JP2022098347A - Road traffic control system - Google Patents

Abstract

To provide a road traffic control system capable of performing future prediction corresponding to a change of a traffic state over a wide range which cannot be fully recognized only by an automatic operation vehicle.SOLUTION: A road traffic control system performs: predicting a future position of a mobile body detected by a mobile body sensor; determining a probability of a collision with the mobile body from position information and route information received from an automatic operation vehicle 16; notifying the automatic operation vehicle; correcting a determination result of the probability of the collision based on signal information SA1 from a traffic signal control device 12 and environment information SA3 including a road surface state from an environment sensor 14; and notifying the automatic operation vehicle of a degree of the risk of the collision by performing division into a plurality of levels.SELECTED DRAWING: Figure 1

Description

The present invention relates to a road traffic control system that provides a wide range of future prediction information that cannot be grasped only by an autonomous driving vehicle to an autonomous driving vehicle passing through an intersection so that the autonomous driving vehicle can travel safely.

Patent Document 1 describes a vehicle collision prevention device for preventing a collision at an intersection. This vehicle collision prevention device calculates the probability distribution of the intersection approach time of each vehicle according to the running conditions of multiple vehicles entering the intersection, obtains the probability of collision from the comparison of this probability distribution, and avoids the collision. To give an alarm for.

Japanese Unexamined Patent Publication No. 6-23396

By the way, at intersections, due to changes in the color of signal lights and the presence of pedestrians, vehicles entering the intersection are stopped, started, accelerated or decelerated, and road surface conditions change due to changes in the weather such as rainfall and snowfall. To do.

However, the above-mentioned technique of Patent Document 1 does not consider the influence of changes in the color of the signal lamp, the presence or absence of pedestrians, and changes in the road surface condition, and the collision probability is based only on the acceleration / deceleration of the vehicle and the traveling condition depending on the position. Because it is seeking, it is not in line with changes in actual traffic conditions.

On the other hand, since the autonomous driving vehicle makes various judgments based on the surrounding environment and the driving condition that can be recognized by the sensor of the own vehicle, the system installed in the autonomous driving vehicle alone can change the traffic condition over a wide range. There is a problem that it is difficult to predict the future.

The present invention has been made in view of the above circumstances, and an object thereof is road traffic that can predict the future in accordance with changes in traffic conditions over a wide range that cannot be grasped only by an autonomous driving vehicle. It is to provide a control system.

The road traffic control system according to one aspect of the present invention predicts the future position of the moving body detected by the moving body sensor, and may collide with the moving body from the position information and the route information received from the automatically driving vehicle. Is a system that determines and notifies the automatically driven vehicle, and corrects the determination result of the possibility of collision based on the signal information from the traffic signal controller and the environmental information including the road surface condition from the environment sensor. However, it is characterized in that the risk of collision is divided into levels and notified.

According to the road traffic control system of the present invention, the possibility of collision or contact accident between a moving body and an automatically driven vehicle is determined by using signal information from a traffic signal controller and environmental information including road surface conditions at an intersection. Since the correction is made based on the above, it is possible to predict the future in line with changes in actual traffic conditions over a wide range that cannot be grasped only by the self-driving vehicle.

It is a block diagram which shows the schematic structure of the road traffic control system which concerns on embodiment of this invention. FIG. 3 is a perspective view for explaining collision determination when a right-turning vehicle and an oncoming straight-ahead vehicle approach each other at an intersection to which the road traffic control system shown in FIG. 1 is applied. It is a schematic plan view of the intersection shown in FIG. It is a schematic plan view for demonstrating the collision determination at the intersection shown in FIG. 2 when a right-turning vehicle temporarily stops in the intersection. It is a perspective view for demonstrating the collision determination of a left-turning vehicle or a right-turning vehicle, and a pedestrian or a bicycle crossing a pedestrian crossing at an intersection to which the road traffic control system shown in FIG. 1 is applied. It is a figure for demonstrating the area range of a straight part and a curved part. It is a figure for demonstrating the setting method of the area range of a curved part. It is a figure for demonstrating the determination of the area position. It is a figure for demonstrating the unit position. It is a figure for demonstrating the size of the object used in the collision determination. It is a schematic diagram for demonstrating the collision determination in detail. FIG. 3 is a block diagram showing a configuration example in which direct communication with a vehicle is performed using an IP network in the road traffic control system shown in FIG. 1. FIG. 3 is a block diagram showing a configuration example in the case of communicating with a vehicle via a server using an IP network in the road traffic control system shown in FIG. 1. It is a block diagram which shows the configuration example in the case of applying the first (last) one mile management system in the road traffic control system shown in FIG.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 shows a schematic configuration of a road traffic control system according to an embodiment of the present invention. This system is composed of a roadside machine 1 provided in the vicinity of an intersection and an in-vehicle device 2 mounted on an autonomous driving vehicle traveling in the vicinity of the intersection.
The roadside unit 1 includes a control module 11, a traffic signal controller 12, a multi-sensor module (mobile sensor) 13, an environment sensor 14, a wireless device 15, and the like.

The in-vehicle device 2 includes various sensors mounted on the autonomous driving vehicle 16, a vehicle control device, an automatic driving (or driving support) device, and a wireless device 17. The vehicle control device includes an engine control device that controls an engine, a steering control device that controls steering, a brake control device that controls brakes, and the like. The automatic driving device calculates the current position and the traveling route from the position information and the route information obtained from various sensors, and controls the vehicle control device based on the calculation result to drive the vehicle.

Signal information SA1 is input from the traffic signal controller 12, mobile information SA2 is input from the multi-sensor module 13, and environmental information SA3 is input from the environment sensor 14 to the control module 11. Further, the wireless device 15 receives the driving information SA4 transmitted from the wireless device 17 of the autonomous driving vehicle 16.

The control module 11 predicts the future position of a moving body such as a non-autonomous vehicle, a bicycle, or a pedestrian by using the moving body information SA2 from the multi-sensor module 13, and the driving information SA4 (received from the automatic driving vehicle 16). It is configured to determine the possibility of collision with a moving body based on the identification information SA4a, the position information SA4b, the route information SA4c, etc.). At the time of this determination, the determination result is corrected based on the signal information SA1 from the traffic signal controller 12 and the environmental information SA3 including the road surface condition by the environment sensor 14. Then, the roadside information SA5 (identification information SA4a, signal information SA1, danger information SA5a, etc.) is transmitted from the wireless device 15 to the autonomous driving vehicle 16.

The traffic signal controller 12 controls the light color and signal switching of the signal lamp, and outputs signal information SA1 such as the light color and the time (seconds) until the signal switching to the control module 11. The control module 11 transmits the signal information SA1 from the radio device 15 before the autonomous driving vehicle 16 enters the intersection. By receiving the timing at which the light color and the signal are switched in advance, the autonomous driving vehicle 16 predicts the light color of the signal lamp when it approaches the intersection.

The multi-sensor module 13 detects vehicles (vehicles that are not automatically driven) from inside the intersection to a distance of about 150 m around the intersection, and bicycles and pedestrians around the pedestrian crossing and the intersection (as far as possible). be. In this example, the multi-sensor module 13 is an image pickup sensor and includes a vehicle lamp camera 21 and a pedestrian lamp camera 22 that act as mobile sensors. The vehicle lamp camera 21 is attached to, for example, a high position of a signal pole, and takes a bird's-eye view of a vehicle passing through an intersection. The pedestrian lamp camera 22 is attached, for example, near a pedestrian traffic light on a signal pole, and photographs pedestrians (including bicycles) crossing a pedestrian crossing.

The images taken by these cameras 21 and 22 are processed by the image processing unit 23 to detect vehicles and pedestrians around the intersection. Then, the detected vehicle information SA2a such as the position (longitude and latitude), speed and moving direction, and the pedestrian information SA2b such as the detected pedestrian position (longitude and latitude), speed and moving direction are used as the moving body information SA2. Input to the control module 11.
Although the case where an image sensor is used as the multi-sensor module 13 has been described as an example, LiDAR (LIDAR) which measures the time until it hits an object and bounces off by irradiating a laser beam and measures the distance and direction to the object. light detection and ranging) may be used. By using LiDAR, it is possible to grasp the distance, shape, and positional relationship of vehicles, bicycles, pedestrians, etc. in three dimensions.

The environment sensor 14 includes a weather sensor 14a for detecting the weather and a road surface monitoring sensor 14b for monitoring the road surface condition. The meteorological sensor 14a measures meteorological conditions such as wind direction, wind speed, temperature, humidity, atmospheric pressure, rainfall, and snowfall, and outputs meteorological information SA3a. Further, the road surface monitoring sensor 14b monitors the road surface condition such as wetness, snow cover, and freezing of the road surface, and outputs the road surface information SA3b. Based on these weather information SA3a and road surface information SA3b, the control module 11 determines the surrounding environment of the intersection.

The control module 11 not only determines whether or not the position and travel path of the autonomous driving vehicle 16 match the detected positions, speeds, and moving directions of the vehicle or pedestrian, and whether or not there is a possibility of collision, as well as whether or not the autonomous driving vehicle 16 has a possibility of collision. Predicts the light color of the signal lamp when the vehicle approaches the intersection, determines the risk of collision based on the road surface condition (road surface slip friction coefficient) of the intersection, and sends the danger information SA5a to the autonomous driving vehicle 16. Set. If the danger information SA5a has already been set and the surrounding environment changes, the danger information SA5a is reset, in other words, the rank of the level division is changed.

Specifically, the control module 11 includes identification information such as a vehicle ID transmitted from the automatically driven vehicle 16 via the wireless device 17, position information such as longitude / latitude and speed, traveling lane, moving direction and speed, and the like. When the route information is received by the wireless device 15, a peripheral map is created from the vehicle information SA2a and the pedestrian information SA2b, and the automatic driving vehicle 16 is specified from the peripheral map. In addition, the future position of the vehicle and the pedestrian is predicted, and it is determined whether or not there is a danger when the autonomous driving vehicle 16 travels (whether or not there is a possibility of collision or contact with another vehicle or pedestrian). do.

In this determination, the slip friction coefficient of the road surface is calculated based on the environmental information obtained from the environment sensor 14, and the braking distance considering the friction coefficient is obtained according to, for example, the following equation, and the future position of the automatic driving vehicle 16 and the vehicle is obtained. Used for prediction of.
Braking distance = square of speed (km / hour) before braking ÷ (254 x coefficient of friction)
When it is determined that there is a danger, the risk level of the collision is classified into levels to determine the control necessary for avoidance, and the determination result danger information SA5a is transmitted from the wireless device 15 to the autonomous driving vehicle 16. ..

On the autonomous driving vehicle 16 side, for example, the signal information SA1 is used to predict the light color and the timing (time) of the next signal switching, and control such as speed maintenance, deceleration, stop, and start is performed. That is, when the light color is blue at the time when it is predicted to approach the intersection, the future position of passing through the intersection is predicted, and when it becomes yellow or red, it is predicted to stop. When the light color is red at the time predicted to approach the intersection, the future position is predicted to stop.
In addition, the brake operation and engine output are suppressed according to the level of the danger information SA5a, and control such as deceleration and stop for avoiding collision and contact is performed.

The level of the risk of collision is divided into a plurality of levels according to the deceleration required for avoiding the danger of the autonomous driving vehicle. For example, the first stage (normal deceleration), the second stage (maximum deceleration), the third stage (emergency brake), and the fourth stage (damage mitigation brake).
The first stage is a braking operation in which a collision occurs when the vehicle moves at the current speed, but when the vehicle decelerates at 0.2 G, the vehicle can stop in front of the collision prediction area. The vehicle is expected to decelerate (0.2G) to avoid danger.
The second stage is a braking operation in which a collision occurs when the vehicle moves at the current speed, but when the vehicle decelerates at 0.3 G, the vehicle can stop in front of the collision prediction area. To avoid danger, the vehicle is expected to decelerate (0.3G).

The third stage is a braking operation in which a collision occurs when the vehicle moves at the current speed, but when the vehicle decelerates at 0.5 G, the vehicle can stop in front of the collision prediction area. To avoid danger, the vehicle is expected to decelerate suddenly (0.5G or more).
The fourth stage is a case where even if the vehicle decelerates at 0.5 G, it cannot be stopped before the collision prediction area. Although the danger cannot be avoided, it is assumed that the vehicle will decelerate to the maximum extent to reduce the damage.

As described above, according to the road traffic control system of the present invention, the possibility of collision or contact accident between a non-automatically driven vehicle or a pedestrian and an automatically driven vehicle is determined by the signal information from the traffic signal controller and the intersection. Since the correction is made based on the environmental information including the road surface condition of the vehicle, it is possible to predict the future according to the change of the actual traffic condition over a wide range that cannot be grasped only by the automatically driven vehicle.
Moreover, by dividing the degree of danger into a plurality of levels and notifying the autonomous driving vehicle, it is possible not only to operate the brake to stop the vehicle but also to decelerate according to the degree of danger.

In addition, in FIG. 1, the control module 11 and the wireless device 15 may be built in the housing 18 of the traffic signal controller 12 as shown by being surrounded by the alternate long and short dash line, or may be mounted externally on a signal pole. Is also good.
Further, for the sake of simplicity, the case where the roadside unit 1 is provided with one control module 11, one traffic signal controller 12, one multi-sensor module 13, an environment sensor 14, and one wireless device 15 has been described. It is preferable to provide a plurality of multi-sensor modules 13 so that a wide range around the intersection can be photographed. A plurality of environment sensors 14 may be provided as needed, or one of the weather sensor 14a and the road surface monitoring sensor 14b may be provided.

Next, in the above-mentioned road traffic control system, a case where danger information (here, oncoming vehicle approach information) is generated in units of the autonomous driving vehicle 16 will be described with reference to FIGS. 2 and 3. FIG. 2 is a perspective view for explaining collision determination when a right-turning vehicle and an oncoming straight-ahead vehicle approach each other at an intersection to which the road traffic control system shown in FIG. 1 is applied, and FIG. 3 is a perspective view shown in FIG. It is a schematic plan view of an intersection.

FIG. 2 shows a situation in which a right-turning vehicle (automated driving vehicle 16) and an oncoming straight-moving vehicle (non-automated driving vehicle 33) enter an intersection after a predetermined time, and the estimated positions of the vehicles 16 and 33 after a predetermined time. Is represented by a broken line (vehicles 16A, 33A).
The roadside machine 1 is attached to a signal pole 31, and driving information SA4 such as identification information SA4a, position information SA4b, and route information SA4c transmitted from the radio device 17 of the autonomous driving vehicle 16 is built in the roadside machine 1. It is received by the wireless device 15. Further, the vehicle lamp camera 21 attached to the signal pole 32 takes a picture of the vehicle (vehicle not automatically driven) 33 traveling in the hatched area AA, and the image processing unit 23 performs image processing on the vehicle. 33 is detected.

Similar to FIG. 2, the vehicles 16A and 16B shown by the broken lines in FIG. 3 are the future positions of the autonomous driving vehicle 16 after a predetermined time, respectively, and the vehicles 33A and 33B shown by the broken lines are the future positions of the vehicle 33 after a predetermined time, respectively. Future position. When the self-driving vehicle 16 and the vehicle 33 proceed along the broken line arrow, the future positions of the vehicle 16B and the vehicle 33B overlap in the intersection, so that the control module 11 determines that the self-driving vehicle 16 and the vehicle 33 collide. Therefore, the control module 11 determines the control necessary for avoidance, and wirelessly transmits the danger information SA5a in which the danger levels are divided into a plurality of levels to the autonomous driving vehicle 16.

FIG. 4 is for explaining the collision determination when the right-turning vehicle 16 temporarily stops in the intersection (shown by the broken line vehicle 16C) at the intersection shown in FIG. 2, and is for automatic driving traveling on the right-turn route 34. When the vehicle 16 is stopped at the right turn waiting point 35, it is determined whether or not the vehicle collides (or comes into contact with) the vehicle 33C traveling on the straight route 36 when the vehicle starts. That is, during the period from the right turn waiting point 35 to passing the intersection passage point 37 (for example, from the present time to 6 seconds later), the vehicle 16 starts in the area AB where the right turn route 34 and the straight route 36 indicated by the arrows intersect. If there is an oncoming straight vehicle 33C, it is judged that it is dangerous to turn right, and if it does not exist, it is judged that it is possible to turn right.

By considering the signal information SA1 from the traffic signal controller 12 and the environmental information SA3 including the road surface condition at the intersection at the time of this danger determination, it covers a wide range that cannot be grasped only by the autonomous driving vehicle 16. , It is possible to predict the future according to changes in actual traffic conditions.
The time from the right turn waiting point 35 to passing through the intersection passing point 37 is set to 6 seconds later, but it depends on the size of the intersection, the acceleration performance of the autonomous driving vehicle 16, the speed of the oncoming straight vehicle 33, and the like. Of course, it is good to set in consideration of these.

In this way, the roadside machine 1 monitors the possibility of collision or contact between the autonomous driving vehicle 16 and the non-automated vehicle 33, and when it is determined that there is a danger, the danger information SA5a that classifies the degree of danger is provided. By wirelessly transmitting to the autonomous driving vehicle 16, it is possible to predict the future in line with changes in traffic conditions over a wide range that cannot be grasped by the autonomous driving vehicle 16 alone.

Next, the function of generating danger information for each intersection will be described with reference to FIG. FIG. 5 is for explaining collision determination between a left-turning vehicle or a right-turning vehicle and a pedestrian or a bicycle crossing a pedestrian crossing at an intersection to which the road traffic control system shown in FIG. 1 is applied.
The roadside unit 1 is attached to a signal pole 31, and receives driving information SA4 such as identification information SA4a, position information SA4b, and route information SA4c transmitted from the wireless devices 17 of the autonomous driving vehicles 40 and 41 by the wireless device 15. do. In addition, the pedestrian light camera 22 photographs pedestrians and bicycles on the pedestrian crossing (area AC with hatching). Although not shown, pedestrian light cameras are also installed on the pedestrian crossings facing each other across the intersection to photograph pedestrians and bicycles.

Here, a state in which a left-turning vehicle (automated driving vehicle) 40 and a right-turning vehicle (automated driving vehicle) 41 are about to enter an intersection at almost the same time is shown as an example. When the left-turning vehicle 40 and the right-turning vehicle 41 approach the intersection, the control module 11 determines the identification information SA4a such as the vehicle ID transmitted from the autonomous driving vehicles 40 and 41, the position information SA4b such as the longitude / latitude and the speed, and the traveling lane. Receives route information SA4c such as travel direction and speed. Further, a peripheral map is created from the vehicle information SA2a and the pedestrian information SA2b, and the autonomous driving vehicles 40 and 41 are specified from the peripheral map. Further, it predicts the future positions of non-autonomous vehicles and pedestrians and bicycles, and determines whether or not there is a danger when the autonomous vehicles 40 and 41 run. At this time, it also detects pedestrian and bicycle signal ignorance, and if it is determined that there is a danger, it determines the control necessary for avoidance and divides the risk of collision into levels of danger information SA5a (pedestrian caution information). ) Is transmitted to the autonomous driving vehicles 40 and 41.

On the side of the autonomous driving vehicles 40 and 41, for example, the signal information SA1 is used to predict the light color and the next signal switching timing, and control such as starting, decelerating, and stopping is performed. This makes it possible to improve the accuracy of future position prediction when approaching an intersection. In addition, the brake operation and engine output are suppressed according to the level of the danger information SA5a, and control such as deceleration and stop is performed. After the automatic driving vehicle 40 has passed the intersection, if it is determined that there are no pedestrians or bicycles on the pedestrian crossing (area AC), the automatic driving vehicle 41 passes through the intersection.

In FIG. 5, for the sake of simplicity, the case of detecting a pedestrian or a bicycle on a pedestrian crossing has been described. For example, a plurality of pedestrian lamp cameras 22 are mounted around an intersection (as far as possible). It may be provided so that pedestrians and bicycles can be detected.
Further, by combining with the vehicle lamp camera 21 shown in FIG. 2, a moving object can be detected in a wide range around the intersection, and the moving speed and direction can be accurately grasped from a plurality of images.

Next, the estimation of the future position of the moving object (object) will be described in detail. This system predicts a future position from vector information of an object such as a vehicle or a pedestrian input from an image processing unit 23, for example, an AI image processing unit, and determines whether or not it is dangerous for an automatically driven vehicle. In this prediction of future position, the object is basically treated as performing an action that is considered appropriate from the surrounding situation.

However, if the current vector information of the object deviates from the operation considered to be valid, the operation estimated from the current vector information shall be performed. For example, the vehicle object stops before the pedestrian crossing if a pedestrian is moving on the pedestrian crossing when turning left, but stops before the pedestrian crossing if it is moving with a vector that cannot be stopped before the pedestrian crossing. Instead, it is assumed that the movement is continued with the current vector.

The future position of each object is estimated based on the surrounding road shape, the current position of each object, the speed, and the like.
Next, the flow of processing for estimating the future position of the object, area / route setting, area determination, route determination, area position determination, light color determination, behavior determination, object collision determination, and object avoidance behavior will be described. ..

<Processing flow>
To give an example of the processing flow in the future position estimation, the future position is calculated for each object at intervals of 0.2 seconds from the current position to the future of 15 seconds (specified by a program constant). Each object basically moves along the route corresponding to the area in which it exists, accelerates / decelerates according to the surrounding conditions (other objects, signals, areas), and determines the final destination. Then, the future position is calculated by repeating the process until the time for calculating the future position.

<Area route setting>
An area is defined as a lane divided into multiple sections. The area is defined by two points, a start point and an end point in the case of a straight line, four points of a start point and an end point in the case of a curved line, and a control point of a cubic Bezier curve, and an area width.
In addition, the route that an object takes is usually defined as a route. A route is defined in the form of a set of consecutive areas. A single block is defined as one area, not multiple areas. However, the starting point of branching exists across a plurality of areas. Areas are basically represented by lines and dots. This method has the advantage that the path such as turning left or right can be expressed more naturally than the area method, and the length of the path itself can be obtained in the form of an approximate value.

The range of the area is the line 101, 102 or 111, parallel to the input route information value (represented by the straight line 100 or the curve 110), as shown by the dashed lines in FIGS. 6 (a) and 6 (b). 112 is calculated and its width is lane width 103, 104 or 113, 114. That is, the areas 105 and 115 are defined as a place surrounded by two parallel lines and a straight line having an area width drawn in the normal direction of the start point and the end point.

As shown in FIG. 7, in the Bezier curve defined by the curved portion, a normal line is drawn perpendicular to the straight line connecting the start point and the control point on the start point side, and the end point and the control point on the end point side. When calculating the lines of two lines parallel to the area, after determining the start point and end point, the control point is determined according to the ratio to the original start point and end point. After that, the points through which parallel lines pass are calculated according to the Bezier curve equation.

In the Bezier curve formula, when the start point (x ₁ , y ₁ ), the start point control point (x ₂ , y ₂ ), the end point control point (x ₃ , y ₃ ), and the end point (x ₄ , y ₄ ), the start point is set. With t = 0 and the end point being t = 1, the coordinates of x and y are expressed by the following equations.
x (t) = t ³ x ₄ + 3t ² (1-t) x ₃ + 3t (1-t) ² x ₂ + (1-t) ³ x ₁
y (t) = t ³ y ₄ + 3t ² (1-t) y ₃ + 3t (1-t) ² y ₂ + (1-t) ³ y ₁
When determining the area range, the area between the start point and the end point may be divided into about 10 and approximated by a plurality of straight lines connecting the points.

<Area judgment>
The area determination is to determine in which area each object is located in the area set by the constant.
Since lanes are separated and set as areas, multiple areas overlap at points where lanes intersect, such as at branches and intersections. If multiple areas overlap, select the area included in the route that the object has traveled in the past. If there are multiple routes, such as a straight left turn lane, it is considered to exist across multiple areas.
If an object suddenly occurs in an intersection due to a factor such as omission of detection, it will exist at the place where multiple routes and areas intersect, but even in such a case, it is treated as if it exists across multiple areas. ..

<Route judgment>
The route determination is to determine which route the target is moving when the area where the object exists is determined. If multiple routes can be taken, such as going straight or turning left, all routes are treated as moving (possible).
However, if it is determined by the destination determination that the vehicle cannot travel on the route, it is assumed that an appropriate route does not exist, and the destination is calculated based on the current speed, direction, and acceleration.

<Area position judgment>
The area position determination determines where the object exists in the area with respect to the current position.
As shown in FIGS. 8A and 8B, the coordinates of the point on the line of the area where the distance ΔD from the current position is the smallest with respect to the line of the area where the object exists, and the current position is in the latitude / longitude direction. Calculate how much the deviation is in the (vertical and horizontal directions) (hereinafter referred to as the position offset). The position offset is normalized so that the direction of the line of the area is due north (normalization makes the vertical direction 0).

The position of the object on the area uses the distance from the upstream of the area calculated using the coordinates of the points on the area. When restoring to the coordinates from the distance from the upstream of the area, the value is obtained by adding the position offset to the coordinates calculated from the position on the area according to the traveling direction of the area on the coordinates.
Since it is a heavy load to calculate the normal from an arbitrary point with respect to the cubic Bezier curve, the position offset may be calculated using a straight line that approximates the cubic Bezier curve.
As shown in FIG. 9, the unit position is a position obtained by adding a position offset perpendicular to the traveling direction (tangential direction) of the area.

<Light color judgment>
The light color determination is performed by calculating the light color at the current date and time or the date and time at the future position using the information creation time of the signal information and the current date and time. The accuracy (error) of the signal information is not taken into consideration.
If there is a range of seconds for each light color in the signal information (the shortest and longest do not match), it is basically treated as operating at the shortest time. However, if there is a light color whose longest is unknown, it is treated as if the light color continues to appear. Further, when a problem occurs in the consistency monitoring of the signal information or when the signal information is not received for 3 seconds (specified by the program constant), the light color received by the light color information is treated as being continuously output. If the light color information cannot be received, all the lights are treated as extinguished.

<Behavior judgment>
The current speed and acceleration are determined from the current and past velocities.
If you don't know the current velocity of the object, or if there are objects in the same group on the route, use the velocity of the most recent object (to match the surrounding flow). If the object does not exist, the speed is the set speed (upper limit speed) of the existing area, and the direction is the direction to move from the current position to the next area. However, as a result of the situation judgment, if the situation requires deceleration, the speed is reduced to a speed at which deceleration is unnecessary.

If the current velocity of the object is known, the velocity and acceleration of the object are calculated from the velocities of the last 3 seconds (specified by the program constant) using a linear approximation formula by the method of least squares. If the speed is unknown within 3 seconds, the approximation is performed using only the known information.

<Object collision detection>
It is determined whether the future positions of all other objects whose routes may intersect collide with the future positions of all the calculated times for the specified object. For the future position of the specified object at a specific time (future position T1 after t seconds), other objects are all from the future position after "t-A" seconds to the future position after "t + A" seconds. Judgment shall be made. For example, A is 10% of t (specified by a constant), and the fraction is rounded up.

Based on the future position (current position) of the object at the target time (hereinafter referred to as T), the area where the object can exist is calculated using the total length and the full width according to the type of the object.
FIG. 10 shows an example of the size of the object used in the collision determination. Here, it is classified into automobiles, buses, bicycles, motorcycles and pedestrians, and the total length and width are determined in advance. The front of the object (direction of the total length) is the traveling direction of the object at the time. Internally, the area where the object can exist is treated as a rectangle.

FIG. 11 is a schematic diagram for explaining the collision determination in detail. From the future position of the specified object at time T and the future position immediately before time T (if it does not exist, the future position immediately before), there is a possibility that the routes will intersect as a polygon with the maximum area with each point as the apex. Similarly, the polygon having the maximum area is calculated for all other objects (other objects), and it is determined whether or not the polygons overlap each other.
If even one point overlaps, it is considered to be a collision.

<Object avoidance behavior>
If the specified object performs an action to avoid another object, it is considered that the specified object has performed a collision avoidance action, and if the specified object does not perform the avoidance action, it is considered to collide.

FIG. 12 shows a configuration example in which the road traffic control system shown in FIG. 1 directly communicates with a vehicle using an IP network. In this example, the control module 11, the traffic signal controller 12, the hub 26, and the mobile router 28 are housed in the housing 18. Signal information SA1 is input from the traffic signal controller 12, environmental information SA3 is input from the environment sensor 14, and positioning information SA6 is input from the GNSS (Global Navigation Satellite System) system to the control module 11. Further, the image of the vehicle taken by the vehicle lamp camera 21 is processed by the image processing unit 23-1, and the vehicle information SA2a is input to the control module 11 via the hubs 25 and 26. Similarly, the image of the pedestrian taken by the pedestrian lamp camera 22 is processed by the image processing unit 23-2, and the pedestrian information SA2b is input to the control module 11 via the hubs 25 and 26.

The driving information SA4 from the autonomous driving vehicle 16-1, 16-2, 16-3, ... (Here, the autonomous driving vehicle 16-1 is taken as an example) is a base using a mobile line provided by a telecommunications carrier. It is transmitted to the station 29 and input from the base station 29 to the control module 11 via the LTE antenna 27, the mobile router 28, and the hub 26. On the other hand, the roadside information SA5 such as the signal information SA1 and the danger information SA5a is transmitted from the control module 11 to the base station 29 via the hub 26, the mobile router 28, the LTE antenna 27, and the mobile line, and the base station 29 responds. The identification information SA4a is transmitted to the automatic driving vehicle 16-1.
According to such a configuration, it is possible to communicate with the autonomous driving vehicle 16-1, 16-2, 16-3, ... Using a mobile line provided by a communication carrier such as a mobile phone carrier.

FIG. 13 shows a configuration example in which the road traffic control system shown in FIG. 1 communicates with a vehicle via a server using an IP network. In FIG. 13, the same parts as those in FIG. 12 are designated by the same reference numerals, and detailed description thereof will be omitted. In this example, the distribution server 20A is provided on the cloud, and the driving information SA4 from the autonomous driving vehicle 16-1, 16-2, 16-3, ... (Here, the autonomous driving vehicle 16-1 is taken as an example). Is transmitted to the base station 29 using the mobile line provided by the telecommunications carrier, and is input from the base station 29 to the distribution server 20A. Then, the operation information SA4 is input to the control module 11 from the distribution server 20A via the base station 29 via the LTE antenna 27, the mobile router 28, and the hub 26.

On the other hand, the roadside information SA5 such as the signal information SA1 and the danger information SA5a is transmitted from the control module 11 to the base station 29 via the hub 26, the mobile router 28, the LTE antenna 27, and the mobile line, and is distributed from the base station 29. It is input to the server 20A. It is transmitted from the distribution server 20A to the autonomous driving vehicle 16-1 of the corresponding identification information SA4a via the base station 29.
According to such a configuration, it is possible to communicate with the autonomous driving vehicle via the distribution server 20A on the cloud by using the mobile line provided by the communication carrier such as the mobile phone carrier. In addition, danger information can be distributed from the distribution server 20A to an autonomous driving vehicle traveling at a plurality of intersections.

FIG. 14 shows a configuration example in which the first (last) one-mile management system is applied in the road traffic control system shown in FIG. In FIG. 14, the same parts as those in FIGS. 12 and 13 are designated by the same reference numerals, and detailed description thereof will be omitted. In this example, the system management server 20B is provided on the cloud, and the driving information from the autonomous driving vehicle 16-1, 16-2, 16-3, ... (Here, the autonomous driving vehicle 16-1 is taken as an example). The SA4 is transmitted to the base station 29 using the mobile line provided by the telecommunications carrier, and is input to the control module 11 from the base station 29 via the LTE antenna 27, the mobile router 28, and the hub 26.

On the other hand, the roadside information SA5 such as the signal information SA1 and the danger information SA5a is transmitted from the control module 11 to the base station 29 via the hub 26, the mobile router 28, the LTE antenna 27, and the mobile line, and the identification information is transmitted from the base station 29. It is transmitted to the automatic driving vehicle 16-1 corresponding to SA4a.

Further, various detection information input to the control module 11 is transmitted to the base station 29 via the hub 26, the mobile router 28, the LTE antenna 27, and the mobile line, and is input from the base station 29 to the system management server 20B. To.
According to such a configuration, it is possible to communicate with the autonomous driving vehicle by using the mobile line provided by the communication carrier such as the mobile phone carrier. Moreover, the system management server 20B on the cloud can manage a plurality of roadside machines via the base station 29, and can manage information of a plurality of intersections in an integrated manner.

As described above, in the present invention, the control module recognizes the vehicle around the intersection by the input from the sensor (current position, vector), and predicts the future position from the current position and vector of the vehicle around the intersection. It receives the current position and route information from the autonomous driving vehicle, and calculates whether it will come into contact with the autonomous driving vehicle from the results of the route of the autonomous driving vehicle and the future position prediction of surrounding vehicles. Then, when there is a possibility of contact, danger information is transmitted to the autonomous driving vehicle, so that the accuracy of future prediction can be improved over a wide range that cannot be grasped only by the autonomous driving vehicle. Therefore, the safety of traffic near the intersection can be further improved.

The circuit configuration, operating procedure, and the like described in the above embodiments are merely schematically shown to the extent that the present invention can be understood and implemented. Therefore, the present invention is not limited to the described embodiment, and can be changed to various embodiments as long as it does not deviate from the scope of the technical idea shown in the claims.

1 ... Roadside device, 2 ... In-vehicle device, 11 ... Control module, 12 ... Traffic signal controller, 13 ... Multi-sensor module (mobile sensor), 14 ... Environmental sensor, 14a ... Meteorological sensor, 14b ... Road surface monitoring sensor, 15 , 17 ... wireless device, 16, 40, 41 ... automatic driving vehicle, 21 ... vehicle lamp camera, 22 ... pedestrian lamp camera, 23, 23-1, 23-2 ... image processing unit, 31, 32 ... signal Pillar, 33 ... vehicle, 34 ... right turn route, 35 ... right turn waiting point, 36 ... straight route, 37 ... intersection passage point, SA1 ... signal information, SA2 ... moving object information, SA2a ... vehicle information, SA2b ... pedestrian information, SA3 ... Environmental information, SA3a ... Meteorological information, SA3b ... Road surface information, SA4 ... Driving information, SA4a ... Identification information, SA4b ... Position information, SA4c ... Route information, SA5 ... Roadside information, SA5a ... Danger information

Claims (8)

A system that predicts the future position of a moving body detected by a moving body sensor, determines the possibility of collision with the moving body from the position information and route information received from the autonomous driving vehicle, and notifies the autonomous driving vehicle. There,
The determination result of the possibility of collision is corrected based on the signal information from the traffic signal controller and the environmental information including the road surface condition from the environment sensor, and the risk of collision is classified into levels and notified. A characteristic road traffic control system. The road traffic control according to claim 1, wherein the level of the risk of collision is divided into a plurality of levels according to the deceleration required for avoiding the danger of the autonomous driving vehicle. system. The deceleration includes normal deceleration that can be stopped before the collision prediction area, maximum deceleration that can be stopped before the collision prediction area, deceleration due to the operation of the emergency brake that can be stopped before the collision prediction area, and stop before the collision prediction area. The road traffic control system according to claim 2, wherein the road traffic control system includes deceleration by a damage mitigation brake that decelerates to the maximum for damage mitigation. The future position of the moving body is predicted from the current position and vector of the moving body detected by the moving body sensor, and the possibility of collision with the moving body is possible from the position information and the route information from the autonomous driving vehicle. The road traffic control system according to claim 1, wherein the sex is determined. The road traffic control system according to claim 1, wherein the signal information includes a light color of a signal lamp and a time until signal switching. The road traffic control system according to claim 1, wherein the road surface condition includes wetness, snow accumulation and freezing of the road surface. The road traffic control system according to claim 1, wherein the moving body sensor is an image pickup sensor or LiDAR (light detection and ranging). The road traffic control system according to claim 1, wherein the moving body is a vehicle, a bicycle, or a pedestrian that is not automatically driven.

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