Modeling method and car-following method considering multi-vehicle influence and acceleration information in front

Abstract

The modeling method and following method of car-following model considering the influence of multiple vehicles in front and acceleration information belong to the technical fields of vehicle-road cooperation and traffic flow modeling. In order to solve the problem that the existing car-following model does not consider comprehensive factors, thereby the overall starting time is long and the operation efficiency of the team is affected. The application considers the influence of the position and speed of the front vehicle and the second front vehicle on the host vehicle, and establishes a car-following model considering the influence of the acceleration of the vehicle in front, and then describes the car-following behavior based on the established car-following model. The application is suitable for modeling and following process of car-following model.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of vehicle-road cooperation and traffic flow modeling, and particularly relates to a car-following model modeling method and car-following method considering the influence of multiple vehicles in front and acceleration information. BACKGROUND

[0002] Car-following (CF) behavior is the most basic microscopic driving behavior, which describes the interaction between two adjacent vehicles in a single lane on which overtaking is restricted. By modeling the car-following behavior of drivers to optimize the car-following model and capturing the influencing factors in the operation process of multiple vehicles to more objectively reflect the operation state of the vehicle, the operation stability of the vehicle can be improved to relieve traffic congestion. Vehicle-road cooperation and vehicle-to-vehicle communication technology can realize information sharing between vehicles, and vehicles can interact with surrounding vehicles in real time in terms of speed, headway and other information, but the operation of the vehicle still depends on the reaction delay and operation behavior of the driver. At the same time, the car-following behavior of the vehicle is influenced by multiple vehicles in front, and different types of information of the vehicles in front have certain differences on the operation of the host vehicle.

[0003] At present, most of the car-following models used in many studies only consider a certain type of information (i.e., the headway or speed difference of the other vehicle) of the immediately preceding vehicle, ignore the influence of multiple vehicles in front on the car-following behavior of the host vehicle, and cannot well describe the running characteristics of the actual traffic flow. On the other hand, the acceleration information of the vehicle has a certain influence on the operation state and stability of the vehicle, but the research on considering the acceleration information in the car-following model has not been in-depth, and the persuasiveness of the car-following model is insufficient. It is particularly necessary to more objectively describe the car-following behavior in this scenario and combine the related information of multiple vehicles in front and the acceleration information of the preceding vehicle for traffic flow modeling. SUMMARY

[0004] The present application solves the problem that the existing car-following model does not consider enough factors, thereby having a long overall start-up time and affecting the operation efficiency of the team.

[0005] A car-following model modeling method considering the influence of multiple vehicles in front and acceleration information, which considers the influence of the position and speed of the preceding vehicle and the second preceding vehicle on the host vehicle and establishes a car-following model considering the acceleration of the immediately preceding vehicle on the basis of the IDM model, and the specific process includes the following steps:

[0006] Based on the actual distance between the head of vehicle n and the tail of the preceding vehicle, the actual distance between the head of vehicle n and the tail of the preceding vehicle is represented by s n-j+1 (t) as follows:

[0007]

[0008] Where, x n (t) represents the position of vehicle n at time t, x n-1 (t) represents the position of vehicle n-1 at time t, and l represents the length of the vehicle; m j Let m1 and m2 represent the influence weights of the preceding vehicle and the next preceding vehicle on the main vehicle, respectively.

[0009] Then, the total speed difference is determined based on the impact of the vehicle speed information of the preceding vehicle and the next preceding vehicle on the main vehicle. The total speed difference is the product of the vehicle impact weight and the speed difference between the preceding vehicle and the main vehicle.

[0010] By introducing an acceleration difference sensitivity coefficient λ into the acceleration of the vehicle in front, we obtain the acceleration information influence term λa that takes into account the vehicles in front. n-1 (t);

[0011] Based on the intelligent driver model, the driver's reaction time t d Introduced into the intelligent driving model, the improved acceleration expression is determined as follows: Then, based on the subsequent acceleration expression, it is... The intelligent driving model incorporates the speed and location information of nearby and second-nearest vehicles ahead, as well as the acceleration information of nearby vehicles ahead, to establish a car-following model.

[0012] Furthermore, the intelligent driver model is as follows:

[0013]

[0014]

[0015] In the formula, a represents the maximum acceleration, v n (t) represents the velocity of vehicle n at time t, v e δ represents the desired velocity; δ represents the acceleration exponent, which is taken as 4 in this embodiment; s a s represents the actual distance between the front of vehicle n and the rear of the vehicle in front; * It represents the expected distance between vehicle n and vehicle n-1, s0 represents the minimum safe distance between vehicles under congested conditions, and Tv n (t) represents the speed-related distance, T represents the safe headway, and Δv n (t) represents the speed difference between vehicle n and the vehicle in front at time t, and b represents the comfortable deceleration.

[0016] Furthermore, the actual distance s between the front of vehicle n and the rear of the vehicle in front. a =x n-1 (t)-x n (t)-l.

[0017] Further, the total speed difference

[0018] Further, the established car-following model is as follows:

[0019]

[0020] The stability condition of the car-following model is as follows:

[0021]

[0022] In the formula, a represents the maximum acceleration, v n (t) represents the speed of vehicle n at time t, v e represents the expected speed; s0 represents the minimum safety distance of the vehicle in the congestion state, Tv n (t) represents the speed-related distance, T represents the safety headway, and Delta v n (t) represents the speed difference between vehicle n and the preceding vehicle at time t, and b represents the comfort deceleration.

[0023] Further, the influence weight of the preceding vehicle and the second preceding vehicle on the host vehicle is m1+m2=1.

[0024] Further, the influence weight of the preceding vehicle on the host vehicle is m1, and the influence weight of the second preceding vehicle on the host vehicle is m2.

[0025] A car-following method considering the influence of multiple vehicles in front and acceleration information, wherein the car-following method is based on a car-following model established by the car-following modeling method considering the influence of multiple vehicles in front and acceleration information.

[0026] A computer storage medium, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the car-following method considering the influence of multiple vehicles in front and acceleration information.

[0027] A car-following device considering the influence of multiple vehicles in front and acceleration information, wherein the device comprises a processor and a memory, and at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the car-following method considering the influence of multiple vehicles in front and acceleration information.

[0028] Beneficial effects:

[0029] The present application provides a car-following model and a car-following method considering the influence of multiple vehicles in front and acceleration information, wherein the positions and speeds of the preceding vehicle and the second preceding vehicle are considered, the acceleration of the preceding vehicle is considered, and a time delay factor is combined to establish a car-following model, and the traffic flow stability and traffic flow operation characteristics of the model are analyzed.

[0030] Theoretical and numerical simulation results show that the model of the application can effectively improve the stability of the traffic flow, the stability region considering the stability of the preceding vehicle and the next preceding vehicle is larger than the stability region only considering the stability of the preceding vehicle, and considering the influence of acceleration information can improve the stability of the whole traffic flow. The next preceding vehicle has less contribution to the stability of the traffic flow than the preceding vehicle, but the superposition of the information of the preceding vehicle has good benefits. At the same time, the improved model has a smoother vehicle starting process at the signal intersection and higher vehicle starting efficiency compared with the original intelligent driver model. The application provides a theoretical reference for the modeling of the car following model and the research on the effects of considering multiple information factors. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 is a flow chart of the car following model modeling method considering the influence of multiple vehicles in front and acceleration information according to the application;

[0032] Figure 2 is the stability curve of t d =0 under different λ I and m1;

[0033] Figure 3 is the stability curve of t d =1 under different λ I and m1;

[0034] Figure 4 is the time speed curve of the intelligent driver model under the condition of basic parameter setting;

[0035] Figure 5 is the time speed curve of the improved intelligent driver model under the condition of basic parameter setting;

[0036] Figure 6 is the time acceleration curve of the intelligent driver model under the condition of basic parameter setting;

[0037] Figure 7 is the time acceleration curve of the improved intelligent driver model under the condition of basic parameter setting. DETAILED DESCRIPTION

[0038] The purpose of the application is to provide a car following model modeling method considering the influence of multiple vehicles in front and acceleration information to overcome the defects of the prior art, considering the influence of multiple information of adjacent and next adjacent vehicles in front and the acceleration information of the preceding vehicle on the car following behavior, improving the accuracy of the car following model and better describing the driving behavior. The application will be described in detail below in combination with specific embodiments.

[0039] Specific embodiment one: in combination with Figure 1 the description of the embodiment,

[0040] The embodiment is a car-following model modeling method considering the influence of multiple front vehicles and acceleration information, which is based on an intelligent driver model and considers the influence of multiple front vehicles in modeling the car-following model, wherein the multiple front vehicles include front adjacent and next adjacent vehicles.

[0041] The car-following model modeling method considering the influence of multiple front vehicles and acceleration information comprises the following steps:

[0042] First, an intelligent driver model, i.e., an IDM model, is determined, and the intelligent driver model is as follows:

[0043]

[0044]

[0045] In the formula, a represents the maximum acceleration, v n (t) represents the speed of vehicle n at time t, v e represents the expected speed; δ represents the acceleration exponent, which is 4 in the embodiment; s a represents the actual distance between the front of vehicle n and the tail of the front vehicle; s * is the expected distance between vehicle n and vehicle n-1, s0 represents the minimum safety distance of the vehicle in a congestion state, Tv n (t) represents the speed-related distance, T represents the safety headway, Δv n (t) represents the speed difference between vehicle n and the front vehicle at time t, and b represents the comfortable deceleration.

[0046] The actual distance between the front of vehicle n and the tail of the front vehicle is as follows:

[0047] s a =x n-1 (t)-x n (t)-l

[0048] In the formula, x n (t) represents the position of vehicle n at time t, x n-1 (t) represents the position of vehicle n-1 at time t, and l represents the vehicle length.

[0049] On the basis of the IDM model, the positions and speeds of the front vehicle and the next front vehicle are considered to affect the host vehicle, and the acceleration of the front adjacent vehicle is considered to establish the car-following model.

[0050] In order to represent the influence degree of the front vehicle on the host vehicle, the influence weight m j of the front vehicle on the host vehicle is introduced, j=1, 2, m1 and m2 represent the influence weights of the front vehicle and the next front vehicle on the host vehicle respectively, and m1+m2=1. Since the host vehicle is more affected by the front adjacent vehicle, m1>m2.

[0051] To express the influence of the vehicle position information of the preceding vehicle and the second preceding vehicle on the host vehicle, the actual distance between the vehicle head of the vehicle n and the vehicle tail of the preceding vehicle is denoted as s n-j+1 (t) represents:

[0052]

[0053] To express the influence of the vehicle speed information of the preceding vehicle and the second preceding vehicle on the host vehicle, the total speed difference is denoted as The product of the vehicle influence weight and the speed difference between the preceding vehicle and the host vehicle is denoted as:

[0054]

[0055] To explore the influence of the acceleration information of the preceding vehicle on the running state of the vehicle, the acceleration difference sensitive coefficient λ is introduced into the model, the coefficient describes the influence degree of the acceleration factor of the preceding vehicle on the acceleration of the host vehicle, and is denoted as λa n-1 (t), wherein a n-1 (t) represents the acceleration of the vehicle n-1 at the time t.

[0056] Since the running characteristics of the vehicle are affected by the reaction time of the driver, the reaction time t d of the driver is introduced into the modeling of the car following model, and the improved acceleration expression is

[0057] According to the car following model modeling method considering the influences of multiple preceding vehicles and acceleration information, the speed and position information of the adjacent and second adjacent vehicles in front of the host vehicle and the acceleration information of the adjacent vehicle in front of the host vehicle are introduced into the intelligent driver model, and an optimized car following model is obtained.

[0058]

[0059] According to the car following model modeling method considering the influences of multiple preceding vehicles and acceleration information, it is assumed that is the average vehicle head time interval of the adjacent vehicles in the stable state. is the average speed of the vehicles in the stable flow. The stability condition of the optimized car following model is:

[0060]

[0061] The current research does not introduce the acceleration information into the intelligent driver model without considering the influences of multiple preceding vehicles, and does not analyze the influence of the acceleration factor on the stability of the model. The present application considers the influences of multiple preceding vehicles on the running of the host vehicle, considers the acceleration influence of the immediately preceding vehicle, analyzes the influences of the acceleration influence coefficient and other parameters on the stability of the intelligent driver model, and analyzes the influences of different influence coefficients on the running characteristics of the traffic flow.

[0062] The model constructed by the application considers acceleration information, can effectively reduce the disturbance of the sharp fluctuation of the front vehicle speed on the main vehicle, reduce the overall starting time of the vehicle, and is beneficial to enhance the stability of the vehicle operation and improve the efficiency of the vehicle operation. Specific implementation two:

[0064] The embodiment is a car-following method considering the influence of multiple vehicles in front and acceleration information, and the car-following method considering the influence of multiple vehicles in front and acceleration information is based on the car-following model established by the car-following modeling method considering the influence of multiple vehicles in front and acceleration information in specific implementation one. Specific implementation three:

[0066] The embodiment is a computer storage medium, and the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to realize the car-following method considering the influence of multiple vehicles in front and acceleration information in specific implementation two.

[0067] It should be understood that the instructions include a computer program product, software or computerized method corresponding to any method described in the application; the instructions can be used to program a computer system or other electronic device. The computer storage medium can include a readable medium having instructions stored thereon, and can include but is not limited to a magnetic storage medium, an optical storage medium, a magneto-optical storage medium, a read-only memory (ROM), a random access memory (RAM), an erasable programmable memory (such as an EPROM and an EEPROM), and a flash layer, or other types of media suitable for storing electronic instructions. Specific implementation four:

[0069] The embodiment is a car-following device considering the influence of multiple vehicles in front and acceleration information, and the device includes a processor and a memory. It should be understood that any device described in the application includes a processor and a memory, and the device can also include other units, modules, etc. that display, interact, process, control, etc. through signals or instructions, and other functions;

[0070] The storage stores at least one instruction, and the at least one instruction is loaded and executed by the processor to realize the car-following method considering the influence of multiple vehicles in front and acceleration information in specific implementation two.

[0071] Embodiment

[0072] Compared with the traditional intelligent driver model, the linear stability region of the improved intelligent driver model is increased, and the following embodiments illustrate that the model of the application can improve the stability of the traffic flow.

[0073] To investigate the impact of the driving information of the nearest and second-nearest vehicles on traffic flow stability and the impact of acceleration information, a linear stability analysis can be performed on the car-following model proposed in this invention.

[0074] Figure 2 and Figure 3 They are respectively in t d =0 and t d Critical stability curves for different parameters when = 1. The area above the critical stability curve is the stable region, where traffic flow is stable and no congestion occurs; the area below the critical stability curve is the unstable region, where traffic flow is unstable and prone to speed changes when disturbed, leading to traffic congestion. The effects of changing the acceleration influence coefficient λ and position influence coefficient m of the vehicle in front on the main vehicle are also considered. j Stability curves under different coefficients were obtained.

[0075] Figure 2 The black solid line in the figure represents the stability curve of the original IDM model when λ = 0 and m1 = 1. The other five lines represent the stability curves obtained under different acceleration influence coefficients. It can be observed that as the acceleration influence coefficient λ increases, the unstable region in the stability curve decreases significantly, while the traffic flow stability region gradually expands. This indicates that considering the acceleration information of the preceding vehicle can effectively improve the stability of the traffic flow. This is because the driving state of the following vehicle is affected by the acceleration and deceleration behavior of the preceding vehicle, and the following vehicle adjusts its own driving state based on the driving state of the preceding vehicle.

[0076] Secondly, the influence of location on stability is analyzed. (Comparison) Figure 2 As shown in the curve, when λ is constant, the stability region in the stability curve increases to some extent as the positional influence coefficient m decreases, which can improve the stability of traffic flow and thus reduce traffic congestion. When λ = 0.2 and m1 = 0.8, the area of ​​the stability region of the stability curve is larger than that when m1 = 0.9. As m1 further decreases, the improvement in traffic flow stability is not significant. This indicates that the stability region considering the preceding and next-to-the-leader vehicles is larger than the stability region considering only the preceding vehicle. Although the impact of the next-to-the-leader vehicle on the main vehicle is much smaller than that of the preceding vehicle, its impact on the main vehicle cannot be ignored. Considering the acceleration information of the preceding vehicle can have a positive effect on the stability of the entire traffic flow.

[0077] Finally, the impact of latency on stability is analyzed. (Comparison) Figure 2 and Figure 3 It can be seen that t d The unstable region when =1 is compared to t dThe time of the fifteenth vehicle is obviously increased when the value of =0, which is due to the fact that the driver needs a certain reaction time to judge the actual situation of the road, resulting in a delay in the response of the vehicle, and thus changing the motion state of the traffic flow in which the vehicle is located.

[0078] Next, the improvement effect of the constructed car-following model is further explored by numerical simulation, and a vehicle starting scene is built to analyze the running characteristics of the traffic flow.

[0079] Vehicle initial position x n (0) = 7, and the simulation basic parameter settings are as follows:

[0080] b = 2 m / s 2 , a0= 1.8 m / s 2 , l = 6 m, v e = 15 m / s, s0= 2 m, T = 1.1 s, t d = 1 s

[0081] m1= 0.9, v0= 0 m / s, v max = 15 m / s, λ1= 0.10, λ2= 0.05, N = 15

[0082] Figure 4 and Figure 5 are the time-speed curves before and after the improvement of the intelligent driver model, respectively. In the starting process of the vehicle (when the vehicle acceleration is less than 0.5 m / s 2 ), the fifteenth vehicle starts at 10.1 s in Figure 5 , while the fifteenth vehicle starts at 7.4 s in the improved model. By comparison, it can be obtained that the vehicle starting with the improved model is faster, which can reduce the starting delay time. At 25 s, the speed of the tail vehicle in the IDM model is 7.85 m / s, while the speed of the tail vehicle in the improved model is 8.53 m / s, which shows that all vehicles applying the improved model can accelerate to the maximum speed in a shorter time, making the overall starting time shorter, and thus improving the passing efficiency of all vehicles.

[0083] Figure 6 and Figure 7 are the time-acceleration curves before and after the improvement of the intelligent driver model, respectively. Figure 6 In Figure 7 , the fifteenth vehicle reaches the maximum acceleration at 22.5 s, and the maximum acceleration is 0.94 m / s 2 , while in the improved model Figure 7 , the fifteenth vehicle reaches the maximum acceleration at 20.4 s, and the maximum acceleration is 0.90 m / s 2The vehicle acceleration of the improved model is less than that of the original IDM model from the third vehicle, the acceleration of the starting process of all vehicles is smaller, and the overall operation is more stable. Among them, the second vehicle is affected by the acceleration of the first vehicle and starts faster, although the acceleration is greater than that of the IDM model, the acceleration is improved, which makes the overall starting efficiency of the rear vehicle better, so it is considered appropriate. Therefore, the time for the vehicle to reach the maximum acceleration in the improved model is shorter, and the overall acceleration is significantly lower than that of the IDM model, which can avoid excessive acceleration caused by the starting process of the vehicle, thereby reducing the fuel consumption of the vehicle.

[0084] Figure 6 The completion time of the starting process of the vehicle at the tail of the IDM model is 27.7s, Figure 7 The completion time of the starting process of the improved IDM model is 25.5s. The time consumed by the starting process of the vehicle at the tail of the IDM model is 10.8s, and that of the improved IDM model is 10.1s. It can be obtained that the overall starting time of the improved model is shorter than that of the IDM model, which is beneficial to improve the operation efficiency of the vehicle group.

[0085] The above examples of the application are only to illustrate the calculation model and calculation process of the application, and are not limited to the embodiments of the application. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above description, and it is impossible to enumerate all the embodiments here, and any obvious changes or variations derived from the technical solutions of the application still fall within the protection scope of the application.

Claims

1. A car-following model modeling method considering the influence of multiple vehicles ahead and acceleration information, characterized in that, On the basis of the IDM model, a car-following model is established by considering the influences of the positions and speeds of the preceding vehicle and the second preceding vehicle on the host vehicle and the acceleration of the immediately preceding vehicle, and the specific process includes the following steps: the actual distance between the front of vehicle n and the rear of the preceding vehicle the actual distance between the front of vehicle n and the rear of the preceding vehicle is denoted by ， wherein, represents the position of vehicle n at time t, represents the position of vehicle n-1 at time t, l represents the vehicle length; m j is the influence weight of the front vehicle on the host vehicle, j = 1, 2, wherein m1 and m2 represent the influence weight of the front vehicle and the preceding vehicle on the host vehicle, respectively, ; Then the total speed difference is determined based on the influence of the preceding vehicle and the vehicle ahead of the preceding vehicle on the host vehicle speed information, and the total speed difference is the product of the vehicle influence weight and the speed difference between the vehicle ahead and the host vehicle, i.e. total speed difference , denotes the speed of vehicle n at time t; Introducing an acceleration difference sensitivity coefficient in the front vehicle acceleration , obtaining an acceleration information influence term considering the front adjacent vehicle ; Based on the intelligent driver model, the driver reaction time is introduced into the intelligent driver model, and the improved acceleration expression is determined as ; then based on the intelligent driver model with the improved acceleration expression , the speed, position information of the front adjacent and next adjacent vehicles, and the acceleration information of the front adjacent vehicle are introduced to establish the car-following model; The intelligent driver model is as follows: ， ， where a represents the maximum acceleration, represents the desired speed; represents the acceleration exponent, taken as 4; represents the actual distance between the front of vehicle n and the rear of the preceding vehicle; is the desired inter-vehicle distance between vehicle n and vehicle n-1, represents the minimum safe inter-vehicle distance in a congestion state, represents the speed-dependent distance, represents the safe headway distance, represents the speed difference between vehicle n and the preceding vehicle at time t, and b represents the comfortable deceleration; The car-following model is established as follows: ， The stability condition of the car-following model is as follows: 。 2. The car-following model modeling method considering the influence of multiple vehicles ahead and acceleration information according to claim 1, characterized in that, influence weight of the preceding vehicle on the host vehicle influence weight of the preceding vehicle on the host vehicle .

3. A car-following method considering the influence of multiple vehicles ahead and acceleration information, characterized in that, The car-following method is based on the car-following model established by the car-following model modeling method of claim 1 or 2.

4. A computer storage medium, characterized in that, The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the car-following method of claim 3.

5. A car-following device considering the influence of multiple vehicles ahead and acceleration information, characterized in that, The device includes a processor and a memory, and the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the car-following method of claim 3.

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