and β=γ=0.4. For example, when P<0.25 (N2=3, N1=2, 1 / Tw or Ts=2.6), the traveling_ECU 21 does not execute any driving assistance.
[0080] For example, when 0.25≦P<0.50 (N2=6, N1=5, 1 / Tw or Ts=1.3), the traveling_ECU 21 issues a warning to the driver of the vehicle 100a. For example, the traveling_ECU 21 outputs a video signal to a head-up display that displays an image on the windshield, in which a shape image in a color (e.g., yellow) indicating the presence of the vehicle 100b on the non-priority road Ls is superimposed. This allows the driver of the vehicle 100a to recognize the presence of the vehicle 100b on the non-priority road Ls from the image displayed on the windshield, and allows the driver to pass through the intersection CL while decelerating, for example.
[0081] For example, when 0.50≦P<0.75 (N2=10, N1=8, 1 / Tw, or Ts=0.2), the traveling_ECU 21 issues a warning to the driver of the vehicle 100a. For example, the traveling_ECU 21 outputs a video signal to a head-up display (HUD) that displays an image on the windshield, in which a vehicle shape image colored red (e.g., red) indicating the presence of the vehicle 100b on the non-priority road Ls is superimposed. For example, the traveling_ECU 21 outputs an audio signal that emits an intermittent sound to a speaker. This allows the driver of the vehicle 100a to recognize the presence of the vehicle 100b on the non-priority road Ls from the image displayed on the windshield, and further recognizes the risk of the vehicle 100b jumping out onto the non-priority road Ls from the intermittent sound from the speaker. As a result, the driver of the vehicle 100a can, for example, pass through the intersection CL at a slower speed.
[0082] For example, when 0.75≦P (N2=15, N1=12, 1 / Tw or Ts=0.1), the traveling_ECU 21 performs risk avoidance control, such as braking, on the vehicle 100a. For example, the traveling_ECU 21 performs a predetermined risk avoidance braking when, for example, three seconds or less remain until a collision between the vehicle 100a and the vehicle 100b occurs. This makes it possible to avoid a collision between the vehicle 100a and the vehicle 100b.
[0083] [Effects] Next, effects of the cruise control system 1 according to one embodiment of the present disclosure will be described.
[0084] In this embodiment, data is acquired indicating the presence of vehicle 100b, the presence of multiple vehicles in at least one lane (traveling lane L1, oncoming lane L2) ahead of vehicle 100a, and the presence of a space SP (entry space) into which vehicle 100b can enter within one or more spaces formed by two adjacent vehicles in a common lane (oncoming lane L2). When vehicle 100b is waiting at stop line SL, a waiting time Tw of vehicle 100b is estimated based on the acquired data. When vehicle 100b is traveling toward stop line SL, a margin time Ts for vehicle 100b to enter the entry space is estimated based on the acquired data. Furthermore, a probability (passing probability P) of vehicle 100b entering the entry space is predicted based on the waiting time Tw or margin time Ts. This makes it possible to predict the possibility that vehicle 100b will pass through intersection CL due to psychological influences on the driver of vehicle 100b. As a result, it is possible to issue a warning, issue a warning, or perform braking control that can avoid a collision between the vehicle 100a and the vehicle 100b.
[0085] In this embodiment, the probability (passing probability P) that vehicle 100b will enter the entry space is predicted based on the number of vehicles N1, N2, the waiting time Tw, or the margin time Ts. This makes it possible to predict the probability that vehicle 100b will pass through intersection CL due to the psychological influence of the driver of vehicle 100b. As a result, it is possible to issue a warning, issue a braking control, or the like that can avoid a collision between vehicle 100a and vehicle 100b.
[0086] In this embodiment, when road information Da and vehicle information Db are obtained from sensors installed in vehicle 100a, the possibility (passage probability P) of vehicle 100b entering the entry space can be predicted even when it is difficult for vehicle 100a to communicate with the network environment NW.
[0087] In this embodiment, when road information Da and vehicle information Db are obtained from sensors installed on vehicle 100a and the network environment NW, the possibility (passing probability P) of vehicle 100b entering the entry space can be predicted more accurately than when road information Da and vehicle information Db are generated only by sensors installed on vehicle 100a.
[0088] 3. Modifications The present disclosure has been described above by giving embodiments, but the present disclosure is not limited to these embodiments and various modifications are possible.
[0089] [Variation 3-1] In the above embodiment, the evaluation target area Rb may be, for example, an area including the nearby area Ra and the area of the lane (traveling lane L1) on which the vehicle 100a is traveling, from the position of the vehicle 100a to the entry space, as shown in FIG. 6. In this case, the number of vehicles traveling in an area that is relatively less affected by the entry of the vehicle 100b into the entry space (the area between the vehicle 100a and the entry space in the oncoming lane L2) can be excluded from the number of vehicles N2. As a result, the passing probability P can be calculated more accurately.
[0090] [Variation 3-2] In the above embodiment and its variations, the traveling_ECU 21 may be capable of predicting the possibility (passing probability P) of the vehicle 100b entering the entry space based on the congestion degree Cd of the nearby area Ra and the evaluation target area Rb, instead of the waiting time Tw and the margin time Ts, as shown in step S113 of Figure 7, for example.
[0091] [Variation 3-3] In the above embodiment, the present disclosure is applied to driving assistance at an intersection CL where a priority road Lm and a non-priority road Ls intersect. However, in the above embodiment and its variations, the present disclosure may also be applied to driving assistance at a merging point where a non-priority road Ls merges with a priority road Lm, for example. In such a case, as in the above embodiment and its variations, it is possible to predict the possibility that vehicle 100b will merge due to the psychological influence of the driver of vehicle 100b.
[0092] [Variation 3-4] In the above-described embodiment and its variations, if it is difficult for the vehicle 100a to communicate with the network environment NW, the travel_ECU 21 may acquire road information Da and vehicle information Db based on various data of the sensor detection area SR obtained from various sensors mounted on the vehicle 100a. Here, the road information Da includes information about the priority road Lm, the driving lane L1, the oncoming lane L2, the non-priority road Ls, and the intersection CL detected by the driving environment recognition unit 13c. The vehicle information Db includes information about the speed (vehicle speed) of the vehicle 100a acquired from the vehicle position estimation unit 13b, as well as information about vehicles on the priority road Lm (e.g., vehicles 100a, 100c-100f) and vehicles on the non-priority road Ls (e.g., vehicle 100b). Even in this case, it is possible to predict the possibility that the vehicle 100b will merge or cross a road due to psychological influences on the driver of the vehicle 100b.
[0093] Note that the effects described in this specification are merely examples. The effects of the present disclosure are not limited to the effects described in this specification. The present disclosure may have effects other than the effects described in this specification.
[0094] Furthermore, for example, the present disclosure can be configured as follows. (1) A control unit is provided that is capable of predicting the behavior of a first vehicle when a non-priority road that merges with or intersects with a priority road having one or more lanes in each direction exists ahead of the first vehicle, and when a prediction target vehicle is stopped at a waiting point on the non-priority road or traveling toward the waiting point exists on the non-priority road, the control unit: acquires first data indicating the presence of the prediction target vehicle, the presence of a plurality of second vehicles in at least one lane ahead of the first vehicle, and the presence of an entry space into which the prediction target vehicle can enter within one or more spaces formed by two of the second vehicles adjacent to each other on a common lane; when the prediction target vehicle is waiting at the waiting point, estimates a waiting time of the prediction target vehicle at the waiting point based on the acquired first data, and when the prediction target vehicle is traveling toward the waiting point, estimates a margin time when the prediction target vehicle enters the entry space based on the acquired first data; and predicts the possibility that the prediction target vehicle will enter the entry space based on the waiting time or the margin time. (2) The driving assistance device according to (1), wherein the control unit is capable of estimating, as the margin time, a time required for the prediction target vehicle to enter the entry space, or a time having a predetermined correlation with that time, based on the acquired first data. (3) The driving assistance device according to (1) or (2), wherein the control unit is capable of acquiring second data indicating that the entry space does not exist, and estimating the waiting time based on the first data and the second data.(4) The driving assistance device according to any one of (1) to (3), wherein the control unit is capable of: estimating the number of vehicles in a predetermined area ahead of the first vehicle based on the acquired set of data; and predicting the possibility that the prediction target vehicle will enter the entry space based on the number of vehicles and the waiting time or the margin time. (5) The driving assistance device according to (4), wherein the control unit is capable of: estimating the number N1 of vehicles near the entry space on the priority road and the number N2 of vehicles in an evaluation target area on the priority road extending from the position of the first vehicle to the vicinity of the entry space based on the number N1 of vehicles, the number N2 of vehicles, and the waiting time or the margin time. (6) The control unit is capable of: estimating, based on the acquired data 1, a number N1 of vehicles near the entry space on the priority road; and a number N3 of vehicles in an evaluation target area extending from the position of the first vehicle to the vicinity of the entry space in the same lane as the first vehicle; and predicting the possibility that the prediction target vehicle will enter the entry space based on the number N1 of vehicles, the number N3 of vehicles, and the waiting time or the margin time.(7) A vehicle including a control unit capable of predicting the behavior of a first vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction exists ahead of the first vehicle, and when a prediction target vehicle is stopped at a waiting point on the non-priority road or traveling toward the waiting point exists on the non-priority road, wherein the control unit is capable of: acquiring data indicating the presence of the prediction target vehicle, the presence of a plurality of second vehicles in at least one lane ahead of the first vehicle, and the presence of an entry space into which the prediction target vehicle can enter within one or more spaces formed by two of the second vehicles adjacent to each other on a common lane; estimating a waiting time of the prediction target vehicle at the waiting point based on the acquired data when the prediction target vehicle is waiting at the waiting point; and estimating a margin time when the prediction target vehicle enters the entry space based on the acquired data when the prediction target vehicle is traveling toward the waiting point; and predicting the possibility that the prediction target vehicle will enter the entry space based on the waiting time or the margin time.(8) A driving assistance method capable of predicting the behavior of a prediction target vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction exists ahead of a first vehicle, and further when a prediction target vehicle is stopped at a waiting point on the non-priority road or traveling toward the waiting point exists, the driving assistance method comprising: acquiring data indicating the presence of the prediction target vehicle, the presence of a plurality of second vehicles in at least one lane ahead of the first vehicle, and the presence of an entry space into which the prediction target vehicle can enter within one or more spaces formed by two of the second vehicles adjacent to each other on a common lane; estimating a waiting time of the prediction target vehicle at the waiting point based on the acquired data when the prediction target vehicle is waiting at the waiting point, and estimating a margin time when the prediction target vehicle enters the entry space based on the acquired data when the prediction target vehicle is traveling toward the waiting point; and predicting the possibility of the prediction target vehicle entering the entry space based on the waiting time or the margin time. (9) A driving assistance device comprising: a control unit capable of predicting the behavior of a first vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction exists ahead of the first vehicle, and when a prediction target vehicle is parked at a waiting point on the non-priority road or traveling toward the waiting point on the non-priority road, the control unit: acquires data indicating the presence of the prediction target vehicle, the presence of a plurality of second vehicles in at least one lane ahead of the first vehicle, and the presence of an entry space into which the prediction target vehicle can enter within one or more spaces formed by two of the second vehicles adjacent to each other on a common lane; based on the acquired data, estimates a first congestion degree in the vicinity of the entry space on the priority road and a second congestion degree of an evaluation target area on the priority road that extends from the position of the first vehicle to the vicinity of the entry space; and predicts the possibility that the prediction target vehicle will enter the entry space based on the first congestion degree and the second congestion degree.(10) A driving assistance device comprising: a control unit capable of predicting the behavior of a first vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in each direction exists ahead of the first vehicle, and when a prediction target vehicle is stopped at a waiting point on the non-priority road or traveling toward the waiting point on the non-priority road, the control unit: acquires data indicating the presence of the prediction target vehicle, the presence of a plurality of second vehicles in at least one lane ahead of the first vehicle, and the presence of an entry space into which the prediction target vehicle can enter within one or more spaces formed by two of the second vehicles adjacent to each other on a common lane; based on the acquired data, estimates a first congestion degree in the vicinity of the entry space on the priority road and a third congestion degree of an evaluation target area extending from the position of the first vehicle to the vicinity of the entry space in the same lane as the first vehicle; and predicts the possibility that the prediction target vehicle will enter the entry space based on the first congestion degree and the third congestion degree.
Claims
1. The system includes a control unit capable of predicting the behavior of a target vehicle when a non-priority road merges with or intersects with a priority road having one or more lanes in one direction, and when a target vehicle is stationary at a waiting point on the non-priority road or traveling toward the waiting point. The control unit, The first data is obtained indicating that the vehicle to be predicted exists, that there are multiple second vehicles in at least one lane ahead of the first vehicle, and that there is an entry space into which the vehicle to be predicted can enter within one or more spaces formed by two adjacent second vehicles in a common lane. Based on the acquired first data, if it is determined that the predicted vehicle is waiting at the waiting point and that there is an entry space for the waiting predicted vehicle, the waiting time of the predicted vehicle at the waiting point is estimated based on the acquired first data. Based on the acquired first data, if it is determined that the predicted vehicle is traveling toward the waiting point and that there is an entry space for the traveling predicted vehicle, the margin of time for the predicted vehicle to enter the entry space is estimated based on the acquired first data. Based on the aforementioned waiting time or buffer time, predict the likelihood that the vehicle to be predicted will enter the entry space. It is possible to do so. Driving assistance system.
2. The control unit is capable of estimating, based on the acquired first data, the time required for the predicted target vehicle to enter the entry space, or a time having a predetermined correlation with that time, as the buffer time. The driving support device according to claim 1.
3. The control unit, To obtain second data indicating that the aforementioned entry space does not exist, Based on the first data and the second data, estimate the waiting time. It is possible to do so. The driving support device according to claim 1.
4. The control unit, Based on the acquired data from step 1, the number of vehicles in a predetermined area in front of the first vehicle is estimated, Based on the number of vehicles and the waiting time or buffer time, predict the likelihood that the target vehicle will enter the entry space. It is possible to do so. The driving support device according to claim 1.
5. The control unit estimates, based on the acquired data from the first, the number of vehicles N1 in the vicinity of the entry space on the priority road and the number of vehicles N2 in the evaluation target area of the priority road, from the position of the first vehicle to the vicinity of the entry space. Based on the number of vehicles N1, the number of vehicles N2, and the waiting time or buffer time, the probability of the target vehicle entering the entry space is predicted. It is possible to do so. The driving support device according to claim 4.
6. The control unit, Based on the acquired data from item 1, estimate the number of vehicles N1 in the vicinity of the entry space on the priority road, and the number of vehicles N3 in the evaluation target area, which extends from the position of the first vehicle to the vicinity of the entry space within the same lane as the first vehicle. Based on the number of vehicles N1, the number of vehicles N3, and the waiting time or buffer time, predict the likelihood that the target vehicle will enter the entry space. It is possible to do so. The driving support device according to claim 4.
7. The system includes a control unit capable of predicting the behavior of a target vehicle when a non-priority road merges with or intersects with a priority road having one or more lanes in one direction, and when a target vehicle is stationary at a waiting point on the non-priority road or traveling toward the waiting point. The control unit, To obtain data indicating that the predicted vehicle exists, that there are multiple second vehicles in at least one lane ahead of the first vehicle, and that there is an entry space into which the predicted vehicle can enter within one or more spaces formed by two adjacent second vehicles in a common lane, Based on the acquired first data, if it is determined that the predicted vehicle is waiting at the waiting point and that there is an entry space for the waiting predicted vehicle, the waiting time of the predicted vehicle at the waiting point is estimated based on the acquired data. Based on the acquired first data, if it is determined that the predicted vehicle is traveling toward the waiting point and that there is an entry space for the traveling predicted vehicle, the margin of time for the predicted vehicle to enter the entry space is estimated based on the acquired data. Based on the aforementioned waiting time or buffer time, predict the likelihood that the vehicle to be predicted will enter the entry space. It is possible to do so. vehicle.
8. A driving assistance method capable of predicting the behavior of a predicted target vehicle when a non-priority road that merges with or intersects with a priority road with one or more lanes in one direction exists in front of a first vehicle, and furthermore, when a predicted target vehicle is stopped at a waiting point on the said non-priority road or is traveling toward the said waiting point, To obtain data indicating that the predicted vehicle exists, that there are multiple second vehicles in at least one lane ahead of the first vehicle, and that there is an entry space into which the predicted vehicle can enter within one or more spaces formed by two adjacent second vehicles in a common lane, Based on the acquired first data, if it is determined that the predicted vehicle is waiting at the waiting point and that there is an entry space for the waiting predicted vehicle, the waiting time of the predicted vehicle at the waiting point is estimated based on the acquired data. Based on the acquired first data, if it is determined that the predicted vehicle is traveling toward the waiting point and that there is an entry space for the traveling predicted vehicle, the margin of time for the predicted vehicle to enter the entry space is estimated based on the acquired data. Based on the aforementioned waiting time or buffer time, predict the likelihood that the vehicle to be predicted will enter the entry space. including Driving assistance methods.