CN112750318A - Ramp confluence control method and system based on edge cloud - Google Patents

Ramp confluence control method and system based on edge cloud Download PDF

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CN112750318A
CN112750318A CN202011617118.0A CN202011617118A CN112750318A CN 112750318 A CN112750318 A CN 112750318A CN 202011617118 A CN202011617118 A CN 202011617118A CN 112750318 A CN112750318 A CN 112750318A
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confluence
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ramp
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CN112750318B (en
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童星
曹正彬
王建生
奚赵明
荣文
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Shandong High Speed Information Group Co Ltd
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Abstract

A ramp confluence control method and system based on edge cloud comprises the following steps: acquiring dynamic information of vehicles in a ramp control area; distributing the confluence sequence and confluence time according to the vehicle information; and planning the vehicle running track based on the confluence time and transmitting control information back to the vehicle. According to the method and the device, the ramp confluence control model based on the edge cloud is constructed, so that the efficient distribution of the passing sequence and the passing time of the vehicles passing through the main road and the ramp confluence area is realized, and finally, the shortest total passing time and the least oil consumption of the vehicles passing through the ramp confluence area are realized.

Description

Ramp confluence control method and system based on edge cloud
Technical Field
The application relates to a ramp confluence control method and system based on edge cloud.
Background
The automobile brings great convenience to people for traveling, but as the number of vehicles on the road increases, road traffic jam becomes more common and serious. Traffic jam reduces traffic efficiency, increases the risk of traffic accidents, increases travel time, brings discomfort to passengers, and causes excessive fuel consumption and tail gas emission. The junction of the ramps is one of the main causes of traffic congestion. Typically, a vehicle attempting to merge may decelerate at the ramp junction, wait for the appropriate merge entity, and look for safe vehicle separation and appropriate trunk vehicle speed to determine the degree and time of acceleration. The vehicles on the arterial road are decelerated to avoid sudden vehicles entering the ramp.
With the development of 5G communication, moving edge computing makes cooperative driving a reality. Cooperative driving is a promising method for improving traffic efficiency and safety, and cooperative confluence is to solve the coordination problem that vehicles on a main road and a ramp enter a confluence area and ensure that the vehicles safely and efficiently pass through the confluence area. The merging order of vehicles is a core issue of collaborative fusion. There is usually a trade-off between computational effort and optimization results, and using existing methods to determine the order of merging of optimizations usually requires a large amount of computation, where only the server can be relied upon, but this results in too poor real-time performance. Therefore, the main challenge today is how to find a method with a large number of vehicles to optimize the merging order in real time.
Disclosure of Invention
In order to solve the above problem, the present application discloses, on one hand, a ramp merging control method based on edge cloud, including the following steps:
obtaining vehicle information in a ramp control area;
distributing the confluence sequence and confluence time according to the vehicle information;
and planning the vehicle running track based on the confluence time and transmitting control information back to the vehicle. According to the method and the device, the ramp confluence control model based on the edge cloud is constructed, so that efficient distribution of sequence and time of main road vehicles and ramp vehicles passing through a confluence area is realized, and finally, the shortest total passing time and the least oil consumption of the vehicles passing through the ramp confluence area are realized.
Preferably, the ramp control area comprises a merging area, a main road and an auxiliary road, wherein the main road and the auxiliary road are connected to the ramp area, and the lengths of the main road and the auxiliary road are not less than 500 m.
Preferably, the confluence order and confluence time are determined as follows:
obtaining an initial merging sequence according to vehicle information and a first-in first-out based strategy;
adjusting the initial merging sequence based on the same-road priority strategy to obtain a corrected merging sequence;
and distributing the merging time for the vehicles in the ramp control area according to the corrected merging sequence.
Preferably, the initial merging order is obtained as follows:
assuming that the vehicle k runs at a constant speed in the control area, the initial speed of each vehicle G (G e G ═ 1, 2., | G | } on the main road
Figure BDA0002871559230000021
And initial distance from the clutch flow region
Figure BDA0002871559230000022
The initial speed of each vehicle R (R belongs to R { | G | +1, | G | +2, · M | + | G | } on the auxiliary road
Figure BDA0002871559230000023
And initial distance from the clutch flow region
Figure BDA0002871559230000024
According to the time of each vehicle reaching the merging area, obtaining an initial sequence L ═ { L ═ of the vehicles reaching the merging areai|i＝1，2...，|G|+|R|}。
Preferably, the modified merging order is obtained as follows:
during traversal of the initialized merge order, a swap operation occurs when the following conditions are satisfied:
when l isiAnd li-2For joining vehicles on a side road,/i-1And li+1If the following formula is satisfied for the main road vehicle, exchange li-1And liPosition in the merging order:
Figure BDA0002871559230000025
wherein
Figure BDA0002871559230000026
For a vehicle li-2The time from the initial speed to the confluence area at a constant speed,
Figure BDA0002871559230000027
for a vehicle liThe time of the fastest reaching the confluence area is calculated according to the following formula:
Figure BDA0002871559230000031
in the formula: v. ofmaxThe maximum speed of the vehicle allowed to pass on the road,
Figure BDA0002871559230000032
is the maximum acceleration allowed for the vehicle k,
Figure BDA0002871559230000033
the speed of the vehicle k reaching the confluence area;
when l isiAnd li-2For vehicles in the main lane,. li-1And li+1If the following formula is satisfied when a vehicle is to be connected to the auxiliary road, exchange li-1And liPosition in the merging order:
Figure BDA0002871559230000034
preferably, the vehicle assigned merging time is obtained as follows:
for the corrected merging sequence S ═ S { [ S ]iFirst vehicle s of | i ═ 1, 2., | G | + | R | }1If the vehicle is in the same lane as the last vehicle in the previous merging sequence, the pair is assigned to the vehicle s1Merging time ofAs shown in the following formula:
Figure BDA0002871559230000035
in the formula:
Figure BDA0002871559230000036
for vehicles s1The distributed merging time is K, and K is the last vehicle in the last merging sequence;
if the vehicle s1And the last vehicle in the last merging sequence is not in the same lane, and is allocated to the vehicle s1The combination time of (a) is shown as follows:
Figure BDA0002871559230000037
for the remaining vehicles in the merge order, if vehicle si-1And a vehicle siOn the same lane, to the assigned vehicle s1The combination time of (a) is shown as follows:
Figure BDA0002871559230000038
if the vehicle si-1And a vehicle siOn the same lane, to the assigned vehicle s1The combination time of (a) is shown as follows:
Figure BDA0002871559230000039
preferably, the time for the vehicles to reach the confluence area is obtained based on the confluence time and the oil consumption efficiency, and the driving track of each vehicle is planned, so that the oil consumption of each vehicle is minimum.
Preferably, the oil consumption is calculated according to the following formula:
Figure BDA0002871559230000041
in the formula:
Figure BDA0002871559230000042
and
Figure BDA0002871559230000043
for the real-time acceleration and speed of the vehicle k, q0～q3And p0～p3Is a constant term; the vehicle driving track planning problem with the fuel consumption model as the objective function is a constrained nonlinear optimization problem, and the requirement of vehicle real-time performance cannot be met due to huge calculation amount caused by non-convexity and nonlinearity of the vehicle driving track planning problem. The invention disperses the vehicle track planning problem into a dynamic planning problem to solve.
By setting the time step to Δ t, the velocity resolution to Δ v, and the acceleration resolution to Δ v, respectively
Figure BDA0002871559230000044
Discretizing a vehicle trajectory planning problem. For each vehicle k, from the initial time 0 to the merge time
Figure BDA0002871559230000045
Is divided into finite phases at Δ t time intervals;
initial state of each stage
Figure BDA0002871559230000046
Wherein
Figure BDA0002871559230000047
vminFor the road to allow a minimum speed of the passing vehicle,
Figure BDA0002871559230000048
the distance between the vehicle k and the clutch flow area at the moment t; the decision of each phase is the acceleration taken at the Δ t time interval
Figure BDA0002871559230000049
Wherein
Figure BDA00028715592300000410
The initial state and the decision of each stage affect the initial state of the next stage for the maximum deceleration allowed by the vehicle k, and the evaluation function corresponding to each decision is the reciprocal of the fuel consumption in the time interval of delta t and is shown by the following formula:
Figure BDA00028715592300000411
in the formula: t is twIs the initial moment of the current stage;
solving the dynamic planning model obtained in the step 2 by using an inverse sequence solution of dynamic planning to obtain the optimal running track plan of each vehicle, namely an acceleration decision sequence
Figure BDA00028715592300000412
Where W is the number of divided time segments. According to the method and the system, the planning of the vehicle tracks of the main road and the vehicles connected into the auxiliary road under the confluence scene of the automatic driving ramps is realized, and finally, the time and the oil consumption of the vehicles passing through the confluence area are reduced.
On the other hand, the application also discloses a control system of ramp confluence based on edge cloud, which comprises the following modules:
the local ramp monitoring module is used for acquiring vehicle information in a ramp control area;
and the mobile edge host is used for acquiring the vehicle information from the ramp local detection module, distributing the confluence sequence and confluence time according to the vehicle information, planning the vehicle running track based on the confluence time and returning the control information to the vehicle. According to the method, the vehicles are planned to be combined in sequence through the ramp confluence control, the time of the vehicles passing through the confluence area is distributed, the running track of each vehicle is planned, the vehicle control information is transmitted to the corresponding vehicle through the virtualization infrastructure, and therefore the total passing time and the oil consumption of the vehicles passing through the confluence area are reduced.
Preferably, the mobile edge host is locally configured.
This application can bring following beneficial effect:
1. according to the method, the ramp confluence control model based on the edge cloud is constructed, so that efficient distribution of sequence and time of main road vehicles and ramp vehicles passing through a confluence area is realized, and finally, the shortest total passing time and the least oil consumption of the vehicles passing through the ramp confluence area are realized;
2. the method comprises the steps of planning vehicle merging sequence through ramp confluence control, distributing the time of vehicles passing through a confluence area, planning the running track of each vehicle, and transmitting vehicle control information to corresponding vehicles through a virtualization infrastructure, so as to reduce the total passing time and oil consumption of the vehicles passing through the confluence area;
3. the vehicle driving track planning problem with the oil consumption model as the objective function is a constrained nonlinear optimization problem, the vehicle real-time requirement cannot be met due to huge calculation amount caused by non-convexity and nonlinearity of the vehicle driving track planning problem, the vehicle driving track planning problem is dispersed into a dynamic planning problem to be solved so as to realize the characteristic that real-time edge optimization can be carried out by locally moving an edge host, and dependence on a central server is eliminated as far as possible.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic illustration of the present application as practiced in the field;
FIG. 2 is a schematic flow chart of the first embodiment;
FIG. 3 is a flow chart showing the merging order and merging time in the first embodiment;
FIG. 4 is a schematic view of a flow section of a second embodiment.
Detailed Description
In order to clearly explain the technical features of the present invention, the present application will be explained in detail by the following embodiments in combination with the accompanying drawings.
In a first embodiment, as shown in figures 1-3,
s101, obtaining vehicle information in a ramp control area;
the method can be obtained through video identification, GPS, Internet of things and other modes;
s102, distributing the confluence sequence and confluence time according to the vehicle information;
the situation that two vehicles passing through the confluence area in sequence come from different roads is more complicated than that of the same road, namely, the headways delta t of the vehicles from different roads2Greater than the headway deltat from the same road vehicle1. Alternating confluence from two roads (i.e., one vehicle from the main road and one vehicle from the ramp) will result in more transit time and travel delays. Thus, it is possible to allow a plurality of vehicles from one road to pass through the merging zone first and then let a group of vehicles from another road pass through later, avoiding alternate merging, so as to minimize the total passing time and travel delay.
S1021, obtaining an initial merging sequence according to vehicle information and a first-in first-out based strategy;
assuming that the vehicle k runs at a constant speed in the control area, the initial speed of each vehicle G (G e G ═ 1, 2., | G | } on the main road
Figure BDA0002871559230000061
And initial distance from the clutch flow region
Figure BDA0002871559230000062
The initial speed of each vehicle R (R belongs to R { | G | +1, | G | +2, · M | + | G | } on the auxiliary road
Figure BDA0002871559230000063
And initial distance from the clutch flow region
Figure BDA0002871559230000064
According to the time of each vehicle reaching the merging area, obtaining an initial sequence L ═ { L ═ of the vehicles reaching the merging areai|i＝1，2...，|G|+|R|}。
S1022, adjusting the sequence of the initial merging sequence based on the same-road priority strategy to obtain a corrected merging sequence;
during traversal of the initialized merge order, a swap operation occurs when the following conditions are satisfied:
when l isiAnd li-2For joining vehicles on a side road,/i-1And li+1If the following formula is satisfied for the main road vehicle, exchange li-1And liPosition in the merging order:
Figure BDA0002871559230000071
wherein
Figure BDA0002871559230000072
For a vehicle li-2The time from the initial speed to the confluence area at a constant speed,
Figure BDA0002871559230000073
for a vehicle liThe time of the fastest reaching the confluence area is calculated according to the following formula:
Figure BDA0002871559230000074
in the formula: v. ofmaxThe maximum speed of the vehicle allowed to pass on the road,
Figure BDA0002871559230000075
is the maximum acceleration allowed for the vehicle k,
Figure BDA0002871559230000076
the speed of the vehicle k reaching the confluence area;
when l isiAnd li-2For vehicles in the main lane,. li-1And li+1If the following formula is satisfied when a vehicle is to be connected to the auxiliary road, exchange li-1And liPosition in the merging order:
Figure BDA0002871559230000077
and S1023, according to the corrected merging sequence, allocating merging time to the vehicles in the ramp control area.
The vehicle distribution confluence time is obtained according to the following method:
for the corrected merging sequence S ═ S { [ S ]iFirst vehicle s of | i ═ 1, 2., | G | + | R | }1If the vehicle is in the same lane as the last vehicle in the previous merging sequence, the pair is assigned to the vehicle s1The combination time of (a) is shown as follows:
Figure BDA0002871559230000078
in the formula:
Figure BDA0002871559230000079
for vehicles s1The distributed merging time is K, and K is the last vehicle in the last merging sequence;
if the vehicle s1And the last vehicle in the last merging sequence is not in the same lane, and is allocated to the vehicle s1The combination time of (a) is shown as follows:
Figure BDA00028715592300000710
for the remaining vehicles in the merge order, if vehicle si-1And a vehicle siOn the same lane, to the assigned vehicle s1The combination time of (a) is shown as follows:
Figure BDA0002871559230000081
if the vehicle si-1And a vehicle siOn the same lane, to the assigned vehicle s1The combination time of (a) is shown as follows:
Figure BDA0002871559230000082
and S103, planning the vehicle running track based on the confluence time and the oil consumption efficiency.
The oil consumption is calculated according to the following formula:
Figure BDA0002871559230000083
in the formula:
Figure BDA0002871559230000084
and
Figure BDA0002871559230000085
for the real-time acceleration and speed of the vehicle k, q0～q3And p0～p3Is a constant term; the vehicle driving track planning problem with the fuel consumption model as the objective function is a constrained nonlinear optimization problem, and the requirement of vehicle real-time performance cannot be met due to huge calculation amount caused by non-convexity and nonlinearity of the vehicle driving track planning problem. The invention disperses the vehicle track planning problem into a dynamic planning problem to solve.
By setting the time step to Δ t, the velocity resolution to Δ v, and the acceleration resolution to Δ v, respectively
Figure BDA0002871559230000086
Discretizing a vehicle trajectory planning problem. For each vehicle k, from the initial time 0 to the merge time
Figure BDA0002871559230000087
Is divided into finite phases at Δ t time intervals;
initial state of each stage
Figure BDA0002871559230000088
Wherein
Figure BDA0002871559230000089
vminVehicle allowing traffic for roadThe minimum speed of the vehicle is set by the speed of the vehicle,
Figure BDA00028715592300000810
the distance between the vehicle k and the clutch flow area at the moment t; the decision of each phase is the acceleration taken at the Δ t time interval
Figure BDA00028715592300000811
Wherein
Figure BDA00028715592300000812
The initial state and the decision of each stage affect the initial state of the next stage for the maximum deceleration allowed by the vehicle k, and the evaluation function corresponding to each decision is the reciprocal of the fuel consumption in the time interval of delta t and is shown by the following formula:
Figure BDA00028715592300000813
in the formula: t is twIs the initial moment of the current stage;
solving the dynamic planning model obtained in the step 2 by using an inverse sequence solution of dynamic planning to obtain the optimal running track plan of each vehicle, namely an acceleration decision sequence
Figure BDA00028715592300000814
Where W is the number of divided time segments.
In a second embodiment, as shown in fig. 4, a control system for edge cloud-based ramp merging includes the following modules: the ramp local monitoring module 201 is used for acquiring vehicle information in a ramp control area; and the mobile edge host 202 is used for acquiring the vehicle information from the ramp local detection module, distributing the confluence sequence and confluence time according to the vehicle information, planning the vehicle running track based on the confluence time and returning the control information to the vehicle. The mobile edge host is locally configured.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A ramp confluence control method based on edge cloud is characterized in that: the method comprises the following steps:
obtaining vehicle information in a ramp control area;
distributing the confluence sequence and confluence time according to the vehicle information;
and planning the vehicle running track based on the confluence time and the fuel consumption efficiency.
2. The ramp confluence control method based on the edge cloud as claimed in claim 1, wherein: the ramp control area comprises a confluence area, a main road and an auxiliary road, wherein the main road and the auxiliary road are connected to the ramp area, and the lengths of the main road and the auxiliary road are not less than 500 m.
3. The ramp confluence control method based on the edge cloud as claimed in claim 2, wherein: the confluence sequence and confluence time are determined as follows:
obtaining an initial merging sequence according to vehicle information and a first-in first-out based strategy;
adjusting the initial merging sequence based on the same-road priority strategy to obtain a corrected merging sequence;
and distributing the merging time for the vehicles in the ramp control area according to the corrected merging sequence.
4. The edge cloud-based ramp confluence control method according to claim 3, wherein:
the initial merging order is obtained as follows:
assuming that the vehicle k runs at a constant speed in the control area, the initial speed of each vehicle G (G e G ═ 1, 2., | G | } on the main road
Figure FDA0002871559220000011
And initial distance from the clutch flow region
Figure FDA0002871559220000012
The initial speed of each vehicle R (R belongs to R { | G | +1, | G | +2, · M | + | G | } on the auxiliary road
Figure FDA0002871559220000013
And initial distance from the clutch flow region
Figure FDA0002871559220000014
According to the time of each vehicle reaching the merging area, obtaining an initial sequence L ═ { L ═ of the vehicles reaching the merging areai|i＝1，2...，|G|+|R|}。
5. The edge cloud-based ramp confluence control method according to claim 4, wherein:
the modified merging order is obtained as follows:
during traversal of the initialized merge order, a swap operation occurs when the following conditions are satisfied:
when l isiAnd li-2For joining vehicles on a side road,/i-1And li+1If the following formula is satisfied for the main road vehicle, exchange li-1And liPosition in the merging order:
Figure FDA0002871559220000021
wherein
Figure FDA0002871559220000022
For a vehicle li-2The time from the initial speed to the confluence area at a constant speed,
Figure FDA0002871559220000023
for a vehicle liWhen the fastest reaches the merging areaThe calculation method is shown as the following formula:
Figure FDA0002871559220000024
in the formula: v. ofmaxThe maximum speed of the vehicle allowed to pass on the road,
Figure FDA0002871559220000025
is the maximum acceleration allowed for the vehicle k,
Figure FDA0002871559220000026
the speed of the vehicle k reaching the confluence area;
when l isiAnd li-2For vehicles in the main lane,. li-1And li+1If the following formula is satisfied when a vehicle is to be connected to the auxiliary road, exchange li-1And liPosition in the merging order:
Figure FDA0002871559220000027
6. the edge cloud-based ramp confluence control method according to claim 5, wherein:
the vehicle distribution confluence time is obtained according to the following method:
for the corrected merging sequence S ═ S { [ S ]iFirst vehicle s of | i ═ 1, 2., | G | + | R | }1If the vehicle is in the same lane as the last vehicle in the previous merging sequence, the pair is assigned to the vehicle s1The combination time of (a) is shown as follows:
Figure FDA0002871559220000028
in the formula:
Figure FDA0002871559220000029
for vehicles s1The distributed merging time is K, and K is the last vehicle in the last merging sequence;
if the vehicle s1And the last vehicle in the last merging sequence is not in the same lane, and is allocated to the vehicle s1The combination time of (a) is shown as follows:
Figure FDA0002871559220000031
for the remaining vehicles in the merge order, if vehicle si-1And a vehicle siOn the same lane, to the assigned vehicle s1The combination time of (a) is shown as follows:
Figure FDA0002871559220000032
if the vehicle si-1And a vehicle siOn the same lane, to the assigned vehicle s1The combination time of (a) is shown as follows:
Figure FDA0002871559220000033
7. the ramp confluence control method based on the edge cloud as claimed in claim 1, wherein: and obtaining the time of the vehicles reaching the confluence area based on the confluence time, and planning the driving track of each vehicle to minimize the oil consumption of each vehicle.
8. The edge cloud-based ramp confluence control method according to claim 7, wherein: the oil consumption is calculated according to the following formula:
Figure FDA0002871559220000034
in the formula:
Figure FDA0002871559220000035
and
Figure FDA0002871559220000036
for the real-time acceleration and speed of the vehicle k, q0～q3And p0～p3Is a constant term;
by setting the time step to Δ t, the velocity resolution to Δ v, and the acceleration resolution to Δ v, respectively
Figure FDA0002871559220000037
Discretizing a vehicle trajectory planning problem; for each vehicle k, from the initial time 0 to the merge time
Figure FDA0002871559220000038
Is divided into finite phases at Δ t time intervals;
initial state of each stage
Figure FDA0002871559220000039
Wherein
Figure FDA00028715592200000310
vminFor the road to allow a minimum speed of the passing vehicle,
Figure FDA00028715592200000311
the distance between the vehicle k and the clutch flow area at the moment t; the decision of each phase is the acceleration taken at the Δ t time interval
Figure FDA00028715592200000312
Wherein
Figure FDA00028715592200000313
Maximum deceleration allowed for vehicle k, initial state and decision of each phaseIn the initial state of a stage, the evaluation function corresponding to each decision is the reciprocal of the oil consumption in the time interval of delta t, and is represented by the following formula:
Figure FDA00028715592200000314
in the formula: t is twIs the initial moment of the current stage;
solving the dynamic planning model obtained in the step 2 by using an inverse sequence solution of dynamic planning to obtain the optimal running track plan of each vehicle, namely an acceleration decision sequence
Figure FDA0002871559220000041
Where W is the number of divided time segments.
9. A ramp confluence control system based on edge cloud is characterized in that: the system comprises the following modules:
the local ramp monitoring module is used for acquiring vehicle information in a ramp control area;
and the mobile edge host is used for acquiring the vehicle information from the ramp local detection module, distributing the confluence sequence and confluence time according to the vehicle information, planning the vehicle running track based on the confluence time and returning the control information to the vehicle.
10. The edge cloud-based ramp confluence control device of claim 9, wherein: the mobile edge host is locally configured.
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CN114067559A (en) * 2021-09-27 2022-02-18 北京交通大学 Confluence optimization control method for merging special lane for automatic vehicle into common lane
CN114999152A (en) * 2022-05-25 2022-09-02 清华大学 Ramp confluence edge cloud control method facing mixed traffic flow
CN115641717A (en) * 2022-10-19 2023-01-24 长沙理工大学 Highway main line-ramp vehicle cooperative confluence control method and device based on mixed traffic flow and storage medium
CN115830885A (en) * 2022-11-29 2023-03-21 南京理工大学 Vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption
CN118012035A (en) * 2022-11-03 2024-05-10 北京三快在线科技有限公司 Control method and device of unmanned equipment

Citations (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003313824A (en) * 2002-04-25 2003-11-06 Kajima Corp Construction method of overhead crossing, overhead crossing and moving frame
CN102157072A (en) * 2011-03-29 2011-08-17 北京航空航天大学 Inducing device and inducing method of vehicle confluence at intersection based on vehicle and road collaboration
JP5490295B1 (en) * 2013-08-02 2014-05-14 株式会社タスカーマリーナサービス Variable slope levitated slope pier
CN104464317A (en) * 2014-12-03 2015-03-25 武汉理工大学 Expressway entrance ring road converging zone guiding control system and method
JP2018044833A (en) * 2016-09-14 2018-03-22 日産自動車株式会社 Automatic drive support method and apparatus
CN108538069A (en) * 2018-05-24 2018-09-14 长安大学 A kind of ring road merging area car speed managing and control system and method
CN108806291A (en) * 2018-07-27 2018-11-13 东南大学 High saturation ring road vehicle based on roadside device imports bootstrap technique and system
CN108806252A (en) * 2018-06-19 2018-11-13 西南交通大学 A kind of Mixed Freeway Traffic Flows collaboration optimal control method
CN108986492A (en) * 2018-07-27 2018-12-11 东南大学 A kind of fast road ramp wagon flow remittance bootstrap technique and system based on roadside device
CN108986488A (en) * 2018-07-23 2018-12-11 东南大学 Ring road imports collaboration track and determines method and apparatus under a kind of truck traffic environment
CN109598950A (en) * 2018-12-04 2019-04-09 东南大学 A kind of the ring road collaboration remittance control method and system of intelligent network connection vehicle
JP2019067280A (en) * 2017-10-04 2019-04-25 パイオニア株式会社 Control device, control method, and control program
CN110930697A (en) * 2019-11-12 2020-03-27 南京航空航天大学 Rule-based intelligent networked vehicle cooperative convergence control method
CN111192455A (en) * 2020-01-10 2020-05-22 西南交通大学 Traffic flow organization optimization method for urban expressway intersection area
CN111243296A (en) * 2020-01-15 2020-06-05 清华大学 Ramp confluence cooperative control method and system based on confluence time optimization
CN111310603A (en) * 2020-01-19 2020-06-19 南京瑞芯科技有限公司 High-performance intelligent road side system based on edge cloud
CN111325967A (en) * 2020-02-28 2020-06-23 清华大学 Intelligent networking automobile formation control method and device based on cooperative assignment
CN111369813A (en) * 2020-03-23 2020-07-03 江苏大学 Ramp division and confluence cooperative control method and system for intelligent network-connected automobile
CN111447264A (en) * 2020-03-24 2020-07-24 腾讯科技（深圳）有限公司 Scheduling method, device and storage medium for edge cloud service
CN111599198A (en) * 2019-02-20 2020-08-28 阿里巴巴集团控股有限公司 Road side unit and road related prompt information issuing method and system
CN111815949A (en) * 2020-03-26 2020-10-23 同济大学 Branch vehicle afflux early warning system for main road traffic vehicles
CN111833628A (en) * 2019-04-18 2020-10-27 华为技术有限公司 Control method of unmanned vehicle and related device
CN111968377A (en) * 2020-08-31 2020-11-20 姜忠太 Vehicle network-based vehicle track optimization method for fuel saving and driving comfort
CN112116822A (en) * 2020-09-21 2020-12-22 长沙理工大学 Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation

Patent Citations (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003313824A (en) * 2002-04-25 2003-11-06 Kajima Corp Construction method of overhead crossing, overhead crossing and moving frame
CN102157072A (en) * 2011-03-29 2011-08-17 北京航空航天大学 Inducing device and inducing method of vehicle confluence at intersection based on vehicle and road collaboration
JP5490295B1 (en) * 2013-08-02 2014-05-14 株式会社タスカーマリーナサービス Variable slope levitated slope pier
CN104464317A (en) * 2014-12-03 2015-03-25 武汉理工大学 Expressway entrance ring road converging zone guiding control system and method
JP2018044833A (en) * 2016-09-14 2018-03-22 日産自動車株式会社 Automatic drive support method and apparatus
JP2019067280A (en) * 2017-10-04 2019-04-25 パイオニア株式会社 Control device, control method, and control program
CN108538069A (en) * 2018-05-24 2018-09-14 长安大学 A kind of ring road merging area car speed managing and control system and method
CN108806252A (en) * 2018-06-19 2018-11-13 西南交通大学 A kind of Mixed Freeway Traffic Flows collaboration optimal control method
CN108986488A (en) * 2018-07-23 2018-12-11 东南大学 Ring road imports collaboration track and determines method and apparatus under a kind of truck traffic environment
CN108986492A (en) * 2018-07-27 2018-12-11 东南大学 A kind of fast road ramp wagon flow remittance bootstrap technique and system based on roadside device
CN108806291A (en) * 2018-07-27 2018-11-13 东南大学 High saturation ring road vehicle based on roadside device imports bootstrap technique and system
CN109598950A (en) * 2018-12-04 2019-04-09 东南大学 A kind of the ring road collaboration remittance control method and system of intelligent network connection vehicle
CN111599198A (en) * 2019-02-20 2020-08-28 阿里巴巴集团控股有限公司 Road side unit and road related prompt information issuing method and system
CN111833628A (en) * 2019-04-18 2020-10-27 华为技术有限公司 Control method of unmanned vehicle and related device
CN110930697A (en) * 2019-11-12 2020-03-27 南京航空航天大学 Rule-based intelligent networked vehicle cooperative convergence control method
CN111192455A (en) * 2020-01-10 2020-05-22 西南交通大学 Traffic flow organization optimization method for urban expressway intersection area
CN111243296A (en) * 2020-01-15 2020-06-05 清华大学 Ramp confluence cooperative control method and system based on confluence time optimization
CN111310603A (en) * 2020-01-19 2020-06-19 南京瑞芯科技有限公司 High-performance intelligent road side system based on edge cloud
CN111325967A (en) * 2020-02-28 2020-06-23 清华大学 Intelligent networking automobile formation control method and device based on cooperative assignment
CN111369813A (en) * 2020-03-23 2020-07-03 江苏大学 Ramp division and confluence cooperative control method and system for intelligent network-connected automobile
CN111447264A (en) * 2020-03-24 2020-07-24 腾讯科技（深圳）有限公司 Scheduling method, device and storage medium for edge cloud service
CN111815949A (en) * 2020-03-26 2020-10-23 同济大学 Branch vehicle afflux early warning system for main road traffic vehicles
CN111968377A (en) * 2020-08-31 2020-11-20 姜忠太 Vehicle network-based vehicle track optimization method for fuel saving and driving comfort
CN112116822A (en) * 2020-09-21 2020-12-22 长沙理工大学 Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
KWANG-WOOK YUN: "Vehicular Acceleration Advisory Algorithm Using V2V Communication", 《I2018 TENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS》 *
罗孝羚: "智能网联车环境下高速匝道汇入车流轨迹优化模型", 《交通运输系统工程与信息》 *
陈兴: "自主重型车队的油耗优化问题研究", <中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑> *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114067559A (en) * 2021-09-27 2022-02-18 北京交通大学 Confluence optimization control method for merging special lane for automatic vehicle into common lane
CN114999152A (en) * 2022-05-25 2022-09-02 清华大学 Ramp confluence edge cloud control method facing mixed traffic flow
CN114999152B (en) * 2022-05-25 2024-04-30 清华大学 Ramp converging edge cloud control method for mixed traffic flow
CN115641717A (en) * 2022-10-19 2023-01-24 长沙理工大学 Highway main line-ramp vehicle cooperative confluence control method and device based on mixed traffic flow and storage medium
CN115641717B (en) * 2022-10-19 2023-09-19 长沙理工大学 Highway main line-ramp vehicle cooperative confluence control method, device and storage medium based on mixed traffic flow
CN118012035A (en) * 2022-11-03 2024-05-10 北京三快在线科技有限公司 Control method and device of unmanned equipment
CN115830885A (en) * 2022-11-29 2023-03-21 南京理工大学 Vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption
CN115830885B (en) * 2022-11-29 2024-05-07 南京理工大学 Vehicle ramp confluence cooperative control method considering energy consumption of multiple vehicle types

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