Collision avoidance assistance device, collision avoidance assistance program, and collision avoidance assistance method

Abstract

The present invention comprises: a host vehicle state-of-travel acquisition unit 200 that acquires the state of travel of a host vehicle 1; an obstacle information acquisition unit 212 that acquires information on a target 320; a predicted collision point computation unit 220 that computes a predicted collision point where a collision between the host vehicle 1 and the target 320 is predicted on the basis of information on the state of travel of the host vehicle 1 and the information on the target 320; a road environment information acquisition unit 211 that acquires road environment information on a road on which the host vehicle 1 is traveling; a driver attention level computation unit 230 that computes the attention level of the driver driving the host vehicle 1 with respect to the surroundings of the predicted collision point on the basis of the information on the state of travel, the road environment information, and information on the predicted collision point; and a control actuation determination unit 240 that determines whether or not to issue an alarm to the driver and / or apply braking to the host vehicle 1 on the basis of the information on the state of travel and the attention level, wherein the control actuation determination unit 240 issues an alarm and / or applies braking when the attention level is determined to be at or above a first threshold.

Description

Collision avoidance support device, collision avoidance support program, and collision avoidance support method

[0001] The present invention relates to a collision avoidance assistance device, a collision avoidance assistance program, and a collision avoidance assistance method that provide collision avoidance assistance for a moving body such as a vehicle.

[0002] As an example of a system for improving the detection speed and accuracy of a moving object so that the moving object can be immediately detected from the image captured by the camera device when it comes into the field of view of the camera device of the vehicle, Patent Document 1 describes a technology that uses location information obtained from a vehicle positioning device installed in the vehicle and location information received periodically from a mobile terminal carried by the moving object.

[0003] Patent Document 2 describes a vehicle collision warning device that can reduce the annoyance felt by the driver while making the driver aware of the risk of a collision in an appropriate manner. The device detects the risk of collision between the vehicle and an obstacle, sounds an alarm from an alarm buzzer based on this risk of collision, estimates the driver's level of concentration, and suppresses the sound of the alarm when the level of concentration is high.

[0004] JP 2015-125669 A JP 2004-231017 A

[0005] BACKGROUND ART Various technologies have been proposed for detecting targets around a vehicle, such as vehicles, motorcycles, bicycles, pedestrians, and structures, using external environment recognition sensors such as on-board cameras and radars.

[0006] Furthermore, because the detection range of the external recognition sensors in these technologies is limited, technologies have been developed that use communication technologies such as vehicle-to-vehicle communication and road-to-vehicle communication to obtain information necessary for driving assistance for the vehicle from vehicles with other communication terminals or infrastructure installed on the roadside, and then use this information to implement driving assistance.However, the information obtained through the above communication has a problem in that there is a large time delay when the vehicle obtains the information due to communication delays.

[0007] Furthermore, in the case of vehicle-to-vehicle communication, there are precision errors specific to the external recognition sensor of the vehicle that is the source of the information, and position errors when calculating the relative position between the source vehicle and the vehicle itself using a GPS (Global Positioning System). Therefore, Patent Document 1 discloses a vehicle control device that uses information from vehicle-to-vehicle communication and road-to-vehicle communication to improve the detection speed and detection accuracy of the external recognition device (a camera device in Patent Document 1) of the vehicle itself.

[0008] Furthermore, Patent Document 2 discloses a vehicle collision warning device that sets the timing for activating assistance to avoid a collision between the vehicle and a target based on the driver's concentration level estimated from the driving operation state, thereby reducing the annoyance felt by the driver and allowing the driver to appropriately recognize the risk of a collision.

[0009] By using the technology disclosed in the above-mentioned Patent Document 1, it is possible to detect a target at an earlier stage than conventionally when the target enters the detection range of the camera. However, in Patent Document 1, since the target is detected when it enters the detection range of the camera of the host vehicle, it is not possible to detect targets on the road that are shielded from the camera of the host vehicle by a wall, fence, etc., and it has become clear that there is room for appropriate execution of assistance to avoid a collision between the host vehicle and the target.

[0010] Furthermore, by using the technology disclosed in Patent Document 2, it is possible to suppress the activation of assistance for avoiding a collision when the driver is concentrating on driving. However, Patent Document 2 has a problem in that it is not possible to identify what the driver is concentrating on, and therefore it is not possible to identify the predicted collision point where the driver is paying attention. In other words, since it is not possible to accurately detect the driver's level of attention, it has become clear that there is room for improvement in the timing of providing appropriate assistance to the driver.

[0011] The present invention has been made in consideration of the above-mentioned circumstances, and aims to provide a collision avoidance assistance device, a collision avoidance assistance program, and a collision avoidance assistance method that are capable of appropriately performing collision avoidance assistance compared to conventional technology.

[0012] The present invention includes multiple means for solving the above-mentioned problems. One example is a collision avoidance assistance device that avoids and assists in collisions between a vehicle and an obstacle, and includes: a vehicle driving state acquisition unit that acquires the driving state of the vehicle; an obstacle information acquisition unit that acquires information about the obstacle; a collision prediction point calculation unit that calculates a predicted collision point where a collision between the vehicle and the obstacle is predicted based on the information about the driving state of the vehicle and the information about the obstacle; a road environment information acquisition unit that acquires road environment information of the road on which the vehicle is traveling; a driver attention level calculation unit that calculates a level of attention of the driver of the vehicle regarding the area around the predicted collision point based on the information about the driving state, the road environment information, and the information about the predicted collision point; and a control operation determination unit that determines whether or not to issue an alarm to the driver and / or apply braking to the vehicle based on the information about the driving state and the level of attention, and the control operation determination unit issues the alarm and / or applies braking when it is determined that the level of attention is equal to or higher than a first threshold.

[0013] According to the present invention, collision avoidance assistance can be performed more appropriately than with the prior art. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments.

[0014] 1 is a schematic configuration diagram of a collision avoidance assistance device in an embodiment. FIG. 2 is a functional block configuration of the collision avoidance assistance device in an embodiment. FIG. 3 is an example of a traffic situation in which collision avoidance assistance in an embodiment is performed. FIG. 4 is a flowchart showing an example of an overall processing procedure of the collision avoidance assistance device in an embodiment. FIG. 5 is a time chart showing an example of operation of the collision avoidance assistance device in an embodiment. FIG. 6 is a flowchart showing an example of processing for predicting a predicted collision point. FIG. 7 is a flowchart showing an example of processing for estimating a driver's attention level. FIG. 8 is a flowchart showing an example of processing for calculating a change in attention level during collision avoidance assistance. FIG. 9 is a flowchart showing an example of processing for calculating a predicted collision point departure degree. FIG. 10 is a time chart showing an example of operation of the collision avoidance assistance device in an embodiment. FIG. 11 is a flowchart showing an example of a learning process related to attention level estimation. FIG. 12 is a diagram showing the assistance operation of the collision avoidance assistance device in an embodiment. FIG. 13 is a diagram comparing the functional block configurations of an embodiment of the present invention with the prior art.

[0015] Embodiments of the collision avoidance assistance device, collision avoidance assistance program, and collision avoidance assistance method of the present invention will be described with reference to Figures 1 to 13. In the drawings used in this specification, identical or corresponding components are denoted by identical or similar reference numerals, and repeated description of these components may be omitted.

[0016] First, the configuration related to the collision avoidance assist device will be described with reference to Fig. 1. Fig. 1 is a schematic diagram of the configuration related to the collision avoidance assist device according to an embodiment of the present invention.

[0017] As shown in FIG. 1 , the host vehicle 1 is equipped with a forward camera 2 mounted in front of the host vehicle 1, a radar 3, a right front wheel speed sensor 5FR that detects the wheel speed of the right front wheel 4FR, a right rear wheel speed sensor 5RR that detects the wheel speed of the right rear wheel 4RR, a left rear wheel speed sensor 5RL that detects the wheel speed of the left rear wheel 4RL, a left front wheel speed sensor 5FL that detects the wheel speed of the left front wheel 4FL, a steering angle sensor 6, a yaw rate sensor 7, a driver monitoring device 8, a meter 9, a buzzer 10, a communication information device 11, a collision avoidance assistance device 12, a braking control device 13, and the like.

[0018] The front camera 2 is equipped with a lens and an image sensor, and is appropriately positioned so as to capture images of the environment surrounding the vehicle 1. The images captured by the front camera 2 are transmitted to the collision avoidance support device 12, where they are processed. The collision avoidance support device 12 identifies the type of object 320 (hereinafter referred to as the object 320, as appropriate) around the vehicle 1 based on the images transmitted from the front camera 2.

[0019] Examples of types of target objects 320 include automobiles, pedestrians, motorcycles, driving paths, lane lines such as white and yellow lines, traffic signals, traffic signs, and obstacles.

[0020] In this embodiment, one front camera 2 is provided to capture images of the environment surrounding the vehicle 1, but multiple cameras may be provided. The front camera 2 may be a monocular camera or a stereo camera, and the type of camera and the functions of the camera may be changed as needed. Hereinafter, unless otherwise specified, the camera will be referred to as the camera 2.

[0021] The radars 3 are installed at the four corners of the vehicle 1, and each radar 3, for example, emits electromagnetic waves and receives reflected waves from surrounding targets 320, thereby measuring the location and speed of the targets 320 around the vehicle 1, and transmits the measurement results to the collision avoidance support device 12. The radar 3 may be, for example, a millimeter-wave radar or a laser radar, or an ultrasonic sensor may be used instead of the radar. Furthermore, the speed and location of the targets 320 may be measured using a combination of multiple sensors.

[0022] In this embodiment, as an example, a combination of a camera 2 and a radar 3 is used as a means for acquiring information about targets 320 around the vehicle 1, but for example, LiDAR (Light Detection and Ranging) may be used in place of the radar 3, or two or more of each of the above-mentioned sensors may be used, or two or more of the sensors may be used and then further combined.

[0023] A right front wheel 4FR, a right rear wheel 4RR, a left rear wheel 4RL, and a left front wheel 4FL are arranged on the front, rear, left, and right sides of the vehicle body of the host vehicle 1. Each of these wheels 4FR, 4RR, 4RL, and 4FL is provided with a right front wheel speed sensor 5FR, a right rear wheel speed sensor 5RR, a left rear wheel speed sensor 5RL, and a left front wheel speed sensor 5FL. Each wheel speed sensor 5FR, 5RR, 5RL, and 5FL detects the rotational speed of its corresponding pair of wheels 4FR, 4RR, 4RL, and 4FL, and transmits the wheel speeds to a collision avoidance assist device 12. The collision avoidance assist device 12 calculates the speed of the host vehicle 1 based on the wheel speed information.

[0024] Hereinafter, unless otherwise specified, the right front wheel speed sensor 5FR, the right rear wheel speed sensor 5RR, the left rear wheel speed sensor 5RL, and the left front wheel speed sensor 5FL will be referred to as wheel speed sensors 5.

[0025] The steering angle sensor 6 is a sensor that detects the rotation angle (steering angle) of the steering wheel of the vehicle 1 , and the steering angle detected by the steering angle sensor 6 is transmitted to the collision avoidance assistance device 12 .

[0026] The yaw rate sensor 7 detects the yaw rate of the host vehicle 1 , and the yaw rate detected by the yaw rate sensor 7 is transmitted to the collision avoidance assistance device 12 .

[0027] The driver monitoring device 8 detects the driver's biological information such as the driver's line of sight, facial direction, pulse rate, etc. The detected biological information of the driver is transmitted to the collision avoidance support device 12 and the driver identification calculation unit 250. The driver's biological information may be detected using a known imaging device that captures an image including the driver using an imaging element, or a known wearable device that can be worn by the driver (for example, a hat or glasses).

[0028] For example, when the collision avoidance assist device 12 determines that there is a high possibility of a collision between the host vehicle 1 and the target 320, the meter 9 displays a warning image to notify the driver of the high possibility of a collision. In this embodiment, the meter 9 is provided as an example of a means for displaying a warning image, but instead of the meter 9, for example, a part of a car navigation system may be used, or an image may be displayed using a head-up display.

[0029] The buzzer 10 emits a warning sound to notify the driver of the high possibility of a collision, for example, when the collision avoidance assistance device 12 determines that there is a high possibility of a collision between the host vehicle 1 and the target object 320. In this embodiment, the buzzer 10 is provided as an example of a means for emitting a warning sound, but instead of the buzzer 10, for example, a part of a car navigation system may be used, or the warning sound may be emitted from a speaker.

[0030] The communication information device 11 communicates with the outside of the vehicle 1 using a wireless communication method such as Wi-Fi (IEEE802.11), 4G, or 5G, and receives information necessary for driving assistance of the vehicle 1. The communication partners outside the vehicle 1 include communication devices mounted on other vehicles, communication devices of various infrastructures installed on the roadside (traffic lights, ETC gates, dedicated facilities, etc.), mobile phones carried by people, data centers (facilities that store multiple pieces of information and transmit necessary information), etc.

[0031] The information received by the vehicle 1 via the communication information device 11 includes information on the target object 320 (information on the communication partner itself, information sensed by the communication partner, etc.), traffic light information (current traffic light color, time until the traffic light color changes to another color, etc.), speed limit information (limited speed, restricted section, etc.), lane restriction information (restricted lanes, restricted section, etc.), congestion information (congestion section, end point of congestion, etc.), road construction information (construction location, restriction details, etc.), accident information (accident location, restriction details, etc.), weather information (weather details, section information, etc.), and road surface condition information (road surface condition, section / location information, etc.).

[0032] In addition, the information about the target object 320 may include the type of target object 320 (such as a passenger car, large vehicle, emergency vehicle, motorcycle, bicycle, person, or other unidentifiable object), location, speed, direction of movement, and reliability (the degree to which the information can be trusted, for example, on a scale of 1 to 100, with 100 being the most reliable value).

[0033] The collision avoidance assistance device 12 is configured to be able to perform collision avoidance assistance operations to avoid a collision between the vehicle 1 and a target 320 or to reduce the damage caused by the collision, and is composed of a program to be executed by a control device mounted on the vehicle 1, or a recording medium on which the program is stored, and a processor that executes the program.

[0034] The collision avoidance assistance device 12 is configured to be able to output control signals for operating the meter 9, the buzzer 10, and the brake control device 13 based on the information received from the above-mentioned communication information device 11, information on the target object 320 detected by the camera 2 and the radar 3, and the driver's attention to the predicted collision point, which will be described later. Details thereof will be described later.

[0035] In this embodiment, the collision avoidance assistance device 12 is configured as, for example, an ECU (Electronic Control Unit) mounted on the vehicle 1, and assists in any or all of the following to realize collision avoidance assistance operations: displaying a warning screen on the meter 9, sounding an alarm from the buzzer 10, or automatically activating the brakes via the brake control device 13.

[0036] The braking control device 13 controls the braking device of the host vehicle 1. The braking control device 13 is a component that can adjust the braking force of the braking device in response to a control signal output from the collision avoidance assistance device 12, and includes brake actuators such as a hydraulic pump and a valve unit.

[0037] Fig. 2 shows the internal functional block configuration of the collision avoidance assist device 12 shown in Fig. 1. Such functional blocks are realized by hardware, software, or a combination of these.

[0038] As shown in FIG. 2 , the collision avoidance assistance device 12 basically includes a vehicle driving state acquisition unit 200, a sensor information calculation unit 210, a collision prediction point calculation unit 220, a driver attention level calculation unit 230, a control operation determination unit 240, and a driver identification calculation unit 250.

[0039] The host vehicle driving state acquisition unit 200 is a part that acquires information about the driving state of the host vehicle 1, and preferably is the main body that executes the host vehicle driving state acquisition procedure and host vehicle driving state acquisition step that acquire the driving state of the host vehicle 1.

[0040] In this host vehicle driving state acquisition unit 200, for example, based on the speed of the host vehicle 1 (hereinafter referred to as the host vehicle speed as appropriate) acquired from the wheel speed sensor 5, the driver attention level calculation unit 230 calculates the required host vehicle speed, acceleration of the host vehicle 1, and whether the host vehicle has stopped, and outputs the calculation results to the driver attention level calculation unit 230. In addition, based on the yaw rate of the host vehicle 1 (hereinafter referred to as the host vehicle yaw rate as appropriate) acquired from the yaw rate sensor 7 and the steering angle of the host vehicle 1 acquired from the steering angle sensor 6, the collision prediction point calculation unit 220 calculates the turning radius of the host vehicle 1 required to predict the host vehicle's path, and outputs the calculation results to the driver attention level calculation unit 230. Furthermore, the host vehicle driving state acquisition unit 200 determines whether the host vehicle 1 has stopped or suddenly decelerated before the predicted collision point, and outputs the determination result to the driver attention level calculation unit 230.

[0041] The sensor information calculation unit 210 includes a road environment information acquisition unit 211 , an obstacle information acquisition unit 212 , and an obstacle visibility calculation unit 213 .

[0042] The road environment information acquisition unit 211 is the part that acquires road environment information of the road on which the vehicle 1 is traveling, and preferably is the entity that executes the road environment information acquisition procedure and road environment information acquisition step that acquires road environment information of the road on which the vehicle 1 is traveling.

[0043] This road environment information acquisition unit 211 acquires, for example, the width of the road on which the vehicle 1 is traveling and weather information such as sunny or rainy, obtained from the camera 2, radar 3, and communication information device 11, and outputs this to the driver attention level calculation unit 230.

[0044] The obstacle information acquisition unit 212 is a part that acquires information about the target object 320, and preferably is the main body that executes the obstacle information acquisition procedure and obstacle information acquisition step that acquires information about the target object 320.

[0045] This obstacle information acquisition unit 212 unifies, for example, the type of target 320, such as a vehicle, bicycle, or pedestrian, acquired from each of the camera 2, radar 3, and communication information device 11, as well as the format and coordinate system of the current location and speed information of the target 320, and outputs them to the collision prediction location calculation unit 220 and the driver attention level calculation unit 230.

[0046] The coordinate system used in this embodiment, as an example, has the center of the front end of the vehicle 1 as the origin, and determines the current position of the target 320 (hereinafter referred to as the target 320 current position) and the speed of the target 320 (hereinafter referred to as the target 320 speed) with the entire length of the vehicle 1 as the vertical direction and the entire width as the horizontal direction.

[0047] When the same target object 320 is detected by the above-mentioned multiple sensors, the obstacle information acquisition unit 212 can determine the current position and speed of the target object 320, taking into consideration errors in the forward / backward and left / right directions of the camera 2 and the radar 3. Furthermore, the obstacle information acquisition unit 212 calculates information about the target object 320 required for the collision prediction point calculation unit 220, such as the acceleration of the target object 320, from the speed of the target object 320, and outputs the information to the collision prediction point calculation unit 220 and the driver attention level calculation unit 230.

[0048] The obstacle visibility calculation unit 213 is a part that calculates whether or not the target object 320 is present at a point that can be seen by the driver.

[0049] This obstacle visibility calculation unit 213 determines whether the acquired target object 320 is located in a position visible to the driver of the vehicle 1, based on information on the target object 320 and obstructions acquired from, for example, the camera 2, the radar 3, and the communication information device 11, and outputs the result to the driver attention level calculation unit 230.

[0050] The collision prediction point calculation unit 220 is a part that calculates a collision prediction point where a collision between the host vehicle 1 and the target 320 is predicted based on information on the driving state of the host vehicle 1 and information on the target 320, and preferably is the main entity that executes the collision prediction point calculation procedure and collision prediction point calculation step that calculates a collision prediction point where a collision between the host vehicle 1 and the target 320 is predicted based on information on the driving state of the host vehicle 1 and information on the target 320.

[0051] The collision prediction point calculation unit 220 predicts the paths of the host vehicle 1 and the target 320 based on the current position, speed, and acceleration of the target 320 acquired from the sensor information calculation unit 210, and the host vehicle position, speed, acceleration, yaw rate, and turning radius of the host vehicle 1 acquired from the host vehicle traveling state acquisition unit 200. Thereafter, based on the predicted path of the host vehicle 1 (hereinafter referred to as the host vehicle predicted path) and the predicted path of the target 320 (hereinafter referred to as the target 320 predicted path), the point (hereinafter referred to as the collision prediction point) and time (hereinafter referred to as the collision margin time) at which the host vehicle 1 and the target 320 are predicted to collide are calculated, and a determination is made as to whether or not there is a possibility of a collision between the host vehicle 1 and the target 320 (hereinafter referred to as the collision determination). Furthermore, the calculation results and the determination results are output to the driver attention calculation unit 230 and the control operation determination unit 240 .

[0052] The driver identification calculation unit 250 is a part that judges the difference between drivers, and identifies the current driver of the vehicle 1 from the information identified by the driver monitoring device 8, and outputs the identified information to the driver attention level calculation unit 230. There are no particular limitations on the method of identifying the driver in this driver identification calculation unit 250, and the specific configuration and processing thereof are determined appropriately depending on the configuration of the driver monitoring device 8.

[0053] The driver attention level calculation unit 230 includes a collision prediction point attention level calculation unit 231, a collision prediction point passage calculation unit 232, a collision prediction point attention level learning unit 233, an assistance time attention level change calculation unit 234, and a collision prediction point passage learning unit 235.

[0054] The predicted collision point attention level calculation unit 231 is a part that calculates the level of attention of the driver of the vehicle 1 regarding the area around the predicted collision point based on information on the driving conditions, road environment information, and information on the predicted collision point, and preferably is the main entity that executes the driver attention level calculation procedure and driver attention level calculation step that calculates the level of attention of the driver of the vehicle 1 regarding the area around the predicted collision point based on information on the driving conditions, road environment information, and information on the predicted collision point.

[0055] The predicted collision point attention level calculation unit 231 calculates the level of the driver's attention to the predicted collision point based on, for example, the host vehicle 1's position, host vehicle speed, host vehicle acceleration, host vehicle yaw rate, and steering angle obtained from the host vehicle driving state acquisition unit 200, road environment information such as the width of the road on which the host vehicle 1 is traveling, the number of obstacles, weather information, and illuminance obtained from the sensor information calculation unit 210, the predicted collision point and time to collision calculated by the predicted collision point calculation unit 220, and whether or not there is a possibility of a collision between the host vehicle 1 and the target 320. At this time, the driver's biological information obtained from the driver monitoring device 8 can also be taken into consideration.

[0056] The predicted collision point passage calculation unit 232 is a part that determines whether a predicted collision point has been passed and initializes the level of attention based on the determination result.For example, based on the predicted collision point obtained from the predicted collision point calculation unit 220, the determination result of whether the host vehicle has stopped or suddenly decelerated obtained from the host vehicle driving state acquisition unit 200, and the determination result by the control operation determination unit 240, when the host vehicle 1 has passed the predicted collision point or has stopped or suddenly decelerated near the predicted collision point, the calculation unit 232 initializes the driver's level of attention to the predicted collision point.

[0057] The collision prediction point attention level learning unit 233 is a part that learns the calculation method of the attention level based on the determination result of whether the collision prediction point has been passed and the determination result by the control operation determination unit 240.For example, it learns the calculation method of the collision prediction point attention level calculation unit 231 based on the collision prediction point obtained from the collision prediction point calculation unit 220, the determination result of passing the collision prediction point obtained from the collision prediction point passage calculation unit 232, and the determination result by the control operation determination unit 240.

[0058] The assistance time attention level change calculation unit 234 is a part that calculates the amount of change in the driver's attention level during control operation based on whether or not control operation is performed by the control operation judgment unit 240 and the difference in the calculation results of the attention level before and after the control operation judgment, and learns the calculation method for the attention level when the amount of change is higher than a second threshold value.For example, based on the judgment result by the control operation judgment unit 240, it calculates the change in the driver's attention level regarding the predicted collision point during control operation based on the difference in the calculation results of the collision prediction point attention level calculation unit 231 before and after the control operation and judgment, and learns the calculation method for the collision prediction point attention level calculation unit 231 when the change is higher than a second predetermined value.

[0059] The collision prediction point passing learning unit 235 is a part that calculates the amount of change in the driver's attention level during control operation based on the calculation results of the attention level before and after the judgment by the control operation judgment unit 240 during control operation, and learns the calculation method for the attention level when the amount of change is higher than a third threshold value.For example, based on the judgment results by the collision prediction point passing calculation unit 232 and the calculation results of the attention level change during assistance calculation unit 234, the collision prediction point is passed, and the calculation method of the collision prediction point attention level calculation unit 231 is learned when the amount of change in the driver's attention level to the collision prediction point during control operation is higher than a third predetermined value.

[0060] The control operation determination unit 240 is the part that determines whether or not to issue an alarm to the driver and / or brake the vehicle 1 based on information about the driving condition and the level of attention, and that issues an alarm and / or brakes the vehicle 1 when the level of attention is determined to be above a first threshold, and preferably is the main entity that executes the control operation determination procedure and control operation determination step that determines whether or not to issue an alarm to the driver and / or brake the vehicle 1 based on information about the driving condition and the level of attention.

[0061] The control operation determination unit 240 requests the operation of controls such as an alarm or braking based on the level of the driver's attention to the predicted collision point calculated by the driver attention level calculation unit 230 and the collision determination and time to collision calculated by the predicted collision point calculation unit 220. The alarm operation request outputs a request to display a warning screen on the meter 9, or a request to sound an alarm to the buzzer 10, or both a request to display a warning screen and a request to sound an alarm. The braking operation request outputs a braking command value required to avoid collision with the target 320 to the braking control device 13.

[0062] Next, specific processing of the collision avoidance assist device 12 will be described with reference to FIGS.

[0063] First, a situation in which such specific processing is performed will be described with reference to Fig. 3. Fig. 3 shows an example of a traffic situation in which collision avoidance assistance is performed by the collision avoidance assistance device 12.

[0064] 3 , when the host vehicle 1 is passing through an intersection 300 where an obstacle 301, such as a building or a wall, obstructs the driver's view, there is a very high possibility that a target object 320 will suddenly appear at the intersection from a direction that is a blind spot while the host vehicle 1 is traveling. The target object 320 is outside a detection range 310 for the target object 320 of the camera 2 and radar 3 mounted on the host vehicle 1, but is detected within a detection range 331 for the target object 320 of the road-to-vehicle communication device 330, and the road-to-vehicle communication device 330 provides information about the target object 320 to the host vehicle 1.

[0065] FIG. 4 is a flowchart showing an example of the overall processing procedure in the collision avoidance assist device 12, and FIG. 5 is a timing chart showing an example of the operation of the collision avoidance assist device 12.

[0066] 5 , time T1 is the timing when the road-to-vehicle communication device 330 detects the target object 320 and the host vehicle 1 starts receiving information about the target object 320. Time T2 is the timing when, just before the intersection 300, the driver of the host vehicle 1 senses danger that the target object 320 may suddenly appear from a blind spot and starts decelerating by the driver's operation. Time T3 is the timing when, just before the intersection 300, the collision avoidance assistance device 12 determines that the driver is paying high attention to the predicted point of collision with the target object 320 and starts control such as issuing an alarm. Time T4 is the timing when the host vehicle 1 enters the intersection 300.

[0067] 4, the collision prediction point calculation unit 220 predicts the predicted path of the host vehicle 1 and the predicted path of the target 320 based on the information output from the host vehicle traveling state acquisition unit 200 and the obstacle information acquisition unit 212, calculates the predicted collision point and time to collision, and executes collision determination. Details will be described later with reference to FIG.

[0068] In step S402, the predicted collision point attention level calculation unit 231 calculates the level of attention the driver should pay to the predicted collision point, as will be described in detail later with reference to FIG.

[0069] In process S403, the predicted collision point attention level calculation unit 231 determines whether or not a control operation such as an alarm or braking is required by comparing the output of process S401 with the level of the driver's attention to the predicted collision point calculated in process S402 to determine whether the level of attention to the area around the predicted collision point is equal to or greater than a first threshold (the attention state determination threshold in FIG. 5 ). If the level of attention is determined to be equal to or greater than the first threshold, i.e., it is determined that the driver is paying high attention to the predicted collision point, the process proceeds to process S404, indicating that a control operation is required. On the other hand, if the level of attention is less than the first threshold, i.e., it is determined that the driver is not paying high attention to the predicted collision point, the process returns to process S401, indicating that a control operation is not required.

[0070] In step S404, the control operation determination unit 240 issues an alarm or applies braking based on the determination result of step S403.

[0071] In process S405, the assistance attention level change calculation unit 234 calculates the difference in the driver's attention level with respect to the predicted collision point before and after the collision avoidance assistance executed in process S403 (hereinafter referred to as "change in attention level during collision avoidance assistance" as appropriate), and determines whether the driver has responded to the collision avoidance assistance based on the change in attention level during collision avoidance assistance. Details will be described later with reference to FIG. 8.

[0072] In step S406, the predicted collision point passage calculation unit 232 and the assistance time attention level change calculation unit 234 calculate the degree of departure from the predicted collision point, as will be described in detail later with reference to FIG.

[0073] In process S407, the collision prediction point passage calculation unit 232 determines whether or not the vehicle has left the collision prediction point based on the output of process S406. At this time, if the attention level A was initialized in process S406, the result is determined to be NO. If it is determined that the vehicle has left the collision prediction point, the process proceeds to process S408, and if it is determined that the vehicle has not left the collision prediction point, the process is completed.

[0074] In process S408, the collision prediction point passage calculation unit 232 determines whether or not the driver has responded to the collision avoidance support based on the output of process S405. If it is determined that the driver has responded, the process proceeds to process S409, and if it is determined that the driver has not responded, the process is terminated.

[0075] In process S409, the collision prediction point passing learning unit 235 updates the learning value used in process S402 to calculate the driver's level of attention to the collision prediction point based on the output of process S408, and completes the process.

[0076] 6 is a flowchart showing an example of a process for predicting a predicted collision point in the predicted collision point calculation unit 220 of the collision avoidance assist device 12. The predicted collision point calculation unit 220 mainly executes each process in FIG.

[0077] In step S601 of FIG. 6, the collision prediction point calculation unit 220 acquires information received from the communication information device 11 via communication.

[0078] In step S602, information relating to the running state of the vehicle 1, such as the vehicle speed, acceleration of the vehicle 1, steering angle, and shift position, is acquired.

[0079] In process S603, the course of the target 320 is predicted based on the information acquired in process S601, the current position of the target 320, the speed of the target 320, and the acceleration of the target 320 acquired from the camera 2 and the radar 3, respectively.

[0080] In process S604, the course of the host vehicle 1 is predicted based on the host vehicle position, host vehicle speed, host vehicle acceleration, host vehicle yaw rate, and turning radius of the host vehicle 1 acquired in process S602.

[0081] In process S605, it is determined whether or not the information on the target 320 acquired in process S601 has been detected by the camera 2 and radar 3 mounted on the vehicle 1. If it is determined that the target 320 has been detected, the process proceeds to process S606, and if it is determined that the target 320 has not been detected, the process proceeds to process S607.

[0082] In process S606, based on the result of process S605, a collision determination threshold is calculated for when the target object 320 is detected by the sensors (camera 2 and radar 3) mounted on the host vehicle 1. The collision determination threshold may be the distance to the collision prediction viewpoint or the time to collision.

[0083] In process S607, a collision determination threshold is calculated based on the result of process S605 when the sensor mounted on the host vehicle 1 does not detect the target 320. The collision determination threshold may be the distance to the collision prediction viewpoint or the time to collision. In addition, in order to facilitate activation of the collision avoidance assistance control, the collision determination threshold when the sensor mounted on the host vehicle 1 does not detect the target 320 can be set so as to make it easier to determine that there is a high possibility of a collision between the host vehicle 1 and the target 320 than when the sensor mounted on the host vehicle 1 detects the target 320, as shown in FIG. 5 .

[0084] In process S608, the predicted collision point is calculated based on the predicted path of the target 320 predicted in process S603 and the predicted path of the vehicle predicted in process S604, and the time to collision is calculated based on the vehicle speed and the distance from the vehicle 1 to the predicted collision point.

[0085] In step S609, the proximity between the vehicle 1 and the predicted collision point is calculated based on the output of step S608.

[0086] In process S610, the proximity between the target object 320 and the predicted collision point is calculated based on the outputs of processes S603 and S608.

[0087] In process S611, it is determined whether or not there is a possibility of a collision between the vehicle 1 and the target object 320 based on the output of process S608, the output of process S609, the output of process S610, and the collision determination threshold calculated in process S606 or process S607.

[0088] 7 is a flowchart showing an example of a process for estimating the driver's attention level in the collision prediction point attention level calculation unit 231 of the collision avoidance support device 12. The main body that executes each process in FIG. 7 is the collision prediction point attention level calculation unit 231 of the driver attention level calculation unit 230.

[0089] 7, the collision prediction point attention level calculation unit 231 determines whether or not the host vehicle 1 is close to the collision prediction point. If it is determined that the host vehicle 1 is close, the process proceeds to step S702, and if it is determined that the host vehicle 1 is not close, the process is completed.

[0090] In process S702, the attention level X1 is estimated based on the driver's biological information. In this process S702, biological information such as the driver's gaze and time-series changes and frequency changes in the heart rate, and the correlation between the amount of head movement and the acceleration of the vehicle 1 is acquired from the driver monitoring device 8. In this embodiment, the gaze is used as an example of the driver's biological information.

[0091] As shown in Figure 5 from time T2 to time T4, when a driver is concentrating on driving and paying attention, the driver's gaze when entering an intersection changes in small increments over time, with large amplitude changes. Conversely, when the driver is distracted, the gaze changes roughly over time, with small amplitude changes. Furthermore, there is a correlation between the movement of the driver's head, including the face, and the movement of the vehicle 1. When the driver is paying attention, the correlation between the acceleration of the driver's head and the acceleration of the vehicle 1 decreases. This is because the driver minimizes changes in the vehicle 1 to check the surrounding conditions and moves their gaze independently of the vehicle's movement. Therefore, when the attention level X1 is estimated using the amount of gaze movement of the driver, the time-series change amount can be used as a feature, or the time-series data obtained by frequency analysis can be used as a feature. Furthermore, the correlation between the acceleration of the driver's head and the acceleration of the vehicle 1 can be calculated and used as a feature, or a combination of these can be used.

[0092] In step S703, the attention level X2 is estimated based on the driving characteristics of the driver. The driving characteristics used for the estimation in step S703 refer to the time series changes and frequency changes of the host vehicle 1's location, host vehicle speed, host vehicle acceleration, host vehicle yaw rate, and turning radius.

[0093] 5, if the driver senses danger and pays attention to what is ahead when the vehicle 1 enters an intersection, the driver tends to sufficiently decelerate before entering the intersection and steer the vehicle toward the center of the lane to avoid a collision with the approaching target 320. Therefore, when the driver's driving characteristics are used to estimate the attention level X2, the acceleration of the vehicle 1 can be used as a feature quantity, or a change in steering can be used as a feature quantity, or a combination of these may be used.

[0094] In process S704, the attention level δ is calculated based on the driving environment of the host vehicle 1. An example of a driving environment that affects the driver's attention level δ is an intersection with poor visibility. An intersection with poor visibility has many obstacles, and the driver must enter while paying attention, so the driver's line of sight is concentrated along the road width, and the amount of line of sight movement is small. In addition, the host vehicle speed tends to be lower than at intersections with good visibility. Therefore, the more obstacles there are and the narrower the road width on which the host vehicle 1 is traveling, the higher the attention level δ is calculated. Therefore, the attention level δ can be calculated using the number of obstacles or the road width as a feature value, or a combination of these.

[0095] In process S705, the attention level α is calculated based on visibility. In conditions where the effective field of view is wide and visibility is good, such as sunny days or daytime, the amplitude of the line of sight change tends to be large, while in conditions where the effective field of view is narrow and visibility is poor, such as rainy days or nighttime, the amplitude of the line of sight change tends to be small. Also, in terms of driving characteristics, the vehicle speed tends to be high in conditions where visibility is good, but tends to be low in conditions where visibility is poor. Therefore, the attention level correction value α is calculated so that it increases as visibility worsens. Therefore, the attention level correction value α based on visibility can be calculated using illuminance, weather information, or a combination of these.

[0096] In process S706, the driver's attention level A is calculated based on the outputs of processes S702, S703, S704, and S705. For example, the driver's attention level A can be calculated using the following (Equation 1).

[0097] Driver's attention level A = α × δ × (β × X1 + γ × X2) (1) Because the attention levels X1 and X2 are estimated based on the driver's biometric information and driving characteristics, respectively, either one may be used alone or a combination of them may be used to calculate the driver's attention level A. Furthermore, because the attention levels α and δ adjust the estimated values ​​of the attention levels X1 and X2 according to visibility and the driving environment of the vehicle 1, the calculation of the driver's attention level A is reflected by multiplying the attention levels X1 and X2.

[0098] Here, the feature quantities used to calculate the attention levels X1 and X2, such as the amount of eye movement, the distribution of facial orientation, the amount of deceleration, and the amount of steering, vary depending on the driver's personality and skill level. Therefore, when calculating the attention levels, the attention levels X1 and X2 can be multiplied by learning values ​​β and γ to enable estimation that is appropriate for the driver. Furthermore, since α and δ vary depending on the driver's personality and skill level, they can be corrected using the learning values.

[0099] In process S707, the driver's attention level is determined based on the output of process S706. The determination result has at least three states with respect to the predicted collision point: distracted state, normal state, and attention state.

[0100] 8 is a flowchart showing an example of a process for calculating a change in the attention level during assistance in the attention level change calculation unit 234 of the collision avoidance assistance device 12. The main body that executes each process in FIG. 8 is the attention level change calculation unit 234 of the driver attention level calculation unit 230.

[0101] In process S801 of Figure 8, the assistance-time attention level change calculation unit 234 calculates the attention level Z1 when collision avoidance assistance is provided based on the driver's biometric information, and calculates the deviation between this attention level Z1 and the attention level X1 calculated in process S702.

[0102] In process S802, the attention level Z2 when collision avoidance assistance is performed is calculated based on the driving characteristics of the driver, and the deviation between this attention level Z2 and the attention level X2 calculated in process S703 is calculated.

[0103] In process S803, it is determined whether the driver has responded to the collision avoidance assistance based on the outputs of processes S702 and S703.

[0104] Fig. 9 is a flowchart showing an example of a process for calculating the predicted collision point departure degree in the collision avoidance support device 12, and Fig. 10 is a timing chart showing an example of an operation for calculating the predicted collision point departure degree in the collision avoidance support device 12. The processes in Fig. 9 below are executed by the predicted collision point passage calculation unit 232 and the assistance-time attention level change calculation unit 234 of the driver attention level calculation unit 230.

[0105] 9, the collision prediction point passage calculation unit 232 and the assistance time attention level change calculation unit 234 determine whether the host vehicle 1 is close to the collision prediction point after entering the collision prediction point. If it is determined that the host vehicle 1 is close, the process proceeds to step S902, and if it is determined that the host vehicle 1 is not close, the process proceeds to step S908.

[0106] In step S902, it is determined whether the host vehicle 1 has stopped near the predicted collision point. If it is determined that the host vehicle 1 has not stopped, the process proceeds to step S903, and if it is determined that the host vehicle has stopped, the process proceeds to step S908.

[0107] In process S903, the attention level Y1 is estimated based on the driver's biological information. As shown in Fig. 10, between times T1 and T4 before the host vehicle 1 enters the intersection, the driver carefully monitors the surroundings, so the driver's line of sight movements are small and the amplitude is large. However, after time T4 after the host vehicle 1 enters the intersection, the driver's line of sight movements become rough and the amplitude is small.

[0108] Therefore, when the attention level Y1 is estimated using the amount of eye movement of the driver, the amount of change over time can be used as a feature, or the time-series data can be frequency-analyzed as a feature, or the correlation between the acceleration of the driver's head and the acceleration of the vehicle 1 can be calculated as a feature, or a combination of these can be used.

[0109] In process S904, the attention level Y2 is estimated based on the driving characteristics of the driver. As shown in Fig. 10, between times T1 and T4 before the host vehicle 1 enters the intersection, the driver performs deceleration operations to carefully monitor the surroundings, but after time T4 after the host vehicle 1 enters the intersection, the driver stops deceleration operations and maintains the speed or accelerates in order to pass through the intersection.

[0110] Furthermore, the driver steers the vehicle in accordance with the road after passing through the intersection. Therefore, when the driving characteristics of the driver are used to estimate the attention level Y2, the acceleration of the vehicle 1 can be used as a feature quantity, or the change in steering can be used as a feature quantity, or a combination of these may be used.

[0111] In step S905, the attention level δ' is calculated based on the driving environment of the vehicle 1. The calculation method is the same as that for the attention level δ.

[0112] In step S906, the attention level α' is calculated based on the visibility in the same manner as for the attention level α.

[0113] In process S907, a collision prediction point departure degree B is calculated based on the outputs of processes S903, S904, S905, and S906. For example, the collision prediction point departure degree B may be calculated using the following (Equation 2).

[0114] Degree of departure from predicted collision point B = α' × δ' × (β' × Y1 + γ' × Y2) (2) Because the attention levels Y1 and Y2 are estimated based on the driver's biometric information and driving characteristics, either one may be used alone or a combination of them may be used to calculate the degree of departure from predicted collision point B. Furthermore, because the attention levels α' and δ' adjust the estimated values ​​of the attention levels Y1 and Y2 according to visibility and the driving environment of the host vehicle 1, the degree of departure from predicted collision point B is calculated by multiplying the attention levels Y1 and Y2.

[0115] Here, the feature quantities used to calculate the attention levels Y1 and Y2, such as the amount of eye movement, the distribution of facial orientation, the amount of deceleration, and the amount of steering, vary depending on the driver's personality and skill level. Therefore, when calculating the attention levels, the attention levels Y1 and Y2 can be multiplied by learning values ​​β' and γ' to enable estimation that is appropriate for the driver. Furthermore, since α' and δ' vary depending on the driver's personality and skill level, they can be corrected using the learning values.

[0116] In process S908, the attention level A is initialized and recalculated. This is because in a situation where there are consecutive intersections, such as in a residential area, if the driver passes through or stops at a previous intersection, the point where the driver needs to pay attention will change to the next intersection. Therefore, by initializing the attention level A for the driver's predicted collision point and calculating it for the next intersection, the driver's attention level can be accurately estimated even in a series of intersections.

[0117] 11 is a flowchart showing an example of a learning process related to estimating the attention level in the collision avoidance assist device 12. The process shown in FIG.

[0118] In process S1101 of FIG. 11, the collision prediction point passage learning unit 235 performs multi-learning because there is a partial correlation between the learned values ​​α, β, γ, and δ for calculating the caution level and the learned values ​​α', β', γ', and δ' when leaving the collision prediction point.

[0119] To update the learning values ​​related to the driver's biometric information and driving characteristics when passing the predicted collision point, the timing for updating the learning values ​​can be when the driver activates the collision avoidance assistance and passes the predicted collision point without stopping or suddenly slowing down before the point, or when the difference in the level of attention before and after passing the predicted collision point is higher than a predetermined value, or the learning values ​​can be updated based on the learning values ​​stored for each driver by determining differences between drivers.

[0120] In addition, in the embodiment, the control is made more likely to be activated when the driver's level of attention to the predicted collision point is high, but in the ideal form of collision avoidance assistance, it is better to notify the driver of the possibility of a collision even when the driver's level of attention is low and the driver is in a distracted state, so the control can be made more likely to be activated when the driver's level of attention is lower than usual and the driver is in a distracted state.

[0121] Furthermore, when information about the target object 320 from the communication information device 11 is interrupted, the predicted path of the target object 320 may be estimated by interpolating with information before the interruption, or the collision determination threshold may be switched to that detected by the vehicle's own sensor. Furthermore, when some of the information necessary for calculating the driver's attention level cannot be acquired, calculation may be performed using only the normally acquired information, and the attention level determination threshold may be changed to that detected by the vehicle's own sensor, or the collision determination threshold may be switched to that detected by the vehicle's own sensor, or the driver may be notified that collision avoidance assistance is not possible.

[0122] By executing the flow described above, the control operation determination unit 240 can appropriately assess the situation and provide assistance based on the driver's level of attention to the predicted collision point and the result of the collision determination. Therefore, in a situation where there is no collision with the target object 320 and the communication information device 11 can acquire information about the target object 320 if the distance is close, as shown in Figures 12 and 13 , in the prior art, assistance is not provided until the vehicle's sensor detects the target object 320. Alternatively, the driver is in an attention state, but assistance is suppressed to avoid excessive activation because it is unclear where the driver's attention is directed. However, with the present invention, collision avoidance assistance can be appropriately provided without excessive activation, depending on the driver's biological information, driving characteristics, the environment around the vehicle, and other conditions.

[0123] Next, the effects of this embodiment will be described.

[0124] The collision avoidance assistance device 12 of the present embodiment described above is a device, program, and method for avoiding and assisting in collision between the host vehicle 1 and the target 320, and includes a host vehicle driving state acquisition unit 200 that acquires the driving state of the host vehicle 1, an obstacle information acquisition unit 212 that acquires information on the target 320, a collision prediction point calculation unit 220 that calculates a collision prediction point where a collision between the host vehicle 1 and the target 320 is predicted based on the information on the driving state of the host vehicle 1 and the information on the target 320, and a road environment information acquisition unit 230 that acquires road environment information of the road on which the host vehicle 1 is traveling. The device is equipped with a road environment information acquisition unit 211, a driver attention calculation unit 230 that calculates the level of attention of the driver of the vehicle 1 regarding the area around the predicted collision point based on information on the driving conditions, road environment information, and information on the predicted collision point, and a control operation determination unit 240 that determines whether or not to issue an alarm to the driver and / or apply braking to the vehicle 1 based on the information on the driving conditions and the level of attention, and the control operation determination unit 240 issues an alarm and / or applies braking when it is determined that the level of attention is above a first threshold.

[0125] In this way, rather than asking the driver for their level of attention to the predicted collision point and intervening if that level is low as in the past, by issuing an alert to the driver and braking the vehicle 1 when the level of attention exceeds the attention state judgment threshold, it is possible to improve the timing of appropriately executing collision avoidance assistance compared to conventional technology, and it also has the effect of communicating to the driver that the safe driving technology is working properly and making them aware of its reliability.

[0126] In addition, the driver attention level calculation unit 230 determines whether a predicted collision point has been passed and initializes the attention level based on the determination result.Since there are many intersections in residential areas, etc., and there are often many predicted collision points in succession when driving in such places, by appropriately initializing the level each time a predicted collision point is passed, it becomes possible to more appropriately provide collision avoidance assistance according to the situation.

[0127] Furthermore, the vehicle driving state acquisition unit 200 determines whether the vehicle 1 has stopped or suddenly decelerated just before the predicted collision point, and the driver attention level calculation unit 230 initializes the attention level based on the determination result by the vehicle driving state acquisition unit 200.By this, the collision avoidance assistance device 12 can determine whether the driver is driving carefully when there are successive intersections, etc., and initializes the level as appropriate, thereby more appropriately providing collision avoidance assistance according to the situation.

[0128] In addition, the driver attention level calculation unit 230 learns the method for calculating the attention level based on the determination result of whether the predicted collision point has been passed and the determination result by the control operation determination unit 240, and can therefore learn whether the control based on the determined attention level is in line with the driver's wishes, thereby realizing collision avoidance assistance that is more appropriate for the driver.

[0129] Furthermore, the driver attention level calculation unit 230 calculates the amount of change in the driver's attention level when control is activated based on whether or not control is activated by the control activation determination unit 240 and the difference in the calculation results of the attention level before and after the control activation determination, and by learning the attention level calculation method when the amount of change is higher than the second threshold, it is possible to learn whether or not the driver actually reacted to control based on the calculated attention level, making it possible to determine the attention level in a way that is more in line with the driver's characteristics, thereby achieving more appropriate assistance.

[0130] In addition, the driver attention level calculation unit 230 calculates the amount of change in the driver's attention level during control operation based on the calculation results of the attention level before and after the judgment made by the control operation judgment unit 240 during control operation, and by learning the attention level calculation method when the amount of change is higher than the third threshold, it can be determined that the driver has paid attention to the predicted collision point due to the control operation.Since the attention level calculation method at that time can be learned as having room for improvement, it becomes possible to determine the attention level more in line with the driver's characteristics, and more appropriate assistance can be achieved.

[0131] Furthermore, the system is further equipped with a driver identification calculation unit 250 that determines differences between drivers, and the driver attention calculation unit 230 learns a calculation method for the attention level for each driver based on the differences between drivers determined by the driver identification calculation unit 250, thereby being able to reflect the tendency for differences in individual attention levels regarding predicted collision points, thereby enabling collision avoidance assistance to be performed that is more suited to the driver's personality and abilities.

[0132] In addition, the system is further provided with an obstacle visibility calculation unit 213 that calculates whether or not the target object 320 is present at a point visible to the driver, and the driver attention level calculation unit 230 calculates a higher level of attention the more likely it is that the target object 320 will not be visible, so that assistance can be expected to be provided in cases where it is determined that there is a high risk of collision, thereby realizing appropriate collision avoidance assistance control.

[0133] <Others> Furthermore, the present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all of the described configurations.

[0134] Furthermore, the aforementioned configurations, functions, processing units, processing means, etc. may be realized in hardware, for example, by designing some or all of them as integrated circuits, or they may be realized in software, by a processor interpreting and executing a program that realizes each function.

[0135] Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, hard disk, or SSD (Solid State Drive), or in a recording medium such as an IC card, SD card, or DVD.

[0136] In addition, the control lines and information lines shown are those that are considered necessary for explanation, and do not necessarily represent all the control lines and information lines that are necessary for implementation. In reality, it can be assumed that almost all components are interconnected.

[0137] The embodiment of the present invention may be in the following form.

[0138] (1) A collision avoidance assistance device that avoids and assists in collisions between a vehicle and an obstacle, comprising: a vehicle driving state acquisition unit that acquires the driving state of the vehicle; an obstacle information acquisition unit that acquires information about the obstacle; a collision prediction point calculation unit that calculates a collision prediction point where a collision between the vehicle and the obstacle is predicted based on information about the driving state of the vehicle and information about the obstacle; a road environment information acquisition unit that acquires road environment information about the road on which the vehicle is traveling; a driver attention level calculation unit that calculates a level of attention of a driver of the vehicle regarding the area around the predicted collision point based on information about the driving state, the road environment information, and information about the collision prediction point; and a control operation determination unit that determines whether to issue an alarm to the driver and / or apply braking to the vehicle based on information about the driving state and the level of attention, wherein the control operation determination unit issues the alarm and / or applies braking when it is determined that the level of attention is equal to or higher than a first threshold.

[0139] (2) In the collision avoidance assistance device described in (1), the driver attention level calculation unit determines whether the predicted collision point has been passed and initializes the attention level based on the determination result.

[0140] (3) In the collision avoidance assistance device described in (1) or (2), the vehicle driving state acquisition unit determines whether the vehicle has stopped or suddenly decelerated just before the predicted collision point, and the driver attention level calculation unit initializes the attention level based on the determination result by the vehicle driving state acquisition unit.

[0141] (4) In the collision avoidance assistance device described in (2) or (3), the driver attention calculation unit learns the calculation method for the attention level based on the judgment result of whether the predicted collision point has been passed and the judgment result by the control operation judgment unit.

[0142] (5) In a collision avoidance assistance device described in any one of (1) to (4), the driver attention level calculation unit calculates the amount of change in the driver's attention level during control operation based on whether or not control operation is performed by the control operation determination unit and the difference in the calculation results of the attention level before and after the determination of the control operation, and when the amount of change is higher than a second threshold value, learns the calculation method for the attention level.

[0143] (6) In a collision avoidance assistance device described in any one of (1) to (5), the driver attention level calculation unit calculates the amount of change in the driver's attention level during control operation based on the difference in the calculation results of the attention level before and after the control operation determination unit determines whether the control operation is to be performed, and when the amount of change is higher than a third threshold value, learns the method for calculating the attention level.

[0144] (7) In the collision avoidance assistance device described in any one of (1) to (6), a driver identification calculation unit is further provided that determines the differences between the drivers, and the driver attention calculation unit learns the calculation method of the attention level for each driver based on the differences between the drivers determined by the driver identification calculation unit.

[0145] (8) In the collision avoidance assistance device described in any one of (1) to (7), an obstacle visibility calculation unit is further provided which calculates whether the obstacle is present at a location visible to the driver, and the driver attention level calculation unit calculates the attention level to be higher the more likely it is that the obstacle is not visible.

[0146] DESCRIPTION OF SYMBOLS 1...Own vehicle (vehicle) 2...Camera 3...Radar 4FL...Left front wheel 4FR...Right front wheel 4RL...Left rear wheel 4RR...Right rear wheel 5...Wheel speed sensor 5FL...Left front wheel speed sensor 5FR...Right front wheel speed sensor 5RL...Left rear wheel speed sensor 5RR...Right rear wheel speed sensor 6...Steering angle sensor 7...Yaw rate sensor 8...Driver monitoring device 9...Meter 10...Buzzer 11...Communication information device 12...Collision avoidance support device 13...Braking control device 200...Own vehicle driving state acquisition unit 210...Sensor information calculation unit 211...Road environment information acquisition unit 212...Obstacle information acquisition unit 213...Obstacle visibility calculation unit 220...Collision prediction point calculation unit 230...Driver attention level calculation unit 231...Collision prediction point attention level calculation unit 232...Collision prediction point passage calculation unit 233: Collision prediction point attention level learning unit 234: Attention level change calculation unit during assistance 235: Collision prediction point passage learning unit 240: Control operation determination unit 250: Driver identification calculation unit 300: Intersection 301: Obstruction 310: Detection range of camera 2 or radar 3 mounted on host vehicle 1 320: Target (obstacle) 330: Road-to-vehicle communication device 331: Detection range of road-to-vehicle communication device

Claims

1. A collision avoidance support device that avoids and supports a collision between a vehicle and an obstacle, comprising: a vehicle driving state acquisition unit that acquires the driving state of the vehicle; an obstacle information acquisition unit that acquires information about the obstacle; a collision prediction point calculation unit that calculates a collision prediction point where a collision between the vehicle and the obstacle is predicted based on information about the driving state of the vehicle and information about the obstacle; a road environment information acquisition unit that acquires road environment information of the road along which the vehicle is traveling; a driver attention level calculation unit that calculates a level of attention of a driver of the vehicle regarding the vicinity of the predicted collision point based on the driving state information, the road environment information, and information about the collision prediction point; and a control operation determination unit that determines whether or not to issue an alarm to the driver and / or apply braking to the vehicle based on the driving state information and the attention level, wherein the control operation determination unit issues the alarm and / or applies the braking when it is determined that the attention level is equal to or higher than a first threshold.

2. A collision avoidance support device according to claim 1, wherein the driver attention level calculation unit determines whether the vehicle has passed the predicted collision point, and initializes the attention level based on the result of the determination.

3. A collision avoidance support device as described in claim 1, wherein the host vehicle driving state acquisition unit determines whether the vehicle has stopped or suddenly decelerated just before the predicted collision point, and the driver attention level calculation unit initializes the attention level based on the determination result by the host vehicle driving state acquisition unit.

4. A collision avoidance support device as described in claim 2, wherein the driver attention calculation unit learns a calculation method for the attention level based on the result of the determination of whether the predicted collision point has been passed and the result of the determination by the control operation determination unit.

5. A collision avoidance assistance device as described in claim 1, wherein the driver attention calculation unit calculates the amount of change in the driver's attention level during control operation based on the presence or absence of control operation by the control operation determination unit and the difference in the calculation results of the attention level before and after the control operation determination unit determines whether or not a control operation has been performed, and learns the calculation method for the attention level when the amount of change is higher than a second threshold value.

6. A collision avoidance assistance device as described in claim 1, wherein the driver attention calculation unit calculates the amount of change in the driver's attention during control operation based on the difference in the calculation results of the attention before and after the control operation determination unit determines whether or not a control operation is to be performed, and learns the method of calculating the attention when the amount of change is higher than a third threshold value.

7. A collision avoidance support device as described in claim 1, further comprising a driver identification calculation unit that determines the differences between the drivers, and the driver attention level calculation unit learns a calculation method for the attention level for each driver based on the differences between the drivers as determined by the driver identification calculation unit.

8. A collision avoidance support device as described in claim 1, further comprising an obstacle visibility calculation unit which calculates whether or not the obstacle is present at a location visible to the driver, and the driver attention level calculation unit calculates the attention level to be higher the higher the possibility that the obstacle is not visible.

9. A collision avoidance assistance program which causes a control device mounted on a vehicle to execute the following programs: a vehicle driving state acquisition procedure for acquiring the driving state of the vehicle; an obstacle information acquisition procedure for acquiring information about an obstacle; a collision prediction point calculation procedure for calculating a collision prediction point where a collision between the vehicle and the obstacle is predicted based on information about the driving state of the vehicle and information about the obstacle; a road environment information acquisition procedure for acquiring road environment information of the road along which the vehicle is traveling; a driver attention calculation procedure for calculating a level of attention of the driver of the vehicle regarding the vicinity of the collision prediction point based on the driving state information, the road environment information, and information about the collision prediction point; and a control operation determination procedure for determining whether or not to issue an alarm to the driver and / or to apply braking to the vehicle based on the driving state information and the attention level, wherein the control operation determination procedure issues the alarm and / or applies braking when it is determined that the attention level is equal to or higher than a first threshold value.

10. A recording medium storing the collision avoidance assistance program according to claim 9.

11. A method for avoiding and assisting in collision between a vehicle and an obstacle, comprising: a vehicle driving state acquisition step for acquiring the driving state of the vehicle; an obstacle information acquisition step for acquiring information about the obstacle; a collision prediction point calculation step for calculating a collision prediction point where a collision between the vehicle and the obstacle is predicted based on the vehicle driving state information and the obstacle information; a road environment information acquisition step for acquiring road environment information of the road along which the vehicle is traveling; a driver attention calculation step for calculating a level of attention of a driver of the vehicle regarding the vicinity of the predicted collision point based on the driving state information, the road environment information, and the collision prediction point information; and a control operation determination step for determining whether or not to issue an alarm to the driver and / or to apply braking to the vehicle based on the driving state information and the attention level, wherein in the control operation determination step, the alarm is issued and / or the braking is performed when it is determined that the attention level is equal to or higher than a first threshold value.

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