CN110992676A - Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method - Google Patents
Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method Download PDFInfo
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Abstract
The invention relates to a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method, which comprises the following steps: obtaining a saturated headway average value and a layout type: obtaining the number of the networked automatic driving vehicles in the research range; obtaining the number of queues formed by the networked automatic driving vehicles; obtaining a mixed saturated traffic flow headway average value in a specific arrangement form; calculating the probability of a specific arrangement form through a probability mass function; obtaining the average value of the locomotive time intervals of the final mixed saturated traffic flow; obtaining road traffic capacity
And the equivalent coefficient PCE of the networked automatic driving vehicle. Compared with the prior art, the method effectively simplifies the calculation, and simultaneously makes up for the common neglect net in the current researchThe weakness of the formation strategy of the automatic driving vehicle.
Description
Technical Field
The invention relates to the field of road traffic planning and management oriented to the internet automatic driving technology, in particular to a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method.
Background
The internet automatic driving vehicle can fundamentally improve the road traffic capacity, the stability of traffic flow and the traffic safety. The internet automatic driving vehicle obtains running state information such as real-time speed, position and the like of a plurality of internet automatic driving vehicles in front through vehicle-to-vehicle (V2V) communication or a vehicle-road cooperative system. A plurality of networked automatic driving vehicles can form a motorcade by sharing the information in real time, and the significantly lower time interval of the vehicle head compared with the time interval between human driving vehicles is maintained inside the motorcade, so that the road traffic capacity is improved. When the networked automatic driving vehicles form formation, different formation strategies can be adopted to form networked automatic driving vehicle fleets with different lengths and positions, so that the traffic capacity, the fuel economy and the traffic safety are further improved. On the other hand, the popularization of the internet automatic driving technology is still in a preliminary stage, the permeability of the internet automatic driving vehicle in a road network is gradually increased, and a novel mixed traffic flow consisting of the internet automatic driving vehicle and human driving vehicles exists for a long time. Therefore, the influence of the internet automatic driving technology on the road traffic capacity needs to be quantified by considering the permeability of the internet automatic driving vehicle and the formation strategy under the condition of a novel mixed traffic flow. However, existing research at home and abroad attempting to quantify this effect has generally ignored the effect of the formation strategy of networked autonomous vehicles. In addition, because the setting of the behaviors of the networked automatic driving vehicle and the human driving vehicle in each study is different, the calculation result of the traffic capacity improvement degree is not consistent, and the calculation result is not consistent with the running state of the actual novel mixed traffic flow.
The vehicle model equivalent coefficient is an important tool for researching mixed traffic flow, and the function of the vehicle model equivalent coefficient is to convert the mixed traffic flow into the traffic flow of a standard vehicle (usually a passenger car), so that the mixed traffic flow with different compositions has comparability. At present, no matter in theoretical research or engineering practice, the target vehicle types of the equivalence coefficient are mainly concentrated on large-sized vehicles such as trucks and buses, and small-sized vehicles such as non-motor vehicles and motorcycles are considered, so that the novel mixed traffic flow which is served for processing the traditional mixed traffic flow and the novel mixed traffic flow which introduces the internet automatic driving technology cannot be processed. Only Bujanovic and Lochrane in the United states study the equivalence factor of networked automatic driving vehicles, but the study also does not relate to the formation strategy of the networked automatic driving vehicles, and only limits the fleet length of the networked automatic driving vehicles according to the communication range of V2V.
The problems existing at present are as follows: 1. neglecting a formation strategy of the networked automatic driving vehicles when the road traffic capacity of the mixed traffic flow is quantified; 2. when the equivalent coefficient of the networked automatic driving vehicle is obtained, the formation strategy of the networked automatic driving vehicle is not involved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method.
The purpose of the invention can be realized by the following technical scheme:
a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method comprises the following steps:
step S1: obtaining a saturated headway average value and a layout type;
step S2, obtaining the number N of networked automatic driving vehicles in the research range based on the permeability η and the total number N of the vehicles in the research rangeA；
Step S3: based on total number N of vehicles and number N of networked automatic driving vehiclesAObtaining α the number of queues formed by the networked automatic driving vehicles;
step S4, obtaining the average value of the headway of the mixed saturated traffic flow in a specific arrangement form based on the number α of queues, the layout type and the average value of the saturated headway
Step S5, number α based on queue, layout type, total number N of vehicles and number N of networked autopilot vehiclesACalculating the probability Pr ((α, J) of a specific arrangement form through a probability mass function)|(N,NA))；
Step S6: obtaining the final average value of the headway of the mixed saturated traffic flow based on the average value and the probability of the headway of the mixed saturated traffic flow
Step S7: vehicle head time interval average value based on final mixed saturated traffic flow
Obtaining road traffic capacity
And the equivalent coefficient PCE of the networked automatic driving vehicle.
The number N of the networked automatic driving vehiclesAComprises the following steps:
NA＝N·η。
the saturated headway average includes a saturated headway average of a human-driven vehicle following the human-driven vehicle
Saturated headway time average value of human-driven vehicle following net-connected automatic driving vehicle
Saturated headway average value of network connection automatic driving vehicle and human driving vehicle
Saturated headway average value of grid-connected automatic driving vehicle and following grid-connected automatic driving vehicle
Said
Obtained by counting the saturated traffic flow of pure human-driven vehicles, the method comprises the following steps
Obtaining experimental data of closed or open site of the automatic driving vehicle through internet connection, wherein the experimental data are obtained through the automatic driving vehicle
And
the method is obtained by carrying out simulation experiments on the saturated traffic flow of the pure network connection automatic driving vehicle.
The layout types comprise an E layout type that a head queue and a tail queue are human-driven vehicles, the head queue is an automatic internet-connected driving vehicle, the tail queue is an F layout type that the human-driven vehicles are human-driven vehicles, the head queue is a G layout type that the human-driven vehicles are internet-connected automatic driving vehicles, and the head queue and the tail queue are H layout types that the automatic internet-connected driving vehicles are.
The average value of the locomotive time distances of the mixed saturated traffic flow in the specific arrangement form
Comprises the following steps:
if the formation strategy of arbitrary formation of human-driven vehicles and networked automatic driving vehicles is adopted, the probability Pr ((α, J) | (N, N)A) Include Pr ((α, E) | (N, N)A))、Pr((α,F)|(N,NA))、Pr((α,G)|(N,NA) And Pr ((α, H) | (N, N)A))：
Wherein, Pr ((α, E) | (N, N)A) Probability of arrangement form of E layout type and the number of queues of α, Pr ((α, F) | (N, N)A) Probability of arrangement form of F layout type and number of queues α, Pr ((α, G) | (N, N)A) Probability of arrangement form of G layout type and the number of queues of α, Pr ((α, H) | (N, N)A) Probability of an arrangement form of H layout type and the number of queues of α;
if a formation strategy of fixing the length of the queue formed by the networked automatic driving vehicles is adopted, and the length of the queue is limited to lambda0The probability Pr ((α, J) | (N, N)A) ) is:
wherein J is a layout type,
comprises that
And
the average time headway value of the final mixed saturated traffic flow
Comprises the following steps:
the road traffic capacity
Comprises the following steps:
the equivalent coefficient PCE of the networked automatic driving vehicle is as follows:
compared with the prior art, the invention has the following advantages:
(1) the saturated headway average value obtained in the saturated traffic flow statistics, closed or open field experiments and simulation experiments of the pure human-driven vehicles is used as model input, the actual running state of the novel mixed traffic flow is effectively reflected, the method is suitable for the running environment of the actual traffic flow, the data obtaining method has operability, the reliability of the estimation result is high, and the estimation method is convenient to popularize and apply.
(2) According to a queue formation strategy adopted by the networked automatic driving vehicle (including a formation strategy adopting human driving vehicles and any formation of the networked automatic driving vehicle and a formation strategy adopting the length of a queue formed by fixed networked automatic driving vehicles), a probability mass function is adopted to reflect the influence of the formation strategy on the time-distance average value of the locomotive of the final mixed saturated traffic flow, and modeling is directly carried out from the result of the formation strategy without relating to a specific algorithm for forming the formation, so that the calculation is effectively simplified, and meanwhile, the weakness that the networked automatic driving vehicle formation strategy is generally ignored in the current research is overcome.
(3) The obtained road traffic capacity and the equivalent coefficient of the internet automatic driving vehicle can be used for guiding the traffic planning and management practice of the internet automatic driving technology facing the internet automatic driving technology, such as the setting of special roads of the internet automatic driving vehicle, the setting of newly-built or reconstructed road vehicle roads and the like, the practical requirements are met, the popularization and application of the internet automatic driving technology are promoted, and the operation efficiency and the safety of the national public road network and the urban road network are further improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of the headway category of the present invention;
fig. 3 is a schematic diagram of the layout type of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The embodiment provides a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method, which comprises the following steps:
step S1: obtaining a saturated headway average and a layout type, the saturated headway average of a human-driven vehicle following the human-driven vehicle
Saturated headway time average value of human-driven vehicle following net-connected automatic driving vehicle
Saturated headway average value of network connection automatic driving vehicle and human driving vehicle
Automatic driving vehicle connected with internetSaturated headway average value of following networked automatic driving vehicle
Step S2, obtaining the number N of networked automatic driving vehicles in the research range based on the permeability η and the total number N of the vehicles in the research rangeA；
Step S3: based on total number N of vehicles and number N of networked automatic driving vehiclesAObtaining α the number of queues formed by the networked automatic driving vehicles;
step S4, based on the number of queues α, layout type,
And
obtaining the average value of the locomotive time distances of the mixed saturated traffic flow in a specific arrangement form
Step S5, number α based on queue, layout type, total number N of vehicles and number N of networked autopilot vehiclesACalculating probability Pr ((α, J) | (N, N) of a specific arrangement form through a probability mass functionA))；
Step S6: obtaining the final average value of the headway of the mixed saturated traffic flow based on the average value and the probability of the headway of the mixed saturated traffic flow
Step S7: vehicle head time interval average value based on final mixed saturated traffic flow
Obtaining road traffic capacity
And the equivalent coefficient PCE of the networked automatic driving vehicle.
Specifically, the method comprises the following steps:
in step S1
Obtained by counting the saturated traffic flow of pure human-driven vehicles,
the experimental data of the closed or open site of the automatic driving vehicle connected with the internet is obtained, and the assumption can also be made
And
the phase of the two phases is equal to each other,
and
the method is obtained by carrying out simulation experiments on the saturated traffic flow of the pure network connection automatic driving vehicle.
The layout types comprise four types, namely an E layout type that a head queue and a tail queue are human-driven vehicles respectively, an F layout type that the head queue is an internet automatic driving vehicle and the tail queue is a human-driven vehicle, a G layout type that the head queue is a human-driven vehicle and the tail queue is an internet automatic driving vehicle, and an H layout type that the head queue and the tail queue are internet automatic driving vehicles respectively. If the traffic flow within the scope of the study has only one queue for a particular vehicle type, then the queue is considered to be both a head queue and a tail queue.
The number N of networked automatic driving vehicles in step S2AComprises the following steps:
NA＝N·η
and N is selected so that the change of the road traffic capacity and the equivalent coefficient of the network connection automatic driving vehicle is smaller than a preset threshold value when the research range is continuously enlarged, namely the road traffic capacity and the equivalent coefficient of the network connection automatic driving vehicle estimated in the research range can reflect the real running state of the mixed traffic flow.
The number of queues α multiplied by the length of each queue in step S3 is not greater than N.
Mixed saturated traffic flow headway average value in specific arrangement form in step S4
Comprises the following steps:
in step S5, if the formation strategy of arbitrary formation of human-driven vehicles and internet-connected automatic driving vehicles is adopted, the probability Pr ((α, J) | (N, N) | (A) Include Pr ((α, E) | (N, N)A))、Pr((α,F)|(N,NA))、Pr((α,G)|(N,NA) And Pr ((α, H) | (N, N)A))：
Wherein, Pr ((α, E) | (N, N)A) Probability of arrangement form of E layout type and the number of queues of α, Pr ((α, F) | (N, N)A) Probability of arrangement form of F layout type and number of queues α, Pr ((α, G) | (N, N)A) Probability of arrangement form of G layout type and the number of queues of α, Pr ((α, H) | (N, N)A) Probability of an arrangement form of H layout type and the number of queues of α;
if a formation strategy of fixing the length of the queue formed by the networked automatic driving vehicles is adopted, and the length of the queue is limited to lambda0，λ0The value of (A) is predetermined, and the probability Pr ((α, J) | (N, N)A) ) is:
wherein J is a layout type,
the total number of the vehicles is N, and the number of the networked automatic driving vehicles is NAThen, forming the number of arrangement forms of the layout type J;
the total number of the vehicles is N, and the number of the networked automatic driving vehicles is NAThen, a layout type J is formed, and the number of arrangement forms of the queue of α networked automatic driving vehicles is included
And
final mixed saturated traffic flow in step S6Mean time headway
Comprises the following steps:
road traffic capability in step S7
Comprises the following steps:
the equivalent coefficient PCE of the networked automatic driving vehicle is dimensionless and is expressed as follows:
the following is set forth with reference to a specific example:
1) as shown in FIG. 2, the saturated traffic flow statistics of the pure human-driven vehicles at the intersection entrance lane is obtained
Is taken from
Equal;
and
the values are obtained by simulating the saturated traffic flow of the networked automatic driving vehicle through a computer simulation platform, and the specific values are shown in table 1, and the unit is second: the layout types can be according to the vehicles to which the head queue and the tail queue belongTypes are classified into E, F, G, H category 4, as shown in FIG. 3.
TABLE 1
And
specific value of
2) In this example, the permeability is η ═ 0.5, and the road traffic capacity and the internet automatic driving vehicle equivalence factor at this permeability were calculated.
3) A continuous section is taken as a research range in the mixed traffic flow, as shown in fig. 2, the scale is N equal to 100, and the number of the networked automatic driving vehicles in the research range is NA＝N·η＝50。
4) Within the research range in the step 3), the arrangement forms of the networked automatic driving vehicle queues are shared
And (4) seed preparation.
5) And calculating the average value of the locomotive time distances of the mixed saturated traffic flows in different arrangement forms. For example, calculating the average value of the headway time of the mixed saturated traffic flow in the arrangement form of the F layout types of the 2 networked automatic driving vehicle queues, wherein the calculation process is shown as the following formula:
the arrangement forms of the other layout types and the queue numbers of different networked automatic driving vehicles adopt the same algorithm, and the calculation results are not listed in a row.
6) In this example, a formation strategy of arbitrary formation of human-driven vehicles and networked automatic driving vehicles is adopted, and then the probability of generating a queue F layout type with 2 networked automatic driving vehicles is calculated as follows:
and calculating the probability by adopting the same method in the arrangement modes of other layout types and the queue numbers of different networked automatic driving vehicles, and not counting the calculation results in a row.
7) Calculating the average value and the probability of the headway of the mixed saturated traffic flow according to the steps 5) and 6), and calculating the average value of the headway of the final mixed saturated traffic flow according to the following formula
8) Road traffic capacity is calculated according to the following formula:
calculating the equivalent coefficient of the networked automatic driving vehicle according to the following formula:
the result shows that under the formation strategy conditions that the permeability of the networked automatic driving vehicles is 0.5 and human-driven vehicles and networked automatic driving vehicles are randomly formed, the road traffic capacity is 1964veh/(h · ln), the equivalent coefficient of the networked automatic driving vehicles is 0.53, and the networked automatic driving vehicles can be used for converting the mixed traffic flow into the traffic flow of standard vehicles (usually passenger cars), so that the mixed traffic flows with different compositions have comparability.
Claims (8)
1. A road traffic capacity and internet automatic driving vehicle equivalence coefficient estimation method is characterized by comprising the following steps:
step S1: obtaining a saturated headway average value and a layout type;
step S2: base ofObtaining the number N of the networked automatic driving vehicles in the research range according to the permeability η and the total number N of the vehicles in the research rangeA；
Step S3: based on total number N of vehicles and number N of networked automatic driving vehiclesAObtaining α the number of queues formed by the networked automatic driving vehicles;
step S4, obtaining the average value of the headway of the mixed saturated traffic flow in a specific arrangement form based on the number α of queues, the layout type and the average value of the saturated headway
Step S5, number α based on queue, layout type, total number N of vehicles and number N of networked autopilot vehiclesACalculating probability Pr ((α, J) | (N, N) of a specific arrangement form through a probability mass functionA))；
Step S6: obtaining the final average value of the headway of the mixed saturated traffic flow based on the average value and the probability of the headway of the mixed saturated traffic flow
Step S7: vehicle head time interval average value based on final mixed saturated traffic flow
Obtaining road traffic capacity
And the equivalent coefficient PCE of the networked automatic driving vehicle.
2. The method of claim 1, wherein the number N of networked autopilots is greater than the number of networked autopilotsAComprises the following steps:
NA＝N·η。
3. a roadway according to claim 1The method for estimating the equivalent coefficient of the traffic capacity and the network-connected automatic driving vehicle is characterized in that the saturated headway average value comprises the saturated headway average value of a human-driven vehicle following the human-driven vehicle
Saturated headway time average value of human-driven vehicle following net-connected automatic driving vehicle
Saturated headway average value of network connection automatic driving vehicle and human driving vehicle
Saturated headway average value of grid-connected automatic driving vehicle and following grid-connected automatic driving vehicle
Said
Obtained by counting the saturated traffic flow of pure human-driven vehicles, the method comprises the following steps
Obtaining experimental data of closed or open site of the automatic driving vehicle through internet connection, wherein the experimental data are obtained through the automatic driving vehicle
And
the method is obtained by carrying out simulation experiments on the saturated traffic flow of the pure network connection automatic driving vehicle.
4. The method according to claim 3, wherein the layout types include an E layout type in which a head queue and a tail queue are human-driven vehicles, the head queue is the Internet automatic driving vehicle, the tail queue is an F layout type in which the tail queue is the human-driven vehicle, the head queue is the human-driven vehicle, the tail queue is a G layout type in which the Internet automatic driving vehicle, and the head queue and the tail queue are H layout types in which the Internet automatic driving vehicle.
5. The method of claim 4, wherein the vehicle headway average value of the mixed saturated traffic flow in the specific arrangement form is used as the vehicle headway average value
Comprises the following steps:
6. the method according to claim 5, wherein the probability Pr ((α, J) | (N, N) is determined by a formation strategy of arbitrary formation of human-driven vehicles and Internet-connected automatic-driven vehiclesA) Include Pr ((α, E) | (N, N)A))、Pr((α,F)|(N,NA))、Pr((α,G)|(N,NA) And Pr ((α, H) | (N, N)A))：
Wherein, Pr ((α, E) | (N, N)A) Probability of arrangement form of E layout type and the number of queues is α, Pr ((α, F)|(N,NA) Probability of arrangement form of F layout type and number of queues α, Pr ((α, G) | (N, N)A) Probability of arrangement form of G layout type and the number of queues of α, Pr ((α, H) | (N, N)A) Probability of an arrangement form of H layout type and the number of queues of α;
if a formation strategy of fixing the length of the queue formed by the networked automatic driving vehicles is adopted, and the length of the queue is limited to lambda0The probability Pr ((α, J) | (N, N)A) ) is:
wherein J is a layout type,
comprises that
And
7. the method of claim 6, wherein the vehicle headway average value of the final mixed saturated traffic flow is obtained by a method of estimating road traffic capacity and internet automatic driving vehicle equivalence coefficient
Comprises the following steps:
8. the method of claim 7, wherein the road traffic capacity and the equivalence coefficient of the Internet-connected automatic driving vehicle are estimated according to the road traffic capacity and the Internet-connected automatic driving vehicle
Comprises the following steps:
the equivalent coefficient PCE of the networked automatic driving vehicle is as follows:
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021073523A1 (en) * | 2019-10-15 | 2021-04-22 | 同济大学 | Method for estimating road capacity and connected automatic driving vehicle equivalent coefficient |
| CN113487854A (en) * | 2021-06-30 | 2021-10-08 | 吉林大学 | Pre-formation management system and method for vehicle cooperative formation on expressway |
| CN113781788A (en) * | 2021-11-15 | 2021-12-10 | 长沙理工大学 | Automatic driving vehicle management method based on stability and safety |
| CN115116217A (en) * | 2022-05-26 | 2022-09-27 | 东北林业大学 | Dynamic measuring and calculating method and system for lane saturation flow rate and start loss time |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116704775B (en) * | 2023-06-27 | 2024-01-30 | 大连海事大学 | Mixed traffic flow traffic capacity calculation method considering intelligent network bus |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101042805A (en) * | 2007-03-09 | 2007-09-26 | 吉林大学 | Control method for single crossing mixed traffic signal |
| CN101510355A (en) * | 2009-03-30 | 2009-08-19 | 东南大学 | Method for determining traffic lane length of crossing inlet road for forbidding lane exchange |
| US9195938B1 (en) * | 2012-12-27 | 2015-11-24 | Google Inc. | Methods and systems for determining when to launch vehicles into a fleet of autonomous vehicles |
| US20170003681A1 (en) * | 2015-05-13 | 2017-01-05 | Uber Technologies, Inc. | Autonomous vehicle operated with guide assistance of human driven vehicles |
| CN106708057A (en) * | 2017-02-16 | 2017-05-24 | 北理慧动（常熟）车辆科技有限公司 | Intelligent vehicle formation driving method |
| DE102016111447A1 (en) * | 2016-06-22 | 2017-12-28 | Terex Mhps Gmbh | System for transporting containers, in particular ISO containers, by means of heavy-duty vehicles |
| CN108417026A (en) * | 2017-12-01 | 2018-08-17 | 安徽优思天成智能科技有限公司 | A kind of intelligent vehicle ratio acquisition methods for keeping road passage capability optimal |
| CN108415245A (en) * | 2018-01-26 | 2018-08-17 | 华南理工大学 | The fault tolerant control method of autonomous fleet operations under the conditions of a kind of heterogeneous car networking |
| CN109725639A (en) * | 2018-12-13 | 2019-05-07 | 北京工业大学 | The linear control method and device of cruise system |
| EP3316062B1 (en) * | 2016-10-31 | 2019-09-04 | Nxp B.V. | Platoon control |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102157064B (en) * | 2011-05-26 | 2013-12-18 | 东南大学 | Method for designing signal intersection of bus lanes |
| CN102622878B (en) * | 2012-04-11 | 2014-08-13 | 天津市市政工程设计研究院 | Setting method of straight lanes special for trucks |
| US9848813B2 (en) * | 2012-08-14 | 2017-12-26 | Volvo Lastvagnar Ab | Method for determining the operational state of a driver |
| CN103383816B (en) * | 2013-07-01 | 2015-09-02 | 青岛海信网络科技股份有限公司 | The traffic signal control method that multiplexing electronic police mixed traffic flow detects and device |
| CN104916135B (en) * | 2015-06-19 | 2017-05-10 | 南京全司达交通科技有限公司 | Method and system for acquiring cargo transport lane traffic capacity of passenger and cargo separating expressway |
| CN105741555B (en) * | 2016-04-28 | 2017-12-01 | 华南理工大学 | A kind of method that vehicle conversion factor is determined based on macroscopical parent map |
| CN106971546B (en) * | 2017-05-18 | 2020-07-24 | 重庆大学 | Road section bus permeability estimation method based on bus GPS data |
| CN109118770A (en) * | 2018-09-11 | 2019-01-01 | 东南大学 | A kind of road section capacity method for digging based on Traffic monitoring data |
| CN109859456B (en) * | 2018-12-06 | 2019-11-22 | 浙江大学 | Platooning's initial scheme under car networking environment determines method |
| CN109709956B (en) * | 2018-12-26 | 2021-06-08 | 同济大学 | Multi-objective optimized following algorithm for controlling speed of automatic driving vehicle |
| CN110992676B (en) * | 2019-10-15 | 2021-06-04 | 同济大学 | Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method |
-
2019
- 2019-10-15 CN CN201910978887.4A patent/CN110992676B/en active Active
-
2020
- 2020-10-14 WO PCT/CN2020/120821 patent/WO2021073523A1/en active Application Filing
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101042805A (en) * | 2007-03-09 | 2007-09-26 | 吉林大学 | Control method for single crossing mixed traffic signal |
| CN101510355A (en) * | 2009-03-30 | 2009-08-19 | 东南大学 | Method for determining traffic lane length of crossing inlet road for forbidding lane exchange |
| US9195938B1 (en) * | 2012-12-27 | 2015-11-24 | Google Inc. | Methods and systems for determining when to launch vehicles into a fleet of autonomous vehicles |
| US20170003681A1 (en) * | 2015-05-13 | 2017-01-05 | Uber Technologies, Inc. | Autonomous vehicle operated with guide assistance of human driven vehicles |
| DE102016111447A1 (en) * | 2016-06-22 | 2017-12-28 | Terex Mhps Gmbh | System for transporting containers, in particular ISO containers, by means of heavy-duty vehicles |
| EP3316062B1 (en) * | 2016-10-31 | 2019-09-04 | Nxp B.V. | Platoon control |
| CN106708057A (en) * | 2017-02-16 | 2017-05-24 | 北理慧动（常熟）车辆科技有限公司 | Intelligent vehicle formation driving method |
| CN108417026A (en) * | 2017-12-01 | 2018-08-17 | 安徽优思天成智能科技有限公司 | A kind of intelligent vehicle ratio acquisition methods for keeping road passage capability optimal |
| CN108415245A (en) * | 2018-01-26 | 2018-08-17 | 华南理工大学 | The fault tolerant control method of autonomous fleet operations under the conditions of a kind of heterogeneous car networking |
| CN109725639A (en) * | 2018-12-13 | 2019-05-07 | 北京工业大学 | The linear control method and device of cruise system |
Non-Patent Citations (4)
| Title |
|---|
| KOUVELAS A 等: "Exploring the impact of autonomous vehicles in urban networks and potential new capabilities for perimeter control", 《2017 5TH IEEE INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS (MT-ITS)》 * |
| ZHAO WM 等: "A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection", 《TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES》 * |
| 王振华: "车路协同环境下城市道路交织区驾驶行为特性及控制策略研究", 《中国博士学位论文全文数据库工程科技II辑》 * |
| 马丽娜: "基于元胞自动机的自动驾驶—手动驾驶交通流特性研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021073523A1 (en) * | 2019-10-15 | 2021-04-22 | 同济大学 | Method for estimating road capacity and connected automatic driving vehicle equivalent coefficient |
| CN113487854A (en) * | 2021-06-30 | 2021-10-08 | 吉林大学 | Pre-formation management system and method for vehicle cooperative formation on expressway |
| CN113781788A (en) * | 2021-11-15 | 2021-12-10 | 长沙理工大学 | Automatic driving vehicle management method based on stability and safety |
| CN115116217A (en) * | 2022-05-26 | 2022-09-27 | 东北林业大学 | Dynamic measuring and calculating method and system for lane saturation flow rate and start loss time |
| CN115116217B (en) * | 2022-05-26 | 2023-09-26 | 东北林业大学 | Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane |
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