CN114355883B - Self-adaptive car following method and system - Google Patents
Self-adaptive car following method and system Download PDFInfo
- Publication number
- CN114355883B CN114355883B CN202111443522.5A CN202111443522A CN114355883B CN 114355883 B CN114355883 B CN 114355883B CN 202111443522 A CN202111443522 A CN 202111443522A CN 114355883 B CN114355883 B CN 114355883B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- speed
- vehicles
- following
- traffic flow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000035945 sensitivity Effects 0.000 claims abstract description 31
- 238000004458 analytical method Methods 0.000 claims abstract description 30
- 230000004044 response Effects 0.000 claims abstract description 28
- 230000001133 acceleration Effects 0.000 claims abstract description 17
- 230000006870 function Effects 0.000 claims description 27
- 238000004590 computer program Methods 0.000 claims description 22
- 230000003044 adaptive effect Effects 0.000 claims description 16
- 230000008859 change Effects 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000009826 distribution Methods 0.000 abstract description 8
- 230000008569 process Effects 0.000 abstract description 8
- 238000010586 diagram Methods 0.000 description 23
- 238000004088 simulation Methods 0.000 description 17
- 230000000694 effects Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005755 formation reaction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention provides a self-adaptive vehicle following method and a self-adaptive vehicle following system, which belong to the technical field of intelligent traffic control, and construct a vehicle following model of current traffic flow based on motion speed data of a plurality of front vehicles, sensitivity coefficients of the plurality of front vehicles and response coefficients of speed differences of the plurality of front vehicles and by combining an optimized speed function; based on the constructed vehicle following model, performing vehicle following linear stability analysis, and determining constraint conditions of the current traffic flow 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 following. According to the method, the speed difference of the following vehicles is combined with the speed data of the multiple front vehicles to influence the traffic flow distribution and the traffic flow stability influence analysis, the relative speed of the vehicles and the disturbance in the starting and driving processes are considered, the safe and reliable self-adaptive following is 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 vehicle following method and system for acquiring driving information of a plurality of vehicles in front based on a current vehicle.
Background
The study of the following problem not only can be used for traffic simulation, but also has great significance for the autonomous navigation management and control system of the vehicle and the unmanned technique. The following model is now the research focus technology in the transportation field and the vehicle control field.
The traffic flow model is a key measure step for researching and discussing traffic flow theory, and is also a means and mode for exploring actual traffic phenomenon. The traffic flow microscopic model comprises a cellular automaton model and a following model. Traffic flow is defined in a model as a pattern of discrete particle formations, with a single vehicle as a representation, and the effect of each vehicle is studied to understand the characteristics of the traffic flow. Second, from a mechanical point of view, it is a particle mechanical model. It is assumed that the fleet of each vehicle must be maintained at a distance from the preceding vehicle to prevent a collision of the vehicle, or that the following vehicle slows down according to the preceding vehicle under consideration, the effect of the vehicle's reaction to stimuli and uncertainty in the vehicle's movement resulting in a relationship between the preceding vehicle and the following vehicle.
The following theory was first proposed by pins, mainly using a stimulus-response model, and using differential equations to analyze and describe the phenomenon of following under vehicle following conditions. It is assumed that each following vehicle needs to keep a distance proportional to the speed of the preceding vehicle and the sum of the minimum safe distance between the two vehicles when the vehicle is parked when driving, so that the safe driving of the vehicle can be ensured, and a following model is provided.
The following model proposed by pins does not take into account the time for the following vehicle driver to react to driving changes in the preceding vehicle. Taking this effect into account, chandler and Herman et al propose improved vehicle models that take into account the delay in the driver's response. Kometani and Sasaki conducted this study to the same conclusion that in order to overcome the disadvantage that the following vehicle will not give feedback no matter how far the two workshops are from when the front and rear vehicle speeds of the model are equal, gazis et al set up a nonlinear model in relation to the distance between the two vehicle heads but the following vehicle will not react, with the sensitivity coefficient not being a fixed value.
Bando et al build an Optimal Velocity (OV) model that can address many of the issues of real traffic flow, such as traffic imbalance, stop-and-go, etc. Helbing and Tilch. The model solves the problem that the optimal speed (OV) model is excessively accelerated, and compared with actual measurement data, the model has better consistency than the optimal speed model, but the model needs seven specified parameters.
Jiang Rui et al, on the basis of an Optimal Velocity (OV) model and a Generalized Force (GF) model, consider that the following vehicle is to adjust its own velocity so that the following vehicle can still have acceleration when the inter-vehicle distance is smaller than a safe inter-vehicle distance, and construct a completely new full velocity difference model. He Min et al comprehensively summarize the history and discussion status of the vehicle following models; gu Hongfei et al build a desired pitch model. Xue Yu et al, consider the relative speed from vehicle to vehicle and build an optimal speed model. Wang Xiaoyuan et al, comprehensive overview of a safe-interval-based model, illustrate the construction of thought and kernel algorithms, and generalize the advantages and disadvantages of the algorithms. Wang Haojian constructs a fuzzy inference model obtained by using a neural network; wang Hao and Ma Shoufeng consider an optimized vehicle-following model constructed in a sloped and curved road environment.
The vehicle following model is the most important component of traffic flow, although the existing following models described above are capable of observing the state of motion of the vehicle in microscopic terms, that is, the motion of the following vehicle can be modeled under known motion of the vehicle. However, it only considers the influence of the speed difference between two vehicles and the following vehicle, when two vehicles travel at the same speed, the following vehicle does not react regardless of the distance of the vehicles, which is against reality, 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 vehicle following method and a self-adaptive vehicle following system which simultaneously consider the influence of vehicle position data and speed data on traffic stability and improve traffic flow stability and vehicle following reliability, so as to solve at least one technical problem in the background technology.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention provides a self-adaptive car following method, which comprises the following steps:
based on the movement speed data of a plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles, constructing a vehicle following model of the current traffic flow by combining the optimized speed function;
Based on the constructed vehicle following model, performing vehicle following linear stability analysis, and determining constraint conditions of the current traffic flow 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 following.
Preferably, the built following model is:
；
Wherein, Represent the firstVehicle atThe speed at which the time of day is reached,Representing the function of the optimized speed,Represent the firstVehicle atThe speed at which the time of day is reached,Represent the firstThe speed difference between the vehicle and the vehicle in front of it,The number of vehicles is indicated and,Representing the sensitivity coefficients of a plurality of front vehicles,A response coefficient indicating a difference between the speeds of the plurality of preceding vehicles. Wherein the sensitivity coefficient is the inverse of the vehicle adaptation time, and is as shown in equation 2.1The model is similar, but can be set according to different conditions under different models, and is usually larger than 1; the sensitivity to the vehicle speed differential is also typically the inverse of time, but typically takes a value greater than zero and less than 1, typically 0.6s -1.
Preferably, when the adjacent vehicles have the same vehicle distance, disturbance caused by vehicle position change is applied to the following vehicle model, and the first derivative of the current traffic flow vehicle speed under the disturbance is obtained;
And carrying out vehicle following linear stability analysis by combining the response coefficients of the differences between the sensitivity coefficients of the plurality of front vehicles and the speeds of the plurality of front vehicles.
Preferably, the first and second derivatives of the applied disturbance are obtained, and the first derivative of the current traffic flow vehicle speed under the disturbance is obtained by Taylor expansion and Fourier series expansion.
Preferably, in combination with the lobby rule, the constraints in the steady state are: the first derivative of the current traffic flow vehicle speed under disturbance is less than the sum of the response coefficients of the differences between the half of the sensitivity coefficients of the plurality of lead vehicles and the plurality of lead vehicle speeds.
In a second aspect, the present invention provides an adaptive vehicle following system comprising:
The construction module is used for constructing a vehicle following model of the current traffic flow based on the motion speed data of the plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles by combining the optimized speed function;
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 of the current traffic flow 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 following.
Preferably, the determining module includes:
The calculation unit is used for applying disturbance caused by vehicle position change to the following model when the adjacent vehicles have the same vehicle distance, and obtaining the first derivative of the current traffic flow vehicle speed under the disturbance;
And the analysis unit is used for carrying out the following linear stability analysis by combining the response coefficients of the differences between the sensitivity coefficients of the plurality of front vehicles and the speeds of the plurality of front vehicles.
In a third aspect, the present invention provides a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement an adaptive vehicle following method as described 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 run on one or more processors.
In a fifth aspect, the invention features an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory, which processor executes the computer program stored in the memory when the electronic device is running, to cause the electronic device to execute instructions for implementing the adaptive following method as described above.
The invention has the beneficial effects that: the speed difference of the following vehicles is combined with the speed data of the multiple front vehicles to influence the traffic flow distribution and the traffic flow stability influence analysis, the relative speed of the vehicles and the disturbance in 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.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of vehicle speed distribution at different time steps in an OV model feature analysis according to an embodiment of the present invention. Fig. 1 (a) is a schematic diagram of a case where the time step is 300s, and fig. 1 (b) is a schematic diagram of a case where the time step is 1000 s.
Fig. 2 is a schematic diagram of a relationship between a vehicle distance and a vehicle speed, which are analyzed through data simulation in an OV model feature analysis according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a movement state curve of a fleet head in the OV model feature analysis according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a vehicle speed distribution in a steady state in an OV model feature analysis according to an 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 of changes in position, speed and acceleration of a vehicle during start according to an embodiment of the present invention. Fig. 5 (a) is a position-time change map, fig. 5 (b) is a velocity-time change map, and fig. 5 (c) is an acceleration-time change map.
Fig. 6 is a schematic diagram of curves of head distances and sensitivity coefficients of different following models according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a vehicle speed distribution of a following model in different time steps under a disturbance state according to an embodiment of the present invention. Fig. 7 (a) is a schematic diagram of 300s, and fig. 7 (b) is a schematic diagram of 800 s.
FIG. 8 is a schematic view showing the speed distribution of all vehicles at different time steps according to the different following models of the present invention. Fig. 8 (a) is a schematic diagram of the case where the time step is 300s, and fig. 8 (b) is a schematic diagram of the case where the time step is 1000 s.
FIG. 9 is a graph showing the velocity profile of a vehicle over time for an improved vehicle following model and OV model in accordance with an embodiment of the present invention. Fig. 9 (a) is a distribution diagram of the OV model, and fig. 9 (b) is a distribution diagram of the improved 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 like or similar elements throughout or elements having like or similar functionality. The embodiments described below by way of the drawings are exemplary only and should not 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 expressly stated otherwise, as understood by those skilled in the art. 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, and/or groups thereof.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present 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, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
In order that the invention may be readily understood, a further description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings and are not to be construed as limiting embodiments of the invention.
It will be appreciated by those skilled in the art that the drawings are merely schematic representations of examples and that the elements of the drawings are not necessarily required to practice the invention.
Example 1
Embodiment 1 provides an adaptive vehicle following system, which includes:
The construction module is used for constructing a vehicle following model of the current traffic flow based on the motion speed data of the plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles by combining the optimized speed function;
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 of the current traffic flow 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 following.
Wherein, the decision module includes:
The calculation unit is used for applying disturbance caused by vehicle position change to the following model when the adjacent vehicles have the same vehicle distance, and obtaining the first derivative of the current traffic flow vehicle speed under the disturbance;
And the analysis unit is used for carrying out the following linear stability analysis by combining the response coefficients of the differences between the sensitivity coefficients of the plurality of front vehicles and the speeds of the plurality of front vehicles.
In this embodiment 1, an adaptive vehicle following method is implemented by using the adaptive vehicle following system, including:
The method comprises the steps that a construction module is utilized, and a vehicle following model of a current traffic flow is constructed based on motion speed data of a plurality of front vehicles, sensitivity coefficients of the plurality of front vehicles and response coefficients of speed differences of the plurality of front vehicles in combination with an optimized speed function;
Based on the constructed following model, carrying out following linear stability analysis by utilizing a judging module, and determining constraint conditions of the current traffic flow 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 following.
The built car following model is as follows:
；
Wherein, Represent the firstVehicle atThe speed at which the time of day is reached,Representing the function of the optimized speed,Represent the firstVehicle atThe speed at which the time of day is reached,Represent the firstThe speed difference between the vehicle and the vehicle in front of it,The number of vehicles is indicated and,Representing the sensitivity coefficients of a plurality of front vehicles,A response coefficient indicating a difference between the speeds of the plurality of preceding vehicles.
Specifically, when adjacent vehicles have the same vehicle distance, a calculation unit is utilized to apply disturbance caused by vehicle position change to a following vehicle model, and a first derivative of the current traffic flow vehicle speed under the disturbance is obtained;
And carrying out the following linear stability analysis by using an analysis unit in combination with response coefficients of the differences between the sensitivity coefficients of the plurality of front vehicles and the speeds of the plurality of front vehicles.
And (3) obtaining a first-order second derivative of the applied disturbance, and obtaining a first-order derivative of the current traffic flow vehicle speed under the disturbance by Taylor expansion and Fourier series expansion. Combining with the lobida rule, the constraint conditions under the steady state are: the first derivative of the current traffic flow vehicle speed under disturbance is less than the sum of the response coefficients of the differences between the half of the sensitivity coefficients of the plurality of lead vehicles and the plurality of lead vehicle speeds.
Example 2
In this embodiment 2, first, a conventional OV model is subjected to a feature analysis, and a vehicle motion state can be observed in a microscopic aspect, that is, a motion of a following vehicle can be modeled under a known vehicle motion.
The optimal speed model proposed by Bando et al addresses the problem of the Newell model by redefining the driver's dynamic process of determining the optimal driving speed of the vehicle using the inter-vehicle distance optimized speed function, namely:
（2.1）
Wherein, Is a sensitivity coefficient; And Respectively represent the firstVehicle atThe position and speed of the moment; the distance between the front vehicle and the following vehicle at the time t is shown; to optimize the speed function, namely:
（2.2）
In the method, in the process of the invention, Is the maximum running speed of the vehicle; is the safe distance of the vehicle.
The OV model is capable of simulating realistic traffic conditions, such as: traffic stability imbalance, congestion, traffic light waiting, etc. When such stable conditions are not satisfactory, traffic jams and road imbalance may result for an evenly distributed vehicle.
The small interference is analyzed in terms of propagation in traffic, which is divided into stable and unstable cases.
1) When unstable
When l=200, n=100, vehicle-to-vehicle spacing=L/n=2. From the stability criteria, it is apparent that the spacing of the vehicles is in an unstable range.
As shown in fig. 1, the graph of the analog velocity at each time node is analyzed by data simulation, and the small disturbance added at the beginning is amplified as time increases. At the beginning of evolution, a slight disturbance will occur later due to interactions between vehicles, and it is not appropriate to explain this process with linear stability theory when the disturbance spreads to all vehicles.
It can be seen from the figure that slight disturbances cause stop-and-go phenomena to occur and that in the case of a ringing, the evolution of the vehicle conditions does not occur where it is not reasonable. Such as those prohibited in actual traffic, are not shown. This demonstrates that the OV model is able to simulate traffic conditions in real life.
As shown in fig. 2, the relationship between the vehicle distance and the vehicle speed is analyzed by the data simulation. After a long time of change, the vehicle repeatedly moves, constituting a loop-type hysteresis. The vehicle state balance is the time when the density reaches its maximum and minimum values.
By using the data simulation, the head-car situation can be obtained, and the slope is the car speed as shown in fig. 3. It can be obtained that the speed of the car is never negative when the time varies, and the distance between the car heads is not affected to be negative. This may indicate that some actual traffic conditions may be fed back using the optimized speed function equation (2.4).
（2.4）
2) At the time of stabilization
Setting two cases of L=400, N=100 and L=100, and N=100, wherein the intervals between vehicles are respectively as follows=4 Sum=0.5. Figure 4 shows a velocity profile of the device,The small interference at=0 is rapidly reduced by time. Fig. 4 (a) shows a case where l=400, and fig. 4 (b) shows a case where l=100.
Helbing the test results were simulated using the optimized speed function of Helbing, indicating that Bando is under certain conditions with too high acceleration, and that there may be traffic accidents. The following is a case study of numerical modeling.
An OV model of Bando is used, where model parameters: Selecting Helbing an optimized speed function; initially arranged in a row of eleven vehicles, the coordinate points of each vehicle are:
（2.5）
Definition of the definition =7.6M. When the traffic light is changed from stop to forward, the first vehicle is) The speed is 0. Since Helbing is used in the simulation to optimize the speed function,Therefore, it is. Thus 11 vehicles are all at rest.
The evolution of traffic conditions over time in the case of red-to-green when t=0. The simulation results are shown in fig. 5, consistent with those given by Jiang Rui. From Helbing experiments, it was concluded that changes in vehicle acceleration generally fluctuate within [ -4,4 ]. However, the OV model for Bando indicates that excessive acceleration was given when the vehicle was started. This is rarely the case in real traffic except when racing.
In summary, the OV model uses a data simulation to discuss the propagation of small disturbances in the vehicle and the start-up procedure of traffic lights, thereby proving the inadequacies of the OV model. Because the OV model does not determine accurate simulation in all traffic conditions, the OV model cannot be fully applied to actual traffic, such as traffic problems of congestion, rear-end collision and the like caused by stagnation which may occur when a traffic light is started.
For this reason, in this embodiment 2, based on the acquisition of the multi-lead vehicle information at 5G, an improved OV model more suitable for traffic characteristics is provided by taking into consideration the influence of the position and speed on the train of the plurality of vehicles. In an actual traffic environment, there is a delay in responding to a front end impact of a vehicle, and a response delay time of a driver and an engine delay time are generally included. During driving, the driver is not only affected by the speed of the adjacent preceding vehicle, but also changes due to a number of vehicle conditions in front of the driver. The lead vehicle motion data may be obtained in advance in consideration of the speed data of a plurality of lead vehicles so that the driver accelerates and decelerates earlier by one step. The delay time is shortened to avoid frequent speed changes of the driver, which contributes to the stability of the traffic flow. Therefore, it is regular to consider 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 vehicles:
；
Wherein, Represent the firstVehicle atThe speed at which the time of day is reached,Representing the function of the optimized speed,Represent the firstVehicle atThe speed at which the time of day is reached,Represent the firstThe speed difference between the vehicle and the vehicle in front of it,The number of vehicles is indicated and,Representing the sensitivity coefficients of a plurality of front vehicles,A response coefficient indicating a difference between the speeds of the plurality of preceding vehicles.
In this example 2, the linear stability analysis was performed on the above-described optimization model:
assuming an initial condition of uniformity and stability, i.e. the vehicles have the same distance between the heads of the vehicles The corresponding vehicle speed isThe method comprises the following steps:
（3.2）
Wherein, Indicating the initial timeThe position of the vehicle.
Adding a disturbance to the above-mentioned formula (3.2)Obtaining:
（3.3）
taking the first and second derivatives of this equation (3.3) and bringing the result back into equation (3.1) yields:
（3.4）
obtained by taylor expansion:
（3.5）
The method is developed according to the Fourier series:
（3.6）
Wherein, Represent the firstThe sensitivity coefficient of the vehicle is set to be,；Representing the imaginary part.
Order the，The method can obtain:
（3.7）
Wherein, Represents angular frequency; will beSubstituting 3.6 to obtain the expression
Let the real and imaginary parts of (3.7) be zero, it is possible to obtain:
（3.8）
（3.9）
Wherein, AndIs a temporary parameter or function generated during the computational derivation.
Bringing formula (3.9) back to formula (3.8) yields:
（3.10）
Representing the derivative of the optimal speed function.
The stability constraint is obtained from the lobida rule:
（3.11）
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 lead vehicle speed data on the speed of the following vehicle. Data simulation is used to observe the evolution 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 the disturbance propagates in the vehicle stream:
the data simulation usage functions are as follows:
（4.1）
Set road length Number of vehiclesThe initial conditions are uniform and stable, and a small interference is added, namely:
（4.2）
The simulation is convenient, and other parameters are set as follows:
，，，，，。
The vehicle speeds of all vehicles at 300s and 800s are shown in fig. 6. As shown in fig. 6, the change in vehicle speed is small, but there is also a range in which some of the vehicles move almost simultaneously and are stationary at the lowest speed. The simulation describes that the model feeds back 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 advantageous to elucidate the speed effect of a plurality of lead vehicle speed data on the following vehicle to increase the stability, and the model results are compared using data simulation. Length of the road Number of vehicles。
（4.3）
The rest parameters are valued:，，，，，。
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 evolutionary wave height of the OV model is concentrated and oscillates up and down. In addition, the improved model has smaller evolution wave amplitude, and can recover a stable state in a short time, and the following system based on the multi-front vehicle data can effectively simulate the motion state of the vehicle by utilizing information sharing in a 5G environment, so that the design method of the improved model is reasonable.
In summary, in this example 2, the optimal velocity model was compared with the modified model by numerical simulation, and the relative velocity was considered and the start-up and process were focused. Compared with an optimal speed model, the data simulation result shows that the speed difference of the following vehicles is greatly influenced by the speed data of the multiple front vehicles, so that the stability characteristics of the improved model, which are beneficial to increasing the traffic flow, are greatly influenced, and the self-adaptive following vehicles are effectively realized.
Example 3
Embodiment 3 of the present invention provides a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement an adaptive following method as described above, the method comprising:
based on the movement speed data of a plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles, constructing a vehicle following model of the current traffic flow by combining the optimized speed function;
Based on the constructed vehicle following model, performing vehicle following linear stability analysis, and determining constraint conditions of the current traffic flow 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 following.
Example 4
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 run on one or more processors, the method comprising:
based on the movement speed data of a plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles, constructing a vehicle following model of the current traffic flow by combining the optimized speed function;
Based on the constructed vehicle following model, performing vehicle following linear stability analysis, and determining constraint conditions of the current traffic flow 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 following.
Example 5
Embodiment 5 of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is coupled to the memory and the computer program is stored in the memory, the processor executing the computer program stored in the memory when the electronic device is running to cause the electronic device to execute instructions for implementing an adaptive following method as described above, the method comprising:
based on the movement speed data of a plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles, constructing a vehicle following model of the current traffic flow by combining the optimized speed function;
Based on the constructed vehicle following model, performing vehicle following linear stability analysis, and determining constraint conditions of the current traffic flow 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 following.
In summary, the embodiments of the present invention are described.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it should be understood that various changes and modifications could be made by one skilled in the art without the need for inventive faculty, which would fall within the scope of the invention.
Claims (6)
1. An adaptive vehicle following method, comprising:
based on the movement speed data of a plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles, constructing a vehicle following model of the current traffic flow by combining the optimized speed function;
Based on the constructed vehicle following model, performing vehicle following linear stability analysis, and determining constraint conditions of the current traffic flow in a stable state;
Controlling the acceleration of the current vehicle according to the constraint condition in the stable state to realize stable following;
The constructed car following model is as follows:
；
Wherein, Represent the firstVehicle atThe speed at which the time of day is reached,Representing the function of the optimized speed,Represent the firstVehicle atThe speed at which the time of day is reached,Represent the firstThe speed difference between the vehicle and the vehicle in front of it,The number of vehicles is indicated and,Representing the sensitivity coefficients of a plurality of front vehicles,A response coefficient indicating a difference between the speeds of the plurality of front vehicles;
when the adjacent vehicles have the same vehicle distance, disturbance caused by vehicle position change is applied to the following vehicle model, and the first derivative of the current traffic flow vehicle speed under the disturbance is obtained;
combining the sensitivity coefficients of the plurality of front vehicles and the response coefficients of the differences between the speeds of the plurality of front vehicles to perform vehicle following linear stability analysis;
Combining with the lobida rule, the constraint conditions under the steady state are: the first derivative of the current traffic flow vehicle speed under disturbance is less than the sum of the response coefficients of the differences between the half of the sensitivity coefficients of the plurality of lead vehicles and the plurality of lead vehicle speeds.
2. The adaptive vehicle following method of claim 1, wherein the first and second derivatives of the applied disturbance are derived from taylor expansion and fourier series expansion to derive the first derivative of the current traffic flow vehicle speed under the disturbance.
3. An adaptive vehicle following system, comprising:
The construction module is used for constructing a vehicle following model of the current traffic flow based on the motion speed data of the plurality of front vehicles, the sensitivity coefficient of the plurality of front vehicles and the response coefficient of the speed difference of the plurality of front vehicles by combining the optimized speed function;
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 of the current traffic flow in a stable state;
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 following;
The constructed car following model is as follows:
；
Wherein, Represent the firstVehicle atThe speed at which the time of day is reached,Representing the function of the optimized speed,Represent the firstVehicle atThe speed at which the time of day is reached,Represent the firstThe speed difference between the vehicle and the vehicle in front of it,The number of vehicles is indicated and,Representing the sensitivity coefficients of a plurality of front vehicles,A response coefficient indicating a difference between the speeds of the plurality of front vehicles;
The determination module includes:
The calculation unit is used for applying disturbance caused by vehicle position change to the following model when the adjacent vehicles have the same vehicle distance, and obtaining the first derivative of the current traffic flow vehicle speed under the disturbance;
The analysis unit is used for carrying out vehicle following linear stability analysis by combining the response coefficients of the differences between the sensitivity coefficients of the plurality of front vehicles and the speeds of the plurality of front vehicles;
Combining with the lobida rule, the constraint conditions under the steady state are: the first derivative of the current traffic flow vehicle speed under disturbance is less than the sum of the response coefficients of the differences between the half of the sensitivity coefficients of the plurality of lead vehicles and the plurality of lead vehicle speeds.
4. A non-transitory computer readable storage medium storing computer instructions which, when executed by a processor, implement the adaptive vehicle following method of any of claims 1-2.
5. A computer program product comprising a computer program for implementing the adaptive vehicle following method according to any of claims 1-2 when run on one or more processors.
6. An electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and wherein the computer program is stored in the memory, which processor, when the electronic device is running, executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the adaptive vehicle following method according to any of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111443522.5A CN114355883B (en) | 2021-11-30 | 2021-11-30 | Self-adaptive car following method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111443522.5A CN114355883B (en) | 2021-11-30 | 2021-11-30 | Self-adaptive car following method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114355883A CN114355883A (en) | 2022-04-15 |
| CN114355883B true CN114355883B (en) | 2024-07-19 |
Family
ID=81097158
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111443522.5A Active CN114355883B (en) | 2021-11-30 | 2021-11-30 | Self-adaptive car following method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114355883B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110979309A (en) * | 2019-12-23 | 2020-04-10 | 北京航空航天大学 | Vehicle following model stability control method considering driver perception error |
| CN113012426A (en) * | 2021-02-04 | 2021-06-22 | 山东师范大学 | Car following method and system under mixed traffic flow |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3520331B2 (en) * | 2000-11-27 | 2004-04-19 | 国土交通省国土技術政策総合研究所長 | Traffic flow simulation device |
| CN106407563B (en) * | 2016-09-20 | 2020-03-27 | 北京工业大学 | Following model generation method based on driving type and front vehicle acceleration information |
| CN107103749B (en) * | 2017-05-19 | 2020-03-10 | 长安大学 | Following traffic flow characteristic modeling method under Internet of vehicles environment |
| CN113219962B (en) * | 2021-02-26 | 2023-02-28 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | Control method and system for hybrid queue following safety and storage medium |
-
2021
- 2021-11-30 CN CN202111443522.5A patent/CN114355883B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110979309A (en) * | 2019-12-23 | 2020-04-10 | 北京航空航天大学 | Vehicle following model stability control method considering driver perception error |
| CN113012426A (en) * | 2021-02-04 | 2021-06-22 | 山东师范大学 | Car following method and system under mixed traffic flow |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114355883A (en) | 2022-04-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113219962B (en) | Control method and system for hybrid queue following safety and storage medium | |
| Zhang et al. | Data-driven based cruise control of connected and automated vehicles under cyber-physical system framework | |
| CN111341104B (en) | Speed time-lag feedback control method of traffic flow following model | |
| Kuutti et al. | End-to-end reinforcement learning for autonomous longitudinal control using advantage actor critic with temporal context | |
| CN112965501A (en) | Automatic driving speed planning method and device | |
| Zhu et al. | LMI-based synthesis of string-stable controller for cooperative adaptive cruise control | |
| Al-Gabalawy et al. | Model predictive control for a basic adaptive cruise control | |
| CN113291304B (en) | Control method, device and system for automatic driving vehicle | |
| CN114074680B (en) | Vehicle channel change behavior decision method and system based on deep reinforcement learning | |
| Richardson et al. | Reinforcement learning with communication latency with application to stop-and-go wave dissipation | |
| CN114355883B (en) | Self-adaptive car following method and system | |
| CN113391553B (en) | Self-adaptive optimal control method of heterogeneous CACC system with actuator saturation | |
| CN111221329B (en) | Autonomous vehicle queuing interval control method | |
| CN112622922A (en) | Non-autonomous vehicle speed prediction with autonomous vehicle reference | |
| KR101930163B1 (en) | Apparatus and method for lane Keeping control | |
| Kavas-Torris et al. | Modelling and Analysis of Car Following Algorithms for Fuel Economy Improvement in Connected and Autonomous Vehicles (CAVs) | |
| Wan et al. | Lane-changing tracking control of automated vehicle platoon based on ma-ddpg and adaptive mpc | |
| CN115782918A (en) | Automatic driving longitudinal control method, device, equipment and storage medium | |
| CN114971152A (en) | Cooperative control method, system, equipment and medium for truck group | |
| Memon et al. | Longitudinal control of a platoon of road vehicles equipped with adaptive cruise control system | |
| CN113589690A (en) | Robust fault-tolerant method and device for cooperative adaptive cruise control | |
| CN113635900A (en) | Energy management-based lane change decision control method in prediction cruise process | |
| Zong et al. | Modeling of the Traffic Vehicle Lane Change Execution in Autonomous Driving Virtual Test | |
| Zhang et al. | An Eco-driving Control Strategy for Connected Electric Vehicles at Intersections Based on Preceding Vehicle Speed Prediction | |
| Luu et al. | Using the lqi2r control method on the adaptive cruise control system for a platoon of vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |