JP2007047972A - Traffic flow simulation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a simulation device for reducing the increase of a load to hardware even if the number of vehicles to be calculated is increased even in a traffic flow simulation device where any traffic accident occurs due to a perceptual mistake, a recognition mistake and a decision mistake of a virtual driver. <P>SOLUTION: An object put in a positional relationship at a dead angle when it is viewed from a traveling object as a target is removed based on at least location information relating to the location of the traveling object. Then, the traveling object is moved with an object left without being removed is moved, and whether or not any traffic accident occurs is simulated. Overlooking processing to remove an object left without being removed may be performed. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a traffic flow simulation apparatus that simulates the movement of a moving body that moves in a traffic network, and more particularly, to a traffic flow simulation apparatus that takes into account the perceptual characteristics of operators of moving bodies such as vehicles, two-wheeled vehicles, ships, and pedestrians.

  When designing a new road in city planning, etc., or when improving or expanding an existing road as the traffic volume increases, a simulation is performed to predict the occurrence of traffic volume or traffic jams. And layout of intersections, width, etc. are performed. As a traffic flow simulation system for that purpose, traffic corresponding to individual cases is considered by taking into account variables such as day of the week, time of day, weather, presence / absence of events, and long-term holidays, which are factors that affect traffic conditions. A simulation technique for predicting the amount is known (Patent Document 1).

  On the other hand, when planning measures such as improving existing roads for the purpose of reducing traffic accidents, the effects of traffic safety measures on accident reduction have not been predicted in advance by traffic flow simulation. . Although it does not directly evaluate the effects of traffic safety measures on accident reduction, at the intersections or roads in the existing or design stage, the vehicle model group is moved according to classical mechanics based on the vehicle model definition information and situation information, and the driver model Patent literature that allows driver models to drive based on definition information, simulation execution condition information, and simulation process status information, and obtains risk information from driver models based on intersections and road geometric and physical characteristics No. 2 risk evaluation device is known.

Japanese Patent Laid-Open No. 05-250594 JP 2002-140786 A

  In order to evaluate the effect of traffic safety measures on accident reduction in advance using a traffic flow simulation device, traffic accidents may occur by faithfully simulating the accident generation process created by complex interactions in actual traffic. A traffic flow simulation device is required. In other words, it is said that the main cause of traffic accidents in actual traffic is the driver's perception, recognition and judgment mistakes, and the ideal traffic flow simulation is the “perception”, “recognition” and “determination” of virtual drivers. , With a series of cognitive actions called “operation”, each of the virtual vehicles in the traffic flow simulation is moved to provide an interactive mechanism that makes a complicated traffic flow appear. It must be programmed to simulate a driver's mistake in "recognition" and "judgment" and thereby cause a traffic accident.

  Here, the virtual driver “perceives” in the traffic flow simulation program means that the virtual driver from a plurality of virtual vehicles existing over a wide area in a region (a road composed of an intersection or a single road) in the traffic flow simulation device. This means searching for a virtual vehicle that is located near the virtual vehicle on which the virtual driver is boarded and that is not shielded by the shielding object. Further, “recognition” of the virtual driver means labeling whether the virtual vehicle searched by the perception of the virtual driver is a preceding vehicle or an oncoming vehicle. When the virtual driver “determines”, it means that the driving behavior necessary to reach the driving destination given to the virtual driver is determined from the arrangement status of the virtual vehicles around the recognized own vehicle. means. Further, “operating” represents determining an input to the virtual vehicle for moving the virtual vehicle as intended.

  Conventionally, there is a simulation device that simulates such “perception”, “recognition”, “determination”, and “operation” of a virtual driver, and the virtual driver judges and acts in the same way as an actual driver. There wasn't.

  The present invention has been invented in view of the above problems, and is a traffic flow simulation apparatus in which a traffic accident occurs due to a virtual driver's perception error, recognition error, or determination error. It is an object of the present invention to provide a simulation apparatus that increases the load on hardware even when the number of hardware increases.

  In order to achieve the above-described object of the present invention, a traffic flow simulation apparatus according to the present invention includes a mobile body information storage unit that stores position information related to at least a position of a mobile body, and a mobile body that is a target based on the position information. Perceptual simulation means for simulating the perception of an agent on a moving body, which removes an object in a positional relationship that becomes a blind spot when viewed, and moving body motion simulation means for operating a target moving body based on information by the perceptual simulation means It comprises.

  Furthermore, an oversight processing means for further removing an object with a predetermined probability with respect to an object remaining without being removed by the perceptual simulation means may be provided.

  Furthermore, agent information storage means for storing visual field information related to at least the visual field of the agent on the moving body is provided, and the perceptual simulation means identifies and identifies an object existing in the visual field using the position information and the visual field information. You may make it remove the object in the positional relationship which becomes a blind spot seeing from the mobile body used as a target with respect to each object.

  Moreover, the traffic flow simulation program of the present invention for realizing such a traffic flow simulation apparatus with a computer is a program for causing the computer to function as each of the above means.

  The traffic flow simulation apparatus of the present invention has an advantage that a traffic flow simulation closer to reality is possible because the virtual driver simulates the perception of an actual driver. In addition, by considering the field of view of the virtual driver, unnecessary arithmetic processing can be eliminated, so that there are effects such as improvement in processing speed, improvement in the number of processes per unit time, and reduction in load on the system. .

  The best mode for carrying out the present invention will be described below with reference to the drawings. Hereinafter, in the present specification, a vehicle such as an automobile will be described as a moving body. However, the present invention is not limited to this, and a driver or a person such as a ship or a pedestrian moves by his / her own judgment. Anything is applicable. The virtual driver and the virtual pedestrian according to the present invention are referred to as an agent.

  FIG. 1 is a schematic diagram of a traffic flow simulation apparatus according to the present invention. The traffic flow simulation apparatus 1 is an electronic computer such as a personal computer, and includes a computing means 2 such as a CPU, a storage means 3 such as a hard disk, RAM, and ROM that stores data processed by the computing means 2 or data after processing. The input / output means 4 is provided.

  The storage means 3 stores information related to the agent in the present invention, a program for simulating the operation according to the information, and the like. The storage means 3 stores information on the traffic network, specifically data on the road, and further information on a moving body moving on the road, such as information on the position of the vehicle and its size, Data such as moving direction and moving speed are also stored.

  Of course, one traffic flow simulation apparatus 1 may be used, but it is also possible to perform distributed processing by a plurality of computers. In this case, by using a vehicle presence information matrix, which will be described later, it is possible to specify the minimum necessary information to be communicated between computers, and it is possible to reduce traffic between computers and improve processing speed. .

  The agent will be described below. FIG. 2 is a block diagram for explaining an agent in the present invention. As illustrated, the agent 20 mainly includes a perception unit 201, a recognition unit 202, a determination unit 203, and an operation unit 204. The agent 20 operates the moving body. When the simulation is performed, the description will be made centering on the target mobile object that is the target of the operation to be moved. The perception unit 201 is the position of the target mobile object among the information on the mobile object stored in the storage unit 3 On the basis of the information on the object, an object having a positional relationship that becomes a blind spot as seen from the target moving body is calculated and detected by the calculation means 2, and the object that becomes the blind spot is removed from the perceptual target. It should be noted that the objects in a positional relationship that becomes a blind spot include various objects such as traffic lights, street trees, and pedestrians as well as other moving objects. The blind spot is caused by various factors such as the position of the shadow of another moving body and the tip of a curved road. In this way, the perception unit 201 performs an agent perception simulation.

  Then, based on the perception result of the perception unit 201, the recognition unit 202 identifies the presence, position, movement direction, and the like of other perceived moving objects, and whether to proceed or stop in order to avoid collision with them. The determination unit 203 determines whether to bend or not, and the operation unit 204 operates the target moving body. As a result, it is possible to perform a simulation such as an actual driver scolding a pedestrian who was in the blind spot in a form close to actual.

  In addition, an oversight process for further removing an object with a predetermined probability may be performed on an object remaining without being removed by the perception unit 201 so that a simulation closer to reality is possible. This is done in order to simulate an oversight as if a side-viewing operation was performed even though it was originally visible without becoming a blind spot. This makes it possible to perform a simulation in a form that is closer to the actual situation. Other parameters, for example, parameters that take into account individuality such as the driver's age, driving preference, driving experience, physiological / mental state, and safety attitude may be added.

  Since it is wasteful from the viewpoint of processing time to determine whether or not it is a blind spot for all the moving bodies for which traffic flow simulation is performed, information regarding the visual field of the agent is also stored in the storage means 3. The perception unit 201 uses this to identify an object that exists in the field of view, and removes an object that is in a positional relationship that becomes a blind spot when viewed from the target moving object with respect to each identified object. It is preferable.

  In the realization of the traffic flow simulation device, that is, in the programming of the cognitive behavior of the agent, the largest amount of calculation processing is the search processing of the near-visible virtual vehicle for “perception”. The simplest programming method is to examine the distance and visibility between virtual vehicles by brute force. However, in order to obtain the positional relationship and the like for each vehicle existing in large numbers by this method, if N is the number of vehicles existing in the area, N-1 calculations are performed for each vehicle. Since it is necessary, N (N-1) operations are required. When N increases, the amount of calculation becomes enormous. Further, each vehicle is transiting on the region one after another as a traffic flow, and it is necessary to repeat the above-described search process based on the relative positional relationship between the vehicles as time passes. . Thus, in the brute force search method, the load on the hardware to be used increases as the amount of calculation processing increases, and the time for calculating the simulation result increases. Traffic accidents are a rare event that rarely occur, and in order to evaluate the effects of traffic safety measures on accident reduction, it is necessary to perform calculations for at least two to three months within the simulation, and the speed of simulation execution If it is slow, it will not be practically used. Therefore, in the traffic flow simulation of the present invention, the calculation amount is reduced by various methods as described below to speed up the simulation.

  Hereinafter, a more specific example for realizing the above traffic flow simulation apparatus will be described. The storage means 3 in FIG. 1 stores road-related data as simulation setting data. In this embodiment, as an example, data related to roads in an area centered on the “Miharabashi intersection” in Chuo-ku, Tokyo. Is remembered. The data relating to the road includes road section coordinate data, vehicle presence information matrix data, and transitionable section matrix data.

  First, road section coordinate data will be described. FIG. 3A is a map showing an area having a radius of about 100 mm centered on the “Miharabashi intersection”. A plurality of circles by two-dot chain lines described in the map are lines drawn at a radius of about 20 m with the center of the intersection as the center. In this embodiment, in order to make the map (mainly the roadway) near the “Miharabashi intersection” data that can be processed, coordinate data is created by dividing the roadway into continuous rectangular areas. The coordinate data may be coordinate data modeled using XY coordinates (referred to as road coordinates) instead of absolute coordinates represented by north latitude and east longitude. In this embodiment, coordinate data modeled using the latter XY coordinates is created and used for subsequent calculations. In FIG. 3B, in order to model two main roads that intersect the center of the map shown in FIG. 3A, the lane is divided by about 20 m from the center of the intersection, and the divided roads are separated. Each region is assigned with a symbol (S1 to S28) as one section. In FIG. 3B, sections are defined at equal intervals from the center of the intersection, but it is not always necessary to create sections at equal intervals. The size of the section can be arbitrarily set according to the traffic volume of the road, for example, a location where the traffic volume is high is dense and a location where the traffic volume is low is large.

  FIG. 4 is a model of the region shown in each of the sections S1 to S28 shown in FIG. 3B by XY coordinates where the center coordinates of the intersection are (X, Y) = (0, 0). Is. In this embodiment, each section is defined by road section coordinates (road coordinates) as shown in FIG. For example, the areas indicated by the sections S1, S2, S3, and S4 obtained by dividing the inside of the intersection shown in FIG. 3B into four are the following (X, Y) coordinates in the modeled road coordinates shown in FIG. It is represented by That is, the section S1 is an area defined by (X0, Y0) and (X1, Y1), the section S2 is an area defined by (X-1, Y0) and (X0, Y1), and the section S3 is An area defined by (X-1, Y-1) and (X0, Y0), and section S4 is alternatively represented as an area defined by (X0, Y-1) and (X1, Y0). . In FIG. 4, for convenience of explanation, the coordinate values are integer values such as 0, 1, and 2, but as actual coordinate values of the road coordinate system, real values approximate to actual dimension values are input. It may be like this.

  As described above, the road section coordinate data is road data representing a coordinate system for defining the position of the section when the road is divided into a plurality of blocks and modeled.

  Next, vehicle presence information matrix data will be described. FIG. 5 is a table showing vehicle presence information. The table is a table for representing the ID numbers of the vehicles existing in the section on the road as time passes. The data represented as the table is a data group in which the number of vehicles and the ID number existing therein can be extracted if a section number is designated.

  The transitionable section matrix data will be described with reference to FIG. FIG. 6 is a table showing a transition matrix. The transition matrix is adjacent area data in which an arbitrary section on road coordinates is arranged as a road with other sections. As will be described later, a vehicle (vehicle model) moves continuously on a two-dimensional plane according to kinematics, and instantaneously moves in a discretized space from one section to another. Do not mean. The transition matrix only represents the latest movable range of the vehicle existing in the section.

  Further, the storage means 3 stores vehicle data related to the vehicle and the agent as simulation setting data. As other objects, for example, an object related to a traffic light and an object related to a pedestrian are also stored.

The vehicle data is vehicle data that models a vehicle moving on a road, holds the position information of the own vehicle, and changes its position and posture angle according to kinematics. Each vehicle has a different ID number (a natural number in the present embodiment) for each vehicle, and is represented by a position (X _v , Y _v ) on the road coordinates (XY coordinates), for example. has a position information according to point type variables, the information of the attitude angle represented by the angle .PHI _v on the X-Y coordinates.

The data related to the agent is data having various kinds of information such as perceived data storage variables of other objects, vehicle operation commands, visual field information such as driver visual field direction and visual field angle (θ _v , α _v ). is there.

  Now, the traffic flow simulation process using these data will be described with reference to FIG. FIG. 7 is a flowchart for explaining the processing steps of the traffic flow simulation apparatus of the present invention. When the simulation is started, road coordinates (see FIG. 4), vehicle presence information matrix (see FIG. 5), transitionable section matrix (see FIG. 6), vehicle data, agent data, traffic light object stored in the storage means 3 are stored. Various data such as a pedestrian object are read, and variables and the like possessed by each data are initialized (step S1).

  Next, the time in the virtual world created by the traffic flow simulation, the execution time of how many hours (or days, months) the simulation is performed in that time, and the virtual world created by the traffic flow simulation A simulation time interval (or a sampling time) indicating how many seconds the time in the unit is advanced is set using the input / output means 4 or the like (step S2). In the traffic flow simulation in this embodiment, if a simulation for one hour is performed in the virtual world, even if the actual time required for the calculation is only a few minutes, the time in the traffic flow simulation is 1 Time has passed. The sampling time is a unit for advancing the time in the virtual world created by the traffic flow simulation. For example, the sampling time is advanced every 0.1 seconds. Therefore, if the calculation for the execution time of 1 hour is performed in units of 0.1 seconds, 60 minutes × 60 seconds × 10 times = 36000 times of repetitive calculations are performed.

  The following steps will be described. For every vehicle object (agent) present on the road coordinates, a simulation of perception (steps S3 to S7) is performed for each vehicle object, and then the vehicle is determined based on the perception simulation. -Operate (step S8). That is, a necessary driving operation is determined based on the perception simulation, and the operation is executed. Based on this determination / operation, simulation (step S10) of actually moving each vehicle object is performed, and processing (steps S11 to S13) when there is a transition between sections accompanying the movement of each vehicle object is performed. And the time is advanced by one sampling time (step S14). Thereafter, the above processing is repeated every sampling time until the designated execution time is reached. Hereinafter, these steps will be described in more detail.

  In the simulation relating to the simulation of the agent's perception, first, the section in the field of view is specified (step S4). This process specifies a section included in the field of view using the field of view information of the agent with reference to a section where one vehicle (hereinafter referred to as “target vehicle” or the like) exists. If you do not consider calculation efficiency etc., you may simulate perception for all vehicles other than the target vehicle, but in order to reduce the calculation time, specify a section in the field of view and Just simulate the perception of the section. Moreover, even in an actual traffic flow, there is no need to consider vehicles other than the driver's field of view, so it can be said that it is realistic.

  Next, in order to remove an object in a positional relationship that becomes a blind spot, an out-of-view object is removed (step S5). In order to obtain other vehicles included in the viewing angle viewed from the target vehicle from among the objects existing in the section within the field of view identified in step S4, this processing is performed. Vehicle, pedestrian, motorcycle, traffic light, etc.). Specifically, visual field information relating to the visual field of the agent who operates the vehicle is used. The visual field information includes the visual field angle. The visual field angle is an angle that can be visually recognized centered on the visual field direction that is the direction of the line of sight and the eye line. Is set to a range of about ± 60 degrees. Out-of-view means outside the range of the viewing angle. Humans cannot see all 360 degrees around them at the same time, and the range (field of view) that can be seen well in a moment is the direction in which the face is facing forward (to be precise, the direction of the line of sight and eyes) ) And the range of about ± 60 degrees. Therefore, for example, when driving a car facing forward, a car behind him cannot be seen (without a room mirror), and therefore, a process of removing an object that is not a perceptible object is performed. If the vehicle is facing backward, the vehicle behind is subject to perception, and the vehicle in front of the vehicle is removed as an object outside the field of view.

FIG. 8 is a diagram showing the positional relationship between the own vehicle (target vehicle) and an object such as another vehicle using XY coordinates. First, it is assumed that the position of the own vehicle is (X _V , Y _V ), and the position of other vehicles is (X _p , Y _p ). Further, it is assumed that the attitude angle of the vehicle is Ф _V , and the viewing direction and viewing angle of the agent are (θ _V , α _V ).

Hereinafter, calculation contents for determining whether or not another object exists within the range of the forward angle (viewing angle) that can be visually recognized by the agent boarding the host vehicle will be described. The relative position r (r _relX , r _relY ) of another object with respect to the vehicle position (X _V , Y _V ) can be expressed by the following equation.
Further, vectors s ₁ and s ₂ that give the limits of the left and right visual fields can be expressed by equations 2 and 3, respectively.
When r is expressed using the above formulas 2 and 3, the following formula is obtained.
According to the linear algebra, if a> 0 and b> 0, r exists in the region between s ₁ and s ₂ . Whether it is in the field of view or outside the field of view can be determined.

Here, a and b can be expressed by the following equations.
Here, since sin α> 0, a and b are obtained by Equation 6 and Equation 7, respectively.
As a result of this calculation, when both a and b are larger than 0 (a> 0, b> 0), the relative position r of another object is within the field of view, and either a or b is 0 or less. Then, it is determined that the relative position r of the object is out of the field of view.

  Note that the processing of Equations (1) to (7) above is performed for all other objects existing in the section in the field of view, and the vehicle ID of the vehicle in the field of view is stored and the total number of vehicles is counted. To do.

  After removing objects outside the field of view in this way, hidden object exclusion processing is performed (step S6). In this process, the object existing in the field of view that has been removed in step S5 is perceived because it is shielded by an object that has a blind spot as viewed from the target vehicle, that is, by another object. This process excludes hidden objects that cannot be performed.

  Since an actual driver cannot see what is hidden and cannot be seen, the same simulation is performed in the traffic flow simulation of the present invention. The actual cause of the accident is that something that was hidden behind something and could not be seen suddenly came out and collided, so in order to simulate such a situation, it can not be seen hidden This processing is performed in order not to convey the information of the thing to the agent. In the traffic flow simulation apparatus in this embodiment, the occurrence of an accident is simulated by such a perception / recognition / judgment mistake. When an accident occurs, it is the same as the occurrence of an accident in an actual traffic flow. , It is totally stochastic.

As shown in FIG. 9, the removal process of the object that becomes such a blind spot includes the position and posture angle (X _O , Y _O , Z _O , Φ _O ) of the object in the field of view, the length, width, and height of the object. (L, w, h), agent visual field direction and viewpoint coordinates (θ _V , X _E , Y _E , Z _E ), target vehicle position and posture angle (X _V , Y _V , _{Ｖ V} ) Based on the above. In this process, the objects in the plurality of fields of view (input objects) obtained by removing the object outside the field of view are subjected to perspective transformation to the virtual screen in front of the driver, and the distance from the own vehicle is calculated. (Step 1), objects within a plurality of fields of view obtained by removing out-of-field objects are sorted according to the distance from the vehicle (Step 2), visibility determination is performed from the back toward the front, By performing the determining process (Step 3), the total number of visible objects in the field of view and the ID number are obtained. It is preferable to use a quick sort algorithm for the sorting process. The virtual screen is introduced for convenience of processing. For example, an object of the same size will appear small if it is placed far away from you, and it will appear large if it is close. As described above, the position of the object is important in addition to the size of the object itself. In order to know how it looks in consideration of the size of the object and the distance at which it is placed, you can move the 3D space to the 2D plane by a method called perspective transformation (perspective method). The dimension plane is called a virtual screen here. Note that “virtual” is assumed to be virtual because the actual driver does not do this, but has been introduced for convenience.

FIG. 10 is an explanatory diagram for explaining the details of the specific processing of Step 1. The coordinates (X _e , Y _e , Z _e ) in the road coordinate system of the viewpoint of the agent boarding the vehicle are expressed by the following equations.

Assuming that the other vehicle is a substantially rectangular object, the coordinates (X ₁ , Y ₁ , ± Z ₁ ), (X ₂ , Y ₂ , ± Z ₂ ) of the other vehicle in the road coordinate system are assumed. , (X ₃ , Y ₃ , ± Z ₃ ) and (X ₄ , Y ₄ , ± Z ₄ ) are represented by Equation 9, Equation 10, Equation 11, and Equation 12, respectively.

As pre-processing for projecting these objects onto the virtual screen, the values in the (X, Y) coordinate system at the four corners of these objects are set, the direction of the agent's line of sight is the x axis, and the horizontal direction of the virtual screen is the y axis If the coordinate is converted into a value in the (x, y) coordinate system, it can be expressed as the following equation.

  In FIG. 10, d represents the distance from the driver to the virtual screen. This scale changes the scale of the projected figure. However, since the magnitude relationship between objects does not change, it is not very important to set this value to remove hidden objects. In this embodiment, d is set to 1.0 m as the distance from the agent to the windshield.

When the coordinates of the four corners of the object obtained as described above are perspective-transformed, the coordinates of other objects on the virtual screen can be expressed by the following equations.
The coordinates that give the outline of the object seen through on the virtual screen are determined as follows.

Moreover, the distance from the own vehicle can be calculated by the following equation (Step 2).
As a result, the size on the screen and the distance from the own vehicle can be known, so that visibility can be determined using the Z sort method. Note that the Z sort method is a method in which the object behind is overwritten by drawing in order from the object far from the viewpoint.

Next, the visibility determination (Step 3) will be described. Visibility determination is performed after objects are rearranged from the near (frontmost) side to the farthest (backmost) side according to the distance between the own vehicle calculated by the above equation 16 and other vehicles and other objects. This is a process for determining whether or not these objects are visible. FIG. 11 shows the visibility determination vector. The visibility determination vector is expressed by “1” or “0” as to whether or not each of the objects located on the foreground side to the object located on the farthest side is visible from the own vehicle. A specific procedure of the visibility determination will be described below. The vector element is a numerical value constituting a vector such as 1 or 2 when there is a vector a = [1 2]. The term “element” refers to the last element of the visibility determination vector, which means the last numerical value in the sequence of values constituting the visibility determination vector. In the case of the present embodiment, since the numerical value constituting the visibility determination vector and the object that can be perceived by the agent have a one-to-one correspondence, it can be considered that element = object. Therefore, setting the last element of the visibility determination vector to 1 means that the farthest object is visible. Specifically, the visibility determination is performed according to the following procedure.
(1) The value of the last element of the visibility determination vector is set to 1. (2) The following (3) and (4) are repeated for I = 1 to N−1. (4) The occlusion determination is performed between the NI-th object and the NI-th and subsequent objects whose element value of the visibility determination vector is 1. If it is visible, the value of the visibility determination vector remains as it is, and if necessary, the visible region is corrected. If it is not visible, the value of the visibility determination vector is changed to 0.

Here, the above-described shielding determination will be specifically described. FIG. 12 is a diagram for explaining the occlusion determination, and represents a case where it is determined whether or not the I = 2nd object is occluded by the I = 1st object. The coordinates of the outer shape of the I = 1st object (object 1) on the virtual screen are represented by (y _max1 , z _max1 ), (y _min1 , z _min1 ). Further, the coordinates of the outer shape of the I = 2nd object (object 2) on the virtual screen are represented by (y _max2 , z _max2 ), (y _min2 , z _min2 ). Then, whether or not the object 2 is shielded by the object 1 is determined as follows.
y _max1 > y _max2 , y _min1 <y _min2 , z _max1 > z _max2 , z _min1 <z _min2
If all of these conditions are satisfied, the object 2 is completely hidden by the object 1 and is thus determined to be invisible.
y _max1 > y _max2 , y _min1 > y _min2 , y _max2 > y _min1
If all of these conditions are satisfied, the object 2 is partially occluded by the object 1, so that it is determined to be visible, and y _max2 = y _min1 is changed.
y _max1 <y _max2 , y _min1 <y _min2 , y _max1 > y _min2
When all of these conditions are satisfied, the object 2 is partially covered by the object 1 and thus determined to be visible, and is changed to y _min2 = y _max1 .
y _min1 > y _max2 , y _min2 > y _max1 , z _min1 > z _max2 , z _min2 > z _max1
If any one of these conditions is satisfied, it is determined that the object 2 is visible because it does not cover the object 1.

  FIG. 13 shows a specific example of hidden object removal, and FIG. 13A shows an arrangement of the vehicle and other objects as an example, as shown in FIG. 13B. Represents the outline of each other object represented on the virtual screen as seen from the vehicle. In the case of FIG. 13 (a), the “large vehicle” (I = 1), “passenger car” (I = 2), “passenger car” (I = 3), “traffic light” (I = 4) Each object is arranged in the order. The arrangement of the objects is determined based on the formula shown in Equation 16. FIG. 13B shows the arrangement of the objects shown in FIG. 13A on a virtual screen viewed from the host vehicle. That is, the “large vehicle” (I = 1) is seen on the foremost side, the “passenger vehicle” (I = 3) and the “traffic light” (I = 4) are hidden by the “large vehicle”, and the “passenger vehicle” (I = 2) A part of it is hidden by a “large car”. In this case, “passenger car” (I = 3) and “traffic light” (I = 4) are “hidden objects”.

  Whether each object is a “hidden object” is determined based on the visibility determination vector described with reference to FIGS. Specifically, as shown in FIG. 14, first, the value of the element of I = 4 of the visibility determination vector of the farthest “traffic light” (I = 4) is set to “1”. Next, it is determined whether or not the “traffic light” (I = 4) is blocked by the “passenger car” (I = 3). In this example, since it is not shielded, “1” is recorded as the value of the element of I = 4 in the visibility determination vector, and “1” is also recorded as the value of the element of “passenger car” (I = 3). Next, whether the "passenger car" (I = 3) is shielded by the "passenger car" (I = 2) and whether the "traffic light" (I = 4) is shielded by the "passenger car" (I = 2) Judge whether or not. In this example, the “traffic light” (I = 4) and the “passenger car” (I = 3) are not shielded by the “passenger car” (I = 2), so that the visibility determination vectors I = 2, I = 3, I “1” is recorded as the value of each element of = 4. The “passenger car” (I = 3) has a portion that partially overlaps with the “passenger car” (I = 2), so the visible region of the “passenger car” (I = 3) is corrected. Next, whether the “passenger car” (I = 2) is shielded by the “large car” (I = 1), or the “passenger car” (I = 3) is shielded by the “large car” (I = 1). And whether or not the “traffic light” (I = 4) is blocked by the “large vehicle” (I = 1). In the illustrated example, the “passenger car” (I = 2) is not shielded by the “large car” (I = 1), and the “passenger car” (I = 3) and “traffic light” (I Since it is determined that I = 4) is shielded by the “large vehicle” (I = 1), “1” is recorded in the values of the elements of the visibility determination vector I = 1 and I = 2. “0” is recorded as the value of the element of = 3 and I = 4.

  With the above processing, the “passenger car” (I = 3) and “traffic light” (I = 4) whose visibility determination vector is “0” are removed because they are in a visual positional relationship.

  Then, after removing the object in the positional relationship that becomes the blind spot, a process for simulating a human error of overlooking a visible object in the field of view is performed (step S7 in FIG. 7). As shown in FIG. 15, this processing is performed based on the total number of visible objects in the field of view that remain without being removed by the previous processing, the ID number, and the probability that an oversight will occur. As shown in FIG. 15, first, a real value between 0 and 1 according to a uniform distribution is randomly generated. Next, it is determined whether the randomly generated numerical value is smaller (yes) or not (no) than the “probability of occurrence of oversight”. When the randomly generated numerical value is smaller than the “probability of occurrence of oversight” (yes), a process of excluding it as an object that is not perceived by oversight is performed. If the randomly generated numerical value is larger than the “probability of occurrence of oversight” (no), a process of storing the total number and the ID number in the storage variables as perceived objects is performed.

  As described above, after the simulation of perception (steps S4 to S7) shown in FIG. 7 is performed, the agent is caused to make a judgment and operate the vehicle based on the information based on the simulation of the perception (step S8). . Next, for all the vehicles existing in the simulation area, a process for causing the simulation of the perception and the determination of the agent based on the information by the simulation of the perception and the operation of the vehicle is performed.

  After these processes, a process for simulating and moving the vehicle of the target host vehicle is performed (step S10), and a transition between sections described below is detected based on the movement of the host vehicle (step S11). Processing such as determination of transition between sections (step S12), update of the vehicle presence information matrix (step S13), and the like are performed.

  After the vehicle motion simulation and movement process shown in step S10, when the vehicle moves to another section, it is necessary to rewrite the contents of the vehicle presence information matrix. Update the information matrix.

Hereinafter, the processing from the detection of the transition between sections in step S11 to the update of the vehicle presence information matrix in step S13 will be described with reference to FIG. The position of the vehicle, the section number so far, and the coordinates of the two corners of each section are given by (X _V , Y _V ), S _i , (X _Smax , Y _Smax ) (X _Smin , Y _Smin ), respectively. Further, the information shown in the transitionable section matrix shown in FIG. 6 is given. The coordinates of the two corners of each section and the transitionable section are set in advance before executing the simulation. Based on these pieces of information, transition (movement) between sections is detected for each vehicle (step S11), and when there is a transition (step S12), the vehicle presence information matrix is updated (step S13).

FIG. 17 is an explanatory diagram of transition detection between sections. First, based on (X _V , Y _V ), S _i , (X _Smax , Y _Smax ) (X _Smin , Y _Smin ), it is confirmed whether or not it exists in the previous section (existence confirmation). . Existence confirmation is performed by the method described below with reference to FIG. In the figure, the position of the vehicle is represented by (X _V1 , Y _V1 ), (X _V2 , Y _V2 ). At this time, the position vector from the vertex at the lower left and upper right of the section to the vehicle is given by the following equation.
Using these position vector elements, PXPX and PY shown in the following equations are calculated.
Using PX and PY, if PX ≦ 0 and PY ≦ 0, it is determined that the vehicle exists in the section, and if PX> 0 or PY> 0, it is determined that the vehicle does not exist in the section. The

If it is determined that such a section still exists in the previous section, the determined section number S _j is set as the input section number S _i . On the other hand, if it is determined that it does not exist in the previous section, the presence check is performed for the section specified in the transitionable section shown in FIG. Is the determined section number S _j .

In the determination of the transition between sections (step S12), the determined section number S _j is compared with the input section number S _i, and if they are different, it is determined whether or not a transition between sections has occurred. The variable Flag_Trans shown is set to 1 and is set to 0 in the same case. Through the above processing, the section where the vehicle exists and the value (0 or 1) of the variable Flag_Trans are obtained.

Next, the update of the vehicle presence information matrix will be specifically described with reference to FIGS. 19 and 20. FIG. 19A shows a vehicle presence information matrix before correction, the number of vehicles existing in the section S _i is four, and the ID numbers of the vehicles existing in the section are “7” “W "9" and "10", respectively, representing a state of being arranged in the order. Further, the number of vehicles present within the section S _j adjacent to the section S _i is three, the ID number of the vehicles existing in the section, "2", "4", "13", respectively the order It represents the state of being lined up.

The correction of the vehicle presence information matrix when the vehicle “W” transitions from section S _i to section S _j from the state shown in FIG. When the vehicle presence information matrix is corrected, the following processing is performed based on “section number before transition”, “section number after transition”, and “vehicle ID number” (see FIG. 18). A column having a value of W is searched from the _i- th row (section S _i ) of the vehicle presence information matrix. Then, 0 is assigned to the part where W is present, and if there is another vehicle (other ID) in the right column of the part where 0 is assigned, a process of reducing the total number of vehicles by 1 is performed. By the process, the vehicle presence information matrix shown in FIG. 19 (a) is modified as shown in the line section S _i in FIG. 19 (b).

  Next, from the j-th row of the vehicle presence information matrix, W is substituted for the element of the first column where the value becomes 0, and 1 is added to the total number of vehicles. With the above processing, the vehicle presence information matrix is updated when a vehicle (a vehicle with ID = W as an example) transitions between sections.

  As described above, when a transition between sections is detected and there is a transition, the vehicle presence information matrix is updated for all the vehicles existing in the simulation area, and the time in the simulation is set as the sampling time. The process is advanced by the amount of time (step S14), one process cycle is terminated, and thereafter the same process is repeated until the first set execution time.

  By the traffic flow simulation of the present invention as described above, each virtual vehicle moves by a series of cognitive actions of agent perception, recognition, judgment and operation, and further, the agent detects perception errors, recognition errors, A traffic flow simulation in which a traffic accident occurs due to a judgment error can be executed at high speed. In such a traffic flow simulation, for example, when trying to change the lane, the vehicle was hidden in the area that was just a blind spot from the own vehicle, so the lane change was executed without noticing its existence. Simulates many accidents that occur in reality, such as situations where a car collides with you, or you do not realize that the preceding vehicle has slowed down because you were looking aside, and when you apply the brakes, It will be possible.

  Furthermore, if the traffic flow simulation is carried out before and after the introduction of the traffic safety measure and the accident rates obtained as a result are compared, the effect on the reduction of the accident can be directly evaluated. As already mentioned, traffic accidents are extremely rare events, and even in traffic flow simulations, it is necessary to perform calculations for at least two to three months within the simulation time. The value is extremely high.

  The determination of the occurrence of an accident is made after the end of one processing cycle and before the start of the next processing cycle. The determination of the occurrence of an accident may be made by searching for the closest vehicle and determining whether the distance has become smaller than the size of the vehicle. This is performed for all vehicles existing in the simulation area. Even when determining the occurrence of an accident, if calculation is performed by brute force, if there are a total of N vehicles, a total of N (N-1) / 2 calculations for collision determination are required. Become. Obviously, the brute force calculation to judge accidents is also useless even for combinations where there is no possibility of contact due to the distance between vehicles being too far apart, and problems such as delay in simulation time and increased load on hardware Invite. However, the simulation apparatus according to the present invention provides information on vehicles existing in each subdivided area by providing a vehicle presence information matrix obtained by subdividing the road area as information relating to the road. Furthermore, it has a transitionable section matrix that can determine whether or not the subdivided areas are adjacent to each other. By combining the vehicle presence information matrix and the transitionable section matrix, a combination of vehicles that require computation can be obtained. In addition to searching for nearby vehicles to simulate the driver's perception, it is possible to eliminate the combination of vehicles that have no possibility of an accident when determining the occurrence of an accident, and the amount of calculation It has the feature that reduction of can be aimed at.

  Also, when traffic flow simulation is performed by operating multiple computers in parallel, distributed processing is not performed like grid computing, but the divided sections are grouped and simulations within each group are performed. It is preferable that each computer perform. In the traffic flow simulation apparatus of the present invention, the ID number of the vehicle in the section is managed using the vehicle presence information matrix. Therefore, if you request a computer in charge of an adjacent section to provide location information for vehicles in that section, the computer that received the request does not need to send location information for all vehicles managed by that computer, Since only the vehicle position information in the section according to the request needs to be transmitted, the transmission data amount can be reduced and the simulation execution speed can be increased.

  The traffic flow simulation apparatus according to the present invention is not limited to the illustrated examples described above, and it is needless to say that various changes can be made without departing from the gist of the present invention.

FIG. 1 is a block diagram of a simulation apparatus according to the present invention. FIG. 2 is a block diagram for explaining an agent in the present invention. FIG. 3 is a diagram showing an example of a simulation area and a diagram of divided small areas. FIG. 4 is a diagram modeling a road that is a simulation region. FIG. 5 is a table showing an example of the vehicle presence information matrix. FIG. 6 is a table showing an example of a transitionable section matrix. FIG. 7 is a flowchart of the simulation process of the present invention. FIG. 8 is a diagram used for explaining calculation processing for excluding objects that are out of the field of view for one vehicle. FIG. 9 is a flowchart for explaining a calculation process for excluding an object existing at a position that becomes a blind spot when viewed from one vehicle. FIG. 10 is a diagram used for explaining the arithmetic processing performed in the flowchart of FIG. FIG. 11 is a diagram used for explaining the visibility determination performed in the flowchart of FIG. 9. FIG. 12 is a diagram used for explaining the shielding determination performed in the visibility determination of FIG. 11. FIG. 13 is a diagram for explaining a specific example of hidden object removal. FIG. 14 is a diagram used for explaining the visibility determination performed in the removal of the hidden object in FIG. 13. FIG. 15 is a flowchart for explaining the oversight process. FIG. 16 is a flowchart for explaining the processing contents from the movement of the vehicle to the update of the vehicle presence information matrix. FIG. 17 is a diagram for describing processing for detecting whether or not there is a transition between sections of the vehicle during the processing shown in FIG. 16. FIG. 18 is a diagram used for explaining the arithmetic processing in the processing shown in FIG. FIG. 19 is a diagram for explaining the contents of the vehicle presence information matrix update process during the process shown in FIG. 16. FIG. 20 is a flowchart for explaining the processing shown in FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Traffic simulation apparatus 2 Calculation means 3 Storage means 4 Input / output means 20 Agent 201 Perception part 202 Recognition part 203 Judgment part 204 Operation part

Claims (4)

A traffic flow simulation device for simulating the movement of a moving body moving in a traffic network, the device comprising:
Mobile body information storage means for storing position information relating to at least the position of the mobile body;
A perceptual simulation means for simulating the perception of an agent on the moving body, which removes an object in a positional relationship that becomes a blind spot as seen from the target moving body based on the position information;
Mobile body motion simulation means for operating a target mobile body based on information by the perceptual simulation means;
A traffic flow simulation apparatus comprising:   2. The traffic flow simulation apparatus according to claim 1, further comprising an oversight processing means for further removing an object with a predetermined probability with respect to an object remaining without being removed by the perceptual simulation means. Traffic flow simulation device. The traffic flow simulation device according to claim 1 or 2, further comprising:
Comprising agent information storage means for storing visual field information relating to at least the visual field of the agent on the moving object;
The perceptual simulation means identifies an object existing in the field of view using the position information and field of view information, and identifies an object in a positional relationship that is a blind spot when viewed from the target moving body with respect to each identified object. A traffic flow simulation device characterized by removing. A traffic flow simulation program for realizing the traffic flow simulation apparatus according to any one of claims 1 to 3 with a computer, wherein the traffic flow simulation program causes the computer to function as each of the above means.