Merging position evaluation device and method for vehicle

Abstract

To properly perform evaluation of a merging position in a short time, in a scene where a vehicle merges.SOLUTION: A merging position evaluation device comprises: an obtaining part (12) that obtains a traffic environment around an own vehicle; an extracting part (12) that extracts merging position candidates that are candidates for cut-in destination gaps for the own vehicle cuts in an other vehicle group; a simulating part (13) that simulates future behavior of the own vehicle and of the other vehicle group using an operation behavior model to calculate a first evaluation value concerning a success or a failure of merging and a second evaluation value that is different from the first evaluation value, for each merging position candidate; and an evaluating part (14) that narrows down the merging position candidates down on the basis of the determined first evaluation value and evaluates superiority or inferiority of the narrowed-down merging position candidates, on the basis of the determined second evaluation value.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an apparatus and method for evaluating a merging position when a vehicle passes through a branch line and merges into a main line. [Background technology]

[0002] In recent years, as part of research into vehicle driving assistance or autonomous driving, assistance or automation of a merging operation of a vehicle merging from a branch line onto a main line has been considered. For example, Patent Document 1 below discloses that in a behavior plan generation device that generates a behavior plan for a vehicle merging onto a main line, a learning model generated from driving data obtained through a simulator function that allows a user to experience simulated vehicle driving is used to calculate a yielding degree that indicates the likelihood that other vehicles traveling on the main line will yield to the user's vehicle, and a behavior plan for the user's vehicle merging onto the main line is generated based on the calculated yielding degree. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-146576 Summary of the Invention [Problem to be solved by the invention]

[0004] When the vehicle merges onto the main lane, it is necessary to determine one of a plurality of gaps (vehicle gaps) in a group of other vehicles traveling on the main lane as a target merging position. However, in the above-mentioned Patent Document 1, the determination of whether or not the vehicle can actually merge at the target merging position, that is, the evaluation of the merging position, is performed based on the output value (degree of yielding) of a learning model obtained from repeated experiments by subjects using a simulator function, which results in the problem of time and effort required to establish the evaluation logic.

[0005] The present invention has been made in consideration of the above circumstances, and has an object to provide a vehicle merging position evaluation device and method that can appropriately evaluate a merging position in a short amount of time. [Means for solving the problem]

[0006] In order to solve the above problem, the vehicle merging position evaluation device of the present invention is a device that evaluates a merging position when a host vehicle merges onto a main line via a branch line, and includes: an acquisition unit that acquires a traffic environment around the host vehicle, including the positions and speeds of other vehicles traveling on the main line and road shape; an extraction unit that extracts merging position candidates that are candidates for cut-in gaps when the host vehicle cuts in into the other vehicle group based on the traffic environment acquired by the acquisition unit; a simulation unit that calculates a first evaluation value regarding the success or failure of merging and a second evaluation value different from the first evaluation value for each of the merging position candidates extracted by the extraction unit by simulating the future behavior of the host vehicle and the other vehicle group using a driving behavior model that predicts the behavior of the host vehicle and the other vehicle group; and an evaluation unit that narrows down the merging position candidates based on the first evaluation value calculated by the simulation unit, and evaluates the merits of the narrowed down merging position candidates based on the second evaluation value.

[0007] According to the present invention, each potential merging location is evaluated through a simulation using a driving behavior model that predicts the behavior of the vehicle itself and other vehicles, eliminating the need for complex processes such as machine learning or reinforcement learning for the evaluation, thereby reducing the time required for the evaluation.

[0008] Furthermore, when evaluating the merging position candidates, the merging position candidates are first narrowed down based on a first evaluation value, and then the merits of the narrowed down merging position candidates are evaluated based on a second evaluation value. In this way, by performing evaluation using multiple evaluation values ​​in stages, the calculation load can be reduced compared to calculating and comparing each evaluation value for all merging position candidates, and the time required for evaluation can be effectively shortened.

[0009] Preferably, the simulation unit calculates, as the first evaluation value, a margin evaluation value based on the remaining distance or remaining time to the end of the branch line at the time when the vehicle has completed merging or is expected to complete merging, and the evaluation unit narrows down the merging location candidates extracted by the extraction unit to those with relatively large margin evaluation values.

[0010] In this aspect, it is possible to appropriately select a candidate merging position that allows for ample merging, in other words, a candidate merging position that is expected to result in successful merging with a sufficient probability.

[0011] Preferably, the simulation unit calculates a following vehicle influence degree, which represents the magnitude of the influence that the merging of the host vehicle will have on a following vehicle, that is, another vehicle behind the host vehicle, as the second evaluation value, and the evaluation unit evaluates the merging position candidate with a small following vehicle influence degree as being better than the merging position candidate with a large following vehicle influence degree.

[0012] In this embodiment, a merging position candidate that will have as little impact as possible on following vehicles can be determined as the merging target, and a smooth merging operation that does not disrupt traffic flow can be achieved.

[0013] Preferably, the driving behavior model includes a following driving model that predicts the behavior of the group of other vehicles and outputs acceleration required for driving while maintaining an appropriate inter-vehicle distance from the preceding vehicle.

[0014] Furthermore, the driving behavior model includes a proportional model that outputs acceleration proportional to the difference between a predetermined target position and a predetermined target speed as a model for predicting the behavior of the host vehicle.

[0015] In these aspects, the future behavior of the other vehicle group and the host vehicle can be appropriately simulated through calculations with a relatively light load using specified calculation formulas.

[0016] A method for evaluating a merging position for a vehicle according to another aspect of the present invention is a method for evaluating a merging position when a host vehicle merges onto a main line via a branch line, the method including the steps of: acquiring a traffic environment around the host vehicle, including the positions and speeds of a group of other vehicles traveling on the main line, and road shape; extracting merging position candidates, which are candidates for cut-in gaps when the host vehicle cuts in into the group of other vehicles, based on the acquired traffic environment; calculating, for each of the extracted merging position candidates, a first evaluation value regarding the success or failure of merging and a second evaluation value different from the first evaluation value by simulating the future behavior of the host vehicle and the group of other vehicles using a driving behavior model that predicts the behavior of the host vehicle and the group of other vehicles; and narrowing down the merging position candidates based on the calculated first evaluation value, and evaluating the merits of the narrowed down merging position candidates based on the second evaluation value.

[0017] According to the invention of the merging position evaluation method, similar to the invention of the merging position evaluation device described above, it is possible to appropriately evaluate the merging position in a short time. [Effects of the Invention]

[0018] As described above, according to the merging position evaluation device or method of the present invention, it is possible to appropriately evaluate a merging position in a short time. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a functional block diagram showing the configuration of a vehicle control system to which a merging position evaluation device according to an embodiment of the present invention is applied. [Figure 2] FIG. 1 is a schematic diagram illustrating an example of a merging scene in which a vehicle merges from a branch line onto a main line. [Figure 3A] FIG. 10 is a schematic diagram for explaining a situation in which the merging margin distance is positive. [Figure 3B] FIG. 10 is a schematic diagram for explaining a situation in which the merging margin distance becomes negative. [Figure 4] FIG. 10 is a schematic diagram showing an example of a convoy of vehicles after merging. [Figure 5]10 is an equation showing the contents of a driving behavior model that predicts the behavior of the host vehicle and other vehicles when merging. [Figure 6] 10 is a flowchart showing the first half of a procedure for evaluating a candidate meeting position. [Figure 7] 10 is a flowchart showing the second half of the evaluation procedure. [Figure 8] 7 is a subroutine showing details of step S4 in FIG. 6. [Figure 9] 9 is a subroutine showing details of step S35 in FIG. 8. [Figure 10A] 3A and 3B are schematic diagrams showing examples of setting a target position and a target speed of a host vehicle; [Figure 10B] 10A and 10B are schematic diagrams showing other examples of setting the target position and target speed of the host vehicle. [Figure 11] 1 is a graph showing the relationship between the value of a model parameter and the frequency of occurrence thereof; [Figure 12] 10 is a graph showing the relationship between the merging margin distance calculated by the above simulation and the frequency of occurrence thereof. [Figure 13] 10 is a graph for explaining that the margin evaluation value calculated from the merging margin distance changes depending on a coefficient. [Figure 14] 10 is a graph showing a comparison of margin evaluation values ​​of four merging position candidates. [Figure 15] 10 is a graph illustrating data of the acceleration of the following vehicle obtained by the above simulation. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, preferred embodiments of the vehicle merging position evaluation device and method of the present invention will be described in detail with reference to the drawings. The vehicle to which the present invention is applied may be any of an engine vehicle powered by an internal combustion engine, an electric vehicle powered by an electric motor, and a hybrid vehicle powered by both an internal combustion engine and an electric motor. Furthermore, the merging position evaluation device and method may be used to evaluate a merging position during a merging operation of an autonomous vehicle that does not require driver operation, or may be used to evaluate a merging position during a merging operation of a vehicle with a driving assistance function that assists the driver in driving.

[0021] (1) Overall structure Fig. 1 is a functional block diagram showing the configuration of a vehicle control system to which a merging position evaluation device according to one embodiment of the present invention is applied. As shown in the diagram, the vehicle control system includes a controller 10 that controls the vehicle, a group of internal sensors 30 that detect the vehicle's driving state, a group of external sensors 31 that detect the vehicle's surrounding conditions, a GNSS receiver 32 that identifies the vehicle's position, and a group of actuators 40 that drive the vehicle. The vehicle control system is mounted on a vehicle 1 shown in Fig. 2. Hereinafter, the vehicle 1 may be referred to as the host vehicle 1 to distinguish it from another vehicle 2.

[0022] The controller 10 is configured with a microcomputer as its main components, including an arithmetic unit 11 including a processor such as a CPU, a storage unit 20 including a ROM, a RAM, and an HDD, and peripheral circuits such as an I / O interface.

[0023] The controller 10 is electrically connected to the internal sensor group 30, the external sensor group 31, the GNSS receiver 32, and the actuator group 40. That is, the controller 10 receives various information output from the sensor groups 30, 31 and the GNSS receiver 32, and outputs control signals to the actuator group 40.

[0024] The internal sensor group 30 is a collective term for sensors for detecting various information representing the traveling state of the host vehicle 1. The internal sensor group 30 includes a vehicle speed sensor for detecting the speed (traveling speed) of the host vehicle 1 and an acceleration sensor for detecting the acceleration of the host vehicle 1.

[0025] The GNSS receiver 32 is a receiver for determining the vehicle's own position using the Global Navigation Satellite System (GNSS). The GNSS receiver 32 receives and processes radio signals transmitted from GNSS satellites to determine the position of the vehicle 1 on Earth.

[0026] The external sensor group 31 is a collective term for sensors for recognizing targets around the host vehicle 1, and includes at least one of an optical sensor and an imaging sensor. Examples of optical sensors include radar such as millimeter-wave radar or a ranging device using LiDAR (light detection and ranging). Examples of imaging sensors include cameras including imaging elements such as CCDs and CMOSs. Alternatively, the external sensor group 31 may be a sensor fusion system including multiple types of sensors including optical sensors and imaging sensors and integration means for integrating and processing signals obtained from the sensors. Note that at least a portion of the external sensor group 31 may be optical sensors or imaging sensors installed on the road on which the host vehicle 1 travels. In this case, the controller 10 of the host vehicle 1 acquires information detected by the sensors on the road via appropriate communication means.

[0027] The actuator group 40 is a general term for actuators for driving that enable the vehicle 1 to drive. The actuator group 40 includes, for example, a power source such as an engine or a motor that generates power for driving, and a motor (steering motor) that drives a steering mechanism for changing the direction of travel.

[0028] The memory unit 20 stores various data necessary for driving control of the host vehicle 1. The data stored in the memory unit 20 includes road map data M1 and a driving behavior model M2. The road map data M1 is, for example, high-precision three-dimensional map data (HD map). In this case, the road map data M1 includes, for example, information on the position, shape, gradient, etc. of the road, position information on traffic lights, signs, etc. on the road, and information on the width and position of lanes, etc. The driving behavior model M2 is a mathematical model for simulating the future behavior of the host vehicle 1 and the other vehicle 2 in a merging scene (FIG. 2) described later. At least one of the road map data M1 and the driving behavior model M2 does not have to be stored in the memory unit 20 in advance, but may be input from an external server via a communication device (not shown) and temporarily stored in the memory unit 20.

[0029] The calculation unit 11 functionally includes a recognition unit 12, a simulation unit 13, an evaluation unit 14, a route generation unit 15, and a travel control unit 16.

[0030] The recognition unit 12 is a module that recognizes the state of the host vehicle 1 and the state of the surroundings of the host vehicle 1, including the other vehicle 2. For example, the recognition unit 12 acquires the speed and acceleration of the host vehicle 1 and the position of the host vehicle 1 on the road based on information input from the internal sensor group 30 and the GNSS receiver 32. The recognition unit 12 also acquires the position and speed of the other vehicle 2 based on information input from the external sensor group 31. Furthermore, the recognition unit 12 acquires the shape of the road around the host vehicle 1 from the road map data M1. The recognition unit 12 corresponds to an example of the "acquisition unit" and the "extraction unit" in the present invention.

[0031] The simulation unit 13 is a module that uses the driving behavior model M2 to simulate the future behavior of the host vehicle 1 and the other vehicle 2. Specifically, the simulation unit 13 performs a simulation to determine the merging operation of the host vehicle 1 at the time of merging shown in Fig. 2, that is, at the time of merging when the host vehicle 1 passes through the branch line 102 and merges onto the main line 101.

[0032] In the example of FIG. 2, the host vehicle 1 is traveling on a branch line 102 including a parallel section that extends parallel to the main line 101 in order to merge onto the main line 101. Furthermore, a plurality of other vehicles 2 (hereinafter also referred to as the other vehicle group 2) are traveling on the main line 101. A gap 3 is formed between each of the vehicles adjacent to each other in the fore-and-aft direction (travel direction) in the other vehicle group 2. When merging onto the main line 101, the host vehicle 1 determines which gap 3 in the other vehicle group 2 the host vehicle 1 will enter, and moves toward the determined gap 3. In order to determine the merging behavior of the host vehicle 1 in advance, the simulation unit 13 repeatedly simulates the future behavior of the host vehicle 1 and the other vehicles 2 using the driving behavior model M2.

[0033] The evaluation unit 14 is a module that evaluates the gap 3 into which the host vehicle 1 will cut in, i.e., the merging position, based on the results of the simulation performed by the simulation unit 13. That is, the evaluation unit 14 regards each of the multiple gaps 3 into which the host vehicle 1 may cut in as a merging position candidate, and evaluates each of the merging position candidates based on the results of the simulation in order to compare the merits of these merging position candidates.

[0034] Specifically, in the example of FIG. 2, a group of other vehicles 2 includes a first vehicle 2A, a second vehicle 2B, a third vehicle 2C, a fourth vehicle 2D, and a fifth vehicle 2E traveling on the main road 101 in this order from the front in the traveling direction. In this case, a first gap 3A between the first vehicle 2A and the second vehicle 2B, a second gap 3B between the second vehicle 2B and the third vehicle 2C, a third gap 3C between the third vehicle 2C and the fourth vehicle 2D, and a fourth gap 3D between the fourth vehicle 2D and the fifth vehicle 2E are respectively candidate merging positions. Based on this, the simulation unit 13 repeatedly simulates the future behavior of the host vehicle 1 and the other vehicles 2 (2A-2E) using the driving behavior model M2 for each case in which the host vehicle 1 is forced to intervene in the first to fourth gaps 3A-3D. From the results of this simulation, an evaluation value representing the appropriateness as a merging position is obtained for each of the first to fourth gaps 3A-3D. The evaluation unit 14 evaluates the merits and demerits of each of the gaps 3A to 3D based on the obtained evaluation value.

[0035] The simulation unit 13 calculates two types of evaluation values: a first evaluation value relating to the success or failure of merging, and a second evaluation value different from the first evaluation value. In this embodiment, the first evaluation value is a margin evaluation value E1 (see formula (1) described later) that indicates how much margin is available for merging. The second evaluation value is a following vehicle influence E2 (see formula (2) described later) that indicates the magnitude of the influence on a following vehicle, which is another vehicle 2 behind the host vehicle, at the time of merging.

[0036] The margin evaluation value E1 is a value calculated based on a merging margin, which is the remaining distance or remaining time to the terminus 102a of the branch line 102 (hereinafter also referred to as the branch line terminus 102a) at the time when the host vehicle 1 has completed merging or is expected to complete merging. As shown in FIG. 3A, in this embodiment, the merging margin is calculated as a merging margin, which is the distance from the host vehicle 1 to the branch line terminus 102a at the time when merging completion is expected. Here, the time when merging completion is expected is the time when merging will be completed if the host vehicle 1 simply changes lanes, moving laterally from the branch line 102 to the main line 101. As will be described in detail later, in this embodiment, it is determined that merging completion is expected when merging possible conditions are met, including conditions that the size of the gap at the cut-in destination (third gap 3C in the example of FIG. 3A) is larger than a predetermined value and the host vehicle 1 is traveling near the gap at a speed close to that of the other vehicle 2.

[0037] The merging margin distance Lm can be calculated as a negative value. In other words, in the above simulation, even if the merging condition is not yet met when the host vehicle 1 approaches the branch line terminal end 102a and merging completion is not expected, in other words, even if it is determined that merging will fail, the calculation using the driving behavior model M2 continues, assuming that there is another lane beyond the branch line terminal end 102a. The calculation continues until the merging condition is met (merging completion is expected), and the negative merging margin distance Lm shown in FIG. 3B is calculated. In other words, the situation in FIG. 3B is a situation in which merging is not possible unless the host vehicle 1 moves beyond the branch line terminal end 102a, which does not actually exist, so the merging margin distance Lm is negative. Conversely, if the merging condition is met just before the branch line terminal end 102a as shown in FIG. 3A, the merging margin distance Lm is calculated as a positive value. Thus, the merging margin distance Lm can be calculated regardless of whether merging is successful or not, and is an evaluation value that can continuously change from positive to negative.

[0038] The evaluation unit 14 evaluates each of the merging position candidates (gaps 3A to 3D) based on the merging margin distance Lm calculated as described above. The larger the merging margin distance Lm, the more margin there is for merging. Therefore, roughly speaking, the evaluation of the merging position candidates can be performed in such a way that the merging position candidate with the larger merging margin distance Lm is given a higher evaluation. However, in this embodiment, a simulation is repeatedly performed for each merging position candidate to calculate the merging margin distance Lm each time, and the merging position candidates are statistically evaluated based on the distribution (mean μ and standard deviation σ) of the merging margin distance Lm obtained from the simulation results. In other words, a margin evaluation value E1, which is a statistical evaluation value, is calculated from the distribution of the merging margin distance Lm, and each gap 3 is evaluated based on this margin evaluation value E1. This will be described in detail later.

[0039] The following vehicle influence level E2 is a value calculated based on the deceleration of the following vehicle that has decelerated as a result of merging. The following vehicle here refers to the other vehicle 2 that was behind the host vehicle 1, i.e., behind the gap 3 that the host vehicle 1 will cut into, at the time the merging possible condition was met. That is, in this embodiment, the simulation targeting the following vehicle continues even after the merging possible condition is met, thereby calculating the deceleration of the following vehicle that has decelerated as a result of merging. Then, the following vehicle influence level E2 is calculated based on the calculated deceleration of the following vehicle.

[0040] Specifically, the following vehicle influence degree E2 is calculated based on the deceleration of a following vehicle that has decelerated within a predetermined period of time since the merging condition was met. That is, the simulation unit 13 assumes a convoy with the host vehicle 1 at the front and another vehicle 2 behind the host vehicle 1 as a follower after the merging condition is met, and predicts the behavior of the convoy through a simulation using the driving behavior model M2. For example, when the host vehicle 1 enters the second gap 3B between the second vehicle 2B and the third vehicle 2C, the simulation unit 13 performs a simulation on the convoy consisting of the host vehicle 1 and the third to fifth vehicles 2C to 2E behind it, as shown in FIG. 4. Then, through the simulation, the following vehicle that has decelerated at a certain level or more within a predetermined period of time since the merging condition was met is extracted, and the following vehicle influence degree E2 is calculated based on the deceleration (more specifically, the maximum deceleration) of the extracted following vehicle. Although the details will be described later, the following vehicle influence degree E2 is calculated so as to increase as the deceleration (maximum deceleration) of the following vehicle generally increases.

[0041] The evaluation unit 14 evaluates each of the merging position candidates (gaps 3A to 3D) based on the following vehicle influence level E2 calculated as described above. The smaller this following vehicle influence level E2 is, the smaller the influence on the following vehicle, meaning that the deceleration experienced by the following vehicle will be small. For this reason, the evaluation of the merging position candidates can be performed in such a way that the smaller the following vehicle influence level E2 is, the higher the evaluation is given to the merging position candidate.

[0042] As described above, in this embodiment, merging position candidates are evaluated based on two evaluation values: the margin evaluation value E1 (first evaluation value) and the following vehicle influence level E2 (second evaluation value). However, evaluation based on these two evaluation values ​​is not performed in parallel, but is performed stepwise. Specifically, in this embodiment, the evaluation unit 14 narrows down the merging position candidates based on the margin evaluation value E1, and further evaluates the merits of the narrowed down merging position candidates based on the following vehicle influence level E2. In other words, the evaluation unit 14 narrows down the merging position candidates to only those with a relatively large margin evaluation value E1, and identifies the merging position candidate with the smallest following vehicle influence level E2 among the narrowed down merging position candidates as the merging position candidate with the highest evaluation.

[0043] The route generation unit 15 is a module that generates a movement route for the host vehicle 1 to a target merging position, i.e., a merging target. That is, the route generation unit 15 determines the merging position candidate with the highest evaluation as the merging target based on the evaluation of each merging position candidate by the evaluation unit 14. Then, assuming that the host vehicle 1 will move to the determined merging target, the route generation unit 15 generates a movement route for the host vehicle 1 required for this purpose.

[0044] The traveling control unit 16 is a module that controls the movement of the host vehicle 1 along the travel route generated by the route generation unit 15. That is, the traveling control unit 16 controls the actuator group 40 that causes the host vehicle 1 to travel, thereby moving the host vehicle 1 along the travel route and leading it to the merging target.

[0045] (2) Driving behavior model 5 shows the equations of the driving behavior model M2 used in the above simulation. The driving behavior model M2 shown in this figure includes an own vehicle model equation F1 that predicts the behavior of the own vehicle 1, and an other vehicle model equation F2 that predicts the behavior of the other vehicle 2.

[0046] The vehicle model formula F1 is a model formula that outputs acceleration ACCx proportional to the difference between the target position and the target speed, and expresses a proportional model such as a spring mass model. In this vehicle model formula F1, Δx is the difference between the target position and the current position, and Δv is the difference between the target speed and the current speed. These Δx and Δv are variables that change with time. Also, a max is the upper limit acceleration, a min is the lower limit acceleration, and c1 and c2 are weighting coefficients. max ,a min , c1, and c2 are parameters that are determined in advance. Note that the weighting coefficients c1 and c2 may be changed as appropriate depending on the situation of the host vehicle 1 or the other vehicle 2, etc.

[0047] The acceleration ACCx of the host vehicle 1 calculated by such host vehicle model formula F1 is basically calculated as the sum of a value proportional to the difference (Δx) between the target position and the current position and a value proportional to the difference (Δv) between the target speed and the current vehicle speed. However, the acceleration ACCx includes a lower limit acceleration a min not fall below the upper limit acceleration a max A limiter is applied to prevent it from exceeding this.

[0048] The other vehicle model formula F2 is a model formula that outputs the acceleration ACCy required to travel while maintaining an appropriate distance from the preceding vehicle, and represents a following driving model generally known as the IDM (Intelligent Driver Model). In this other vehicle model formula F2, s is the distance from the preceding vehicle, v is the speed, and Δv is the relative speed from the preceding vehicle. These s, v, and Δv are variables that change over time. Also, v0 is the desired speed, s0 is the safe distance from the preceding vehicle (in other words, the distance between vehicles when stopped), T is the minimum time between vehicles (reaction time), a is the maximum acceleration, b is the maximum deceleration, and δ is the power of the acceleration term (smoothness of acceleration). These v0, s0, T, a, b, and δ are characteristic parameters that can change depending on the driver's characteristics and the vehicle's performance.

[0049] Simply put, this other vehicle model formula F2 (follow-up driving model) calculates the appropriate inter-vehicle distance (s * ) and calculates the acceleration ACCy from the calculated inter-vehicle distance. As a result, the acceleration of the other vehicle 2 is adjusted so as to maintain an appropriate inter-vehicle distance from the preceding vehicle.

[0050] (3) Details of evaluation of candidate merging locations Next, the details of the evaluation of the merging position candidate performed by the evaluation unit 14 will be described. FIG. 6 is a flowchart showing the procedure for evaluating the merging position candidate. As a premise for starting the control shown in this figure, it is assumed that the host vehicle 1 is traveling on the branch line 102 (FIG. 2), and that multiple other vehicles 2 are traveling within the range of the main line 101 that the host vehicle 1 can recognize. When this control starts, the recognition unit 12 acquires the traveling state of the host vehicle 1 and the traffic environment around the host vehicle 1 (step S1). That is, the recognition unit 12 acquires information including the position and speed of the host vehicle 1 as information representing the traveling state of the host vehicle 1. The recognition unit 12 also acquires information including the road shape around the host vehicle 1 and the position and speed of other vehicles 2 as information representing the traffic environment around the host vehicle 1. This information is determined from the detection values ​​of the internal sensor group 30 and the external sensor group 31, the road map data M1 stored in the memory unit 20, and the received signal of the GNSS receiver 32.

[0051] More specifically, the recognition unit 12 acquires information including the speed of the vehicle 1 and the position of the vehicle 1 on the branch line 102 based on the detection values ​​of the internal sensor group 30 and the received signal of the GNSS receiver 32. The recognition unit 12 also acquires information about the road shape around the vehicle 1 based on the acquired position (current position) of the vehicle 1 and detailed road map data M1 stored in the memory unit 20. This information may include the length of an acceleration section, which is the distance from the current position of the vehicle 1 traveling on the branch line 102 to the branch line end 102a, and information about lane change prohibited sections, which are sections where changing lanes from the branch line 102 to the main line 101 is prohibited. If a lane change prohibited section exists, the position of the branch line end 102a may be set to the end of the branch line 102 excluding the lane change prohibited section, in other words, the end of the section where changing lanes from the branch line 102 to the main line 101 is permitted. Furthermore, the recognition unit 12 acquires information including the position and speed of each of a plurality of other vehicles 2 (other vehicle group) traveling on the main road 101 in a range from diagonally ahead to diagonally behind the host vehicle 1, based on the detection values ​​of the external sensor group 31. In this case, the acquired positions of the other vehicle group 2 are relative positions with the host vehicle 1 as the reference.

[0052] Next, based on the information about the other vehicle group 2 acquired in step S1, the recognition unit 12 extracts candidate merging positions, which are candidates for gaps into which the host vehicle 1 will cut in when cutting into the other vehicle group 2 (step S2). For example, if the other vehicle group 2 recognized in step S1 is five vehicles, namely, first to fifth vehicles 2A to 2E, as shown in Fig. 2, the recognition unit 12 extracts four gaps, consisting of a first gap 3A between the first vehicle 2A and the second vehicle 2B, a second gap 3B between the second vehicle 2B and the third vehicle 2C, a third gap 3C between the third vehicle 2C and the fourth vehicle 2D, and a fourth gap 3D between the fourth vehicle 2D and the fifth vehicle 2E, as candidate merging positions.

[0053] Next, the evaluation unit 14 designates one merging position candidate to be evaluated from the merging position candidates extracted in step S2 (step S3). For example, if the extracted merging position candidates are the first to fourth gaps 3A to 3D as in the example of Fig. 2, the evaluation unit 14 designates an appropriate one of these four gaps 3A to 3D as the target for evaluation.

[0054] Next, the evaluation unit 14 calculates a margin evaluation value E1 for the merging position candidate designated in step S3 (step S4). The margin evaluation value E1 is calculated based on the results of a simulation performed by the simulation unit 13 using the driving behavior model M2.

[0055] 8 is a subroutine showing detailed procedures for evaluating merge position candidates in step S4 above. When the control shown in this figure starts, the simulation unit 13 sets the initial positions and initial velocities of the host vehicle 1 and the other vehicle group 2 (step S31). The initial positions and initial velocities are the positions and velocities of the vehicles 1 and 2 at the start of the simulation. The simulation unit 13 sets the position and speed of the host vehicle 1 acquired in step S1 above as the initial position and initial speed of the host vehicle 1, and sets the position and speed of the other vehicle group 2 acquired in step S1 above as the initial position and initial speed of the other vehicle group 2.

[0056] Next, the simulation unit 13 sets the acceleration section length at the start of the simulation based on the information acquired in step S1 (step S32). The acceleration section length is the distance from the initial position of the vehicle 1 to the branch line end 102a.

[0057] Next, the simulation unit 13 sets a target position and a target speed of the host vehicle 1 (step S33). The target position and the target speed are the position and the speed that the host vehicle 1 aims to achieve in the host vehicle model formula F1 of the driving behavior model M2 described above, and are determined according to the merging position candidate specified in step S3 (FIG. 6).

[0058] For example, as shown in FIG. 10A, if the specified merging position candidate is the second gap 3B between the second vehicle 2B and the third vehicle 2C, the target position is set to the side of the second gap 3B. Also, as shown in FIG. 10B, if the specified merging position candidate is the fourth gap 3D between the fourth vehicle 2D and the fifth vehicle 2E, the target position is set to the side of the fourth gap 3D. In the former case (FIG. 10A), the target position is set in front of the host vehicle 1, so the host vehicle 1 moves toward the target position while accelerating. On the other hand, in the latter case (FIG. 10B), the target position is set behind the host vehicle 1, so the host vehicle 1 moves toward the target position while suppressing acceleration (or decelerating in some cases).

[0059] The target position of the vehicle 1 may be approximately to the side of the gap 3 designated as the candidate merging position in step S3 (hereinafter referred to as designated gap 3), and various specific methods for setting the target position are conceivable. For example, the target position may be to the side of the center of the designated gap 3, or to the side of another vehicle 2 (leading vehicle) located in front of the designated gap 3.

[0060] The target speed of the host vehicle 1 is set based on the speeds of the other vehicles 2 that form the gap 3 of the candidate merging position, in other words, the speeds of the other vehicles 2 before and after the gap 3. For example, if the candidate merging position is the second gap 3B as shown in FIG. 10A, the target speed of the host vehicle 1 is set based on the speeds of the second vehicle 2B in front of the second gap 3B and the third vehicle 2C behind the second gap 3B. There are various possible specific setting methods, but for example, the average speed of the second vehicle 2B and the third vehicle 2C may be set as the target speed of the host vehicle 1. This is also true when the candidate merging position is another gap (the first gap 3A, the third gap 3C, or the fourth gap 3D).

[0061] Next, the simulation unit 13 randomly sets the model parameters of the other vehicle model formula F2 described above that predicts the behavior of the other vehicle 2 (step S34). Specifically, in this embodiment, four parameters included in the other vehicle model formula F2, namely, the desired speed (v0), the minimum inter-vehicle time (T), the maximum acceleration (a), and the maximum deceleration (b), are randomly set based on statistical occurrence frequency distribution data. On the other hand, the remaining parameters, namely, the exponent (δ) of the acceleration term and the minimum inter-vehicle distance (s0), are maintained at fixed values. This is because it is known that sufficient accuracy can be obtained even if these two parameters are treated as fixed values.

[0062] Here, the statistical occurrence frequency distribution data that forms the basis for random setting is statistical data that defines the relationship between parameter values ​​and occurrence frequencies, as shown in FIG. 11, and is obtained in advance by observing actual vehicles traveling at merging sections on expressways, etc. The four model parameters described above depend on the characteristics of the driver of the other vehicle 2 and the performance of the other vehicle 2, and therefore cannot be known by the own vehicle 1. Therefore, in step S34, the model parameters are randomly set based on the occurrence frequency distribution data of FIG. 11 obtained from observation of actual traffic. This is equivalent to statistically inferring the driver characteristics of the other vehicle 2 from the observation results of actual traffic. Note that there are already several publicly available data for occurrence frequency distribution data (statistical data) such as that shown in FIG. 11, and it is possible to obtain them without independently observing actual traffic.

[0063] For example, suppose the occurrence frequency distribution data in FIG. 11 is data on the occurrence frequency of each parameter in the range from P1 to P2, and the mode is Px. In this case, the simulation unit 13 randomly sets the model parameters within the range from P1 to P2 and varies the occurrence probability based on the distribution curve. That is, the model parameter value generated by the random setting is most likely to be Px. Conversely, the further a value is from Px, the less likely it is to be set as a model parameter. Thus, in step S34, the simulation unit 13 sets the model parameters with probabilities according to the distribution curve in FIG. 11. Therefore, when step S34 is repeated and model parameters are randomly set many times, the occurrence frequency of the model parameters will be higher as they are closer to the mode Px in FIG. 11 and lower as they are further from the mode Px.

[0064] Furthermore, the random setting of the model parameters in step S34 above is performed individually for each other vehicle 2. That is, the random setting of the model parameters for one other vehicle 2 is performed independently of the random setting of the model parameters for the other other vehicles 2. For this reason, in most cases, different model parameters will be set for each other vehicle 2.

[0065] As will be described later, the random setting of the model parameters in step S34 is repeated until a predetermined number of sampling times N1 (S36) is reached, and the model parameters set each time are used in the next merging simulation (S35). In other words, the values ​​of the model parameters used in the merging simulation vary as the random setting in step S34 is repeated. For this reason, the processing in step S34 can be said to be processing that imparts fluctuations to the model parameters based on statistical occurrence frequency distribution data.

[0066] When the random setting of the model parameters of the other vehicle model formula F2 is completed as described above, the simulation unit 13 performs a merging simulation using the set model parameters (step S35). The merging simulation is a process of model predicting the future behavior of each vehicle 1, 2 in a merging scene where the host vehicle 1 cuts in to a group of other vehicles 2. In this merging simulation, a simulation is performed using the driving behavior model M2 including the host vehicle model formula F1 and the other vehicle model formula F2 described above. At this time, the parameters randomly set in step S34 above are used as the model parameters (v0, T, a, b) of the other vehicle model formula F2.

[0067] 9 is a subroutine showing the detailed procedure for performing the merging simulation in step S35. When the control shown in this figure starts, the simulation unit 13 determines the variables of each of the model formulas F1 and F2 from the initial positions and initial velocities of the host vehicle 1 and the other vehicle group 2, and the target position of the host vehicle 1 (step S41).

[0068] Specifically, the simulation unit 13 determines, for each other vehicle 2, the values ​​of the speed (v), the relative speed (Δv) from the preceding vehicle, and the inter-vehicle distance (s) from the preceding vehicle, which are variables of the other vehicle model formula F2, from the position (initial position) and speed (initial speed) of the other vehicle group 2 acquired in step S1 (FIG. 6). For example, for the second vehicle 2B shown in FIG. 2, the simulation unit 13 determines the speed of the second vehicle 2B acquired in step S1 as the speed (v) of the other vehicle model formula F2. Furthermore, the simulation unit 13 calculates the relative speed and inter-vehicle distance of the second vehicle 2B with respect to the first vehicle 2A from the positions and speeds of the first vehicle 2A and second vehicle 2B acquired in step S1, and determines the calculated values ​​as the relative speed (Δv) and inter-vehicle distance (s) of the other vehicle model formula F2. The simulation unit 13 performs this process for the other other vehicles 2 in a similar manner. Note that for the first vehicle 2A, the above variables can be determined using information on other vehicles (not shown) located further ahead. However, if no other vehicles are recognized ahead of the first vehicle 2A, the above variables can be determined under an appropriate assumption, such as, for example, that there is another vehicle sufficiently ahead of the first vehicle 2A traveling at the same speed as the desired speed (v0) of the first vehicle 2A.

[0069] Furthermore, the simulation unit 13 determines the difference (Δx) between the target position and the current position and the difference (Δv) between the target speed and the current speed, which are variables of the host vehicle model formula F1, based on the position (initial position) and speed (initial speed) of the host vehicle 1 acquired in step S1 and the target position and target speed of the host vehicle 1 set in step S33 (FIGS. 10A and 10B). For example, the simulation unit 13 determines the difference (Δx) between the target position and the current position in the host vehicle model formula F1 based on the position of the host vehicle 1 acquired in step S1 and the target position of the host vehicle set in step S33. Furthermore, the simulation unit 13 determines the difference (Δv) between the target speed and the current speed in the host vehicle model formula F1 based on the speed of the host vehicle 1 acquired in step S1 and the target speed of the host vehicle set in step S33.

[0070] Next, the simulation unit 13 calculates the acceleration ACCy of the other vehicle group 2 from the other vehicle model formula F2 (step S42). That is, the simulation unit 13 performs calculations for each other vehicle 2 using the other vehicle model formula F2 using the variables (v, Δv, s) determined for each other vehicle 2 in step S41 above, thereby calculating the acceleration ACCy of each other vehicle 2. At this time, the values ​​randomly set for each other vehicle 2 in step S34 above are used as the model parameters (v0, T, a, b) of the other vehicle model formula F2.

[0071] Next, the simulation unit 13 calculates the position and speed of each of the other vehicles 2 at the time when the small time Δt has elapsed, using the acceleration ACCy calculated for each other vehicle 2 in step S42 (step S43). That is, the simulation unit 13 calculates the position and speed of each other vehicle 2 after Δt has elapsed, based on the current position and speed of each other vehicle 2 obtained in the previous step and the acceleration ACCy of each other vehicle 2 calculated in step S42.

[0072] Next, the simulation unit 13 calculates the acceleration ACCx of the host vehicle 1 from the host vehicle model formula F1 (step S44). That is, the simulation unit 13 performs calculations using the host vehicle model formula F1 using the variables (Δx, Δv) determined for the host vehicle 1 in step S41, thereby calculating the acceleration ACCx of the host vehicle 1.

[0073] Next, the simulation unit 13 calculates the position and speed of the host vehicle 1 at the time when the small time Δt has elapsed, using the acceleration ACCx of the host vehicle 1 calculated in step S44 (step S45). That is, the simulation unit 13 calculates the position and speed of the host vehicle 1 after Δt has elapsed, based on the current position and speed of the host vehicle 1 obtained in the previous step and the acceleration ACCx of the host vehicle 1 calculated in step S44.

[0074] Next, the simulation unit 13 updates the positions and velocities of the host vehicle 1 and the other vehicle group 2, as well as the target position and target speed of the host vehicle 1 (step S46). That is, the simulation unit 13 updates the positions and velocities of the host vehicle 1 and the other vehicle group 2 to the positions and velocities after Δt has elapsed, which were calculated in steps S43 and S45. The simulation unit 13 also updates the target position and target speed of the host vehicle 1 based on the updated position and speed of the other vehicle group 2.

[0075] Next, the simulation unit 13 determines whether or not the merging possible condition is satisfied (step S47). The merging possible condition is a condition under which the vehicle 1 can start changing lanes, moving laterally from the branch lane 102 to the main lane 101. When the merging possible condition is satisfied, the merging is completed only by changing lanes, and therefore, it can be considered that the completion of the merging is expected at this point. In other words, determining whether or not the merging possible condition is satisfied is equivalent to determining whether or not the completion of the merging is expected.

[0076] Specifically, the confluence enabling conditions may include, for example, the following requirements (i) to (iv). (i) The vehicle 1 is located near the gap 3 designated as a candidate merging position, i.e., the target position set to the side of the designated gap 3. For example, the vehicle 1 is located within a range of ±5 m of the target position. (ii) The vehicle 1 is traveling at a speed close to the target speed set so that it can travel alongside other vehicles 2 before and after the designated gap 3. For example, the vehicle 1 is traveling within a range of ±5 km / h of the target speed. (iii) The above requirements (i) and (ii) continue for a predetermined period of time (for example, approximately 3 seconds) or more. (iv) The distance between vehicles in designated gap 3 is sufficient. For example, the distance between vehicles in designated gap 3 is a distance equivalent to a time gap of 1.5 seconds or more.

[0077] If the determination in step S47 above is NO and it is confirmed that the merging condition is not met, the simulation unit 13 updates the variables of the model formulas F1 and F2 described above (step S48). That is, the simulation unit 13 calculates the variables (v, Δv, s) of the other vehicle model formula F2 and the variables (Δx, Δv) of the host vehicle model formula F1, respectively, from the positions and velocities of the host vehicle 1 and the other vehicle group 2 updated in step S46 above and the target position and target velocity of the host vehicle 1, in the same manner as in step S41 above. Then, using the calculated values ​​as the updated variables, the simulation unit 13 performs the calculations in steps S42 to S45 above to calculate the positions and velocities of the host vehicle 1 and the other vehicle group 2 after Δt has elapsed. The simulation unit 13 repeats this calculation to calculate the position and velocity of each vehicle every Δt until the merging condition in step S47 above is met.

[0078] It should be noted that, even if the position of the vehicle 1 approaches the branch line terminal end 102a during the repetition of the above calculations, the calculations continue unless the merging condition (S47) is met at that point. In other words, the calculations indicate that the vehicle 1 may move beyond the branch line terminal end 102a. However, if the merging condition is not met even after the vehicle 1 has significantly passed the branch line terminal end 102a, the calculations may be terminated at that point and the merging condition may be deemed to be met.

[0079] If the determination in step S47 above is YES and the merging condition is confirmed to be met, the simulation unit 13 ends the above calculations for determining the position and speed of each vehicle every Δt (step S49), and calculates the merging margin distance Lm using the conditions at the time of completion (step S50). That is, the simulation unit 13 calculates the merging margin distance Lm (FIGS. 3A and 3B), which is the distance from the host vehicle 1 to the branch line end 102a, based on the position of the host vehicle 1 on the branch line 102 when the merging condition is met, i.e., the position of the host vehicle 1 on the branch line 102 calculated in the last of the repeated calculations.

[0080] The merging margin distance Lm calculated here can be considered the remaining distance to the branch line terminal end 102a at the time when merging is expected to be completed, and as described above, is a value that can be either positive or negative. That is, if the position of the host vehicle 1 at the time when the merging condition is met is before the branch line terminal end 102a, a positive merging margin distance Lm is calculated. On the other hand, if the position of the host vehicle 1 at the time when the merging condition is met is beyond the branch line terminal end 102a, a negative merging margin distance Lm is calculated. The larger the positive merging margin distance Lm, the more leeway there is for merging. Furthermore, a negative merging margin distance Lm means that merging is not possible using behavior according to the model formula.

[0081] When the calculation of the merging margin distance Lm is completed in the above manner, the simulation unit 13 returns to the flow of FIG. 8 and determines whether or not the number of times the model parameters of the other vehicle model formula F2 have been randomly set has reached a predetermined number of sampling times N1 (step S36).

[0082] If the determination in step S36 above is NO and it is confirmed that the number of random settings has not reached the sampling number N1, the simulation unit 13 returns to step S34 above and randomly sets new model parameters for the other vehicle group 2. Then, using the randomly set model parameters here, a merging simulation (S35) is performed and the merging margin distance Lm is calculated. The simulation unit 13 repeats this merging simulation (calculation of the merging margin distance Lm) for each condition of the model parameters until the number of random settings reaches the sampling number N1.

[0083] When it is determined as YES in the above step S36 and it is confirmed that the number of random settings has reached the sampling number N1, the simulation unit 13 obtains the distribution of a large number of merging margin distances Lm obtained by repeating the merging simulation in the above step S35, that is, the distribution of the merging margin distance Lm corresponding to various conditions in which the model parameters are varied (step S37). Specifically, the simulation unit 13 obtains the mean μ and the standard deviation σ shown in FIG. 12 as the distribution of the merging margin distance Lm. As is well known, the mean μ is a value obtained by dividing the sum of the obtained data of the merging margin distance Lm by the number of data. The standard deviation σ is a value indicating the degree of variation with respect to the mean value μ, and is represented by the square root of the value (variance) obtained by dividing the sum of the squares of the differences (deviations) between the values of the target data and the mean μ by the number of data.

[0084] Next, the simulation unit 13 calculates a margin evaluation value E1 of the merging position candidate from the distribution of the merging margin distance Lm obtained in the above step S37, that is, the mean μ and the standard deviation σ (step S38). In the present embodiment, the margin evaluation value E1 is a value defined by the following formula (1) using the mean μ and the standard deviation σ of the merging margin distance Lm.

[0085]

Equation

[0086] The coefficient k in the above formula (1) may be appropriately set based on values such as values to be emphasized, and may be a constant value regardless of conditions, or a value that changes depending on conditions. In the latter case, as an example, a method of changing the coefficient k according to the degree of margin can be considered. For example, in a condition that requires a careful judgment such as when the mean μ of the merging margin distance Lm is relatively small, the coefficient k is made smaller than zero (-1≦k<0), and in a condition that allows an optimistic judgment such as when the mean μ of the merging margin distance Lm is relatively large, the coefficient k is made larger than zero (0<k≦+1), and so on.

[0087] 13 is a graph showing the range of the margin evaluation value E1 calculated by the above formula (1). As shown in the figure, the margin evaluation value E1 is maximum E1b (=μ+σ) when the coefficient k is +1, and is minimum E1a (=μ-σ) when the coefficient k is -1. In this way, the margin evaluation value E1 is calculated to vary within the range from E1a to E1b depending on the setting of the coefficient k.

[0088] When the calculation of the margin evaluation value E1 is completed in this manner, the simulation unit 13 returns to the flow of Fig. 6 and determines whether or not the calculation of the margin evaluation value E1 for all of the merging position candidates is completed (step S5). For example, if there are a total of four merging position candidates (first to fourth gaps 3A to 3D) as in the example of Fig. 2, the simulation unit 13 determines whether or not the calculation of the margin evaluation value E1 (Fig. 8) for all of these four gaps 3A to 3D is completed.

[0089] If the determination in step S5 above is NO and it is confirmed that there are still merging position candidates for which the margin evaluation value E1 has not been calculated, the simulation unit 13 designates the next merging position candidate for which the margin evaluation value E1 should be calculated (step S6).The simulation unit 13 then calculates a new margin evaluation value E1 for the designated merging position candidate (S4).The simulation unit 13 repeats the designation of merging position candidates and the calculation of margin evaluation values ​​E1 (S4, S6) until the calculation of margin evaluation values ​​E1 has been performed for all merging position candidates.

[0090] If the determination in step S5 is YES and it is confirmed that the margin evaluation values ​​E1 have been calculated for all of the meeting position candidates, the evaluation unit 14 identifies the number of tentative candidates with relatively large margin evaluation values ​​E1 (step S7). Here, the tentative candidates are meeting position candidates with margin evaluation values ​​E1 that exceed a predetermined reference value Ez.

[0091] FIG. 14 is a graph showing an example of the difference in the margin evaluation value E1 when there are four candidate merging positions, candidates 1 to 4. When the margin evaluation value E1 of candidate 1 is E11, the margin evaluation value E1 of candidate 2 is E12, the margin evaluation value E1 of candidate 3 is E13, and the margin evaluation value E1 of candidate 4 is E14, in the example of FIG. 14, the margin evaluation value E1 increases in the order of candidate 1, candidate 2, candidate 4, and candidate 3 (E11 < E12 < E14 < E13). Also, the reference value Ez is greater than the margin evaluation value E11 of candidate 1 and less than the margin evaluation value E12 of candidate 2. In other words, the relationship E11 < Ez < E12 < E14 < E13 holds. In this case, the provisional candidates whose margin evaluation value E1 exceeds the reference value Ez are three, namely candidate 2, candidate 3, and candidate 4. On the other hand, since the margin evaluation value E11 of candidate 1 is below the reference value Ez, candidate 1 is excluded from the provisional candidates. As a result, in the example of FIG. 14, the number of provisional candidates whose margin evaluation value E1 exceeds the reference value Ez is counted as "3".

[0092] When the graph of FIG. 14 is associated with the layout example of each vehicle shown in FIG. 2, candidate 1 may correspond to the first gap 3A, candidate 2 may correspond to the second gap 3B, candidate 3 may correspond to the third gap 3C, and candidate 4 may correspond to the fourth gap 3D. Of course, there may be other correspondence relationships depending on the situation.

[0093] When the number of provisional candidates whose margin evaluation value E1 exceeds the reference value Ez is specified as described above, the evaluation unit 14 determines whether or not the number of the provisional candidates is equal to or greater than a predetermined specified number n0 (step S8). The specified number n0 is an integer of 2 or more. The specified number n0 can be appropriately set within the range of 2 or more, but for example, it can be set to 3 or 4.

[0094] If the determination in step S8 above is NO and it is confirmed that the number of tentative candidates is less than the specified number n0, the evaluation unit 14 identifies the tentative candidate with the highest headroom evaluation value E1 (step S9). For example, if the specified number n0 is 3 or 4 and the number of tentative candidates is 2, the evaluation unit 14 identifies the candidate with the higher headroom evaluation value E1 from among the two tentative candidates as the tentative candidate with the highest headroom evaluation value E1. Furthermore, if there is only one tentative candidate, the evaluation unit 14 identifies this candidate as the tentative candidate with the highest headroom evaluation value E1.

[0095] Next, the route generation unit 15 determines the tentative candidate identified in step S9 above, i.e., the merging position candidate having the highest margin evaluation value E1 in the range exceeding the reference value Ez, as the merging target for the host vehicle 1 (step S10). For example, if the tentative candidate identified in step S9 above is candidate 3 and this corresponds to the third gap 3C shown in Fig. 2, the route generation unit 15 determines the third gap 3C as the merging target. As a result, the host vehicle 1 will travel along the travel route to the third gap 3C and will enter the third gap 3C.

[0096] Although rare, one possible case in which the determination in step S8 above is NO is when the number of tentative candidates becomes zero. In this case, the merging position candidates are reevaluated using a different criterion, and an action plan is formulated based on the results. That is, when the number of tentative candidates becomes zero, it means that the margin evaluation values ​​E1 of all the extracted merging position candidates are equal to or less than the reference value Ez. In this case, for example, one candidate with the highest positive margin evaluation value E1 (closest to the reference value Ez) is identified from the extracted merging position candidates, and the margin evaluation value E1 of the identified candidate is compared with an alternative reference value set to a value smaller than the reference value Ez. The alternative reference value is set to an appropriate value that is smaller than the reference value Ez and that allows for a substantially problem-free merging operation. If the comparison confirms that the margin evaluation value E1 exceeds the alternative reference value, the identified candidate is set as the merging target. On the other hand, if the margin evaluation value E1 is equal to or less than the substitution reference value, none of the merging position candidates can be a merging target, so it may be necessary to take emergency measures such as temporarily stopping the vehicle 1 at a safe location.

[0097] Next, a description will be given of the control when the determination in step S8 above is YES, that is, when the number of provisional candidates whose margin evaluation values ​​E1 exceed the reference value Ez is equal to or greater than the specified number n0. In this case, the simulation unit 13 proceeds to the flow in Fig. 7 and designates one candidate from the provisional candidates as a target for examining the following vehicle influence degree E2 (step S11). For example, as in the example in Fig. 14, when the number of provisional candidates whose margin evaluation values ​​E1 exceed the reference value Ez is three, i.e., candidates 2 to 4, the simulation unit 13 designates an appropriate one of these candidates 2 to 4 as the target.

[0098] Next, the simulation unit 13 sets a post-merging convoy based on the provisional candidate designated in step S11 (step S12). The post-merging convoy is a convoy with the host vehicle 1 at the head, assuming that the host vehicle 1 has cut into gap 3 of the designated provisional candidate. For example, if the designated provisional candidate is second gap 3B, the host vehicle 1 will cut in between the second vehicle 2B and the third vehicle 2C, as shown in FIG. 4, so the post-merging convoy will be a convoy with the host vehicle 1 at the head and the third vehicle 2C, fourth vehicle 2D, and so on, as the following vehicles. By setting the post-merging convoy in this way, in the simulation, the vehicle traveling in front of the third vehicle 2C will be switched from the second vehicle 2B to the host vehicle 1.

[0099] The conditions of the vehicle convoy set in step S12 are inherited from the results of the merging simulation (S35) performed to calculate the margin evaluation value E1. That is, the longitudinal position and speed of each vehicle in the set vehicle convoy are the same as the longitudinal position and speed of each vehicle obtained by the merging simulation when the merging conditions are met. For example, if the designated tentative candidate is the second gap 3B (see FIG. 4), the position and speed of the vehicle convoy are set to the same as the longitudinal position and speed of the subject vehicle 1 and the group of other vehicles 2 behind it (third vehicle 2C, fourth vehicle 2D, ...) obtained by the merging simulation with the second gap 3B as the merging position when the merging conditions are met.

[0100] As described above, the merging simulation is repeated for each merging position (gap 3) while varying the model parameters of the other vehicle model formula F2 (S34 to S36). That is, there are multiple sets of position and speed data for each vehicle at the time when the merging condition is met, the number of times that variations are applied to the model parameters (the number of samplings N1). Therefore, the simulation unit 13 obtains a representative value by, for example, averaging the multiple sets of position and speed data for each vehicle generated at each merging position, and sets the representative value as the position and speed of the vehicle train.

[0101] Next, the simulation unit 13 determines the behavior of the host vehicle 1 (step S13). The behavior of the host vehicle 1 can be determined by various methods. In this embodiment, the behavior of the other vehicle 2 traveling in front of the host vehicle 1, i.e., the preceding vehicle, is simplified, and the behavior of the host vehicle 1 is determined so that the host vehicle 1 travels while maintaining a constant distance from the preceding vehicle. For example, as shown in the example of FIG. 4, if the preceding vehicle in front of the host vehicle 1 is a second vehicle 2B, the simulation unit 13 assumes that the second vehicle 2B travels at a constant speed that is the same as the speed at the time when the vehicle train was set in step S11 (in other words, when the merging possible condition is met). Then, the behavior of the host vehicle 1 is determined so that the host vehicle 1 travels at a position a predetermined distance behind the second vehicle 2B (preceding vehicle) traveling at a constant speed. The behavior of the host vehicle 1 at this time can be determined by the same proportional model as the host vehicle model formula F1 (FIG. 5) described above. In this case, the simulation unit 13 determines the behavior of the host vehicle 1 using a host vehicle model formula F1 (proportional model) in which the target speed is the same as that of the preceding vehicle traveling at a constant speed, and the target position is a position that is the above-mentioned specified distance behind the preceding vehicle.

[0102] Next, the simulation unit 13 determines the behavior of the other vehicle 2 behind the host vehicle 1, i.e., the following vehicle, through a simulation using the other vehicle model formula F2 (FIG. 5) described above (step S14). That is, the simulation unit 13 determines the behavior of each following vehicle through a simulation using the other vehicle model formula F2 representing a following traveling model, so that each following vehicle travels behind the host vehicle 1 while maintaining an appropriate inter-vehicle distance. At this time, the mode Px as shown in FIG. 11 can be used as a model parameter of the other vehicle model formula F2.

[0103] As in the case of the merging simulation (FIG. 8), it is also possible to determine the behavior of the following vehicle by repeatedly performing a simulation while varying the model parameters. In this case, the following vehicle influence degree E2 (S17), which will be described later, is calculated through appropriate processing, such as averaging the data group of the deceleration of the following vehicle obtained as a result of such repeated simulations.

[0104] The calculations for determining the behavior of the host vehicle 1 and the following vehicle in steps S13 and S14 are processes for deriving the acceleration, speed, and position of each vehicle while updating them at short time intervals, similar to the merging simulation described above (steps S42 to S46). The simulation unit 13 determines whether the time virtually progressing through such calculations, that is, the time elapsed in the simulation since the vehicle train was set in step S12, has reached a predetermined time (step S15).

[0105] If the determination in step S15 is NO and it is confirmed that the predetermined time has not elapsed, the simulation unit 13 continues the simulation of the host vehicle 1 and the following vehicle in steps S13 and S14.

[0106] On the other hand, if the determination in step S15 is YES and it is confirmed that the predetermined time has elapsed, that is, if it is confirmed that the elapsed time in the simulation from the setting of the train of vehicles in step S12 has reached the predetermined time, the simulation unit 13 extracts a following vehicle that has decelerated (step S16). Here, a following vehicle that has decelerated is a following vehicle that has decelerated by a predetermined threshold or more during the predetermined time. The threshold can be set as appropriate, but in this embodiment, it is set to 1 m / s 2 In this case, the simulation unit 13 sets the speed to 1 m / s during the predetermined time. 2 The following vehicle has decelerated by more than this, in other words -1 m / s 2 A following vehicle that accelerates as follows is extracted as a following vehicle that has decelerated.

[0107] Next, the simulation unit 13 calculates the following vehicle influence degree E2 based on the deceleration of the following vehicle extracted in the above step S16 (step S17). 2 The maximum deceleration of each of the following vehicles during the predetermined time period is calculated, and the sum of the maximum decelerations of the following vehicles is calculated as the following vehicle influence E2.

[0108] More specifically, the following vehicle influence degree E2 is a value defined by the following equation (2).

number

[0109] In the above formula (2), the velocity is 1 m / s during the above predetermined time. 2 The number of following vehicles that have decelerated by more than this, in other words -1 m / s 2 The number of following vehicles that accelerate as follows is set to n (n is an integer equal to or greater than 1). In this case, the following vehicle influence degree E2 is the minimum value Am of each acceleration of these n following vehicles. i The minimum acceleration value Am is equal to the sum of the above values ​​multiplied by -1. i The value obtained by multiplying this by -1 is synonymous with the maximum deceleration, that is, the maximum deceleration. In other words, the following vehicle influence E2 defined by the above formula (2) is calculated based on the following vehicle influence E2 when the following vehicle exceeds the threshold (1 m / s 2 ) is the sum of the maximum accelerations of the following vehicles that have decelerated by more than 100%.

[0110] FIG. 15 is a graph illustrating the acceleration data of the following vehicles obtained by the above simulation. Specifically, this graph illustrates the acceleration data for the above-mentioned predetermined time when there are three following vehicles behind the host vehicle 1. In the graph, Am1 represents the minimum value of the acceleration of the first following vehicle behind the host vehicle 1, Am2 represents the minimum value of the acceleration of the second following vehicle further behind the first following vehicle, and Am3 represents the minimum value of the acceleration of the third following vehicle further behind the second following vehicle. Of these, Am1 and Am2 represent the minimum value of the acceleration of the first following vehicle behind the host vehicle 1, Am2 represents the minimum value of the acceleration of the second following vehicle behind the second following vehicle, and Am3 represents the minimum value of the acceleration of the third following vehicle behind the second following vehicle. Of these, Am1 and Am2 are -1 m / s 2 Below, Am3 is -1m / s 2 In other words, the first and second following vehicles are moving at speeds exceeding 1 m / s 2 The third following vehicle is decelerating at 1m / s 2 Therefore, in this case, the following vehicle influence degree E2 is calculated as the sum of the minimum acceleration values ​​Am1 and Am2 of the first following vehicle and the second following vehicle multiplied by -1 (-1 × (Am1 + Am2)).

[0111] When the calculation of the following vehicle impact degree E2 is completed in the above manner, the simulation unit 13 determines whether or not the calculation of the following vehicle impact degree E2 for all tentative candidates is completed (step S18). For example, as in the example of Fig. 14, if the number of tentative candidates whose margin evaluation value E1 exceeds the reference value Ez is three, i.e., candidates 2 to 4, the simulation unit 13 determines whether or not the calculation of the following vehicle impact degree E2 for all of these three candidates 2 to 4 (S17) is completed.

[0112] If the determination in step S18 is NO and it is confirmed that there are still tentative candidates for which the following vehicle impact degree E2 has not been calculated, the simulation unit 13 designates a tentative candidate for which the following vehicle impact degree E2 should be calculated next (step S19). Then, the simulation unit 13 newly calculates the following vehicle impact degree E2 for the designated tentative candidate (S12 to S17). The simulation unit 13 repeats the designation of tentative candidates and the calculation of the following vehicle impact degree E2 until the calculation of the following vehicle impact degree E2 has been performed for all tentative candidates.

[0113] If the determination in step S18 is YES and it is confirmed that the calculation of the following vehicle impact degree E2 has been completed for all the provisional candidates, the evaluation unit 14 identifies the provisional candidate with the smallest following vehicle impact degree E2 (step S20). The provisional candidate identified here is a candidate with a small overall deceleration occurring in the following vehicle, and can be said to be the candidate with the smallest impact on the following vehicle.

[0114] Next, the route generating unit 15 determines the tentative candidate identified in step S20, that is, the merging position candidate with the smallest following vehicle influence degree E2, as the merging target for the host vehicle 1 (step S21).

[0115] (4) Effects As described above, in this embodiment, when the host vehicle 1 merges into the group of other vehicles 2, multiple candidate merging locations (e.g., gaps 3A to 3D) that are candidates for the cut-in location are evaluated through a simulation using the driving behavior model M2. Specifically, from the results of a simulation of the future behavior of the host vehicle 1 and the group of other vehicles 2, a margin evaluation value E1, which is an evaluation value (first evaluation value) regarding the success or failure of merging, and a following vehicle influence level E2, which is a different evaluation value (second evaluation value), are calculated, and the merits of the candidate merging locations are evaluated based on the calculated evaluation values ​​E1 and E2. Specifically, the candidate merging locations are first narrowed down based on the margin evaluation value E1, and then the merits of the narrowed down candidate merging locations are evaluated based on the following vehicle influence level E2. This configuration has the advantage of allowing the candidate merging locations to be evaluated appropriately in a short amount of time.

[0116] In other words, in this embodiment, evaluation of each potential merging position is performed through a simulation using a driving behavior model M2 that predicts the behavior of the vehicle 1 and the group of other vehicles 2, so there is no need to go through complex processes such as machine learning or reinforcement learning for the evaluation, and the time required for the evaluation can be shortened.

[0117] Furthermore, when evaluating merge position candidates, the merge position candidates are first narrowed down based on the margin evaluation value E1, and then the merits of the narrowed down merge position candidates are evaluated based on the following vehicle influence E2. By performing evaluation using multiple evaluation values ​​E1 and E2 in stages in this way, the calculation load can be reduced compared to calculating and comparing each evaluation value E1 and E2 for all merge position candidates, and the time required for evaluation can be effectively shortened.

[0118] In this embodiment, the following vehicle influence level E2 is a value that indicates the magnitude of the influence that the merging of the host vehicle 1 has on the following vehicle. With this configuration, a merging position candidate that will have as little influence as possible on the following vehicle can be determined as the merging target based on the following vehicle influence level E2, thereby realizing a smooth merging operation that does not disrupt traffic flow.

[0119] Specifically, the following vehicle influence level E2 is calculated for a following vehicle that has decelerated within a predetermined time (i.e., within a predetermined period) since the merging condition was met, and the greater the deceleration of the following vehicle, the greater the following vehicle's influence level E2 is calculated. With this configuration, by evaluating merging location candidates with a small following vehicle influence level E2 as being better than merging location candidates with a large following vehicle influence level E2, it is possible to select an appropriate merging location candidate that is unlikely to force following vehicles to suddenly decelerate, thereby smoothing traffic after merging.

[0120] More specifically, in this embodiment, the sum of the maximum decelerations of the following vehicles that have decelerated during the predetermined time is calculated as the following vehicle influence degree E2. With this configuration, it is possible to select merging position candidates that reduce the maximum deceleration of each following vehicle overall, thereby minimizing the influence on following vehicles.

[0121] In this embodiment, the margin evaluation value E1 is calculated based on the distribution (mean μ and standard deviation σ) of the merging margin distance Lm, which is the remaining distance to the branch line end 102a at the time when the host vehicle 1 is expected to have completed merging (when the merging condition is met). With this configuration, by narrowing down the merging position candidates to those with a relatively high margin evaluation value E1, it is possible to appropriately select a merging position candidate that allows merging with ample margin, in other words, a merging position candidate that is expected to have a sufficient probability of successful merging.

[0122] The merging margin distance Lm, which is the remaining distance to the branch line end 102a, is a value that can change continuously depending on the situation, and therefore can be used as an index to estimate how much leeway is available for merging, unlike a dichotomous evaluation value such as success / failure. Therefore, according to this embodiment, which evaluates merging location candidates using the margin evaluation value E1 based on the merging margin distance Lm, it is possible to determine not only a dichotomous judgment of success / failure, but also how much leeway is available for merging, and to appropriately select a merging location candidate that is expected to have a sufficient probability of successful merging.

[0123] In this embodiment, the driving behavior model M2 includes an other vehicle model formula F2 that predicts the behavior of the other vehicle group 2, and a host vehicle model formula F1 that predicts the behavior of the host vehicle 1. The other vehicle model formula F2 corresponds to a following driving model that outputs the acceleration required to drive while maintaining an appropriate inter-vehicle distance from the preceding vehicle, and the host vehicle model formula F1 corresponds to a proportional model that outputs acceleration proportional to the difference between a predetermined target position and a predetermined target speed. With this configuration, the future behavior of the other vehicle group 2 and the host vehicle 1 can be appropriately simulated through relatively light calculations using specified calculation formulas.

[0124] (5) Variations Although the preferred embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and various modifications are possible without departing from the spirit of the present invention.

[0125] For example, in the above embodiment, in the simulation for examining the following vehicle influence degree E2, the behavior of the host vehicle 1 at the front of the vehicle convoy was determined to be a predetermined distance behind the preceding vehicle, which was assumed to be traveling at a constant speed, but the behavior of the host vehicle 1 may be simplified further and determined to be the same constant speed as when the vehicle convoy was set up. Alternatively, the behavior of all vehicles, including the host vehicle 1 and the other vehicles 2 before and after it, may be determined using the same following traveling model as the other vehicle model formula F2.

[0126] In the above embodiment, in order to check the margin evaluation value E1, the simulation is continued until the merging conditions are met (merging is expected to be completed), and the distance from the vehicle 1 to the end of the branch line 102a at that time, in other words, the remaining distance to the end of the branch line 102a, is calculated as the merging margin distance Lm. However, the simulation may also be continued until the vehicle 1 actually enters the specified gap 3 of the other vehicle group 2 and merging is completed, and the remaining distance to the end of the branch line 102a at that time (the time of merging completion) is calculated as the merging margin distance Lm.

[0127] Furthermore, instead of the merging margin distance Lm as described above, a merging margin time, which is the remaining time until the branch line end 102a at the time when merging has been completed or is expected to be completed, may be calculated by simulation, and the merging position candidate may be evaluated based on the merging margin time. In other words, the merging margin in the present invention may be any value that indicates how much margin the host vehicle 1 has to merge, and may be a value expressed in terms of either distance or time.

[0128] Furthermore, the evaluation value (first evaluation value) for evaluating the merits and demerits of the merging position candidate may be any value related to the success or failure of merging, and is not limited to the merging margin (merging margin distance or time). For example, an evaluation value may be used that integrates multiple index values, including a first index value that indicates whether merging can be performed before the branch line end 102a, and a second index value that indicates the degree of burden on the driver, such as the smoothness of acceleration and deceleration.

[0129] In the above embodiment, the merging position candidates narrowed down based on the margin evaluation value E1 as the first evaluation value are further evaluated based on the following vehicle influence E2 as the second evaluation value, but the second evaluation value need only be different from the first evaluation value and is not limited to the following vehicle influence E2. For example, an evaluation value set so that the merging position candidate that is closer to the host vehicle 1 at the start of the simulation is given a higher evaluation may be used as the second evaluation value.

[0130] In the above embodiment, a following-traveling model (other vehicle model formula F2) is used as a model for predicting the behavior of the other vehicle 2, and a proportional model (subject vehicle model formula F1) is used as a model for predicting the behavior of the host vehicle 1, but the behavior of the host vehicle 1 and the other vehicle 2 may be predicted using the same model. For example, the behavior of both the host vehicle 1 and the other vehicle 2 may be predicted using a following-traveling model. Furthermore, since various models with a similar purpose have already been proposed as the following-traveling model (IDM), it goes without saying that a following-traveling model other than the model formula (other vehicle model formula F2) shown in FIG. 4 can be used as appropriate.

[0131] In the above embodiment, a simulation was performed while driving to predict the future behavior of the host vehicle 1 and the other vehicle group 2 in an actual merging scene in which the host vehicle 1 cuts into one of the gaps 3 in the other vehicle group 2, and the merits of the merging position candidates, which are candidates for the cut-in gap, were evaluated based on the results of the simulation, but a similar evaluation may also be performed to generate a regression model. That is, by performing a large number of simulations under various virtual initial conditions with different positions and speeds of the host vehicle 1 and the other vehicle group 2, a large number of data sets including initial conditions and evaluation values ​​(for example, merging margins) may be created as training data, and a regression model may be generated based on this training data. [Explanation of symbols]

[0132] 1. Your vehicle 2 Other vehicles (groups of other vehicles) 3. Gap 12 Recognition unit (acquisition unit, extraction unit) 13 Simulation Department 14 Evaluation Section 101 Main Line 102 branch line 102a (branch line) termination E1 Margin evaluation value E2 Influence of following vehicles M2 driving behavior model

Claims

1. A device for evaluating a merging position when a vehicle passes through a branch line and merges into a main line, an acquisition unit that acquires a traffic environment around the vehicle, including the positions and speeds of other vehicles traveling on the main road and road shapes; an extraction unit that extracts merge position candidates that are candidates for a gap to be cut in when the host vehicle cuts in to the group of other vehicles, based on the traffic environment acquired by the acquisition unit; a simulation unit that calculates a first evaluation value regarding success or failure of merging and a second evaluation value different from the first evaluation value, for each of the merging position candidates extracted by the extraction unit, by simulating future behaviors of the host vehicle and the other vehicle group using a driving behavior model that predicts behaviors of the host vehicle and the other vehicle group; an evaluation unit that narrows down the merging position candidates based on the first evaluation value calculated by the simulation unit, and evaluates the merits of the narrowed down merging position candidates based on the second evaluation value.

2. 2. The vehicle merging position evaluation device according to claim 1, the simulation unit calculates, as the first evaluation value, a margin evaluation value based on a remaining distance or remaining time to an end point of the branch line at a time when the host vehicle has completed merging or is expected to complete merging; The evaluation unit narrows down the merging position candidates extracted by the extraction unit to merging position candidates having a relatively large margin evaluation value.

3. 3. The vehicle merging position evaluation device according to claim 2, the simulation unit calculates, as the second evaluation value, a following vehicle influence degree that indicates a magnitude of an influence that the merging of the host vehicle has on a following vehicle that is another vehicle behind the host vehicle; The evaluation unit evaluates the merging position candidate with a small degree of influence of the following vehicle as being better than the merging position candidate with a large degree of influence of the following vehicle.

4. The vehicle merging position evaluation device according to any one of claims 1 to 3, The driving behavior model is a model that predicts the behavior of the group of other vehicles, and includes a following driving model that outputs the acceleration required to drive while maintaining an appropriate distance from the preceding vehicle.

5. 4. The vehicle merging position evaluation device according to claim 3, The vehicle merging position evaluation device, wherein the driving behavior model includes a proportional model that outputs acceleration proportional to the difference between a predetermined target position and a predetermined target speed as a model for predicting the behavior of the vehicle.

6. A method for evaluating a merging position when a vehicle passes through a branch line and merges into a main line, comprising: acquiring a traffic environment around the vehicle, including the positions and speeds of other vehicles traveling on the main road and road shapes; extracting, based on the acquired traffic environment, merge position candidates that are candidates for gaps into which the host vehicle will cut in when cutting into the group of other vehicles; a step of calculating a first evaluation value relating to success or failure of merging and a second evaluation value different from the first evaluation value, for each of the extracted merging position candidates, by simulating future behaviors of the host vehicle and the other vehicle group using a driving behavior model that predicts behaviors of the host vehicle and the other vehicle group; a step of narrowing down the merging position candidates based on the calculated first evaluation value, and evaluating the merits of the narrowed down merging position candidates based on the second evaluation value.

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