CN117079451A - Control method and device for mixed traffic system in urban continuous intersection scene - Google Patents
Control method and device for mixed traffic system in urban continuous intersection scene Download PDFInfo
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Abstract
The application discloses a control method and a device of a mixed traffic system under a scene of an urban continuous intersection, wherein the method comprises the following steps: establishing a hybrid vehicle queue model and a traffic signal lamp model, and dividing road sections of an intersection into a free lane change section, a formation generation section and an energy-saving control section; based on virtual distribution of vehicle positions of formation generation intervals, determining a passing sequence of a free lane change interval, constructing behavior conflicts among the mixed vehicle queues in the free lane change interval to obtain an optimal solution or a suboptimal solution, further obtaining a feasible path of an energy-saving control interval, obtaining an optimal control mode, generating an optimal speed track of a head vehicle in the mixed vehicle queues, tracking the optimal speed track, and controlling an intelligent network vehicle to lead and follow vehicles. Therefore, the problems that the safety of vehicles can be improved and the energy consumption of a hybrid traffic system can be reduced due to the fact that the vehicles are forced to form a team to generate frequent channel changing actions in the related technology are solved.
Description
Technical Field
The application relates to the technical field of Internet of vehicles, in particular to a control method and a device of a hybrid traffic system under a scene of an urban continuous intersection.
Background
In the related art, a method based on the full intelligent network connection automobile queue can be used for generating the full intelligent network connection automobile queue in a centralized planning mode to carry out planning and control on the vehicles, or the method of the mixed vehicle queue is used for carrying out planning and control on the vehicles through the mixed vehicle queue which is formed freely, so that energy-saving control on the vehicles in a scene of a continuous urban intersection is realized.
However, in the related art, in the process of generating the full intelligent network-connected automobile queue, since the vehicles are forced to form a team to generate frequent channel changing actions, the safety of the vehicles is reduced, especially, the characteristics of the following vehicles in the hybrid vehicle queue are not considered enough, so that the energy saving efficiency is limited, the energy consumption in the hybrid traffic system is increased, the driving experience of the users cannot be met, and the problem needs to be solved.
Disclosure of Invention
The present application is based on the inventors' knowledge and knowledge of the following problems:
the intersection is an important component and key node of urban road traffic, is an important scene of vehicle engineering and traffic engineering field research, and the research discovers that the idle speed and frequent start-stop behavior of the vehicle caused by the intersection are main reasons for increasing the energy consumption of the vehicle in the urban road, and the development of ITS (Intelligent Transportation Systems, intelligent traffic system) and ICV (Intelligent and Connected Vehicle, intelligent network connected automobile) provides a new opportunity for solving the problem of serious energy consumption.
It should be noted that, as the market penetration of intelligent network vehicles increases, the traffic system will experience a traffic scene from all-human-drive vehicles, to a mixed traffic scene where intelligent network vehicles and human-drive vehicles coexist, and finally to a traffic scene of all-intelligent network vehicles, where the mixed traffic scene will last for a long time, and thus, the energy-saving control problem of the mixed traffic system needs to be considered under the urban continuous intersection scene.
Aiming at a multi-intersection scene, the existing intelligent network automobile energy-saving control research aspect facing the mixed traffic scene can be divided into a method based on a single vehicle, a method based on a full intelligent network automobile queue and a method based on a mixed queue, wherein the method based on the single vehicle adopts a distributed planning method, ignores the guiding control effect of the intelligent network automobile on human driving vehicles, does not form a systematic system, leads to limited overall energy-saving effect, the method based on the full intelligent network automobile queue adopts a centralized planning method, but generates frequent channel changing actions due to forced queue formation in the process of generating the full intelligent network automobile queue, and brings about the problem of safety.
In addition, the above researches mostly simplify the road of the multi-intersection scene into a single lane, do not consider the situation of a plurality of lanes, and have insufficient simulation degree on a real traffic system, so as to be clear that the current mixed traffic research on the urban multi-intersection regions does not model the real road traffic environment in detail, is unfavorable for the realization of reality, does not consider the uncertainty of human driving vehicles with emphasis, is unfavorable for the safety of the system, has no clear-layered method flow and limited energy-saving effect, and therefore, the mixed traffic energy-saving control method under the urban road multi-intersection scene needs to be discussed with emphasis so as to solve the technical bottleneck that the energy-saving effect of the current mixed traffic system is limited.
The application provides a control method and a device of a hybrid traffic system in a scene of an urban continuous intersection, which are used for solving the problems that in the related art, in the process of generating a full intelligent network-connected automobile queue, the vehicles are forced to form a queue, so that frequent channel changing behaviors are generated, the safety of the vehicles is reduced, and particularly, the characteristics of the following vehicles in the hybrid vehicle queue are not considered enough, so that the energy saving efficiency is limited, the energy consumption in the hybrid traffic system is increased, and the driving experience of a user cannot be met.
An embodiment of a first aspect of the present application provides a method for controlling a hybrid traffic system in a scene of an urban continuous intersection, including the steps of: establishing a hybrid vehicle queue model and a traffic signal lamp model, and dividing road sections of an intersection into a free lane change section, a formation generation section and an energy-saving control section; determining a passing sequence of the free lane change interval based on virtual distribution of vehicle positions of the formation generation interval, constructing behavior conflict among mixed vehicle queues in the free lane change interval, searching by utilizing the behavior conflict to obtain an optimal solution or a feasible suboptimal solution, and obtaining a feasible track of the energy-saving control interval based on the optimal solution or the feasible suboptimal solution; based on the feasible track, planning centralized energy-saving control on intelligent network vehicles to obtain an optimal control mode, and generating an optimal speed track of a head vehicle in the mixed vehicle queue according to the optimal control mode; and tracking the optimal speed track by using a preset bidirectional following vehicle model, and controlling the intelligent network vehicle to lead the following vehicle.
Optionally, in an embodiment of the present application, the determining, according to the vehicle position virtual distribution of the formation generating section, a traffic order of the free lane changing section, building a behavior conflict between the mixed vehicle queues in the free lane changing section, searching for an optimal solution or a possible suboptimal solution by using the behavior conflict, and obtaining a possible track based on the optimal solution or the possible suboptimal solution includes: determining virtual distribution of the vehicle positions of the formation generating section according to the green time length of the traffic signal lamp at the intersection and the average speed of traffic flow, and obtaining a final formation configuration according to the configuration of the selected final mixed vehicle queue; performing target allocation of multiple vehicles based on the final formation configuration, determining an allocation cost function, performing allocation solution based on integer programming to obtain a target allocation result of the vehicles, and performing position allocation of all the vehicles according to the target allocation result to obtain a position allocation result of all the vehicles; and searching to obtain the optimal solution or the feasible suboptimal solution by utilizing the behavior conflict based on the position distribution results of all vehicles, and obtaining the feasible track according to the optimal solution or the feasible suboptimal solution.
Optionally, in an embodiment of the present application, the planning, based on the feasible track, of the centralized energy-saving control for the intelligent network-connected vehicle, and generating an optimal speed track of the head vehicle in the hybrid vehicle queue by adopting an optimal control method includes: generating an optimal control problem aiming at energy conservation according to the constructed model of the hybrid traffic system; and solving the optimal control problem by using a model theory capable of being satisfied, and generating an optimal speed track of the head car in the mixed vehicle queue.
Optionally, in one embodiment of the present application, the optimal control problem targeting energy conservation includes constructing an objective function targeting engine fuel consumption characteristics of the vehicle, wherein an expression of the objective function is:
wherein T represents torque, N represents rotational speed, Q s (, N) represents a function of torque T and rotational speed N.
Optionally, in one embodiment of the present application, the tracking the optimal speed track by using a bi-directional following vehicle model, controlling the intelligent networked vehicle to lead the following vehicle includes: constructing a hybrid vehicle queue parameterized model based on the bi-directional following vehicle model; identifying at least one parameter in the hybrid vehicle queue parameterized model by utilizing a data-driven manner to output a deterministic model of the hybrid traffic system according to the at least one parameter to obtain deterministic parameters of the hybrid vehicle queue parameterized model based on the deterministic model; and solving a mixed integer problem generated by non-convex optimization and gear influence by utilizing a branch-and-bound algorithm, solving the non-convex optimization problem based on a numerical optimization method to obtain a control sequence of the intelligent network-connected vehicle, optimizing a rolling time domain based on the control sequence to obtain an optimization result, and controlling the intelligent network-connected vehicle to lead the following vehicle based on the optimization result.
An embodiment of a second aspect of the present application provides a control device for a hybrid traffic system in a scene of an urban continuous intersection, including: the system comprises a building module, a control module and a control module, wherein the building module is used for building a hybrid vehicle queue model and a traffic signal lamp model and dividing road sections of an intersection into a free lane change section, a formation generation section and an energy-saving control section; the determining module is used for determining the passing sequence of the free lane change interval based on the virtual distribution of the vehicle positions of the formation generation interval, constructing behavior conflict among the mixed vehicle queues in the free lane change interval, searching by utilizing the behavior conflict to obtain an optimal solution or a feasible suboptimal solution, and obtaining a feasible track of the energy-saving control interval based on the optimal solution or the feasible suboptimal solution; the generation module is used for planning the centralized energy-saving control of the intelligent network-connected vehicles based on the feasible track to obtain an optimal control mode, and generating an optimal speed track of the head vehicle in the mixed vehicle queue according to the optimal control mode; and the control module is used for tracking the optimal speed track by utilizing a preset bidirectional following vehicle model and controlling the intelligent network vehicle to lead the following vehicle.
Optionally, in one embodiment of the present application, the determining module includes: the first determining unit is used for determining virtual distribution of the vehicle positions of the formation generating interval according to the green light time length of the intersection traffic signal lamp and the average vehicle speed of the traffic flow, and obtaining a final formation configuration according to the configuration of the selected final mixed vehicle queue; the second determining unit is used for carrying out target allocation of multiple vehicles based on the final formation configuration, determining an allocation cost function, carrying out allocation solution based on integer programming to obtain a target allocation result of the vehicles, and carrying out position allocation of all the vehicles according to the target allocation result to obtain a position allocation result of all the vehicles; and the acquisition unit is used for searching to obtain the optimal solution or the feasible sub-optimal solution by utilizing the behavior conflict based on the position distribution results of all vehicles, and obtaining the feasible track according to the optimal solution or the feasible sub-optimal solution.
Optionally, in one embodiment of the present application, the generating module includes: the first construction unit is used for generating an optimal control problem aiming at energy conservation according to a constructed model of the hybrid traffic system; and the generating unit is used for solving the optimal control problem by using a model theory and generating an optimal speed track of the head car in the mixed vehicle queue.
Optionally, in one embodiment of the present application, the optimal control problem targeting energy conservation includes constructing an objective function targeting engine fuel consumption characteristics of the vehicle, wherein an expression of the objective function is:
wherein T represents torque, N represents rotational speed, Q s (, N) represents a function of torque T and rotational speed N.
Optionally, in one embodiment of the present application, the control module includes: the second construction unit is used for constructing a hybrid vehicle queue parameterized model based on the bidirectional following vehicle model; a third determining unit, configured to identify at least one parameter in the hybrid vehicle queue parameterized model by using a data-driven manner, so as to output a deterministic model of the hybrid traffic system according to the at least one parameter, so as to obtain a deterministic parameter of the hybrid vehicle queue parameterized model based on the deterministic model; the control unit is used for solving a mixed integer problem generated by non-convex optimization and gear influence by utilizing a branch-and-bound algorithm, solving the non-convex optimization problem based on a numerical optimization method to obtain a control sequence of the intelligent network-connected vehicle, optimizing a rolling time domain based on the control sequence to obtain an optimization result, and controlling the intelligent network-connected vehicle to lead the following vehicle based on the optimization result.
An embodiment of a third aspect of the present application provides a vehicle including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the control method of the hybrid traffic system in the urban continuous intersection scene as described in the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method for controlling a hybrid traffic system in a urban continuous intersection scene as above.
According to the embodiment of the application, a mixed vehicle queue model and a traffic signal lamp model can be established, road sections of an intersection are divided into a free lane change section, a formation generation section and an energy-saving control section, the passing sequence of the free lane change section is determined based on the virtual distribution of the vehicle positions of the formation generation section, and the behavior conflict among the mixed vehicle queues is constructed in the free lane change section so as to obtain an optimal solution or a suboptimal solution, further obtain a feasible track of the energy-saving control section, obtain an optimal control mode, generate an optimal speed track of a head vehicle in the mixed vehicle queue, track the optimal speed track and control an intelligent network-connected vehicle to lead and follow vehicles, so that the safety of the vehicles is effectively improved, and the energy consumption of the vehicles in a mixed traffic system is reduced. Therefore, the problems that the energy-saving efficiency is limited and the energy consumption of the vehicle in a hybrid traffic system is increased due to the fact that frequent channel changing behaviors are generated by forced vehicle team formation, the safety of the vehicle is reduced, and particularly, the following vehicle characteristics are not considered enough in the related art are solved.
Additional aspects and advantages of the application 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 application.
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The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a control method of a hybrid traffic system in a scene of an urban continuous intersection according to an embodiment of the present application;
FIG. 2 is a schematic view of a multi-intersection interval segment divided into three sub-interval segments according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an intelligent networked vehicle traffic order search within multiple consecutive intersection intervals according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a trajectory planning for energy conservation based on an optimal control theory according to an embodiment of the present application;
FIG. 5 is a schematic illustration of a human-driven vehicle model with a bi-directional follow-up model in a hybrid vehicle consist, according to an embodiment of the present application;
FIG. 6 is a schematic diagram of hybrid vehicle fleet control based on a two-way driver model in accordance with one embodiment of the present application;
Fig. 7 is a schematic structural diagram of a control device of a hybrid traffic system in a scene of a city continuous intersection according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The following describes a control method and a device of a hybrid traffic system in a scene of an urban continuous intersection according to an embodiment of the application with reference to the accompanying drawings. Aiming at the problems that in the related art mentioned in the background technology center, in the process of generating a full intelligent network automobile queue, the vehicle forced formation generates frequent channel changing behaviors, so that the safety of vehicles is reduced, particularly, the energy saving efficiency is limited due to insufficient consideration of following vehicle characteristics in a mixed vehicle queue, the energy consumption in a mixed traffic system is increased, and the riding experience of users cannot be met. Therefore, the problems that the energy-saving efficiency is limited and the energy consumption of the vehicle in a hybrid traffic system is increased due to the fact that frequent channel changing behaviors are generated by forced vehicle team formation, the safety of the vehicle is reduced, and particularly, the following vehicle characteristics are not considered enough in the related art are solved.
Specifically, fig. 1 is a schematic flow diagram of a control method of a hybrid traffic system in a scene of an urban continuous intersection according to an embodiment of the present application.
As shown in fig. 1, the control method of the hybrid traffic system in the scene of the urban continuous intersection comprises the following steps:
in step S101, a hybrid vehicle queue model and a traffic light model are established, and the road sections of the intersection are divided into a free lane change section, a formation generation section, and an energy-saving control section.
It can be understood that the embodiment of the application can establish a hybrid vehicle queue model and a traffic signal lamp model, and divide the road segments of the intersection into a free lane change section, a formation generation section and an energy-saving control section, for example, as shown in fig. 2, a schematic view of a scene in which the road segments of the multi-intersection section of one embodiment of the application are divided into three subinterval road segments can be used for effectively improving the control performability of the hybrid traffic system by considering the hybrid traffic scene.
In step S102, based on the virtual distribution of the vehicle positions of the formation generation section, the traffic order of the free lane change section is determined, the behavior conflict between the hybrid vehicle queues is constructed in the free lane change section, the optimal solution or the feasible sub-optimal solution is obtained by searching the behavior conflict, and the feasible track of the energy-saving control section is obtained based on the optimal solution or the feasible sub-optimal solution.
It can be understood that the embodiment of the application can determine the passing sequence of the free lane change interval based on the virtual distribution of the vehicle positions of the formation generation interval in the following steps, build the behavior conflict among the mixed vehicle queues in the free lane change interval, obtain the optimal solution or the possible suboptimal solution by utilizing the behavior conflict search, obtain the possible track of the energy-saving control interval based on the optimal solution or the possible suboptimal solution, and effectively reduce the energy consumption of the vehicle in the mixed traffic system.
In one embodiment of the present application, determining a traffic order of a free lane change interval according to a vehicle position virtual distribution of a formation generation interval, constructing behavior conflicts among hybrid vehicle queues in the free lane change interval, searching for an optimal solution or a feasible sub-optimal solution by using the behavior conflicts, and obtaining a feasible track based on the optimal solution or the feasible sub-optimal solution, including: determining virtual distribution of vehicle positions of formation generating intervals according to green time length of traffic lights at intersections and average speed of traffic flows, and obtaining final formation configuration according to the selected final mixed vehicle queue configuration; performing target allocation of multiple vehicles based on a final formation configuration, determining an allocation cost function, performing allocation solution based on integer programming to obtain a target allocation result of the vehicles, and performing position allocation of all the vehicles according to the target allocation result to obtain a position allocation result of all the vehicles; based on the results of the position distribution of all vehicles, an optimal solution or a feasible suboptimal solution is obtained by utilizing the behavior conflict search, and a feasible track is obtained according to the optimal solution or the feasible suboptimal solution.
In the actual implementation process, as shown in fig. 3, the schematic diagram of the intelligent network-connected vehicle traffic sequence search in a plurality of continuous intersection regions according to an embodiment of the present application is shown, and in the embodiment of the present application, the final state configuration of formation, that is, the virtual distribution of the positions of vehicles entering the formation generation region, needs to be obtained for determining the traffic sequence, where the virtual distribution may be determined according to the green light time length of the multi-intersection traffic light and the average speed of the traffic flow in the following steps.
First, the embodiment of the application can be based on the average speed in the roadCalculating the average head distance +.>Then, the average headway +.>Assume that the green light window duration of the next signal lamp is T g It is possible to estimate the length of the final queue as +.>And finally, according to the selected final state mixed vehicle queue configuration, the final state configuration of the formation can be obtained.
Further, after obtaining the final vehicle position distribution, the embodiment of the application can perform target distribution of multiple vehicles, firstly, the distributed cost function can be determined, and the cost function can be selected in various ways, wherein the euclidean distance is an intuitive index for measuring the cost, so that the matrix for obtaining the cost function by taking the euclidean distance cost function as an example is as follows:
Where i and j denote that the vehicle i arrives at position j,and->The abscissa representing the current position and the target position of the vehicle, respectively,/->And->Representing the ordinate of the current position and the target position, c, respectively, of the vehicle ij Representing the distance between the current position coordinates and the target position coordinates.
The coordinates of the target position are relative to the road, that is, the absolute position of the vehicle relative to the road coordinates.
Next, the embodiment of the present application may obtain a defined vehicle allocation matrix, where the vehicle allocation matrix is expressed as:
wherein,representing a vehicle allocation matrix, a ij Indicating whether the vehicle is assigned to the final position.
Further, an objective function at the cost of euclidean distance from the initial position to the final position of all vehicles in the vehicle distribution task can be constructed, and can be expressed as:
wherein, solving the optimization problem needs to satisfy the following constraint:
the method ensures that only one final state position can be allocated to one vehicle, vehicles do not cross, and the target allocation result of the vehicle can be obtained by solving the optimization problem.
Secondly, the embodiment of the application can solve and obtain the corresponding distribution matrix of the vehicle reaching the final state position, distribute the positions of all vehicles, and perform conflict-free track planning after determining that the distribution task is completed.
Specifically, the embodiment of the application can solve the problem of collision-free passing in the process of reaching the final state position from the initial position by a method based on behavior collision search, avoid collision events, obtain an optimal solution or a feasible suboptimal solution, and perform compromise processing of optimality and calculation cost.
Finally, the distributed feasible tracks can be obtained and output to the vehicle to realize real-time tracking control of the tracks, for example, the influence of a human driving vehicle can be considered during tracking control to ensure the safety, and the control is performed by combining a control mechanism of a rolling time domain, so that a better energy-saving tracking effect of the vehicle is achieved.
It should be noted that, the determination of the traffic sequence of the free lane change interval in the embodiment of the application can lay a solid foundation for the subsequent energy-saving control, and the wide-area sensing communication technology provides support for the cooperative lane change technology of the hybrid traffic system.
In step S103, based on the feasible track, a centralized energy-saving control plan is performed on the intelligent networked vehicles to obtain an optimal control mode, and an optimal speed track of the head vehicle in the hybrid vehicle queue is generated according to the optimal control mode.
It can be understood that the embodiment of the application can plan the intelligent network-connected vehicles in a centralized energy-saving control manner based on the feasible track in the following steps to obtain an optimal control manner, and generate the optimal speed track of the head vehicle in the mixed vehicle queue according to the optimal control manner, thereby effectively improving the safety of the vehicles, increasing the consideration of the following vehicle characteristics and improving the energy-saving efficiency of the vehicles.
In one embodiment of the present application, a centralized energy-saving control plan is performed on an intelligent network-connected vehicle based on a feasible track, and an optimal speed track of a head car in a hybrid vehicle queue is generated by adopting an optimal control method, including: generating an optimal control problem aiming at energy conservation according to the constructed model of the hybrid traffic system; and solving the optimal control problem by using the model theory, and generating an optimal speed track of the head car in the mixed vehicle queue.
As a possible implementation manner, the centralized energy-saving control method in the embodiment of the present application mainly acts on the control area between two intersections to perform energy-saving control on the hybrid vehicle queue, as shown in fig. 4, which is a schematic diagram of a trajectory planning with energy saving as a target based on an optimal control theory in a specific embodiment of the present application, firstly, the embodiment of the present application may establish a vehicle dynamics model and a traffic constraint model to complete modeling of a hybrid traffic system in a whole multi-intersection scene, and then may construct an optimal control problem with energy saving as a target, specifically, may construct a target function with a fuel consumption characteristic of a vehicle engine as a target, that is:
Wherein T represents torque, N represents rotational speed, Q s (, N) represents a function of torque T and rotational speed N, p f Representing the distance of the final state.
Wherein, polynomial functions can be generally applied for fitting, and constraint conditions mainly comprise vehicle dynamics constraint, namely:
where p represents the position of the vehicle, v represents the vehicle speed, a represents the vehicle acceleration, τ represents the inertial time constant, m represents the vehicle mass, η T Representing mechanical efficiency, r w Represents the radius of the wheel, I 0 Representing the transmission ratio of the main speed reducer, I g Representing transmission ratio, T e The engine torque is represented, and g represents the gravitational acceleration constant.
In addition, the embodiment of the application also comprises other constraints, such as space-time constraints caused by traffic lights, maximum vehicle speed and minimum vehicle speed constraints caused by traffic speed limit, and speed constraints and acceleration constraints caused by driving comfort.
And finally, generating an optimal speed track of the head car in the mixed vehicle queue, transmitting the generated optimal speed track to the vehicle, and performing tracking control execution.
Finally, the embodiment of the application can introduce a rolling time domain optimization strategy, update the system with a certain frequency, ensure the safety of vehicle running and the energy-saving high efficiency, and realize the closed-loop control of the whole hybrid traffic system.
In summary, the embodiment of the application can take the mixed vehicle team as a medium, guide the following vehicles by the intelligent network vehicle, reduce the problem of the behavior of the following vehicles with uncertainty in mixed traffic, solve the problem of energy-saving control of a mixed traffic system, and is beneficial to energy-saving calculation resources.
In step S104, the optimal speed track is tracked by using the preset bidirectional following vehicle model, and the intelligent networked vehicle is controlled to lead the following vehicle.
It can be understood that the embodiment of the application can track the optimal speed track by utilizing the following vehicle model with bidirectional following in the following steps to control the intelligent network vehicle to guide the following vehicle, thereby effectively improving the safety of the vehicle, reducing the energy consumption of the vehicle in the hybrid traffic system and improving the energy-saving control effect of the hybrid traffic system.
Wherein, in one embodiment of the application, the following vehicle model of the bi-directional following is utilized to track the optimal speed track, and the intelligent network vehicle is controlled to lead the following vehicle, comprising: constructing a hybrid vehicle queue parameterized model based on the bi-directional following vehicle model; identifying at least one parameter in the hybrid vehicle queue parameterized model by utilizing a data driving mode, and outputting a deterministic model of the hybrid traffic system according to the at least one parameter so as to obtain deterministic parameters of the hybrid vehicle queue parameterized model based on the deterministic model; and solving a mixed integer problem generated by non-convex optimization and gear influence by utilizing a branch-and-bound algorithm, solving the non-convex optimization problem based on a numerical optimization method to obtain a control sequence of the intelligent network-connected vehicle, optimizing a rolling time domain based on the control sequence to obtain an optimization result, and controlling the intelligent network-connected vehicle to lead and follow the vehicle based on the optimization result.
For example, as shown in fig. 5, a schematic diagram of a human driving vehicle model with a bidirectional following model in a hybrid vehicle queue according to an embodiment of the present application is mainly responsible for tracking a track generated by a centralized planning manner, so as to improve the energy-saving control effect of the hybrid traffic system.
Firstly, as shown in fig. 6, a schematic diagram of a hybrid vehicle queue control based on a bidirectional driver model according to a specific embodiment of the present application is shown, and the embodiment of the present application is different from a conventional unidirectional forward following vehicle of a following vehicle, and selects a bidirectional following vehicle model to perform parametric modeling of the hybrid vehicle queue, where the model state quantity includes an intelligent network vehicle state and a following vehicle state, and the control quantity is acceleration of a head intelligent network vehicle and a tail intelligent network vehicle, so that the behavior of the following vehicle can be effectively regulated by adjusting the motion state of the intelligent network vehicle, and the overall energy saving effect of the hybrid vehicle queue is improved.
Then, because the parameterized model of the hybrid vehicle queue has the problem of uncertainty of model parameters, a data-driven method can be used for carrying out system identification, such as a method adopting a Koopman theory to quickly acquire parameters in the model and then output a deterministic model of the system, but the embodiment of the application does not fully believe the model obtained through the data-driven theory, therefore, the parameter obtained through parameter identification can be given an uncertainty range, the uncertainty range can be calibrated according to actual driver data, thereby based on the idea of predictive control of the robust model, a robust invariant set is generated by taking the uncertainty into account with emphasis, and a new control problem taking energy conservation as an objective function is constructed, wherein corresponding state quantity and control quantity of the control problem also carry out corresponding contraction under the condition of taking the uncertainty into account, so as to complete the construction of the economical robust control problem of the hybrid vehicle queue with a bidirectional following model, wherein the constructed new expression taking energy conservation as the objective function is as follows:
Wherein l represents an objective function, x represents a state, k represents a k moment, u represents a control amount, N represents a total step size, and i represents an ith step.
Wherein, the constraints of the state quantity and the control quantity are as follows:
wherein,representing the set of state quantities after removal of the robust invariant set, formed taking into account the uncertainty,/->The set of control quantities is typically limited to a selected range of acceleration of the intelligent networked vehicle.
In addition, the problem of economical robust control is also constrained by vehicle dynamics and practical conditions, such as road speed limit, comfort and other factors.
Finally, the control problem is converted into a constrained optimization problem, wherein an objective function in the control problem can be a non-convex optimization problem, and if the influence of a gear is considered, the control problem is converted into a mixed integer problem, so that when the constrained optimization problem is solved, a branch-and-bound algorithm can be adopted to solve the mixed integer problem, then the non-convex optimization problem is solved based on a numerical optimization method, a control sequence of the intelligent network connected vehicle is obtained, a first control quantity is acted on the vehicle, finally, the optimization of a rolling time domain is carried out, the state of the vehicle is output, and the closed loop control of the system is completed.
In summary, the embodiment of the application carries out parameter identification of the system by a data driving method, completes accurate capturing of the vehicle state, further considers the problem of parameter uncertainty, constructs a robust energy-saving tracking control problem targeting energy saving, carries out numerical solution, is beneficial to ensuring the safety of tracking control, and is beneficial to improving the energy-saving benefits of an intelligent traffic system and an intelligent network-connected automobile.
According to the control method of the mixed traffic system in the urban continuous intersection scene, which is provided by the embodiment of the application, a mixed vehicle queue model and a traffic signal lamp model can be established, road sections of an intersection are divided into a free lane change section, a formation generation section and an energy-saving control section, the traffic sequence of the free lane change section is determined based on the virtual distribution of the vehicle positions of the formation generation section, the behavior conflict among the mixed vehicle queues is constructed in the free lane change section so as to obtain an optimal solution or a feasible suboptimal solution, the feasible track of the energy-saving control section is further obtained, the optimal control mode is obtained, the optimal speed track of the head vehicle in the mixed vehicle queues is generated, the optimal speed track is tracked, and the intelligent network vehicle guiding following vehicle is controlled, so that the safety of the vehicle is effectively improved, and the energy consumption of the vehicle in the mixed traffic system is reduced. Therefore, the problems that the energy-saving efficiency is limited and the energy consumption of the vehicle in a hybrid traffic system is increased due to the fact that frequent channel changing behaviors are generated by forced vehicle team formation, the safety of the vehicle is reduced, and particularly, the following vehicle characteristics are not considered enough in the related art are solved.
Next, a control device of a hybrid traffic system in a scene of an urban continuous intersection according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 7 is a block schematic diagram of a control device of a hybrid traffic system in a city continuous intersection scene according to an embodiment of the present application.
As shown in fig. 7, the control device 10 of the hybrid traffic system in the urban continuous intersection scene includes: the system comprises a building module 100, a determining module 200, a generating module 300 and a control module 400.
Specifically, the building module 100 is configured to build a hybrid vehicle queue model and a traffic light model, and divide a road section of an intersection into a free lane change section, a formation generation section, and an energy-saving control section.
The determining module 200 is configured to determine a traffic order of the free lane change interval based on a virtual distribution of vehicle positions of the formation generation interval, construct a behavior conflict between the hybrid vehicle queues in the free lane change interval, search for an optimal solution or a possible suboptimal solution by using the behavior conflict, and obtain a possible track of the energy-saving control interval based on the optimal solution or the possible suboptimal solution.
The generating module 300 is configured to perform centralized energy-saving control planning on the intelligent network-connected vehicle based on the feasible track, obtain an optimal control mode, and generate an optimal speed track of the head vehicle in the hybrid vehicle queue according to the optimal control mode.
The control module 400 is configured to track an optimal speed track by using a preset bidirectional following vehicle model, and control the intelligent networked vehicle to lead the following vehicle.
Optionally, in one embodiment of the present application, the determining module 200 includes: the device comprises a first determining unit, a second determining unit and an acquiring unit.
The first determining unit is used for determining virtual distribution of vehicle positions of the formation generating section according to the green time length of the traffic signal lamp of the intersection and the average speed of traffic flow, and obtaining a final formation configuration according to the selected final mixed vehicle queue configuration.
The second determining unit is used for carrying out target allocation of multiple vehicles based on the final formation configuration, determining an allocation cost function, carrying out allocation solution based on integer programming to obtain a target allocation result of the vehicles, and carrying out position allocation of all the vehicles according to the target allocation result to obtain a position allocation result of all the vehicles.
The acquisition unit is used for searching to obtain an optimal solution or a feasible suboptimal solution by utilizing behavior conflict based on the position distribution results of all vehicles, and obtaining a feasible track according to the optimal solution or the feasible suboptimal solution.
Optionally, in one embodiment of the present application, the generating module 300 includes: a first construction unit and a generation unit.
The first construction unit is used for generating an optimal control problem aiming at energy conservation according to the constructed model of the hybrid traffic system.
And the generating unit is used for solving the optimal control problem by using the model theory and generating an optimal speed track of the head car in the mixed vehicle queue.
Optionally, in one embodiment of the present application, the optimal control problem targeting energy conservation includes constructing an objective function targeting engine fuel consumption characteristics of the vehicle, wherein the objective function has an expression of:
wherein T represents torque, N represents rotational speed, Q s (, N) represents a function of torque T and rotational speed N.
Optionally, in one embodiment of the present application, the control module 400 includes: the device comprises a second construction unit, a third determination unit and a control unit.
The second construction unit is used for constructing a hybrid vehicle queue parameterized model based on the vehicle model with bidirectional following.
And a third determining unit for identifying at least one parameter in the hybrid vehicle queue parameterization model by using a data driving mode to output a deterministic model of the hybrid traffic system according to the at least one parameter to obtain deterministic parameters of the hybrid vehicle queue parameterization model based on the deterministic model.
The control unit is used for solving the mixed integer problem generated by the non-convex optimization and the gear influence by utilizing a branch-and-bound algorithm, solving the non-convex optimization problem based on a numerical optimization method to obtain a control sequence of the intelligent network vehicle, optimizing a rolling time domain based on the control sequence to obtain an optimization result, and controlling the intelligent network vehicle to lead and follow the vehicle based on the optimization result.
It should be noted that the explanation of the embodiment of the method for controlling the hybrid traffic system in the urban continuous intersection scene is also applicable to the control device for the hybrid traffic system in the urban continuous intersection scene of the embodiment, and will not be repeated here.
According to the control device of the mixed traffic system in the urban continuous intersection scene, which is provided by the embodiment of the application, a mixed vehicle queue model and a traffic signal lamp model can be established, road sections of an intersection are divided into a free lane change section, a formation generation section and an energy-saving control section, the traffic sequence of the free lane change section is determined based on the virtual distribution of the vehicle positions of the formation generation section, the behavior conflict among the mixed vehicle queues is constructed in the free lane change section so as to obtain an optimal solution or a feasible suboptimal solution, the feasible track of the energy-saving control section is further obtained, the optimal control mode is obtained, the optimal speed track of the head vehicle in the mixed vehicle queues is generated, the optimal speed track is tracked, and the intelligent network vehicle guiding following vehicle is controlled, so that the safety of the vehicle is effectively improved, and the energy consumption of the vehicle in the mixed traffic system is reduced. Therefore, the problems that the energy-saving efficiency is limited and the energy consumption of the vehicle in a hybrid traffic system is increased due to the fact that frequent channel changing behaviors are generated by forced vehicle team formation, the safety of the vehicle is reduced, and particularly, the following vehicle characteristics are not considered enough in the related art are solved.
Fig. 8 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
a memory 801, a processor 802, and a computer program stored on the memory 801 and executable on the processor 802.
The processor 802 executes a program to implement the method for controlling the hybrid traffic system in the urban continuous intersection scene provided in the above embodiment.
Further, the vehicle further includes:
a communication interface 803 for communication between the memory 801 and the processor 802.
A memory 801 for storing a computer program executable on the processor 802.
The memory 801 may include high-speed RAM memory or may further include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 8, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 801, the processor 802, and the communication interface 803 are integrated on a chip, the memory 801, the processor 802, and the communication interface 803 may communicate with each other through internal interfaces.
The processor 802 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the control method of the hybrid traffic system in the urban continuous intersection scene as above.
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 application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N 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.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A control method of a mixed traffic system in a scene of a city continuous intersection is characterized by comprising the following steps:
establishing a hybrid vehicle queue model and a traffic signal lamp model, and dividing road sections of an intersection into a free lane change section, a formation generation section and an energy-saving control section;
determining a passing sequence of the free lane change interval based on virtual distribution of vehicle positions of the formation generation interval, constructing behavior conflict among mixed vehicle queues in the free lane change interval, searching by utilizing the behavior conflict to obtain an optimal solution or a feasible suboptimal solution, and obtaining a feasible track of the energy-saving control interval based on the optimal solution or the feasible suboptimal solution;
based on the feasible track, planning centralized energy-saving control on intelligent network vehicles to obtain an optimal control mode, and generating an optimal speed track of a head vehicle in the mixed vehicle queue according to the optimal control mode; and
And tracking the optimal speed track by using a preset bidirectional following vehicle model, and controlling the intelligent network vehicle to lead the following vehicle.
2. The method according to claim 1, wherein the determining the traffic order of the free lane-changing section according to the vehicle position virtual distribution of the formation generating section, constructing behavior conflicts between the mixed vehicle queues in the free lane-changing section, searching for an optimal solution or a suboptimal solution using the behavior conflicts, and obtaining a feasible track based on the optimal solution or the suboptimal solution comprises:
determining virtual distribution of the vehicle positions of the formation generating section according to the green time length of the traffic signal lamp at the intersection and the average speed of traffic flow, and obtaining a final formation configuration according to the configuration of the selected final mixed vehicle queue;
performing target allocation of multiple vehicles based on the final formation configuration, determining an allocation cost function, performing allocation solution based on integer programming to obtain a target allocation result of the vehicles, and performing position allocation of all the vehicles according to the target allocation result to obtain a position allocation result of all the vehicles;
and searching to obtain the optimal solution or the feasible suboptimal solution by utilizing the behavior conflict based on the position distribution results of all vehicles, and obtaining the feasible track according to the optimal solution or the feasible suboptimal solution.
3. The method according to claim 1, wherein the planning of the centralized energy-saving control for the intelligent network-connected vehicles based on the feasible trajectories and generating the optimal speed trajectory for the head vehicles in the hybrid vehicle train by adopting the optimal control method comprises:
generating an optimal control problem aiming at energy conservation according to the constructed model of the hybrid traffic system;
and solving the optimal control problem by using a model theory capable of being satisfied, and generating an optimal speed track of the head car in the mixed vehicle queue.
4. The method of claim 3, wherein the energy conservation targeted optimal control problem comprises constructing an objective function targeting the fuel consumption characteristics of the vehicle's engine,
wherein, the expression of the objective function is:
wherein T represents torque, N represents rotational speed, Q s (, N) represents a function of torque T and rotational speed N.
5. The method of claim 1, wherein tracking the optimal speed trajectory using a bi-directional following vehicle model, controlling the intelligent networked vehicle to lead a following vehicle, comprises:
constructing a hybrid vehicle queue parameterized model based on the bi-directional following vehicle model;
Identifying at least one parameter in the hybrid vehicle queue parameterized model using a data-driven manner to output a deterministic model of the hybrid traffic system according to the at least one parameter to determine at least one parameter of the hybrid vehicle queue parameterized model based on the deterministic model;
and solving a mixed integer problem generated by non-convex optimization and gear influence by utilizing a branch-and-bound algorithm, solving the non-convex optimization problem based on a numerical optimization method to obtain a control sequence of the intelligent network-connected vehicle, optimizing a rolling time domain based on the control sequence to obtain an optimization result, and controlling the intelligent network-connected vehicle to lead the following vehicle based on the optimization result.
6. A control device for a hybrid traffic system in a scene of a city continuous intersection, comprising:
the system comprises a building module, a control module and a control module, wherein the building module is used for building a hybrid vehicle queue model and a traffic signal lamp model and dividing road sections of an intersection into a free lane change section, a formation generation section and an energy-saving control section;
the determining module is used for determining the passing sequence of the free lane change interval based on the virtual distribution of the vehicle positions of the formation generation interval, constructing behavior conflict among the mixed vehicle queues in the free lane change interval, searching by utilizing the behavior conflict to obtain an optimal solution or a feasible suboptimal solution, and obtaining a feasible track of the energy-saving control interval based on the optimal solution or the feasible suboptimal solution;
The generation module is used for planning the centralized energy-saving control of the intelligent network-connected vehicles based on the feasible track to obtain an optimal control mode, and generating an optimal speed track of the head vehicle in the mixed vehicle queue according to the optimal control mode; and
and the control module is used for tracking the optimal speed track by utilizing a preset bidirectional following vehicle model and controlling the intelligent network vehicle to lead the following vehicle.
7. The apparatus of claim 6, wherein the means for determining comprises:
the first determining unit is used for determining virtual distribution of the vehicle positions of the formation generating interval according to the green light time length of the intersection traffic signal lamp and the average vehicle speed of the traffic flow, and obtaining a final formation configuration according to the configuration of the selected final mixed vehicle queue;
the second determining unit is used for carrying out target allocation of multiple vehicles based on the final formation configuration, determining an allocation cost function, carrying out allocation solution based on integer programming to obtain a target allocation result of the vehicles, and carrying out position allocation of all the vehicles according to the target allocation result to obtain a position allocation result of all the vehicles;
And the acquisition unit is used for searching to obtain the optimal solution or the feasible sub-optimal solution by utilizing the behavior conflict based on the position distribution results of all vehicles, and obtaining the feasible track according to the optimal solution or the feasible sub-optimal solution.
8. The apparatus of claim 6, wherein the generating module comprises:
the first construction unit is used for generating an optimal control problem aiming at energy conservation according to a constructed model of the hybrid traffic system;
and the generating unit is used for solving the optimal control problem by using a model theory and generating an optimal speed track of the head car in the mixed vehicle queue.
9. A vehicle, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of controlling a hybrid transportation system in a urban continuous intersection scene as claimed in any one of claims 1 to 5.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for implementing a control method of a hybrid traffic system in a urban continuous intersection scene according to any one of claims 1-5.
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Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060155427A1 (en) * | 2003-02-27 | 2006-07-13 | Shaopeng Yang | Road traffic control method and traffic facilities |
| US20130297689A1 (en) * | 2012-05-03 | 2013-11-07 | Cisco Technology, Inc. | Activity Stream Tuning Using Multichannel Communication Analysis |
| CN104118451A (en) * | 2014-07-15 | 2014-10-29 | 同济大学 | Method for working out tramcar running chart under sharing right of way and adjusting signal light |
| US9606539B1 (en) * | 2015-11-04 | 2017-03-28 | Zoox, Inc. | Autonomous vehicle fleet service and system |
| GB201802057D0 (en) * | 2018-02-08 | 2018-03-28 | Jaguar Land Rover Ltd | A controller and a method for managing vwhicles |
| CN109410561A (en) * | 2018-10-24 | 2019-03-01 | 清华大学 | A kind of even heterogeneous formation travel control method of highway vehicle |
| US20190206247A1 (en) * | 2018-01-03 | 2019-07-04 | Xiaofeng Xie | Smart In-Vehicle Decision Support Systems and Methods with V2I Communications for Driving through Signalized Intersections |
| CN110718074A (en) * | 2019-11-06 | 2020-01-21 | 清华大学 | Cooperative control method for signal lamp and vehicle of hybrid traffic intersection |
| CN110942645A (en) * | 2019-11-06 | 2020-03-31 | 清华大学 | Vehicle control method for hybrid traffic intersection |
| CN113393690A (en) * | 2021-08-17 | 2021-09-14 | 长沙理工大学 | Hybrid traffic balance distribution method considering automatic driving special lane |
| CN114239272A (en) * | 2021-12-16 | 2022-03-25 | 北京工业大学 | Hybrid bicycle flow microscopic modeling method and device based on retrograde behavior |
| US20220114885A1 (en) * | 2020-10-12 | 2022-04-14 | Cavh Llc | Coordinated control for automated driving on connected automated highways |
| CN114578711A (en) * | 2022-02-28 | 2022-06-03 | 同济大学 | CACC simulation platform for urban scene |
| CN114999227A (en) * | 2022-05-25 | 2022-09-02 | 中国农业大学 | Mixed multi-vehicle model-free prediction cooperative control method for non-signal control intersection |
| WO2022223952A1 (en) * | 2021-04-21 | 2022-10-27 | Zeta Specialist Lighting Limited | Traffic control at an intersection |
| CN115565390A (en) * | 2022-09-24 | 2023-01-03 | 南京理工自动化研究院有限公司 | Intelligent internet automobile multi-lane queue traffic control method and system and computer readable storage medium |
| CN115662121A (en) * | 2022-10-19 | 2023-01-31 | 吉林大学 | Method for estimating upper and lower limits of traffic capacity of mixed traffic signal control intersection in queuing mode |
| US20230050192A1 (en) * | 2021-07-30 | 2023-02-16 | Mitsubishi Electric Research Laboratories, Inc. | Global Multi-Vehicle Decision Making System for Connected and Automated Vehicles in Dynamic Environment |
| CN115909716A (en) * | 2022-09-23 | 2023-04-04 | 清华大学 | Traffic intersection scheduling system, method and equipment based on internet cloud control platform |
| CN116189462A (en) * | 2022-09-07 | 2023-05-30 | 北京航空航天大学 | Mixed traffic flow-oriented vehicle track and traffic signal cooperative control method |
| CN116243603A (en) * | 2023-02-21 | 2023-06-09 | 东南大学 | Vehicle queue following control method and device for mixed traffic |
| US20230192090A1 (en) * | 2021-12-22 | 2023-06-22 | Motional Ad Llc | Precedence determination at multi-way stops |
| CN116403402A (en) * | 2023-04-13 | 2023-07-07 | 交通运输部公路科学研究所 | Traffic state prediction method for urban intersection area in network environment |
-
2023
- 2023-07-11 CN CN202310847870.1A patent/CN117079451B/en active Active
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060155427A1 (en) * | 2003-02-27 | 2006-07-13 | Shaopeng Yang | Road traffic control method and traffic facilities |
| US20130297689A1 (en) * | 2012-05-03 | 2013-11-07 | Cisco Technology, Inc. | Activity Stream Tuning Using Multichannel Communication Analysis |
| CN104118451A (en) * | 2014-07-15 | 2014-10-29 | 同济大学 | Method for working out tramcar running chart under sharing right of way and adjusting signal light |
| US9606539B1 (en) * | 2015-11-04 | 2017-03-28 | Zoox, Inc. | Autonomous vehicle fleet service and system |
| US20190206247A1 (en) * | 2018-01-03 | 2019-07-04 | Xiaofeng Xie | Smart In-Vehicle Decision Support Systems and Methods with V2I Communications for Driving through Signalized Intersections |
| GB201802057D0 (en) * | 2018-02-08 | 2018-03-28 | Jaguar Land Rover Ltd | A controller and a method for managing vwhicles |
| CN109410561A (en) * | 2018-10-24 | 2019-03-01 | 清华大学 | A kind of even heterogeneous formation travel control method of highway vehicle |
| CN110718074A (en) * | 2019-11-06 | 2020-01-21 | 清华大学 | Cooperative control method for signal lamp and vehicle of hybrid traffic intersection |
| CN110942645A (en) * | 2019-11-06 | 2020-03-31 | 清华大学 | Vehicle control method for hybrid traffic intersection |
| US20220114885A1 (en) * | 2020-10-12 | 2022-04-14 | Cavh Llc | Coordinated control for automated driving on connected automated highways |
| WO2022223952A1 (en) * | 2021-04-21 | 2022-10-27 | Zeta Specialist Lighting Limited | Traffic control at an intersection |
| US20230050192A1 (en) * | 2021-07-30 | 2023-02-16 | Mitsubishi Electric Research Laboratories, Inc. | Global Multi-Vehicle Decision Making System for Connected and Automated Vehicles in Dynamic Environment |
| CN113393690A (en) * | 2021-08-17 | 2021-09-14 | 长沙理工大学 | Hybrid traffic balance distribution method considering automatic driving special lane |
| CN114239272A (en) * | 2021-12-16 | 2022-03-25 | 北京工业大学 | Hybrid bicycle flow microscopic modeling method and device based on retrograde behavior |
| US20230192090A1 (en) * | 2021-12-22 | 2023-06-22 | Motional Ad Llc | Precedence determination at multi-way stops |
| CN114578711A (en) * | 2022-02-28 | 2022-06-03 | 同济大学 | CACC simulation platform for urban scene |
| CN114999227A (en) * | 2022-05-25 | 2022-09-02 | 中国农业大学 | Mixed multi-vehicle model-free prediction cooperative control method for non-signal control intersection |
| CN116189462A (en) * | 2022-09-07 | 2023-05-30 | 北京航空航天大学 | Mixed traffic flow-oriented vehicle track and traffic signal cooperative control method |
| CN115909716A (en) * | 2022-09-23 | 2023-04-04 | 清华大学 | Traffic intersection scheduling system, method and equipment based on internet cloud control platform |
| CN115565390A (en) * | 2022-09-24 | 2023-01-03 | 南京理工自动化研究院有限公司 | Intelligent internet automobile multi-lane queue traffic control method and system and computer readable storage medium |
| CN115662121A (en) * | 2022-10-19 | 2023-01-31 | 吉林大学 | Method for estimating upper and lower limits of traffic capacity of mixed traffic signal control intersection in queuing mode |
| CN116243603A (en) * | 2023-02-21 | 2023-06-09 | 东南大学 | Vehicle queue following control method and device for mixed traffic |
| CN116403402A (en) * | 2023-04-13 | 2023-07-07 | 交通运输部公路科学研究所 | Traffic state prediction method for urban intersection area in network environment |
Non-Patent Citations (8)
| Title |
|---|
| 《中国公路学报》编辑部;: "中国汽车工程学术研究综述・2017", 中国公路学报, no. 06 * |
| CHAOYI CHEN等: "Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling", 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》, pages 11351 - 11366 * |
| KEQIANG LI: "Cooperative Merging Strategy in Mixed Traffic Based on Optimal Final-State Phase Diagram With Flexible Highway Merging Points", 《IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS》, 16 May 2023 (2023-05-16) * |
| 俞益;李辉;郑旺;: "城市轨道交通混合供电系统工作模式研究", 城市轨道交通研究, no. 01, 10 January 2020 (2020-01-10) * |
| 巫威眺;沈旅欧;靳文舟;: "基于速度截断分布和流量的车队离散模型", 西南交通大学学报, no. 01 * |
| 常鑫;李海舰;荣建;秦伶巧;杨艳芳;: "混有智能网联车队的交通流基本图模型分析", 东南大学学报(自然科学版), no. 04 * |
| 王春;李文;马纯永;韩勇;陈戈;MARGARET HORNE;: "基于VRGIS的城市道路交通微观仿真系统研究", 中国海洋大学学报(自然科学版), no. 07 * |
| 郭露露;高炳钊;陈虹;: "汽车经济性行驶优化", 中国科学:信息科学, no. 05, 20 May 2016 (2016-05-20) * |
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