CN110264757B - Intelligent networking automobile layered speed planning method based on continuous signal lamp information - Google Patents
Intelligent networking automobile layered speed planning method based on continuous signal lamp information Download PDFInfo
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Abstract
The invention discloses an intelligent networking automobile layering speed planning method based on continuous signal lamp information, which comprises the steps of acquiring traffic light time sequence information of a front continuous intersection and distance information between the current vehicle position and each intersection in real time through a V2X networking technology; establishing a whole vehicle longitudinal dynamics model based on a distance domain; the method comprises the steps that a passable area of a vehicle is determined in advance according to the average driving speed expected by a driver and road speed limit information; based on the time sequence and position information of the traffic lights of the front continuous intersections, the driving task information such as the arrival speed and time of each intersection is obtained on the premise of meeting traffic and road constraints; according to the driving task information, the speed planning of each sub-road section is constructed into an optimal control problem, the optimal control quantity given vehicle is solved by utilizing a maximum value principle and through the display derivation of the control rate, the speed planning is realized, and the efficient and economic traffic network of the intelligent internet automobile passing through the multi-intersection under the condition of no stopping is realized.
Description
Technical Field
The invention belongs to the technical field of vehicle engineering, and relates to an intelligent networked automobile continuous multi-intersection traffic control method based on a rolling time domain optimization framework, in particular to an intelligent networked automobile layered speed planning method based on continuous signal lamp information.
Background
With the vigorous development of smart cities and intelligent transportation technologies, the application of the intelligent networking technology in automobile energy-saving control can improve the energy-saving potential of automobiles by 15-20%. However, how to realize the full utilization of the big data information in the automobile energy-saving control becomes the key for realizing energy conservation and emission reduction, and therefore, establishing the relation between the time-varying traffic information and the vehicle running becomes a necessary way.
Research shows that the traffic network with signal lamps in urban working conditions has great energy-saving potential when implementing a speed planning driving strategy. However, the existing speed planning research is only aimed at a typical working condition of passing through a single intersection, and the strategy does not consider dynamic traffic information such as waiting vehicles at the intersection, so that the method is only suitable for the conditions of simple road running working conditions and smooth roads.
Disclosure of Invention
In order to realize efficient and economical traffic network of an intelligent internet automobile passing through multiple intersections under the condition of no stopping, the invention provides an intelligent internet automobile layered speed planning method based on continuous signal lamp information, the time sequence information of a front signal lamp is obtained in real time through internet technologies such as V2X and the like, the layered speed planning is based on a rolling time domain optimization framework, the upper layer is a driving task planning layer, the lower layer is a speed planning layer, two optimization targets of fuel economy and driving rapidity are balanced, different driver requirements and vehicle self-restraint are considered at the same time, the globally optimal speed track is solved, and the economical and efficient passing of the vehicle through the multiple intersections is realized.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an intelligent networking automobile layered speed planning method based on continuous signal lamp information comprises the following steps:
the method comprises the following steps: acquiring traffic light time sequence information of front continuous intersections and distance information between the current vehicle position and each intersection in real time through a V2X networking technology;
step two: establishing an upper driving task planning layer, which comprises the following specific steps:
2.1) establishing a whole vehicle longitudinal dynamics model based on a distance domain;
2.2) predetermining the passable area of the vehicle according to the average running speed expected by the driver and the road speed limit information;
2.3) based on the traffic light time sequence and the position information of the front continuous intersections, obtaining the driving task information such as the arriving speed and time of each intersection on the premise of meeting traffic and road constraints;
step three: the lower-layer speed planning layer constructs the speed planning of each sub-road section into an optimal control problem according to the driving task information obtained by the upper-layer driving task planning layer, the optimal control quantity given vehicle is solved by utilizing a maximum value principle and through the display derivation of the control rate, and the speed planning is realized by using a rolling time domain control method.
The invention has the beneficial effects that:
1. according to the invention, through a hierarchical control method, the speed of the vehicle is globally planned by utilizing the time sequence information of the front continuous signal lamps, so that the proper globally optimal speed and time for reaching each intersection are obtained, and therefore, the vehicle can efficiently and economically pass through the continuous multi-intersection without stopping.
2. The invention fully considers different driving requirements of the driver, and not only ensures the economy, but also considers the driving rapidity.
3. The method is simple to implement, and a problem construction method based on the distance domain provides a new idea for intelligent networked automobile energy-saving control.
Drawings
FIG. 1 is a control block diagram of the present invention;
FIG. 2 is a schematic view of a passable area in the present invention;
FIG. 3 is a diagram showing simulation results in the present invention.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings.
As shown in fig. 1, an intelligent networked automobile hierarchical speed planning method based on continuous signal lamp information collects road traffic information including information such as road speed limit, continuous multi-intersection signal lamp time sequence and the like through a V2X technology; the upper-layer controller carries out global driving task planning according to the acquired traffic information by establishing a distance domain-based longitudinal dynamic model of the whole vehicle; and the lower layer controller solves the optimal control rate according to the optimal control target and the dynamic equation according to the driving task information such as the arrival speed, the time and the like of each intersection obtained by the upper layer, so that the global speed planning of the vehicle is realized.
The method comprises the following steps:
step one, acquiring time sequence information of traffic lights at continuous intersections ahead and distance information between the current vehicle position and each intersection in real time through a V2X networking technology, and determining a passable area of the vehicle according to road traffic and speed limit information and the maximum and minimum speed limit of the vehicle, which is shown in figure 2.
Due to intersection position determination, equality constraint s (t) exists on distancef)＝sfWherein s isfIs the endpoint displacement information. The timing sequence of signal lamps is different, and inequality constraint t exists in timeg＜tf＜tr，tfIs the terminal time in units of s, tgIs the starting time of green light, and has the unit of s, trIs the red light start time in units of s. In order to solve the problem conveniently, the traditional time-based optimization problem is converted into a distance domain optimization problem through an equivalent transformation of a formula (1):
wherein s is the vehicle running distance in m, v is the vehicle speed in m/s, and t is the vehicle running time in s.
Step two, establishing an upper driving task planning layer, which comprises the following specific steps:
2.1) establishing a distance domain-based whole vehicle longitudinal dynamics model, such as formula (2):
in the formula (2), x is the state quantity of the optimization problem, t is the running time of the vehicle, and the unit is s; v is the running speed of the vehicle and has the unit of m/s; u is a control variable of the vehicle, i.e. driving force or braking force per unit mass, in units of N/kg; m is the mass of the whole vehicle, and the unit is kg; cDIs the air resistance coefficient; rhoaIs the air density in kg/m3(ii) a g is the acceleration of gravityIn the unit of m/s2；AvIs the limited frontal area of the vehicle, and has the unit of m2Theta is the road gradient, and here, when the ground gradient is small, the gradient is approximately expressed, namely cos (theta) is approximately equal to 1 and sin (theta) is approximately equal to theta; μ is a rolling resistance coefficient.
2.2) average driving speed v according to the driver's desirerAnd road speed limit information, predetermining the passable area of the vehicle:
average vehicle speed v according to driver's expectationrIn m/s, location information s of the destinationfinalIn the unit m. The expected arrival time t of the driver is obtained by the formula (3)finalThe unit is s.
Therefore, the time sequence of the vehicle passing through each intersection can be further determined, namely, the specific passable area is determined:
2.3) based on the traffic light time sequence and the position information of the front continuous intersection, the driving task information such as the arriving speed and time of each intersection is obtained on the premise of meeting traffic and road constraints:
the distance domain-based driving task planning problem is constructed as follows:
the objective function is selected to better balance two objectives of fuel economy and driving rapidity, omegai(i is 1,2,3) is a weight coefficient, FtIs the driving force at the wheel, FbIs the wheel braking force with the unit of N; Δ s is the discrete step size, tN-t0Is the travel time; n is the predicted time domain step number; v. oflimThe unit is m/s, and is the road speed limit value; u. ofminAnd umaxThe maximum value and the minimum value are respectively the maximum value and the minimum value of the control quantity; t is tpi(i is 1,2,3) is the time to reach each intersection, tpi,minAnd tpi,maxAre respectively asThe minimum and maximum green time allowed for the ith intersection. The invention solves and obtains the information of driving tasks such as speed, time and the like of arriving at each intersection through a Sequential Quadratic Programming (SQP) algorithm.
Step three: the lower speed planning layer carries out speed planning through the driving task information obtained by the driving task planning layer (upper layer), constructs the speed planning of each sub-road section as an optimal control problem, utilizes the maximum value principle (PMP),
and the optimal speed track is quickly solved through the display derivation of the control rate, so that the vehicle can economically and efficiently pass through multiple intersections.
The speed planning problem is described as: based on the time domain, an optimal speed track is searched under the condition of meeting the automobile longitudinal dynamics equation and the terminal boundary condition, so that the energy consumption in the whole time domain is minimum. The mathematics are described as follows:
wherein the controlled variable u ═ Ft,Fb]，FtIs the driving force at the wheel, FbFor wheel braking force, the state quantity x ═ s, v]S is the vehicle running distance in m, v is the vehicle speed in m/s; ft,maxAnd Fb,maxMaximum driving force and braking force respectively; xi1,ξ2Is a weight coefficient; phi (-) is a terminal penalty term;
respectively the arrival time, the vehicle speed and the distance of the ith intersection; alpha is alpha1,α2Are adjustable parameters.
In view of the object of the present invention, the entire vehicle energy consumption in the prediction time domain is selected as an objective function, and the objective function is constructed as shown in equation (5) taking the braking force into consideration in order to reduce unnecessary braking.
The following hamiltonian equation was constructed in conjunction with the maximum principle (PMP):
wherein λ is1,λ2Is a covariate.
Discretizing the optimal control problem on a delta t time axis by adopting a forward difference method, wherein the optimal necessity condition and the terminal condition to be met are as follows:
in addition, if the optimal trajectory is to be followed, the optimal control variables need to satisfy the following relationship at each time
Based on the optimal necessity condition, the relationship between the control quantity u and the co-modal state lambda is deduced, u*(k),x*(k),λ*(k) Respectively an optimal control quantity, an optimal state quantity and an optimal co-modal variable. The control quantity u is made to be [ F ] by the Hamiltoniant,Fb]In the form of a quadratic function of (c). Thus, an explicit form of the control variable can be derived:
wherein the content of the first and second substances,
finally obtaining the optimal control quantity
Comprises the following steps:
by combining the control laws, the optimal control quantity in the prediction time domain can be obtained, the first control quantity given vehicle is extracted, and the reasonable planning of the vehicle speed is realized by using a rolling time domain control method.
An entire vehicle model is built by AMESim to perform oil consumption simulation analysis on the situation, and the obtained result is as follows:
the upper solid line is the oil consumption simulation result without adopting the method of the invention, and the lower dotted line is the oil consumption simulation result with adopting the layered speed planning method of the invention, and the result shows that the method can realize no-stop waiting at the intersection, thereby effectively reducing the oil consumption and realizing economical driving.
Claims (5)
1. An intelligent networking automobile layered speed planning method based on continuous signal lamp information is characterized by comprising the following steps:
the method comprises the following steps: acquiring traffic light time sequence information of front continuous intersections and distance information between the current vehicle position and each intersection in real time through a V2X networking technology;
step two: establishing an upper driving task planning layer, which comprises the following specific steps:
2.1) establishing a whole vehicle longitudinal dynamics model based on a distance domain;
2.2) predetermining the passable area of the vehicle according to the average running speed expected by the driver and the road speed limit information;
2.3) based on the time sequence and the position information of the traffic lights at the front continuous intersections, obtaining driving task information including the arrival speed and time of each intersection on the premise of meeting traffic and road constraints;
the distance domain-based driving task planning problem is constructed as follows:
in the formula (I), the compound is shown in the specification,ωi(i ═ 1,2,3) is a weight coefficient; ftIs the driving force at the wheels, in N; fbIs the wheel braking force, in units of N; Δ s is the discrete step size, tN-t0Is the travel time; n is the predicted time domain step number; n is the predicted time domain step number; v. ofmin,vmaxThe unit is the road speed limit value, m/s; u. ofminAnd umaxRespectively as the maximum value and the minimum value of the control quantity; t is tpi(i is 1,2,3) is the time to reach each intersection, tpi,minAnd tpi,maxRespectively allowing the i-th intersection to pass through the minimum green time and the maximum green time;
solving to obtain driving task information including the speed and time of reaching each intersection through a sequential quadratic programming algorithm;
step three: the lower-layer speed planning layer constructs the speed planning of each sub-road section into an optimal control problem according to the driving task information obtained by the upper-layer driving task planning layer, the optimal control quantity given vehicle is solved by utilizing a maximum value principle and through the display derivation of the control rate, and the speed planning is realized by using a rolling time domain control method.
2. The method for planning the layering speed of the intelligent networked automobile based on the continuous signal lamp information as claimed in claim 1, wherein in the step one, due to the fact that the intersection position is determined, equality constraint exists in distance, and inequality constraint exists in time when the signal lamp time sequences are different, the traditional time-based optimization problem is equivalently converted into a distance domain optimization problem:
wherein s is the vehicle driving distance in m; v is vehicle speed in m/s; t is the travel time of the vehicle in units of s.
3. The intelligent networked automobile layered speed planning method based on continuous signal lamp information as claimed in claim 1, wherein the distance domain-based automobile longitudinal dynamics model established in the step 2.1) is:
wherein x is the state quantity of the optimization problem; t is the travel time of the vehicle in units of s; v is the running speed of the vehicle in m/s; u is a control variable of the vehicle and has a unit of N/kg; m is the mass of the whole vehicle in kg; cDIs the air resistance coefficient; rhoaIs the air density in kg/m3(ii) a g is gravity acceleration in m/s2；AvIs the limited frontal area of the vehicle, unit m2θ is road slope; μ is a rolling resistance coefficient.
4. The method for intelligent networked automobile hierarchical speed planning based on continuous signal lamp information as claimed in claim 1, wherein the step 2.2) is based on the expected average speed v of the driverrLocation information s of destinationfinalObtaining the expected arrival time t of the driverfinal：
Wherein the desired average vehicle speed vrUnit m/s, location information of destination sfinalIn the unit of m; expected arrival time tfinalThe unit is s;
the time sequence of the vehicles passing through each intersection can be further determined, namely the passing area.
5. The intelligent networked automobile layered speed planning method based on continuous signal lamp information as claimed in claim 1, wherein the specific steps of the third step are as follows:
speed planning is carried out through the driving task information obtained by the driving task planning layer, the whole vehicle energy consumption in a prediction time domain is selected as a target function, and meanwhile, the braking force is taken into consideration, and the target function is constructed as follows:
wherein the controlled variable u ═ Ft,Fb]，FtIs the driving force at the wheel; fbIs the wheel braking force; the state quantity x ═ s, v]S is the vehicle travel distance in m; v is vehicle speed in m/s; ft,maxAnd Fb,maxMaximum driving force and braking force respectively; xi1,ξ2Is a weight coefficient; phi (-) is a terminal penalty term;
respectively the arrival time, the vehicle speed and the distance of the ith intersection; alpha is alpha1,α2Is an adjustable parameter;
the following Hamiltonian equation is constructed in combination with the maximum principle:
wherein λ is1,λ2Is a covariate;
discretizing the optimal control problem on a delta t time axis by adopting a forward difference method, wherein the optimal necessity condition and the terminal condition to be met are as follows:
the optimal control variables need to satisfy the following relationship at each moment:
based on the optimal necessity condition, the relationship between the control quantity u and the co-modal state lambda is deduced, u*(k),x*(k),λ*(k) Respectively an optimal control quantity, an optimal state quantity and an optimal co-modal variable; the explicit form of the control variable is:
wherein the content of the first and second substances,
p1(k)＝ξ1v2(k)
p3(k)＝ξ2
finally obtaining the optimal control quantity Ft *(k),
Comprises the following steps:
and (4) solving the optimal control quantity in the prediction time domain by combining the control laws, extracting a first control quantity given vehicle, and realizing the planning of the vehicle speed by using a rolling time domain control method.
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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 |
| 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 |
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