CN114355883A - Self-adaptive car following method and system - Google Patents
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Abstract
The invention provides a self-adaptive car following method and a self-adaptive car following system, which belong to the technical field of intelligent traffic control, and are used for constructing a car following model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front cars, the sensitivity coefficients of the plurality of front cars and the response coefficient of the speed difference of the plurality of front cars; performing linear stability analysis of car following based on the constructed car following model, and determining a constraint condition that the current traffic flow is in a stable state; and controlling the acceleration of the current vehicle according to the constraint condition in the stable state, so as to realize stable vehicle following. According to the invention, the influence analysis of the speed difference of the following vehicles on the traffic flow distribution and the traffic flow stability is combined with the speed data of the multiple front vehicles, the relative speed of the vehicles and the disturbance of the starting and driving processes are considered, the safe and reliable self-adaptive following vehicles are effectively realized, and the traffic flow stability is improved.
Description
Technical Field
The invention relates to the technical field of intelligent traffic control, in particular to a self-adaptive car following method and a self-adaptive car following system for acquiring driving information of a plurality of front cars based on a current car.
Background
The research on the following problem can be used for traffic simulation, and has great significance for the vehicle autonomous cruise control system and the unmanned technology. The following model is now the research focus technology in the fields of transportation and vehicle control.
The traffic flow model is a key measure step for researching and discussing the traffic flow theory and is a means and a mode for exploring the actual traffic phenomenon. The traffic flow microscopic model comprises a cellular automaton model and a following model. The traffic flow is defined in a model as a formation pattern of discrete particles, and a single vehicle is used as a representation, and the influence of each vehicle is studied to understand the characteristics of the traffic flow. Secondly, from a mechanical point of view, it is a particle mechanical model. Given that each fleet of vehicles must maintain a certain distance from the preceding vehicle to prevent a vehicle collision, or that the following vehicle slows down according to the preceding vehicle under consideration, the effect of the vehicle's response to the stimulus and the uncertainty of the vehicle's motion results in a relationship between the preceding vehicle and the following vehicle.
The following theory was first proposed by Pipes, mainly using a model of stimulus-response, and using differential equations to analyze and describe the phenomenon of following under vehicle following conditions. The following model is provided on the assumption that the sum of a distance which is proportional to the speed of the front vehicle and the minimum safe distance between two vehicles when the vehicles are parked needs to be kept between the front vehicle and the rear vehicle when each following vehicle runs, so that the safe running of the vehicles can be ensured.
The following model proposed by pipe does not take into account the time that the following driver reacts to the traffic change of the preceding vehicle. Taking this effect into account, Chandler and Herman et al propose an improved vehicle model that takes into account driver reaction delays. The same conclusion is obtained by Kometani and Sasaki, and in order to overcome the defect that no feedback is generated when the following vehicle is not driven no matter the distance between two vehicles is far or near when the speeds of the two vehicles are equal before and after the model, Gazis et al establish a nonlinear model which is linked with the distance between the two vehicle heads and can not react when the following vehicle is driven, wherein the sensitivity coefficient is not a fixed value.
Bando et al have established an Optimal Velocity (OV) model that can address many problems with real traffic flow, such as traffic imbalance, stop-and-go, and the like. And (4) establishing an optimized generalized force model by Helbin and Tilch. The model overcomes the problem that the acceleration of an Optimal Velocity (OV) model is too large, and compared with measured data, the consistency is better than that of the optimal velocity model, but the model needs seven specified parameters.
Jiangrui et al consider that the speed of the following vehicle needs to be adjusted on the basis of an optimal speed (OV) model and a Generalized Force (GF) model, so that the following vehicle still can have acceleration when the distance between the vehicles is smaller than the safe distance, and a brand-new full speed difference model is constructed. The citizen and the like comprehensively summarize the history and discussion status of a car-following model; giardia flies et al establish an expected spacing model. Schroer depression et al consider the relative speed between cars and establish an optimal speed model. The comprehensive overview of the model based on the safety interval by the Wangxiangyuan and the like explains the construction of thinking and kernel algorithms and summarizes the advantages and the disadvantages of the algorithms. Wanghao constructs a fuzzy inference model obtained by using a neural network; the Wanghao and Ma goddess consider an optimized car-following model constructed under a road environment with gradient and camber.
The car-following model is the most important component of traffic flow, although the existing car-following model described above enables vehicle motion to be observed in microscopic terms, that is, the motion of the following vehicle can be modeled under known car motion. However, it only considers the influence of the speed difference between two vehicles and the following vehicle, and when two vehicles travel at the same speed, the following vehicle will not react regardless of the distance between the vehicles, which is contrary to the reality, and it is difficult to reflect the actual traffic condition, and the acceleration process of a single vehicle is not accurately represented, and the simulated acceleration is defective and the stability is insufficient.
Disclosure of Invention
The invention aims to provide a self-adaptive car following method and a self-adaptive car following system which simultaneously consider the influence of vehicle position data and speed data on traffic stability and improve the stability of traffic flow and the reliability of car following so as to solve at least one technical problem in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a self-adaptive car following method, which comprises the following steps:
constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed differences of the plurality of front vehicles;
performing linear stability analysis of car following based on the constructed car following model, and determining a constraint condition that the current traffic flow is in a stable state;
and controlling the acceleration of the current vehicle according to the constraint condition in the stable state, so as to realize stable vehicle following.
Preferably, the car following model is constructed by:
wherein v isn+1(t) represents the speed of the (n + 1) th vehicle at time t, V (Δ x)n-a+1) Representing an optimized velocity function, vn-a+1(t) represents the speed of the (n-a + 1) th vehicle at time t, Δ vn＝a+1Representing the speed difference between the (N-a + 1) th vehicle and the vehicle ahead, N representing the number of vehicles, alphaaRepresenting the sensitivity factor, beta, of a number of preceding vehiclesaA response coefficient representing a difference between a plurality of preceding vehicle speeds. The sensitivity coefficient is the reciprocal of the vehicle self-adaptive time, is similar to k in the formula 2.1, can be set according to different conditions under different models and is usually more than 1; beta is aaThe sensitivity to the speed difference of the vehicle is usually also the reciprocal of time, but usually takes a value greater than zero and less than 1, typically 0.6s-1。
Preferably, when the adjacent vehicles have the same distance, vehicle position change disturbance is applied to the vehicle following model, and a first-order derivative of the speed of the current traffic flow vehicle under the disturbance is obtained;
and (4) combining the sensitivity coefficients of a plurality of front vehicles and the response coefficients of the difference between the speeds of the plurality of front vehicles to perform the linear stability analysis of the following vehicles.
Preferably, the first-order second-order derivative of the applied disturbance is calculated, and the current traffic flow vehicle speed first-order derivative under the disturbance is calculated through a Taylor expansion formula and a Fourier series expansion.
Preferably, in combination with the law of lopoda, the constraints at steady state are: and the first derivative of the speed of the current traffic flow vehicle under disturbance is smaller than the sum of response coefficients of the difference between one half of the sensitivity coefficient of the plurality of front vehicles and the speed of the plurality of front vehicles.
In a second aspect, the present invention provides an adaptive car following system, including:
the construction module is used for constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed difference of the plurality of front vehicles;
the judging module is used for carrying out vehicle following linear stability analysis based on the constructed vehicle following model and determining the constraint condition that the current traffic flow is in a stable state;
and the control module is used for controlling the acceleration of the current vehicle according to the constraint condition in the stable state so as to realize stable vehicle following.
Preferably, the determination module includes:
the calculation unit is used for applying vehicle position change disturbance to the following vehicle model when the adjacent vehicles have the same vehicle distance, and solving a first-order derivative of the current traffic flow vehicle speed under the disturbance;
and the analysis unit is used for combining the sensitivity coefficients of a plurality of front vehicles and the response coefficients of the differences of the speeds of the plurality of front vehicles to carry out the linear stability analysis of the following vehicles.
In a third aspect, the invention provides a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement an adaptive car following method as claimed above.
In a fourth aspect, the invention provides a computer program product comprising a computer program for implementing an adaptive car following method as described above, when the computer program runs on one or more processors.
In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein a processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to make the electronic device execute the instructions for implementing the adaptive car following method as described above.
The invention has the beneficial effects that: the influence analysis of the speed difference of the following vehicles on traffic flow distribution and traffic flow stability is combined with the speed data of the multiple front vehicles, the relative speed of the vehicles and the disturbance of the starting and driving processes are considered, the safe and reliable self-adaptive following vehicles are effectively realized, and the traffic flow stability is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of vehicle speed distribution at different time steps in an OV model characteristic analysis according to an embodiment of the present invention. Fig. 1(a) is a schematic diagram when the time step is 300s, and fig. 1(b) is a schematic diagram when the time step is 1000 s.
Fig. 2 is a schematic diagram of a relationship between a vehicle distance and a vehicle speed analyzed through data simulation in the OV model feature analysis according to the embodiment of the present invention.
Fig. 3 is a schematic diagram of a movement state curve of a front vehicle of a fleet in the OV model feature analysis according to the embodiment of the present invention.
Fig. 4 is a schematic diagram of vehicle speed distribution in a steady state in the OV model feature analysis according to the embodiment of the present invention. Fig. 4(a) is a schematic diagram when L is equal to 400m, and fig. 4(b) is a schematic diagram when L is equal to 100 m.
Fig. 5 is a schematic diagram illustrating changes in position, speed, and acceleration of a vehicle during starting according to an embodiment of the present invention. Fig. 5(a) is a diagram showing a relationship between position and time, fig. 5(b) is a diagram showing a relationship between velocity and time, and fig. 5(c) is a diagram showing a relationship between acceleration and time.
Fig. 6 is a schematic graph of curves of the headway distance and the sensitivity coefficient of different following vehicle models according to the embodiment of the present invention.
Fig. 7 is a schematic diagram of vehicle speed distribution of the following vehicle model in different time steps in a disturbance state according to the embodiment of the present invention. Fig. 7(a) is a schematic diagram when the time step is 300s, and fig. 7(b) is a schematic diagram when the time step is 800 s.
Fig. 8 is a schematic diagram of the velocity distribution of all vehicles at different time steps of different vehicle following models according to the embodiment of the present invention. Fig. 8(a) is a schematic diagram when the time step is 300s, and fig. 8(b) is a schematic diagram when the time step is 1000 s.
FIG. 9 is a vehicle velocity profile over time for the improved vehicle following and OV models according to an embodiment of the present invention. In which fig. 9(a) is a distribution diagram of the OV model, and fig. 9(b) is a distribution diagram of the modified following model.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below by way of the drawings are illustrative only and are not to be construed as limiting the invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
For the purpose of facilitating an understanding of the present invention, the present invention will be further explained by way of specific embodiments with reference to the accompanying drawings, which are not intended to limit the present invention.
It should be understood by those skilled in the art that the drawings are merely schematic representations of embodiments and that the elements shown in the drawings are not necessarily required to practice the invention.
Example 1
This embodiment 1 provides a self-adaptation car following system, and this system includes:
the construction module is used for constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed difference of the plurality of front vehicles;
the judging module is used for carrying out vehicle following linear stability analysis based on the constructed vehicle following model and determining the constraint condition that the current traffic flow is in a stable state;
and the control module is used for controlling the acceleration of the current vehicle according to the constraint condition in the stable state so as to realize stable vehicle following.
Wherein the determination module comprises:
the calculation unit is used for applying vehicle position change disturbance to the following vehicle model when the adjacent vehicles have the same vehicle distance, and solving a first-order derivative of the current traffic flow vehicle speed under the disturbance;
and the analysis unit is used for combining the sensitivity coefficients of a plurality of front vehicles and the response coefficients of the differences of the speeds of the plurality of front vehicles to carry out the linear stability analysis of the following vehicles.
In this embodiment 1, the adaptive car following method is implemented by using the adaptive car following system, and includes:
constructing a following vehicle model of the current traffic flow by utilizing a construction module based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed differences of the plurality of front vehicles and combining an optimized speed function;
performing linear stability analysis of car following by using a judgment module based on the constructed car following model, and determining a constraint condition that the current traffic flow is in a stable state;
and controlling the acceleration of the current vehicle by using the control module according to the constraint condition in the stable state, so as to realize stable vehicle following.
Wherein, the car following model is constructed as follows:
wherein v isn+1(t) represents the speed of the (n + 1) th vehicle at time t, V (Δ x)n-a+1) Representing an optimized velocity function, vn-a+1(t) represents the speed of the (n-a + 1) th vehicle at time t, Δ vn＝a+1Representing the speed difference between the (N-a + 1) th vehicle and the vehicle ahead, N representing the number of vehicles, alphaaRepresenting the sensitivity factor, beta, of a number of preceding vehiclesaA response coefficient representing a difference between a plurality of preceding vehicle speeds.
Specifically, when the adjacent vehicles have the same distance, a calculation unit is utilized to apply vehicle position change disturbance to a vehicle following model, and a first-order derivative of the current traffic flow vehicle speed under disturbance is obtained;
and utilizing an analysis unit to combine the sensitivity coefficients of a plurality of front vehicles and the response coefficients of the differences of the speeds of the plurality of front vehicles to analyze the linear stability of the following vehicles.
And solving a first-order second-order derivative of the applied disturbance, and solving a first-order derivative of the speed of the current traffic flow vehicle under the disturbance through Taylor expansion and Fourier series expansion. Combining the law of luobida, the constraint conditions in the steady state are as follows: and the first derivative of the speed of the current traffic flow vehicle under disturbance is smaller than the sum of response coefficients of the difference between one half of the sensitivity coefficient of the plurality of front vehicles and the speed of the plurality of front vehicles.
Example 2
In the embodiment 2, the conventional OV model is first subjected to feature analysis, so that the motion state of the vehicle can be observed in a microscopic way, that is, the motion of the following vehicle can be modeled under the known motion of the automobile.
The optimal speed model proposed by Bando et al deals with the problem of the Newell model, and redefines the dynamic process of determining the optimal driving speed of the vehicle by the driver by using the inter-vehicle distance optimization speed function, namely:
wherein k is a sensitivity coefficient; x is the number ofn(t) and vn(t) respectively representing the position and speed of the nth vehicle at the time t; Δ xn(t)＝xn+1(t)-xn(t) represents the distance between the front vehicle and the following vehicle at the time t; v (Δ x)n(t)) is the optimized speed function, i.e.:
in the formula, vmaxThe maximum driving speed of the vehicle; h iscIs the safe distance of the vehicle.
The OV model can simulate real traffic conditions, such as: traffic stability imbalance, congestion, traffic light waiting, etc. When such stable conditions are not satisfactory, the road is out of balance for evenly distributed vehicles, which can lead to traffic jams.
With regard to the propagation of small disturbances in traffic, the analysis is divided into two cases, stable and unstable.
1) When unstable
When L is 200 and N is 100, the distance between the vehicles is 2. From the stability criteria, it is apparent that the spacing of the vehicles is in the unstable range.
As shown in fig. 1, a graph of simulated velocity at each time node is analyzed by data simulation, and the glitch added at the beginning is amplified as time increases. At the beginning of the evolution, slight disturbances will appear later due to the interaction between the vehicles, and when the disturbance spreads to all vehicles, it is not appropriate to explain this process with the linear stability theory.
It can be seen from the figure that the slight disturbance causes the stop-and-go phenomenon to occur, and in the case of a sloshing, the evolution of the vehicle condition does not occur in an unreasonable place. For example, the reverse running vehicles prohibited in actual traffic are not shown in the figure. This demonstrates that the OV model can simulate traffic conditions in real life.
As shown in fig. 2, the relationship between the vehicle distance and the vehicle speed is analyzed by data simulation. After a long time change, the vehicle is repeatedly moved, constituting a circular hysteresis. The vehicle state equilibrium is at the moment when the intensity reaches its maximum and minimum values.
The head-up situation can be obtained by data simulation, and the slope is the vehicle speed as shown in fig. 3. It can be obtained that when the time changes, the speed of the car never is negative, and the distance between the car heads is not affected to become negative. This may indicate that some actual traffic conditions may be fed back using the optimized speed function equation (2.4).
2) At the time of stabilization
Two cases of 400, 100 and 100 are provided, and the distance between the vehicles is 4 and 0.5 respectively. Fig. 4 shows a velocity diagram, where the small interference at t 0 decreases rapidly with time. Fig. 4(a) shows a case where L is 400, and fig. 4(b) shows a case where L is 100.
Helbin uses Helbin's optimization speed function to simulate the test result, shows that Bando is too high in acceleration under some circumstances, may have the traffic accident. The following is a case study of numerical simulations.
The OV model of Bando was used, where the model parameters: b1Selecting a Helbin optimization speed function as 0.85; the initial arrangement is eleven cars in a row, and the coordinate points for each car are:
xn(0)＝(n-1)s,n＝1,2,...,11 (2.5)
definition s ═ 7.6 m. When the traffic light changes from stop to forward, the speed of the first vehicle (n ═ 11) is 0. Because Helbin is adopted in the simulation to optimize the speed function, vH(7.6) ═ 0, so vn(0) 0, n is 1, 2. So that 11 vehicles are all at rest.
And (3) the evolution process of the traffic state in the case of red-to-green when t is 0. The simulation results are shown in fig. 5, which is consistent with the results given by JiangRui. From Helbin's experimental conclusion, the change in vehicle acceleration typically fluctuates within [ -4,4 ]. However, the OV model of Bando indicates that excessive acceleration is imparted at vehicle start-up. Except at the time of the match, this situation is rare in real traffic.
In conclusion, the OV model discusses the propagation of small interference in vehicles and the starting process of traffic lights in a data simulation manner, thereby proving the deficiency of the OV model. Because the OV model does not determine accurate simulation in all traffic conditions, the OV model may not be fully applicable to actual traffic, such as traffic problems of congestion, rear-end collision, etc. due to stagnation that may occur when a traffic light is started.
For this reason, in embodiment 2, based on the acquisition of the information of the multiple preceding vehicles at 5G, an improved OV model more suitable for the traffic characteristics is provided by considering the influence of the position and the speed on the following vehicles. In an actual traffic environment, there is a delay in response to a vehicle front end impact, typically including a driver's response delay time and an engine delay time. During driving, the driver is affected not only by the speed of the adjacent preceding vehicle, but also by many vehicle conditions in front of the driver. The leading vehicle motion data can be obtained in advance in consideration of the speed data of a plurality of leading vehicles, so that the driver can accelerate and decelerate at one step earlier. Shortening the delay time to avoid frequent speed changes of the driver contributes to the stability of the traffic flow. Therefore, it is in accordance with the law to take into account the influence of the position and speed of many vehicles in front of the vehicle.
Based on the above analysis, the following optimization model will be proposed taking into account the influence of the speeds of the preceding n vehicles on the following vehicle:
wherein v isn+1(t) represents the speed of the (n + 1) th vehicle at time t, V (Δ x)n-a+1) Representing an optimized velocity function, vn-a+1(t) represents the speed of the (n-a + 1) th vehicle at time t, Δ vn＝a+1Representing the speed difference between the (N-a + 1) th vehicle and the vehicle ahead, N representing the number of vehicles, alphaaRepresenting the sensitivity factor, beta, of a number of preceding vehiclesaA response coefficient representing a difference between a plurality of preceding vehicle speeds.
In this example 2, the above optimization model was subjected to linear stability analysis:
assuming that the initial state is uniform and stable, i.e. the vehicles have the same vehicle headway of y, the corresponding vehicle speed is v (y), i.e.:
wherein the content of the first and second substances,
indicates the initial timen vehicle positions.
Adding a disturbance y to the above equation (3.2)n(t), obtaining:
the first and second derivatives are obtained from this equation (3.3), and the result is taken back to equation (3.1) to obtain:
from the Taylor expansion:
and (3) expanding according to a Fourier series to obtain:
wherein alpha iskRepresenting the sensitivity coefficient of the kth vehicle, wherein a is more than or equal to k is more than or equal to b; i denotes an imaginary part.
Let Z be r + ω i, r be 0:
wherein ω represents an angular frequency; the expression obtained by substituting Z ═ r + ω i into 3.6 can be obtained by making the real-imaginary part of equation (3.7) zero:
wherein σcAnd σsIs a temporary parameter or function generated during the computational derivation. Bringing formula (3.9) back to formula (3.8) gives:
v' (b) represents the derivative of the optimal speed function.
From the law of lobida, the stability constraints are:
in this example 2, performance analysis and numerical simulation experiments were performed on the improved model:
the improved model is built by adding a plurality of angles of influence of the leading vehicle speed data on the following vehicle speed. Data simulation is used to observe the evolution process of the model. In addition, to better illustrate the improvement in performance of the model, the difference in distinction between the improved model and the OV model was compared.
As disturbances propagate in the vehicle flow:
the data simulation uses the function as follows:
V(Δxn)＝tanh(Δxn-2)-tanh2 (4.1)
assuming that the road length L is 200, the number of vehicles N is 100, the initial condition is uniform and stable, and a small disturbance is added, that is:
convenient simulation, other parameters are set as follows:
b＝2，α1＝2.0，
β1＝0.2，β2＝0.15。
the vehicle speeds of all vehicles at 300s and 800s are shown in fig. 6. As shown in fig. 6, the variation in the vehicle speed is small, but there is also a smooth range where a part of the vehicle moves almost simultaneously and at the lowest speed. The model feedbacks the safe driving condition of the driver, and basically has consistency with the actual condition of the driver.
Stability of the comparative model:
as shown in fig. 7, it is explained that the influence of the speed data of a plurality of leading vehicles on the speed of the following vehicle is beneficial to increase the stability, and the model results are compared by using data simulation. The road length L is 1500m, and the number of vehicles N is 100.
And taking the other parameters: b 2.01, alpha1＝1.42s-1，
β1＝0.21，β2＝0.16。
As shown in fig. 8 and 9, the velocity change of the improved model is reduced, the low-velocity motion range is large, but the evolution wave height of the OV model is concentrated and oscillates up and down. In addition, the amplitude of the evolution wave of the improved model is small, the stable state is recovered in a short time, the information sharing in the 5G environment is utilized, the following system based on the data of the multiple front vehicles can effectively simulate the motion state of the vehicle, and the design method of the model is reasonable.
In summary, in this embodiment 2, through numerical simulation, the optimal velocity model is compared with the improved model, and the relative velocity is considered and the start and the process are focused on. Compared with the optimal speed model, the data simulation result shows that the speed data of multiple front vehicles has larger influence on traffic flow distribution and traffic flow stability caused by the speed difference of following vehicles, the improved model is helpful for increasing the stability characteristic of the traffic flow, and the self-adaptive following is effectively realized.
Example 3
constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed differences of the plurality of front vehicles;
performing linear stability analysis of car following based on the constructed car following model, and determining a constraint condition that the current traffic flow is in a stable state;
and controlling the acceleration of the current vehicle according to the constraint condition in the stable state, so as to realize stable vehicle following.
Example 4
An embodiment 4 of the present invention provides a computer program (product) comprising a computer program for implementing an adaptive car following method as described above when the computer program runs on one or more processors, the method comprising:
constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed differences of the plurality of front vehicles;
performing linear stability analysis of car following based on the constructed car following model, and determining a constraint condition that the current traffic flow is in a stable state;
and controlling the acceleration of the current vehicle according to the constraint condition in the stable state, so as to realize stable vehicle following.
Example 5
An embodiment 5 of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein a processor is connected to a memory, a computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to make the electronic device execute instructions for implementing the adaptive car following method as described above, the method includes:
constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed differences of the plurality of front vehicles;
performing linear stability analysis of car following based on the constructed car following model, and determining a constraint condition that the current traffic flow is in a stable state;
and controlling the acceleration of the current vehicle according to the constraint condition in the stable state, so as to realize stable vehicle following.
In summary, the embodiments of the present invention are described.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts based on the technical solutions disclosed in the present invention.
Claims (10)
1. An adaptive car following method, comprising:
constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed differences of the plurality of front vehicles;
performing linear stability analysis of car following based on the constructed car following model, and determining a constraint condition that the current traffic flow is in a stable state;
and controlling the acceleration of the current vehicle according to the constraint condition in the stable state, so as to realize stable vehicle following.
2. The adaptive car following method according to claim 1, wherein the car following model is constructed by:
wherein v isn+1(t) represents the speed of the (n + 1) th vehicle at time t，V(Δxn-a+1) Representing an optimized velocity function, vn-a+1(t) represents the speed of the (n-a + 1) th vehicle at time t, Δ vn＝a+1Representing the speed difference between the (N-a + 1) th vehicle and the vehicle ahead, N representing the number of vehicles, alphaaRepresenting the sensitivity factor, beta, of a number of preceding vehiclesaA response coefficient representing a difference between a plurality of preceding vehicle speeds.
3. The adaptive car following method according to claim 2, wherein:
when the adjacent vehicles have the same distance, applying vehicle position change disturbance to the vehicle following model, and solving a first-order derivative of the current traffic flow vehicle speed under disturbance;
and (4) combining the sensitivity coefficients of a plurality of front vehicles and the response coefficients of the difference between the speeds of the plurality of front vehicles to perform the linear stability analysis of the following vehicles.
4. The adaptive car following method according to claim 3, wherein a first-order second-order derivative is obtained for the applied disturbance, and a current traffic flow vehicle speed first-order derivative under the disturbance is obtained through Taylor expansion and Fourier series expansion.
5. The adaptive car following method according to claim 3, wherein in combination with the law of lopida, the constraints at steady state are: and the first derivative of the speed of the current traffic flow vehicle under disturbance is smaller than the sum of response coefficients of the difference between one half of the sensitivity coefficient of the plurality of front vehicles and the speed of the plurality of front vehicles.
6. An adaptive car following system, comprising:
the construction module is used for constructing a following vehicle model of the current traffic flow by combining an optimized speed function based on the motion speed data of a plurality of front vehicles, the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the speed difference of the plurality of front vehicles;
the judging module is used for carrying out vehicle following linear stability analysis based on the constructed vehicle following model and determining the constraint condition that the current traffic flow is in a stable state;
and the control module is used for controlling the acceleration of the current vehicle according to the constraint condition in the stable state so as to realize stable vehicle following.
7. The adaptive vehicle following system according to claim 6, wherein the determination module comprises:
the calculation unit is used for applying vehicle position change disturbance to the following vehicle model when the adjacent vehicles have the same vehicle distance, and solving a first-order derivative of the current traffic flow vehicle speed under the disturbance;
and the analysis unit is used for combining the sensitivity coefficients of a plurality of front vehicles and the response coefficients of the differences of the speeds of the plurality of front vehicles to carry out the linear stability analysis of the following vehicles.
8. A non-transitory computer-readable storage medium storing computer instructions which, when executed by a processor, implement the adaptive car following method according to any one of claims 1-5.
9. A computer program product, comprising a computer program for implementing an adaptive car following method according to any one of claims 1 to 5, when the computer program is run on one or more processors.
10. An electronic device, comprising: a processor, a memory, and a computer program; wherein a processor is connected with a memory, a computer program being stored in the memory, the processor executing the computer program stored by the memory when the electronic device is running, to cause the electronic device to execute instructions implementing the adaptive car following method according to any of the claims 1-5.
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