CN115578849B - Optimization method for centralized formation of automatic driving vehicles in special lane environment - Google Patents
Optimization method for centralized formation of automatic driving vehicles in special lane environment Download PDFInfo
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- CN115578849B CN115578849B CN202211189362.0A CN202211189362A CN115578849B CN 115578849 B CN115578849 B CN 115578849B CN 202211189362 A CN202211189362 A CN 202211189362A CN 115578849 B CN115578849 B CN 115578849B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- 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
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- 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
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
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- 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
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Abstract
The invention discloses an optimization method for centralized formation of automatic driving vehicles in a special lane environment, which comprises the following steps: acquiring journey path information of an automatic driving vehicle in an area; acquiring operation data of an automatic driving vehicle in an area; determining an automatic driving vehicle formation method; calculating the safety, efficiency and comfort cost of automatic driving vehicle formation; the total cost of the autonomous vehicle platoon is calculated and optimized. The method comprehensively considers the requirements on safety, efficiency and comfort of the automatic driving vehicle formation in the special road environment, so that the automatic driving vehicle formation method is more scientific and efficient, further provides safe and comfortable riding experience for passengers of the automatic driving vehicle, and improves the utilization efficiency of road resources.
Description
Technical Field
The invention relates to the technical field of intelligent traffic control, in particular to an optimization method for centralized formation of automatic driving vehicles in a special road environment.
Background
Since the 21 st century, automatic driving technology at home and abroad has been steadily advancing and has been in trend of practical use thanks to the development of sensor technology and information and communication technology. The automatic driving vehicles run in the special lanes to form a motorcade, so that the characteristics of low delay and high efficiency can be fully exerted, and the method has important significance for improving traffic safety, improving road resource utilization efficiency and promoting sustainable development.
The formation is the basis of the automatic driving of the train of vehicles, and the process involves overtaking and lane changing and is directly related to traffic safety conditions. Especially, when the highway is formed into a queue, the potential safety hazard is aggravated due to the influence of the speed of the vehicle. In addition, the order of vehicles in the train is a major factor affecting running efficiency and driving comfort.
According to the vehicle formation driving method disclosed in China patent publication No. 2020104139095, the application date 2020, and the month 05 and the day 15, the following vehicle information is received by a piloting vehicle, and the confirmed vehicle information is integrated and broadcast, so that the vehicle formation driving is realized. The Chinese patent with application number 2021107120606 and application date 2020, month and 25 discloses an intersection autonomous vehicle dispatching and controlling method based on vehicle formation, which mainly combines vehicles close to an intersection through positioning information to realize intersection autonomous vehicle dispatching.
In general, the existing researches lack consideration of safety, efficiency and comfort of the formation process, and neglect the influence of the formation sequence on traffic safety, running efficiency and driving comfort, so that a new formation optimization method is urgently needed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an optimization method for centralized formation of automatic driving vehicles in a special road environment, which takes the travel path planning, the speed limit requirement of a road along the way and the height, speed and position data of the automatic driving vehicles in a special road area of a highway as basic information to determine the formation method of the automatic driving vehicles, calculate the safety cost, the efficiency cost and the comfort cost of the corresponding formation method and calculate and optimize the total cost of the formation of the automatic driving vehicles. The invention uses a centralized method to optimize, provides safe and comfortable riding experience for passengers of the automatic driving vehicle, and improves the utilization efficiency of road resources.
In order to solve the technical problems, the invention adopts the following technical scheme:
an optimization method for centralized formation of automatic driving vehicles in a special lane environment comprises the following steps:
step 1, acquiring journey path information of an automatic driving vehicle in a specific area; the journey path information comprises journey path planning and road speed limit information;
step 2, acquiring running data of an automatic driving vehicle in a specific area; the operational data includes vehicle altitude, vehicle speed, and position data;
step 3, forming a queue of the automatic driving vehicles in the specific area range;
step 4, calculating the safety cost, the efficiency cost and the comfort cost of the automatic driving vehicle formation according to the travel path information and the operation data of the automatic driving vehicle formation;
and 5, calculating and optimizing the total cost of the automatic driving vehicle formation according to the safety cost, the efficiency cost and the comfort cost of the automatic driving vehicle formation.
As a further preferred aspect of the present invention, in step 3, the method for forming a queue of automatic driving vehicles includes:
step 3-1, collecting route planning information of the automatic driving vehicles within the range of 500m based on a navigation platform, and forming a queue for all vehicles with the planned driving mileage of more than or equal to 20km on the current special road;
step 3-2, numbering 1,2, …, n and n from front to back along the driving direction according to the longitudinal relative positions determined by the formation for all the automatic driving vehicles participating in the formation, wherein n is the number of the automatic driving vehicles participating in the formation;
and 3-3, in the formation process, the vehicles are sequentially accelerated to drive to the longitudinal relative positions represented by the serial numbers according to the serial numbers 1,2, … and n.
As a further preferred aspect of the present invention, in step 4, the method for calculating the safety cost for formation of the automatic driving vehicle is as follows:
wherein C is a Representing the safety costs of the formation of autonomous vehicles,an arithmetic average of all participating formation vehicle speeds at the start of formation; v m The speed unit is km/h for the highest speed limit of the special lane of the automatic driving vehicle; λ represents a constant coefficient obtained by experiment, and λ is 3;
the number of overtaking times of the automatic driving vehicle with the sequence number i in the formation process when the formation is finished; />When the formation starts, the autonomous vehicles with the representative number i are ordered from front to back in the driving direction according to the longitudinal relative position, +.>
As a further preferred aspect of the present invention, in step 4, the method for calculating the efficiency cost of the automatic driving vehicle formation is as follows:
wherein C is x Representing efficiency costs for automated driving vehicle formation, L i The unit is km for the driving mileage on the special road determined according to the path planning;alpha represents a constant coefficient obtained through experiments, and alpha is 1.
As a further preferred aspect of the present invention, in step 4, the method for calculating the comfort cost of the automated driving vehicle formation includes:
wherein C is s Indicating comfort cost of automatic driving vehicle formation, h i The height of the automatic driving vehicle is numbered i, and the unit is m;h is the time interval of the automatic driving vehicle, 1 is taken, and the unit is s; beta represents a constant coefficient obtained through experiments, and beta is 0.1.
As a further preferred aspect of the present invention, in step 5, the optimization method of the total cost of the automatic driving vehicle formation is:
C min ＝min(C 1 ,C 2 ,...,C n！-1 ,C n！ )，
wherein c=μ a C a +μ x C x +μ s C s C represents the total cost of the autonomous vehicle formation; mu (mu) a ,μ x ,μ s Weighting coefficients of 0.6, 0.25 and 0.15 are respectively taken for forming the safety cost, the efficiency cost and the comfort cost.
The invention has the following beneficial effects: the invention discloses an optimization method for centralized formation of automatic driving vehicles in a special road environment, which is used for determining an automatic driving vehicle formation method based on travel path information, height and operation data of the automatic driving vehicles, calculating the safety cost, efficiency cost and comfort cost of formation, and calculating and optimizing the total cost of automatic driving vehicle formation. The invention provides a scientific and reasonable formation method for the automatic driving vehicles from the system angle by integrating the characteristics of routes, roads and vehicles, improves the running efficiency and improves the comfort of drivers and passengers while improving the road safety.
Drawings
FIG. 1 is a flow chart of an optimization method for centralized formation of autonomous vehicles in a lane environment according to the present invention.
Detailed Description
In the description of the present invention, it should be understood that the terms "left", "right", "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and "first", "second", etc. do not indicate the importance of the components, and thus are not to be construed as limiting the present invention. The specific dimensions adopted in the present embodiment are only for illustrating the technical solution, and do not limit the protection scope of the present invention.
The invention will be described in further detail with reference to the accompanying drawings and specific preferred embodiments.
Preferred traffic embodiment: within a 500m range of a special lane of a certain automatic driving vehicle, three automatic driving vehicles run in the same direction, the speed limit of the special lane is known to be 120km/h, the travel path information and the running data of the three automatic driving vehicles are shown in table 1, and the relative position is positive and indicates that the three automatic driving vehicles are positioned in front of the running direction:
| vehicle name | Planning driving mileage of current road section | Speed limiting | Vehicle height | Relative position | Current vehicle speed |
| A | 60km | 120km/h | 3.5m | 0m | 100km/h |
| B | 50km | 120km/h | 2.8m | 100m | 110km/h |
| C | 40km | 120km/h | 1.5m | 200m | 120km/h |
TABLE 1 trip path information and operational data for automated guided vehicle formation
According to the information, the optimization method for the centralized formation of the automatic driving vehicles in the special road environment is adopted to calculate and optimize the total cost of the formation of the automatic driving vehicles. As shown in fig. 1:
step 1, acquiring journey path information of an automatic driving vehicle in a specific area; the journey path information comprises journey path planning and road speed limit information along the journey, and specific data are shown in table 1.
Step 2, acquiring running data of an automatic driving vehicle in a specific area; the operational data included vehicle height, vehicle speed and position data, and the specific data are shown in table 1.
And 3, forming a queue of the automatic driving vehicles in the specific area range.
Three automatic driving automobiles are all located in a 500m area, and the current planning driving mileage of a special road is more than 20km, so that the formation requirement is met; the longitudinal relative positions determined according to the formation plan, numbered from front to rear in the direction of travel are shown in table 2:
| numbering device | Plan 1 | Plan 2 | Plan 3 | Plan 4 | Plan 5 | Plan 6 |
| 1 | A | A | B | B | C | C |
| 2 | B | C | A | C | A | B |
| 3 | C | B | C | A | B | A |
TABLE 2 longitudinal relative position of automatic drive automobile formation
Step 4, according to the travel path information and the operation data of the automatic driving vehicle formation, calculating the corresponding safety cost C of each formation plan a Cost of efficiency C x Comfort cost C s Taking plan 5 as an example:
similarly, calculate the safety cost C of the remaining 5 formation plans a Cost of efficiency C x Comfort cost C s As shown in table 3:
| cost type | Plan 1 | Plan 2 | Plan 3 | Plan 4 | Plan 5 | Plan 6 |
| Safety cost | 0.92 | 0.61 | 0.61 | 0.31 | 0.31 | 0.00 |
| Cost of efficiency | 0.67 | 0.75 | 0.67 | 0.33 | 0.42 | 0.00 |
| Comfort cost | 0.60 | 0.60 | 0.60 | 0.39 | 0.21 | 0.00 |
TABLE 3 safety, efficiency and comfort costs for formation planning
And 5, calculating and optimizing the total cost of the automatic driving vehicle formation according to the safety cost, the efficiency cost and the comfort cost of the automatic driving vehicle formation.
Taking plan 5 as an example:
C 5 ＝μ a C a +μ x C x +μ s C s ＝0.31×0.6+0.42×0.25+0.21×0.15＝0.32
similarly, the total cost for the remaining 5 formation plans is calculated as shown in table 4:
| cost type | Plan 1 | Plan 2 | Plan 3 | Plan 4 | Plan 5 | Plan 6 |
| Total cost of | 0.81 | 0.64 | 0.62 | 0.33 | 0.32 | 0.00 |
TABLE 4 Total cost of formation plans
C min ＝min(C 1 ,C 2 ,...,C n！-1 ,C n！ )＝C 6 I.e. party corresponding to plan 6The method has the lowest cost, and 3 automatic driving automobiles are queued according to the order of C, B, A.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.
Claims (1)
1. An optimization method for centralized formation of automatic driving vehicles in a special lane environment is characterized by comprising the following steps of: the method comprises the following steps:
step 1, acquiring journey path information of an automatic driving vehicle in a specific area; the journey path information comprises journey path planning and road speed limit information;
step 2, acquiring running data of an automatic driving vehicle in a specific area; the operational data includes vehicle altitude, vehicle speed, and position data;
step 3, forming a queue of the automatic driving vehicles in a specific area range, wherein the automatic driving vehicle forming method comprises the following steps:
step 3-1, collecting route planning information of the automatic driving vehicles within the range of 500m based on a navigation platform, and forming a queue for all vehicles with the planned driving mileage of more than or equal to 20km on the current special road;
step 3-2, numbering 1,2, …, n and n from front to back along the driving direction according to the longitudinal relative positions determined by the formation for all the automatic driving vehicles participating in the formation, wherein n is the number of the automatic driving vehicles participating in the formation;
step 3-3, in the formation process, the vehicles are sequentially accelerated to run to the longitudinal relative positions represented by the serial numbers according to the serial numbers 1,2, … and n;
step 4, calculating the safety cost, the efficiency cost and the comfort cost of the automatic driving vehicle formation according to the travel path information and the operation data of the automatic driving vehicle formation;
the calculation method of the automatic driving vehicle formation safety cost comprises the following steps:
c in the formula a Representing the safety costs of the formation of autonomous vehicles,an arithmetic average of all participating formation vehicle speeds at the start of formation; v m The speed unit is km/h for the highest speed limit of the special lane of the automatic driving vehicle; λ represents a constant coefficient obtained by experiment, and λ is 3;
the number of overtaking times of the automatic driving vehicle with the sequence number i in the formation process when the formation is finished; />When the formation starts, the autonomous vehicles with the representative number i are ordered from front to back in the driving direction according to the longitudinal relative position, +.> To find a temporary variable in the process, represent a 1 to i-1 traversal; />
The calculation method of the efficiency cost of the automatic driving vehicle formation comprises the following steps:
wherein C is x Representing efficiency costs for automated driving vehicle formation, L i The unit is km for the driving mileage on the special road determined according to the path planning;alpha represents a constant coefficient obtained through experiments, and alpha is 1;
the method for calculating the comfort cost of the automatic driving vehicle formation comprises the following steps:
wherein C is s Indicating comfort cost of automatic driving vehicle formation, h i The height of the automatic driving vehicle is numbered i, and the unit is m;h is the time interval of the automatic driving vehicle, 1 is taken, and the unit is s; beta represents a constant coefficient obtained through experiments, and beta is 0.1;
step 5, calculating and optimizing total cost of the automatic driving vehicle formation according to the safety cost, the efficiency cost and the comfort cost of the automatic driving vehicle formation;
the optimization method of the total cost of the automatic driving vehicle formation comprises the following steps:
C min ＝min(C 1 ,C 2 ,...,C n！-1 ,C n！ )，
wherein c=μ a C a +μ x C x +μ s C s C represents the total cost of the automated driving vehicle formation, C n！ A total cost of formation representing all the arrangements of the n autonomous vehicles; mu (mu) a ,μ x ,μ s Weighting coefficients of 0.6, 0.25 and 0.15 are respectively taken for forming the safety cost, the efficiency cost and the comfort cost.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109859456A (en) * | 2018-12-06 | 2019-06-07 | 浙江大学 | Platooning's initial scheme under car networking environment determines method |
| CN111275987A (en) * | 2020-01-21 | 2020-06-12 | 东南大学 | Automobile driving speed optimization method considering intersection queue influence |
| CN112258830A (en) * | 2020-10-23 | 2021-01-22 | 上海博泰悦臻电子设备制造有限公司 | Method for evaluating reliability of vehicle formation driving and application thereof |
| CN113591269A (en) * | 2021-06-29 | 2021-11-02 | 东南大学 | Special road control method for intelligent networked vehicles on congested road sections based on traffic simulation |
| CN113808436A (en) * | 2021-08-31 | 2021-12-17 | 东南大学 | Motorcade control method for off-ramp vehicles to leave intelligent internet dedicated lane |
| CN114419903A (en) * | 2021-12-17 | 2022-04-29 | 东南大学 | Intelligent network connection automobile queue intersection traffic control method and device and vehicle |
| CN114611911A (en) * | 2022-03-02 | 2022-06-10 | 博雷顿科技有限公司 | Multi-vehicle unmanned cooperative operation design method, algorithm system and vehicle |
| CN114783170A (en) * | 2022-05-17 | 2022-07-22 | 厦门金龙联合汽车工业有限公司 | Intelligent unmanned vehicle formation system |
| CN114973735A (en) * | 2022-05-10 | 2022-08-30 | 阿波罗智联(北京)科技有限公司 | Formation method, device, equipment, vehicle and medium for automatic driving vehicle |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200293960A1 (en) * | 2019-03-13 | 2020-09-17 | The Board Of Trustees Of The University Of Illinois | System for simulating commodities and emulating disruptions in domain networks |
-
2022
- 2022-09-28 CN CN202211189362.0A patent/CN115578849B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109859456A (en) * | 2018-12-06 | 2019-06-07 | 浙江大学 | Platooning's initial scheme under car networking environment determines method |
| CN111275987A (en) * | 2020-01-21 | 2020-06-12 | 东南大学 | Automobile driving speed optimization method considering intersection queue influence |
| CN112258830A (en) * | 2020-10-23 | 2021-01-22 | 上海博泰悦臻电子设备制造有限公司 | Method for evaluating reliability of vehicle formation driving and application thereof |
| CN113591269A (en) * | 2021-06-29 | 2021-11-02 | 东南大学 | Special road control method for intelligent networked vehicles on congested road sections based on traffic simulation |
| CN113808436A (en) * | 2021-08-31 | 2021-12-17 | 东南大学 | Motorcade control method for off-ramp vehicles to leave intelligent internet dedicated lane |
| CN114419903A (en) * | 2021-12-17 | 2022-04-29 | 东南大学 | Intelligent network connection automobile queue intersection traffic control method and device and vehicle |
| CN114611911A (en) * | 2022-03-02 | 2022-06-10 | 博雷顿科技有限公司 | Multi-vehicle unmanned cooperative operation design method, algorithm system and vehicle |
| CN114973735A (en) * | 2022-05-10 | 2022-08-30 | 阿波罗智联(北京)科技有限公司 | Formation method, device, equipment, vehicle and medium for automatic driving vehicle |
| CN114783170A (en) * | 2022-05-17 | 2022-07-22 | 厦门金龙联合汽车工业有限公司 | Intelligent unmanned vehicle formation system |
Non-Patent Citations (1)
| Title |
|---|
| 关于成品油公路运输道路交通安全考核的若干建议;郭松岭;;物流工程与管理(第04期);全文 * |
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