CN110264757A - Intelligent network based on continuous signal lamp information joins vehicle layered speed planning method - Google Patents
Intelligent network based on continuous signal lamp information joins vehicle layered speed planning method Download PDFInfo
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- G—PHYSICS
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- 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
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Abstract
The invention discloses a kind of, and the intelligent network based on continuous signal lamp information joins vehicle layered speed planning method, obtains the continuous traffic lights timing information of intersection in front and the range information of current vehicle location and each intersection in real time by V2X net connection technology；Establish the vehicle Longitudinal Dynamic Model based on distance domain；According to the desired average overall travel speed of driver and road speed-limiting messages, predefine vehicle can traffic areas；Traffic lights timing and location information based on the continuous intersection in front obtain the driving tasks information such as speed and the time that each intersection reaches under the premise of meeting traffic and road constraint；According to driving task information, the speed planning of each pavement branch sections is configured to an optimal control problem, utilize maximal principle, and it is derived by the display of control rate, it solves optimum control amount and gives vehicle, it realizes speed planning, realizes the transportation network by multi-intersection of intelligent network connection automobile high-efficiency and economic in the case where not parking.
Description
Technical field
The invention belongs to Vehicle Engineering technical fields, are related to a kind of intelligent network connection automobile based on rolling time horizon Optimization Framework
A kind of continuous multi-intersection passing control method, and in particular to vehicle layered speed of intelligent network connection based on continuous signal lamp information
Planing method.
Background technique
With booming, the application that intelligent network connection technology is controlled in vehicle energy saving of smart city and intelligent transport technology
It can be improved the energy-saving potential of automobile 15%-20%.But how to realize abundant benefit of the big data information in vehicle energy saving control
With the key for realizing energy-saving and emission-reduction is become, for this purpose, the relationship established between the traffic information of time-varying and vehicle driving becomes necessary
Road.
Research shows that implementing speed planning driving strategy in transportation network of the city operating condition containing signal lamp has biggish section
It can potentiality.But existing speed planning research is often only for this typical condition of single intersection is passed through, not due to the strategy
Consider to wait the dynamic informations such as vehicle at crossing, therefore is only applicable to the case where road running mode is simple, and the road is clear.
Summary of the invention
To realize that intelligent network joins the transportation network by multi-intersection of automobile high-efficiency and economic in the case where not parking, this
Invention proposes that a kind of intelligent network based on continuous signal lamp information joins vehicle layered speed planning method, joins skill by nets such as V2X
Art obtains front signal light timing information in real time, and the layered velocity planning based on rolling time horizon Optimization Framework, upper layer is to drive to appoint
Business planning layer, lower layer is speed planning layer, tradeoff fuel economy and traveling two optimization aims of rapidity, while considering difference
Operator demand and vehicle itself constraint, solve the speed trajectory of global optimum, realize vehicle economy, is efficient by handing over more
Prong.
To achieve the above object, the technical solution of the present invention is as follows:
A kind of vehicle layered speed planning method of intelligent network connection based on continuous signal lamp information, comprising the following steps:
Step 1: the traffic lights timing information of the continuous intersection in front and current is obtained by V2X net connection technology in real time
The range information of vehicle location and each intersection；
Step 2: upper layer driving task planning layer is established, the specific steps are as follows:
2.1) the vehicle Longitudinal Dynamic Model based on distance domain is established；
2.2) according to the desired average overall travel speed of driver and road speed-limiting messages, predefine vehicle can FOH
Domain；
2.3) traffic lights timing and location information based on the continuous intersection in front, before meeting traffic and road constraint
It puts to obtain the driving tasks information such as speed and the time of each intersection arrival；
Step 3: the driving task information that lower layer's speed planning layer is obtained according to upper layer driving task planning layer, it will be each
The speed planning of pavement branch sections is configured to an optimal control problem, derives using maximal principle, and by the display of control rate,
It solves optimum control amount and gives vehicle, speed planning is realized by using roll stablized loop method.
The invention has the benefit that
1, the present invention carries out the speed of vehicle using front continuous signal lamp timing information by hierarchical control method
Global motion planning obtains speed and the time for the suitable global optimum for reaching each intersection, to realize vehicle not parking
In the case of high-efficiency and economic pass through continuous multi-intersection.
2, the present invention fully considers the different drive demands of driver, and traveling is had also contemplated while both guaranteeing economy
Rapidity.
3, the present invention is implemented simple, and construction method is that intelligent network joins vehicle energy saving control and mentions the problem of be based on distance domain
New approaches are supplied.
Detailed description of the invention
Fig. 1 is control block diagram of the invention；
Fig. 2 is can traffic areas schematic diagram in the present invention；
Fig. 3 is simulation result diagram in the present invention.
Specific embodiment
A specific embodiment of the invention is elaborated below in conjunction with technical solution and attached drawing.
As shown in Figure 1, a kind of intelligent network based on continuous signal lamp information joins vehicle layered speed planning method, pass through
V2X technology acquires the information such as Traffic Information, including road speed limit, continuous multi-intersection signal lamp timing；Upper controller
By establishing the vehicle Longitudinal Dynamic Model based on distance domain, according to collected traffic information, global driving task is carried out
Planning；The driving tasks information such as the arrival rate for each intersection that lower layer's controller is obtained according to upper layer and time, according to optimal
Target, dynamic equation solution optimum control rate are controlled, to realize the global speed planning of vehicle.
Step of the invention:
Step 1 obtains the traffic lights timing information of the continuous intersection in front and current by V2X net connection technology in real time
The range information of vehicle location and each intersection, and according to the minimax speed limit of road traffic and speed-limiting messages and vehicle,
Can determine that vehicle can traffic areas, referring to fig. 2.
Since intersection position determines that there are equality constraint s (t onf)=sf, wherein sfFor end displacement information.Letter
The difference of signal lamp timing, there are inequality constraints t on the timeg< tf< tr, tfFor terminal juncture, unit s, tgStart for green light
Moment, unit s, trFor red light start time, unit s.In order to facilitate this is solved the problems, such as, tradition is based on the time by the present invention
Optimization problem become the optimization problem of distance domain through formula (1) equivalency transform:
Wherein, s is vehicle driving distance, and unit m, v are speed, and unit m/s, t are the running time of vehicle, unit
For s.
Step 2 establishes upper layer driving task planning layer, the specific steps are as follows:
2.1) the vehicle Longitudinal Dynamic Model based on distance domain is established, such as formula (2):
X is the quantity of state of optimization problem in formula (2), and t is the running time of vehicle, unit s；V is the traveling of vehicle
Speed, unit m/s；U is the control variable of vehicle, the i.e. driving force or brake force of unit mass, unit N/kg；M is whole
Vehicle quality, unit kg；CDFor coefficient of air resistance；ρaFor atmospheric density, unit kg/m3；G is acceleration of gravity, and unit is
m/s2；AvFor the limited front face area of vehicle, unit m2, θ is road grade, here, when ground line gradient is smaller, to the gradient
Make approximate expression, i.e. cos (θ) ≈ 1 and sin (θ) ≈ θ；μ is coefficient of rolling resistance.
2.2) according to the desired average overall travel speed v of driverrWith road speed-limiting messages, passing through for vehicle is predefined
Region:
Expectation average speed v according to driverr, unit m/s, the location information s of destinationfinal, unit m.It is logical
It crosses formula (3) and obtains the expected approach time t of driverfinal, unit s.
Therefore vehicle can be further determined that by the time series of each intersection, that is, determining specifically can traffic areas:
2.3) traffic lights timing and location information based on the continuous intersection in front, before meeting traffic and road constraint
It puts to obtain the driving tasks information such as speed and the time of each intersection arrival:
Driving task planning problem building based on distance domain is as follows:
Objective function is chosen preferably to balance fuel economy and traveling two targets of rapidity, ωi(i=1,2,
It 3) is weight coefficient, FtFor driving force at wheel, FbFor wheel braking force, unit N；Δ s is discrete steps, tN-t0For traveling
Time；N is prediction time domain step number；vlimFor road speed limit value, unit m/s；uminAnd umaxFor the minimax for not being control amount
Value；tpi(i=1,2,3) is respectively the time for reaching each intersection, tpi,minAnd tpi,maxRespectively i-th of intersection allows to lead to
The minimum and maximum green time crossed.The present invention obtains reaching each intersection by sequential quadratic programming (SQP) algorithm, solution
The driving tasks information such as speed and time.
Step 3: lower layer's speed planning layer passes through the driving task information that driving task planning layer (upper layer) obtains, and carries out
The speed planning of each pavement branch sections is configured to an optimal control problem by speed planning, using maximal principle (PMP),
And derived by the display of control rate, it realizes the optimal speed trajectory of rapid solving, realizes the economic, efficient of vehicle
Pass through multi-intersection.
Velocity planning problem description are as follows: it is based on time-domain, under the conditions of meeting vehicle overall design equation and end boundary,
Finding an optimal velocity track makes the energy consumption in entire time domain minimum.Mathematical description is as follows:
Wherein, control amount u=[Ft,Fb], FtFor driving force at wheel, FbFor wheel braking force, quantity of state x=[s, v], s
For vehicle driving distance, unit m, v are speed, unit m/s；Ft,maxAnd Fb,maxRespectively maximum driving force and brake force；
ξ1,ξ2For weight coefficient；φ () is terminal penalty item；Arrival time of respectively i-th intersection, speed and
Locating distance；α1,α2For adjustable parameters.
In view of target of the invention, the vehicle energy consumption selected in prediction time domain is objective function, while in order to subtract
Few unnecessary braking, brake force is taken into account, and constructs objective function as shown in formula (5).
Following Hamilton's equation is constructed in conjunction with maximal principle (PMP):
Wherein, λ1,λ2To assist state variable.
Optimal control problem is used to the method discretization of forward difference on Δ t time shaft, then what is met is optimal
Necessity condition and terminal condition are as follows:
In addition to this, to along optimal trajectory, optimum control variable needs to meet following relationship at each moment
Based on the above optimal necessity condition, relationship between control amount u and association state state λ, u are derived*(k),x*(k),λ*
It (k) is respectively optimum control amount, optimum state amount, optimal association's state variable.Since the Hamiltonian function makes control amount u=[Ft,Fb]
Quadratic function form.Therefore, the Explicit Form of available control variable:
Wherein,
Finally obtain optimum control amountAre as follows:
In conjunction with the above control law, the optimum control amount in prediction time domain can be found out, it is given to extract first control amount
Vehicle realizes making rational planning for for speed by using roll stablized loop method.
Whole vehicle model is built to carry out oil consumption simulation analysis to said circumstances with AMESim below, and it is as follows to obtain result:
Top solid line is the oil consumption simulation result for not taking method in the present invention, and lower broken line is to take in the present invention to divide
The oil consumption simulation result of interval velocity planing method, the results showed that, since this method can be realized the not parking waiting in crossing, Ke Yiyou
The reduction oil consumption of effect realizes that economy drives.
Claims (6)
1. a kind of intelligent network based on continuous signal lamp information joins vehicle layered speed planning method, which is characterized in that including with
Lower step:
Step 1: the traffic lights timing information and current vehicle of the continuous intersection in front are obtained in real time by V2X net connection technology
The range information of position and each intersection；
Step 2: upper layer driving task planning layer is established, the specific steps are as follows:
2.1) the vehicle Longitudinal Dynamic Model based on distance domain is established；
2.2) according to the desired average overall travel speed of driver and road speed-limiting messages, predefine vehicle can traffic areas；
2.3) traffic lights timing and location information based on the continuous intersection in front, under the premise of meeting traffic and road constraint
Obtain the driving tasks information such as speed and the time that each intersection reaches；
Step 3: the driving task information that lower layer's speed planning layer is obtained according to upper layer driving task planning layer, by each branch
The speed planning of section is configured to an optimal control problem, derives, solves using maximal principle, and by the display of control rate
Optimum control amount gives vehicle, realizes speed planning by using roll stablized loop method.
2. a kind of intelligent network based on continuous signal lamp information as described in claim 1 joins vehicle layered speed planning method,
It is characterized in that, in the step 1, since intersection position determines that there are equality constraints on, and signal lamp timing is not
Together, there are inequality constraints on the time, and therefore, by the time-based optimization problem of tradition, equivalency transform is the optimization of distance domain
Problem:
Wherein, s is vehicle driving distance, unit m；V is speed, unit m/s；T is the running time of vehicle, unit s.
3. a kind of intelligent network based on continuous signal lamp information as described in claim 1 joins vehicle layered speed planning method,
It is characterized in that, the vehicle Longitudinal Dynamic Model based on distance domain established in the step 2.1) are as follows:
Wherein, x is the quantity of state of optimization problem；T is the running time of vehicle, unit s；V is the travel speed of vehicle, unit m/
s；U is the control variable of vehicle, unit N/kg；M is complete vehicle quality, units/kg；CDFor coefficient of air resistance；ρaFor atmospheric density,
Units/kg/m3；G is acceleration of gravity, unit m/s2；AvFor the limited front face area of vehicle, unit m2, θ is road grade；μ is
Coefficient of rolling resistance.
4. a kind of intelligent network based on continuous signal lamp information as described in claim 1 joins vehicle layered speed planning method,
It is characterized in that, expectation average speed v of the step 2.2) according to driverr, destination location information sfinal, driven
The expected approach time t for the person of sailingfinal:
Wherein, it is expected that average speed vr, unit m/s, the location information s of destinationfinal, unit m；Expected approach time
tfinal, unit s；
It can further determine that vehicle passes through the time series of each intersection, it can traffic areas.
5. a kind of intelligent network based on continuous signal lamp information as described in claim 1 joins vehicle layered speed planning method,
It is characterized in that, driving task planning problem building of the step 2.3) based on distance domain is as follows:
In formula, ωiIt (i=1,2,3) is weight coefficient；FtFor driving force at wheel, unit N；FbFor wheel braking force, unit N；
Δ s is discrete steps, tN-t0For running time；N is prediction time domain step number；vlimFor road speed limit value, unit m/s；uminAnd umax
For the maximum value and minimum value for not being control amount；tpi(i=1,2,3) is respectively the time for reaching each intersection, tpi,minWith
tpi,maxRespectively i-th of intersection allow by minimum green time and maximum green time；
By sequential quadratic programming algorithm, solution obtains reaching the driving tasks information such as speed and time of each intersection.
6. a kind of intelligent network based on continuous signal lamp information as described in claim 1 joins vehicle layered speed planning method,
It is characterized in that, the specific steps of the step 3 are as follows:
The driving task information obtained by driving task planning layer carries out speed planning, selectes the vehicle energy in prediction time domain
Amount consumption is objective function, while brake force being taken into account, and building objective function is as follows:
Wherein, control amount u=[Ft,Fb], FtFor driving force at wheel；FbFor wheel braking force；Quantity of state x=[s, v], s are vehicle
Operating range, unit m；V is speed, unit m/s；Ft,maxAnd Fb,maxRespectively maximum driving force and brake force；ξ1,ξ2For power
Weight coefficient；φ () is terminal penalty item；Arrival time, speed and the locating distance of respectively i-th intersection；
α1,α2For adjustable parameters；
Following Hamilton's equation is constructed in conjunction with maximal principle:
Wherein, λ1,λ2To assist state variable；
Optimal control problem is used to the method discretization of forward difference, the then optimal necessity to be met on Δ t time shaft
Property condition and terminal condition are as follows:
Optimum control variable needs to meet following relationship at each moment:
Based on the above optimal necessity condition, relationship between control amount u and association state state λ, u are derived*(k),x*(k),λ*(k) divide
It Wei not optimum control amount, optimum state amount, optimal association's state variable；Control the Explicit Form of variable are as follows:
Wherein,
p1(k)=ξ1v2(k)
p3(k)=ξ2
Finally obtain optimum control amountAre as follows:
In conjunction with the above control law, the optimum control amount in prediction time domain is found out, first control amount is extracted and gives vehicle, pass through
The planning of speed is realized using roll stablized loop method.
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| CN112991801A (en) * | 2021-03-05 | 2021-06-18 | 合肥工业大学 | Optimal safety path obtaining method based on time-varying road condition |
| CN113192347A (en) * | 2021-03-16 | 2021-07-30 | 东南大学 | Intelligent internet vehicle economical driving control method in dynamic traffic scene |
| CN113299107A (en) * | 2021-05-08 | 2021-08-24 | 东南大学 | Multi-target fusion intersection dynamic vehicle internet speed guiding control method |
| CN113470407A (en) * | 2021-06-21 | 2021-10-01 | 上汽通用五菱汽车股份有限公司 | Vehicle speed guiding method for multi-intersection passing, server and readable storage medium |
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| CN115410369B (en) * | 2022-08-30 | 2024-02-20 | 合肥工业大学 | Real-time road traffic speed interval construction method |
| CN116092310A (en) * | 2023-01-28 | 2023-05-09 | 西南交通大学 | Intersection collaborative ecological driving control method and system for mixed traffic environment |
| CN116092310B (en) * | 2023-01-28 | 2023-07-18 | 西南交通大学 | Intersection collaborative ecological driving control method and system for mixed traffic environment |
| CN116476828A (en) * | 2023-03-08 | 2023-07-25 | 华中科技大学 | Vehicle speed planning method and system considering influence of front vehicles and traffic light information |
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