Vehicle platoon prediction control method, device, electronic device and storage medium

Abstract

The present invention relates to the field of intelligent transportation technology, and in particular to a vehicle queue prediction control method, device, electronic device and storage medium. The method of the present invention first obtains a head vehicle data set and multiple follower vehicle data sets; then, based on the head vehicle data set, the multiple follower vehicle data sets, navigation routes and follow-up constraints, a position prediction optimization equation is constructed to optimize the positions of multiple vehicles; then, based on the position prediction optimization equation, the positions of multiple vehicles are optimized with the goal of minimizing the following distance, and multiple position predictions are obtained; finally, based on the multiple position predictions, the current positions of the multiple vehicles and the current time, the multiple vehicles are controlled. The position prediction equation of the embodiment of the present invention takes into account the following distance and driving speed constraints. Therefore, the position prediction can obtain the minimum following distance under the premise of ensuring driving safety, thereby improving the efficiency of vehicle queue passage.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a vehicle queue prediction control method, device, electronic equipment and storage medium. Background Art

[0002] With the development of intelligent driving technology and communication technology, platooning of autonomous vehicles with vehicle-to-vehicle communication capabilities has attracted much attention because it can effectively improve traffic efficiency. Vehicle platoon control enables vehicles to travel safely at smaller distances, increase traffic density, and reduce the occurrence of traffic accidents. At the same time, smaller vehicle spacing means reduced air resistance, thereby improving energy efficiency and reducing greenhouse gas emissions. Opportunities and challenges coexist. How to design a high-precision, robust, and fast-response control system to achieve safe, energy-saving, and comfortable platooning is a problem that needs to be studied and solved.

[0003] At present, most of the vehicle platoon control algorithms set a large driving distance for the vehicle platoon from the perspective of safety. The disadvantage of this is that it causes communication delay and instability. Some control methods try to use a small driving distance to plan the vehicle platoon, but they do not consider the difference in vehicle acceleration and the impact of some vehicles' deceleration when turning on the distance between the vehicle platoons, which brings great safety risks to the vehicle platoon.

[0004] Based on this, it is necessary to develop and design a vehicle queue prediction control method. Summary of the invention

[0005] The embodiments of the present invention provide a vehicle queue prediction control method, device, electronic device and storage medium, which are used to solve the problem of unreasonable setting of driving distance in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a vehicle queue prediction control method, comprising:

[0007] Acquire a leading vehicle data set and a plurality of following vehicle data sets, wherein the leading vehicle data set and the following vehicle data set represent the driving state of the leading vehicle and the driving state of the following vehicles respectively;

[0008] Constructing a position prediction optimization equation for optimizing the positions of multiple vehicles according to the lead vehicle data set, the multiple following vehicle data sets, the navigation route, and the following constraint conditions;

[0009] According to the position prediction optimization equation, the positions of the multiple vehicles are optimized with the goal of minimizing the following distance to obtain multiple position predictions, wherein each position prediction corresponds to one vehicle;

[0010] The multiple vehicles are controlled based on the multiple position predictions, the current positions of the multiple vehicles, and the current time.

[0011] In a possible implementation, the lead vehicle data set and the following vehicle data set both include: position data and driving speed data, and the position prediction optimization equation for optimizing the positions of multiple vehicles is constructed based on the lead vehicle data set, the multiple following vehicle data sets, the navigation route and the following constraint conditions, including:

[0012] According to the position data of the lead vehicle, the driving speed data of the lead vehicle and the navigation route, a position prediction equation of the lead vehicle is constructed;

[0013] Determine the position prediction equations of the multiple following vehicles according to the positions of the multiple following vehicles, the driving speed data of the multiple following vehicles, the navigation route, the following distance constraint condition and the position prediction equation of the lead vehicle;

[0014] A position prediction optimization equation is constructed according to the position prediction equation of the lead vehicle, the position prediction equations of the plurality of following vehicles, and vehicle speed constraints.

[0015] In one possible implementation, the position prediction optimization equation is:

[0016]

[0017] In the formula, is the speed vector prediction of the kth vehicle, NR k (x, y) is the predicted position of the kth vehicle, Pos k (x, y) is the vehicle position of the kth vehicle, is the speed vector of the kth vehicle, a limit is the acceleration limit value, Δt is the prediction duration, w1 is the following distance coefficient, and w2 is the steering speed limit coefficient.

[0018] In a possible implementation, the positions of multiple vehicles are optimized according to the position prediction optimization equation with the goal of minimizing the following distance to obtain multiple position predictions, including:

[0019] Acquire and initialize multiple location data sets, wherein each data set includes multiple candidate locations;

[0020] From the multiple position data sets, three position data sets that satisfy the following distance constraint condition, the vehicle speed constraint condition and have the best following distance adaptability are selected as three preferred position data sets;

[0021] Calculating the distance between each location data set to be optimized and the three preferred location data sets, wherein the location data set to be optimized is a location data set other than the three preferred location data sets;

[0022] Optimizing each location data set to be optimized according to the distance between the location data set to be optimized and the three preferred location data sets;

[0023] If the iteration termination condition is not met, then reselect three position data sets that meet the following distance constraint condition, meet the vehicle speed constraint condition and have the best following distance fitness from the multiple optimized position data sets to be optimized and the three preferred position data sets as the three preferred position data sets, and jump to the step of calculating the distance between each position data set to be optimized and the three preferred position data sets, wherein the iteration termination condition is that the number of iterations or the following distance fitness reaches a preset value;

[0024] From the multiple optimized position data sets to be optimized and the three preferred position data sets, select a position data set that satisfies the following distance constraint condition, satisfies the vehicle speed constraint condition and has the best following distance adaptability as the optimal position data set;

[0025] A plurality of candidate positions in the optimal position data set are used as the plurality of position predictions.

[0026] In a possible implementation, the following distance adaptability is determined according to a first formula, which is:

[0027]

[0028] In the formula, FS is the following distance fitness, CNR k (x, y) is the kth candidate position in the data set, kmax is the total number of candidate positions in the data set, and Pos k (x, y) is the vehicle position of the kth vehicle.

[0029] In a possible implementation manner, calculating the distance between each to-be-optimized location data set and the three preferred location data sets respectively includes:

[0030] The distances between each location data set to be optimized and the three preferred location data sets are calculated according to a second formula, where the second formula is:

[0031] The distances between each location data set to be optimized and the three preferred location data sets are calculated according to a second formula, where the second formula is:

[0032]

[0033] In the formula, is the position data set to be optimized, DPosα is the optimal position data set among the three preferred position data sets, is the second best location dataset among the three best location datasets. is the third best position data set among the three best position data sets, is the distance between the position data set to be optimized and the optimal position data set, is the distance between the location dataset to be optimized and the suboptimal location dataset, is the distance between the position dataset to be optimized and the third best position dataset, is the first coefficient vector, is the second coefficient vector, is the third coefficient vector;

[0034] The optimizing each location data set to be optimized according to the distance between the location data set to be optimized and the three preferred location data sets includes:

[0035] Each position data set to be optimized is optimized according to a third formula and the distance between the position data set to be optimized and the three preferred position data sets, wherein the third formula is:

[0036]

[0037] Wherein, MDPos1 is the first intermediate position data set, MDPos2 is the second intermediate position data set, and MDPos3 is the third intermediate position data set. is the fourth coefficient vector, is the fifth coefficient vector, is the sixth coefficient vector, is the optimized location data set to be optimized.

[0038] In a possible implementation, controlling the multiple vehicles according to the multiple position predictions, the current positions of the multiple vehicles, and the current time includes:

[0039] The multiple vehicles are regulated according to a fourth formula, the multiple position predictions, the current positions of the multiple vehicles, and the current time, wherein the fourth formula is:

[0040]

[0041] In the formula, e k (t) is the speed error, t0 is the time node before prediction, Δt is the prediction duration, t1 is the current time, is the current velocity vector of the kth vehicle, PWR k (t) is the driving force of the kth vehicle, w k1 is the proportionality coefficient, w k2 is the differential coefficient, w k3 is the integration coefficient.

[0042] In a second aspect, an embodiment of the present invention provides a vehicle queue prediction control device, which is used to implement the vehicle queue prediction control method described in the first aspect or any possible implementation of the first aspect, wherein the vehicle queue prediction control device includes:

[0043] A driving state acquisition module is used to acquire a lead vehicle data set and a plurality of follower vehicle data sets, wherein the lead vehicle data set and the follower vehicle data set represent the driving state of the lead vehicle and the driving state of the follower vehicle respectively;

[0044] A position optimization equation construction module, used to construct a position prediction optimization equation for optimizing the positions of multiple vehicles according to the lead vehicle data set, the multiple following vehicle data sets, the navigation route and the following constraint conditions;

[0045] A position prediction module, configured to optimize the positions of the plurality of vehicles according to the position prediction optimization equation with the goal of minimizing the following distance, and obtain a plurality of position predictions, wherein each position prediction corresponds to one vehicle;

[0046] as well as,

[0047] The vehicle control module is used to control the multiple vehicles according to the multiple position predictions, the current positions of the multiple vehicles and the current time.

[0048] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0050] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0051] The embodiment of the present invention discloses a vehicle queue prediction control method, which first obtains a lead vehicle data set and multiple follower vehicle data sets, wherein the lead vehicle data set and the follower vehicle data set respectively characterize the driving state of the lead vehicle and the following vehicle; then, according to the lead vehicle data set, the multiple follower vehicle data sets, the navigation route and the following constraint conditions, a position prediction optimization equation for optimizing the positions of multiple vehicles is constructed; then, according to the position prediction optimization equation, the positions of the multiple vehicles are optimized with the minimum following interval as the goal, and multiple position predictions are obtained, wherein each position prediction corresponds to a vehicle; finally, according to the multiple position predictions, the current positions of the multiple vehicles and the current time, the multiple vehicles are controlled.

[0052] The embodiment of the present invention establishes a position prediction equation with driving speed and following distance as constraints, takes the minimum following distance as the goal, and obtains the optimal position prediction through a position optimization method. Since the position prediction equation takes the following distance and driving speed constraints into consideration, the position prediction can obtain the minimum following distance while ensuring driving safety, thereby improving the efficiency of vehicle queue passage. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0054] Figure 1 is a flow chart of a vehicle queue prediction control method provided by an embodiment of the present invention;

[0055] Figure 2 This is a diagram of a vehicle queue application scenario provided by an embodiment of the present invention;

[0056] Figure 3 is a functional block diagram of a vehicle queue prediction control device provided in an embodiment of the present invention;

[0057] Figure 4 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0060] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0061] Figure 1 A flow chart of a vehicle queue prediction control method provided in an embodiment of the present invention.

[0062] like Figure 1 As shown, it shows a flow chart of the implementation of the vehicle queue prediction control method provided by an embodiment of the present invention, which is described in detail as follows:

[0063] In step 101, a leading vehicle data set and a plurality of following vehicle data sets are obtained, wherein the leading vehicle data set and the following vehicle data set represent the driving state of the leading vehicle and the driving state of the following vehicles respectively.

[0064] In step 102, a position prediction optimization equation for optimizing the positions of multiple vehicles is constructed based on the lead vehicle data set, the multiple following vehicle data sets, the navigation route, and the following constraints.

[0065] In some implementations, the leading vehicle data set and the following vehicle data set both include: position data and driving speed data, and step 102 includes:

[0066] According to the position data of the lead vehicle, the driving speed data of the lead vehicle and the navigation route, a position prediction equation of the lead vehicle is constructed;

[0067] Determine the position prediction equations of the multiple following vehicles according to the positions of the multiple following vehicles, the driving speed data of the multiple following vehicles, the navigation route, the following distance constraint condition and the position prediction equation of the lead vehicle;

[0068] A position prediction optimization equation is constructed according to the position prediction equation of the lead vehicle, the position prediction equations of the plurality of following vehicles, and vehicle speed constraints.

[0069] In some implementations, the position prediction optimization equation is:

[0070]

[0071] In the formula, is the speed vector prediction of the kth vehicle, NR k (x, y) is the predicted position of the kth vehicle, Pos k (x, y) is the vehicle position of the kth vehicle, is the speed vector of the kth vehicle, a limit is the acceleration limit value, Δt is the prediction duration, w1 is the following distance coefficient, and w2 is the steering speed limit coefficient.

[0072] For example, Figure 2 As shown, this figure is a diagram of a vehicle queue application scenario provided by an embodiment of the present invention. A vehicle queue consisting of multiple vehicles advances along a navigation route 201. This queue is led by a head vehicle 202 and multiple following vehicles 203. When encountering a curve or an intersection, they should slow down in advance. Considering the effect of communication delay, the distance between the head vehicle 202 and the following vehicle 203 will be reduced, which will affect the driving safety of the following vehicle 203.

[0073] One control method is to increase the distance between multiple vehicles in the convoy, which will cause communication delay, increase the length of the vehicle queue, and interfere with the movement of the queue by other vehicles joining the queue. Therefore, it is necessary to plan the distance, travel speed and acceleration between the leading vehicle 202 and the following vehicle 203 before entering a curve or intersection.

[0074] Based on the above starting point, the implementation method of the present invention obtains a data set for each vehicle, which includes the speed, direction and current position of the vehicle. Since these vehicles need to move along the navigation route, it is necessary to ensure that the coordinates of the vehicle should conform to the navigation route curve. In other words, the coordinates of the vehicle should be on the curve of the navigation route. After obtaining the above data set, the implementation method of the present invention can predict the position of each vehicle at the end of the prediction time according to the prediction time, that is, the position prediction of multiple vehicles, and the speed of the vehicle can be controlled based on this prediction.

[0075] The vehicle position prediction should ensure a reasonable safety distance, which is positively correlated with the square of the driving speed. The driving distance constraint formula is:

[0076]

[0077] In the formula, is the speed vector prediction of the kth vehicle, NR k (x, y) is the predicted position of the kth vehicle, and w1 is the following distance coefficient.

[0078] In addition, since some vehicles in the vehicle queue may be located on a curve, the driving speed of these vehicles on the curve should not be higher than the driving speed of the curve. The embodiment of the present invention uses the change of the vehicle's driving vector to obtain the curvature index of the curve, and constrains the vehicle's driving speed by the index. The steering constraint formula is:

[0079]

[0080] In the formula, is the speed vector prediction of the kth vehicle, is the driving speed vector of the kth vehicle, and w2 is the steering speed limiting coefficient.

[0081] In the above formula, the predicted value of the vehicle segment travel speed vector can be determined based on the vehicle position prediction. The travel speed vector prediction formula of the embodiment of the present invention is:

[0082]

[0083] In the formula, NR k (x, y) is the predicted position of the kth vehicle, Pos k (x, y) is the vehicle position of the kth vehicle, is the speed vector of the kth vehicle, and Δt is the prediction time.

[0084] In the above formula, the vehicle's driving speed vector is obtained through the leading vehicle data set and the following vehicle data set, and the position prediction is a point on the navigation route (NR) curve.

[0085] In addition, the embodiments of the present invention also need to consider the constraints of the acceleration (deceleration) performance of the vehicle. The acceleration constraint formula of the embodiments of the present invention is:

[0086]

[0087] In the formula, NR k (x, y) is the predicted position of the kth vehicle, Pos k (x, y) is the vehicle position of the kth vehicle, is the speed vector of the kth vehicle, a limit is the acceleration limit value, and Δt is the prediction duration.

[0088] Combining the above equations, we can get the position optimization equation:

[0089]

[0090] In the formula, is the speed vector prediction of the kth vehicle, NR k (x, y) is the predicted position of the kth vehicle, Pos k (x, y) is the vehicle position of the kth vehicle, is the speed vector of the kth vehicle, a limit is the acceleration limit value, Δt is the prediction duration, w1 is the following distance coefficient, and w2 is the steering speed limit coefficient.

[0091] In step 103, according to the position prediction optimization equation, the positions of multiple vehicles are optimized with the goal of minimizing the following distance to obtain multiple position predictions, wherein each position prediction corresponds to one vehicle.

[0092] In some implementations, step 103 includes:

[0093] Acquire and initialize multiple location data sets, wherein each data set includes multiple candidate locations;

[0094] From the multiple position data sets, three position data sets that satisfy the following distance constraint condition, the vehicle speed constraint condition and have the best following distance adaptability are selected as three preferred position data sets;

[0095] Calculating the distance between each location data set to be optimized and the three preferred location data sets, wherein the location data set to be optimized is a location data set other than the three preferred location data sets;

[0096] Each location data set to be optimized is optimized according to the distance between the location data set to be optimized and the three preferred location data sets:

[0097] If the iteration termination condition is not met, then reselect three position data sets that meet the following distance constraint condition, meet the vehicle speed constraint condition and have the best following distance fitness from the multiple optimized position data sets to be optimized and the three preferred position data sets as the three preferred position data sets, and jump to the step of calculating the distance between each position data set to be optimized and the three preferred position data sets, wherein the iteration termination condition is that the number of iterations or the following distance fitness reaches a preset value;

[0098] From the multiple optimized position data sets to be optimized and the three preferred position data sets, select a position data set that satisfies the following distance constraint condition, satisfies the vehicle speed constraint condition and has the best following distance adaptability as the optimal position data set;

[0099] A plurality of candidate positions in the optimal position data set are used as the plurality of position predictions.

[0100] In some implementations, the following spacing adaptability is determined according to a first formula, where the first formula is:

[0101]

[0102] Where Fs is the following distance fitness, cNR k (x, y) is the kth candidate position in the data set, kmax is the total number of candidate positions in the data set, and Pos k (x, y) is the vehicle position of the kth vehicle.

[0103] In some implementations, calculating the distance between each to-be-optimized location data set and the three preferred location data sets includes:

[0104] The distances between each location data set to be optimized and the three preferred location data sets are calculated according to a second formula, where the second formula is:

[0105] The distances between each location data set to be optimized and the three preferred location data sets are calculated according to a second formula, where the second formula is:

[0106]

[0107] In the formula, is the position data set to be optimized, DPosα is the optimal position data set among the three preferred position data sets, is the second best location dataset among the three best location datasets. is the third best position data set among the three best position data sets, is the distance between the position data set to be optimized and the optimal position data set, is the distance between the location dataset to be optimized and the suboptimal location dataset, is the distance between the position dataset to be optimized and the third best position dataset, is the first coefficient vector, is the second coefficient vector, is the third coefficient vector;

[0108] The optimizing each location data set to be optimized according to the distance between the location data set to be optimized and the three preferred location data sets includes:

[0109] Each position data set to be optimized is optimized according to a third formula and the distance between the position data set to be optimized and the three preferred position data sets, wherein the third formula is:

[0110]

[0111] Wherein, MDPos1 is the first intermediate position data set, MDPos2 is the second intermediate position data set, and MDPos3 is the third intermediate position data set. is the fourth coefficient vector, is the fifth coefficient vector, is the sixth coefficient vector, is the optimized location data set to be optimized.

[0112] Exemplarily, in terms of finding a method that satisfies the constraints and minimizes the spacing according to the above-mentioned position prediction optimization equation, an embodiment of the present invention first prepares a plurality of position data sets, each of which is provided with a plurality of candidate position data corresponding to a plurality of vehicles, and then, the candidate data in these position data sets are respectively substituted into the above-mentioned driving spacing constraint formula, steering constraint formula and acceleration constraint formula, and the position data sets that satisfy these constraint formulas are selected from these position data sets before the following spacing fitness is calculated.

[0113] The embodiment of the present invention adopts a first formula to determine the fitness of each location data set, and the first formula is:

[0114]

[0115] In the formula, FS is the following distance fitness, CNR k (x, y) is the kth candidate position in the data set, kmax is the total number of candidate positions in the data set, and Pos k (x, y) is the vehicle position of the kth vehicle.

[0116] According to the result of fitness calculation, three position data with the best fitness (according to the first formula, the larger the value, the better the fitness) are selected from the position data set as the preferred position data set.

[0117] Then, the other data sets are adjusted in the direction of the three data sets. Before the adjustment, each data set determines the distance between each position data set and the three preferred position data sets according to the second formula:

[0118]

[0119] In the formula, is the position data set to be optimized, DPpsα is the optimal position data set among the three preferred position data sets, is the second best location dataset among the three best location datasets. is the third best position data set among the three best position data sets, is the distance between the position data set to be optimized and the optimal position data set, is the distance between the location dataset to be optimized and the suboptimal location dataset, is the distance between the position dataset to be optimized and the third best position dataset, is the first coefficient vector, is the second coefficient vector, is the third coefficient vector.

[0120] After the location is determined, the location data sets other than the three optimized location data sets can be optimized using the third formula. The third formula is:

[0121]

[0122] Wherein, MDPos1 is the first intermediate position data set, MDPos2 is the second intermediate position data set, and MDPos3 is the third intermediate position data set. is the fourth coefficient vector, is the fifth coefficient vector, is the sixth coefficient vector, is the optimized location data set to be optimized.

[0123] The coefficient vectors in the second and third formulas are determined according to the following formula:

[0124]

[0125] In the above formula, i is the number of iterations, i max is the maximum number of iterations, r1 and r2 obey a uniform distribution between [0, 1].

[0126] After multiple rounds of iterations between searching for the preferred location data set and adjusting the location data set, all location data sets are close to the optimal solution. When the number of iterations reaches the upper limit, or the fitness of the best data set in the preferred location data set reaches the preset value, the iteration can be ended and the candidate location in the best location data set can be selected as the location prediction.

[0127] In step 104, the multiple vehicles are controlled according to the multiple position predictions, the current positions of the multiple vehicles, and the current time.

[0128] In some implementations, step 104 includes:

[0129] The multiple vehicles are regulated according to a fourth formula, the multiple position predictions, the current positions of the multiple vehicles, and the current time, wherein the fourth formula is:

[0130]

[0131] In the formula, e k(t) is the speed error, t0 is the time node before prediction, Δt is the prediction duration, t1 is the current time, is the current velocity vector of the kth vehicle, PWR k (t) is the driving force of the kth vehicle, w k1 is the proportionality coefficient, w k2 is the differential coefficient, w k3 is the integration coefficient.

[0132] For example, after obtaining the position prediction (the position to be reached at the scheduled time point), the fourth formula can be used to control each vehicle:

[0133] The fourth formula is:

[0134]

[0135] In the formula, e k (t) is the speed error, t0 is the time node before prediction, Δt is the prediction duration, t1 is the current time, is the current velocity vector of the kth vehicle, PWR k (t) is the driving force of the kth vehicle, w k1 is the proportionality coefficient, w k2 is the differential coefficient, w k3 is the integration coefficient.

[0136] The implementation method of the vehicle queue prediction control method of the present invention first obtains a lead vehicle data set and multiple follower vehicle data sets, wherein the lead vehicle data set and the follower vehicle data set respectively characterize the driving state of the lead vehicle and the driving state of the follower vehicle; then, according to the lead vehicle data set, the multiple follower vehicle data sets, the navigation route and the following constraint conditions, a position prediction optimization equation for optimizing the positions of multiple vehicles is constructed; then, according to the position prediction optimization equation, the positions of multiple vehicles are optimized with the minimum following distance as the goal, and multiple position predictions are obtained, wherein each position prediction corresponds to one vehicle; finally, according to the multiple position predictions, the current positions of the multiple vehicles and the current time, the multiple vehicles are controlled. The implementation method of the present invention establishes a position prediction equation with driving speed and following distance as constraints, and obtains the preferred position prediction through the position optimization method with the minimum following distance as the goal. Since the position prediction equation takes into account the following distance and driving speed constraints, the position prediction can obtain the minimum following distance under the premise of ensuring driving safety, thereby improving the efficiency of vehicle queue passage.

[0137] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0138] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0139] Figure 3 is a functional block diagram of a vehicle queue prediction control device provided by an embodiment of the present invention, referring to Figure 3 The vehicle queue prediction control device includes: a driving state acquisition module 301, a position optimization equation construction module 302, a position prediction module 303 and a vehicle control module 304, wherein:

[0140] The driving state acquisition module 301 is used to acquire a leading vehicle data set and a plurality of following vehicle data sets, wherein the leading vehicle data set and the following vehicle data set represent the driving state of the leading vehicle and the driving state of the following vehicles respectively;

[0141] A position optimization equation construction module 302 is used to construct a position prediction optimization equation for optimizing the positions of multiple vehicles according to the lead vehicle data set, the multiple following vehicle data sets, the navigation route and the following constraint conditions;

[0142] A position prediction module 303 is used to optimize the positions of multiple vehicles according to the position prediction optimization equation with the goal of minimizing the following distance, and obtain multiple position predictions, wherein each position prediction corresponds to one vehicle;

[0143] The vehicle control module 304 is used to control the multiple vehicles according to the multiple position predictions, the current positions of the multiple vehicles and the current time.

[0144] Figure 4 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can be run on the processor 400. When the processor 400 executes the computer program 402, the steps in the above-mentioned vehicle queue prediction control method and the embodiment are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0145] Exemplarily, the computer program 402 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to implement the present invention.

[0146] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0147] The processor 400 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0148] The memory 401 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.

[0149] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0150] Each functional unit and module in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit, and the above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0151] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0152] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0153] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0154] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0155] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0156] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various methods and device implementations can be implemented.

[0157] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0158] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A vehicle platoon prediction control method, characterized in that: include: Acquire a leading vehicle data set and a plurality of following vehicle data sets, wherein the leading vehicle data set and the following vehicle data set represent the driving state of the leading vehicle and the driving state of the following vehicles respectively; Constructing a position prediction optimization equation for optimizing the positions of multiple vehicles according to the lead vehicle data set, the multiple following vehicle data sets, the navigation route, and the following constraint conditions; According to the position prediction optimization equation, the positions of multiple vehicles are optimized with the goal of minimizing the following distance to obtain multiple position predictions, wherein each position prediction corresponds to one vehicle; controlling the plurality of vehicles according to the plurality of position predictions, the current positions of the plurality of vehicles, and a current time; in, The method of optimizing the positions of multiple vehicles according to the position prediction optimization equation and taking the minimum following distance as the goal to obtain multiple position predictions includes: Acquire and initialize multiple location data sets, wherein each data set includes multiple candidate locations; From the multiple position data sets, three position data sets that satisfy the following distance constraint condition, the vehicle speed constraint condition and have the best following distance adaptability are selected as three preferred position data sets; Calculating the distance between each location data set to be optimized and the three preferred location data sets, wherein the location data set to be optimized is a location data set other than the three preferred location data sets; Optimizing each location data set to be optimized according to the distance between the location data set to be optimized and the three preferred location data sets; If the iteration termination condition is not met, then reselect three position data sets that meet the following distance constraint condition, meet the vehicle speed constraint condition and have the best following distance fitness from the multiple optimized position data sets to be optimized and the three preferred position data sets as the three preferred position data sets, and jump to the step of calculating the distance between each position data set to be optimized and the three preferred position data sets, wherein the iteration termination condition is that the number of iterations or the following distance fitness reaches a preset value; From the multiple optimized position data sets to be optimized and the three preferred position data sets, select a position data set that satisfies the following distance constraint condition, satisfies the vehicle speed constraint condition and has the best following distance adaptability as the optimal position data set; A plurality of candidate positions in the optimal position data set are used as the plurality of position predictions.

2. The vehicle platoon prediction control method according to claim 1, characterized in that: The leading vehicle data set and the following vehicle data set both include: position data and driving speed data. The position prediction optimization equation for optimizing the positions of multiple vehicles is constructed based on the leading vehicle data set, the multiple following vehicle data sets, the navigation route and the following constraint conditions, including: According to the position data of the lead vehicle, the driving speed data of the lead vehicle and the navigation route, a position prediction equation of the lead vehicle is constructed; Determine the position prediction equations of the multiple following vehicles according to the positions of the multiple following vehicles, the driving speed data of the multiple following vehicles, the navigation route, the following distance constraint condition and the position prediction equation of the lead vehicle; A position prediction optimization equation is constructed according to the position prediction equation of the lead vehicle, the position prediction equations of the plurality of following vehicles, and vehicle speed constraints.

3. The vehicle queue prediction control method according to claim 2, characterized in that: The position prediction optimization equation is: In the formula, is the speed vector prediction of the kth vehicle, NR k (x, y) is the predicted position of the kth vehicle, Pos k (x, y) is the vehicle position of the kth vehicle, is the speed vector of the kth vehicle, a limit is the acceleration limit value, Δt is the prediction duration, w1 is the following distance coefficient, and w2 is the steering speed limit coefficient.

4. The vehicle queue prediction control method according to claim 1, characterized in that: The following distance adaptability is determined according to a first formula, which is: In the formula, FS is the following distance fitness, CNR k (x, y) is the kth candidate position in the data set, kmax is the total number of candidate positions in the data set, and Pos k (x, y) is the vehicle position of the kth vehicle.

5. The vehicle queue prediction control method according to claim 1, characterized in that: The calculating the distance between each to-be-optimized location data set and the three preferred location data sets respectively includes: The distances between each location data set to be optimized and the three preferred location data sets are calculated according to a second formula, where the second formula is: In the formula, is the location dataset to be optimized, is the optimal location dataset among the three preferred location datasets, is the second best location dataset among the three best location datasets. is the third best position data set among the three best position data sets, is the distance between the position data set to be optimized and the optimal position data set, is the distance between the location dataset to be optimized and the suboptimal location dataset, is the distance between the position dataset to be optimized and the third best position dataset, is the first coefficient vector, is the second coefficient vector, is the third coefficient vector; The optimizing each location data set to be optimized according to the distance between the location data set to be optimized and the three preferred location data sets includes: Each position data set to be optimized is optimized according to a third formula and the distance between the position data set to be optimized and the three preferred position data sets, wherein the third formula is: Wherein, MDPos1 is the first intermediate position data set, MDPos2 is the second intermediate position data set, and MDPos3 is the third intermediate position data set. is the fourth coefficient vector, is the fifth coefficient vector, is the sixth coefficient vector, is the optimized location data set to be optimized.

6. The vehicle queue prediction control method according to any one of claims 1 to 5, characterized in that: The controlling the multiple vehicles according to the multiple position predictions, the current positions of the multiple vehicles and the current time includes: The multiple vehicles are regulated according to a fourth formula, the multiple position predictions, the current positions of the multiple vehicles, and the current time, wherein the fourth formula is: In the formula, e k (t) is the speed error, t0 is the time node before prediction, Δt is the prediction duration, t1 is the current time, is the current velocity vector of the kth vehicle, PWR k (t) is the driving force of the kth vehicle, w k1 is the proportionality coefficient, w k2 is the differential coefficient, w k3 is the integration coefficient, NR k (x, y) is the predicted position of the kth vehicle, Pos k (x, y) is the vehicle position of the kth vehicle.

7. A vehicle queue prediction control device, characterized in that: For implementing the vehicle queue prediction control method according to any one of claims 1 to 6, the vehicle queue prediction control device comprises: A driving state acquisition module is used to acquire a lead vehicle data set and a plurality of follower vehicle data sets, wherein the lead vehicle data set and the follower vehicle data set represent the driving state of the lead vehicle and the driving state of the follower vehicle respectively; A position optimization equation construction module, used to construct a position prediction optimization equation for optimizing the positions of multiple vehicles according to the lead vehicle data set, the multiple following vehicle data sets, the navigation route and the following constraint conditions; A position prediction module, configured to optimize the positions of the plurality of vehicles according to the position prediction optimization equation with the goal of minimizing the following distance, and obtain a plurality of position predictions, wherein each position prediction corresponds to one vehicle; as well as, The vehicle control module is used to control the multiple vehicles according to the multiple position predictions, the current positions of the multiple vehicles and the current time.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images