CN111586130B - Performance evaluation method and device of longitudinal control algorithm in mixed traffic scene - Google Patents

Performance evaluation method and device of longitudinal control algorithm in mixed traffic scene Download PDF

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CN111586130B
CN111586130B CN202010353582.7A CN202010353582A CN111586130B CN 111586130 B CN111586130 B CN 111586130B CN 202010353582 A CN202010353582 A CN 202010353582A CN 111586130 B CN111586130 B CN 111586130B
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icv
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CN111586130A (en
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李克强
王嘉伟
许庆
王建强
陈超义
边有钢
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Tsinghua University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing

Abstract

The invention discloses a performance evaluation method and a device of an intelligent networked vehicle control algorithm in a mixed traffic scene, wherein the method comprises the following steps: s1, initializing road scenes and vehicle states; s2, modeling the longitudinal movement behavior of the ICV; s3, modeling the longitudinal motion behavior of the HDV; s4, carrying out two simulation experiments of Type1 and Type 2; s5, according to the experimental data output by the Type1 experiment, one or more of vehicle track data, normalized disturbance mean values and segmented disturbance mean value change curve analysis methods are used for evaluating the longitudinal control algorithm of the intelligent networked vehicle; s6, calculating the average value of preset indexes of multiple experiments according to experiment data output by the Type2 experiment, and evaluating the performance of the intelligent networked vehicle algorithm in a mixed traffic scene; the preset index comprises one or more of average speed, traffic shock wave characteristic parameters and traffic flow average homogeneity indexes. The invention can systematically evaluate the performance of the intelligent networked automobile longitudinal control algorithm in the mixed traffic scene.

Description

Performance evaluation method and device of longitudinal control algorithm in mixed traffic scene
Technical Field
The invention relates to the technical field of intelligent networked vehicles, in particular to a performance evaluation method and device of an intelligent networked vehicle control algorithm in a mixed traffic scene of a single lane and a straight road.
Background
In the last two decades, with the explosive growth of the automobile holding capacity in China, traffic congestion becomes an important factor restricting the social and economic development of modern cities. The traditional traffic infrastructure, traffic service capability and traffic management capability cannot deal with the increasingly severe traffic jam problem, and the Intelligent and Connected Vehicle (ICV) technology based on the internet of vehicles and the automatic driving technology is considered to be capable of effectively improving the traffic jam problem and improving the traffic efficiency in recent years.
In the field of internet Vehicle longitudinal Control, many technologies have been introduced, typically represented by Adaptive Cruise Control (ACC), Cooperative Adaptive Cruise Control (CACC), internet Cruise Control (CCC), Vehicle queue Control (Vehicle plant Control), and the like. Under these techniques, existing research has developed a number of different algorithms. Under the condition that all vehicles are networked and controllable, the algorithms can effectively avoid the influence of reaction delay and behavior uncertainty of drivers, so that the condition of traffic jam is reduced; in addition, on the basis of guaranteeing the driving safety, the distance between the vehicle heads is reduced, the driving speed is increased, and therefore the traffic efficiency is greatly improved.
In the existing research, when the performance of the algorithms is evaluated, the existing research is mostly aimed at the working condition of all automatic driving of all vehicles, and in the process of popularization of the actual Intelligent networked Vehicle technology, a long-term transition stage exists, namely, the mixed traffic working condition that ICV (the English is totally called 'Intelligent Connected Vehicle', the Chinese is totally called 'Intelligent networked Vehicle') and HDV (the English is totally called 'Human-drive Vehicle', the Chinese is totally called 'driver-Driven Vehicle') coexist. In a mixed traffic scene, the evaluation of the performance of the intelligent networked vehicle control algorithm has important practical significance on the development and popularization of the intelligent networked vehicle technology. However, the existing research lacks a performance evaluation method systematically aiming at an intelligent networked vehicle control algorithm in a mixed traffic scene.
Disclosure of Invention
The invention aims to provide a performance evaluation method and a performance evaluation device of an intelligent networked vehicle control algorithm in a hybrid traffic scene, so as to overcome or at least alleviate at least one of the above defects of the prior art.
In order to achieve the purpose, the invention provides a performance evaluation method of an intelligent networked vehicle control algorithm in a mixed traffic scene, which comprises the following steps:
s1, initializing the states of all vehicles in the road scene and the vehicle queue; the method comprises the following steps that an 'initialization road scene' comprises the steps of arranging a single-lane straight road scene, designing the total length of a road, the total number of vehicles and the speed of a balanced state traffic flow; "initializing each vehicle state" includes an initial velocity, an initial acceleration, and an initial spacing between adjacent vehicles of each vehicle in the vehicle train; each vehicle in the vehicle queue comprises an ICV and an HDV;
s2, modeling the longitudinal movement behavior of the ICV to obtain an intelligent networked vehicle longitudinal control algorithm to be evaluated;
s3, modeling the longitudinal movement behavior of the HDV to obtain a vehicle driving model of the driver;
s4, performing two types of simulation experiments of Type1 and Type2 according to the initialized road scene, the states of all vehicles, the intelligent networked vehicle longitudinal control model and the driver driving vehicle model to obtain corresponding types of experiment data; the Type1 is a single simulation experiment under the condition of ICV typical distribution, and the Type2 is a plurality of simulation experiments under the condition of ICV random distribution; the simulation experiment comprises introducing external weak disturbance and strong disturbance into a steady operation traffic flow;
s5, according to experimental data output by a Type1 simulation experiment, evaluating a tested intelligent networked vehicle longitudinal control algorithm, wherein the evaluation method comprises one or more of a vehicle track data analysis method, a normalized disturbance mean analysis method and a segmented disturbance mean change curve analysis method;
s6, calculating a plurality of preset indexes according to a plurality of times of experiment data output by a Type2 simulation experiment, analyzing the average value of a plurality of times of experiments of each preset index, drawing each index-permeability curve, and comprehensively evaluating the performance of the tested intelligent networked vehicle algorithm in a mixed traffic scene; wherein the preset index comprises: one or more of average speed, traffic shock wave characteristic parameters and traffic flow average homogeneity indexes.
Further, the Type1 simulation experiment specifically includes:
the market permeability r of the preset ICV is { 10%, 20%, …, 90%, 100% }, and one simulation experiment is performed at each preset market permeability r, and each simulation experiment is set as: under a certain preset market penetration rate, the Nr-th vehicle is ICV, then every Nr-1 vehicles are ICV, and the rest vehicles are HDV.
Further, the normalized disturbance mean analysis method comprises the following steps:
under each permeability r, a calculation formula for defining an ICV normalized disturbance mean value PET _ ICV is shown as the following formula (10), a calculation formula for defining an HDV normalized disturbance mean value PET _ HDV is shown as the following formula (11), the smaller the PET _ ICV is, the better the disturbance control capability of the corresponding intelligent networked vehicle algorithm on the PET _ ICV is, and the smaller the PET _ HDV is, the better the disturbance attenuation capability of the corresponding intelligent networked vehicle algorithm on the HDV is:
Figure BDA0002472695720000031
Figure BDA0002472695720000032
in the equations (10) and (11), the fleet in the simulation test includes 1 pilot vehicle and N following vehicles, i is the vehicle number in the fleet, S is the set formed by the numbers of the vehicles in the fleetAVSet of numbers for ICV, TfTo simulate the end time, vi(t) is the speed at the moment t of the intelligent networked vehicle with the serial number i, v*The traffic flow speed is a preset equilibrium state traffic flow speed.
Further, the method for analyzing the segmented disturbance mean value change curve comprises the following steps:
at each preset market permeability r, there are Nr segments in total, and based on the HDV between two adjacent ICVs, the segmented disturbance mean value of the k-th segment (k ═ 1., Nr) is defined as formula (12):
Figure BDA0002472695720000033
drawing a PET (k) -k curve based on the segmented disturbance mean value calculated by the formula (12), wherein the lower the value of the k point on the curve is, the smaller the disturbance degree of the segment is; the faster the curve decreases along with the increase of k, the faster the disturbance attenuation rate is shown, so as to comprehensively evaluate the influence degree of the intelligent networked vehicle algorithm on the local disturbance of the traffic flow.
Further, the Type2 simulation experiment specifically includes:
the market permeability r of the ICV is preset to be { 10%, 20%, 90%, 100% }, n simulation experiments are performed at each preset market permeability, and each simulation experiment is set as: the number of ICVs is r/N, and the positions of the ICVs are randomly distributed in N +1 vehicles.
Furthermore, the traffic shock wave characteristic parameters comprise a shock wave range SR and a shock wave duration SDT, a calculation formula of the shock wave range SR is a formula (14), a calculation formula of the shock wave duration SDT is a formula (15), and the smaller the shock wave range SR and the shock wave duration SDT is, the better the performance of the intelligent internet vehicle algorithm corresponding to the shock wave range SR and the shock wave duration SDT for improving the traffic efficiency in a mixed traffic scene is;
Figure BDA0002472695720000034
Figure BDA0002472695720000035
in formulae (14) and (15), pi(t) the vehicle number i is located at the position on the road at time t,
Figure BDA0002472695720000041
at the initial moment when the vehicle numbered i is affected by the shock,
Figure BDA0002472695720000042
is the final moment of the shock wave influence of the vehicle with the number i, SwNumbering a set of shock affected vehicles, aiAnd (t) the vehicle with the acceleration smaller than the acceleration threshold and the duration reaching the preset time length.
Further, the average homogeneity index of the traffic flow is an average value of HOM values of the traffic flow homogeneity obtained by n times of simulation experiments, a calculation formula of the HOM is shown as a formula (16) below, and the smaller the average homogeneity index of the traffic flow is, the better the performance of the intelligent internet vehicle algorithm corresponding to the HOM for improving the traffic efficiency in a mixed traffic scene is;
Figure BDA0002472695720000043
in the formula (16), vi(t) represents the speed at time t of the vehicle numbered i, vi-1(T) represents the speed at time T of the vehicle numbered i-1, TfIs the simulation end time.
Further, the simulation experiment further includes:
initializing each vehicle state: setting initial velocity v of each vehiclei(0) Initial acceleration ai(0) Equilibrium state traffic flow velocity v*And an initial spacing between adjacent vehicles;
the stably operating traffic flow includes: from the beginning of the simulation experiment to t1Keeping the pilot vehicle speed at the equilibrium state traffic flow speed v within the time period and the time period from the weak disturbance and the strong disturbance to the simulation experiment ending time*；
In the weak disturbance, the movement track of the pilot vehicle is set as follows:
in self-simulation experiment t1From time until t2，1Within the time period, the pilot vehicle takes 1m/s2The acceleration of the brake is used for emergency braking;
in self-simulation experiment t2，1From time until t3，1In the time period, the pilot vehicle keeps running at a constant speed;
in self-simulation experiment t3，1From time until t4，1Within the time period, the pilot vehicle is 1m/s2Acceleration returns to v*The speed of (d);
in the strong disturbance, the movement track of the pilot vehicle is set as follows:
in self-simulation experiment t1From time until t2，2In the time period, the pilot vehicle carries out emergency braking with the maximum braking intensity;
in self-simulation experiment t2，2From time until t3，2In the time period, the pilot vehicle keeps running at a constant speed;
in self-simulation experiment t3，2From time until t4，2During the time, the pilot vehicle returns to v at the maximum acceleration*The speed of (2).
The invention also provides a performance evaluation device of the intelligent networked vehicle control algorithm in a mixed traffic scene, which comprises the following steps:
the system comprises an initialization module, a processing module and a control module, wherein the initialization module is used for initializing a road scene and the state of each vehicle in a vehicle queue; the method comprises the following steps that an 'initialization road scene' comprises the steps of arranging a single-lane straight road scene, designing the total length of a road, the total number of vehicles and the speed of a balanced state traffic flow; "Pair of initialized vehicle states" includes initial velocity, initial acceleration, and initial spacing between adjacent vehicles of each vehicle in the vehicle fleet; each vehicle in the vehicle queue comprises an ICV and an HDV;
the simulation module is used for respectively modeling the longitudinal motion behaviors of the HDV and the ICV to respectively obtain an intelligent networked vehicle longitudinal control algorithm to be evaluated and a driver-driven vehicle model, and carrying out Type1 and Type2 simulation experiments according to the initialized road scene, the states of the vehicles, the intelligent networked vehicle longitudinal control model and the driver-driven vehicle model to obtain corresponding types of experiment data; the Type1 is a single simulation experiment under the condition of ICV typical distribution, and the Type2 is a plurality of simulation experiments under the condition of ICV random distribution; the simulation experiment comprises introducing external weak disturbance and strong disturbance into a steady operation traffic flow;
the output module is used for recording data output by the simulation module, and the data comprise the information of the position, the speed and the acceleration of each vehicle in the vehicle queue along with the change of time;
the performance analysis module is used for evaluating a tested intelligent internet vehicle longitudinal control algorithm according to experimental data output by a Type1 simulation experiment, the evaluation method comprises one or more of a vehicle track data analysis method, a normalized disturbance mean value analysis method and a segmented disturbance mean value change curve analysis method, and is used for calculating a plurality of preset indexes according to a plurality of times of experimental data output by a Type2 simulation experiment, analyzing the average value of a plurality of times of experiments of each preset index, drawing each index-permeability curve, and comprehensively evaluating the performance of the tested intelligent internet vehicle algorithm in a mixed traffic scene; wherein the preset index comprises: one or more of average speed, traffic shock wave characteristic parameters and traffic flow average homogeneity indexes.
The invention can systematically aim at the performance evaluation method of the intelligent networked automobile longitudinal control algorithm in the mixed traffic scene.
Drawings
Fig. 1 is a schematic block diagram of a performance evaluation device of an intelligent networked vehicle control algorithm in a hybrid traffic scene according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of an evaluation method provided by an embodiment of the present invention.
Fig. 3 is a schematic diagram of a mixed traffic scene of a single-lane straight road according to an embodiment of the present invention.
Fig. 4 is a schematic flow chart of a single simulation experiment performed by using the evaluation method provided by the embodiment of the invention.
Fig. 5 is a schematic view of a traffic shock wave under a single simulation experiment performed by using the evaluation method provided by the embodiment of the invention.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
Fig. 1 is a schematic block diagram of a performance evaluation device of an intelligent networked vehicle control algorithm in a hybrid traffic scene according to an embodiment of the present invention. Where "hybrid" is understood to mean that each vehicle in the vehicle fleet comprises an ICV and an HDV. The performance evaluation device of the intelligent networked vehicle control algorithm in the mixed traffic scene comprises an initialization module, a simulation module, an output module and a performance analysis module, wherein:
the initialization module is used for initializing a road scene and the state of each vehicle in the vehicle queue. The 'initialization road scene' comprises the steps of arranging a single-lane straight road scene, designing the total length of a road, counting vehicles and the speed of balanced traffic flow. "Pair of initialized vehicle states" includes initial velocity, initial acceleration, and initial spacing between adjacent vehicles of each vehicle in the vehicle fleet; each vehicle in the vehicle fleet includes an ICV and an HDV.
The simulation module is used for respectively modeling the longitudinal motion behaviors of the HDV and the ICV to respectively obtain an intelligent networked vehicle longitudinal control algorithm to be evaluated and a driver-driven vehicle model, and carrying out simulation experiments of Type1 and Type2 according to the initialized road scene, the vehicle states, the intelligent networked vehicle longitudinal control model and the driver-driven vehicle model to obtain corresponding types of experiment data; the Type1 is a single simulation experiment under the condition of ICV typical distribution, and the Type2 is a plurality of simulation experiments under the condition of ICV random distribution; the simulation experiment comprises introducing external weak disturbance and strong disturbance into the steady operation traffic flow.
The output module is used for recording data output by the simulation module, and the data comprise information of the position, the speed and the acceleration of each vehicle in the vehicle queue, which change along with time.
The performance analysis module is used for evaluating a tested intelligent internet vehicle longitudinal control algorithm according to experimental data output by a Type1 simulation experiment, the evaluation method comprises one or more of a vehicle track data analysis method, a normalized disturbance mean value analysis method and a segmented disturbance mean value change curve analysis method, and is used for calculating a plurality of preset indexes according to a plurality of times of experimental data output by a Type2 simulation experiment, analyzing the average value of a plurality of times of experiments of each preset index, drawing each index-permeability curve, and comprehensively evaluating the performance of the tested intelligent internet vehicle algorithm in a mixed traffic scene; wherein the preset index comprises: one or more of average speed, traffic shock wave characteristic parameters and traffic flow average homogeneity indexes.
Fig. 2 is a schematic flow chart of a method 200 for evaluating performance of an intelligent networked vehicle control algorithm according to an embodiment of the present invention, where the method 200 may be executed by a network device in the communication system 100 shown in fig. 1, and as shown in fig. 2, the method 200 may include the following steps:
s1, initializing the states of the vehicles in the road scene and the vehicle queue; the method comprises the following steps that an 'initialization road scene' comprises the steps of arranging a single-lane straight road scene, designing the total length of a road, the total number of vehicles and the speed of a balanced state traffic flow; "Pair of initialized vehicle states" includes initial velocity, initial acceleration, and initial spacing between adjacent vehicles of each vehicle in the vehicle fleet; each vehicle in the vehicle fleet includes an ICV and an HDV. S1 specifically includes:
s1-1, as shown in FIG. 3, selecting proper road total length L, vehicle total number N +1 and balanced state traffic flow speed v*. Such as: l100 m, N100, v*＝15m/s。
And S1-2, arranging 1 pilot vehicle and N following vehicles at the entrance section of the road, wherein the following vehicles comprise HDV and ICV. The method comprises the steps of numbering all vehicles from a pilot vehicle to be 0, 1, 2, and N in sequence, and taking the numbers of intelligent networked vehicles to form a set SAV。
And S2, modeling the longitudinal movement behavior of the ICV to obtain the longitudinal control algorithm of the intelligent networked vehicle to be evaluated. The intelligent networked vehicle longitudinal control algorithm comprises the following steps:
s2-1, a vehicle dynamics model, which may be selected as a third order vehicle dynamics model as follows:
Figure BDA0002472695720000071
in the formula (1), pi(t) represents the position of ICVt moment i, which is the initial position of the pilot vehicle as the reference point, vi(t) represents the numberi speed at moment ICVt, ai(t) represents the acceleration at the moment of ICVt, η, numbered iiRepresenting a vehicle powertrain time lag factor, is often taken to be 0.5.
If the time lag factor is ignored, a second-order vehicle dynamics model can also be selected as follows:
Figure BDA0002472695720000072
in the formula (2), pi(t) denotes the position of the ICVt moment with the number i, vi(t) represents the speed at the moment of ICVt with number i, ai(t) represents the acceleration at the moment of ICV with number i.
S2-2, information flow topology: the intelligent networked vehicle control algorithm needs to acquire information of other vehicles and records epsilon for ICV with number iiA set of numbers for other vehicles for which the algorithm needs to obtain information, then
Figure BDA0002472695720000073
S2-3, expected spacing model: the ICV generally needs to determine its expected following distance, which is denoted as the ICV with the number i and the vehicle with the number j
Figure BDA0002472695720000081
S2-4, a controller model. Based on the third order vehicle dynamics model in S2-1, the general form of the controller model is:
Figure BDA0002472695720000082
if based on the second order vehicle dynamics model in S2-1, the general form of the controller model is:
Figure BDA0002472695720000083
in the formulae (3) and (4), function Fi() All the intelligent networked vehicle longitudinal control algorithms are directly determined by the evaluated intelligent networked vehicle longitudinal control algorithms. For example, if a linear feedback control algorithm based on a third order vehicle dynamics model is evaluated, the algorithm may be embodied as:
Figure BDA0002472695720000084
in the formula (5), the reaction mixture is,
Figure BDA0002472695720000085
for controller parameters determined by the evaluated intelligent networked vehicle longitudinal control algorithm, e.g.
Figure BDA0002472695720000086
And S3, modeling the longitudinal movement behavior of the HDV to obtain a driver driving vehicle model.
The driver driving vehicle model is composed of a vehicle dynamic model and a driver following behavior model. Since the output of the driver following behavior model is generally the actual acceleration of the vehicle, the driver often selects the second order dynamics model (2) from among the vehicle dynamics models driven by the driver. For the driver following behavior model, an IDM + model can be selected for specific modeling:
Figure BDA0002472695720000087
in the formula (6), si(t)＝pi-1(t)-pi(t) is the following distance, Δ vi(t)＝vi-1(t)-vi(t) is the following speed difference, s*(vi(t-τ)，Δvi(t- τ)) is the driver's desired headway, which satisfies the following equation (7):
Figure BDA0002472695720000088
the physical meanings and common specific values of the parameters in the IDM + model are given in table 1 below:
TABLE 1 IDM + model parameter meanings and values
Figure BDA0002472695720000091
Besides the IDM + model, the driver following behavior model can also select a 0VM model for specific modeling:
ui(t)＝α(V(si(t-τ))-vi(t-τ))+βΔvi(t-τ) (8)
in formula (8), V(s)i(t- τ)) represents a desired speed of the driver at the time (t- τ), which satisfies equation (9):
Figure BDA0002472695720000092
the physical meanings and common specific values of the parameters in the OVM model are shown in table 2 below:
TABLE 2 OVM model parameter meanings and values
Figure BDA0002472695720000093
And S4, as shown in FIG. 4, performing two types of simulation experiments of Type1 and Type2 according to the traffic simulation scene, the intelligent networked vehicle longitudinal control model and the driver driving vehicle model to obtain corresponding types of experiment data.
Wherein, Type1 Type is single simulation experiment under typical distribution condition of ICV: the traffic simulation experiment is sequentially carried out by selecting the market permeability r of the intelligent internet vehicles (10%, 20%, 90%, 100%). Under each market penetration rate, the numbers of the intelligent networked vehicles are taken to form a set SAV(Nr) · (Nr), i.e. the Nr-th vehicle is ICV, then every Nr-1 vehicles is ICV, and the rest are all vehiclesHDV. Step S4 was performed once for each market penetration, and two perturbation conditions were done once each, and experimental data were recorded.
Type2 is a multiple simulation experiment with random distribution of ICV: the market permeability r of the intelligent networked vehicle is selected as { 10%, 20%, 90%, 100% }, multiple random experiments are carried out under each permeability, and the experiment times are recorded as n. In each test, the number of the intelligent networked vehicles is r/N, and the numbers of the intelligent networked vehicles form a set SAVAnd (4) randomly selecting, namely randomly distributing the intelligent networked vehicle positions in the N vehicles. Step S4 is performed n times at each market penetration rate, each time for two disturbance conditions, and experimental data is recorded.
Wherein, the simulation experiment specifically comprises:
s4-1, initializing each vehicle state: setting initial velocity v of each vehiclei(0) Initial acceleration ai(0) And an initial spacing between adjacent vehicles. Such as: v. ofi(0)＝15m/s，ai(0)＝0m/s2The initial spacing between adjacent vehicles was set to 20 m.
S4-2, the setting of the stable operation traffic flow comprises the following steps: from the beginning of the simulation experiment to t1The pilot speed is kept at the speed v of the traffic flow in a balanced state in the time period*And the whole traffic flow tends to a stable state.
S4-3, introducing external disturbance. Self-simulated t1From time, a disturbance is introduced. The disturbances can be classified into two categories:
1) and (4) weak disturbance. The following table is set for the navigation vehicle motion track:
TABLE 3 movement locus of piloting vehicle under weak disturbance
Figure BDA0002472695720000101
2) And (4) strong disturbance. The following table is set for the navigation vehicle motion track:
TABLE 4 movement locus of piloting vehicle under strong disturbance
Figure BDA0002472695720000102
T in tables 3 and 41Indicating the braking start time, t2，1、t2，2Indicating the start of low-speed travel, t3，1、t3，2Indicates the acceleration start time, t4，1、t4，2Indicating the acceleration stop time.
And S4-4, stably operating the traffic flow. After both disturbances have ended (t under weak disturbance)4，1T at time of day or strong disturbance4，2Time), the speed of the pilot vehicle (vehicle No. 0) is kept at the speed v of the traffic flow in the balanced state*Unchanged until the end time t of the simulation5。
And S4-5, recording and outputting data. Recording position, velocity and acceleration information, i.e. p, for each vehicle at each momenti(t)，vi(t)，ai(t)，i＝1，2，...，N，0≤t≤t5。
And S5, according to the experimental data output by the Type1 Type simulation experiment, taking the data output by a single simulation experiment, and evaluating the tested intelligent networked vehicle longitudinal control algorithm around traffic disturbance, wherein the evaluation method comprises one or more of a vehicle track data analysis method, a normalized disturbance mean value analysis method and a segmented disturbance mean value change curve analysis method.
The vehicle trajectory data analysis method comprises the following steps:
as shown in FIG. 5, the position-time curve (i.e., p) for each vehicle is plotted on the same graphi(t), i ═ 1, 2.., n), the shade of each curve at a time t being determined by the speed v at that timei(t) determination, viThe larger (t) the lighter the color and vice versa. Under each disturbance condition, 10 vehicle trajectory maps can be obtained, which correspond to different permeabilities respectively. The evolution of the dark color area in the vehicle track map reflects the change condition of traffic disturbance, the smaller the range of the dark color area is, the faster the disturbance dissipation is shown, and the better the traffic flow stability is, so that the better the performance of the algorithm of the intelligent internet vehicle for improving the traffic efficiency in a mixed traffic scene is.
The normalized disturbance mean value analysis method comprises the following steps:
at each market permeability r, defining the normalized disturbance mean value PET _ ICV of the intelligent networked vehicle and the normalized disturbance mean value PET _ HDV of the driver-driven vehicle as follows:
Figure BDA0002472695720000111
Figure BDA0002472695720000112
in the formulas (10) and (11), Nr represents nxr, N (1-r) represents nx (1-r), a fleet in a simulation test includes 1 pilot vehicle and N following vehicles, i is a vehicle number in the fleet, S is a set of numbers of vehicles in the fleet, and S is a set of numbers of vehicles in the fleetAVSet of numbers for ICV, TfTo simulate the end time, vi(t) is the speed at the moment t of the intelligent networked vehicle with the serial number i, v*The traffic flow speed is a preset equilibrium state traffic flow speed.
The intelligent network connection vehicle normalized disturbance mean value PET _ ICV and the driver-driven vehicle normalized disturbance mean value PET _ HDV respectively reflect disturbance mean values of the intelligent network connection vehicle and the driver-driven vehicle in the mixed traffic flow. The smaller the normalized disturbance mean value PET _ ICV of the intelligent networked vehicle is, the better the disturbance control capability of the intelligent networked vehicle algorithm on the intelligent networked vehicle (intelligent networked vehicle) is; the smaller the normalized disturbance average value PET _ HDV of the vehicle driven by the driver is, the better the disturbance attenuation capacity of the intelligent networked vehicle algorithm on other vehicles (the vehicle driven by the driver) is.
The method for analyzing the sectional disturbance mean change curve comprises the following steps:
because the intelligent networked vehicles are distributed in the traffic flow typically, the mixed traffic flow can be separated by the intelligent networked vehicles in sequence, and the intelligent networked vehicles exhibit the typical segmented property. At each permeability r, there are Nr segments in total. Based on a driver driving a vehicle between two adjacent intelligent networked vehicles, a segmented disturbance mean value of a k-th segment (k is 1.., Nr) is defined as:
Figure BDA0002472695720000121
in the formula (12), Nr represents Nxr, kNr represents kXNxr, and (k-1) Nr represents (k-1). times.Nxr. And drawing a variation curve of the segmented disturbance mean value, namely a PET (k) -k curve based on the segmented disturbance mean value. The lower the value at the k point on the curve, the smaller the disturbance degree in the section; the faster the curve falls as k increases, indicating a faster rate of decay of the perturbation. Based on the segmented disturbance mean value change curve, the influence degree of the intelligent networked vehicle algorithm on the local disturbance of the traffic flow can be comprehensively evaluated.
S6, calculating a plurality of preset indexes according to a plurality of times of experiment data output by a Type2 simulation experiment, analyzing the average value of a plurality of times of experiments of each preset index, drawing each index-permeability curve, and comprehensively evaluating the performance of the tested intelligent networked vehicle algorithm in a mixed traffic scene; wherein the preset index comprises: one or more of average speed, traffic shock wave characteristic parameters and traffic flow average homogeneity indexes.
The average speed obtaining method comprises the following steps:
the average speed AVS of all vehicles is defined as follows
Figure BDA0002472695720000122
Averaging the results of AVS values of average speed obtained by multiple experiments
Figure BDA0002472695720000123
The value of
Figure BDA0002472695720000124
The larger the traffic flow speed is, the better the performance of the intelligent networked vehicle algorithm for improving the traffic efficiency in a mixed traffic scene is.
The method for obtaining the traffic shock wave characteristic parameters comprises the following steps:
and extracting the traffic shock waves from the acceleration angle, and calculating two characteristic parameters of the shock wave range and the duration. If the acceleration of a certain vehicle is less than-1 m/s2And the duration exceeds 1s, the shock wave is considered to be in the shock wave, and the acceleration is less than-1 m/s2And the corresponding time is the time when the vehicle is influenced by the shock wave. Defining the set of shock-affected vehicle numbers as Sw，
Figure BDA0002472695720000131
At the initial moment when the vehicle numbered i is affected by the shock,
Figure BDA0002472695720000132
the end time when the vehicle numbered i is affected by the shock wave. The calculation formula for defining the shock wave range SR and the shock wave duration SDT is
Figure BDA0002472695720000133
Figure BDA0002472695720000134
In formulae (14) and (15), pi(t) the vehicle number i is located at the position on the road at time t,
Figure BDA0002472695720000135
at the initial moment when the vehicle numbered i is affected by the shock,
Figure BDA0002472695720000136
for the end time of the shock wave influence of the vehicle numbered i, NsNumbering a set of shock affected vehicles, aiAnd (t) the vehicle with the acceleration smaller than the acceleration threshold and the duration reaching the preset time length.
The smaller the shock wave range SR and the shock wave duration SDT are, the smaller the traffic flow disturbance degree is, and the better the performance of the algorithm of the intelligent networked vehicle for improving the traffic efficiency in a mixed traffic scene is.
The method for obtaining the average homogeneity index of the traffic flow comprises the following steps:
defining a traffic flow homogeneity index based on an accumulation of speed differences of neighboring vehicles as
Figure BDA0002472695720000137
In the formula (16), vi(t) represents the speed at time t of the vehicle numbered i, vi-1(T) represents the speed at time T of the vehicle numbered i-1, TfIs the simulation end time.
And averaging the results of the HOM values of the traffic flow homogeneity obtained by multiple experiments to obtain the average homogeneity index of the traffic flow. The smaller the index is, the smaller the cumulative speed difference between vehicles is, and the better the traffic flow homogeneity is, the better the performance of the intelligent networked vehicle algorithm in improving the traffic efficiency in a mixed traffic scene is.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Those of ordinary skill in the art will understand that: modifications can be made to the technical solutions described in the foregoing embodiments, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (9)

1. A performance evaluation method of an intelligent networked vehicle control algorithm in a hybrid traffic scene is characterized by comprising the following steps:
s1, initializing the states of all vehicles in the road scene and the vehicle queue; the method comprises the following steps that an 'initialization road scene' comprises the steps of arranging a single-lane straight road scene, designing the total length of a road, the total number of vehicles and the speed of a balanced state traffic flow; "initializing each vehicle state" includes an initial velocity, an initial acceleration, and an initial spacing between adjacent vehicles of each vehicle in the vehicle train; each vehicle in the vehicle queue comprises an ICV and an HDV;
s2, modeling the longitudinal movement behavior of the ICV to obtain an intelligent networked vehicle longitudinal control algorithm to be evaluated;
s3, modeling the longitudinal movement behavior of the HDV to obtain a vehicle driving model of the driver;
s4, performing two types of simulation experiments of Type1 and Type2 according to the initialized road scene, the states of all vehicles, the intelligent networked vehicle longitudinal control model and the driver driving vehicle model to obtain corresponding types of experiment data; the Type1 is a single simulation experiment under the condition of ICV typical distribution, and the Type2 is a plurality of simulation experiments under the condition of ICV random distribution; the simulation experiment comprises introducing external weak disturbance and strong disturbance into a steady operation traffic flow;
s5, according to experimental data output by a Type1 simulation experiment, evaluating a tested intelligent networked vehicle longitudinal control algorithm, wherein the evaluation method comprises one or more of a vehicle track data analysis method, a normalized disturbance mean analysis method and a segmented disturbance mean change curve analysis method;
s6, calculating a plurality of preset indexes according to a plurality of times of experiment data output by a Type2 simulation experiment, analyzing the average value of a plurality of times of experiments of each preset index, drawing each index-permeability curve, and comprehensively evaluating the performance of the tested intelligent networked vehicle algorithm in a mixed traffic scene; wherein the preset index comprises: one or more of average speed, traffic shock wave characteristic parameters and traffic flow average homogeneity indexes.
2. The performance evaluation method of the intelligent networked vehicle control algorithm in the mixed traffic scene as claimed in claim 1, wherein the Type1 Type simulation experiment specifically comprises:
the market permeability r of the preset ICV is { 10%, 20%, …, 90%, 100% }, and one simulation experiment is performed at each preset market permeability r, and each simulation experiment is set as: under a certain preset market penetration rate, the Nr-th vehicle is ICV, then every Nr-1 vehicles are ICV, and the rest vehicles are HDV.
3. The method for evaluating the performance of the intelligent networked vehicle control algorithm in the hybrid traffic scene as claimed in claim 2, wherein the normalized disturbance mean analysis method comprises:
under each permeability r, a calculation formula for defining an ICV normalized disturbance mean value PET _ ICV is shown as the following formula (10), a calculation formula for defining an HDV normalized disturbance mean value PET _ HDV is shown as the following formula (11), the smaller the PET _ ICV is, the better the disturbance control capability of the corresponding intelligent networked vehicle algorithm on the PET _ ICV is, and the smaller the PET _ HDV is, the better the disturbance attenuation capability of the corresponding intelligent networked vehicle algorithm on the HDV is:
Figure FDA0002918826830000021
Figure FDA0002918826830000022
in the equations (10) and (11), the fleet in the simulation test includes 1 pilot vehicle and N following vehicles, i is the vehicle number in the fleet, S is the set formed by the numbers of the vehicles in the fleetAVSet of numbers for ICV, TfTo simulate the end time, vi(t) is the speed at the moment t of the intelligent networked vehicle with the serial number i, v*The traffic flow speed is a preset equilibrium state traffic flow speed.
4. The method for evaluating the performance of the intelligent networked vehicle control algorithm in the mixed traffic scene as claimed in claim 2, wherein the method for analyzing the segmented disturbance mean value change curve comprises the following steps:
at each preset market penetration rate r, there are Nr segments in total, and based on the HDV between two adjacent ICVs, the average of the segmental disturbances defining the k-th segment (k ═ 1, …, Nr) is expressed as formula (12):
Figure FDA0002918826830000023
drawing a PET (k) -k curve based on the segmented disturbance mean value calculated by the formula (12), wherein the lower the value of the k point on the curve is, the smaller the disturbance degree of the segment is; the faster the curve decreases along with the increase of k, the faster the disturbance attenuation rate is shown, so as to comprehensively evaluate the influence degree of the intelligent networked vehicle algorithm on the local disturbance of the traffic flow.
5. The method for evaluating the performance of the intelligent networked vehicle control algorithm in the hybrid traffic scene according to any one of claims 1 to 4, wherein the vehicle queue in S1 comprises 1 pilot vehicle and N following vehicles;
the Type2 simulation experiment specifically comprises the following steps:
the market penetration rate r of the preset ICV is { 10%, 20%, …, 90%, 100% }, n simulation experiments are performed at each preset market penetration rate, and each simulation experiment is set as: the number of ICVs is r/N, and the positions of the ICVs are randomly distributed in N +1 vehicles.
6. The method for evaluating the performance of the intelligent internet vehicle control algorithm in the hybrid traffic scenario according to claim 5, wherein the traffic shock characteristic parameters include a shock wave range SR and a shock wave duration SDT, a calculation formula of the shock wave range SR is as follows (14), a calculation formula of the shock wave duration SDT is as follows (15), and a smaller shock wave range SR and shock wave duration SDT indicates a better performance of the corresponding intelligent internet vehicle algorithm in improving traffic efficiency in the hybrid traffic scenario;
Figure FDA0002918826830000031
Figure FDA0002918826830000032
in formulae (14) and (15), pi(t) the vehicle number i is located at the position on the road at time t,
Figure FDA0002918826830000033
at the initial moment when the vehicle numbered i is affected by the shock,
Figure FDA0002918826830000034
is the final moment of the shock wave influence of the vehicle with the number i, SwNumbering a set of shock affected vehicles, aiAnd (t) the vehicle with the acceleration smaller than the acceleration threshold and the duration reaching the preset time length.
7. The method for evaluating the performance of the intelligent internet vehicle control algorithm in the mixed traffic scene as claimed in claim 5, wherein the average value of the average homogeneity index of the traffic flow is an average value of the HOM values of the traffic flow obtained by n times of simulation experiments, the calculation formula of the HOM is shown as the following formula (16), and the smaller the average homogeneity index of the traffic flow is, the better the performance of the corresponding intelligent internet vehicle algorithm in the mixed traffic scene for improving the traffic efficiency is;
Figure FDA0002918826830000035
in the formula (16), vi(t) represents the speed at time t of the vehicle numbered i, vi-1(T) represents the speed at time T of the vehicle numbered i-1, TfIs the simulation end time.
8. The method for evaluating the performance of the intelligent networked vehicle control algorithm in the mixed traffic scene as recited in claim 5, wherein the simulation experiment further comprises:
initializing each vehicle state: setting initial velocity v of each vehiclei(0) Initial acceleration ai(0) Equilibrium state traffic flow velocity v*And an initial spacing between adjacent vehicles;
the stably operating traffic flow includes: from the beginning of the simulation experiment to t1Keeping the pilot vehicle speed at the equilibrium state traffic flow speed v within the time period and the time period from the weak disturbance and the strong disturbance to the simulation experiment ending time*；
In the weak disturbance, the movement track of the pilot vehicle is set as follows:
in self-simulation experiment t1From time until t2，1Within the time period, the pilot vehicle takes 1m/s2The acceleration of the brake is used for emergency braking;
in self-simulation experiment t2，1From time until t3，1In the time period, the pilot vehicle keeps running at a constant speed;
in self-simulation experiment t3，1From time until t4，1Within the time period, the pilot vehicle is 1m/s2Acceleration returns to v*The speed of (d);
in the strong disturbance, the movement track of the pilot vehicle is set as follows:
in self-simulation experiment t1From time until t2，2In the time period, the pilot vehicle carries out emergency braking with the maximum braking intensity;
in self-simulation experiment t2，2From time until t3，2In the time period, the pilot vehicle keeps running at a constant speed;
in self-simulation experiment t3，2From time until t4，2During the time, the pilot vehicle returns to v at the maximum acceleration*The speed of (2).
9. A performance evaluation device of an intelligent networked vehicle control algorithm in a hybrid traffic scene is characterized by comprising the following components:
the system comprises an initialization module, a processing module and a control module, wherein the initialization module is used for initializing a road scene and the state of each vehicle in a vehicle queue; the method comprises the following steps that an 'initialization road scene' comprises the steps of arranging a single-lane straight road scene, designing the total length of a road, the total number of vehicles and the speed of a balanced state traffic flow; "Pair of initialized vehicle states" includes initial velocity, initial acceleration, and initial spacing between adjacent vehicles of each vehicle in the vehicle fleet; each vehicle in the vehicle queue comprises an ICV and an HDV;
the simulation module is used for respectively modeling the longitudinal motion behaviors of the HDV and the ICV to respectively obtain an intelligent networked vehicle longitudinal control algorithm to be evaluated and a driver-driven vehicle model, and carrying out Type1 and Type2 simulation experiments according to the initialized road scene, the states of the vehicles, the intelligent networked vehicle longitudinal control model and the driver-driven vehicle model to obtain corresponding types of experiment data; the Type1 is a single simulation experiment under the condition of ICV typical distribution, and the Type2 is a plurality of simulation experiments under the condition of ICV random distribution; the simulation experiment comprises introducing external weak disturbance and strong disturbance into a steady operation traffic flow;
the output module is used for recording data output by the simulation module, and the data comprise the information of the position, the speed and the acceleration of each vehicle in the vehicle queue along with the change of time;
the performance analysis module is used for evaluating a tested intelligent internet vehicle longitudinal control algorithm according to experimental data output by a Type1 simulation experiment, the evaluation method comprises one or more of a vehicle track data analysis method, a normalized disturbance mean value analysis method and a segmented disturbance mean value change curve analysis method, and is used for calculating a plurality of preset indexes according to a plurality of times of experimental data output by a Type2 simulation experiment, analyzing the average value of a plurality of times of experiments of each preset index, drawing each index-permeability curve, and comprehensively evaluating the performance of the tested intelligent internet vehicle algorithm in a mixed traffic scene; wherein the preset index comprises: one or more of average speed, traffic shock wave characteristic parameters and traffic flow average homogeneity indexes.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113382064A (en) * 2021-06-08 2021-09-10 重庆大学 Traffic capacity estimation method and device considering CAV (vehicle Access control) special lane of intelligent internet vehicle
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CN114999227B (en) * 2022-05-25 2023-10-20 中国农业大学 Non-signal control intersection mixed multi-vehicle model-free prediction cooperative control method
CN115169890A (en) * 2022-07-07 2022-10-11 深圳大学 Intelligent networked vehicle hybrid traffic evaluation system based on microscopic traffic simulation
CN116486612B (en) * 2023-04-21 2023-12-26 清华大学 Mixed traffic queue stability evaluation method and device based on vehicle Lu Yun cooperation
CN116343485B (en) * 2023-05-19 2023-10-20 深圳市城市交通规划设计研究中心股份有限公司 Intelligent networking vehicle performance degradation evaluation method, electronic equipment and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109445404A (en) * 2018-09-17 2019-03-08 中南大学 Intelligent driving programmed decision-making control system enhances in ring test method
CN110718074A (en) * 2019-11-06 2020-01-21 清华大学 Cooperative control method for signal lamp and vehicle of hybrid traffic intersection
CN110942645A (en) * 2019-11-06 2020-03-31 清华大学 Vehicle control method for hybrid traffic intersection

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9053636B2 (en) * 2012-12-30 2015-06-09 Robert Gordon Management center module for advanced lane management assist for automated vehicles and conventionally driven vehicles
CN108536117A (en) * 2018-02-13 2018-09-14 北京联合大学 A kind of unmanned Che Yueche system and methods of plugging into

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109445404A (en) * 2018-09-17 2019-03-08 中南大学 Intelligent driving programmed decision-making control system enhances in ring test method
CN110718074A (en) * 2019-11-06 2020-01-21 清华大学 Cooperative control method for signal lamp and vehicle of hybrid traffic intersection
CN110942645A (en) * 2019-11-06 2020-03-31 清华大学 Vehicle control method for hybrid traffic intersection

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
智能网联汽车（ICV）技术的发展现状及趋势;李克强等;《汽车安全与节能学报》;20170315;全文 *
通信时延与丢包下智能网联汽车控制性能分析;常雪阳等;《中国公路学报》;20190615;全文 *

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