JP7769800B2 - Vehicle control device - Google Patents

Description

The present invention relates to a vehicle control device that includes a vehicle path calculation device capable of detecting the vehicle's driving path and that estimates the accuracy of determining the presence and/or position of an obstacle based on the vehicle's driving path.

Patent document 1 describes a system in which, in a situation where multiple vehicles are traveling on a road, a base station receives information transmitted from the traveling vehicles, identifies the vehicle's traveling behavior, including its traveling route, based on the received information, performs statistics or machine learning on the identified traveling behavior of the multiple vehicles, and determines the presence and location of obstacles on the road based on the results.

Japanese Patent Application Laid-Open No. 2021-22221

The system described in Patent Document 1 is based on the premise that the base station estimates obstacles on the road based on information received from the vehicle. However, if the vehicle's position recognition accuracy is insufficient due to communication problems between the GNSS satellite and the vehicle, even if it determines that an obstacle is present on the road, there is a concern that the estimated location of the obstacle may not be accurately determined. In the event of communication problems, it is effective to use dead reckoning (DREC), which estimates the vehicle's position based on each wheel speed and/or steering angle. However, parameter errors with vehicle specifications due to changes in vehicle state are integrated into DREC position coordinates, which also raises the concern that the location of the obstacle may not be accurately determined.

In addition, in the system described in Patent Document 1, to solve the problem of increasing the processing load on each vehicle, the vehicle constantly transmits the necessary information to the base station so that the base station can process the determination of the presence or absence of obstacles on the road. However, this poses the problem of increasing the communication traffic burden per vehicle. Furthermore, if the communication situation between the base station and the vehicle is unstable, the vehicle will not be able to transmit information, and there is a concern that unsent time-series data will strain the memory of the vehicle control unit.

The present invention has been made in consideration of the above-mentioned problems, and aims to provide a vehicle control device that is more robust against communication problems with base stations and GNSS satellites, and that can reduce the amount of communication information.

In order to solve the above problem, the vehicle control device of the present invention has an obstacle avoidance judgment unit that calculates the position of the host vehicle based on information obtained by a sensor mounted on the host vehicle, calculates the traveling trajectory of the host vehicle based on the calculated position of the host vehicle, and detects that the host vehicle has avoided an obstacle on the road based on the calculated traveling trajectory of the host vehicle.When the obstacle avoidance judgment unit detects that the host vehicle has avoided the obstacle, it calculates a judgment accuracy according to the obstacle detection method, and estimates the presence and position of the obstacle based on the calculated judgment accuracy.

This invention allows the vehicle to detect an obstacle while communication between the vehicle and GNSS satellites is interrupted by structures around the vehicle, resulting in communication failure. The obstacle's position is estimated based on the DedReco position coordinates. When communication with the GNSS satellites is restored, the position accuracy at the time of obstacle detection is estimated from the deviation between the GNSS position coordinates when communication with the GNSS satellites was good and the DedReco position coordinates. This increases robustness against communication failures with GNSS satellites. Furthermore, by using edge computing to organize the accuracy of determining the presence and location of an obstacle, as well as its position accuracy, as obstacle information, the vehicle can reduce the burden of communication traffic by transmitting the obstacle information to a server only when an obstacle is detected by the vehicle.

Furthermore, in road traffic information and communication systems such as VICS _{(registered trademark)} on Japanese expressways, information on obstacles on the road is collected from television cameras, patrols, and reports from the general public, and the obstacle information is aggregated and shared. However, the present invention can estimate the position of an obstacle based on the behavior of the vehicle and the operation of the driver, so that obstacle information can be shared quickly.

Other issues, configurations, and effects will become clear from the description of the following embodiments.

1 is a diagram showing the configuration of a vehicle equipped with a vehicle control device according to a first embodiment of the present invention; 2 is a block diagram showing an example of the internal configuration of a vehicle control ECU 9 used in a vehicle control device according to a first embodiment of the present invention, and showing the relationship between input and output signals of the vehicle control ECU 9 shown in FIG. 1. FIG. 2 is a functional block diagram of the inside of a vehicle control ECU 9 according to the first embodiment of the present invention. 3 is a flowchart showing an outline of the entire vehicle control process performed by a vehicle control ECU 9 according to the first embodiment of the present invention. 3 is a flowchart showing an outline of obstacle avoidance determination according to the first embodiment of the present invention. 1 is a bird's-eye view image showing an overview of obstacle position estimation according to the first embodiment of the present invention. 1 is a table of weights used in calculating position accuracy according to the first embodiment of the present invention. 1 is an image of calculation of the position accuracy of an obstacle according to the first embodiment of the present invention. 10 is a bird's-eye view image showing an outline of obstacle position estimation when a vehicle travels over an obstacle according to a second embodiment of the present invention.

Embodiments of the present invention will be described below with reference to the drawings. For convenience, the following embodiments will be divided into multiple sections or embodiments, but unless otherwise specified, they are not unrelated to one another, and one may be a partial or complete modification, detail, supplementary explanation, etc. of the other.

(Embodiment 1)
Hereinafter, a first embodiment of the present invention will be described in detail with reference to FIGS.

First, the configuration of a vehicle equipped with a vehicle control device will be described using Figure 1. The vehicle control ECU (Electronic Control Unit) 9 used in the vehicle control device is mounted on the vehicle (host vehicle) 100, calculates the driving route of the vehicle 100 and/or a preceding vehicle (also called a leading vehicle), estimates the position of an obstacle from the driving route of the host vehicle 100 and/or the preceding vehicle, and calculates the accuracy of determining the presence of the obstacle and the accuracy of its position.

The vehicle 100 is primarily composed of a right front wheel speed sensor 3FR that detects the wheel speed of the right front wheel 2FR, a right rear wheel speed sensor 3RR that detects the wheel speed of the right rear wheel 2RR, a left rear wheel speed sensor 3RL that detects the wheel speed of the left rear wheel 2RL, and a left front wheel speed sensor 3FL that detects the wheel speed of the left front wheel 2FL (hereinafter, unless otherwise specified, 2FR, 2RR, 2RL, and 2FL will be referred to as wheels 2, 3FR, 3RR, 3RL, and 3FL will be referred to as wheel speed sensors 3), an electric power steering device 5 that changes the direction of each wheel 2 in accordance with the steering angle of a steering wheel 4 located in the driver's cab of the vehicle 100, a camera 6 mounted on the front of the vehicle, a GNSS receiver 7, a vehicle communication unit 8 that transmits and receives obstacle position information, etc. via wireless communication with a base station (also called a server or data center) located outside the vehicle, and a vehicle control ECU 9.

Next, the internal configuration of the vehicle control ECU 9 will be explained using Figures 2 and 3.

As shown in FIG. 2, the vehicle control ECU 9 is composed of an I/O LSI 9a including an A/D converter, a CPU 9b, etc. As described above, the vehicle control ECU 9 receives signals from the wheel speed sensor 3, the electric power steering device 5, the camera 6, and the GNSS receiver 7, and has both input and output signals with the vehicle communication unit 8. Note that the communication method is not limited, and the connection may be via a Car Aria Network (CAN) or a direct connection. The vehicle control ECU 9 transmits obstacle information estimated based on the driving state of the vehicle 100 and the image captured by the camera 6 from the vehicle communication unit 8 to a base station, where the obstacle information is collected.

In detail, as shown in Figure 3, the vehicle control ECU 9 basically includes a host vehicle path evaluation unit 301, a vehicle-in-front path evaluation unit 302, a host vehicle and vehicle-in-front path evaluation unit 303, an obstacle presence evaluation unit 304, a vehicle position reliability calculation unit 305, and an obstacle position estimation unit 306. The host vehicle path evaluation unit 301 and the vehicle-in-front path evaluation unit 302 constitute an obstacle avoidance determination unit 307 of this embodiment, which will be described later.

The vehicle path evaluation unit 301 acquires the wheel speed of each wheel 2 from the wheel speed sensor 3 and/or the steering angle of the steering wheel 4 from the electric power steering device 5 (steering angle sensor), calculates the position of the vehicle 100 from the acquired information, calculates the driving trajectory (driving route) of the vehicle 100 from the calculated position of the vehicle 100, detects the behavior of the vehicle 100 in avoiding an obstacle on the road from the calculated driving trajectory of the vehicle 100, and calculates the accuracy of determining the presence of an obstacle from the behavior of the vehicle 100 in avoiding the obstacle on the road.

The preceding vehicle path evaluation unit 302 acquires the lateral position of the preceding vehicle and/or the distance (relative relationship) between the preceding vehicle and the host vehicle 100 from the camera 6, calculates the position of the preceding vehicle from the acquired information, calculates the driving trajectory (driving path) of the preceding vehicle from the calculated position of the preceding vehicle, detects the behavior of the preceding vehicle as it avoids an obstacle on the road from the calculated driving trajectory of the preceding vehicle, and calculates the accuracy of determining the presence of an obstacle from the behavior of the preceding vehicle as it avoids the obstacle on the road. Here, the means for detecting the relative relationship between the preceding vehicle and the host vehicle 100 is not limited to the camera 6, which captures and transmits captured images of the area ahead of the vehicle, but may also be radar or LiDAR, which recognizes the area ahead of the vehicle. In other words, the host vehicle path evaluation unit 301 and the preceding vehicle path evaluation unit 302 constitute an obstacle avoidance determination unit 307 that calculates the position of the host vehicle 100 and/or the preceding vehicle based on information obtained by sensors mounted on the host vehicle 100, calculates the driving trajectory (driving route) of the host vehicle 100 and/or the preceding vehicle based on the calculated positions of the host vehicle 100 and/or the preceding vehicle, and detects that the host vehicle 100 and/or the preceding vehicle have avoided an obstacle on the road based on the calculated driving trajectory of the host vehicle 100 and/or the preceding vehicle.

The route evaluation unit 303 for the host vehicle and the preceding vehicle acquires the accuracy of determining the presence of an obstacle calculated by the host vehicle route evaluation unit 301 and the accuracy of determining the presence of an obstacle calculated by the preceding vehicle route evaluation unit 302, and calculates the final accuracy of determining the presence of the obstacle according to the detection method used to calculate (estimate) the presence of the obstacle.

The obstacle presence evaluation unit 304 obtains the accuracy of the obstacle presence determination calculated by the route evaluation unit 303 for the vehicle itself and the vehicle in front, and determines whether or not an obstacle exists based on the obtained accuracy of the obstacle presence determination.

The vehicle position reliability calculation unit 305 obtains the wheel speed of each wheel 2 from the wheel speed sensor 3 and the GPS coordinates (also called GNSS coordinates) of the vehicle 100 from the GNSS receiver 7, and calculates the position of the vehicle 100 and its accuracy (also called reliability).

The obstacle position estimation unit 306 estimates the position and accuracy of the obstacle from the position information of the vehicle 100 calculated by the vehicle position reliability calculation unit 305 and the presence or absence information of the obstacle determined by the obstacle presence evaluation unit 304, more specifically, from the position information of the vehicle 100 calculated by the vehicle position reliability calculation unit 305 in accordance with the result of the obstacle presence determination by the obstacle presence evaluation unit 304.

The vehicle communication unit 8 acquires the accuracy of determining the presence of an obstacle calculated by the route evaluation unit 303 of the vehicle itself and the vehicle in front, as well as the position and accuracy of the obstacle calculated by the obstacle position estimation unit 306, and transmits this as obstacle information to a base station outside the vehicle.

In this embodiment, obstacles are estimated based on vehicle behavior, and the presence and location of obstacles are calculated at a base station based on the obstacle information estimated by each vehicle, and the obstacle information is shared. On expressways, road obstacle information is collected by television cameras, patrols, and reports from the public, as in the case of VICS _{(registered trademark)} , and then consolidated at a traffic control center. In contrast, according to this embodiment, obstacle information can be collected by vehicles using edge computing instead of human eyes, so obstacle information can be shared across the entire road, not just expressways.

Next, we will explain the overview of the entire vehicle control process using the flowchart in Figure 4.

In obstacle avoidance judgment S401 based on the driving path of the preceding vehicle, the first obstacle avoidance (that the preceding vehicle has avoided an obstacle on the road) is judged based on the lateral or longitudinal behavior of the preceding vehicle obtained from the camera 6 (corresponding to the relative relationship between the vehicle and the preceding vehicle) (preceding vehicle path evaluation unit 302).

In obstacle avoidance judgment S402 based on the vehicle's driving path, the lateral or longitudinal behavior of the vehicle 100 is detected based on the wheel speed of each wheel 2 obtained from the wheel speed sensor 3 and/or the steering angle of the steering wheel 4 obtained from the electric power steering device 5 (steering angle sensor), and a second obstacle avoidance (that the vehicle 100 has avoided an obstacle on the road) is judged (vehicle path evaluation unit 301).

In obstacle position estimation S403, the position of the vehicle 100 is estimated based on the wheel speed of each wheel 2 obtained from the wheel speed sensor 3 and/or the GPS coordinates (GNSS coordinates) of the vehicle 100 obtained from the GNSS receiver 7 (vehicle position reliability calculation unit 305), the position of the vehicle in front is estimated from the position of the vehicle 100 and the relative position of the vehicle 100 and the vehicle in front obtained from the camera 6, and when it is determined that the vehicle in front has avoided the obstacle (obstacle presence evaluation unit 304), the position of a first obstacle is estimated based on the position of the vehicle in front (obstacle position estimation unit 306), and when it is determined that the vehicle in front has avoided the obstacle (obstacle presence evaluation unit 304), the position of a second obstacle is estimated based on the position of the vehicle 100 (obstacle position estimation unit 306).

In the obstacle presence determination accuracy calculation S404, the accuracy of determining the presence of a first obstacle avoided by the vehicle in front is calculated based on the degree of deviation (deviation amount) of the behavior of the vehicle in front from normal conditions (vehicle in front path evaluation unit 302), and similarly, the accuracy of determining the presence of a second obstacle avoided by the vehicle in front is calculated based on the degree of deviation (deviation amount) of the behavior of the vehicle in front from normal conditions (vehicle in front path evaluation unit 301), and the accuracy of determining the presence of the first obstacle and the accuracy of determining the presence of the second obstacle are combined depending on the detection method used to calculate (estimate) the presence of the obstacle to calculate the final accuracy of determining the presence of the obstacle (vehicle in front and vehicle in front path evaluation unit 303).

In the obstacle position accuracy calculation S405, the dead-rec error at the time of obstacle avoidance judgment (in other words, the time when the obstacle is detected) is estimated from the difference between the dead-rec coordinates (vehicle position coordinates) of the vehicle 100 based on the wheel speed of each wheel 2 obtained from the wheel speed sensor 3 and the GPS coordinates (GNSS coordinates) of the vehicle 100 obtained from the GNSS receiver 7, and the obstacle position accuracy is calculated (obstacle position estimation unit 306).

Next, using Figure 5, we will explain the details of the obstacle avoidance judgment S401 (vehicle-in-front route evaluation unit 302) based on the driving route of the vehicle in front and the obstacle avoidance judgment S402 (vehicle-in-front route evaluation unit 301) based on the vehicle's own route in Figure 4. Note that the target vehicle in the following explanation refers to the vehicle in front or the vehicle in-front 100.

In S501, it is determined whether the camera 6 recognizes the lane the vehicle 100 is traveling in. If the lane is recognized, S502 and S503 are executed to calculate the obstacle avoidance judgment threshold Thp and index y using the lane width and the vehicle's traveling path. If the lane is not recognized, S502a and S503a are executed to calculate the obstacle avoidance judgment threshold Thp and index y using the vehicle's traveling path. By dividing the processing depending on whether or not a lane is present, if a lane is present, the lane is treated as the basis for the target vehicle's traveling path, allowing for accurate determination of the target vehicle's obstacle avoidance behavior.

In S502, an avoidance judgment threshold Thp, which determines the obstacle avoidance behavior based on the lateral position of the vehicle ahead (S401) or the vehicle itself 100 (S402), is calculated based on the width of the driving lane obtained from the camera 6. For example, Thp may be a predetermined multiple of the driving lane width, or a predetermined multiple of the standard deviation of the lateral position change of the vehicle ahead or the vehicle itself may be added to Thp as a margin to make it less likely that vehicle sway will be mistaken for an avoidance judgment.

In S503, an index y is calculated to determine obstacle avoidance behavior based on the lateral position of the vehicle ahead (S401) or the vehicle itself (S402). y is the lateral position of the vehicle ahead relative to the center of the driving lane based on the relative positions of the vehicle ahead and the driving lane acquired from the camera 6 (S401), or the lateral position of the vehicle itself (S402) relative to the center of the driving lane based on the relative positions of the vehicle ahead and the driving lane acquired from the camera 6.

In S502a, the avoidance judgment threshold Thp, which determines the obstacle avoidance behavior based on the lateral position change of the vehicle in front (S401) or the vehicle itself (S402), is set to a predetermined value multiplied by the standard deviation of the lateral position change of the vehicle in front or the vehicle itself (S402). Thp does not have to be calculated based on the standard deviation, but may simply be a predetermined value. Furthermore, the accuracy of the judgment may be improved by calculating Thp after traveling a predetermined distance and/or for a predetermined time or more and accumulating behavior information of the target vehicle.

In S503a, an index y for determining obstacle avoidance behavior is calculated based on the amount of change in lateral position of the vehicle in front (S401) or the vehicle itself (S402). If the target vehicle is the vehicle in front, y is the amount of change in lateral position of the vehicle in front based on the relative positions of the vehicle itself (100) and the vehicle in front acquired from camera 6 (S401). If the target vehicle is the vehicle itself, y is the amount of change in lateral position of the vehicle itself (100) relative to the center of the lane of travel based on the relative position of the lane of travel and the vehicle itself (S402). Here, the target vehicle may be equipped with a navigation system or the like that generates a route to a destination, and the direction of travel of the target vehicle is known in advance. If a change in the speed and/or lateral position of the target vehicle in the lane of travel is detected before a turn point, it may be determined to be a preparatory movement for a turn, and the target vehicle may be excluded from the obstacle avoidance behavior determination.

In S504, the current index y calculated in S503 or S503a is compared with the avoidance judgment threshold Thp calculated in S502 or S502a. If the current y exceeds Thp, it is determined that the target vehicle has begun obstacle avoidance behavior, and S505, S506, and S507 are executed. If the current y is less than or equal to Thp, it is determined that the target vehicle is traveling normally, and processing ends.

In other words, the system continuously calculates the average values and standard deviations of the longitudinal and lateral behavior of the target vehicle (host vehicle and/or preceding vehicle), and when it detects that the difference between the most recent longitudinal and lateral behavior average values deviates from the variance by a predetermined value or more, it infers that the longitudinal and/or lateral behavior is an obstacle avoidance behavior, and recognizes the obstacle.

In S505, the position of the target vehicle when it is determined that y is greater than Thp is set as the obstacle avoidance start point Pos. If the target vehicle is the subject vehicle 100, the subject vehicle position is estimated based on the GPS coordinates (GNSS coordinates) and/or the wheel speed of each wheel 2 acquired from the wheel speed sensor 3 (S402). If the subject vehicle is a leading vehicle, the position of the leading vehicle is estimated from the subject vehicle position and the relative relationship between the leading vehicle and the subject vehicle 100 acquired from the camera 6 (S401).

In S506, Pom is set to the position where the target vehicle's lateral position is at its maximum after determining that y is greater than Thp. This is used to estimate the point where the obstacle and the target vehicle are likely to be closest.

In S507, while the target vehicle is traveling for a predetermined time or distance from the time when it is detected that the target vehicle has begun obstacle avoidance behavior (S504, S505), the current index y calculated in S503 or S503a is compared with the avoidance judgment threshold Thp calculated in S502 or S502a. If the current y is less than or equal to Thp, the target vehicle is deemed to have completed its obstacle avoidance behavior, and S508 is executed. If the current y continues to exceed Thp while traveling for a predetermined time or distance, the target vehicle's avoidance behavior is deemed to have been misjudged, and the target vehicle is deemed to be traveling normally, and processing is terminated.

In S508, the position of the target vehicle when it is determined that y is less than or equal to Thp is set as the obstacle avoidance end point Poe.

In this embodiment, the obstacle avoidance behavior of the target vehicle is determined based on the lateral position of the target vehicle, which is based on information obtained from the camera 6. However, this determination method is not limited to this. It may also be determined based on the amount of change in the steering angle of the steering wheel 4, the amount of change in brake fluid pressure required by the driver, or the amount of change in vehicle speed along the entire length of the target vehicle, which are obtained from the electric power steering device 5. It may also be determined by combining the above-mentioned conditions. For example, as a method of combining the above-mentioned conditions, the obstacle avoidance behavior may be determined when a predetermined number or more of the determination conditions, which are a simple comparison of y and Thp, are met, as in this embodiment. Alternatively, each y may follow a predetermined probability distribution, such as a normal distribution, and the probability P of occurrence of each y may be calculated. The opposite probability (1-P) of the calculated probability of occurrence P may be used as the probability of existence of each obstacle. A behavior with a low probability P, such that the product of the probability of existence of each obstacle is equal to or greater than a predetermined value, may be determined to be an obstacle avoidance behavior.

Next, using Figure 6, we will explain the details of obstacle position estimation S403 (vehicle position reliability calculation unit 305, obstacle position estimation unit 306) in Figure 4.

Figure 6 shows a bird's-eye view of a scene in which a target vehicle 601 according to this embodiment travels along a route 602 that first passes through the obstacle avoidance start point Pos, then the position Pom where the target vehicle's lateral position is at its maximum, and finally the obstacle avoidance end point Poe. This information is used to estimate the obstacle's position. Of the Y coordinates of Pos and Poe, the point near the obstacle that is farthest from the Y coordinate of Pom is selected (Pos in Figure 6). An obstacle is estimated to be located at a Y coordinate that is a predetermined distance OB603 away from the Y coordinate of the obstacle near point in the Y-axis direction opposite Pom. The obstacle's X coordinate is assumed to be the same as the X coordinate of Pom, allowing the obstacle position 604 to be estimated. For example, the obstacle position 604 can be estimated to be within the lane in which the target vehicle 601 is traveling. Depending on the application of the obstacle position 604, such as vehicle avoidance control by an automated driving system or transmission of obstacle information to the driver of a following vehicle, the shape of the obstacle may be regarded as a circle parallel to the road surface, and the size of the obstacle may be estimated with the obstacle position 604 as the center of the circle, and the free space through which the vehicle can pass may be calculated. In this embodiment, the shape of the obstacle is regarded as a circle parallel to the road surface, but it is not limited to a circle, and may be a rectangle or a three-dimensional shape.

Next, using Figure 7, we will explain the details of the obstacle presence determination accuracy calculation S404 (route evaluation unit 303 for the host vehicle and the vehicle in front) in Figure 4.

The accuracy of obstacle presence determination is calculated based on the degree of deviation (amount of deviation) between the index y used in determining the target vehicle's avoidance behavior and its average value. For example, calculation can be performed using a predetermined function such that the greater the degree of deviation, the greater the accuracy of obstacle presence determination. Alternatively, the index y is assumed to follow a predetermined probability distribution, such as a normal distribution, based on the degree of deviation, and the probability P of y's occurrence is calculated. The greater the opposite probability of the calculated probability P (1-P), the greater the accuracy of obstacle presence determination. Here, the accuracy of obstacle presence determination can be weighted according to the detection means, as shown in FIG. 7. For example, if the avoidance behavior determination means uses the index y as the amount of change in the lateral position of the leading vehicle obtained from the camera 6, weighting the calculated accuracy of obstacle presence determination can be used to determine the accuracy of obstacle presence determination, taking into account the accuracy error of the camera 6 and the degree of confidence that the vehicle's movement matches the avoidance behavior, thereby improving the accuracy of obstacle estimation.

In addition, the deviation of the longitudinal and/or lateral behavior of each of the vehicle and the preceding vehicle from normal (average value) can be calculated, and the sum of the calculated deviations of each of the vehicle and the preceding vehicle from normal (average value) can be used as the accuracy of determining the presence of an obstacle.

Next, using Figure 8, we will explain the details of the obstacle position accuracy calculation S405 (obstacle position estimation unit 306) in Figure 4.

The GPS coordinates acquired from the GNSS receiver 7 are highly accurate, with relative coordinate errors between two points within a few centimeters. If the number of detected GPS satellites is less than a predetermined number, the system determines that the position recognition accuracy of the GPS coordinates is insufficient, and stores the GPS coordinates with sufficient position recognition accuracy from the previous time. When the position recognition accuracy is insufficient, the obstacle avoidance determination shown in S401 or S402 is performed, and the position of the vehicle 100 is estimated using the GPS coordinates and/or the dead-record coordinates based on the wheel speed of each wheel 2 from the wheel speed sensor 3. Next, the system determines that the position recognition accuracy is sufficient, and estimates the position accuracy (distance 801) of the vehicle 100 by tracing back to the time when the obstacle avoidance determination was made based on the deviation between the vehicle 100's current position and the vehicle's previous position with insufficient position recognition accuracy, as well as the distance traveled when the position recognition accuracy was insufficient. Here, the basis for calculating the position accuracy is not limited to GPS coordinates, but the position of the vehicle 100 may be estimated based on azimuth information based on the phase difference of radio waves acquired from a radio transmitter that is a landmark installed outside the vehicle and that is equipped with a radio receiver. Note that the position accuracy is not limited to the accuracy of the DedReco coordinates, but may also include the accuracy error of the reference GPS coordinates, or, if the target vehicle that has avoided an obstacle is a vehicle ahead, the accuracy of the position of the vehicle ahead acquired from the camera 6.

In other words, the accuracy of position recognition is determined based on the detection status of the GNSS satellites used to recognize the position of the vehicle, and if it is determined that the determined accuracy of position recognition is insufficient, the position of the obstacle is estimated based on the position of the vehicle 100 calculated based on information obtained by a sensor mounted on the vehicle 100 (dead-record coordinates based on the wheel speed of each wheel 2 from the wheel speed sensor 3).

In addition, when it is determined that the position recognition accuracy determined based on the detection status of the GNSS satellites is sufficient, the position accuracy at the time the obstacle was detected is estimated based on the difference in the position of the vehicle 100 calculated based on the GNSS coordinates of the vehicle 100 and information obtained by a sensor mounted on the vehicle 100 (dead-record coordinates based on the wheel speed of each wheel 2 from the wheel speed sensor 3) and the distance traveled by the vehicle 100.

Finally, the vehicle communication unit 8 transmits obstacle information, including the location and presence determination accuracy of the obstacle, and location accuracy, to the base station, and the base station calculates the presence and location of the obstacle based on the obstacle information acquired (received) from multiple vehicles. For example, when acquiring obstacle information close to multiple vehicles, the base station may determine that an obstacle is present if the cumulative accuracy of the obstacle presence determination exceeds a predetermined value. There are no limitations on the method for calculating the obstacle location at the base station.

In addition, the accuracy of obstacle presence determination collected by the base station decreases over time, and when the cumulative accuracy of obstacle presence determination falls below a predetermined value, the base station determines that the obstacle has disappeared and deletes the obstacle information. In other words, when the base station (its computer) does not receive obstacle information for the location where the occurrence of an obstacle was determined from multiple vehicles equipped with the vehicle control ECU 9 for a predetermined period of time, it determines that the obstacle has disappeared and deletes the obstacle information.

With the above configuration, the base station can estimate the position of an obstacle by observing the behavior of the preceding vehicle and/or the vehicle itself 100 as it avoids the obstacle, and further calculate the accuracy of determining the presence of the obstacle based on the detection means and the avoidance behavior, and the position accuracy based on high-precision position recognition such as GNSS, thereby accurately collecting obstacle information.

(Embodiment 2)
The second embodiment is a modification of the outline of the obstacle position estimation S403 process shown in Fig. 4 of the first embodiment. The second embodiment of the present invention will be described in detail below with reference to Fig. 9.

Figure 9 shows an example of obstacle position estimation when an obstacle avoidance determination is made based on the lateral position change in the obstacle avoidance determinations S401 and S402 shown in Figure 4 of embodiment 1, but the movement direction of the host vehicle 901 based on the lateral position change is detected to be different from the lateral movement direction of the host vehicle 901 based on the steering angle change, and it is determined that the host vehicle 901 has traveled over an obstacle (behavior due to climbing over an obstacle). Note that the conditions for determining that the host vehicle 901 has traveled over an obstacle are not limited to the lateral position change, but may also refer to the vehicle speed change, suspension stroke, G sensor output, etc. as vehicle behavior, and not limited to the steering angle change, but may also refer to the required brake fluid pressure, required engine torque, etc. as driver operation amounts. The host vehicle 901 behaves differently from normal (average value), passes the obstacle avoidance start point Pos, then passes the point Pom where the lateral position is at its maximum, and behaves similarly to normal, traveling along a route 902 that passes the obstacle avoidance end point Poe. Here, if it is determined that the host vehicle 901 has traveled over an obstacle, Pom is estimated as the obstacle position 903. If the configuration is such that the amount of operation by the driver of the vehicle in front can be measured, the host vehicle 901 according to this embodiment can be replaced with the vehicle in front, it can be determined that the vehicle in front has traveled over an obstacle, and the obstacle position can be estimated.

With the above configuration, the position of an obstacle can be estimated by observing vehicle behavior that differs from the driver's operation based on the behavior of the vehicle in front and/or the vehicle itself 100, making it possible to collect information on icy or wet roads where there is a risk of unavoidable slippage or spinning, and/or obstacles that are difficult for the driver to notice.

(Summary of Embodiments 1 and 2)
As described above, the vehicle control ECU (vehicle control device) 9 of the first and second embodiments has an obstacle avoidance determination unit 307 that calculates the position of the host vehicle based on information acquired by a sensor mounted on the host vehicle, calculates a travel trajectory (travel route) of the host vehicle based on the calculated position of the host vehicle, and detects that the host vehicle has avoided an obstacle on the road based on the calculated travel trajectory of the host vehicle, and when the obstacle avoidance determination unit 307 detects that the host vehicle has avoided an obstacle, it calculates a determination accuracy (of the presence of the obstacle) according to the obstacle detection method, and estimates the position and presence or absence of the obstacle based on the calculated determination accuracy.

More specifically, the vehicle control ECU (vehicle control device) 9 of Embodiments 1 and 2 includes an obstacle avoidance determination unit 307 (subject vehicle path evaluation unit 301, preceding vehicle path evaluation unit 302) that calculates the position of the subject vehicle based on information acquired by a sensor mounted on the subject vehicle, calculates the travel trajectory (travel route) of the subject vehicle based on the calculated subject vehicle position, and detects that the subject vehicle has avoided an obstacle on the road based on the calculated travel trajectory of the subject vehicle; a determination accuracy calculation unit (subject vehicle and preceding vehicle path evaluation unit 303) that, when the obstacle avoidance determination unit 307 detects that the subject vehicle has avoided the obstacle, calculates the determination accuracy (of the presence of the obstacle) according to the obstacle detection method; an obstacle presence evaluation unit 304 that estimates the presence or absence of the obstacle based on the determination accuracy calculated by the determination accuracy calculation unit; and an obstacle position estimation unit 306 that estimates the position of the obstacle according to the obstacle presence estimation result of the obstacle presence evaluation unit 304.

The obstacle avoidance determination unit 307 (front vehicle path evaluation unit 302) further detects that the front vehicle has avoided an obstacle on the road from the relative relationship (lateral distance relationship) between the host vehicle and the preceding vehicle (front vehicle) traveling in front of the host vehicle.

Furthermore, when the vehicle control ECU (vehicle control device) 9 of this embodiment 2 detects a deviation of the longitudinal behavior and/or the lateral behavior from normal (average value) behavior without detecting any sudden operation that causes longitudinal behavior and lateral behavior, it infers that the longitudinal behavior and/or the lateral behavior is behavior caused by running over the obstacle, and infers that the location of the obstacle is on the route traveled by the host vehicle and/or the preceding vehicle.

In other words, the vehicle control ECU (vehicle control device) 9 in this embodiment estimates the accuracy of determining the presence of an obstacle from the vehicle's driving route using statistics or machine learning, and estimates the position accuracy when an obstacle is detected based on the difference between the amount of movement of the GNSS position coordinates when communication is good and the amount of movement of the DedReco position coordinates.

According to this embodiment, each vehicle estimates the position of obstacles on the road based on its travel route, and further estimates the judgment accuracy according to the obstacle detection means, thereby increasing robustness against communication problems with base stations and GNSS satellites, and furthermore, by transmitting an estimated value of the obstacle at the time of obstacle detection, it is possible to reduce the amount of communication information.

In other words, with this embodiment, when an obstacle is detected on the vehicle while communication between the vehicle and GNSS satellites is blocked by structures around the vehicle, causing communication problems, the position of the obstacle is estimated based on the DedReco position coordinates. When communication with the GNSS satellites is restored, the position accuracy at the time of obstacle detection is estimated from the deviation between the GNSS position coordinates when communication with the GNSS satellites was good and the DedReco position coordinates, thereby increasing robustness against communication problems with GNSS satellites. Furthermore, by using edge computing to organize the accuracy of determining the presence and position of an obstacle, as well as its position accuracy, as obstacle information, and sending the obstacle information to the server only when an obstacle is detected on the vehicle side, the burden of communication traffic can be reduced.

Furthermore, in road traffic information and communication systems such as VICS _{(registered trademark)} on Japanese expressways, information on obstacles on the road is collected from television cameras, patrols, and reports from the general public, and the obstacle information is aggregated and shared. However, according to this embodiment, the position of an obstacle can be estimated based on the behavior of the vehicle and the operation of the driver, so that obstacle information can be shared quickly.

Note that the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those having all of the configurations described.

Furthermore, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or in software by a processor interpreting and executing programs that realize each function. Information such as programs, tables, and files that realize each function can be stored in storage devices such as memory, hard disks, and solid-state drives (SSDs), or on recording media such as IC cards, SD cards, and DVDs.

Furthermore, the control lines and information lines shown are those considered necessary for explanation, and do not necessarily represent all control lines and information lines necessary for implementation. In reality, it is safe to assume that almost all components are interconnected.

100...vehicle equipped with vehicle control device (own vehicle)
2. Wheel 3. Wheel speed sensor 4. Steering wheel 5. Electric power steering device 6. Camera 7. GNSS receiver 8. Vehicle communication unit 9. Vehicle control ECU (vehicle control device)
9a...I/O LSI
9b...CPU
301: Vehicle route evaluation unit 302: Vehicle-in-front route evaluation unit 303: Vehicle and vehicle-in-front route evaluation unit (determination accuracy calculation unit)
304: Obstacle presence evaluation unit 305: Vehicle position reliability calculation unit 306: Obstacle position estimation unit 307: Obstacle avoidance determination unit

Claims (9)

an obstacle avoidance determination unit that calculates a position of the host vehicle based on information acquired by a sensor mounted on the host vehicle, calculates a travel path of the host vehicle based on the calculated position of the host vehicle, and detects that the host vehicle has avoided an obstacle on a road based on the calculated travel path of the host vehicle;
When the obstacle avoidance determination unit detects the avoidance of the obstacle, it calculates a determination accuracy according to the obstacle detection method, and estimates the presence and position of the obstacle based on the calculated determination accuracy ;
The obstacle avoidance determination unit further detects that the preceding vehicle has avoided an obstacle on the road from a relative relationship between the host vehicle and a preceding vehicle traveling ahead of the host vehicle,
The vehicle control device is characterized in that the obstacle avoidance judgment unit continuously calculates the average value and standard deviation of the longitudinal behavior and lateral behavior of the host vehicle and/or the preceding vehicle, and when it detects that the difference between the latest longitudinal behavior and the average value of the lateral behavior deviates by a predetermined value or more with respect to the variance, it infers that the longitudinal behavior and/or the lateral behavior is an avoidance behavior of the obstacle, and recognizes the obstacle . The vehicle control device according to claim 1, characterized in that, when the vehicle control device estimates that the longitudinal behavior and/or the lateral behavior is an avoidance behavior of the obstacle, the vehicle control device estimates that the position of the obstacle is within the lane in which the host vehicle and/or the preceding vehicle are traveling. The vehicle control device according to claim 1, characterized in that, when the vehicle control device detects a deviation of the longitudinal behavior and/or the lateral behavior from normal without detecting a sudden operation that causes the longitudinal behavior and the lateral behavior, the vehicle control device infers that the longitudinal behavior and/or the lateral behavior is a behavior caused by climbing over the obstacle, and infers that the position of the obstacle is on a route traveled by the host vehicle and/or the preceding vehicle. The vehicle control device according to claim 1, characterized in that the vehicle control device calculates the amount of deviation from normal of the longitudinal behavior and/or the lateral behavior of each of the host vehicle and the preceding vehicle, and the sum of the calculation results of the amount of deviation from normal of each of the host vehicle and the preceding vehicle is used as the judgment accuracy. The vehicle control device described in claim 1, characterized in that the vehicle control device determines the position recognition accuracy based on the detection state of GNSS satellites used to recognize the position of the vehicle, and when it determines that the position recognition accuracy is insufficient, estimates the position of the obstacle based on the position of the vehicle calculated based on information obtained by the sensor mounted on the vehicle. The vehicle control device according to claim 5, characterized in that, when it determines that the position recognition accuracy determined based on the detection state of the GNSS satellites is sufficient, it estimates the position accuracy at the time the obstacle was detected based on the difference between the GNSS coordinates of the vehicle and the position of the vehicle calculated based on information acquired by the sensor mounted on the vehicle, and the distance traveled by the vehicle. The vehicle control device transmits the position, determination accuracy, and position accuracy of the obstacle to a computer installed outside the vehicle,
2. The vehicle control device according to claim 1, wherein the computer determines the presence and position of the obstacle based on the position, determination accuracy, and position accuracy of the obstacle received from the vehicle control device. The vehicle control device according to claim 7, wherein the computer determines that the obstacle has disappeared by not receiving obstacle information at the position where the occurrence of the obstacle was determined from the vehicle equipped with the vehicle control device for a predetermined period of time. an obstacle avoidance determination unit that calculates a position of the host vehicle based on information acquired by a sensor mounted on the host vehicle, calculates a travel trajectory of the host vehicle based on the calculated position of the host vehicle, and detects that the host vehicle has avoided an obstacle on a road based on the calculated travel trajectory of the host vehicle;
a determination accuracy calculation unit that calculates a determination accuracy according to a method for detecting the obstacle when the obstacle avoidance determination unit detects that the obstacle has been avoided;
an obstacle presence evaluation unit that estimates the presence or absence of the obstacle based on the determination accuracy calculated by the determination accuracy calculation unit;
an obstacle position estimation unit that estimates a position of the obstacle in accordance with the obstacle presence estimation result of the obstacle presence evaluation unit ,
The obstacle avoidance determination unit further detects that the preceding vehicle has avoided an obstacle on the road from a relative relationship between the host vehicle and a preceding vehicle traveling ahead of the host vehicle,
The vehicle control device is characterized in that the obstacle avoidance judgment unit continuously calculates the average value and standard deviation of the longitudinal behavior and lateral behavior of the host vehicle and/or the preceding vehicle, and when it detects that the difference between the latest longitudinal behavior and the average value of the lateral behavior deviates by a predetermined value or more with respect to the variance, it infers that the longitudinal behavior and/or the lateral behavior is an avoidance behavior of the obstacle, and recognizes the obstacle .