CN115578849A - Optimization method for centralized formation of automatic driving vehicles in special road environment - Google Patents

Optimization method for centralized formation of automatic driving vehicles in special road environment Download PDF

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CN115578849A
CN115578849A CN202211189362.0A CN202211189362A CN115578849A CN 115578849 A CN115578849 A CN 115578849A CN 202211189362 A CN202211189362 A CN 202211189362A CN 115578849 A CN115578849 A CN 115578849A
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CN115578849B (en
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董长印
王昊
刘云杰
陈雨佳
吕科赟
尹芳至
熊卓智
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    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/052Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/09Arrangements for giving variable traffic instructions
    • G08G1/0962Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708Systems 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
    • G08G1/096725Systems 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
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/09Arrangements for giving variable traffic instructions
    • G08G1/0962Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096805Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/20Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/10Internal combustion engine [ICE] based vehicles
    • Y02T10/40Engine management systems

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Abstract

The invention discloses an optimization method for centralized formation of automatically driven vehicles in a special road environment, which comprises the following steps: acquiring the travel path information of an automatic driving vehicle in an area; acquiring operation data of an autonomous vehicle in an area; determining an autonomous vehicle formation method; calculating the safety, efficiency and comfort cost of the formation of the automatic driving vehicles; the total cost of the formation of autonomous vehicles is calculated and optimized. The method provided by the invention comprehensively considers the requirements of safety, efficiency and comfort of the formation of the automatic driving vehicles in the special road environment, so that the method for forming the automatic driving vehicles is more scientific and efficient, safe and comfortable riding experience is further provided for passengers of the automatic driving vehicles, and meanwhile, the utilization efficiency of road resources is improved.

Description

Optimization method for centralized formation of automatic driving vehicles in special road environment
Technical Field
The invention relates to the technical field of intelligent traffic control, in particular to an optimization method for automatic vehicle centralized formation under a special road environment.
Background
Since the 21 st century, the development of sensor technology and information and communication technology has been benefited, and domestic and foreign automatic driving technology has been steadily advanced and is showing a practical trend. The automatic driving vehicles form a motorcade to run on the special road, can fully play the characteristics of low time delay and high efficiency, and has important significance for improving traffic safety, improving road resource utilization efficiency and promoting sustainable development.
Formation is the basis for the driving of an autonomous vehicle train, which involves overtaking, changing lanes, and is directly related to traffic safety conditions. Particularly, when the expressway special lanes are formed into a team, the potential safety hazard is aggravated under the influence of the speed of the automobile. In addition, the sequencing of the vehicles in the fleet is a major factor affecting operating efficiency and driving comfort.
The method for driving the vehicles in formation is disclosed in Chinese patent application No. 2020104139095 filed 2020, 05, month 15, and is mainly characterized in that following vehicle information is received by a pilot vehicle, and the confirmed vehicle information is integrated and broadcasted, so that the vehicles are driven in formation. The Chinese patent with the application number of 2021107120606, the application date of 2020, 06 and 25 discloses a crossing autonomous vehicle scheduling and controlling method based on vehicle formation, and mainly realizes the crossing autonomous vehicle scheduling by merging the vehicles close to the crossing through positioning information.
Generally speaking, the existing researches lack the consideration on the safety, efficiency and comfort of the formation process, and the influence of the formation sequence on the traffic safety, the operation efficiency and the driving comfort is ignored, so that a new formation optimization method is urgently needed.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an optimization method for centralized formation of automatically driven vehicles in a special road environment aiming at the defects of the prior art, wherein the method for forming the automatically driven vehicles is determined by taking the travel path planning, the speed limit requirement of a road along the highway and the height, the speed and the position data of the automatically driven vehicles in a special road area of an expressway as basic information, the safety cost, the efficiency cost and the comfort cost of the corresponding formation method are calculated, and the total cost of forming the automatically driven vehicles is calculated and optimized. The invention is optimized by a centralized method, provides safe and comfortable riding experience for passengers who automatically drive the vehicle, and improves the utilization efficiency of road resources.
In order to solve the technical problems, the invention adopts the technical scheme that:
an optimization method for centralized formation of automatic driving vehicles in a special road environment comprises the following steps:
step 1, obtaining the travel path information of an automatic driving vehicle in a specific area; the travel path information comprises travel path planning and road speed limit information along the route;
step 2, obtaining the operation data of the automatic driving vehicle in the specific area; the operational data includes vehicle height, vehicle speed, and position data;
step 3, forming a team of the automatic driving vehicles in a specific area range;
step 4, calculating the safety cost, the efficiency cost and the comfort cost of the formation of the automatic driving vehicles according to the travel path information and the operation data of the formation of the automatic driving vehicles;
and 5, calculating and optimizing the total cost of the formation of the automatic driving vehicles according to the safety cost, the efficiency cost and the comfort cost of the formation of the automatic driving vehicles.
As a further preferred aspect of the present invention, in step 3, the method for formation of autonomous vehicles comprises:
3-1, collecting travel route planning information of the automatic driving vehicles within a range of 500m based on a navigation platform, and forming all vehicles with the planned driving range of more than or equal to 20km on the current special road;
3-2, numbering 1,2, \ 8230for all the automatic driving vehicles participating in formation from front to back along the driving direction according to the longitudinal relative position determined by the formation, wherein n and n are the number of the automatic driving vehicles participating in the formation;
and 3-3, accelerating the vehicles to the longitudinal opposite positions represented by the serial numbers sequentially according to the serial numbers 1,2, \ 8230;, n in the formation process.
As a further preferred aspect of the present invention, in step 4, the method for calculating the safety cost of the formation of the autonomous vehicles comprises:
Figure BDA0003868729140000021
in the formula, C a Represents a safety cost for the formation of autonomous vehicles,
Figure BDA0003868729140000022
the arithmetic mean value of the speeds of all the vehicles participating in formation when formation starts; v. of m The speed unit is km/h for the highest speed limit of the lane special for the automatic driving vehicle; lambda represents a constant coefficient obtained through experiments, and lambda is 3;
Figure BDA0003868729140000023
the overtaking times of the automatic driving vehicles with the sequence number i in the formation process when the formation is finished;
Figure BDA0003868729140000024
when the formation begins, the automatic driving vehicles with the representative serial number i are sequenced from front to back along the driving direction according to the longitudinal relative position,
Figure BDA0003868729140000025
as a further preferred aspect of the present invention, in step 4, the method for calculating the efficiency cost of the formation of the autonomous vehicles comprises:
Figure BDA0003868729140000026
in the formula, C x Representing the efficiency cost of autonomous vehicle formation, L i The unit of the driving mileage on the special road is km determined according to the path planning;
Figure BDA0003868729140000031
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 comfort cost of the formation of autonomous vehicles is calculated by:
Figure BDA0003868729140000032
in the formula, C s Represents the comfort cost of the formation of autonomous vehicles, h i Is the autonomous vehicle height numbered i in m;
Figure BDA0003868729140000033
h is the headway of the automatic driving vehicle, 1 is taken, and the unit is s; beta represents a constant coefficient obtained by experiments, and beta is 0.1.
As a further preferred aspect of the present invention, in step 5, the method for optimizing the total cost of the formation of the autonomous vehicles comprises:
C min =min(C 1 ,C 2 ,...,C n!-1 ,C n! ),
wherein C = μ a C ax C xs C s C represents the total cost of the autonomous vehicle formation; mu.s axs Weighting coefficients of 0.6, 0.25 and 0.15 are respectively taken for the safe cost, the efficiency cost and the comfortable cost of formation.
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. The invention integrates the characteristics of routes, roads and vehicles, provides a scientific and reasonable formation method for automatically driving the vehicles from the system perspective, improves the road safety, the running efficiency and the comfort of drivers and passengers.
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FIG. 1 is a flow chart of a method for centralized formation of autonomous vehicles in a dedicated lane environment according to the present invention.
Detailed Description
In the description of the present invention, it is to be understood that the terms "left side", "right side", "upper part", "lower part", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and that "first", "second", etc., do not represent an important degree of the component parts, and thus are not to be construed as limiting the present invention. The specific dimensions used in the present example are only for illustrating the technical solution and do not limit the protection scope of the present invention.
The present invention will be described in further detail with reference to the accompanying drawings and specific preferred embodiments.
Preferred traffic embodiments: in a 500m area of a lane dedicated to a certain autonomous vehicle, three autonomous vehicles travel in the same direction, the speed limit of the lane is known to be 120km/h, the travel route information and the operation data of the three autonomous vehicles are shown in table 1, and the relative position is positive and indicates that the autonomous vehicle is located in front of the traveling direction:
vehicle name Current road section planning mileage Speed limit Height of vehicle 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 formation of journey route information and operational data for autonomous vehicles
According to the information, the method for optimizing the automatic driving vehicle centralized formation under the special road environment is adopted to calculate and optimize the total cost of the automatic driving vehicle formation. As shown in fig. 1:
step 1, obtaining the travel path information of an automatic driving vehicle in a specific area; the travel path information comprises travel path planning and speed limit information of roads along the route, and specific data are shown in table 1.
Step 2, obtaining the operation data of the automatic driving vehicle in the specific area; the operational data includes vehicle height, vehicle speed and position data, as shown in table 1.
And 3, forming a team of the automatic driving vehicles in the specific area range.
The three automatic driving automobiles are all positioned in the area of 500m, and the planned driving mileage of the current special lane is more than 20km, so that the formation requirement is met; the longitudinal relative positions determined according to the formation plan are numbered from front to back in the direction of travel as shown in table 2:
numbering Plan 1 Plan 2 Plan 3 Scheme 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 formation of autonomous vehicles
Step 4, calculating safety cost C corresponding to each formation plan according to the travel path information and the operation data of the automatic driving vehicle formation a Efficiency cost C x Comfort cost C s Taking plan 5 as an example:
Figure BDA0003868729140000041
Figure BDA0003868729140000042
Figure BDA0003868729140000043
similarly, the safety cost C of the other 5 formation plans is calculated a Efficiency cost C x Comfort cost C s As shown in table 3:
type of cost Scheme 1 Scheme 2 Scheme 3 Scheme 4 Scheme 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 safe, efficiency, and comfort costs of formation planning
And 5, calculating and optimizing the total cost of the formation of the automatic driving vehicles according to the safety cost, the efficiency cost and the comfort cost of the formation of the automatic driving vehicles.
Taking plan 5 as an example:
C 5 =μ a C ax C xs C s =0.31×0.6+0.42×0.25+0.21×0.15=0.32
similarly, the total cost corresponding to the remaining 5 formation plans is calculated, as shown in table 4:
type of cost Plan 1 Scheme 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 plan
C min =min(C 1 ,C 2 ,...,C n!-1 ,C n! )=C 6 Namely, the formation method corresponding to the plan 6 has the lowest cost, and 3 automatic driving automobiles are formed according to the sequence of C, B and A.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent changes may be made within the technical spirit of the present invention, and the technical scope of the present invention is also covered by the present invention.

Claims (6)

1. An optimization method for centralized formation of automatic driving vehicles in a special road environment is characterized by comprising the following steps: the method comprises the following steps:
step 1, obtaining the travel path information of an automatic driving vehicle in a specific area; the travel path information comprises travel path planning and road speed limit information along the route;
step 2, obtaining the operation data of the automatic driving vehicle in the specific area; the operational data includes vehicle height, vehicle speed, and position data;
step 3, formation of automatic driving vehicles in a specific area range;
step 4, calculating the safety cost, the efficiency cost and the comfort cost of the formation of the automatic driving vehicles according to the travel path information and the operation data of the formation of the automatic driving vehicles;
and 5, calculating and optimizing the total cost of the formation of the automatic driving vehicles according to the safety cost, the efficiency cost and the comfort cost of the formation of the automatic driving vehicles.
2. The method of claim 1, wherein the method comprises the following steps: in step 3, the automatic driving vehicle formation method comprises the following steps:
step 3-1, collecting travel route planning information of the automatic driving vehicles within a range of 500m based on a navigation platform, and forming all vehicles with the planned driving range of more than or equal to 20km on the current special road;
3-2, numbering 1,2, \ 8230for all the automatic driving vehicles participating in formation from front to back along the driving direction according to the longitudinal relative position determined by the formation, wherein n and n are the number of the automatic driving vehicles participating in the formation;
and 3-3, sequentially accelerating the vehicles to the longitudinal relative positions represented by the serial numbers according to the serial numbers 1,2, \ 8230and n in the formation process.
3. The method for optimizing centralized formation of autonomous vehicles in a dedicated lane environment according to claim 2, wherein: in step 4, the method for calculating the safety cost of the formation of the automatic driving vehicles comprises the following steps:
Figure FDA0003868729130000011
in the formula C a Represents a safety cost for the formation of autonomous vehicles,
Figure FDA0003868729130000012
the arithmetic mean value of the speeds of all the vehicles participating in formation when the formation is started; v. of m The speed unit is km/h for the highest speed limit of the lane special for the automatic driving vehicle; lambda represents a constant coefficient obtained through experiments, and lambda is 3;
Figure FDA0003868729130000013
Figure FDA0003868729130000014
the overtaking times of the automatic driving vehicles with the sequence number i in the formation process when the formation is finished;
Figure FDA0003868729130000015
when the formation begins, the automatic driving vehicles with the representative serial number i are sequenced from front to back along the driving direction according to the longitudinal relative position,
Figure FDA0003868729130000016
4. the method of claim 3, wherein the method comprises the following steps: in step 4, the method for calculating the efficiency cost of the formation of the autonomous vehicles comprises the following steps:
Figure FDA0003868729130000021
in the formula, C x Representing the cost of efficiency of formation of autonomous vehicles, L i The unit of the driving mileage on the special road is km which is determined according to the path planning;
Figure FDA0003868729130000022
α represents a constant coefficient obtained by an experiment, and α is 1.
5. The method of claim 4, wherein the method comprises the following steps: in step 4, the method for calculating the comfort cost of the formation of the automatic driving vehicles comprises the following steps:
Figure FDA0003868729130000023
in the formula, C s Represents the comfort cost of the autonomous vehicle formation, h i Is the autonomous vehicle height numbered i in m;
Figure FDA0003868729130000024
h is the headway of the automatic driving vehicle, 1 is taken, and the unit is s; beta represents a constant coefficient obtained by an experiment, and beta is 0.1.
6. The method of claim 5, wherein the method comprises the following steps: in step 5, the method for optimizing the total cost of the formation of the automatic driving vehicles comprises the following steps:
C min =min(C 1 ,C 2 ,...,C n!-1 ,C n! ),
wherein C = μ a C ax C xs C s And C represents the total cost of the formation of autonomous vehicles; mu.s axs Weighting coefficients of 0.6, 0.25 and 0.15 are respectively taken for the safe cost, the efficiency cost and the comfortable cost of formation.
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Citations (10)

* Cited by examiner, † Cited by third party
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
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
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

Patent Citations (10)

* Cited by examiner, † Cited by third party
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
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
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)

* Cited by examiner, † Cited by third party
Title
郭松岭;: "关于成品油公路运输道路交通安全考核的若干建议", 物流工程与管理, no. 04 *

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