CN113611122A - Vehicle speed guiding method, device and equipment - Google Patents
Vehicle speed guiding method, device and equipment Download PDFInfo
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- CN113611122A CN113611122A CN202111172498.6A CN202111172498A CN113611122A CN 113611122 A CN113611122 A CN 113611122A CN 202111172498 A CN202111172498 A CN 202111172498A CN 113611122 A CN113611122 A CN 113611122A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Abstract
A vehicle speed guiding method, a vehicle speed guiding device and vehicle speed guiding equipment are provided, wherein each divided driving road section is obtained by dividing a target driving path; predicting future traffic phase information of each traffic signal lamp and future average speed of each driving road section in a first future time period of the current moment; obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp in the first future time period, future average vehicle speed of each driving road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation; according to the first speed sequence, the target guiding speed of the vehicle at the next moment of the current moment is determined, the method is suitable for various complex scenes such as a level road or a ramp, a road section provided with a traffic signal lamp or without the traffic signal lamp, different road vehicle speed, different vehicle driving behaviors and the like, and vehicle passing efficiency and energy economy are improved through vehicle speed guiding.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a vehicle speed guiding method, a vehicle speed guiding device and vehicle speed guiding equipment.
Background
Under the current urban environment, the traffic jam phenomenon is very common, and the traffic jam is relieved to a certain extent through the optimized design of roads and peak shifting travel. However, due to the lack of traffic information and traffic signal information in front of the driver, frequent waiting at the intersection where the traffic signal is located, and sudden stop and rapid acceleration are caused, which affects the traffic efficiency and energy economy to some extent.
In order to solve the problems, the vehicle speed guidance can be provided for the driver by acquiring the information of 1-2 adjacent traffic lights and taking the optimal fuel consumption and emission as the optimization target at present. However, since urban roads are dense and the traffic flow on the roads is also large, the improvement of the vehicle passing efficiency brought by the method is limited, and the improvement of the energy economy is also limited.
Disclosure of Invention
The embodiment of the invention provides a vehicle speed guiding method, a vehicle speed guiding device and vehicle speed guiding equipment, which are used for improving traffic passing efficiency and energy economy.
According to a first aspect, an embodiment provides a vehicle speed guidance method, the method comprising:
acquiring a target driving path of a vehicle, determining position information of each traffic signal lamp included in the target driving path, and dividing the target driving path according to the position information of each traffic signal lamp to obtain each divided driving road section;
obtaining historical traffic phase information of each traffic signal lamp included in the target driving path in a first historical time period before the current time, inputting the historical traffic phase information of each traffic signal lamp included in the target driving path in the first historical time period into a first target neural network, and obtaining future traffic phase information of each traffic signal lamp included in the target driving path in a first future time period after the current time, wherein the first target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path;
acquiring historical average vehicle speed of each driving road section in a second historical time period before the current moment and historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period, and inputting the historical average vehicle speed of each driving road section in the second historical time period, the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period and future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period into a second target neural network to obtain the future average vehicle speed of each driving road section in the first future time period, wherein the second target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path and the historical average vehicle speed of each driving road section, the duration of the second historical time period is less than the duration of the first historical time period;
obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period, future average vehicle speed of each driving road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation, wherein the vehicle state equation includes a power equation, a fuel consumption equation and an engine speed equation of the vehicle, the power equation is obtained according to road gradient information of the target driving path, and the evaluation equation is used for comprehensively evaluating fuel consumption and traffic efficiency of the vehicle;
and determining the target guiding vehicle speed of the vehicle at the next moment of the current moment according to the first speed sequence.
Optionally, the determining, according to the first speed sequence, a target guidance vehicle speed of the vehicle at a time next to the current time includes:
obtaining a second speed sequence of the vehicle in a second future time period after the current time according to the first speed sequence, the vehicle state equation, a second preset vehicle speed constraint condition and the evaluation equation, wherein the duration of the second future time period is less than that of the first future time period;
determining a target guidance vehicle speed of the vehicle at a time next to the current time from the second speed sequence.
Optionally, the power equation is:
wherein the content of the first and second substances,mis the mass of the vehicle in question,ais the longitudinal acceleration of the vehicle in question,r g is the gear ratio of the current gear of the transmission,r d the transmission ratio of the main speed reducer is set,T e in order to obtain a driving torque for the engine,T b in order to obtain the braking torque,R w which is the radius of the wheel, is,gin order to be the acceleration of the gravity,θin order to be the gradient of the road,C r in order to be the rolling resistance,ρin order to be the density of the air,Athe area of the wind-facing surface is,C d in order to be the air resistance coefficient,vis the longitudinal speed of the vehicle.
Optionally, the inputting the historical average vehicle speed of each driving section in the second historical time period, the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period, and the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period into the second target neural network to obtain the future average vehicle speed of each driving section in the first future time period includes:
sequentially and uniformly dividing the first future time period into a plurality of future sub-time periods;
inputting the historical average vehicle speed of each driving road section in the second historical time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period into a second target neural network to obtain the future average vehicle speed of each driving road section in a first future sub-time period, wherein the time length of each future sub-time period is equal to the time length of the second historical time period;
inputting the future average speed of each driving road section in the first future sub-time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the first future sub-time period into a second target neural network to obtain the average speed of each driving road section in a second future sub-time period;
and iterating the loop until the average speed of each driving road section in the last future sub-time period is obtained.
Optionally, the method further includes:
and outputting prompt information, wherein the prompt information is used for prompting the target guiding vehicle speed of the vehicle at the next moment of the current moment.
According to a second aspect, an embodiment provides a vehicle speed guidance apparatus, the apparatus comprising:
the first acquisition module is used for acquiring a target driving path of a vehicle, determining position information of each traffic signal lamp included in the target driving path, and dividing the target driving path according to the position information of each traffic signal lamp to obtain each divided driving road section;
the second acquisition module is used for acquiring historical traffic phase information of each traffic signal lamp included in the target driving path in a first historical time period before the current time, inputting the historical traffic phase information of each traffic signal lamp included in the target driving path in the first historical time period into a first target neural network, and acquiring future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period after the current time, wherein the first target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path;
a third obtaining module, configured to obtain a historical average vehicle speed of each traveling road segment in a second historical time period before a current time and historical traffic phase information of each traffic light included in the target traveling path in the second historical time period, and input the historical average vehicle speed of each traveling road segment in the second historical time period, the historical traffic phase information of each traffic light included in the target traveling path in the second historical time period, and future traffic phase information of each traffic light included in the target traveling path in the first future time period into a second target neural network, so as to obtain a future average vehicle speed of each traveling road segment in the first future time period, where the second target neural network is obtained by training the historical traffic phase information of each traffic light included in the target traveling path and the historical average vehicle speed of each traveling road segment, the duration of the second historical time period is less than the duration of the first historical time period;
the fourth obtaining module is used for obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp included in the target running path in the first future time period, future average vehicle speed of each running road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation, wherein the vehicle state equation includes a power equation, a fuel consumption equation and an engine speed equation of the vehicle, the power equation is obtained according to road gradient information of the target running path, and the evaluation equation is used for comprehensively evaluating the fuel consumption and traffic efficiency of the vehicle;
and the fifth acquisition module is used for determining the target guiding vehicle speed of the vehicle at the next moment of the current moment according to the first speed sequence.
Optionally, the fifth obtaining module is specifically configured to obtain, according to the first speed sequence, the vehicle state equation, a second preset vehicle speed constraint condition, and the evaluation equation, a second speed sequence of the vehicle in a second future time period after the current time, where a duration of the second future time period is smaller than a duration of the first future time period; determining a target guidance vehicle speed of the vehicle at a time next to the current time from the second speed sequence.
Optionally, the power equation is:
wherein the content of the first and second substances,mis the mass of the vehicle in question,ais the longitudinal acceleration of the vehicle in question,r g is the gear ratio of the current gear of the transmission,r d the transmission ratio of the main speed reducer is set,T e in order to obtain a driving torque for the engine,T b in order to obtain the braking torque,R w which is the radius of the wheel, is,gin order to be the acceleration of the gravity,θin order to be the gradient of the road,C r in order to be the rolling resistance,ρin order to be the density of the air,Athe area of the wind-facing surface is,C d in order to be the air resistance coefficient,vis the longitudinal speed of the vehicle.
Optionally, the third obtaining module is specifically configured to divide the first future time period into a plurality of future sub-time periods sequentially and uniformly; inputting the historical average vehicle speed of each driving road section in the second historical time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period into a second target neural network to obtain the future average vehicle speed of each driving road section in a first future sub-time period, wherein the time length of each future sub-time period is equal to the time length of the second historical time period; inputting the future average speed of each driving road section in the first future sub-time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the first future sub-time period into a second target neural network to obtain the average speed of each driving road section in a second future sub-time period; and iterating the loop until the average speed of each driving road section in the last future sub-time period is obtained.
Optionally, the apparatus further comprises: and the output module is used for outputting prompt information, and the prompt information is used for prompting the target guide vehicle speed of the vehicle at the next moment of the current moment.
According to a third aspect, there is provided in one embodiment an electronic device comprising: a memory for storing a program; a processor for implementing the vehicle speed guidance method according to any one of the first aspect described above by executing a program stored in the memory.
According to a fourth aspect, an embodiment provides a computer-readable storage medium having a program stored thereon, the program being executable by a processor to implement the vehicle speed guidance method of any one of the first aspects described above.
The embodiment of the invention provides a vehicle speed guiding method, a vehicle speed guiding device and vehicle speed guiding equipment, wherein the vehicle speed guiding method comprises the steps of obtaining a target driving path of a vehicle, determining position information of each traffic signal lamp included in the target driving path, and dividing the target driving path according to the position information of each traffic signal lamp to obtain each divided driving road section; obtaining historical traffic phase information of each traffic signal lamp included in a target driving path in a first historical time period before the current time, inputting the historical traffic phase information of each traffic signal lamp included in the target driving path in the first historical time period into a first target neural network, and obtaining the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period after the current time, wherein the first target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path; acquiring historical average vehicle speeds of all driving road sections in a second historical time period before the current time and historical traffic phase information of all traffic lights included in a target driving path in the second historical time period, and the historical average speed of each driving road section in the second historical time period, the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period and the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period are input into a second target neural network to obtain the future average speed of each driving road section in the first future time period, the second target neural network is obtained by training historical traffic phase information of each traffic signal lamp and historical average vehicle speed of each driving road section, wherein the historical traffic phase information and the historical average vehicle speed are included in a target driving path, and the duration of the second historical time period is less than that of the first historical time period; obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp, future average vehicle speed of each driving road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation, wherein the future traffic phase information of each traffic signal lamp is included in a target driving path in the first future time period; and determining the target guiding vehicle speed of the vehicle at the next moment of the current moment according to the first speed sequence. The method is suitable for various complex scenes such as level roads or ramps, road sections with or without traffic lights, different road vehicle flow speeds, different vehicle driving behaviors (straight running, left turning, right turning and turning around) and the like, and vehicle passing efficiency and energy economy can be improved by determining the target guide vehicle speed of the vehicle at the next moment of the current moment.
Drawings
Fig. 1 is a schematic flowchart of a first embodiment of a vehicle speed guidance method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a vehicle speed guidance system according to an embodiment of the present invention;
FIG. 3 is a schematic view of a target travel path;
FIG. 4 is a schematic diagram of path segmentation according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a second exemplary embodiment of a vehicle speed guiding method according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating a third exemplary embodiment of a vehicle speed guiding method according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating a vehicle speed guidance method according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a vehicle speed guiding device according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning. The term "connected" and "coupled" when used in this application, unless otherwise indicated, includes both direct and indirect connections (couplings).
In the prior art, the vehicle speed guidance can be provided for the driver by acquiring the information of 1-2 adjacent traffic signal lamps and taking the optimal fuel consumption and emission as optimization targets. However, since urban roads are dense and the traffic flow on the roads is also large, the improvement of the vehicle passing efficiency brought by the method is limited, and the improvement of the energy economy is also limited. In order to improve traffic efficiency and energy economy, embodiments of the present invention provide a vehicle speed guiding method, a vehicle speed guiding device, and a vehicle speed guiding apparatus, which are described in detail below.
Fig. 1 is a schematic flowchart of a first embodiment of a vehicle speed guidance method according to an embodiment of the present invention, and as shown in fig. 1, the vehicle speed guidance method according to the present embodiment may include:
s101, acquiring a target driving path of the vehicle, determining position information of each traffic light included in the target driving path, and dividing the target driving path according to the position information of each traffic light to obtain each divided driving road section.
The execution subject of the embodiment of the present invention is any device with processing capability, for example, the device may be a vehicle controller installed in a vehicle, and the vehicle speed guiding method provided by the embodiment may correspond to a vehicle speed guiding system as shown in fig. 2, and the vehicle speed guiding system may include a vehicle controller installed in a vehicle, a display terminal, an operation terminal, a vehicle-mounted positioning and navigation unit, and a vehicle communication unit, and a road section communication unit and other vehicle communication unit which are not installed in the vehicle. In specific implementation, the position information of each traffic signal lamp and the speed information of other vehicles included in the target driving path can be acquired through the external road section communication unit and other vehicle communication units, and then the acquired information is sent to the vehicle communication unit through the external road section communication unit and other vehicle communication units.
In a specific implementation, the target driving path may be selected by the driver, for example, after the starting position and the destination position are input, a plurality of driving paths may be provided to the driver, and one driving path may be selected as the target driving path from the plurality of driving paths by the driver; the target travel path may also be selected by other priority strategies, e.g.The route having the shortest distance may be preferentially selected as the target travel route. FIG. 3 is a schematic view of a target travel path, as shown in FIG. 3, now having three alternative travel paths, the driver may select the path bolded in FIG. 3 as the target travel path, wherein the target travel path is takenS f The starting time of the origin is recordedT 0 At the moment, the vehicle is drivenT 0 Starting at a time along the target travel pathS f Movement, when moving to a certain position, the time elapsed is recorded astVehicle running on the whole target pathS f The time spent is expressed asT f Target travel routeS f Has a length ofs f 。
After the target driving path is determined, the target driving path is divided according to the position information of each traffic signal lamp to obtain each divided driving road section. Specifically, the target travel path may be divided by the following equations (1) to (4):
L(S j )=
∈[10,50]，j∈[
N k +1，
N k + N i ]，N i ∈N + ，i≥2 (1)
L(S j )=
∈[10,50]，j∈[1,N 1]，N 1∈N + ，i=1 (2)
L(S j )=
∈[10,50]，j∈[
N k +1，
N k +N M+1]，N M+1∈N + ，i=M (3)
l=
N k +N M+1 (4)
wherein the content of the first and second substances,S j in order to be a divided travel section,L(S j )indicating a travel route sectionS j The length of (a) of (b),d i to travel on a target pathS f To go toiDistance initial point of traffic signal lampx 0 The equation (2) represents the distance between the starting point of the target driving path and the first traffic signal lampN 1Dividing the driving sub-path; formula (1) shows the difference between the first traffic light and the second traffic light on the target driving pathN 2Between the sub-path of travel, the second traffic light and the third traffic lightN 3Travel sub-path … … thM-1 traffic signal lamp andMbetween traffic signal lampsN M Dividing the driving sub-path; formula (3) shows that the last driving sub-path is divided, each divided road is a driving road section,lthe number representing the last travel segment. In specific implementation, the driving sub-paths between every two traffic lights are uniformly divided, but the driving section length of each divided driving sub-path is longThe degrees may be the same or different.
For example, fig. 4 is a schematic diagram of path division according to an embodiment of the present invention, and in fig. 4, a target driving path is takenS f For example, there are 3 intersections, and 1 traffic light is set at each of the 3 intersections, where the traffic light 1 and the initial pointx 0 A distance ofd 1Initial pointx 0 The sub-path of travel with the traffic light 1 isN 1(ii) a Traffic light 2 and initial pointx 0 A distance ofd 2The sub-path of travel between the traffic light 1 and the traffic light 2 isN 2(ii) a Traffic light 3 and initial pointx 0 A distance ofd 3The sub-path of travel between the traffic light 2 and the traffic light 3 isN 3The sub-path of travelN 4Is a target driving pathS f Last driving sub-path, target driving pathS f Has a length ofs f . By the method, the driving sub-path can be matchedN 1、N 2、N 3AndN 4and (6) carrying out uniform segmentation.
S102, obtaining historical traffic phase information of each traffic signal lamp included in the target driving path in a first historical time period before the current time, inputting the historical traffic phase information of each traffic signal lamp included in the target driving path in the first historical time period into a first target neural network, and obtaining the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period after the current time.
The first target neural network can be obtained by training historical traffic phase information of each traffic signal lamp included in the target driving path.
Specifically, the traffic phase information of the traffic signal lamp may include: the current phase of the traffic signal light, the remaining duration of the current phase, and the time period (sum of the durations of all phases). For example, the pass phase information may be in the form of:[d i ,p i (t,T),tp i (t,T),rp i (t,T)]wherein, in the step (A),p i (t,T) Is composed ofTAt the first momentiThe current phase of the individual traffic signal lights,tp i (t,T) Is composed ofTAt the first momentiThe time period of each of the traffic signal lights,rp i (t,T) Is composed ofTAt the first momentiThe remaining duration of the current phase of each traffic light.
Optionally, the first historical time period before the current time may be a time period within the previous 100s from the current time (i.e., 0 to 100s before the current time), or may be a time period 100s before the current time (e.g., 200s to 300s before the current time); the first future period of time after the current time is the future from the current timehA time period during which, among other things,hthe values of (d) may be, for example:h∈[60s，1500s]. For example, the traffic phase information of each traffic light within 100s before the current time is input into the first target neural network, the traffic phase information of each traffic light within 1500s in the future from the current time can be predicted, and the prediction can be completed through one-time neural network calculation.
S103, acquiring historical average vehicle speeds of all driving road sections in a second historical time period before the current time and historical traffic phase information of all traffic lights included in the target driving path in the second historical time period.
And the duration of the second historical time period is less than that of the first historical time period. For example, the second history period before the current time may be within the first 5s from the current time (i.e., 0-5s before the current time), or may be a period of 5s before the current time (e.g., 20s-25s before the current time).
The historical average vehicle speed of each travel section can be obtained by the following formula:
wherein the content of the first and second substances,Tindicating the time of day in the area of the vehicle,Tcan be made for 24 hours;
is composed ofTTime of dayS j The average vehicle speed of the travel section is,
is composed ofTTime of dayS j Vehicle on driving road sectionkThe speed of travel of the vehicle,K j （S j ,T) Is composed ofTTime of dayS j Total number of vehicles on the travel section.
And S104, inputting the historical average speed of each driving road section in the second historical time period, the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period and the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period into the second target neural network to obtain the future average speed of each driving road section in the first future time period.
The second target neural network can be obtained by training historical traffic phase information of each traffic signal lamp and historical average vehicle speed of each driving road section, wherein the historical traffic phase information of each traffic signal lamp is included in the target driving path.
And S105, obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period, future average vehicle speed of each driving road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation.
The vehicle state equation may include a power equation of the vehicle, a fuel consumption equation, and an engine speed equation, the power equation is obtained according to road gradient information of the target travel path, and the evaluation equation is used to comprehensively evaluate fuel consumption and traffic efficiency of the vehicle.
In a specific implementation, the equation of power may be:
wherein the content of the first and second substances,mis the mass of the vehicle,ais the longitudinal acceleration of the vehicle and,r g is the gear ratio of the current gear of the transmission,r d the transmission ratio of the main speed reducer is set,T e in order to obtain a driving torque for the engine,T b in order to obtain the braking torque,R w which is the radius of the wheel, is,gin order to be the acceleration of the gravity,θin order to be the gradient of the road,C r in order to be the rolling resistance,ρin order to be the density of the air,Athe area of the wind-facing surface is,C d in order to be the air resistance coefficient,vis the longitudinal speed of the vehicle. Wherein the content of the first and second substances,C r =C r1+C r2，C r1andC r2is the rolling resistance coefficient.
In specific implementation, the fuel consumption equation may be:
in a specific implementation, the engine speed equation may be:
in a specific implementation, the first preset vehicle speed constraint condition may include the following equations (9) to (16):
v min≤v(S i )≤
，
S i ∈[0,S f ] (12)
N g (S)∈[1,N m ], 
S i ∈[0,S f ] (13)
v(t,d i )=0，
，
S∈[0,S f ] (14)
wherein the content of the first and second substances,S i is as followsiThe number of the traveling sections is one,
is composed ofS i The historical average vehicle speed of the travel section,v maxis composed ofS i The speed limit on the road section of travel,N g is the current gear of the vehicle and,N m is the maximum gear of the vehicle,
is a target driving pathS f Is located atd i Traffic signal light traffic phase information.
In a specific implementation, the evaluation equation may be:
wherein, the lambda is a weight coefficient,m fuel the total amount of the fuel oil consumption is,sis a displacement.
And S106, determining the target guiding vehicle speed of the vehicle at the next moment of the current moment according to the first speed sequence.
Since the guidance vehicle speed of the vehicle at the time next to the current time is included in the first speed sequence, the target guidance vehicle speed of the vehicle at the time next to the current time can be determined from the first speed sequence. Alternatively, the target guidance vehicle speed of the vehicle at the time next to the current time may be determined from the first speed sequence according to another optimization algorithm.
According to the vehicle speed guiding method provided by the embodiment of the invention, the position information of each traffic signal lamp included in the target driving path is determined by acquiring the target driving path of the vehicle, and the target driving path is divided according to the position information of each traffic signal lamp to obtain each divided driving road section; obtaining historical traffic phase information of each traffic signal lamp included in a target driving path in a first historical time period before the current time, inputting the historical traffic phase information of each traffic signal lamp included in the target driving path in the first historical time period into a first target neural network, and obtaining the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period after the current time, wherein the first target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path; acquiring historical average vehicle speeds of all driving road sections in a second historical time period before the current time and historical traffic phase information of all traffic lights included in a target driving path in the second historical time period, and the historical average speed of each driving road section in the second historical time period, the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period and the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period are input into a second target neural network to obtain the future average speed of each driving road section in the first future time period, the second target neural network is obtained by training historical traffic phase information of each traffic signal lamp and historical average vehicle speed of each driving road section, wherein the historical traffic phase information and the historical average vehicle speed are included in a target driving path, and the duration of the second historical time period is less than that of the first historical time period; obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp, future average vehicle speed of each driving road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation, wherein the future traffic phase information of each traffic signal lamp is included in a target driving path in the first future time period; and determining the target guiding vehicle speed of the vehicle at the next moment of the current moment according to the first speed sequence. The method is suitable for various complex scenes such as level roads or ramps, road sections with or without traffic lights, different road vehicle flow speeds, different vehicle driving behaviors (straight running, left turning, right turning and turning around) and the like, and vehicle passing efficiency and energy economy can be improved by determining the target guide vehicle speed of the vehicle at the next moment of the current moment.
As a possible way to achieve, in implementing S106 in the first embodiment, the second speed sequence of the vehicle in the second future time period after the current time may be obtained according to the first speed sequence, the vehicle state equation, the second preset vehicle speed constraint condition and the evaluation equation, and the duration of the second future time period is smaller than that of the first future time period, for example, the first future time period (long time period) is 1500S in the future, and the second future time period (short time period) is 5S in the future; a target guidance vehicle speed of the vehicle at a time next to the current time is determined from the second speed sequence. The second preset vehicle speed constraint condition may be composed of the first preset vehicle speed constraint condition and a terminal constraint condition, and specifically, the terminal constraint condition may be: the speed in the second speed sequence is within the preset error range with respect to the speed in the first speed sequence at the same time, for example, the terminal constraint condition may be as follows:
the above formula (17) represents: corresponding to the same time, e.g. the same time being at the current timeTAt the next future 5s, the speed in the second speed sequence (in the above equation (17)) "v(T+5) ") and the velocity in the first velocity sequence (in the above equation (17)"v dp (T+5) ") may be within an error range of ± 1 m/s.
By the method, the macro speed planning of the long time domain and the micro speed planning of the short time domain can be combined, the vehicle passing efficiency and the economical efficiency of the capacity can be further improved, and therefore better vehicle speed guidance is achieved.
As one possible implementation manner, after the target guidance vehicle speed of the vehicle at the time next to the current time is determined, a prompt message may be output, the prompt message may prompt the driver of the target guidance vehicle speed in a voice manner, and the prompt message may also be displayed on the display terminal to prompt the driver of the target guidance vehicle speed of the vehicle.
Fig. 5 is a flowchart illustrating a second embodiment of a vehicle speed guidance method according to an embodiment of the present invention, and as shown in fig. 5, S104 in the foregoing embodiment may include:
and S1041, sequentially and uniformly dividing the first future time period into a plurality of future sub-time periods.
And S1042, inputting the historical average speed of each running road section in the second historical time period and the historical traffic phase information of each traffic signal lamp included in the target running path in the second historical time period into a second target neural network to obtain the future average speed of each running road section in the first future sub-time period.
And the duration of each future sub-time period is equal to the duration of the second historical time period.
And S1043, inputting the future average speed of each driving road section in the first future sub-time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the first future sub-time period into the second target neural network to obtain the average speed of each driving road section in the second future sub-time period.
And S1044, iterating and circulating until the average speed of each driving road section in the last future sub-time period is obtained.
For example, in specific implementation, the current time history may be set within 5sS j Time series information of the average speed of the traveling road section and the current time history within 5sS jInputting the time sequence information of the historical passing phase of the traffic signal lamps at the upstream and the downstream of the driving road section into a second target neural network, and predicting the first 5s in the future by inputting the two groups of time sequencesS j Time series information of the average vehicle speed of the travel section. Then the predicted future is within the first 5sS j Time series information of average vehicle speed of traveling section, and predicted first 5s in futureS j The time series information of the traffic signal lamp communication phase of the driving road section is input into the second target neural network, so as to predict the next 5s (namely the second 5s in the future)S j Time series information of the average vehicle speed of the travel section. By the iterative loop, the future is obtainedhTime series information of the average vehicle speed of each travel section over time.
The following describes a vehicle speed guidance method provided in an embodiment of the present invention, taking a specific implementation as an example. Fig. 6 is a schematic flowchart of a third embodiment of a vehicle speed guidance method according to an embodiment of the present invention, and as shown in fig. 6, the vehicle speed guidance method according to the present embodiment may include:
s601, determining a target running path.
And S602, determining the position information and the traffic phase information of each traffic signal lamp included in the target driving path according to the map information.
And S603, dividing the target driving path to obtain each divided driving road section.
And S604, predicting the communication phase information of each traffic signal lamp on the target driving path in the future for 1-25 minutes and the average speed information of each driving road section in the future for 1-25 minutes according to the historical traffic phase information of the traffic signal lamp and the historical average speed information of each driving road section.
And S605, determining a first speed sequence of the vehicle within 1-25 minutes in the future from the viewpoint of fuel economy and traffic efficiency according to the vehicle state equation, the first preset vehicle speed constraint condition and the evaluation equation.
In particular implementations, the first speed sequence may be updated every 5 minutes.
And S606, determining a second speed sequence of the vehicle within 1-5 minutes in the future from the perspective of fuel economy and traffic efficiency according to the vehicle state equation, the first preset vehicle speed constraint condition and the evaluation equation.
In particular implementations, the second velocity sequence may be updated every 1 s.
And S607, determining the target guiding vehicle speed of the vehicle in the next second according to the second speed sequence.
Specifically, as shown in the left box of FIG. 7, information is predicted based on the phase of the traffic signal (i.e.,' in FIG. 7) "
"), the vehicle state equation, and the average vehicle speed prediction information for each travel section (i.e.,' in fig. 7)"
"), a first predetermined vehicle speed constraint and an evaluation equation, by dynamicsThe plan can obtain a vehicle speed trajectory of 1500s in the future (i.e., "[ 2 ] in FIG. 7)v dp {T,T+1500}]"). As shown in the right frame of fig. 7, according to the future 1500s vehicle speed trajectory, the vehicle state equation, the second preset vehicle speed constraint condition (the first preset vehicle speed constraint condition and the terminal constraint condition) and the evaluation equation, and through Model Predictive Control (MPC) rolling optimization, the target guiding vehicle speed of the next second of the vehicle (i.e.,' in fig. 7) is finally obtained "v p (T+1)”）。
Fig. 8 is a schematic structural diagram of a vehicle speed guiding device according to an embodiment of the present invention, and as shown in fig. 8, the vehicle speed guiding device 80 may include:
the first obtaining module 810 may be configured to obtain a target driving path of a vehicle, determine position information of each traffic light included in the target driving path, and divide the target driving path according to the position information of each traffic light to obtain each divided driving section.
The second obtaining module 820 may be configured to obtain historical traffic phase information of each traffic signal included in the target travel path in a first historical time period before the current time, and input the historical traffic phase information of each traffic signal included in the target travel path in the first historical time period into the first target neural network, so as to obtain future traffic phase information of each traffic signal included in the target travel path in the first future time period after the current time, where the first target neural network is trained from the historical traffic phase information of each traffic signal included in the target travel path.
The third obtaining module 830 may be configured to obtain a historical average vehicle speed of each driving road segment in a second historical time period before the current time and historical traffic phase information of each traffic signal included in the target driving path in the second historical time period, and the historical average speed of each driving road section in the second historical time period, the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period and the future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period are input into a second target neural network to obtain the future average speed of each driving road section in the first future time period, the second target neural network is obtained by training historical traffic phase information of each traffic signal lamp and historical average vehicle speed of each driving road section, wherein the historical traffic phase information and the historical average vehicle speed of each driving road section are included in a target driving path, and the duration of the second historical time section is smaller than that of the first historical time section.
The fourth obtaining module 840 may be configured to obtain a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp included in the target travel path in the first future time period, a future average vehicle speed of each travel road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition, and an evaluation equation, where the vehicle state equation includes a power equation, a fuel consumption equation, and an engine speed equation of the vehicle, the power equation is obtained according to road gradient information of the target travel path, and the evaluation equation is used to comprehensively evaluate fuel consumption and traffic efficiency of the vehicle.
The fifth obtaining module 850 may be configured to determine a target guiding vehicle speed of the vehicle at a time next to the current time according to the first speed sequence.
The vehicle speed guiding device provided by the embodiment of the invention is suitable for various complex scenes such as level roads or ramps, road sections with or without traffic lights, different road vehicle speed, different vehicle driving behaviors (straight running, left turning, right turning, turning around) and the like, and can improve the vehicle passing efficiency and the energy economy by determining the target guiding vehicle speed of the vehicle at the next moment of the current moment.
Optionally, the fifth obtaining module 850 may be specifically configured to obtain a second speed sequence of the vehicle in a second future time period after the current time according to the first speed sequence, the vehicle state equation, the second preset vehicle speed constraint condition, and the evaluation equation, where a duration of the second future time period is smaller than a duration of the first future time period; a target guidance vehicle speed of the vehicle at a time next to the current time is determined from the second speed sequence.
Optionally, the power equation may be:
wherein the content of the first and second substances,mis the mass of the vehicle,ais the longitudinal acceleration of the vehicle and,r g is the gear ratio of the current gear of the transmission,r d the transmission ratio of the main speed reducer is set,T e in order to obtain a driving torque for the engine,T b in order to obtain the braking torque,R w which is the radius of the wheel, is,gin order to be the acceleration of the gravity,θin order to be the gradient of the road,C r in order to be the rolling resistance,ρin order to be the density of the air,Athe area of the wind-facing surface is,C d in order to be the air resistance coefficient,vis the longitudinal speed of the vehicle.
Optionally, the third obtaining module 830 may be specifically configured to divide the first future time period into a plurality of future sub-time periods sequentially and uniformly; inputting the historical average speed of each running road section in the second historical time period and the historical traffic phase information of each traffic signal lamp included in the target running path in the second historical time period into a second target neural network to obtain the future average speed of each running road section in the first future sub-time period, wherein the duration of each future sub-time period is equal to the duration of the second historical time period; inputting the future average speed of each driving road section in the first future sub-time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the first future sub-time period into a second target neural network to obtain the average speed of each driving road section in the second future sub-time period; and (4) iterating and circulating until the average speed of each driving road section in the last future sub-time period is obtained.
Optionally, the apparatus 80 may further include: and an output module (not shown in the figure) for outputting a prompt message for prompting the target guiding vehicle speed of the vehicle at the next moment of the current moment.
In addition, corresponding to the vehicle speed guiding method provided by the above embodiment, an embodiment of the present invention further provides an electronic device, which may include: a memory for storing a program; and a processor for implementing all the steps of the vehicle speed guidance method provided by the embodiment of the invention by executing the program stored in the memory.
In addition, corresponding to the vehicle speed guiding method provided in the foregoing embodiment, an embodiment of the present invention further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, all steps of the vehicle speed guiding method according to the embodiment of the present invention are implemented.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (10)
1. A vehicle speed guidance method, characterized by comprising:
acquiring a target driving path of a vehicle, determining position information of each traffic signal lamp included in the target driving path, and dividing the target driving path according to the position information of each traffic signal lamp to obtain each divided driving road section;
obtaining historical traffic phase information of each traffic signal lamp included in the target driving path in a first historical time period before the current time, inputting the historical traffic phase information of each traffic signal lamp included in the target driving path in the first historical time period into a first target neural network, and obtaining future traffic phase information of each traffic signal lamp included in the target driving path in a first future time period after the current time, wherein the first target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path;
acquiring historical average vehicle speed of each driving road section in a second historical time period before the current moment and historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period, and inputting the historical average vehicle speed of each driving road section in the second historical time period, the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period and future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period into a second target neural network to obtain the future average vehicle speed of each driving road section in the first future time period, wherein the second target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path and the historical average vehicle speed of each driving road section, the duration of the second historical time period is less than the duration of the first historical time period;
obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period, future average vehicle speed of each driving road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation, wherein the vehicle state equation includes a power equation, a fuel consumption equation and an engine speed equation of the vehicle, the power equation is obtained according to road gradient information of the target driving path, and the evaluation equation is used for comprehensively evaluating fuel consumption and traffic efficiency of the vehicle;
and determining the target guiding vehicle speed of the vehicle at the next moment of the current moment according to the first speed sequence.
2. The method of claim 1, wherein determining the target lead vehicle speed for the vehicle at a time next to the current time according to the first speed sequence comprises:
obtaining a second speed sequence of the vehicle in a second future time period after the current time according to the first speed sequence, the vehicle state equation, a second preset vehicle speed constraint condition and the evaluation equation, wherein the duration of the second future time period is less than that of the first future time period;
determining a target guidance vehicle speed of the vehicle at a time next to the current time from the second speed sequence.
3. The method of claim 1, wherein the power equation is:
wherein the content of the first and second substances,mis the mass of the vehicle in question,ais the longitudinal acceleration of the vehicle in question,r g is the gear ratio of the current gear of the transmission,r d the transmission ratio of the main speed reducer is set,T e in order to obtain a driving torque for the engine,T b in order to obtain the braking torque,R w which is the radius of the wheel, is,gin order to be the acceleration of the gravity,θin order to be the gradient of the road,C r in order to be the rolling resistance,ρin order to be the density of the air,Athe area of the wind-facing surface is,C d in order to be the air resistance coefficient,vis the longitudinal speed of the vehicle.
4. The method of claim 1, wherein the inputting the historical average vehicle speed of each of the travel sections in the second historical time period, the historical traffic phase information of each traffic light included in the target travel path in the second historical time period, and the future traffic phase information of each traffic light included in the target travel path in the first future time period into a second target neural network to obtain the future average vehicle speed of each of the travel sections in the first future time period comprises:
sequentially and uniformly dividing the first future time period into a plurality of future sub-time periods;
inputting the historical average vehicle speed of each driving road section in the second historical time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period into a second target neural network to obtain the future average vehicle speed of each driving road section in a first future sub-time period, wherein the time length of each future sub-time period is equal to the time length of the second historical time period;
inputting the future average speed of each driving road section in the first future sub-time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the first future sub-time period into a second target neural network to obtain the average speed of each driving road section in a second future sub-time period;
and iterating the loop until the average speed of each driving road section in the last future sub-time period is obtained.
5. The method of claim 1, wherein the method further comprises:
and outputting prompt information, wherein the prompt information is used for prompting the target guiding vehicle speed of the vehicle at the next moment of the current moment.
6. A vehicle speed guidance device, characterized by comprising:
the first acquisition module is used for acquiring a target driving path of a vehicle, determining position information of each traffic signal lamp included in the target driving path, and dividing the target driving path according to the position information of each traffic signal lamp to obtain each divided driving road section;
the second acquisition module is used for acquiring historical traffic phase information of each traffic signal lamp included in the target driving path in a first historical time period before the current time, inputting the historical traffic phase information of each traffic signal lamp included in the target driving path in the first historical time period into a first target neural network, and acquiring future traffic phase information of each traffic signal lamp included in the target driving path in the first future time period after the current time, wherein the first target neural network is obtained by training the historical traffic phase information of each traffic signal lamp included in the target driving path;
a third obtaining module, configured to obtain a historical average vehicle speed of each traveling road segment in a second historical time period before a current time and historical traffic phase information of each traffic light included in the target traveling path in the second historical time period, and input the historical average vehicle speed of each traveling road segment in the second historical time period, the historical traffic phase information of each traffic light included in the target traveling path in the second historical time period, and future traffic phase information of each traffic light included in the target traveling path in the first future time period into a second target neural network, so as to obtain a future average vehicle speed of each traveling road segment in the first future time period, where the second target neural network is obtained by training the historical traffic phase information of each traffic light included in the target traveling path and the historical average vehicle speed of each traveling road segment, the duration of the second historical time period is less than the duration of the first historical time period;
the fourth obtaining module is used for obtaining a first speed sequence of the vehicle in the first future time period according to future traffic phase information of each traffic signal lamp included in the target running path in the first future time period, future average vehicle speed of each running road section in the first future time period, a vehicle state equation, a first preset vehicle speed constraint condition and an evaluation equation, wherein the vehicle state equation includes a power equation, a fuel consumption equation and an engine speed equation of the vehicle, the power equation is obtained according to road gradient information of the target running path, and the evaluation equation is used for comprehensively evaluating the fuel consumption and traffic efficiency of the vehicle;
and the fifth acquisition module is used for determining the target guiding vehicle speed of the vehicle at the next moment of the current moment according to the first speed sequence.
7. The apparatus according to claim 6, wherein the fifth obtaining module is specifically configured to obtain, according to the first speed sequence, the vehicle state equation, a second preset vehicle speed constraint condition and the evaluation equation, a second speed sequence of the vehicle for a second future time period after the current time, where a duration of the second future time period is smaller than a duration of the first future time period; determining a target guidance vehicle speed of the vehicle at a time next to the current time from the second speed sequence.
8. The apparatus of claim 6, wherein the third obtaining module is specifically configured to divide the first future time period into a plurality of future sub-time periods sequentially and uniformly; inputting the historical average vehicle speed of each driving road section in the second historical time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the second historical time period into a second target neural network to obtain the future average vehicle speed of each driving road section in a first future sub-time period, wherein the time length of each future sub-time period is equal to the time length of the second historical time period; inputting the future average speed of each driving road section in the first future sub-time period and the historical traffic phase information of each traffic signal lamp included in the target driving path in the first future sub-time period into a second target neural network to obtain the average speed of each driving road section in a second future sub-time period; and iterating the loop until the average speed of each driving road section in the last future sub-time period is obtained.
9. An electronic device, comprising:
a memory for storing a program;
a processor for implementing the method of any one of claims 1-5 by executing a program stored by the memory.
10. A computer-readable storage medium, characterized in that the medium has stored thereon a program which is executable by a processor to implement the method according to any one of claims 1-5.
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