JP4735153B2 - Traffic flow simulation apparatus, method and program - Google Patents

Description

  The present invention relates to a traffic flow simulation apparatus, method, and program, and more particularly, to a traffic flow simulation apparatus, method, and program for simulating behavior of a moving body in consideration of human error of a human operating the moving body.

  When planning a road traffic system, it is necessary to evaluate in advance the effects such as where and what should be maintained to prevent traffic congestion. In view of this, a technique has been proposed in which the behavior of each vehicle is reproduced on a computer, and the flow of traffic and the state of traffic congestion are simulated (Patent Document 1; see Non-Patent Document 1).

  The road traffic simulation apparatus of Patent Document 1 represents a plurality of moving bodies and a road traffic environment on a computer and simulates a traffic situation generated by the moving bodies. Specifically, each of the moving bodies is a moving body that is a combination of a driver model that models a driving operation by a virtual driver and a vehicle motion model that models the physical behavior of each moving body. It is expressed by a model. The mobile body model passes through the road traffic environment expressed on the computer independently of each other. Further, each vehicle model expresses detailed vehicle behavior by giving the output value of the driver model to the vehicle motion model in response to various road traffic environments.

The technology described in Non-Patent Document 1 is a conventional micro traffic flow simulator, which associates the driving environment with the driver's behavior on a one-to-one basis, for example, stops at a temporary intersection, The acceleration / deceleration is determined based on the following equation according to the distance.
JP 2004-199287 A Traffic Engineering Handbook 2005, Traffic Engineering Study Group

  As shown in FIG. 6 of Patent Document 1, the driver model described in Patent Document 1 is based on data such as a passable area obtained from the traffic environment database 20 for the moving body to follow the set route. A path is calculated, and an apparent traffic difficulty distribution is generated by converting the traffic difficulty area and the traffic difficulty of other mobile objects to the field of view from the mobile object, and the mobile objects actually pass based on these calculation results. Find the heading and speed. In other words, the driver model unifies the situation in front with the traffic difficulty distribution and determines the behavior of the vehicle. For this reason, the road traffic simulation apparatus using this driver model cannot reproduce detailed recognition and judgment (recognition error, judgment error, traffic violation) according to the driving scene and the driver's situation. In addition, since static obstacles such as roads and buildings are mainly targeted, it is impossible to determine the behavior of the driver by predicting the situation ahead of the host vehicle or other vehicles.

  The micro traffic flow simulator described in Non-Patent Document 1 reproduces only the exemplary driving behavior of the driver, and does not consider the process of the driver's understanding (recognition) of the environment. For this reason, human errors such as driver recognition errors, judgment errors and traffic violations could not be reproduced.

  The present invention has been proposed to solve the above-described problems, and an object of the present invention is to provide a traffic flow simulation apparatus, method, and program for simulating a traffic flow of a moving object in consideration of a human error of a driver. To do.

A traffic flow simulation apparatus according to the present invention is a traffic flow simulation apparatus for simulating the movement of a moving body traveling on a road, and includes a road environment creation means for creating a road environment and a road created by the road environment creation means. A mobile body traveling means for traveling the mobile body in an environment; and a management means for managing the road environment creating means so as to create a road environment according to the traveling state of the mobile body, the mobile body traveling means comprising: , awareness of the traffic key points, awareness of signs indicating a traffic key points, and the traffic key points confirm easiness at least is one traffic key point based on the feature, the traffic key point of the driver At least one of the presence / absence of recognition, the next action for a traffic important point, and whether or not to violate the traffic is determined, and the moving body is caused to travel based on the determination result.

  The road environment creating means creates a road environment of a moving body traveling on the road. The moving body traveling means causes the moving body to travel in the created road environment. The management means manages the road environment creation means so that a road environment according to the traveling state of the mobile object can be created.

Here, the mobile traveling means based on the features of transportation key points, the presence or absence of recognition of the traffic key points of the driver, the following actions with respect to traffic key point, whether the traffic violations of at least one judge. Further, based on the driver's personal characteristics, at least one of whether or not the driver's traffic key is recognized, the next action for the traffic key, and whether or not to make a traffic violation may be determined. As the personal characteristics of the driver, for example, at least one of a tendency of how to recognize traffic points, a tendency of next judgment for traffic points, a tendency of accuracy of judgment, and a tendency of vehicle operation may be used. . As the feature of the traffic point, at least one of the recognition degree of the traffic point, the recognition degree of the sign indicating the traffic point, and the ease of confirmation of the traffic point may be used.

  Therefore, the traffic flow simulation device determines whether or not the driver recognizes the traffic point, the next action on the traffic point, or whether the traffic violation occurs based on at least one of the personal characteristics of the driver and the characteristics of the traffic point. By determining at least one of the above and running the moving body based on the determination result, it is possible to simulate the traffic flow while taking into account the human error of the driver.

  The present invention is also applied to a traffic flow simulation method and program.

  The traffic flow simulation apparatus, method and program according to the present invention are based on at least one of the driver's personal characteristics and the characteristics of the traffic key, whether the driver recognizes the traffic key, the next action for the traffic key, By determining at least one of whether or not to violate the traffic and running the moving body based on the determination result, it is possible to simulate the traffic flow while taking into account the human error of the driver.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.

[overall structure]
FIG. 1 is a block diagram showing a configuration of a traffic flow simulation apparatus according to an embodiment of the present invention. The traffic flow simulation device can display the result on a display device (not shown) while simulating the traffic flow of the vehicle. The traffic flow simulation apparatus includes a traveling environment creation unit 10 that creates a traveling environment to be displayed on a display device, a vehicle moving unit 20 that moves a vehicle arranged on the traveling environment, and traffic that manages the traffic situation of each vehicle. A situation management unit 30.

  The traveling environment creation unit 10 arranges the vehicle in the road environment based on the vehicle data and the road environment data, and inputs the situation to the vehicle moving unit 20. The vehicle data and road environment data may be stored in advance in a database, or may be transmitted in real time via a network.

  Vehicle data includes, for example, vehicle characteristics (vehicle type, size), driver personal characteristics (for example, stop recognition, stop passage, accuracy of judgment, desired speed, accelerator opening, stop position with respect to stop line) In addition, the vehicle state (position, moving distance, moving speed) at the current time is included. Note that “determination” includes intentional traffic violations such as “you can pass the intersection even if you ignore the temporary stop line” in addition to a determination error such as “if you increase the speed, you can pass the intersection”.

  FIG. 2 is a diagram illustrating the personal characteristics of the driver that represent the driver's recognition and judgment trends. Here, for each driver, “pause recognition degree” indicating the degree of recognition error with respect to the pause line (cognitive error is 1: many ← → 5: none), “pause stop passage” indicating the degree of passage of the pause line Degree ”(passage violation is 1: many ← → 5: no),“ judgment accuracy ”indicating the accuracy of judgment when passing through the temporary stop line (judgment error is 1: many ← → 5: no ) Is defined in five stages.

  FIG. 3 is a diagram illustrating a driver's personal characteristics representing a tendency related to the operability of the driver. Here, for each driver, a “desired speed” indicating the speed desired by the driver with respect to the regulated speed (1: fast ← → 5: slow with respect to the regulated speed), and the accelerator opening degree of the driver with respect to the reference value. “Accelerator opening” (1: large ← → 5: small with respect to the reference value) and “stop position” indicating the stop position with respect to the temporary stop line are defined in five stages.

  The road environment data includes, for example, a road network (road length, width, number of lanes, connection relationship, stop line position, pedestrian crossing position, etc.), intersection characteristics (for example, intersection recognition degree, sign recognition degree in the case of a temporary stop intersection) , Prospects, etc.), pedestrian crossings, pedestrians, etc.

  FIG. 4 is a diagram showing the characteristics of the temporary stop intersection included in the road data. The links a, b, c,... Are the minimum units constituting the road network. That is, a road network is configured by connecting links. In FIG. 4, “intersection recognition” indicating the degree of recognition of the presence of a stop intersection (ease of recognition, 1: difficult ← → 5: easy), “sign recognition” indicating the degree of recognition of a stop sign (priority) Road / non-priority road recognition, 1: difficulty ← → 5: easy), “line of sight” indicating distance of priority road from non-priority road with temporary stop line (distance from temporary stop line [m]) Is defined in five stages.

  Further, the travel environment creation unit 10 creates a new travel environment according to the travel state of each vehicle based on the data of each vehicle updated by the traffic state management unit 30. The traveling environment creation unit 10 repeatedly executes such processing for each time step, and creates a traveling environment representing the traffic situation at each time for each vehicle.

  The vehicle moving unit 20 is provided for each vehicle in the simulation, and includes a traveling environment grasping unit 21 that grasps the traveling environment, a determination unit 22 that determines the next action according to the grasped traveling environment, An operation unit 23 that operates the vehicle according to the determination content and a vehicle update unit 24 that updates the behavior of the vehicle are provided.

  The traveling environment grasping unit 21 simulates how much the driver grasps the traveling environment. From the traveling environment created by the traveling environment creating unit 10, the driving environment grasping unit 21 determines whether the road environment such as a signal or an intersection or a surrounding vehicle. Understand the relationship. Here, depending on the combination of the road environment and the driver's situation, a recognition error such as “oversight” that the driver could not grasp is also expressed.

   The determination unit 22 simulates how the driver will behave next, and determines “how to act” based on the traveling environment grasped by the traveling environment grasping unit 21. The determination unit 22 may make a determination error or a traffic violation due to an estimation error of the position and speed of the surrounding vehicle.

  The operation unit 23 simulates a driver's vehicle operation, and performs at least one of an accelerator operation, a brake operation, a steering wheel operation, and the like based on the content determined by the determination unit 22.

  The vehicle update unit 24 simulates the behavior state of the vehicle based on the operation of the driver, calculates the position, movement distance, and movement speed of the vehicle according to the operation content and elapsed time of the operation unit 23, and stores the vehicle data. Update.

  The traffic condition management unit 30 inputs the vehicle data of each vehicle updated by the vehicle update unit 24 of the vehicle moving unit 20, and associates each vehicle to manage it as a traffic condition.

[Passing through the temporary stop]
The traffic flow simulation apparatus configured as described above simulates the movement of each vehicle as follows. Here, a case where the host vehicle enters the priority road from a non-priority road having a temporary stop line will be described as an example.

  FIG. 5 is a diagram illustrating a case where the vehicle enters the priority road from the non-priority road. 6 to 8 are flowcharts showing a control routine of the vehicle moving unit 20 when the host vehicle enters the priority road from the non-priority road. Here, the non-priority road shown in FIG.

  In step S <b> 1, the traveling environment grasping unit 21 determines whether or not the existence of the temporary stop intersection is grasped. Specifically, the traveling environment grasping unit 21 satisfies the following expression (1) using the “pause recognition degree” of the driver shown in FIG. 2 and the “intersection recognition degree” shown in FIG. Determine whether or not.

    (Intersection recognition degree) · α + (temporary stop recognition degree) · β> threshold value P (1) Here, coefficients α, β, and threshold value P are set to 1, 1, 3, respectively, and the non-priority road shown in FIG. In the case of driver A, the link is as follows. Note that the values of α, β, and P are not limited to this.

3 ・ 1 + 1 ・ 1 = 4> 3
Therefore, the traveling environment grasping unit 21 satisfies the expression (1) in the case of the driver A, and therefore determines that the driver A grasps the intersection of the link a. And the driving | running | working environment grasping | ascertainment part 21 transfers to step S3 at the time of affirmation determination, and transfers to step S2 at the time of a negative determination (recognition mistake).

  In step S2, since the next action is not determined by the determination part 22, the operation part 23 continues operation so far. Then, this control routine ends.

  In step S3, the traveling environment grasping unit 21 determines whether or not the vehicle is in a stopped state. If the determination is affirmative, the process proceeds to step S4. If the determination is negative, the process proceeds to step S7.

  In step S4, the traveling environment grasping unit 21 determines whether or not there is an intersecting vehicle that intersects with the own vehicle on the priority road. If the determination is affirmative, the process proceeds to step S5, and if the determination is negative (recognition error). The process proceeds to step S6.

  In step S5, the determination unit 22 compares each time until one or more crossing vehicles arrive at the intersection with the time until the own vehicle arrives at the intersection, and the smallest time difference until arrival at the intersection is obtained. It is determined whether or not it is larger than the threshold value. This threshold value refers to a time difference at which two vehicles can enter the temporary stop intersection without danger at present. The determination unit 22 proceeds to step S6 when the determination is affirmative, and proceeds to step S7 when the determination is negative (recognition error).

  In this embodiment, the time until the own vehicle and the intersecting vehicle arrive at the intersection is used, but the equivalent parameter, for example, the distance until the own vehicle and the intersecting vehicle arrive at the intersection is used. Alternatively, the speeds of the own vehicle and the crossing vehicle may be used.

  In step S6, the operation unit 23 performs an accelerator operation to start the host vehicle. The vehicle update unit 24 calculates the position, travel distance, and travel speed of the vehicle according to the operation content of the operation unit 23 and the elapsed time, and updates the vehicle data. Then, this control routine ends.

  In step S7, the operation unit 23 performs a brake operation to stop the host vehicle. The vehicle update unit 24 calculates the position, travel distance, and travel speed of the vehicle according to the operation content of the operation unit 23 and the elapsed time, and updates the vehicle data. Then, this control routine ends.

  On the other hand, in step S8, the traveling environment grasping part 21 determines whether or not it is recognized that there is a temporary stop sign. Specifically, the driving environment grasping unit 21 satisfies the following expression (2) using the “pause recognition degree” of the driver shown in FIG. 2 and the “sign recognition degree” shown in FIG. Determine whether or not.

(Intersection recognition) · γ + (pause recognition) · δ> threshold Q (2)
Here, when the coefficients γ and δ and the threshold value Q are 1, 1, and 3, respectively, the case of the driver A is as follows. Note that the values of γ, δ, and Q are not limited to this.

1 ・ 1 + 1 ・ 1 = 2 <3
Therefore, the driving environment grasping unit 21 determines that the driver A does not recognize that there is a stop sign because the driver A does not satisfy the expression (2). And the driving | running | working environment grasping | ascertainment part 21 transfers to step S8 at the time of affirmation determination, and transfers to step S11 at the time of negative determination (recognition mistake).

  In step S9, the traveling environment grasping part 21 determines whether or not the line of sight is good. Specifically, the determination unit 22 compares the value of “line of sight” shown in FIG. 4 with the distance from the position of the host vehicle to the stop line (stop line distance), and the stop line distance is smaller than the above value. When the prospect is good, it is judged that the prospect is not good when it is not exceeded.

  Here, the numerical value of “line of sight” is the distance from the stop line to the vehicle when a vehicle traveling on a certain link (non-priority road) can see all the crossing vehicles traveling on the priority road. In the case of link a, the numerical value of “line of sight” is 10 [m]. And the driving | running | working environment grasping | ascertainment part 21 transfers to step S10 at the time of affirmation determination, and transfers to step S11 at the time of negative determination.

  The “forecast” figure is obtained as follows. Initially, the crossing vehicles are respectively arranged at positions where the free running speed associated with the link (priority road) can be stopped at the intersection by the driver's desired deceleration. Next, the position where the own vehicle can see all the crossing vehicles is determined. Finally, the distance from the temporary stop line is calculated for the position of the host vehicle. Then, the calculated distance may be defined as the “line of sight” of the link a (non-priority road in FIG. 5). In this example, one “line of sight” is defined for one intersection per road. However, it is also possible to define whether each line is visible or not depending on the state of each crossing road or the speed of the crossing vehicle. Good. In addition, the viewpoint of the driver may be taken into consideration.

  In step S10, the determination unit 22 determines what action is to be taken next to the stop sign. Specifically, the determination unit 22 determines whether the next action is stop, slowing down, or neither stopping nor slowing down. When stopping, the process proceeds to step S11, and when slowing down, the process is shown in FIG. When the process proceeds to step S31 and it is selected not to stop or slow down, the process proceeds to step S21 shown in FIG.

  Note that the determination unit 22 may determine whether or not to temporarily stop whether or not the following determination formula is satisfied using the “passing degree” illustrated in FIG. 2.

(Temporary passing degree of driver) ・ η> Threshold R
Here, if the coefficient η and the threshold value R are 1 and 3, respectively, for example, the driver B satisfies the above determination formula, but the driver C does not satisfy the above determination formula. Then, the determination unit 22 proceeds to step S11 when the determination formula is satisfied, and may determine whether to “slow down” or “never stop or stop” otherwise.

  It should be noted that “decelerate” and “do not decelerate or stop” may be determined by the determination unit 22 using the value on the right side of the determination formula as it is. The determination unit 22 determines, for example, whether or not the right side of the determination formula is larger than a threshold value S (<R), and determines “decelerate” when the determination is affirmative, and “does not decelerate or stop” when the determination is negative. do it.

  In step S11, the operation part 23 and the vehicle update part 24 perform a process similarly to step S7 in order to stop the own vehicle. Then, this control routine ends.

  In step S12, the driving environment grasping unit 21 determines whether or not the line of sight is good as in step S9. When the determination is affirmative, the process proceeds to step S21 shown in FIG. 6, and when the determination is negative, the process proceeds to step S13. Transition.

  In step S13, the operation unit 23 operates the brake to slow down the vehicle. The vehicle update unit 24 calculates the vehicle position, travel distance, and travel speed according to the operation content and elapsed time of the operation unit 23, and updates the vehicle data. Then, this control routine ends.

  In step S21 shown in FIG. 6, the driving environment grasping unit 21 determines whether or not there is a crossing vehicle on the priority road, as in step S4. If the determination is affirmative, the process proceeds to step S22, and a negative determination (recognition) If it is missed, the process proceeds to step S23.

  In step S22, as in step S5, the determination unit 22 compares each time until one or more intersection vehicles arrive at the intersection with the time until the host vehicle arrives at the intersection, and arrives at the intersection. It is determined whether or not the smallest time difference until is larger than the threshold value. When the determination is affirmative, the process proceeds to step S23, and when the determination is negative (determination error), the process proceeds to step S24.

In step S23, the operation unit 23 estimates the desired speed of the driver, so that the vehicle travels at a desired speed of the driver, acceleration a using the distance from the current position (time t _c when) to a target position (Equation (3)) and the accelerator or brake is operated.

Here, a is the acceleration / deceleration [m ² / s], v _c is the speed [m / s] at the time t _c , v _opt is the desired speed [m / s] at the target point, and l _i is the time t _c. This is the distance [m] from the current position to the target position. However, it is assumed that the acceleration / deceleration speed a does not exceed the upper limit value set by the vehicle type or the driver.

The desired speed v _opt is determined based on the relationship between the host vehicle and surrounding vehicles, for example, the speed of the host vehicle and the distance to the intersection, the speed of the vehicle on the intersection road, the distance to the intersection, and the like. The desired speed v _opt is, for example, “If the host vehicle that is approaching the temporarily stopped intersection travels near the intersection at a higher speed and the arrival time of the intersection is earlier than the intersection vehicle, increase the speed. As in the case of “passing”, it is possible to express a driver's detailed judgment that does not pass through the intersection at a constant speed. Furthermore, individual differences of drivers, misjudgments, differences in vehicle behavior depending on the road, and the like can be expressed. The desired speed v _opt is calculated as follows using fuzzy control, rule base, and learning.

(Calculation of desired speed v _opt )
The desired speed v _opt at the target point is calculated using a fuzzy control model based on the time difference between the own vehicle and the intersecting vehicle until the intersection arrives, the speed of the own vehicle, and the distance until the own vehicle arrives at the intersection. .

  FIGS. 9A to 9D are diagrams showing membership functions constituting a fuzzy control model. (A) is time difference between own vehicle and intersection vehicle until arrival at intersection (own vehicle arrival time-intersection vehicle arrival time), (B) is own vehicle speed, (C) is own vehicle arrival time at intersection arrival. (D) shows the membership function of the desired speed when passing the intersection. Note that (A) to (C) indicate the antecedent part, and (D) indicates the consequent part.

  Each membership function represents the degree of subjective adaptation of each condition as a truth value. For example, the graph showing “the crossing vehicle is fast” in FIG. 9A is “the crossing vehicle is fast” when the time difference between the own vehicle and the crossing vehicle arrival at the intersection is one of the horizontal axes of the graph. It shows how well it meets the subjective conditions. Similarly, the graph showing “the speed of the host vehicle is medium (about)” in FIG. 9B shows that when the speed of the host vehicle is in any of the horizontal axes of the graph, It shows how well the subjective condition “(degree)” is met.

  FIG. 10 is a diagram illustrating the fuzzy rules constituting the fuzzy control model. This fuzzy rule (rule 0 to rule 26) indicates that “if (1) and (2) and (3) is (4)”. For example, in the case of rule 0, (1) the time of arrival at the intersection is faster (2) the speed of the own vehicle is faster, and (3) the distance to the intersection of the own vehicle is far (4) indicates that the desired speed when passing the intersection is fast. Further, in the case of rule 1, (1) the time of arrival at the intersection is faster (2) the speed of the own vehicle is medium (about), and (3) the distance until the own vehicle arrives at the intersection When is far, (4) indicates that the desired speed when passing the intersection is fast.

  The operation unit 23 is based on the membership function as described above, fuzzy rules representing subjective feelings regarding the desired speed, and further, based on the current running conditions of the own vehicle and the crossing vehicle on the simulation, Estimate the desired speed. Here, as specific numerical values, for example, the time difference until the intersection arrival between the own vehicle and the intersecting vehicle is -3 seconds (the intersecting vehicle arrives at the intersection three seconds faster), the own vehicle speed is 30 km / h, The case where the distance from the own vehicle to the intersection is 40 m will be described.

  Procedure 1: First, the above-described time difference, speed, and distance are applied to FIGS. 9A to 9C, respectively, and a truth value indicating the degree of conformance of each condition is obtained.

  11A is a truth value of each condition obtained from FIG. 9A, FIG. 11B is a truth value of each condition obtained from FIG. 9B, and FIG. It is a figure which shows the truth value of each calculated | required condition. For example, when -3 (sec) is applied to FIG. 9A, the truth value of the graph “the crossing vehicle is fast” is 0.2, and the truth value of the graph “same” is 0.4. The truth value of the graph “Own vehicle is fast” is zero. FIG. 11A shows this result.

  Procedure 2: The truth value described above is entered in each of the rules (1) to (3). Procedure 3: The minimum value is obtained from the truth values (1) to (3) of each fuzzy rule. Procedure 4: The maximum value is obtained for each speed content (fast, medium, slow) when passing.

  12A shows the antecedent part of the fuzzy rule, FIG. 12B shows the truth value in the antecedent part of each fuzzy rule, and FIG. 12C shows the truth values of (1) to (3) of each fuzzy rule. (D) shows the consequent part of the fuzzy rule, and (E) shows the maximum value for each content (fast, medium, slow) of “speed when passing” shown in (D). FIG.

  Procedure 5: The maximum value obtained in Procedure 4 is applied to the consequent part of the membership function (FIG. 9D) to obtain a fuzzy set for each condition, and the centroid is obtained by synthesizing each fuzzy set.

  FIG. 13 is a diagram showing a fuzzy set obtained from the membership function of each condition shown in FIG. The center of gravity of the synthesized fuzzy set is obtained by equation (4).

Here, y is the horizontal axis, y ₀ is the center of gravity, and μ (y) is the synthesized fuzzy set. In this embodiment, y ₀ = 18 and the desired speed is 18 km / h.

  In this way, the operation unit 23 estimates the driver's desired speed based on the fuzzy control model using an ambiguous expression relating to the driver's desired speed, and the running conditions of the host vehicle and the crossing vehicle on the simulation. Note that the graph shape and fuzzy rules of the membership function are not limited to those described above, and may be changed according to the driver characteristics, or the driver's psychological state (for example, a rushing state), You may change in consideration of the difference in a driving road. As a result, the desired speed that changes under the influence of the driver characteristics, the psychological state, and the traveling road can be obtained. Further, in the above-described example, the Min-Max centroid method is used as a fuzzy control solution. However, other solution methods such as a product-sum centroid method, a simple inference method, a functional inference method, etc. may be used. It is.

  And the operation part 23 operates an accelerator or a brake so that a vehicle drive | works at a driver's desired speed as mentioned above. The vehicle update unit 24 calculates the position, travel distance, and travel speed of the vehicle according to the operation content of the operation unit 23 and the elapsed time, and updates the vehicle data. Then, this control routine ends.

  On the other hand, in step S24, the determination unit 22 determines whether the host vehicle traveling on the non-priority road first passes through the intersection based on the traveling environment grasped by the traveling environment grasping unit 21. If the determination is affirmative, the process proceeds to step S25. If the determination is negative (determination error), the process proceeds to step S26.

  In step S25, the operation unit 23 calculates the expression (3) so as to travel at the desired speed, and then operates the accelerator to accelerate the host vehicle. The vehicle update unit 24 calculates the position, travel distance, and travel speed of the vehicle according to the operation content of the operation unit 23 and the elapsed time, and updates the vehicle data. Then, this control routine ends.

  In step S26, the operation part 23 and the vehicle update part 24 process similarly to step S13 in order to slow down the own vehicle. Then, this control routine ends.

  On the other hand, in step S31 shown in FIG. 8, the driving environment grasping part 21 determines whether or not there is a crossing vehicle on the priority road, similarly to step S4. If the determination is affirmative, the process proceeds to step S32, and a negative determination is made. If it is (recognition error), the process proceeds to step S33.

  In step S32, as in step S5, the determination unit 22 compares each time until one or more intersecting vehicles arrive at the intersection with the time until the own vehicle arrives at the intersection, and arrives at the intersection. It is determined whether or not the smallest time difference until is larger than the threshold value. When the determination is affirmative, the process proceeds to step S33, and when the determination is negative (determination error), the process proceeds to step S34.

  In step S33, the operation part 23 and the vehicle update part 24 process similarly to step S13 in order to slow down the own vehicle. Then, this control routine ends.

  In step S34, the operation part 23 and the vehicle update part 24 perform a process similarly to step S7 in order to stop the own vehicle. Then, this control routine ends.

[Passing through signalized intersections]
The traffic flow simulation apparatus can also simulate the movement of each vehicle as follows. Here, a case where the host vehicle enters a signalized intersection will be described as an example.

  14 and 15 are flowcharts showing a control routine of the vehicle moving unit 20 when the host vehicle enters the signalized intersection.

  In step S41, the driving environment grasping part 21 determines whether or not the existence of the signalized intersection is grasped in the same manner as in step S1, and proceeds to step S43 when the determination is affirmative, and when the determination is negative (recognition error). Proceeds to step S42.

  In step S42, since the next action is not determined by the determination part 22, the operation part 23 continues operation so far. Then, this control routine ends.

  In step S43, the driving environment grasping unit 21 determines whether or not the vehicle is in a stopped state as in step S3. When the determination is affirmative, the process proceeds to step S44, and when the determination is negative, FIG. The process proceeds to step S51.

  In step S44, the traveling environment grasping unit 21 determines whether the signal at the signalized intersection in the direction in which the vehicle is traveling is green. If the determination is affirmative, the process proceeds to step S45, where a negative determination (recognition) If it is a miss, the process proceeds to step S48.

  In step S45, the determination unit 22 determines whether to turn right next. If the determination is affirmative, the process proceeds to step S46. If the determination is negative, the process proceeds to step S47.

  In step S46, the determination unit 22 compares each time until one or more opposing straight vehicles arrive at the intersection with the time until the host vehicle arrives at the intersection, and is the smallest until the intersection arrives. It is determined whether the time difference (gap) is larger than a threshold value. This threshold value refers to the time difference at which the host vehicle can turn right without risking the two vehicles. If the determination is affirmative, the determination unit 22 proceeds to step S48. If the determination is negative (determination error), the determination unit 22 proceeds to step S47.

  In step S47, the operation unit 23 performs an accelerator operation to start the host vehicle. The vehicle update unit 24 calculates the position, travel distance, and travel speed of the vehicle according to the operation content of the operation unit 23 and the elapsed time, and updates the vehicle data. Then, this control routine ends.

  In step S48, the operation part 23 and the vehicle update part 24 perform a process similarly to step S7 in order to stop the own vehicle. Then, this control routine ends.

  On the other hand, in step S51 shown in FIG. 15, the determination unit 22 determines the next traveling direction (folding direction) of the host vehicle at the signalized intersection. Specifically, the determination unit 22 determines whether the host vehicle performs a straight turn, a left turn, or a right turn at the signalized intersection. When the vehicle goes straight, the process proceeds to step S52. When the vehicle turns left, the process proceeds to step S54. When making a right turn, the process proceeds to step S56.

  In step S52, as in step S44, the traveling environment grasping unit 21 determines whether the signal at the signalized intersection in the direction in which the vehicle is traveling is green. If the determination is affirmative, the process proceeds to step S53. If the determination is negative (cognitive error), the process proceeds to step S59.

  In step S53, as in step S23, the operation unit 23 and the vehicle update unit 24 cause the vehicle to travel at the speed desired by the driver. Then, this control routine ends.

  In step S54, the driving environment grasping unit 21 determines whether the signal at the signalized intersection is green as in step S52. If the determination is affirmative, the process proceeds to step S55, and if the determination is negative (recognition error). Control goes to step S59.

  In step S55, as in step S13, the operation unit 23 operates the brake to slow down the vehicle. The vehicle update unit 24 calculates the vehicle position, travel distance, and travel speed according to the operation content and elapsed time of the operation unit 23, and updates the vehicle data. Then, this control routine ends.

  In step S56, as in step S52, the driving environment grasping unit 21 determines whether the signal at the signalized intersection is green. When the determination is affirmative, the process proceeds to step S57. When the determination is negative (recognition error). Control goes to step S59.

  In step S57, as in step S46, the determination unit 22 compares each time until one or more opposing straight vehicles arrive at the intersection with the time until the host vehicle arrives at the intersection. It is determined whether or not the smallest time difference (gap) until arrival at the intersection is larger than the threshold value. If the determination is affirmative, the process proceeds to step S58. If the determination is negative (determination error), the process proceeds to step S55 described above.

  In step S58, the operation part 23 and the vehicle update part 24 perform a process similarly to step S48 in order to stop the own vehicle. Then, this control routine ends.

In step S59, the determination unit 22 determines whether or not the distance l ₀ from the current position (time t _c ) to the stop position is smaller than the distance for stopping at the desired deceleration a ₀ . Specifically, the determination unit 22 determines whether or not Expression (5) is satisfied.

Here, v is the current speed [m / s], and v _opt is the desired speed [m / s] at the stop position, which is zero in this embodiment. When the expression (5) is satisfied, that is, when the determination is affirmative, the determination unit 22 proceeds to step S53 described above, and when the expression (5) is not satisfied, that is, when the determination is negative, the process proceeds to step S58 described above. To do.

  As described above, the traffic flow simulation apparatus according to the embodiment of the present invention uses the personal characteristics of each driver and the characteristics of the intersection to accurately recognize the driving environment, whether there is an error in judgment, or traffic violation. Each vehicle is determined whether or not to perform the simulation, and each vehicle is simulated based on the determination result. As a result, it is possible to accurately simulate the traffic flow in consideration of human error that could not be reproduced conventionally, such as recognition errors in the driving environment, judgment errors, and traffic violations intentionally caused by the driver.

  In particular, cognitive errors, judgment errors, and traffic violations vary greatly depending on the traffic points and the individual characteristics of the driver. By preparing these trends for each driver or each traffic point in advance, human error Traffic flow can be simulated in consideration of the possibility of

  Note that the present invention is not limited to the above-described embodiment, and it is needless to say that the present invention can also be applied to a design modified within the scope of the claims.

  For example, in FIG. 2 and FIG. 3, the driver characteristics are shown at five levels, but may be expressed using a distribution diagram centered on the average value. As a result, even if the driver is in exactly the same driving environment, the driver will behave differently.

  Further, in each of the determination formulas described above, an additive operation in which each parameter is weighted and added is used, but the present invention is not limited to this. For example, other formulas or fuzzy models may be used. Furthermore, in the above-described embodiment, a temporary stop intersection and a signalized intersection have been described as examples of traffic points, but it goes without saying that the present invention is not limited to this.

  Furthermore, the traffic flow simulation device is not limited to hardware, and may be one in which a traffic flow simulation program is installed in a computer.

It is a block diagram which shows the structure of the traffic flow simulation apparatus which concerns on embodiment of this invention. It is a figure which shows the personal characteristic of the driver showing the tendency regarding recognition and judgment of a driver. It is a figure which shows the personal characteristic of the driver showing the tendency regarding the operativity of a driver. It is a figure which shows the characteristic of a temporary stop intersection. It is a figure which shows the case where a vehicle approachs a priority road from a non-priority road. It is a flowchart which shows the control routine of the vehicle moving part when the own vehicle approachs a priority road from a non-priority road. It is a flowchart which shows the control routine of the vehicle moving part when the own vehicle approachs a priority road from a non-priority road. It is a flowchart which shows the control routine of the vehicle moving part when the own vehicle approachs a priority road from a non-priority road. It is a figure which shows the membership function which comprises a fuzzy control model, (A) is the time difference of the own vehicle and intersection vehicle until it arrives at an intersection, (B) is the speed of the own vehicle, (C) is the intersection of the own vehicle. Distance to arrival, (D) is a diagram showing the desired speed when passing through an intersection. It is a figure which shows the fuzzy rule which comprises a fuzzy control model. The truth value of each condition obtained from FIG. 9A, (B) the truth value of each condition obtained from FIG. 9B, and (C) of each condition obtained from FIG. 9C. It is a figure which shows a truth value. (A) is the antecedent part of the fuzzy rule, (B) is a diagram in which the truth value is entered in the antecedent part of each fuzzy rule, (C) is the minimum of the truth value of (1) to (3) of each fuzzy rule The figure which shows a value, (D) is a figure which shows the consequent part of a fuzzy rule, and is a figure which shows the maximum value for every content (fast, middle, slow) of "speed at the time of passing" shown in (E) and (D) is there. It is a figure which shows the fuzzy set calculated | required from the membership function of each condition shown in FIG.9 (D). It is a flowchart which shows the control routine of a vehicle moving part when the own vehicle approachs a signalized intersection. It is a flowchart which shows the control routine of a vehicle moving part when the own vehicle approachs a signalized intersection.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Travel environment preparation part 20 Vehicle moving part 21 Travel environment grasping part 22 Judgment part 23 Operation part 24 Vehicle update part 30 Traffic condition management part

Claims (7)

A traffic flow simulation device for simulating the movement of a moving object traveling on a road,
Road environment creation means for creating a road environment;
Moving body traveling means for traveling the moving body in the road environment created by the road environment creating means;
Management means for managing the road environment creation means so as to create a road environment according to the traveling state of the mobile body,
The moving body traveling means is awareness of the traffic key points, awareness of signs indicating a traffic key point, and based on at least is one feature of the traffic strategic points of the traffic key points confirm easiness, A traffic flow simulation device that determines at least one of whether or not a driver has recognized a traffic point, the next action for a traffic point, and whether or not to make a traffic violation, and causes the vehicle to travel based on the determination result. The mobile body travel means further includes a driver's personality which is at least one of a tendency of how to recognize a traffic point, a tendency of the next judgment on the traffic point, a tendency of accuracy of judgment, and a tendency of vehicle operation. Based on the characteristics, at least one of presence / absence of recognition of the traffic point of the driver, the next action for the traffic point, and whether or not to violate the traffic is determined, and the moving body is caused to travel based on the determination result. The traffic flow simulation device according to claim 1. The moving body travel means is configured to request the host vehicle based on a fuzzy rule that represents a desired speed of the host vehicle based on a traveling state of the host vehicle and the intersecting vehicle when the host vehicle passes through the intersection as the traffic point. The traffic flow simulation apparatus according to claim 1, wherein a speed is estimated, and the next action is determined based on the desired speed. A traffic flow simulation method for simulating the movement of a moving object traveling on a road,
Create a road environment,
In created road environment, awareness of traffic key points, awareness of signs indicating a traffic key point, and based on at least one feature of the traffic key point is the transport key points confirm easiness, Determine at least one of the presence or absence of recognition of the traffic point of the driver, the next action for the traffic point, and whether or not to violate the traffic, and drive the mobile based on the determination result,
A traffic flow simulation method for managing the road environment so that the road environment can be created according to the traveling state of a moving object. When driving the mobile body , the driver further includes at least one of a tendency of how to recognize a traffic important point, a tendency of a next determination with respect to the traffic important point, a tendency of accuracy of determination, and a tendency of vehicle operation. Based on the personal characteristics of the vehicle, at least one of whether the driver recognizes the traffic point, the next action for the traffic point, and whether or not to violate the traffic is determined. Make it run
The traffic flow simulation method according to claim 4 . In the traveling of the moving body, when the own vehicle passes through the intersection as the traffic point, the request of the own vehicle based on the fuzzy rule representing the desired speed of the own vehicle based on the traveling state of the own vehicle and the intersecting vehicle. Estimate speed and determine next action based on desired speed
The traffic flow simulation method according to claim 4 or 5 . A traffic flow simulation program that causes a computer to simulate the movement of a moving object traveling on a road,
In the computer,
Create a road environment,
In created road environment, awareness of traffic key points, awareness of signs indicating a traffic key point, and based on at least one feature of the traffic key point is the transport key points confirm easiness, At least one of the presence or absence of recognition of the traffic point of the driver, the next action for the traffic point, and whether or not to violate the traffic is determined , and the vehicle is driven based on the determination result,
A traffic flow simulation program for managing the road environment so that the road environment can be created according to the traveling state of a moving object.

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