CN113415288B - Sectional type longitudinal vehicle speed planning method, device, equipment and storage medium - Google Patents
Sectional type longitudinal vehicle speed planning method, device, equipment and storage medium Download PDFInfo
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- 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
- B60W40/10—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 vehicle motion
- B60W40/105—Speed
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- 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
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- 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
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- 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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/12—Lateral speed
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- 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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/15—Road slope
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- 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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/53—Road markings, e.g. lane marker or crosswalk
Abstract
The invention belongs to the technical field of vehicles, and discloses a sectional type longitudinal vehicle speed planning method, a sectional type longitudinal vehicle speed planning device, sectional type longitudinal vehicle speed planning equipment and a storage medium. The method comprises the following steps: acquiring running environment information, running parameter information, vehicle condition information and road state information of a running road of a target vehicle; segmenting a target route on a driving road according to the road state information to obtain a first segmented route; obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the running environment information, the road state information and the running parameter information; determining a preset dynamic programming optimization strategy based on a target time penalty factor; and obtaining a target segmented speed track through a preset dynamic programming optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route so that the target vehicle runs according to the target segmented speed track. By the method, the vehicle is subjected to the sectional speed planning, and the real-time performance and the global optimality of the speed planning are realized.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a sectional type longitudinal vehicle speed planning method, device, equipment and storage medium.
Background
The existing vehicle speed planning methods are roughly two types: one is a vehicle speed planning method based on rolling time domain optimization, which reduces the calculation time by shortening the path length of the speed planning, and continuously updates the latest optimization result for the implementation of the vehicle in order to adapt to the change of the running environment around the vehicle. The other method is a vehicle speed planning method based on a global optimization algorithm, the method can plan the speed of the whole range from the starting point to the end point according to the acquired running environment information, but the two methods can not ensure that the planned speed track has real-time performance and is optimal.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a sectional type longitudinal vehicle speed planning method, a sectional type longitudinal vehicle speed planning device, sectional type longitudinal vehicle speed planning equipment and a storage medium, and aims to solve the technical problem that the vehicle speed planning in the prior art cannot ensure that a speed track has real-time performance and optimal economy.
In order to achieve the above object, the present invention provides a sectional longitudinal vehicle speed planning method, comprising the steps of:
acquiring running environment information, running parameter information, vehicle condition information and road state information of a running road of a target vehicle;
segmenting a target route on the driving road according to the road state information to obtain a first segmented route;
obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information and the driving parameter information;
determining a corresponding preset dynamic programming optimization strategy based on the target time penalty factor;
and obtaining a target segmented speed track through a preset dynamic programming optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route, so that the target vehicle runs according to the target segmented speed track.
Optionally, the acquiring the running environment information, the running parameter information, the vehicle condition information, and the road state information of the running road of the target vehicle includes:
sending an information acquisition instruction to enable a target cloud end to feed back running environment information around the target vehicle;
and acquiring road state information, driving parameter information and vehicle condition information of a driving road corresponding to the target vehicle, wherein the vehicle condition information comprises target vehicle energy consumption information and target vehicle impact degree information.
Optionally, before obtaining the corresponding target time penalty factor according to the driving environment information, the road state information, and the driving parameter information by a preset multilayer feedforward neural network model, the method further includes:
acquiring training driving environment information, training road state information and training vehicle driving parameter information in a preset database;
determining a corresponding initial time penalty factor through an initial dynamic planning strategy according to the training driving environment information, the training road state information and the training vehicle driving parameter information;
constructing a time penalty factor database;
and training the initial multilayer feedforward neural network model according to the initial time penalty factor and the time penalty factor database to obtain a preset multilayer feedforward neural network model.
Optionally, the obtaining a target segment speed trajectory through a preset dynamic programming optimization strategy based on the driving environment information, the road state information, the vehicle condition information, and the first segment route includes:
obtaining a local segmentation speed track of the first segmentation route through a preset dynamic programming optimization strategy based on the driving environment information, the road state information and the vehicle condition information;
obtaining a global segmentation speed track corresponding to the target route according to the preset dynamic programming optimization strategy based on the local segmentation speed track;
and taking the global segmented velocity track as a target segmented velocity track.
Optionally, the obtaining the local segment speed trajectory of the first segment route through a preset dynamic programming optimization strategy based on the driving environment information, the road state information, and the vehicle condition information includes:
acquiring traffic signal lamp passing state information and traffic state information in the driving environment information;
acquiring intersection information and road speed limit information in the road state information;
and obtaining a local segmentation speed track of the first segmentation route through a preset dynamic planning optimization strategy based on the traffic signal lamp passing state information, the traffic state information, the intersection information, the road speed limit information and the vehicle condition information.
Optionally, the obtaining of the local segmentation speed track of the first segmentation route through a preset dynamic programming optimization strategy based on the traffic signal lamp traffic state information, the intersection information, the road speed limit information, and the vehicle condition information includes:
obtaining average speed information of the periphery of the target vehicle according to the traffic state information;
obtaining corresponding intersection passing state information according to the intersection information and the traffic signal lamp passing state information;
determining a terminal time constraint corresponding to the first sectional route according to the intersection passing state information;
determining a speed range constraint corresponding to the first subsection route according to the road speed limit information;
determining a terminal speed constraint corresponding to the first subsection route according to the average speed information;
determining a corresponding optimization target according to the vehicle condition information;
and obtaining the local segmentation speed track of the first segmentation route according to the terminal time constraint, the speed range constraint, the terminal speed constraint and the optimization target.
Optionally, the obtaining a global segment speed trajectory corresponding to the target route according to the preset dynamic programming optimization strategy based on the local segment speed trajectory includes:
acquiring a target travel time of a target vehicle;
obtaining the corresponding local segmentation end speed according to the local segmentation speed track;
determining corresponding local segmentation travel time according to the target travel time and the local segmentation speed track;
determining a corresponding optimization target according to the vehicle condition information;
and obtaining a global subsection speed track corresponding to the target route according to the local subsection tail end speed, the local subsection travel time and the optimization target.
In addition, to achieve the above object, the present invention further provides a sectional longitudinal vehicle speed planning device, including:
the acquisition module is used for acquiring the running environment information, the running parameter information, the vehicle condition information and the road state information of a running road of a target vehicle;
the segmentation module is used for segmenting a target route on the driving road according to the road state information to obtain a first segmented route;
the obtaining module is used for obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information and the driving parameter information;
the determining module is used for determining a corresponding preset dynamic programming optimization strategy based on the target time penalty factor;
the obtaining module is further configured to obtain a target segment speed track through a preset dynamic programming optimization strategy based on the driving environment information, the road state information, the vehicle condition information and the first segment route, so that the target vehicle drives according to the target segment speed track.
In addition, to achieve the above object, the present invention further provides a sectional longitudinal vehicle speed planning apparatus, including: a memory, a processor and a segmented longitudinal vehicle speed planning program stored on the memory and executable on the processor, the segmented longitudinal vehicle speed planning program configured to implement the steps of the segmented longitudinal vehicle speed planning method as described above.
Furthermore, to achieve the above object, the present invention further proposes a storage medium having stored thereon a segmented longitudinal vehicle speed planning program, which when executed by a processor implements the steps of the segmented longitudinal vehicle speed planning method as described above.
The method comprises the steps of acquiring running environment information, running parameter information, vehicle condition information and road state information of a running road of a target vehicle; segmenting a target route on the driving road according to the road state information to obtain a first segmented route; obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information and the driving parameter information; determining a corresponding preset dynamic programming optimization strategy based on the target time penalty factor; and obtaining a target segmented speed track through a preset dynamic planning optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route, so that the target vehicle runs according to the target segmented speed track, and performing segmented route planning on the target vehicle based on the running environment information, the running parameter information, the vehicle condition information and the road state information of a running road, thereby realizing the globally optimal speed planning, ensuring the segmented speed track of the target vehicle to be the globally optimal economic speed track, ensuring the real-time speed planning of the target vehicle in the running process, and greatly reducing the planning time.
Drawings
FIG. 1 is a schematic structural diagram of a piece-wise longitudinal vehicle speed planning apparatus for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating a first embodiment of a sectional longitudinal vehicle speed planning method according to the present invention;
FIG. 3 is a schematic flow chart diagram of a second embodiment of the segmental longitudinal vehicle speed planning method of the invention;
FIG. 4 is a schematic overall flow chart of an embodiment of the sectional type longitudinal vehicle speed planning method of the present invention
FIG. 5 is a block diagram of a first embodiment of the sectional type longitudinal vehicle speed planning apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a segmented longitudinal vehicle speed planning device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the segmental longitudinal vehicle speed planning apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of the segmented longitudinal vehicle speed planning apparatus, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a segmented longitudinal vehicle speed planning program.
In the segmented longitudinal vehicle speed planning apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the segmental longitudinal vehicle speed planning device of the invention can be arranged in the segmental longitudinal vehicle speed planning device, and the segmental longitudinal vehicle speed planning device calls the segmental longitudinal vehicle speed planning program stored in the memory 1005 through the processor 1001 and executes the segmental longitudinal vehicle speed planning method provided by the embodiment of the invention.
The embodiment of the invention provides a sectional type longitudinal vehicle speed planning method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the sectional type longitudinal vehicle speed planning method.
In this embodiment, the sectional type longitudinal vehicle speed planning method includes the following steps:
step S10: the method comprises the steps of acquiring running environment information, running parameter information, vehicle condition information and road state information of a running road of a target vehicle.
It should be noted that the execution subject of the embodiment is an intelligent controller of a vehicle, the intelligent controller can acquire running environment information around a target vehicle during running, running parameter information and vehicle condition information of the vehicle, and road state information of a target running road of the target vehicle, and after acquiring the information, the intelligent controller performs speed segmentation planning according to the information, so as to obtain an optimal economic speed track when the target vehicle runs on the current road. The execution main body may also be other devices having the same function, which is not limited in this embodiment, and in this embodiment, the intelligent controller is used as the execution main body for description.
The driving environment information refers to driving environment information around the target vehicle during the driving of the target vehicle, and includes, but is not limited to, traffic signal traffic status information, and driving parameter information of surrounding vehicles.
It is understood that the driving parameter information refers to speed parameter information of the target vehicle during driving, including but not limited to a current driving speed, an initial speed, and a preset economical terminal speed of the target vehicle.
It is understood that the vehicle condition information refers to energy consumption information and impact information of the target vehicle, and may further include a speed driving range of the target vehicle.
It should be understood that the road state information refers to state information of a target traveling road of the target vehicle from a start point to a target end point, and includes, but is not limited to, road gradient change information, intersection information, and road speed limit change information.
In a specific implementation, the above information is obtained by the intelligent controller of the target vehicle, and since the intelligent controller cannot collect all information by itself, the information can only be obtained by a communication technology, further, the obtaining of the running environment information, the running parameter information, the vehicle condition information, and the road state information of the running road of the target vehicle includes: sending an information acquisition instruction to enable a target cloud end to feed back running environment information around the target vehicle; and acquiring road state information, driving parameter information and vehicle condition information of a driving road corresponding to the target vehicle, wherein the vehicle condition information comprises target vehicle energy consumption information and target vehicle impact degree information.
It should be noted that the information acquisition instruction is a running environment information acquisition instruction sent by the intelligent controller of the target vehicle to the cloud of the target vehicle, so that the cloud of the target vehicle acquires the real-time running environment information of the target vehicle through the road acquisition device, and feeds the acquired running environment information back to the intelligent controller of the target vehicle in time.
It will be appreciated that the road condition information is acquired by the intelligent controller of the target vehicle from a high accuracy map of the geographic information system in the target vehicle. The driving parameter information and the vehicle condition information are directly obtained by an intelligent controller of the target vehicle.
Step S20: and segmenting the target route on the driving road according to the road state information to obtain a first segmented route.
The first segment route refers to a route obtained by dividing a target route travel of the target vehicle from a starting point to a target end point into a plurality of segments.
In a specific implementation, the segmentation basis is to divide the road according to intersection information, road speed limit change information and road gradient change information in the road state information.
Step S30: and obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information and the driving parameter information.
It should be noted that the preset multilayer feedforward neural network model (BP neural network model) refers to a BP neural network model trained by various parameter information in driving environment information, road state information, and driving parameter information, and the preset BP neural network model can rapidly output a value of a time penalty factor according to input information.
In specific time, the initial speed, the terminal speed, the road speed limit information, the road length information, the road gradient information and the travel time of the target vehicle are used as input information, and a corresponding target time penalty factor is obtained. And the target time penalty factor fitted by the preset BP neural network model is adopted, so that the time of a stage of searching the time penalty factor in the planning process is saved.
In a specific implementation, in order to train an initial BP neural network model to a preset BP neural network model capable of outputting a time penalty factor value according to input information quickly, before obtaining a corresponding target time penalty factor through a preset multilayer feed-forward neural network model according to the driving environment information, the road state information, and the driving parameter information, the method further includes: acquiring training driving environment information, training road state information and training vehicle driving parameter information in a preset database; determining a corresponding initial time penalty factor through an initial dynamic planning strategy according to the training driving environment information, the training road state information and the training vehicle driving parameter information; constructing a time penalty factor database; and training the initial multilayer feedforward neural network model according to the initial time penalty factor and the time penalty factor database to obtain a preset multilayer feedforward neural network model.
It should be noted that the preset database refers to a database storing various sample information during the driving process of the vehicle.
It is understood that the training driving environment information, the training road state information and the training vehicle driving parameter information in the preset database are obtained, and the road information in the training driving environment information, the road speed limit and the road gradient in the training road state information, the initial speed of the automobile, the terminal speed and the travel time in the training vehicle driving parameter information are used as input. And solving the time penalty factor by using an initial dynamic programming strategy, namely a dynamic programming algorithm, in an off-line manner to obtain the initial time penalty factor. Travel time refers to the total set of all times in the travel time range with a time interval of 1s, for example, the current travel time range is 100s to 200s, i.e., the total set of all times in the travel time range from 100s to 200s with an interval of 1 s.
In specific implementation, the obtained initial time penalty factor is stored through an initial BP neural network model, a time penalty factor database is built, the initial BP neural network model is trained on the basis of the time penalty factor database and the initial time penalty factor, and finally a preset BP neural network model capable of rapidly outputting a time penalty factor value according to input information is obtained.
Step S40: and determining a corresponding preset dynamic programming optimization strategy based on the target time penalty factor.
It should be noted that after the target time penalty factor is obtained, time of a stage of searching for the time penalty factor in the planning process is saved, and a preset dynamic planning optimization strategy corresponding to the target vehicle is determined based on the target time penalty factor.
Step S50: and obtaining a target segmented speed track through a preset dynamic programming optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route, so that the target vehicle runs according to the target segmented speed track.
It should be noted that the optimal economic speed trajectory of the first segment route can be solved in the spatial domain based on the current driving environment information, the road state information and the vehicle condition information, the travel time and the speed at the end of the travel of each segment route are optimized by adopting a preset dynamic programming optimization strategy based on the optimal economic speed trajectory of each segment route of the first segment route to obtain a target segment speed trajectory, and finally the target vehicle is driven according to the target segment speed trajectory.
In the specific implementation, the travel time of each segmented road section and the vehicle speed at the end of the travel are determined according to the requirement of a user on the total travel time and a preset dynamic planning optimization strategy, and a global optimal speed track from the start point of the travel to the end point of the travel is further obtained.
The embodiment obtains the running environment information, the running parameter information, the vehicle condition information and the road state information of a running road of a target vehicle; segmenting a target route on the driving road according to the road state information to obtain a first segmented route; obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information and the driving parameter information; determining a corresponding preset dynamic programming optimization strategy based on the target time penalty factor; and obtaining a target segmented speed track through a preset dynamic planning optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route, so that the target vehicle runs according to the target segmented speed track, and performing segmented route planning on the target vehicle based on the running environment information, the running parameter information, the vehicle condition information and the road state information of a running road, thereby realizing the globally optimal speed planning, ensuring the segmented speed track of the target vehicle to be the globally optimal economic speed track, ensuring the real-time speed planning of the target vehicle in the running process, and greatly reducing the planning time.
Referring to fig. 3, fig. 3 is a flowchart illustrating a sectional longitudinal vehicle speed planning method according to a second embodiment of the present invention.
Based on the first embodiment, the step S50 in the segmental longitudinal vehicle speed planning method according to the embodiment includes:
step S501: and obtaining a local segmentation speed track of the first segmentation route through a preset dynamic programming optimization strategy based on the driving environment information, the road state information and the vehicle condition information.
It should be noted that after the driving environment information and the road state information are obtained, the driving environment information and the road state information are combined, and the local segment speed trajectory corresponding to each segment route in the first segment route is obtained through a preset dynamic programming optimization strategy according to the vehicle condition information.
In a specific implementation, to obtain a more accurate local segment speed trajectory, further, obtaining the local segment speed trajectory of the first segment route through a preset dynamic programming optimization strategy based on the driving environment information, the road state information, and the vehicle condition information includes: acquiring traffic signal lamp passing state information and traffic state information in the driving environment information; acquiring intersection information and road speed limit information in the road state information; and obtaining a local segmentation speed track of the first segmentation route through a preset dynamic planning optimization strategy based on the traffic signal lamp passing state information, the traffic state information, the intersection information, the road speed limit information and the vehicle condition information.
It should be noted that the traffic signal light passing state information and the traffic state information in the driving environment information, and the intersection information and the road speed limit information in the road state information are acquired, and the passing state information of the intersection on the driving road of the target vehicle is obtained by combining the traffic signal passing state information and the intersection information.
It can be understood that after the intersection traffic state information is obtained, the local segmentation speed trajectory of the first segmentation route can be obtained by planning through a preset dynamic planning optimization strategy based on the traffic state information, the road information and the vehicle condition information of the target vehicle on the driving road.
In a specific implementation, obtaining a more economical local segmentation speed track based on a preset dynamic programming optimization strategy, and further obtaining the local segmentation speed track of the first segmentation route based on the traffic signal light traffic state information, the intersection information, the road speed limit information and the vehicle condition information through the preset dynamic programming optimization strategy includes: obtaining average speed information of the periphery of the target vehicle according to the traffic state information; obtaining corresponding intersection passing state information according to the intersection information and the traffic signal lamp passing state information; determining a terminal time constraint corresponding to the first sectional route according to the intersection passing state information; determining a speed range constraint corresponding to the first subsection route according to the road speed limit information; determining a terminal speed constraint corresponding to the first subsection route according to the average speed information; determining a corresponding optimization target according to the vehicle condition information; and obtaining the local segmentation speed track of the first segmentation route according to the terminal time constraint, the speed range constraint, the terminal speed constraint and the optimization target.
It should be noted that, the traffic state information fed back by the cloud is identified to obtain the average vehicle speed information around the target vehicle, and the average vehicle speed in the traffic flow is used as the terminal speed constraint of each segment terminal of the target vehicle in the segment route.
It can be understood that the road speed limit information is used as a speed range constraint, namely a solution range constraint, corresponding to each subsection route in the first subsection route, the intersection traffic state information is used as a terminal time constraint corresponding to each subsection route in the first subsection route, the energy consumption information and the impact degree information in the target vehicle condition information are used as optimization targets, and an optimal economic speed track, namely a local subsection speed track, corresponding to each subsection route in the first subsection route is solved in a space domain through a preset dynamic programming optimization strategy.
In the concrete implementation, a whole vehicle energy consumption model is built based on bench experimental data of a target vehicle, a vehicle speed solving space is obtained through calculation according to the current speed of the target vehicle, the expected travel end point vehicle speed, the road speed limit upper and lower limits and the vehicle acceleration range, the vehicle speed solving space is equally divided into N sections, and the cost function of the kth section is as follows:
in the formula, Qe(k) Representing the energy consumption of the vehicle on the k-th road section; lambda [ alpha ]1，λ2Representing the price of electricity and gasoline, respectively; m iseRepresenting the rate at which power is consumed; m isgRepresenting the rate of gasoline consumption; a represents acceleration; t is the travel time of the kth road section; delta3,δ4Respectively represent timeAnd a speed fluctuation penalty factor. The constraint conditions are as follows:
in the formula, vh,ahRepresenting the speed and acceleration of the vehicle, respectively; t is thRepresenting the actual travel time of the vehicle from the starting point to the end point; t is twRepresenting a desired travel time of the vehicle from the starting point to the end point; v. ofmax,vminRepresenting the highest and lowest speed limits of the road; a ismaxRepresenting an acceleration threshold related to ride comfort.
Step S502: and obtaining a global sectional speed track corresponding to the target route according to the preset dynamic programming optimization strategy based on the local sectional speed track.
It should be noted that after the local segment speed trajectory corresponding to each segment route in the first segment route is obtained, the travel time and the speed of the travel end of each segment route need to be optimized according to a preset dynamic programming optimization strategy, so as to obtain a global segment speed trajectory corresponding to the target route.
In a specific implementation, in order to optimize a global segment speed trajectory, further, the obtaining a global segment speed trajectory corresponding to the target route according to the preset dynamic programming optimization strategy based on the local segment speed trajectory includes: acquiring a target travel time of a target vehicle; obtaining the corresponding local segmentation end speed according to the local segmentation speed track; determining corresponding local segmentation travel time according to the target travel time and the local segmentation speed track; determining a corresponding optimization target according to the vehicle condition information; and obtaining a global subsection speed track corresponding to the target route according to the local subsection tail end speed, the local subsection travel time and the optimization target.
It should be noted that the target travel time refers to a preset travel time of the target vehicle on the target route, and is also a sum of travel times obtained in the local segment speed trajectory.
It can be understood that after the target travel time is obtained, the segment travel time and the terminal travel speed corresponding to each segment route are obtained according to the local segment speed trajectory corresponding to each segment route in the first segment route, the segment travel time and the terminal travel speed are respectively used as the local segment travel time and the local segment terminal speed, the local segment travel time and the local segment terminal speed are used as decision variables, the energy consumption information in the vehicle condition information is used as an optimization target, the optimal time distribution and the terminal speed optimization for each segment route are completed, and the global segment speed trajectory corresponding to the target route is finally obtained.
For example, after the target route is divided into M sections according to the speed limit change of the crossing road and the gradient change of the road, the vehicle speed track of the j section is obtained by solving the preset dynamic programming optimization strategy as described above, and the section expected travel time and the expected travel end point vehicle speed required by the method are obtained by optimizing the preset dynamic programming optimization strategy. Dividing the target route into N sections, wherein the state variable is the initial remaining travel time t of the k sectionkFirst speed v of vehicle in phase kk(ii) a The decision variable is the time spent in the k stage xkEnd of phase k vehicle speed uk(ii) a In the preset dynamic programming optimization strategy, the state transition equation is as follows: t is tk+1＝tk-xk；vk+1＝ukThe comprehensive state transition equation is: f. ofk(tk,vk)＝min[L(xk,uk)+fk+1(tk-xk,uk)]k＝3,2,1。
In a specific implementation, as shown in fig. 4, after obtaining the road state information after the driving environment information, dividing a target route from a starting point to a destination point of a target vehicle to obtain a single route, i.e. a first segment route, taking the intersection traffic state as a terminal time constraint based on a preset dynamic planning strategy, taking the road speed limit as a solving range constraint, taking the average speed of a traffic flow as a terminal speed constraint, adding an impact range constraint, taking the vehicle energy consumption as an optimization target, solving in a spatial domain to obtain an optimal economic speed track corresponding to the first segment route, taking the initial speed, the terminal speed, the road speed limit, the road length, the road gradient and the travel time of the target vehicle as inputs, taking a time penalty factor as an output BP neural network, and quickly planning to obtain a local segment speed track based on the vehicle speed of the single road segment, i.e. the first segment route, and determining the travel time and the travel tail end vehicle speed of each segmented route according to the requirement of a user on the total travel time by taking the vehicle energy consumption as an optimization target based on the local segmented travel time and the tail end speed in the local segmented speed track as decision variables, and further obtaining a global optimal speed track from the travel starting point to the travel ending point.
Step S503: and taking the global segmented velocity track as a target segmented velocity track.
The embodiment obtains the local segmentation speed track of the first segmentation route through a preset dynamic programming optimization strategy based on the driving environment information, the road state information and the vehicle condition information; obtaining a global segmentation speed track corresponding to the target route according to the preset dynamic programming optimization strategy based on the local segmentation speed track; and taking the global segmented velocity track as a target segmented velocity track. The local segmented speed track is obtained through a preset dynamic programming optimization strategy, and then the local segmented speed track is further optimized to obtain a final target speed segmented track, so that the efficiency of the segmented speed planning is improved, and the economy and the real-time performance of the target segmented speed track planning are ensured.
In addition, referring to fig. 5, an embodiment of the present invention further provides a sectional longitudinal vehicle speed planning device, including:
the acquiring module 10 is used for acquiring the running environment information, the running parameter information, the vehicle condition information and the road state information of a running road of the target vehicle.
And the segmenting module 20 is configured to segment the target route on the driving road according to the road state information to obtain a first segmented route.
And the obtaining module 30 is configured to obtain a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information, and the driving parameter information.
And the determining module 40 is configured to determine a corresponding preset dynamic programming optimization strategy based on the target time penalty factor.
The obtaining module 30 is further configured to obtain a target segment speed trajectory through a preset dynamic programming optimization strategy based on the driving environment information, the road state information, the vehicle condition information, and the first segment route, so that the target vehicle drives according to the target segment speed trajectory.
The embodiment obtains the running environment information, the running parameter information, the vehicle condition information and the road state information of a running road of a target vehicle; segmenting a target route on the driving road according to the road state information to obtain a first segmented route; obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information and the driving parameter information; determining a corresponding preset dynamic programming optimization strategy based on the target time penalty factor; and obtaining a target segmented speed track through a preset dynamic planning optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route, so that the target vehicle runs according to the target segmented speed track, and performing segmented route planning on the target vehicle based on the running environment information, the running parameter information, the vehicle condition information and the road state information of a running road, thereby realizing the globally optimal speed planning, ensuring the segmented speed track of the target vehicle to be the globally optimal economic speed track, ensuring the real-time speed planning of the target vehicle in the running process, and greatly reducing the planning time.
In an embodiment, the obtaining module 10 is further configured to send an information obtaining instruction, so that the target cloud feeds back the driving environment information around the target vehicle;
and acquiring road state information, driving parameter information and vehicle condition information of a target route corresponding to the target vehicle, wherein the vehicle condition information comprises target vehicle energy consumption information and target vehicle impact degree information.
In an embodiment, the obtaining module 30 is further configured to obtain training driving environment information, training road state information, and training vehicle driving parameter information in a preset database;
determining a corresponding initial time penalty factor through an initial dynamic planning strategy according to the training driving environment information, the training road state information and the training vehicle driving parameter information;
constructing a time penalty factor database;
and training the initial multilayer feedforward neural network model according to the initial time penalty factor and the time penalty factor database to obtain a preset multilayer feedforward neural network model.
In an embodiment, the obtaining module 30 is further configured to obtain a local segment speed trajectory of the first segment route through a preset dynamic planning optimization strategy based on the driving environment information, the road state information, and the vehicle condition information;
obtaining a global segmentation speed track corresponding to the target route according to the preset dynamic programming optimization strategy based on the local segmentation speed track;
and taking the global segmented velocity track as a target segmented velocity track.
In an embodiment, the obtaining module 30 is further configured to obtain traffic signal light passing status information and traffic status information in the driving environment information;
acquiring intersection information and road speed limit information in the road state information;
and obtaining a local segmentation speed track of the first segmentation route through a preset dynamic planning optimization strategy based on the traffic signal lamp passing state information, the traffic state information, the intersection information, the road speed limit information and the vehicle condition information.
In an embodiment, the obtaining module 30 is further configured to obtain average vehicle speed information around the target vehicle according to the traffic status information;
obtaining corresponding intersection passing state information according to the intersection information and the traffic signal lamp passing state information;
determining a terminal time constraint corresponding to the first sectional route according to the intersection passing state information;
determining a speed range constraint corresponding to the first subsection route according to the road speed limit information;
determining a terminal speed constraint corresponding to the first subsection route according to the average speed information;
determining a corresponding optimization target according to the vehicle condition information;
and obtaining the local segmentation speed track of the first segmentation route according to the terminal time constraint, the speed range constraint, the terminal speed constraint and the optimization target.
In an embodiment, the obtaining module 30 is further configured to obtain a target travel time of the target vehicle;
obtaining the corresponding local segmentation end speed according to the local segmentation speed track;
determining corresponding local segmentation travel time according to the target travel time and the local segmentation speed track;
determining a corresponding optimization target according to the vehicle condition information;
and obtaining a global subsection speed track corresponding to the target route according to the local subsection tail end speed, the local subsection travel time and the optimization target.
Since the present apparatus employs all technical solutions of all the above embodiments, at least all the beneficial effects brought by the technical solutions of the above embodiments are achieved, and are not described in detail herein.
Furthermore, an embodiment of the present invention further provides a storage medium having a segmented longitudinal vehicle speed planning program stored thereon, which when executed by a processor implements the steps of the segmented longitudinal vehicle speed planning method as described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not elaborated in the embodiment may be referred to a sectional type longitudinal vehicle speed planning method provided by any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A segmented longitudinal vehicle speed planning method, characterized in that the segmented longitudinal vehicle speed planning method comprises:
acquiring running environment information, running parameter information, vehicle condition information and road state information of a running road of a target vehicle;
segmenting a target route on the driving road according to the road state information to obtain a first segmented route;
obtaining a corresponding target time penalty factor through a preset multilayer feedforward neural network model according to the driving environment information, the road state information and the driving parameter information;
determining a corresponding preset dynamic programming optimization strategy based on the target time penalty factor;
obtaining a target segmented speed track through a preset dynamic programming optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route, so that the target vehicle runs according to the target segmented speed track;
before obtaining a corresponding target time penalty factor according to the driving environment information, the road state information and the driving parameter information through a preset multilayer feedforward neural network model, the method further comprises the following steps:
acquiring training driving environment information, training road state information and training vehicle driving parameter information in a preset database;
determining a corresponding initial time penalty factor through an initial dynamic planning strategy according to the training driving environment information, the training road state information and the training vehicle driving parameter information;
constructing a time penalty factor database;
and training the initial multilayer feedforward neural network model according to the initial time penalty factor and the time penalty factor database to obtain a preset multilayer feedforward neural network model.
2. The segmented longitudinal vehicle speed planning method according to claim 1, wherein the obtaining of the running environment information, the running parameter information, the vehicle condition information, and the road state information of the running road of the target vehicle comprises:
sending an information acquisition instruction to enable a target cloud end to feed back running environment information around the target vehicle;
and acquiring road state information, driving parameter information and vehicle condition information of a driving road corresponding to the target vehicle, wherein the vehicle condition information comprises target vehicle energy consumption information and target vehicle impact degree information.
3. The segmented longitudinal vehicle speed planning method according to claim 1, wherein obtaining a target segmented speed trajectory through a preset dynamic planning optimization strategy based on the driving environment information, the road state information, the vehicle condition information and the first segmented route comprises:
obtaining a local segmentation speed track of the first segmentation route through a preset dynamic programming optimization strategy based on the driving environment information, the road state information and the vehicle condition information;
obtaining a global segmentation speed track corresponding to the target route according to the preset dynamic programming optimization strategy based on the local segmentation speed track;
and taking the global segmented velocity track as a target segmented velocity track.
4. The segmented longitudinal vehicle speed planning method according to claim 3, wherein the obtaining of the local segmented speed trajectory of the first segmented route through a preset dynamic planning optimization strategy based on the driving environment information, the road state information and the vehicle condition information comprises:
acquiring traffic signal lamp passing state information and traffic state information in the driving environment information;
acquiring intersection information and road speed limit information in the road state information;
and obtaining a local segmentation speed track of the first segmentation route through a preset dynamic planning optimization strategy based on the traffic signal lamp passing state information, the traffic state information, the intersection information, the road speed limit information and the vehicle condition information.
5. The segmented longitudinal vehicle speed planning method according to claim 4, wherein the obtaining of the local segmented speed trajectory of the first segmented route through a preset dynamic planning optimization strategy based on the traffic signal light traffic state information, the intersection information, the road speed limit information and the vehicle condition information comprises:
obtaining average speed information of the periphery of the target vehicle according to the traffic state information;
obtaining corresponding intersection passing state information according to the intersection information and the traffic signal lamp passing state information;
determining a terminal time constraint corresponding to the first sectional route according to the intersection passing state information;
determining a speed range constraint corresponding to the first subsection route according to the road speed limit information;
determining a terminal speed constraint corresponding to the first subsection route according to the average speed information;
determining a corresponding optimization target according to the vehicle condition information;
and obtaining the local segmentation speed track of the first segmentation route according to the terminal time constraint, the speed range constraint, the terminal speed constraint and the optimization target.
6. The segmented longitudinal vehicle speed planning method according to claim 3, wherein the obtaining a global segmented speed trajectory corresponding to the target route according to the preset dynamic planning optimization strategy based on the local segmented speed trajectory comprises:
acquiring a target travel time of a target vehicle;
obtaining the corresponding local segmentation end speed according to the local segmentation speed track;
determining corresponding local segmentation travel time according to the target travel time and the local segmentation speed track;
determining a corresponding optimization target according to the vehicle condition information;
and obtaining a global subsection speed track corresponding to the target route according to the local subsection tail end speed, the local subsection travel time and the optimization target.
7. A segmented longitudinal vehicle speed planning apparatus, comprising:
an acquisition module: the system comprises a data processing unit, a data processing unit and a data processing unit, wherein the data processing unit is used for acquiring running environment information, running parameter information, vehicle condition information and road state information of a running road of a target vehicle;
a segmentation module: the road state information is used for segmenting a target route on the driving road according to the road state information to obtain a first segmented route;
obtaining a module: the system comprises a road condition information acquisition module, a multi-layer feedforward neural network model acquisition module and a target time penalty factor acquisition module, wherein the road condition information acquisition module is used for acquiring a target time penalty factor according to the running environment information, the road condition information and the running parameter information through a preset multi-layer feedforward neural network model;
a determination module: the system comprises a target time penalty factor, a dynamic planning optimization strategy and a dynamic planning optimization strategy, wherein the target time penalty factor is used for determining a corresponding preset dynamic planning optimization strategy;
the obtaining module: the target vehicle speed control system is also used for obtaining a target segmented speed track through a preset dynamic programming optimization strategy based on the running environment information, the road state information, the vehicle condition information and the first segmented route so as to enable the target vehicle to run according to the target segmented speed track;
the obtaining module is also used for obtaining training running environment information, training road state information and training vehicle running parameter information in a preset database;
determining a corresponding initial time penalty factor through an initial dynamic planning strategy according to the training driving environment information, the training road state information and the training vehicle driving parameter information;
constructing a time penalty factor database;
and training the initial multilayer feedforward neural network model according to the initial time penalty factor and the time penalty factor database to obtain a preset multilayer feedforward neural network model.
8. A segmented longitudinal vehicle speed planning apparatus, the apparatus comprising: a memory, a processor, and a segmented longitudinal vehicle speed planning program stored on the memory and executable on the processor, the segmented longitudinal vehicle speed planning program configured to implement the segmented longitudinal vehicle speed planning method of any of claims 1-6.
9. A storage medium having stored thereon a segmented longitudinal vehicle speed planning program which, when executed by a processor, implements a segmented longitudinal vehicle speed planning method according to any one of claims 1 to 6.
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