CN113936461A - Simulation method and system for mixed driving of vehicles at signal control intersection - Google Patents
Simulation method and system for mixed driving of vehicles at signal control intersection Download PDFInfo
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Abstract
The invention discloses a simulation method and a system for mixed driving of vehicles at a signalized intersection, wherein the simulation method comprises the following steps: establishing a simulation system containing a signal control intersection; acquiring signal control information, road information, vehicle parameters and vehicle types of the signalized intersection, and the number of vehicles and an intersection driving model corresponding to the vehicle types; configuring a vehicle queue according to vehicle types and the number of vehicles corresponding to each vehicle type, wherein the mixed vehicle queue can form at least two vehicle sequencing schemes; and operating the simulation system based on the mixed vehicle queue, and outputting the operation result of the simulation system. The method can analyze different sequencing schemes of the mixed vehicle queue and what influence can be generated on traffic flow when the quantity of vehicles of the same type is different and the positions in the queue are different, so as to provide theoretical basis for future traffic flow guidance.
Description
Technical Field
The invention belongs to the technical field of traffic simulation, and particularly relates to a simulation method and a system for mixed traveling of vehicles at a signal control intersection.
Background
With the development of vehicle intelligent technology, the mixed traveling of vehicles with different intelligent degrees can become reality, at the moment, manual driving vehicles (MV), autonomous unmanned vehicles (AV), internet-connected manual driving vehicles (CV) and even internet-connected unmanned vehicles (CAV) exist on the road, but no actual mixed traveling scene exists at present, so that the influence on the traffic flow at the signal control intersection can not be generated when the vehicles with different intelligent degrees are mixed traveling.
In order to solve the problems, most of the existing technologies utilize a microscopic traffic simulation platform to construct a simulated traffic scene, and the influence of the mixed running of different types of intelligent vehicles and manually-driven vehicles on the traffic flow is researched by specifying the types of the intelligent-driven vehicles and the permeability of various types of intelligent-driven vehicles in the simulated scene, namely the proportion of the number of the various types of intelligent-driven vehicles to the total number of the vehicles in the simulated scene, so that the method can provide a simulation research basis for the road-going of the intelligent vehicles and make up the defect that the existing traffic cannot realize the mixed running of large-scale intelligent vehicles and manually-driven vehicles, but the method can only macroscopically research the influence of the permeability of the various types of intelligent vehicles on the traffic flow and cannot microscopically analyze the influence of different ranks of vehicle ranking queues and the influence of the traffic flow when the same type of vehicles is positioned at different positions in the queues, therefore, it is necessary to develop a simulation system capable of microscopically analyzing different ranks of the vehicle rank queue and positive or negative influences on traffic flow when the same type of vehicle is located at different positions in the queue, so as to provide a theoretical basis for future traffic flow guidance.
Disclosure of Invention
Aiming at the problems, the invention designs a simulation method and a simulation system for mixed driving of vehicles at a signal control intersection.
In a first aspect, the invention provides a simulation method for mixed traveling of vehicles at a signalized intersection, which comprises the following steps:
establishing a simulation system containing a signal control intersection;
acquiring signal control information, road information, vehicle parameters and vehicle types of the signalized intersection, and the number of vehicles and intersection driving models corresponding to the vehicle types;
configuring a mixed vehicle queue according to vehicle types and the number of vehicles corresponding to each vehicle type, wherein the mixed vehicle queue can form at least two vehicle sequencing schemes;
and operating the simulation system based on the mixed vehicle queue, and outputting the operation result of the simulation system.
Preferably, the information acquisition includes reading, receiving or inputting through a human-computer interaction device. The simulation system sets various different modes for acquiring information, and a worker can select a proper acquisition mode according to actual conditions, so that the flexibility of the simulation system is improved.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: the method can analyze different sorting schemes of the mixed vehicle queue and the influence on the traffic flow when the quantity of vehicles of the same type is different and the positions in the queue are different according to the operation result of the simulation system, further, the vehicle queue meeting the preset condition can be screened out, if the vehicle queue meeting the preset condition can have positive influence on the traffic flow, a theoretical basis is provided for future traffic flow guidance, the mixed vehicles can be guided to form the vehicle queue at the intersection in the real traffic flow, more scientific traffic management rules are conveniently formulated, the waiting time of the vehicles passing through the intersection is reduced, the traffic volume at the intersection is increased, traffic jam is avoided, and traffic accidents are caused.
Preferably, configuring the mixed vehicle queue according to the vehicle types and the number of vehicles corresponding to each vehicle type includes: acquiring the number of vehicle types and the number of vehicles corresponding to each vehicle type, and designating target vehicles, wherein the number of the vehicle types is not less than two; the method comprises the steps of obtaining or configuring the number m of vehicles, the number n of target vehicles and a target vehicle sequencing rule in a mixed vehicle queue, and enabling the target vehicles and other vehicles to form the mixed vehicle queue based on the target vehicle sequencing rule.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: each vehicle queue entering the simulation system conforms to a target vehicle sequencing rule, the target vehicle sequencing rule can be manually formulated, summarized after actual investigation and fed back by a user or a target user, and under the guidance of the target vehicle sequencing rule, an optimal vehicle queue or vehicle queues can be found out by analyzing and researching the running results of the mixed vehicle queues under different target vehicle sequencing rules.
Preferably, the obtaining or configuring the number m of vehicles, the number n of target vehicles, and the target vehicle ranking rule in the vehicle queue, and forming the target vehicle and other vehicles into a mixed vehicle queue based on the target vehicle ranking rule, includes: and arranging the target vehicles in the vehicle queue, screening one or more mixed vehicle queues meeting the target vehicle sequencing rule, and identifying the simulation positions of the target vehicles in the mixed vehicle queues.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: the simulation system can accurately identify the simulation position of the target vehicle in the mixed vehicle queue, is beneficial to researching what kind of influence can be generated on traffic flow when the same type of vehicle is positioned at different positions in the queue, and compares and analyzes the position of the target vehicle in the mixed vehicle queue, and the influence can be generated on the traffic flow in a positive or negative mode.
Preferably, the simulated location of the target vehicle in the vehicle simulation fleet may be identified based on the vehicle type or vehicle parameters or the intersection driving model.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: the simulation position of the target vehicle in the vehicle simulation queue can be identified based on various ways and modes, so that the flexibility of the application of the simulation system is improved; in the system establishing or learning stage, mutual verification can be performed through different identification ways and modes, for example, the simulation position of the target vehicle identified by the vehicle type and the simulation position of the target vehicle identified by the vehicle parameter can be verified, and whether the identification results of the two identification modes are consistent or not is judged, so that the accuracy of the simulation system in identifying the simulation position of the target vehicle is improved.
Preferably, the signal control information includes a signal control mode, a phase design scheme, a phase sequence, time duration of each phase, cycle time duration, full red setting, and the like;
the road information includes the shape and size of the signalized intersection, road class, number of lanes, lane width, lane function, lane number.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: the detailed signal control information and the road information are set for the signal control intersection, so that the scene setting of the signal control intersection is more detailed, the scene setting of the signal control intersection is closer to or even consistent with the scene of the real road intersection, and the accuracy and the practicability of the simulation environment are improved.
Preferably, the vehicle type comprises one or more of a manually driven vehicle, a networked manually driven vehicle, an autonomous unmanned vehicle, and a networked unmanned vehicle, and the vehicle parameters comprise deterministic parameters and stochastic parameters.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: according to the preferred technical scheme, a plurality of vehicle types with different intelligent degrees are set, the vehicles are not roughly classified into unmanned vehicles and manned vehicles, but the unmanned vehicles and the manned vehicles are further classified, more vehicle arrangement modes and a possibility scheme that more vehicles are mixed can be obtained, more sample data can be obtained, vehicle queues in actual traffic flow are simulated, and the operation result of the simulation system has realistic reference and research value.
Preferably, the certainty parameters include vehicle length, vehicle width, maximum acceleration, desired deceleration, desired headway, road speed limit; and/or the randomness parameters comprise the distance from the head car to the stopping line after the head car stops, the maximum driving speed, the reaction time of the driver and the minimum distance between cars.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: dividing vehicle parameters into a deterministic parameter and a stochastic parameter, wherein the deterministic parameter can be set by investigating representative road intersections of corresponding places according to regions to which simulation faces; the value of the randomness parameter has uncertainty, so the randomness parameter can be used as a check parameter of the simulation system, the sampling distribution type, the mean value and the standard deviation of the randomness parameter are set in the simulation system, the simulation system is operated, the value distribution condition of the randomness parameter of one vehicle is randomly selected from a mixed vehicle queue obtained by simulation and compared with the corresponding actual investigation condition, and then whether the sampling distribution type, the mean value and the standard deviation of the randomness parameter in the simulation system are set to follow the actual investigation data or not is judged, and the reasonability and the accuracy of the operation result of the simulation system are improved.
Preferably, when the simulation system is operated based on the hybrid vehicle queue, the manually driven vehicle, the internet-connected manually driven vehicle, the autonomous unmanned vehicle and the internet-connected unmanned vehicle respectively adopt different intersection driving models, and preferably, the intersection driving model adopts a following model.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: the following model is used for researching the reaction of a rear vehicle caused by the speed change of a front vehicle in a running vehicle fleet by using a dynamic method, different following models are set for different vehicle types according to the preferable technical scheme, and parameters such as speed, acceleration, position and the like of the vehicles in the mixed vehicle queue at any moment can be obtained by solving the corresponding following equation through the operation result of the simulation system.
In a second aspect, the present invention provides a simulation system for vehicle mixed-traveling at a signalized intersection, comprising:
the communication module is configured for acquiring signal control information, road information, vehicle parameters and vehicle types of the signal control intersection, and the number of vehicles and intersection driving models corresponding to the vehicle types;
the simulation system establishing module is configured for establishing a simulation system containing the signal control intersection;
the vehicle queue module is configured for configuring a mixed vehicle queue according to vehicle types and the number of vehicles corresponding to each vehicle type, wherein the mixed vehicle queue can at least form two vehicle sequencing schemes;
and the simulation operation module is configured for operating the simulation system based on the mixed vehicle queue and outputting the operation result of the simulation system.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: the simulation system comprises a plurality of modules which supplement each other and cooperate together, so that the normal work of the whole simulation system is ensured. The method can analyze different sorting schemes of the mixed vehicle queue and the influence on the traffic flow when the quantity of vehicles of the same type is different and the positions in the queue are different according to the operation result of the simulation system, further, the vehicle queue meeting the preset condition can be screened out, if the vehicle queue meeting the preset condition can have positive influence on the traffic flow, a theoretical basis is provided for future traffic flow guidance, the mixed vehicles can be guided to form the vehicle queue at the intersection in the real traffic flow, more scientific traffic management rules are conveniently formulated, the waiting time of the vehicles passing through the intersection is reduced, the traffic volume at the intersection is increased, traffic jam is avoided, and traffic accidents are caused.
Preferably, the communication module is further configured to,
the method comprises the steps of obtaining the number of vehicle types and the number of vehicles corresponding to each vehicle type, and obtaining or configuring the number m of vehicles, the number n of target vehicles and a target vehicle sequencing rule in a vehicle queue.
A vehicle queue module comprising:
a specifying unit configured to specify a target vehicle;
and the queue composition unit is configured for composing the target vehicle and other vehicles into a mixed vehicle queue based on the target vehicle sequencing rule.
Adopt above-mentioned preferred technical scheme's beneficial effect to lie in: the simulation system can directionally analyze and research the operation results of the vehicle queues under different target vehicle sequencing rules by designating the target vehicles and forming the hybrid vehicle queues based on the target vehicle sequencing rules, and find out the optimal hybrid vehicle queue or queues.
The beneficial effect of this preferred scheme does: (1) according to the intersection vehicle simulation method provided by the invention, the target vehicle and other vehicles form the mixed vehicle queue by configuring the number m of the vehicles in the vehicle queue, the number n of the target vehicles and the sequencing rule of the target vehicles, and the simulation position of the target vehicle in the mixed vehicle queue can be identified, so that the influence on the traffic flow at the signal control intersection can be analyzed when the same type of vehicle is different in position and number in the mixed vehicle queue.
(2) The invention can judge the vehicle queue meeting the preset condition according to the operation result of the simulation system, and can guide the mixed vehicles to form the sorting mode in the real traffic flow, thereby reducing the waiting time and the passing time of the vehicles at the intersection, increasing the passing amount at the intersection, avoiding traffic jam and causing traffic accidents, and providing a theoretical basis for future traffic flow guidance.
Drawings
FIG. 1 is a flow chart of a method for simulating mixed traveling of vehicles at a signalized intersection according to the present invention;
FIG. 2 is a schematic diagram of a location where a mixed vehicle queue includes 1 AV according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the location of a mixed vehicle queue including 2 adjacent AV's according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a mixed vehicle queue including 2 discrete AV's, one of the AV's being at the head of the queue according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a mixed vehicle queue including 2 discrete AV's, and 2 AV's are not at the head of the queue according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a location where a mixed vehicle queue includes multiple AV's according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a hybrid vehicle queue including 1 AV and 1 CAV adjacent to each other and the position of the CAV following the AV according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a mixed vehicle queue including 1 AV and 1 CAV adjacent to each other and the position of the AV following the CAV according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a mixed-traveling vehicle queue including 1 AV and 1 CAV, and AV vehicles located at the head of the queue according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of a mixed-traveling vehicle queue including 1 AV and 1 CAV in a dispersed manner, with the CAV vehicles being located at the head of the queue according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a mixed-traveling vehicle queue including 1 AV and 1 CAV, both of which are not at the head of the queue, according to an embodiment of the present invention;
FIG. 12 is a schematic diagram of a position of a mixed vehicle queue including a plurality of AV and CAVs according to an embodiment of the present invention;
FIG. 13 is a line graph of the traffic capacity of the approach lane with a target vehicle at various locations in the queue according to one embodiment of the invention;
FIG. 14 is a line graph of the traffic capacity of the approach lane for different numbers of target vehicles in the queue in accordance with one embodiment of the present invention;
FIG. 15 is a line graph of the traffic capacity of the approach lane when the target vehicles in the fleet are of different vehicle types in accordance with one embodiment of the present invention;
FIG. 16 is a line graph of the traffic capacity of the entrance lane when the vehicles of the 2 target vehicles in the fleet are of the same type and in different sequencing plans, in accordance with an embodiment of the present invention;
fig. 17 is a line graph of the entrance lane trafficability when the vehicle types of the 2 target vehicles in the queue are different and the ordering scheme is different in one embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention.
Example 1
The invention provides a signal control intersection vehicle mixed-driving simulation system, which comprises:
a communication module configured to:
and acquiring signal control information, road information, vehicle parameters and vehicle types of the signalized intersection, and the number of vehicles and intersection driving models corresponding to the vehicle types. The signal control information comprises a signal control mode, a phase design scheme, a phase sequence, time length of each phase, period time length and full red setting; the road information comprises the shape and size of an intersection, the road grade, the number of lanes, the lane width, the lane function and the lane number; the vehicle type comprises one or more of a manually driven vehicle, an autonomous unmanned vehicle, a networked unmanned vehicle and a networked manually driven vehicle; the vehicle parameters include a deterministic parameter and a stochastic parameter. Alternatively, the certainty parameters include vehicle length, vehicle width, maximum acceleration, desired deceleration, desired headway, road speed limit; the randomness parameters comprise the distance from the parking line to the parking line after the head car is parked, the maximum driving speed, the response time of a driver and the minimum distance between cars.
Acquiring the number of vehicle types and the number of vehicles corresponding to each vehicle type, and specifying a target vehicle; and acquiring or configuring the number m of vehicles in the vehicle queue, the number n of target vehicles and a target vehicle sequencing rule.
The simulation system establishing module is configured for establishing a simulation system containing the signalized intersection;
the vehicle queue module is configured to configure a mixed vehicle queue according to vehicle types and the number of vehicles corresponding to each vehicle type, wherein the mixed vehicle queue can form at least two vehicle sequencing schemes; specifically, the vehicle queue module includes: a specifying unit that specifies a target vehicle; the queue composition unit is configured for composing a mixed vehicle queue by the target vehicle and other vehicles based on the target vehicle sequencing rule; and the screening and identifying unit is configured to screen out one or more mixed vehicle queues meeting the target vehicle sequencing rule and identify the simulation positions of the target vehicles in the mixed vehicle queues.
And the simulation operation module is configured for operating the simulation system based on the vehicle queue and outputting an operation result of the simulation system.
The acquisition of the information of the embodiment comprises reading, receiving or inputting through a man-machine interaction device and the like. The simulation system sets various different modes for acquiring information, and a worker can select a proper acquisition way according to actual conditions, so that the flexibility of the simulation system is improved.
The invention provides a simulation method of a vehicle mixed-driving simulation system for controlling an intersection by applying the signal, which comprises the following steps:
The signal control information comprises a signal control mode, a phase design scheme, a phase sequence, each phase time length, a period time length and full red setting; the road information includes intersection shape and size, road grade, number of lanes, lane width, lane function, lane number. One or more signally controlled intersections can be set according to actual simulation requirements.
Before the simulation system is established, traffic flow investigation of representative road intersections at corresponding places can be carried out according to regions or cities to which simulation researches are directed, and signal control information, road information and traffic flow information of the representative road intersections are obtained.
For example, in this embodiment, an actual survey is performed on a signal control intersection at an intersection between a release road and a street, and the signal control information, the road information, and the traffic flow information are obtained as follows:
regarding the signal control information: the signal control intersection signal lamp cycle is 190 seconds. Wherein, the south-north straight-going phase is 47 seconds for green light and 3 seconds for yellow light; the south-north left-turn phase, the green light 31 seconds, the yellow light 3 seconds, and the full red 3 seconds; east-west straight phase, green 73 seconds, yellow 3 seconds; east-west left-hand phase, green 25 seconds, yellow 3 seconds. The phase sequence is: south-north straight going, south-north left turning, east-west straight going, east-west left turning.
Regarding the road information: the south-north direction of the signal control intersection is a unidirectional 3-lane, wherein one left-turn entrance lane, one straight-going entrance lane and one straight-right mixed-going entrance lane are arranged; the east-west direction is a unidirectional 4 lanes, wherein two left-turn entrance lanes, one straight entrance lane and one straight right-mixed entrance lane are 3 meters in width.
Information on traffic flow: and (3) surveying the actual traffic flow of the signal control intersection by using an unmanned aerial vehicle, and obtaining the queuing length of each entrance lane, the time of each vehicle in the queue passing through the entrance lane stop line after the green light is turned on, the distance from the queue head vehicle to the stop line when the vehicle stops, and the speed and track information of the vehicle when the vehicle passes through the intersection. The number of investigation samples per entrance lane is not less than 100 vehicle queues, and the investigation period includes peak and flat peaks.
The vehicle parameter obtaining mode includes reading, receiving or man-machine interaction device input, and in this embodiment, 3 vehicle types, MV, AV, and CAV, are used for description.
And 2-1, determining parameters comprise maximum acceleration, expected deceleration, vehicle length, vehicle width, expected headway and road speed limit.
The deterministic parameters of a certain vehicle type are determined generally, so that the deterministic parameters are assigned to a certain position directly after the vehicle type of the position in a vehicle queue is determined, and the deterministic parameters of the MV vehicles are obtained by analyzing actual traffic flow survey data of a liberal road and a street signal control intersection in the Changchun city. Compared with the MV, the AV and the CAV sense the environmental information through the sensor, the central processing unit processes the sensing information to obtain the behavior decision, and the decision and the vehicle control are implemented through the line control system, so the AV and the CAV have quicker response time and more accurate action decision, and the deterministic parameter value of the expected headway time of the AV and the CAV is smaller than the MV when the simulation system is established.
TABLE 1 deterministic parameter values
| Type of vehicle | MV | AV | CAV |
| Maximum acceleration | 2.5 | 2.5 | 2.5 |
| Desired |
4 | 4 | 4 |
| Length of |
4 | 4 | 4 |
| Width of |
2 | 2 | 2 |
| Desired headway | 1.55 | 1.1 | 0.6 |
| Road speed limit | 60 | 60 | 60 |
| Following model | IDM | ACC | CACC |
Step 2-2, the randomness parameters comprise: the distance from the head car to the stop line after stopping, the minimum distance between cars, the reaction time of the driver and the maximum driving speed.
Because of uncertainty of the randomness parameters, the value of the randomness parameters is set to be in accordance with normal distribution, the value range of the randomness parameters can be set according to literature search and vehicle function analysis, the normal distribution in accordance with the randomness parameters can also be obtained through practical investigation of representative road intersections in corresponding areas and summary calculation, preferably, in the embodiment, the time interval of a green light of a liberal road in the Changchun city and a concordant street signal control intersection for queuing vehicles to pass through a stop line after the green light of each entrance road is lightened, the running speed and track of the vehicles when the vehicles pass through the intersection, and the time of the first vehicle to pass through the stop line after the green light is lightened are analyzed to obtain the distance of the stop line after the first vehicle is stopped, the minimum inter-vehicle distance (the time interval of the queuing vehicles to pass through the stop line), the response time of drivers (the time interval of the green light to the first vehicle in the queue of the queuing vehicles) and the time of the drivers, Distribution, mean value and standard deviation of four random parameters of the maximum running speed.
And operating the simulation system after the randomness parameters are set in the simulation system. In this case, all the vehicles in the simulation system are MV vehicles. And analyzing the simulation result to obtain the headway distribution of each vehicle in the queue, comparing the headway distribution obtained by actual investigation of the corresponding position with the headway distribution obtained by simulation, and performing micro-adjustment on the randomness parameter setting in the simulation system according to the comparison result to ensure that the deviation of the simulation data and the actual investigation data is not higher than 5%. The value range of the finally obtained MV randomness parameter is shown in Table 2.
Setting the initialization values of the random parameters of MV, AV and CAV, in this embodiment, the values of the random parameters of MV, AV and CAV are shown in table 2:
TABLE 2 randomness parameter values
| Type of vehicle | MV | AV | CAV |
| Distance to stop line after parking of head car | N(4，0.5) | N(4，0.05) | N(4.5，0.03) |
| Minimum distance between cars | N(2.5，0.5) | N(1，0.05) | N(1，0.03) |
| Maximum driving speed | N(55.5，6) | N(55.5，6) | N(55.5，6) |
| Driver reaction time | N(4.2，0.5) | N(3，0.3) | N(2.8，0.1) |
The value ranges of the distance from a head car to a stopping line after the head car stops and the minimum distance between the cars are as follows: since the MV is manually driven and the control of the distance depends on the perception and experience of the driver, the numerical fluctuation is large. AV relies on sensor perception and system calculation analysis, so the control on distance is better than MV, and the numerical fluctuation is smaller than MV. CAV has a positioning function besides a sensor sensing function, so that CAV can control the distance more accurately, and the numerical fluctuation is minimum compared with MV and AV.
The value range of the maximum driving speed is as follows: the maximum driving speed values of the AV and CAV vehicles are set to be in accordance with the same normal distribution as that of the MV vehicle.
And thirdly, the value range of the reaction time of the driver: the reaction time here refers specifically to the reaction time of the vehicle at the head of the vehicle queue, which is closest to the signal light. The reaction time of the MV is the sum of the perception time and the execution time of the driver to the signal change, the reaction time of the AV is originated from the calculation error of the system, and the reaction time of the CAV is originated from the communication delay and the positioning error. Therefore, the average value and standard deviation of the AV reaction time are set to be smaller than those of MV, and the average value and standard deviation of the CAV reaction time are set to be smaller than those of AV.
And 2, step 3, as an alternative, setting different intersection driving models for different vehicle types, specifically:
I. the MV adopts an IDM following model and has an expression of
Wherein a is the acceleration of the vehicle, v is the current speed of the vehicle, s is the actual distance between the vehicle and the vehicle, v0Is the desired speed of the vehicle, Δ v is the speed difference between the preceding vehicle and the vehicle, amaxB is the maximum acceleration of the self vehicle, b is the expected deceleration of the self vehicle, T is the expected headway time of the front vehicle and the self vehicle, delta is an acceleration index, the value is generally 4, s' is the expected minimum distance between the front vehicle and the self vehicle, and s0The minimum parking distance between the front vehicle and the self vehicle.
II. The AV adopts an ACC following model, and the expression is as follows:
ai，k＝k1ei，k+k2(vi-1，k-1-vi，k-1) (formula 3)
ei，k＝xi-1，k-1-xi，k-1-d0-Tvi，k-1(formula 4)
In the formula ai，kAcceleration at time k, of the vehicle1Control coefficient, k, for AV gap2For AV speed control coefficient, ei，kA distance error between an actual distance between the own vehicle i and the preceding vehicle i-1 at the time k and a desired distance vi-1，k-1Speed of the preceding vehicle at time k-1, vi，k-1The speed of the vehicle at the time k-1, d0Is the clearance margin between the front vehicle and the self vehicle, T is the expected head time distance between the front vehicle and the self vehicle, xi-1，k-1Front vehicle position at time k-1, xi，k-1The position of the own vehicle at the moment k-1.
The ACC following model is divided into three modes:
a cruise mode: when the preceding vehicle does not exist or the distance between the preceding vehicle and the own vehicle exceeds 120m, the influence of the preceding vehicle on the own vehicle is almost negligible, and the own vehicle runs at a desired speed. The ACC following model can be simplified to:
ai，k＝k0(v0-vi，k-1) (formula 5)
In the formula v0To the desired speed of the vehicle, k0The value range of the speed control coefficient is 0.3-0.4.
Approach mode: the self-vehicle gradually approaches to the front vehicle from a far distance, when the distance between the two vehicles is (2-1.5 times of the expected distance, 120m), the self-vehicle is converted from a free running state to a following state, and the ACC following model is as follows:
ai，k＝k1ei，k+k2(vi-1，k-1-vi，k-1) (formula 6)
Clearance threshold d between two vehicles0Comprises the following steps:
k1the values of (A) are as follows: 0.04; k is a radical of2The values of (A) are as follows: 0.8.
③ following mode: when the distance between the self vehicle and the front vehicle is (0, 2-1.5 times of the expected distance), the speed error between the two vehicles fluctuates around 0, and the self vehicle is in a stable following state.
At this time, the AV gap control coefficient k1The values of (A) are as follows: 0.23; AV speed control coefficient k2The values of (A) are as follows: 0.07.
III, CAV adopts CACC following model, and the expression is as follows:
ei，k＝xi-1，k-1-xi，k-1-d0-Tvi，k-1(formula 8)
In the formula kpFor CAV gap control coefficient, kdFor CAV speed control coefficient, ei，kThe distance error between the actual distance between the self vehicle i and the front vehicle i-1 and the expected distance at the moment k,
is the derivative of the spacing error at time k-1, vi-1，k-1The speed of the vehicle ahead at time k-1, d0Is the clearance margin between the front vehicle and the self vehicle, T is the expected headway time of the front vehicle and the self vehicle, vi，k-1The speed of the bicycle at time k-1, xi-1，k-1The position of the preceding vehicle at the time k-1, xi，k-1The position of the own vehicle at the moment k-1.
The CACC follow model is divided into three modes:
a cruise mode: when the front vehicle does not exist or the distance between the two vehicles exceeds 300m, the influence of the front vehicle on the self vehicle can be almost ignored, and the self vehicle runs at the expected speed. At this time, the CACC following model can be simplified as follows:
ai，k＝k0(v0-vi，k-1) (formula 10)
In the formula v0To the desired speed of the vehicle, k0The value range of the speed control coefficient is as follows: 0.3 to 0.4.
Approach mode: when the self-vehicle gradually approaches the front vehicle from a distance, and the distance between the two vehicles is [ 2-1.5 times of the expected distance, 300m), the self-vehicle is converted from a free running state to a following state, and the CACC following model at the moment is as follows:
wherein
At this time, the CAV gap control coefficient kpThe values of (A) are as follows: 0.01; CAV speed control coefficient kdThe values of (A) are as follows: 1.6.
③ following mode: when the distance between the front vehicle and the self vehicle is (0, 2-1.5 times of the expected distance), the speed error between the two vehicles fluctuates around 0, and the self vehicle is in a stable following state.
At this time, the CAV gap control coefficient kpThe values of (A) are as follows: 0.45 of; CAV speed control systemNumber kdThe value range is as follows: 0.0125 to 0.075, with kdThe larger the value of (a), the more remarkable the initial fluctuation of the follow-up model.
The data required by the following model can be read by a third-party system when a simulation system is established, or a database is pre-established in advance, or the data can be directly assigned manually.
And 3, configuring a mixed vehicle queue according to the vehicle types and the vehicle quantity corresponding to each vehicle type, wherein the mixed vehicle queue can form at least two vehicle sequencing schemes. At this time, although the mixed vehicle queue is configured according to the vehicle types and the number of vehicles corresponding to each vehicle type, it cannot be analyzed which vehicle types and the number of vehicles corresponding to each type are specific in the mixed vehicle queue.
Therefore, as a preferable scheme, the method comprises the following steps according to the vehicle types and the number of vehicles corresponding to each vehicle type:
step 3-1, acquiring the number of vehicle types and the number of vehicles corresponding to each vehicle type and designating target vehicles;
3-2, acquiring or configuring the number m of vehicles, the number n of target vehicles and a target vehicle sequencing rule in the vehicle queue, and forming a mixed vehicle queue by the target vehicles and other vehicles based on the target vehicle sequencing rule;
although the above preferred scheme obtains the number of vehicle types in the mixed vehicle queue and the number of vehicles corresponding to each type of vehicle, further specifies the target vehicle, and forms the mixed vehicle queue based on the target vehicle sequencing rule, the scheme cannot accurately identify the simulation position of each target vehicle in the mixed vehicle queue. Therefore, the present embodiment adopts a more preferable scheme,
and arranging the target vehicles in the mixed vehicle queue, screening one or more mixed vehicle queues meeting the target vehicle sequencing rule, and identifying the simulation positions of the target vehicles in the mixed vehicle queues. The identification mode can be based on the vehicle type or the vehicle parameters or the intersection driving model, namely the vehicle type and the vehicle parameters after being assigned to a certain position are identified, the intersection driving model is matched with the vehicle type and the vehicle parameters of the target vehicle, and the intersection driving model is marked in an identifiable mode, and the position information of the target vehicle and the vehicle queue where the target vehicle is located can be associated and stored for convenient research.
How the hybrid vehicle queue is configured is described in detail by way of example as follows: for convenience of description, the present embodiment designates the target vehicle as AV, CAV vehicle; the total number of vehicles in the vehicle queue is m, which is 10. The limitation of the embodiment on the type of the vehicle and the total number of the vehicles is not unique, and the limitation can be adjusted and set according to the actual requirements of the simulation.
In the first case: obtaining the number of vehicle types as 2, wherein the number is MV and AV/CAV; the target vehicle sequencing rule is as follows: one of the two vehicle types, AV/CAV, is mixed with MV (hereinafter, AV is taken as an example, CAV is the same as AV, and will not be described in detail).
Case 1: as shown in fig. 2, taking the AV with only 1 vehicle as an example, when the number m of vehicles is 10 and the number n of target vehicles is 1, in this case, the vehicle queue may constitute 10 kinds of vehicle ranks, and the simulated positions of the AV vehicle in the vehicle queue may be at multiple positions of the 1 st (i.e. the top), 2 nd, 3 rd, etc. of the vehicle fleet.
If the target vehicle sequencing rule is as follows: the target vehicle is positioned at the head of the fleet of vehicles, and the simulation position of the target vehicle in the vehicle simulation queue is identified as the position closest to the signal lamp; if the target vehicle sequencing rule is as follows: the target vehicle is positioned at the non-top-ranking position of the fleet, and the simulation position of the target vehicle in the identification vehicle simulation queue is the position of 2-10 of the distance signal lamp.
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output. As an alternative, the operation result of the simulation system includes macroscopic traffic flow data and microscopic vehicle data, wherein the macroscopic traffic flow data includes hourly traffic flow of a specified entrance lane of the signal control intersection, saturated headway of queued vehicles, queuing position reaching the saturated headway, start loss time of queued vehicles at the intersection, and the microscopic vehicle data includes movement position, speed, vehicle number, headway, and the like of the vehicles at each moment. The preset conditions can be set according to data output by the system, workers can analyze different sorting schemes of the mixed vehicle queue and what influence can be generated on traffic flow when the quantity of vehicles of the same type is different and the vehicles are positioned in different positions in the queue according to the operation result of the simulation system, further, the vehicle queue meeting the preset conditions can be screened out, and if the vehicle queue meeting the preset conditions can generate positive influence on the traffic flow, vehicles can be guided to form the vehicle queue in real traffic in the future. For example, a traffic flow survey of a representative road intersection can be performed in an area targeted by a simulation study, and then a survey result is analyzed to obtain actual macro traffic flow data and micro vehicle data corresponding to a surveyed signalized intersection, specifically, in this embodiment, the actual survey is performed on a signalized intersection at an intersection of a liberal road and a highway in Changchun city, the traffic volume of a specified entrance lane of the signalized intersection is selected as a preset condition, if the hourly traffic flow of the specified entrance lane of the signalized intersection in the actual data is X, the preset condition is set such that the hourly traffic flow of the specified entrance lane of the signalized intersection is greater than X, then the simulated macro traffic flow data and the micro vehicle data in the operation result of the simulation system are compared with the actual data, and a vehicle queue with the hourly traffic flow of the specified entrance lane of the signalized intersection being greater than X is screened out, then, the simulation position of the target vehicle in the vehicle simulation queue is identified based on the vehicle type or the vehicle parameter or the intersection driving model, and the mixed-traveling vehicles can be guided in the real traffic flow to form the sequencing mode at the intersection, so that more scientific traffic management rules can be conveniently formulated, the waiting time of the vehicles passing through the intersection is reduced, the traffic volume at the intersection is increased, and traffic jam and traffic accidents are avoided. In practical application, of course, the staff may also output other data according to the research needs, which is not the technical solution of the present application and is not described in detail again.
In this embodiment, for the following cases and situations, although the number of target vehicles is increased, the corresponding calculation amount is also only increased, and the internal logic is not changed, so how to find out the qualified fleet sequence according to the operation result is not described in each of the following cases.
Case 2: as shown in fig. 3-5, taking 2 vehicles as an example, when the number m of vehicles is 10 and the number n of target vehicles is 2, the vehicle queue may form 45 vehicle orderings, and the simulated positions of the 2 AV vehicles in the vehicle queue may be adjacent or dispersed.
As shown in fig. 3, if the target vehicle sorting rule is that the simulated positions of 2 AV vehicles are adjacent, when the target vehicle is located at the platoon head, it is recognized that the simulated position of the target vehicle in the vehicle simulation queue is 1-2 positions of the distance signal lamp; when the target vehicle is located at the non-top position of the fleet, the simulation position of the target vehicle in the vehicle simulation queue is recognized to be 2-10 positions of the distance signal lamp.
As shown in fig. 4-5, if the target vehicle sorting rule is that the simulated positions of 2 AV vehicles are scattered, when the target vehicle is located at the platoon head, it is recognized that the simulated positions of the target vehicle in the vehicle simulation queue are 1, 3-10 positions of the distance signal lamp; when the target vehicle is positioned at the non-top position of the fleet, the simulation position of the target vehicle in the vehicle simulation queue is recognized to be 2-10 positions away from the signal lamp (and the middle of 2 AV vehicles are separated by a plurality of positions).
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output.
Case 3: taking AV as an example, when the number of vehicles m is 10 and the number of target vehicles n is 4, the vehicle queue may form 210 vehicle ranks, and the simulated positions of the 4 AV vehicles in the vehicle queue may be adjacent or dispersed.
If the target vehicle sequencing rule is that the simulation positions of 4 AV vehicles are adjacent, when the target vehicle is positioned at the head of a fleet of vehicles, recognizing that the simulation position of the target vehicle in the vehicle simulation queue is 1-4 positions of the distance signal lamp; when the target vehicle is located at the non-top position of the fleet, the simulation position of the target vehicle in the vehicle simulation queue is recognized to be 2-10 positions of the distance signal lamp.
If the target vehicle sequencing rule is that the simulation positions of 4 AV vehicles are scattered, when the target vehicle is positioned at the head of a fleet of vehicles, recognizing that the simulation positions of the target vehicle in a vehicle simulation queue are 1, 3-10 positions of the distance signal lamp; when the target vehicle is positioned at the non-top position of the fleet, the simulation position of the target vehicle in the vehicle simulation queue is identified to be 2-10 positions away from the signal lamp (at this time, 4 AV vehicles may be dispersed completely, may be dispersed two by two, and may also be dispersed one by three).
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output.
Case 4: as shown in fig. 6, when there are X AV vehicles, the number of vehicles m is 10, and the number of target vehicles n is X, and these AV vehicles appear in the mixed vehicle queue in order from the top of the vehicle queue, may be adjacent to each other, and may be dispersed.
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output.
In the second case: the number of the obtained vehicle types is 3, the obtained vehicle types are respectively MV, AV and CAV vehicles, and the target vehicle sequencing rule is as follows: the AV and CAV vehicle models are mixed with the MV at the same time.
Case 1: as shown in fig. 7-11, taking 1 AV, 1 CAV as an example, the number of vehicles m is 10, and the number of target vehicles n is 2, in this case, the vehicle queue may constitute 90 kinds of vehicle ranks, and the simulated positions of these 2 vehicles in the vehicle queue may be adjacent or dispersed.
As shown in fig. 7-8, if the target vehicle ordering rule is that the simulation positions of AV and CAV are adjacent, when the target vehicle is at the platoon head, it is recognized that the simulation position of the target vehicle in the vehicle simulation queue is 1-2 positions away from the signal lamp (at this time, the AV vehicle may be at the head of the platoon head, or the CAV vehicle may be at the head of the platoon head); when the target vehicle is located at the non-fleet top position, the simulation position of the target vehicle in the vehicle simulation queue is recognized to be 2-10 positions away from the signal lamp (in this case, the AV can follow the CAV, and the CAV can follow the AV).
As shown in fig. 9-11, if the target vehicle sorting rule is that the simulated positions of AV and CAV are scattered, when the target vehicle is at the platoon head, it is recognized that the simulated position of the target vehicle in the vehicle simulation queue is 1, 3-10 positions of the distance signal lamp (possibly, the AV vehicle is at the head of the platoon head, and possibly, the CAV vehicle is at the head of the platoon head); when the target vehicle is at the non-fleet top position, the simulated position of the target vehicle in the vehicle simulation queue is identified as being 2-10 positions from the signal light (and several positions apart between AV and CAV).
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output.
Case 2: taking AV with 1 vehicle and CAV with 2 vehicles as an example, the number of vehicles m is 10 and the number of target vehicles n is 3, and the simulation positions of the 3 vehicles in the vehicle queue may be adjacent and may be dispersed.
If the target vehicle sequencing rule is that the simulation positions of the 3 vehicles are adjacent, when the target vehicle is positioned at the head of the fleet of vehicles, the simulation position of the target vehicle in the vehicle simulation queue is identified to be the 1-3 positions of the distance signal lamp (at the moment, the sequencing of the 3 vehicles may be AV-CAV-CAV, or CAV-AV-CAV, or CAV-CAV-AV); when the target vehicle is located at the non-fleet top position, the simulation position of the target vehicle in the vehicle simulation queue is recognized to be 2-10 positions away from the signal lamp (at the moment, the sequence of the 3 vehicles may be AV-CAV-CAV, or CAV-AV-CAV, or CAV-CAV-AV).
If the target vehicle sorting rule is that the simulated positions of the 3 vehicles are scattered, when the target vehicle is positioned at the head of the fleet of vehicles, the simulated positions of the target vehicle in the vehicle simulation queue are recognized to be 1 and 3-10 positions of the distance signal lamp (at this time, the AV vehicle may be at the head of the fleet, and the other two CAVs are adjacent or scattered at the 3-10 positions, and the CAV vehicle may be at the head of the fleet, and the other AV and CAVs are adjacent or scattered at the 3-10 positions); when the target vehicle is located at the non-fleet top position, the simulation position of the target vehicle in the vehicle simulation queue is identified to be 2-10 positions away from the signal lamp (in this case, the 3 vehicles may be all dispersed, or 1 vehicle may be dispersed, and the other 2 vehicles are adjacent).
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output.
Case 3: taking AV has 2, CAV has 2 as an example, the number of vehicles m is 10, and the number of target vehicles n is 4, and in this case, the simulation positions of the 4 vehicles in the vehicle queue may be adjacent or may be dispersed.
If the target vehicle sorting rule is that the simulated positions of the 4 vehicles are adjacent, when the target vehicle is positioned at the head of a fleet of vehicles, the simulated position of the target vehicle in the vehicle simulation queue is identified to be 1-4 positions away from the signal lamp (at this time, the sorting of the 4 vehicles may be CAV-AV-AV-CAV, or AV-AV-CAV-CAV, or CAV-AV-CAV-AV, only 3 are listed here as examples, and other sorting ways are not listed one by one); when the target vehicle is located at the non-fleet top position, the simulated position of the target vehicle in the vehicle simulation queue is identified as the 2-10 position of the distance signal lamp (at this time, the sequence of the 4 vehicles may be CAV-AV-CAV, AV-CAV, or CAV-AV-CAV-AV, which is only 3 examples, and the other arrangement is not listed any more).
If the target vehicle sequencing rule is that the simulated positions of the 4 vehicles are scattered, when the target vehicle is positioned at the head of the fleet of vehicles, the simulated positions of the target vehicle in the vehicle simulation queue are identified to be 1, 3-10 positions away from the signal lamp (at this time, the AV vehicle may be at the head of the fleet, the other 3 vehicles may be adjacent or scattered at the 3-10 positions, and the CAV vehicle may be at the head of the fleet, the other 3 vehicles may be adjacent or scattered at the 3-10 positions); when the target vehicle is located at the non-fleet head position, the simulation position of the target vehicle in the vehicle simulation queue is identified to be 2-10 positions away from the signal lamp (at this time, the 4 vehicles may be dispersed completely, may be dispersed two by two, and may also be dispersed one by three).
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output.
Case 4: as shown in fig. 12, when there are Y vehicles in AV and CAV, the number of vehicles m is 10, and the number of target vehicles n is Y, and these vehicles in the vehicle group appear in sequence from the head position, may be adjacent to each other, and may be dispersed.
And after the vehicle queue is configured, the simulation system is operated based on the vehicle queue, and the operation result of the simulation system is output.
In this embodiment, the influence of different mixed vehicle queues on the hourly traffic flow (i.e., traffic capacity) of an entrance lane is studied by taking the south-north direct-driving entrance lane of the liberal road in vinpoch city and the signal control intersection of the city street as an object:
(1) influence of different positions of the target vehicle in the queue on the traffic capacity of the entrance lane:
the traffic flow per hour at the entrance lane with AV at different positions in the mixed-vehicle train obtained by the simulation is shown in fig. 13, where position 0 means that the vehicle train has only MV vehicles. It can be known that when AV vehicles are mixed in the queue, the hourly traffic flow of the entrance lane can be obviously improved; as the queuing position of the AV in the queue moves backwards, the traffic capacity of the entrance lane is reduced; when the AV car is positioned at the head of the row, the traffic capacity of the entrance way is optimal, and when the AV car is positioned at other positions, the traffic capacity of the entrance way is not greatly changed.
This gives: when AV vehicles are mixed in the queue, the AV vehicles should be positioned at the head of the queue as much as possible.
(2) The effect of the different number of target vehicles in the queue on the capacity of the entrance lane:
the simulated hourly traffic flow of entrance roads at different numbers of AV's in the fleet is shown in fig. 14, where 0 means that the fleet has only MV vehicles. It can be seen that as the number of AV cars in the queue increases, the traffic capacity of the entrance lane is significantly improved.
(3) Influence of different vehicle types of the target vehicles in the queue on the traffic capacity of the entrance lane:
the simulated traffic flow per hour at the entrance lane with AV or CAV at different positions in the vehicle train is shown in fig. 15, where 0 means that only MV vehicles are in the train. It is understood that when AV or CAV vehicles are mixed in the queue, the hourly traffic flow of the entrance lane can be significantly increased, and the improvement in the traffic capacity of the entrance lane by CAV is more significant than AV, and thus it is possible to obtain a higher degree of intelligence of the target vehicle and a higher degree of improvement in the traffic capacity of the entrance lane at the same queuing position.
(4) Influence of different sequencing schemes of 2 target vehicles of the same vehicle type on the entrance lane traffic capacity: (preferably, the case of CAV is the same as AV, taking AV as an example, and the description thereof is omitted here)
As shown in fig. 16, the sorting scheme 1 is that 2 AVs in the mixed vehicle queue are adjacent and sequentially move from the head of the queue to the rear; the sorting scheme 2 is that 2 AV in the mixed vehicle queue are dispersed, and one AV is always positioned at the head of the queue.
The comparison of simulation results shows that the improvement of the traffic capacity of the entrance passage by the sequencing scheme 2 is obviously better than that of the sequencing scheme 1. Therefore, the target vehicle is positioned at the head of the row, and the traffic capacity of the entrance way can be obviously improved.
(5) Influence of different sequencing schemes of mixed-driving vehicles on traffic capacity
As shown in fig. 17, in the ordering scheme 1, AV and CAV in the mixed vehicle queue are adjacent, and CAV follows AV and moves from the head of the queue to the rear part in sequence; and the sequencing scheme 2 is that the AV and the CAV in the mixed vehicle queue are adjacent, and the AV follows the CAV and moves from the head of the queue to the rear part in sequence.
The comparison of simulation results shows that when the target vehicle is positioned at the head of the rank, the improvement degree of the traffic capacity of the entrance lane by the ranking scheme 2 is better than that by the ranking scheme 1; when the target vehicle is located at other positions in the queue, the influence of the sequencing scheme 1 and the sequencing scheme 2 on the traffic capacity of the entrance lane is closer. Therefore, when the AV and the CAV are adjacent in the mixed vehicle queue, the AV follows the CAV as much as possible, and the traffic capacity of the entrance lane can be obviously improved.
The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit the technical solutions, based on the embodiments of the present invention, a person skilled in the art may freely change the number of vehicles, the types of vehicles, the number of types of vehicles, and the number of target vehicles in a mixed vehicle queue, and the change of these data only increases or decreases the calculation amount of the calculation result, but the inherent logic and the calculation reasoning manner are the same, and a worker may also optimize the preset conditions step by step according to the needs of his own simulation, and these modifications or optimizations do not make the essence depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and all belong to the protection scope of the present invention.
Claims (10)
1. A simulation method for hybrid driving of vehicles at a signalized intersection is characterized by comprising the following steps:
establishing a simulation system containing a signal control intersection;
acquiring signal control information, road information, vehicle parameters and vehicle types of the signalized intersection, and the number of vehicles and intersection driving models corresponding to the vehicle types;
configuring a mixed vehicle queue according to vehicle types and the number of vehicles corresponding to each vehicle type, wherein the mixed vehicle queue can form at least two vehicle sequencing schemes;
and operating the simulation system based on the mixed vehicle queue, and outputting the operation result of the simulation system.
2. The simulation method for vehicle mixing at a signalized intersection according to claim 1, wherein the step of configuring a mixed vehicle queue according to vehicle types and the number of vehicles corresponding to each vehicle type comprises:
acquiring the number of vehicle types and the number of vehicles corresponding to each vehicle type, and designating target vehicles, wherein the number of the vehicle types is not less than two;
the method comprises the steps of obtaining or configuring the number m of vehicles, the number n of target vehicles and a target vehicle sequencing rule in a mixed vehicle queue, and enabling the target vehicles and other vehicles to form the mixed vehicle queue based on the target vehicle sequencing rule.
3. The simulation method for vehicle mixing at a signalized intersection according to claim 2, wherein the number m of vehicles in the mixed vehicle queue, the number n of target vehicles and a target vehicle sequencing rule are obtained or configured, and the target vehicles and other vehicles form the mixed vehicle queue based on the target vehicle sequencing rule, and the method comprises the following steps: and arranging the target vehicles in the vehicle queue, screening one or more mixed vehicle queues meeting the target vehicle sequencing rule, and identifying the simulation positions of the target vehicles in the mixed vehicle queues.
4. The simulation method for controlling mixed traveling of vehicles at an intersection according to claim 3, wherein the simulation position of the target vehicle in the mixed traveling vehicle queue is identified based on the type of the vehicle or the parameters of the vehicle or the traveling model at the intersection.
5. The simulation method for vehicle mixing at a signalized intersection according to claim 1,
the signal control information comprises a signal control mode, a phase design scheme, a phase sequence, time length of each phase, period time length and full red setting;
the road information includes the shape and size of the signalized intersection, road grade, lane number, lane width, lane function, and lane number.
6. The simulation method for vehicle mixing at a signalized intersection according to claim 1,
the vehicle type comprises one or more of a manually driven vehicle, a networked manually driven vehicle, an autonomous unmanned vehicle and a networked unmanned vehicle;
the vehicle parameters include a deterministic parameter and a stochastic parameter.
7. The simulation method for controlling intersection vehicle mixing according to claim 6,
the certainty parameters comprise vehicle length, vehicle width, maximum acceleration, expected deceleration, expected headway and road speed limit;
and/or
The randomness parameters comprise the distance from the parking line to the parking line after the head car is parked, the maximum driving speed, the response time of a driver and the minimum distance between cars.
8. The simulation method for controlling the mixed running of the vehicles at the intersection according to the claim 1 or 6, wherein when the simulation system is operated based on a mixed running vehicle queue, different intersection running models are respectively adopted for the manually driven vehicles, the networked manually driven vehicles, the autonomous unmanned vehicles and the networked unmanned vehicles.
9. A simulation system for controlling mixed traveling of vehicles at an intersection by signals is characterized by comprising:
the communication module is configured for acquiring signal control information, road information, vehicle parameters and vehicle types of the signal control intersection, and the number of vehicles and intersection driving models corresponding to the vehicle types;
the simulation system establishing module is configured for establishing a simulation system containing the signal control intersection;
the vehicle queue module is configured for configuring a mixed vehicle queue according to vehicle types and the number of vehicles corresponding to each vehicle type, wherein the mixed vehicle queue can at least form two vehicle sequencing schemes;
and the simulation operation module is configured for operating the simulation system based on the mixed vehicle queue and outputting the operation result of the simulation system.
10. The simulation system for signalized intersection vehicle mixing according to claim 9, wherein the communication module is further configured to,
acquiring the number of vehicle types and the number of vehicles corresponding to each vehicle type;
acquiring or configuring the number m of vehicles in the mixed vehicle queue, the number n of target vehicles and a target vehicle sequencing rule,
a vehicle queue module comprising:
a specifying unit configured to specify a target vehicle;
and the queue composition unit is configured for composing the target vehicle and other vehicles into a mixed vehicle queue based on the target vehicle sequencing rule.
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| CN115131965B (en) * | 2022-06-23 | 2023-07-07 | 重庆长安汽车股份有限公司 | Vehicle control method, device, system, electronic equipment and storage medium |
| CN115662121A (en) * | 2022-10-19 | 2023-01-31 | 吉林大学 | Method for estimating upper and lower limits of traffic capacity of mixed traffic signal control intersection in queuing mode |
| CN115662121B (en) * | 2022-10-19 | 2024-05-10 | 吉林大学 | Method for estimating upper and lower limits of traffic capacity of mixed traffic signal control intersection in queuing mode |
| CN117492448A (en) * | 2023-12-28 | 2024-02-02 | 北京主线科技有限公司 | Vehicle formation method, device, equipment and storage medium |
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