CN111785088A - Double-layer collaborative optimization method for merging network vehicle ramps - Google Patents

Double-layer collaborative optimization method for merging network vehicle ramps Download PDF

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CN111785088A
CN111785088A CN202010581563.XA CN202010581563A CN111785088A CN 111785088 A CN111785088 A CN 111785088A CN 202010581563 A CN202010581563 A CN 202010581563A CN 111785088 A CN111785088 A CN 111785088A
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CN111785088B (en
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史彦军
袁志恒
吕玲玲
沈卫明
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Dalian University of Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention belongs to the technical field of intelligent control of networked vehicles in a vehicle networking system, and particularly relates to a double-layer collaborative optimization method for merging networked vehicle ramps. In the invention, the balance between the calculation load and the optimality is considered, the optimality is improved to a great extent and the calculation amount is reduced to the maximum extent through the form of an upper-layer optimization model and a lower-layer analytical solution; the upper layer determines and distributes the time of each intelligent network vehicle entering a merging area, the lower layer determines the specific motion track of each intelligent network vehicle, the traffic efficiency and the energy utilization rate of the merging of the ramps are improved, and the intelligent network vehicles which cannot meet the minimum time interval return to the upper layer for scheduling calculation through the lower layer calculation, so that the system has robustness.

Description

Double-layer collaborative optimization method for merging network vehicle ramps
Technical Field
The invention belongs to the technical field of intelligent control of networked vehicles in a vehicle networking system, and particularly relates to a double-layer collaborative optimization method for merging networked vehicle ramps.
Background
The increasing frequency of traffic activities and traffic jams have had a significant impact on socio-economic issues. As one of the bottlenecks of transportation systems, highway ramp merging not only results in huge economic and transportation costs, but also has side effects on increasing atmospheric pollutant emissions and collision risks. The development of car networking and automation technology provides opportunities to solve the above problems. By utilizing the real-time information transmission capability between the vehicles and the infrastructure, a more advanced and efficient traffic management system can be developed, the traffic jam and the air pollutant emission are reduced, and the safety is improved. The intelligent networked vehicle improves safety and efficiency through information sharing and vehicle coordination.
At present, methods for coordinating and controlling intelligent networked vehicles to improve traffic efficiency and reduce energy consumption can be generally divided into two categories: rule-based and optimization-based methods. The rule-based method is mainly a no-signal ramp-junction vehicle coordination algorithm based on a first-in first-out rule, but does not consider microscopic properties (such as vehicle tracks) and optimize the merging sequence of vehicles. The optimization-based method is divided into two methods, one method only optimizes the motion track of the vehicle, and assumes that the merging sequence of the vehicle is fixed, which is special, and the optimality of the method cannot be guaranteed; another entry ramp control strategy is a constrained nonlinear optimization problem that provides step-by-step control commands for individual vehicles, but ignores the computational difficulty of solving complex optimization problems on-line.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a double-layer collaborative optimization method for merging network vehicle ramps. The upper layer model optimizes the passing efficiency of the target vehicle, and the lower layer model plans the motion track of the vehicle, so that the energy utilization rate is improved. On the premise of not affecting safety, the contradiction between calculation load and optimality can be balanced, and the maneuverability and energy conservation of the vehicle can be improved to the maximum extent.
The invention particularly relates to a double-layer collaborative optimization method for merging network vehicle ramps, which comprises the following steps:
step (1), determining a framework of intelligent networking vehicle ramp merging cooperative control: the system comprises a vehicle clustering module, a vehicle merging sequence coordination module, a vehicle motion track planning layer, an infrastructure communication module and a ramp merging traffic model;
dividing a ramp merging road section into a sequence area, a control area and a merging area, initializing an intelligent internet vehicle serial number id based on a first-in first-out rule entering the sequence area, and sequentially making id equal to 0, 1, 2 … … and N;
step (3), collecting intelligent networked vehicle parameters, real-time speed information and real-time position information;
step (4), clustering the vehicles based on potential conflicts of the vehicles at the merging points by searching the group sources of the initial states of the vehicles;
step (5), establishing an upper and lower layer scheduling optimization model based on a framework of intelligent networked vehicle ramp merging cooperative control, determining and distributing the time of each intelligent networked vehicle entering a merging area by an upper layer, and determining the specific motion track of each intelligent networked vehicle by a lower layer according to an upper layer scheduling plan:
step (6), the vehicle enters a sequence area, and an upper layer model objective function F is optimized by adopting a branch-and-bound algorithm;
step (7), the vehicle enters a control area, and the obtained scheduling plan M is transmitted to a lower-layer motion planner;
step (8), judging whether the vehicle needs to be accelerated or decelerated according to the parameters, real-time information and upper-layer scheduling information of the intelligent networked vehicle with the serial number id:
step (9), if acceleration and deceleration are needed, entering an acceleration and deceleration mode, reporting a finished vehicle speed configuration file and the current required speed after completion, and keeping the current speed;
step (10), if acceleration and deceleration are not needed, the vehicle keeps the current speed;
step (11), judging whether the time interval of the intelligent networked vehicles with the serial numbers id is smaller than a threshold value;
step (12), if yes, the vehicle enters a following mode;
and (13) if not, the vehicle ignores the upper-layer scheduling information and participates in the upper-layer scheduling planning again and returns to the step (5).
The upper and lower layer scheduling models in the step (5) are specifically:
(a) upper optimization model
The upper layer model takes the minimum total running time of the main lane vehicle and the entrance ramp vehicle as an optimization target, and the objective function is as follows:
Figure BDA0002553377670000031
where n and m are the number of vehicles in the main lane concerned and the number of vehicles on the on-ramp road concerned, t0，iTime for the host lane vehicle i to enter the sequencing zone; t is t0，jThe time when the ramp vehicle j enters the sequencing zone; t is tmer,iFor vehicles i and t on the main lanemer,jThe optimal merging time for entering the merging area is determined for the entrance ramp vehicle j;
and (3) vehicle parameter constraint:
tmer,k＞Tmer,k(2)
in the formula, tmer,kThe time T of the kth intelligent networked vehicle entering the merging areamer,kThe time when the kth intelligent networked vehicle enters the limit merging area is the time when the kth intelligent networked vehicle enters the limit merging area;
restraint of safe vehicle distance on the same lane:
tmer,s1-tmer,s2≥tsafe1(3)
in the formula, tmer,s1And tmer,s2Respectively the time when the intelligent network connection vehicle enters the merging area on the same lane, tsafe1The time interval between adjacent confluent vehicles on the same lane can be different for the vehicles on the main lane and the vehicles on the entrance ramp;
restraint of safe vehicle distance on different lanes:
tmer,i-tmer,j+Cwi,j≥tsafe2(4)
tmer,j-tmer,i+C(1-wi,j)≥tsafe2(5)
in the formula, wi、jFor the introduced binary variable, only 0 or 1 can be obtained, and C is a constant large enough to be absolutely greater than tsafe2+|tmer,i-tmer,j|，tsafe2Is the safe time interval between adjacent merging vehicles on different lanes at the merging point.
(b) Lower model
The lower model is a heuristic vehicle trajectory planning method, and determines whether the vehicle can maintain the current speed vi or whether acceleration/deceleration is required to follow the distribution according to the merging time distributed to each vehicle by the upper layer and then the current state attribute of the vehicle. If the time to reach the merge point using the current speed is greater than the remaining time for the specified merge time, the vehicle needs to accelerate to reach the target, and vice versa. We assume that the vehicles have a constant acceleration/deceleration rate until they reach the desired cruising speed.
Further, the acceleration and deceleration pattern in the lower model is:
an acceleration mode:
if it is not
Figure BDA0002553377670000041
Figure BDA0002553377670000042
And (3) deceleration mode:
if it is not
Figure BDA0002553377670000043
Figure BDA0002553377670000044
The invention has the beneficial effects that:
(1) considering the balance between the calculation load and the optimality, the optimality is improved to a great extent and the calculation amount is reduced to the maximum extent through the form of an upper-layer optimization model and a lower-layer analytical solution;
(2) the upper layer determines and distributes the time for each intelligent network vehicle to enter a merging area, and the lower layer determines the specific motion track of each intelligent network vehicle, so that the improvement of traffic efficiency and energy utilization rate of ramp merging is facilitated;
(3) through the lower-layer movement track planning, a central controller or an intelligent networked vehicle local calculation module can be flexibly selected to calculate according to the actual condition of infrastructure;
(4) through lower-layer calculation, the intelligent networked vehicles which cannot meet the minimum time interval return to upper-layer scheduling again for scheduling calculation, so that the system has robustness.
Drawings
FIG. 1 is a framework of intelligent networked vehicle ramps incorporating cooperative control in accordance with the present invention;
FIG. 2 is a diagram of a model of a highway ramp merge in accordance with the present invention;
in the figure, 1 denotes an intelligent networked vehicle on a main road, 2 denotes an intelligent networked vehicle on a ramp, 3 denotes a merging area, and 4 denotes a communication infrastructure.
Fig. 3 is a flow chart of a double-layer cooperative optimization method for merging the internet vehicle ramps.
Detailed Description
The following describes in detail a specific embodiment of a double-layer cooperative optimization method for merging network vehicle ramps according to the present invention with reference to the accompanying drawings:
fig. 1 is a framework of intelligent networked vehicle ramp merging cooperative control, which mainly includes four modules and a model: the system comprises a vehicle clustering module, a vehicle merging sequence coordination module, a vehicle motion track planning layer, an infrastructure communication module and a ramp merging traffic model.
And a perfect communication network is provided between the modules and the models so as to carry out necessary information interaction. The vehicle clustering module and the vehicle merging sequence coordination module collect vehicle parameters and real-time information in the ramp merging traffic model through the infrastructure communication module, and solve through a clustering program and an optimization program to obtain an optimal merging sequence of vehicles and optimal time for entering a merging area; and then transmitting the result to a vehicle motion track planning layer, and transmitting the result to a ramp merging traffic model through an infrastructure communication module through calculation to control the specific motion of the vehicle. As shown in fig. 2, the actual road conditions corresponding to the above processes are calculated by the vehicle clustering module, the vehicle merging sequence coordination module and the vehicle motion trajectory planning module, and will occur before the merging region. The specific ramp merging process occurs in the merging area.
The following description is made for a specific control method:
an upper layer and a lower layer scheduling optimization model are established based on a framework of intelligent networked vehicle ramp merging cooperative control, the upper layer determines and distributes the time of each intelligent networked vehicle entering a merging area, and the lower layer determines the specific motion track of each intelligent networked vehicle according to an upper layer scheduling plan.
(1) Upper optimization model
The upper layer model takes the minimum total running time of the main lane vehicle and the entrance ramp vehicle as an optimization target, and the objective function is as follows:
Figure BDA0002553377670000061
where n and m are the number of vehicles in the main lane concerned and the number of vehicles on the on-ramp road concerned, t0，iTime for the host lane vehicle i to enter the sequencing zone; t is t0，jThe time when the ramp vehicle j enters the sequencing zone; t is tmer,iFor vehicles i and t on the main lanemer,jThe optimal merging time for entering the merging area is determined for the entrance ramp vehicle j; assume that the vehicle enters the sequence area following a poisson distribution with parameter 3.
And (3) vehicle parameter constraint:
tmer,k＞Tmer,k(2)
in the formula, tmer,kIs as followsTime of k intelligent networked vehicles entering the merging area, Tmer,kFor the time when the kth intelligent network connection vehicle enters the limit merging area, assuming Tmer,kThe value is 2.23 seconds;
restraint of safe vehicle distance on the same lane:
tmer,s1-tmer,s2≥tsafe1(3)
in the formula, tmer,s1And tmer,s2Respectively the time when the intelligent network connection vehicle enters the limit merging area on the same lane, tsafe1Is the time interval between adjacent merging vehicles on the same lane, which may be different for a main lane vehicle and an on-ramp vehicle, assuming t for the main lane vehicle and the on-ramp vehiclesafe10.8s and 1.2s, respectively.
Restraint of safe vehicle distance on different lanes:
tmer,i-tmer,j+Cwi,j≥tsafe2(4)
tmer,j-tmer,i+C(1-wi,j)≥tsafe2(5)
in the formula, wi，jFor the introduced binary variable, only 0 or 1 can be taken, C is a large enough constant, specifically 3000, tsafe2Is the safe time interval between adjacent merging vehicles on different lanes at the merging point, assuming tsafe2The value was 1.8 s. And optimizing an upper-layer model objective function F by using a branch-and-bound algorithm and transmitting the obtained scheduling plan M to a lower-layer motion planner.
(2) Lower model
The lower model is a heuristic vehicle trajectory planning method, and determines whether the vehicle can maintain the current speed vi or whether acceleration/deceleration is required to follow the distribution according to the merging time distributed to each vehicle by the upper layer and then the current state attribute of the vehicle. If the time to reach the merge point using the current speed is greater than the remaining time for the specified merge time, the vehicle needs to accelerate to reach the target, and vice versa. We assume that the vehicle has a constant acceleration/deceleration rate of 2.5m/s2And-2.5 m/s2Until they reach the desired cruising speed.
Further, the acceleration and deceleration pattern in the lower model is:
an acceleration mode:
if it is not
Figure BDA0002553377670000071
Figure BDA0002553377670000072
And (3) deceleration mode:
if it is not
Figure BDA0002553377670000073
Figure BDA0002553377670000081
Fig. 3 is a double-layer cooperative optimization method for merging the internet vehicle ramps. And solving the double-layer optimization model by adopting a hierarchical periodicity method, wherein the solution period is assumed to be 2.4 s. And planning the intelligent networked vehicles which continuously enter the ramp merging road section through periodic calculation between the upper layer model and the lower layer model. The upper layer model is calculated and solved through a branch-and-bound method, the lower layer model is a heuristic vehicle trajectory planning method, and whether the vehicle can keep the current speed or needs to accelerate/decelerate to follow distribution or not is determined according to the combination time distributed to each vehicle by the upper layer and the current state attribute of the vehicle. If the time to keep the current speed to reach the merge point is greater than the remaining time for the specified merge time, the vehicle needs to accelerate to reach the target, and vice versa. We assume that the vehicles have a constant rate of acceleration or deceleration until they reach the desired cruising speed. According to the above embodiment, the results of the simulation are compared with the results of the existing simulation based on the "first-in-first-out" method, as shown in the following table.
Figure BDA0002553377670000082
Therefore, the method provided by the invention not only improves the traffic efficiency by about 21%, but also reduces the energy consumption by 29.07%.

Claims (1)

1. A double-layer collaborative optimization method for merging network vehicle ramps is characterized by comprising the following steps:
step 1: determining a framework of intelligent networking vehicle ramp merging cooperative control: the system comprises a vehicle clustering module, a vehicle merging sequence coordination module, a vehicle motion track planning layer, an infrastructure communication module and a ramp merging traffic model;
step 2: dividing a ramp merging road section into a sequence area, a control area and a merging area, initializing an intelligent internet vehicle serial number id based on a first-in first-out rule entering the sequence area, and sequentially setting id to be 0, 1, 2 … … and N;
and step 3: collecting intelligent networked vehicle parameters, real-time speed information and real-time position information;
and 4, step 4: clustering the vehicles based on potential conflicts of the vehicles at the merging points by sourcing the group of the initial states of the vehicles;
and 5: an upper and lower layer scheduling optimization model is established based on a framework of intelligent networked vehicle ramp merging cooperative control, the upper layer determines and distributes the time of each intelligent networked vehicle entering a merging area, and the lower layer determines the specific motion track of each intelligent networked vehicle according to an upper layer scheduling plan:
the upper and lower layers of scheduling models in the step 5 are specifically:
(a) upper optimization model
The upper layer model takes the minimum total running time of the main lane vehicle and the entrance ramp vehicle as an optimization target, and the objective function is as follows:
Figure FDA0002553377660000011
where n and m are the number of vehicles in the main lane concerned and the number of vehicles on the on-ramp road concerned, t0,iTime for the host lane vehicle i to enter the sequencing zone; t is t0，jThe time when the ramp vehicle j enters the sequencing zone; t is tmer,iFor vehicles i and t on the main lanemer,jThe optimal merging time for entering the merging area is determined for the entrance ramp vehicle j;
and (3) vehicle parameter constraint:
tmer,k＞Tmer,k(2)
in the formula, tmer,kThe time T of the kth intelligent networked vehicle entering the merging areamer,kThe time when the kth intelligent networked vehicle enters the limit merging area is the time when the kth intelligent networked vehicle enters the limit merging area;
restraint of safe vehicle distance on the same lane:
tmer,s1-tmer,s2≥tsafe1(3)
in the formula, tmer,s1And tmer,s2Respectively the time when the intelligent network connection vehicle enters the merging area on the same lane, tsafe1The time interval between adjacent confluent vehicles on the same lane can be different for the vehicles on the main lane and the vehicles on the entrance ramp;
restraint of safe vehicle distance on different lanes:
tmer,i-tmer,j+Cwi,j≥tsafe2(4)
tmer,j-tmer,i+C(1-wi,j)≥tsafe2(5)
in the formula, wi、jFor the introduced binary variable, only 0 or 1 can be obtained, and C is a constant large enough to be absolutely greater than tsafe2+|tmer,i-tmer,j|，tsafe2Is the safe time interval between adjacent merging vehicles on different lanes on the merging point;
(b) lower model
The acceleration and deceleration mode in the lower model is as follows:
an acceleration mode:
if it is not
Figure FDA0002553377660000021
Figure FDA0002553377660000022
And (3) deceleration mode:
if it is not
Figure FDA0002553377660000023
Figure FDA0002553377660000024
Step 6: when a vehicle enters a sequence area, optimizing an upper model objective function F by adopting a branch-and-bound algorithm;
and 7: the vehicle enters a control area, and the obtained scheduling plan M is transmitted to a lower-layer motion planner;
and 8: according to the parameters, real-time information and upper-layer scheduling information of the intelligent networked vehicle with the serial number id, judging whether the vehicle needs to be accelerated or decelerated:
and step 9: if acceleration and deceleration are needed, entering an acceleration and deceleration mode, reporting a completed vehicle speed configuration file and a current required speed after completion, and keeping the current speed;
step 10: if acceleration and deceleration are not required, the vehicle keeps the current speed;
step 11: judging whether the time interval of the intelligent networked vehicles with the serial numbers id is smaller than a threshold value or not;
step 12: if so, the vehicle enters a following mode;
step 13: if not, the vehicle ignores the upper-layer scheduling information and participates the upper-layer scheduling planning again and returns to the step 5.
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CN113011634B (en) * 2021-02-09 2024-03-22 北京工业大学 Intelligent network connection ramp merging method based on distributed optimal control
CN115083140A (en) * 2022-04-18 2022-09-20 同济大学 Special intelligent network-connected automobile expressway road management and control method, system and storage medium
CN115083140B (en) * 2022-04-18 2023-09-26 同济大学 Intelligent network-connected automobile expressway special road management and control method, system and storage medium
CN114863681A (en) * 2022-04-29 2022-08-05 上海理工大学 Vehicle track optimization method for collision elimination of main line entrance ramp confluence area

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