CN116729386A - Method and device for hierarchical planning and control of vehicles in multi-intersection scene - Google Patents
Method and device for hierarchical planning and control of vehicles in multi-intersection scene Download PDFInfo
- Publication number
- CN116729386A CN116729386A CN202310834319.3A CN202310834319A CN116729386A CN 116729386 A CN116729386 A CN 116729386A CN 202310834319 A CN202310834319 A CN 202310834319A CN 116729386 A CN116729386 A CN 116729386A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- control
- planning
- track
- saving
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 74
- 230000007246 mechanism Effects 0.000 claims description 14
- 238000005096 rolling process Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 abstract description 13
- 230000009286 beneficial effect Effects 0.000 abstract description 10
- 230000000694 effects Effects 0.000 abstract description 9
- 238000004134 energy conservation Methods 0.000 description 15
- 238000004891 communication Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 238000010276 construction Methods 0.000 description 6
- 230000008685 targeting Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 4
- 239000000446 fuel Substances 0.000 description 4
- 239000000243 solution Substances 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18159—Traversing an intersection
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/04—Traffic conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0037—Mathematical models of vehicle sub-units
- B60W2050/0039—Mathematical models of vehicle sub-units of the propulsion unit
Abstract
The application relates to a vehicle layered planning and control method and device under a multi-intersection scene, wherein the method comprises the following steps: the method comprises the steps of planning and controlling vehicles in a multi-intersection scene in a layering manner, and specifically dividing the planning into a cloud planning layer and a vehicle end control layer; the method comprises the steps that global track speed planning is conducted on the basis of a cloud planning layer and current road information by collecting current road information of a road where a vehicle is located, and global energy-saving track speed tracks of the vehicle are generated; and tracking and controlling the global energy-saving track based on a deterministic model constructed by the vehicle-end control layer so as to realize real-time tracking of the optimal global energy-saving track. Therefore, the problems that the prior art fails to consider a vehicle dynamics model in detail or adopts a deterministic vehicle model, does not model a real road traffic environment, is not beneficial to being applied in a real scene, and cannot ensure that an intelligent network-connected automobile achieves global optimization of energy saving effect in a plurality of intersection intervals are solved.
Description
Technical Field
The application relates to the technical field of vehicle control, in particular to a method and a device for planning and controlling layering of vehicles in a multi-intersection scene.
Background
The parking idling caused by the intersection scene in the urban road is one of the important reasons for increasing the energy consumption of the vehicle, and the green wave passing is realized by optimizing the vehicle track, so that the problem is effectively solved. In recent years, intelligent internet-connected automobiles rapidly develop and gradually realize industrialization, are expected to become a main carrier for transportation in the future, have remarkable potential in improving driving safety, high efficiency and comfort, and are expected to reduce energy consumption problems in urban road traffic.
At present, the intelligent network-connected vehicle planning and control technology in a plurality of intersection intervals is mainly analyzed and researched based on an optimization method, and the optimization planning which aims at energy conservation is carried out by giving a fixed model track strategy, such as a three-stage strategy of uniform acceleration, uniform speed and uniform deceleration, to the vehicles and taking the time spent by each stage of strategy as an optimization variable; however, the fixed track strategy is used, the dynamics of the vehicle is not considered, and the tracking control of the vehicle layer is not facilitated; in addition, the related research at present ignores a vehicle dynamics model, has insufficient simulation degree on a real traffic system, does not consider the problem of model uncertainty, and is difficult to achieve global optimization.
In summary, in the current research about planning and control of intelligent network-connected vehicles in multiple intersection regions, a vehicle dynamics model or a deterministic vehicle model cannot be considered in detail, a real road traffic environment is not modeled, the intelligent network-connected vehicle is not beneficial to being applied in a real scene, global optimization of energy-saving effect of the intelligent network-connected vehicle in the multiple intersection regions cannot be guaranteed, and the problem is to be solved.
Disclosure of Invention
The application provides a vehicle layered planning and controlling method and device under a multi-intersection scene, which are used for solving the problems that the prior art fails to consider a vehicle dynamics model or adopts a deterministic vehicle model in detail, does not model a real road traffic environment, is not beneficial to application in a real scene, and cannot ensure that an intelligent network-connected automobile achieves global optimum energy-saving effect in a plurality of intersection intervals.
An embodiment of a first aspect of the present application provides a method for hierarchical planning and control of vehicles in a scene of multiple intersections, including the steps of: layering vehicles in a multi-intersection scene to obtain a cloud planning layer and a vehicle end control layer of the vehicles; the method comprises the steps of collecting current road information of a road where a vehicle is located, carrying out global track planning based on the cloud planning layer and the current road information, generating a global energy-saving track of the vehicle, and carrying out tracking control on the global energy-saving track based on a deterministic model constructed by the vehicle end control layer so as to obtain an optimal global energy-saving track.
Optionally, in an embodiment of the present application, the collecting current road information of a road where the vehicle is located, performing global track planning based on the cloud planning layer and the current road information, and generating a global energy-saving track of the vehicle includes: constructing a vehicle dynamics model of a space domain and a space-time model of a traffic signal lamp; collecting macroscopic traffic flow information, and estimating the queuing time of the vehicle according to the macroscopic traffic flow information; and solving the vehicle dynamics model and the space-time model based on the queuing time of the vehicle, a preset safety mechanism, vehicle dynamics constraint and traffic light constraint to obtain the global energy-saving track.
Optionally, in an embodiment of the present application, the tracking control of the global energy-saving track based on the deterministic model constructed by the vehicle-end control layer to obtain an optimal global energy-saving track includes: acquiring a historical driving track of the vehicle, and performing system identification by adopting a preset data driving method to construct a deterministic model of the vehicle end control layer; determining an economic robust control problem for the vehicle based on the deterministic model; and extracting a first control quantity in a control sequence of the economic robust control problem, so that the vehicle performs tracking control on the global energy-saving track according to the first control quantity.
Optionally, in one embodiment of the present application, the tracking control of the global energy-saving track based on the deterministic model constructed by the vehicle-end control layer includes: and updating the tracking control result to the cloud planning layer, and performing rolling time domain control on the global energy-saving track to obtain the optimal global energy-saving track.
An embodiment of a second aspect of the present application provides a vehicle hierarchical planning and control device in a multi-intersection scene, including: the layering module is used for layering vehicles in a multi-intersection scene to obtain a cloud planning layer and a vehicle end control layer of the vehicles; the system comprises a planning module, a control module and a control module, wherein the planning module is used for acquiring current road information of a road where a vehicle is located, carrying out global track planning based on the cloud planning layer and the current road information, generating a global energy-saving track of the vehicle, and carrying out tracking control on the global energy-saving track based on a deterministic model constructed by the vehicle-end control layer so as to acquire an optimal global energy-saving track.
Optionally, in one embodiment of the present application, the planning module includes: the first modeling unit is used for constructing a vehicle dynamics model of a space domain and a space-time model of a traffic signal lamp; the estimating unit is used for acquiring macroscopic traffic flow information and estimating the queuing time of the vehicle according to the macroscopic traffic flow information; and the solving unit is used for solving the vehicle dynamics model and the space-time model based on the queuing time of the vehicle, a preset safety mechanism, vehicle dynamics constraint and traffic signal lamp constraint to obtain the global energy-saving track.
Optionally, in one embodiment of the present application, the control module includes: the second modeling unit is used for acquiring the historical driving track of the vehicle, and performing system identification by adopting a preset data driving method so as to construct a deterministic model of the vehicle end control layer; a determining unit configured to determine an economic robust control problem of the vehicle based on the deterministic model; and the extraction unit is used for extracting a first control quantity in the control sequence of the economic robust control problem, so that the vehicle performs tracking control on the global energy-saving track according to the first control quantity.
Optionally, in one embodiment of the present application, the control module further includes: and the optimizing unit is used for updating the tracking control result to the cloud planning layer, and performing rolling time domain control on the global energy-saving track to obtain the optimal global energy-saving track.
An embodiment of a third aspect of the present application provides an electronic device, 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 vehicle layered planning and control method in the multi-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 vehicle hierarchical planning and control method in a multi-intersection scene as above.
Thus, embodiments of the present application have the following beneficial effects:
according to the embodiment of the application, the cloud planning layer and the vehicle end control layer of the vehicle can be obtained by layering the vehicles in the scene of multiple intersections; collecting current road information of a road where a vehicle is located, and carrying out global track planning based on a cloud planning layer and the current road information to generate a global energy-saving track of the vehicle; and tracking and controlling the global energy-saving track based on the deterministic model constructed by the vehicle-end control layer so as to acquire the optimal global energy-saving track. According to the intelligent network vehicle energy-saving control method, the track of the cloud planning layer is tracked and controlled by taking energy conservation as a target, so that the decoupling of planning and control and the energy-saving control of the intelligent network vehicle are realized, and the construction of a green intelligent traffic system is facilitated. Therefore, the problems that the prior art fails to consider a vehicle dynamics model in detail or adopts a deterministic vehicle model, does not model a real road traffic environment, is not beneficial to being applied in a real scene, and cannot ensure that an intelligent network-connected automobile achieves global optimization of energy saving effect in a plurality of intersection intervals 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.
Drawings
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 vehicle hierarchical planning and control method in a multi-intersection scene according to an embodiment of the present application;
FIG. 2 is a schematic illustration of hierarchical planning and control of intelligent networked vehicles for multiple intersections according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a logic architecture for global trajectory planning targeting energy conservation based on optimal control theory according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a logic architecture of a method for hierarchical planning and control of vehicles in a multi-intersection scenario according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a logic architecture of a data-driven robust energy-efficient tracking control with uncertainty issues considered according to an embodiment of the present application;
FIG. 6 is a flow chart of a security event triggering mechanism according to an embodiment of the present application;
FIG. 7 is a schematic diagram of an intelligent networked vehicle track according to an embodiment of the present application;
FIG. 8 is an exemplary diagram of a vehicle hierarchical planning and control apparatus in a multiple intersection scenario in accordance with an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
The system comprises a vehicle layered planning and control device, a 100-layered module, a 200-planning module, a 300-control module, a 901-memory, a 902-processor and a 903-communication interface in a 10-multi-intersection scene.
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 vehicle layered planning and control method and device in a multi-intersection scene according to an embodiment of the application with reference to the accompanying drawings. Aiming at the problems that in the background art, the vehicle dynamics model is not considered in detail or a deterministic vehicle model is adopted in the existing research on planning and control of intelligent network vehicles in a plurality of intersection areas, the real road traffic environment is not modeled in detail, the actual application is not facilitated, and the like, the application provides a vehicle layered planning and control method under a multi-intersection scene, wherein in the method, a cloud planning layer and a vehicle end control layer of the vehicle are obtained by layering the vehicles under the multi-intersection scene; collecting current road information of a road where a vehicle is located, and carrying out global track planning based on a cloud planning layer and the current road information to generate a global energy-saving track of the vehicle; and tracking and controlling the global energy-saving track based on the deterministic model constructed by the vehicle-end control layer so as to acquire the optimal global energy-saving track. According to the intelligent network vehicle energy-saving control method, the track of the cloud planning layer is tracked and controlled by taking energy conservation as a target, so that the decoupling of planning and control and the energy-saving control of the intelligent network vehicle are realized, and the construction of a green intelligent traffic system is facilitated. Therefore, the problems that the prior art fails to consider a vehicle dynamics model in detail or adopts a deterministic vehicle model, does not model a real road traffic environment, is not beneficial to being applied in a real scene, and cannot ensure that an intelligent network-connected automobile achieves global optimization of energy saving effect in a plurality of intersection intervals are solved.
Specifically, fig. 1 is a flowchart of a vehicle hierarchical planning and control method in a multi-intersection scene according to an embodiment of the present application.
As shown in fig. 1, the method for planning and controlling the layering of vehicles in the multi-intersection scene comprises the following steps:
in step S101, vehicles in a multi-intersection scene are layered, and a cloud planning layer and a vehicle end control layer of the vehicles are obtained.
Currently, the development of vehicle-road cloud integration provides a solid foundation for layering planning and control of intelligent network vehicle connection, and the embodiment of the application can layer vehicles in a multi-intersection scene through layering architecture design to obtain a cloud planning layer and a vehicle-end control layer, as shown in fig. 2.
It can be understood that the embodiment of the application establishes a modularized system by clearly distributing functions of the cloud and the vehicle end, realizes hierarchical decoupling of planning and control, solves the problem of unclear planning and control layering of the intelligent network-connected automobile facing the intersection scene in the prior art, can solve the planning layer in real time by relying on the characteristic of great calculation force of the cloud, reduces the calculation consumption of the vehicle end, provides better feasibility for the vehicle layer to concentrate on economic model prediction tracking control, and provides greater possibility for energy conservation of the whole traffic system.
In step S102, current road information of a road where the vehicle is located is collected, global track planning is performed based on the cloud planning layer and the current road information, and a global energy-saving track of the vehicle is generated.
After the intelligent network-connected vehicle is divided into a cloud planning layer and a vehicle end control layer to realize hierarchical decoupling of planning and control, further, the embodiment of the application can receive the information of the whole road through the cloud planning layer, wherein the information comprises the phase information of all traffic lights and the position information of the vehicle; and based on the characteristic of great calculation force of the cloud, the global track planning of the cloud, which aims at energy conservation, is completed.
In addition, the cloud end also needs to consider the influence of other traffic participants in detail, and predicts the queuing length of vehicles in front of the intersection, so that the track planning result of the intelligent network vehicle, which aims at energy conservation, is output through the cloud end planning layer, and the generated track is sent to the vehicle end through the communication technology, so that the intelligent network vehicle tracks the track.
Optionally, in an embodiment of the present application, current road information of a road where a vehicle is located is collected, global track planning is performed based on a cloud planning layer and the current road information, and a global energy-saving track of the vehicle is generated, including: constructing a vehicle dynamics model of a space domain and a space-time model of a traffic signal lamp; collecting macroscopic traffic flow information, and estimating the queuing time of the vehicle according to the macroscopic traffic flow information; and solving a vehicle dynamics model and a space-time model based on the queuing time of the vehicle, a preset safety mechanism, vehicle dynamics constraint and traffic light constraint to obtain a global energy-saving track.
The execution logic of global track planning by using the cloud planning layer in the embodiment of the application is shown in fig. 3, and the specific process is as follows:
1. establishing a vehicle dynamics model of a space domain and a space-time model of a traffic signal lamp, and completing mathematical description of a scene;
2. estimating the vehicle queuing length in front of the signal lamp of the intersection according to macroscopic traffic flow information, such as average vehicle speed or average vehicle distance, or predicting the vehicle queuing length in front of the intersection according to a learning method;
3. the corresponding safety mechanism is designed based on methods such as a triggering mechanism and the like, so that when the action influence (cut-in or cut-out) of the other vehicle is considered and the distance between the front vehicle and the own vehicle is smaller than a certain threshold, the following vehicle model replaces a global track and is transmitted to a vehicle layer for following vehicle control, and the safety of the vehicle is ensured;
4. adopting a centralized planning method, taking the dynamics (vehicle engine) characteristics of a vehicle into consideration, taking energy conservation (the consumption of the Brake Specific Fuel Consumption (BSFC) is minimum) as a target, and constructing an optimal control problem by combining vehicle dynamics constraint, traffic signal lamp constraint, state quantity and control quantity constraint;
5. the optimal control problem can be quickly solved by a numerical solution method, such as a pseudo-spectrum method or a direct multiple targeting method, so as to generate a global optimal track, and the generated track is sent to a vehicle end for execution.
It can be understood that by considering the influence of vehicle queuing before an intersection, the embodiment of the application designs an event trigger switching mechanism for ensuring the safety of the vehicle, divides intelligent network vehicle motion control into two types of global planning and local planning, further constructs an optimal control problem with energy conservation as a target, and adopts a direct and repeated targeting mode to quickly solve the optimal control problem to generate a track to be tracked by the vehicle.
Therefore, the embodiment of the application obtains a large range of traffic information through the cloud planning layer, accurately estimates and predicts traffic, aims at saving energy, obtains the globally optimal intelligent network vehicle driving track by considering the characteristics of the vehicle and the influence of other vehicles, and is beneficial to saving energy, thereby solving the problem that the existing method cannot guarantee globally optimal, and realizing the energy-saving control of the intelligent network vehicle.
In step S103, tracking control is performed on the global energy-saving trajectory based on the deterministic model constructed by the vehicle-side control layer, so as to obtain an optimal global energy-saving trajectory.
After the global energy-saving track of the vehicle is generated, further, the track generated by the cloud planning layer can be utilized, the influence of interference and model uncertainty is considered at the vehicle control layer, the data-driven robust model prediction control vehicle longitudinal controller based on the mixed integer problem, which considers characteristics of gears and the like, is provided, as shown in fig. 4, so that the energy conservation and safety of the vehicle motion are ensured, the system energy conservation performance of the traffic layer and the vehicle layer is analyzed, the energy consumption in the running process of the intelligent network-connected vehicles in a plurality of intersection intervals is optimized, the energy consumption in traffic is reduced, and a powerful supporting technology is provided for realizing intelligent environment-friendly automobiles.
Optionally, in an embodiment of the present application, tracking control is performed on the global energy-saving track based on a deterministic model constructed by a vehicle-end control layer to obtain an optimal global energy-saving track, including: acquiring a historical driving track of a vehicle, and performing system identification by adopting a preset data driving method to construct a deterministic model of a vehicle end control layer; determining an economic robust control problem for the vehicle based on the deterministic model; and extracting a first control quantity in a control sequence of the economic robust control problem, so that the vehicle performs tracking control on the global energy-saving track according to the first control quantity.
It should be noted that, in the embodiment of the present application, the vehicle control layer needs to track and control the track after receiving the track information of the cloud planning layer. In the tracking control process, the safety influence caused by the front vehicle must be considered, so that the embodiment of the application can design a switching mechanism for ensuring safety, complete the safety tracking of the track, and consider the influence of model uncertainty when the tracking controller is designed, thereby carrying out the tracking control with energy conservation as a target.
The logic for executing the data-driven robust energy-saving tracking control of the intelligent network-connected vehicle considering the uncertainty problem in the embodiment of the application is shown in fig. 5, and the specific process is as follows:
1. modeling a vehicle tracking control system by adopting a data driving method:
(1) Determining a standard form of the system model;
(2) The method comprises the steps of obtaining a historical track of actual running of a vehicle, carrying out system identification by adopting a data driving method, and obtaining a deterministic model of the system, wherein the model can be generally expressed in the form of a state equation.
It should be noted that, the state equation in the embodiment of the present application needs to consider the dynamics model of the vehicle, and uses the engine torque and the gear as the control amounts to control, so as to furthest improve the energy-saving control effect.
2. Based on the data-driven model, the economic robust control problem construction is carried out:
(1) Analyzing the reachable set, and constructing a mathematical form of the reachable set of states;
(2) Setting a cost function with energy conservation as a target, and generally, directly selecting a fuel consumption model of the engine;
(3) Solving an optimization problem by considering the reachable set state and the control quantity constraint; the optimal control problem comprises integer data, so that a numerical solution is needed to be solved by adopting methods such as branch delimitation and the like to obtain a sequence of control quantity, and the construction of the economic robust control problem is completed.
3. The first control quantity in the control sequence of the economic robust control problem is extracted, and is acted on the actual system of the intelligent network-connected automobile to control the execution of the vehicle bottom layer executor, so as to complete the control of the intelligent network-connected automobile.
Therefore, the embodiment of the application carries out data-driven robust energy-saving tracking control through the intelligent network-connected vehicle considering the uncertainty problem, has strong scene applicability, can effectively solve the problem that the existing method cannot ensure the energy-saving effect under the condition of uncertainty existence, and carries out real-time modeling through the data-driven method, thereby accurately capturing the vehicle state, objectively reflecting the vehicle working condition and being beneficial to the construction of a green intelligent traffic system.
Optionally, in one embodiment of the present application, tracking control of the global energy-saving trajectory based on a deterministic model constructed by a vehicle-side control layer includes: and updating the tracking control result to the cloud planning layer, and performing rolling time domain control on the global energy-saving track to obtain an optimal global energy-saving track.
After the tracking action is executed, the embodiment of the application can update the tracking control result to the cloud planning layer so as to plan the global energy-saving track in the next period by combining the cloud planning layer with a rolling time domain control mechanism, and complete the closed-loop rolling control of the system, wherein the information updating frequency of the cloud layer is generally lower than that of the vehicle control layer.
Therefore, the embodiment of the application can further obtain and store the actual state of the vehicles in the mixed vehicle queue by performing rolling time domain control on the global energy-saving track of the cloud planning layer, and analyze the state, thereby ensuring the safety of the system and being beneficial to performing energy-saving control on the intelligent network-connected vehicles.
The method for planning and controlling the vehicle layering in the multi-intersection scene is further described and illustrated by the following specific embodiments and the accompanying drawings.
The specific embodiment performs the process of vehicle layered planning and control in a multi-intersection scene as follows:
1. constructing an intelligent network-connected automobile time domain nonlinear model;
the mathematical expression of the intelligent network-connected automobile time domain nonlinear model is as follows:
wherein p is vehicle displacement; v is vehicle speed; a is the acceleration of the vehicle; τ is the time lag constant of the driveline; m is the mass of the vehicle; η (eta) t Is the mechanical efficiency of the transmission system; r is (r) w Is the radius of the wheel; i 0 Is the transmission ratio of the main speed reducer; i g Is the transmission gear ratio; t (T) e Is the desired engine torque;the expression of (2) is as follows:
wherein C is D Is the air resistance coefficient; ρ is the air density; a represents the windward area of the vehicle; g is a gravitational acceleration constant; f is the rolling resistance coefficient.
By simplifying and reducing the model, the time lag tau in the drive train is ignored in the upper energy-saving planning, and a simplified vehicle dynamics model can be obtained, as shown in the following formula:
setting the state vector of the time domain to x t ＝[pv] T According toEquation (3) may be further converted into a spatial domain model as shown in the following equation:
2. modeling the traffic signal lamp:
(1) The position of the ith traffic light is recorded as P i The starting time of the j green light phase and the j red light phase of the i traffic light are respectively recorded asAnd-> And->The time is a relative time to start traveling with respect to ICV.
It should be noted that embodiments of the application provide forThat is, it is ensured that the cycle of the traffic light starts from the green light, and therefore, if the vehicle can pass through the intersection without stopping, the following conditions need to be satisfied:
wherein, the liquid crystal display device comprises a liquid crystal display device,is the time at which the ICV passes through the i-th intersection.
(2) Constructing optimal control problems in a plurality of intersection scenes:
in one embodiment of the present application, the objective function may be expressed as:
wherein p is f Is the final distance; q (Q) s ＝ s /v(p)，q s For fuel injection rate, q s The specific form is as follows:
wherein P is e Engine power (w).
In one embodiment of the present application, constraints on the optimal control problem are as follows:
P e,in <e<e,ax(9)
v min <v< max (10)
the energy-saving optimal control problem constructed by the specific embodiment of the application is a nonlinear optimal control problem, and an analytic solution is difficult to obtain, but a numerical solution can be obtained by a numerical method such as a pseudo-spectrum method or a direct multiple targeting method, so that a global optimal track, namely a global energy-saving track, is obtained.
4. Considering the influence of other vehicles, when the current vehicle and the intelligent network-connected vehicle are too close to each other or other vehicles enter, a corresponding event trigger mechanism should be designed, and the flow of the event trigger mechanism design is shown in fig. 6. As can be seen from fig. 6, when the distance from the preceding vehicle is sufficiently large, a centralized global trajectory optimization can be employed; when the distance is short, energy-saving tracking control can be adopted as a target to output a track for tracking the intelligent network-connected automobile, and the track is transmitted to the intelligent network-connected automobile through communication.
5. The intelligent network-connected vehicle receives the track for tracking control:
(1) Modeling a control system, while there is necessarily uncertainty in the system model, one embodiment of the present application may choose a data-driven approach to modeling.
The data driving method can select a data driving prediction control method, and the method constructs the data driving prediction control of the rolling time domain optimization by combining the data driving dynamics expression based on the Willems basic theorem with a frame of the prediction control with input and output constraints, wherein the core formula is as follows:
wherein U is p And Y p Sub-blocks of Hankel matrix, Y p And Y f The same constitution, u ini And u represents a control input sequence in the past time domain and a control input sequence in the future time domain, y ini And y represents a state output sequence in the past time domain and a state output sequence in the future time domain.
6. Building a robust optimization problem:
one embodiment of the present application can build an economical robust control problem as shown in the following formula:
min g,u,y J(y,u)(13)
the constraints for solving this problem are:
it should be noted that, in the formula, U and Y are control amounts and state constraint sets generated by using the reachable set theory after taking model uncertainty into consideration, and J (Y, U) is an objective function targeting energy conservation.
7. And tracking and controlling the track of the intelligent network-connected vehicle, and completing energy-saving optimal control under the condition of ensuring safety.
In the application case test scene of the embodiment of the application, intersections (numbered 1, 2, 3, 4, 5 and 6 in sequence) with signal lamps are designed, the whole-course speed limit of the road is set to be 18m/s, and the track of the intelligent network-connected vehicle in the embodiment of the application is shown in figure 7. As can be seen from fig. 7, the speed track obtained by the vehicle layered planning and control method in the multi-intersection scene of the application is very smooth, has obvious advantages in fuel saving, can effectively avoid the parking idle condition of the intelligent network-connected vehicle in front of the intersection, and is beneficial to improving the energy-saving effect.
According to the vehicle layered planning and control method under the multi-intersection scene, the cloud planning layer and the vehicle control layer are divided, layered decoupling of planning and control is achieved, the influence of vehicle dynamics characteristics and other vehicles in a road is considered, the problem that the existing method cannot guarantee global optimization is solved, and energy-saving control of an intelligent network-connected vehicle is achieved; and based on the track generated by the planning layer, the intelligent network-connected automobile which considers the uncertainty problem is used for carrying out data-driven robust energy-saving tracking control, so that the problem that the energy-saving effect cannot be ensured under the condition that the uncertainty exists in the existing method can be solved, and the energy-saving performance of the whole traffic system is further improved.
Next, a vehicle hierarchical planning and control device in a multi-intersection scene according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 8 is a block schematic diagram of a vehicle hierarchical planning and control apparatus in a multi-intersection scene according to an embodiment of the present application.
As shown in fig. 8, the vehicle hierarchical planning and control device 10 in the multi-intersection scene includes: layering module 100, planning module 200, and control module 300.
The layering module 100 is configured to layer vehicles in a multi-intersection scene to obtain a cloud planning layer and a vehicle end control layer of the vehicles.
The planning module 200 is configured to collect current road information of a road where the vehicle is located, perform global track planning based on the cloud planning layer and the current road information, and generate a global energy-saving track of the vehicle.
The control module 300 is configured to perform tracking control on the global energy-saving track based on a deterministic model constructed by the vehicle-end control layer, so as to obtain an optimal global energy-saving track.
Optionally, in one embodiment of the present application, the planning module 200 includes: the device comprises a first modeling unit, an estimation unit and a solving unit.
The first modeling unit is used for constructing a vehicle dynamics model of a space domain and a space-time model of a traffic signal lamp.
And the estimating unit is used for acquiring macroscopic traffic flow information and estimating the queuing time of the vehicle according to the macroscopic traffic flow information.
The solving unit is used for solving the vehicle dynamics model and the space-time model based on the queuing time of the vehicle, a preset safety mechanism, vehicle dynamics constraint and traffic signal lamp constraint to obtain a global energy-saving track.
Optionally, in one embodiment of the present application, the control module 300 includes: a second modeling unit, a determining unit and an extracting unit.
The second modeling unit is used for acquiring the historical driving track of the vehicle, and performing system identification by adopting a preset data driving method so as to construct a deterministic model of the vehicle end control layer.
And the determining unit is used for determining the economic robust control problem of the vehicle based on the deterministic model.
And the extraction unit is used for extracting a first control quantity in the control sequence of the economic robust control problem, so that the vehicle performs tracking control on the global energy-saving track according to the first control quantity.
Optionally, in one embodiment of the present application, the control module 300 further includes: and the optimizing unit is used for updating the tracking control result to the cloud planning layer, and performing rolling time domain control on the global energy-saving track to obtain an optimal global energy-saving track.
It should be noted that, the explanation of the embodiment of the method for planning and controlling the vehicle layer in the multi-intersection scene is also applicable to the device for planning and controlling the vehicle layer in the multi-intersection scene of the embodiment, and will not be repeated here.
According to the vehicle layered planning and control device under the multi-intersection scene, the cloud planning layer and the vehicle end control layer of the vehicle are obtained by layering the vehicles under the multi-intersection scene; collecting current road information of a road where a vehicle is located, and carrying out global track planning based on a cloud planning layer and the current road information to generate a global energy-saving track of the vehicle; and tracking and controlling the global energy-saving track based on the deterministic model constructed by the vehicle-end control layer so as to acquire the optimal global energy-saving track. According to the intelligent network vehicle energy-saving control method, the track of the cloud planning layer is tracked and controlled by taking energy conservation as a target, so that the decoupling of planning and control and the energy-saving control of the intelligent network vehicle are realized, and the construction of a green intelligent traffic system is facilitated.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
memory 901, processor 902, and a computer program stored on memory 901 and executable on processor 902.
The processor 902 implements the vehicle hierarchical planning and control method in the multi-intersection scene provided in the above embodiment when executing the program.
Further, the electronic device further includes:
a communication interface 903 for communication between the memory 901 and the processor 902.
Memory 901 for storing a computer program executable on processor 902.
Memory 901 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 901, the processor 902, and the communication interface 903 are implemented independently, the communication interface 903, the memory 901, and the processor 902 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. 9, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 901, the processor 902, and the communication interface 903 are integrated on a chip, the memory 901, the processor 902, and the communication interface 903 may communicate with each other through internal interfaces.
The processor 902 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 embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, realizes the vehicle layered planning and control method under the multi-intersection scene.
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. The vehicle layered planning and control method under the scene of multiple intersections is characterized by comprising the following steps:
layering vehicles in a multi-intersection scene to obtain a cloud planning layer and a vehicle end control layer of the vehicles;
collecting current road information of a road where a vehicle is located, performing global track planning based on the cloud planning layer and the current road information, generating a global energy-saving track of the vehicle, and
and tracking and controlling the global energy-saving track based on a deterministic model constructed by the vehicle-end control layer so as to acquire an optimal global energy-saving track.
2. The method of claim 1, wherein the collecting current road information of a road on which the vehicle is located, performing global track planning based on the cloud planning layer and the current road information, and generating a global energy-saving track of the vehicle, comprises:
constructing a vehicle dynamics model of a space domain and a space-time model of a traffic signal lamp;
collecting macroscopic traffic flow information, and estimating the queuing time of the vehicle according to the macroscopic traffic flow information;
and solving the vehicle dynamics model and the space-time model based on the queuing time of the vehicle, a preset safety mechanism, vehicle dynamics constraint and traffic light constraint to obtain the global energy-saving track.
3. The method according to claim 1, wherein tracking the global energy-saving trajectory based on a deterministic model constructed by the vehicle-side control layer to obtain an optimal global energy-saving trajectory, comprises:
acquiring a historical driving track of the vehicle, and performing system identification by adopting a preset data driving method to construct a deterministic model of the vehicle end control layer;
determining an economic robust control problem for the vehicle based on the deterministic model;
and extracting a first control quantity in a control sequence of the economic robust control problem, so that the vehicle performs tracking control on the global energy-saving track according to the first control quantity.
4. A method according to claim 3, wherein said tracking control of said global energy-saving trajectory based on a deterministic model constructed by said vehicle-side control layer comprises:
and updating the tracking control result to the cloud planning layer, and performing rolling time domain control on the global energy-saving track to obtain the optimal global energy-saving track.
5. A vehicle layered planning and control device in a multi-intersection scene, comprising:
the layering module is used for layering vehicles in a multi-intersection scene to obtain a cloud planning layer and a vehicle end control layer of the vehicles;
the planning module is used for collecting current road information of a road where the vehicle is located, carrying out global track planning based on the cloud planning layer and the current road information, generating a global energy-saving track of the vehicle, and
and the control module is used for tracking and controlling the global energy-saving track based on the deterministic model constructed by the vehicle-end control layer so as to acquire the optimal global energy-saving track.
6. The apparatus of claim 5, wherein the planning module comprises:
the first modeling unit is used for constructing a vehicle dynamics model of a space domain and a space-time model of a traffic signal lamp;
the estimating unit is used for acquiring macroscopic traffic flow information and estimating the queuing time of the vehicle according to the macroscopic traffic flow information;
and the solving unit is used for solving the vehicle dynamics model and the space-time model based on the queuing time of the vehicle, a preset safety mechanism, vehicle dynamics constraint and traffic signal lamp constraint to obtain the global energy-saving track.
7. The apparatus of claim 5, wherein the control module comprises:
the second modeling unit is used for acquiring the historical driving track of the vehicle, and performing system identification by adopting a preset data driving method so as to construct a deterministic model of the vehicle end control layer;
a determining unit configured to determine an economic robust control problem of the vehicle based on the deterministic model;
and the extraction unit is used for extracting a first control quantity in the control sequence of the economic robust control problem, so that the vehicle performs tracking control on the global energy-saving track according to the first control quantity.
8. The apparatus of claim 7, wherein the control module further comprises:
and the optimizing unit is used for updating the tracking control result to the cloud planning layer, and performing rolling time domain control on the global energy-saving track to obtain the optimal global energy-saving track.
9. An electronic device, 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 vehicle hierarchical planning and control in a multiple intersection scene as claimed in any one of claims 1 to 4.
10. A computer-readable storage medium having stored thereon a computer program, wherein the program is executed by a processor for implementing the vehicle hierarchical planning and control method in a multi-intersection scene as set forth in any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310834319.3A CN116729386B (en) | 2023-07-07 | 2023-07-07 | Method and device for hierarchical planning and control of vehicles in multi-intersection scene |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310834319.3A CN116729386B (en) | 2023-07-07 | 2023-07-07 | Method and device for hierarchical planning and control of vehicles in multi-intersection scene |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116729386A true CN116729386A (en) | 2023-09-12 |
| CN116729386B CN116729386B (en) | 2023-12-26 |
Family
ID=87915093
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310834319.3A Active CN116729386B (en) | 2023-07-07 | 2023-07-07 | Method and device for hierarchical planning and control of vehicles in multi-intersection scene |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116729386B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201612281D0 (en) * | 2016-07-15 | 2016-08-31 | Kompetenzzentrum-Das Virtuelle Fahrzeug | Predictive control system for autonomous driving vehicle |
| US20170003685A1 (en) * | 2015-07-02 | 2017-01-05 | Geodigital International Inc. | Attributed roadway trajectories for self-driving vehicles |
| CN111891110A (en) * | 2020-05-15 | 2020-11-06 | 吉林大学 | Intelligent network-connected hybrid electric vehicle energy-heat integrated real-time management system |
| US20210253128A1 (en) * | 2020-02-19 | 2021-08-19 | Nvidia Corporation | Behavior planning for autonomous vehicles |
| CN113525396A (en) * | 2021-08-13 | 2021-10-22 | 北京理工大学 | Hybrid electric vehicle layered prediction energy management method integrating deep reinforcement learning |
| CN114822048A (en) * | 2022-05-25 | 2022-07-29 | 云控智行科技有限公司 | Cloud planning service system and method for Internet vehicles |
| CN115071505A (en) * | 2022-06-20 | 2022-09-20 | 武汉理工大学 | Fuel cell automobile layered planning method, system, device and storage medium |
-
2023
- 2023-07-07 CN CN202310834319.3A patent/CN116729386B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170003685A1 (en) * | 2015-07-02 | 2017-01-05 | Geodigital International Inc. | Attributed roadway trajectories for self-driving vehicles |
| GB201612281D0 (en) * | 2016-07-15 | 2016-08-31 | Kompetenzzentrum-Das Virtuelle Fahrzeug | Predictive control system for autonomous driving vehicle |
| US20210253128A1 (en) * | 2020-02-19 | 2021-08-19 | Nvidia Corporation | Behavior planning for autonomous vehicles |
| CN111891110A (en) * | 2020-05-15 | 2020-11-06 | 吉林大学 | Intelligent network-connected hybrid electric vehicle energy-heat integrated real-time management system |
| CN113525396A (en) * | 2021-08-13 | 2021-10-22 | 北京理工大学 | Hybrid electric vehicle layered prediction energy management method integrating deep reinforcement learning |
| CN114822048A (en) * | 2022-05-25 | 2022-07-29 | 云控智行科技有限公司 | Cloud planning service system and method for Internet vehicles |
| CN115071505A (en) * | 2022-06-20 | 2022-09-20 | 武汉理工大学 | Fuel cell automobile layered planning method, system, device and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 李克强等: "智能网联汽车云控系统原理及其典型应用", 汽车安全与节能学报, vol. 11, no. 3, pages 261 - 275 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116729386B (en) | 2023-12-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Hybrid electric vehicle energy management with computer vision and deep reinforcement learning | |
| Wang et al. | Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios | |
| Asadi et al. | Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time | |
| Dong et al. | Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends | |
| CN111169480B (en) | Power system energy management method, device, equipment and medium | |
| Amini et al. | Sequential optimization of speed, thermal load, and power split in connected HEVs | |
| Luo et al. | Green light optimal speed advisory for hybrid electric vehicles | |
| CN109360409A (en) | A kind of intelligent network connection hybrid vehicle formation control method based on driving style | |
| CN111959492B (en) | HEV energy management hierarchical control method considering lane change behavior in internet environment | |
| CN103158714A (en) | System and method of assisting driver in driving electric vehicle in more environmentally efficient manner | |
| CN112614340B (en) | Method and device for enabling branch vehicles to converge into main road, electronic equipment and storage medium | |
| Yan et al. | Hierarchical predictive energy management of fuel cell buses with launch control integrating traffic information | |
| CN102126496A (en) | Parallel hybrid management control system and management control method thereof | |
| CN103434509A (en) | Control system of hybrid power bus and motive power control method of hybrid power bus | |
| CN111267830A (en) | Hybrid power bus energy management method, device and storage medium | |
| CN113642177A (en) | Digital twin virtual-real multi-vehicle mixed-driving simulation method and device | |
| CN109816978B (en) | Regional group traffic guidance system and method considering dynamic response behaviors of drivers | |
| Zhang et al. | Predictive eco-driving application considering real-world traffic flow | |
| Hao et al. | Deeply integrated vehicle dynamic and powertrain operation for efficient plug-in hybrid electric bus | |
| CN115534929A (en) | Plug-in hybrid electric vehicle energy management method based on multi-information fusion | |
| Huang et al. | Energy saving performance improvement of intelligent connected PHEVs via NN-based lane change decision | |
| Li et al. | A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes | |
| CN116729386B (en) | Method and device for hierarchical planning and control of vehicles in multi-intersection scene | |
| Song et al. | State-of-the-art and development trends of energy management strategies for intelligent and connected new energy vehicles: a review | |
| Pan et al. | Research on multi-lane energy-saving driving strategy of connected electric vehicle based on vehicle speed prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |