CN115880884B - Expressway ramp mixed traffic flow control method based on controllable intelligent network vehicle connection - Google Patents
Expressway ramp mixed traffic flow control method based on controllable intelligent network vehicle connection Download PDFInfo
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Abstract
本发明涉及智能交通领域，尤其涉及一种基于可控智能网联车的高速公路匝道混合交通流管控方法，包括以下步骤：S1，将高速公路匝道路段分为正常行驶路段、编队形成路段和加速汇入路段；S2，智能网联车和人驾跟驰车辆在编队形成路段形成车辆编队；S3，计算车辆编队完全到达汇入点S的时间区间[tmin,tmax]；S4，对主干道智能网联车与匝道路段上的智能网联车进行协同控制，在主干道为匝道上的编队车辆预留出汇入间隙；S5，车辆编队汇入主干道。本发明依靠车联网技术提前获取高速公路主干道和下游合流区的交通状况，通过控制智能网联车的速度引导匝道车辆安全汇入高速公路主干道，避免出现驾驶员仅仅根据自身的驾驶经验和周围的驾驶环境寻找汇入主干道的时机的情况。
The invention relates to the field of intelligent transportation, and in particular to a method for controlling mixed traffic flow on a highway ramp based on controllable intelligent networked vehicles. It includes the following steps: S1, divide the highway ramp section into a normal driving section, a formation formation section and an acceleration section. Merging road section; S2, intelligent networked vehicles and human-driven following vehicles form a vehicle formation in the formation section; S3, calculate the time interval [t min , t max ] for the vehicle formation to completely arrive at the merging point S; S4, for the main trunk The intelligent networked vehicles on the road perform collaborative control with the intelligent networked vehicles on the ramp section, and a merging gap is reserved on the main road for the formation vehicles on the ramp; S5, the vehicle formation merges into the main road. This invention relies on the Internet of Vehicles technology to obtain the traffic conditions of the main road of the highway and the downstream merging area in advance, and guides the ramp vehicles to safely merge into the main road of the highway by controlling the speed of the intelligent networked vehicles, thus avoiding the situation where the driver only relies on his own driving experience and The surrounding driving environment looks for opportunities to merge onto the main road.
Description
Technical Field
The invention relates to the field of intelligent traffic, in particular to a highway ramp mixed traffic flow control method based on a controllable intelligent network car.
Background
With the development of 5G communication technology, internet of vehicles technology, intelligent automobiles and road side equipment, the new era of intelligent network connection has gradually moved to the field of vision of people. The intelligent network-connected vehicle can improve the accuracy and effect of the management and control strategy by means of the accurate sensing, passing and control capability of the intelligent network-connected vehicle, and meanwhile, the negative influence of randomness and uncertainty of the driving behavior of the driver of the traditional driving following vehicle on the operation of a traffic system can be avoided.
The ramp converging area is used as a converging node of the expressway, and is often a disaster area of traffic jam of the expressway due to the randomness of frequent lane changing and driving behaviors, so that traffic safety problems also occur. When the traffic flow of the expressway main road is large, if the ramp vehicles cannot find a proper junction gap, the forced road changing behavior of the main road vehicles is caused, and the traffic jam of the junction area is further aggravated.
The existing method for controlling the traffic flow of the expressway ramp is mainly controlled by a traditional signal lamp, but aiming at complex traffic scenes such as the expressway ramp junction point, the complex traffic flow of the ramp junction region is difficult to be operated by single signal timing control. In addition, the control of intelligent network connected vehicles is also a difficulty in ramp mixed traffic flow control, and unreasonable speed control of intelligent network connected vehicles can aggravate traffic jam of a road section downstream of an expressway.
Disclosure of Invention
Aiming at the problem of highway ramp junction congestion in the prior art, the invention provides a highway ramp mixed traffic flow control method based on a controllable intelligent network car.
The invention is realized by the following technical scheme:
a highway ramp mixed traffic flow control method based on a controllable intelligent network car comprises the following steps:
s1, dividing a ramp road section into a normal driving road section, a formation road section and an acceleration converging road section, marking a head vehicle determining point on the ramp road sectionFormation completion Point->And sink->；
S2, forming a vehicle formation with the intelligent network vehicle as a head vehicle by forming a road section on the intelligent network vehicle and the human driving following vehicle;
s3, calculating that the vehicle formation completely reaches the entry pointTime interval +.>；
S4, cooperatively controlling intelligent network vehicles on the main road and intelligent network vehicles on the ramp section, and reserving an import gap for formation vehicles on the ramp on the main road;
s5, the vehicles are formed and gathered into a main road.
Preferably, in S2, when the formation is completed, the formula is satisfied between the intelligent network vehicle and the following vehicle:
in the formula ,determining the point for the head car>A location on the ramp;
in order to form a formation head gear +.>The running speed at the moment;
the person driving the following vehicle for forming a team is +.>The running speed at the moment;
is the inter-vehicle distance of the queue when in the following state.
Preferably, in S3, it is calculated that the vehicle fleet has arrived completely at the entry pointThe specific steps of the time interval of (a) are as follows: firstly, calculating the lowest speed of the intelligent network vehicle on a ramp, and respectively calculating the arrival of the intelligent network vehicle and a human driving following vehicle at a junction point +.>Finally calculate the time for the vehicle formation to reach the entry point completely +.>Is a time interval of (a).
Preferably, the speed of the intelligent network vehicle running on the ramp satisfies the formula:
in the formula ,the lowest speed for the vehicle to travel on the ramp;
the highest speed of the vehicle running on the ramp;
completion point for formation on ramp>Is a position of (2);
tis the travel time of the vehicle on the ramp.
Preferably, the intelligent network connection arrival sink is calculated according to the vehicle formation completion pointTime of (2):
case one: when the formation completion point of the vehicle formation is marked on the rampWhen the intelligent network connection vehicle is coincident, the intelligent network connection vehicle reaches the sink point +.>Time of (2)t cav_to_S The formula is satisfied:
；
and a second case: when the formation completion point of the vehicle formation is positioned at the head vehicle determination point on the rampAnd formation completion point->When the intelligent network connection vehicle reaches the sink point +.>Time of (2)t cav_to_S The formula is satisfied:
in the formula ,completion point for formation on ramp>Is a position of (2);
is the junction point on the ramp>Is a position of (2);
acceleration for intelligent network connection;
the intelligent network vehicle is the position of the intelligent network vehicle on the ramp when formation is successful.
Preferably, the human driving following vehicle is calculated to reach the sink point based on the New following modelAnd the time of (2) satisfies the formula:
in the formula ,reaction time of following the car for the human driving;
minimum following distance for a human driving following vehicle;
nto the first in formationnThe vehicle is a vehicle that is to be operated,n≠1。
preferably, the vehicle platoon reaches the entry point completelyThe time interval of (2) is>Wherein the shortest timet min The calculation formula of (2) is as follows:
maximum timet max The calculation formula of (2) is as follows:
in the formula ,completion point for formation on ramp>Is a position of (2);
is the junction point on the ramp>Is a position of (2);
the number of vehicles in the ramp vehicle formation;
the distance between the vehicles in the queue when the vehicle is in the following state;
is the length of the body of the vehicle.
Preferably, in S4, when the position of the intelligent network connection on the main road is located in the sectionAnd in the course, the speed of intelligent network linkage on the main road is regulated to reserve a safe afflux gap for ramp vehicle formation, wherein,is the highest speed limit of the expressway main road,M S for the main road from the start point to the sink point +.>Is a distance of (3).
Preferably, in S4, the speed of the intelligent network link on the main roadThe formula is satisfied:
wherein ,when the formation of vehicles in the ramp is successful, the position of the intelligent network vehicle connection is cooperatively controlled on the main road;
for the last vehicle in the vehicle formation to travel to the sink +.>The time required;
for the cooperative control intelligent network car at +.>The travel speed at the moment.
Preferably, in S4, on the main road, the inter-vehicle distance between the intelligent network car and the preceding carThe formula is satisfied:
in the formula ,the number of vehicles in the ramp vehicle formation;
the distance between the vehicles in the queue when the vehicle is in the following state;
in this embodiment, it is assumed that the intelligent network vehicle and the human-driven following vehicle have the same body length, which is the body length of the vehiclel。
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a highway ramp mixed traffic flow control method based on a controllable intelligent network train, which starts from a highway ramp control scene, combines a traffic flow model and a vehicle kinematics model, takes the controllability of ramp mixed traffic flow safety afflux as a target, and is based on the idea of guiding ramp vehicle formation afflux by the intelligent network train.
When intelligent network vehicles and rear vehicles in the queue are successfully formed, firstly, the intelligent network vehicles and rear vehicles arrive at an merging point according to the vehicle formationJudging whether intelligent network vehicles which can be cooperatively controlled exist in the corresponding position interval at the upstream of the expressway main road at the moment, and if so, controlling the speed of the corresponding intelligent network vehicles on the main road to ensure a safety gap when the vehicles are assembled. The intelligent network connection vehicle on the main road and the intelligent network connection vehicle on the ramp are utilized to realize the safety controllability when the ramp vehicles are converged by the cooperative control of the two vehicles.
The method is applicable to different types of expressway ramps. Under the balanced and unbalanced ramp traffic flow, the safety and controllability of ramp vehicle convergence can be effectively ensured.
Drawings
Fig. 1 is a schematic view of a highway traffic scene in an embodiment of the present invention.
FIG. 2 is a schematic diagram illustrating analysis of formation conditions in a ramp in an embodiment of the present invention.
FIG. 3 is a schematic diagram of case one when formation is completed in an embodiment of the present invention.
Fig. 4 is a schematic diagram of a second case when formation is completed in the embodiment of the present invention.
Fig. 5 is a schematic diagram of intelligent network vehicle linkage cooperative control of a main road in an embodiment of the invention.
Detailed Description
The invention will now be described in further detail with reference to specific examples, which are intended to illustrate, but not to limit, the invention.
The invention discloses a highway ramp mixed traffic flow control method based on a controllable intelligent network car, which comprises the following steps:
s1, referring to FIGS. 1 and 2, dividing a ramp road section into a normal driving road section, a formation road section and an acceleration merging road section, and marking a head vehicle determination point on the ramp road sectionFormation completion Point->And sink->The method comprises the steps of carrying out a first treatment on the surface of the Head cart determination point ++>The position on the ramp is +.>Formation completion Point->The position of (2) is +.>Sink->The position on the ramp is +.>The position on the main road is +.>。
S2, forming a vehicle formation with the intelligent network vehicle as a head vehicle by forming a road section on the intelligent network vehicle and the human driving following vehicle;
consider the extreme case, assuming that when the intelligent network connection happens to reach the head-end determination pointWhen the vehicle is in use, a person just drives the vehicle at the ramp entrance, and no other vehicle exists between the vehiclesAnd (5) a vehicle. When formation is completed, the intelligent network vehicle and the following person driving following vehicle satisfy the formula:
in the formula ,determining the point for the head car>A location on the ramp;
in order to form a formation head gear +.>The running speed at the moment;
the person driving the following vehicle for forming a team is +.>The running speed at the moment;
is the inter-vehicle distance of the queue when in the following state.
When the formation completion point of the vehicle formation is marked on the rampWhen the intelligent network vehicle is coincident, the formula is satisfied between the intelligent network vehicle and the human driving following vehicle:
in the formula ,determine the point for the head gear to reach>The vehicle speed at the time of treatment;
deceleration for intelligent network connection;
acceleration of a following vehicle for a person;
deceleration of a following vehicle for a person;
and />The constant-speed running time of the intelligent network car and the constant-speed running time of the human driving following car are respectively.
S3, calculating that the vehicle formation completely reaches the entry pointTime interval +.>Completely reach sink->The time of (2) is the interval value because the running speeds of vehicles in the vehicle formation are different, and the specific calculation steps are as follows:
s31, firstly, calculating the lowest speed of the intelligent network vehicle on the ramp:
when the acceleration and deceleration processes of the head car and the human driving following car are not considered, the speed of the intelligent network car running on the ramp satisfies the formula:
in the formula ,the lowest speed for the vehicle to travel on the ramp;
for the highest speed of the vehicle running on the ramp, the vehicle refers to intelligent network car and human driving following vehicle;
completion point for formation on ramp>Is a position of (2);
tis the travel time of the vehicle on the ramp.
S32, respectively calculating arrival of intelligent network vehicle connection and human driving following vehicle to the sink pointTime of (2):
(1) Calculating arrival sink-in point of intelligent network vehicle linkage according to vehicle formation completion pointTime of (2):
case one: referring to fig. 3, when a formation completion point of a vehicle formation and a formation completion point marked on a rampWhen the intelligent network connection vehicle is coincident, the intelligent network connection vehicle reaches the sink point +.>Time of (2)t cav_to_S The formula is satisfied:
；
and a second case: referring to fig. 4, when a formation completion point of a vehicle formation is located at a first vehicle determination point on a rampAnd formation completion point->When the intelligent network connection vehicle reaches the sink point +.>Time of (2)t cav_to_S The formula is satisfied:
in the formula ,completion point for formation on ramp>Is a position of (2);
is the junction point on the ramp>Is a position of (2);
acceleration for intelligent network connection;
the intelligent network vehicle is the position of the intelligent network vehicle on the ramp when formation is successful.
(2) Calculation of complete arrival of human driving following vehicle at sink point based on New well following modelAnd the time of (2) satisfies the formula:
in the formula ,reaction time of following the car for the human driving;
minimum following distance for a human driving following vehicle;
nto the first in formationnThe vehicle is a vehicle that is to be operated,n≠1。
s33, finally calculating that the vehicle formation completely reaches the entry pointIs a time interval of (2):
vehicle formation fully reaches the point of entryThe time interval of (2) is>Wherein, assuming that the distances between two adjacent vehicles in the vehicle formation are the same, the shortest timet min The calculation formula of (2) is as follows:
maximum timet max The calculation formula of (2) is as follows:
in the formula ,completion point for formation on ramp>Is a position of (2);
is the junction point on the ramp>Is a position of (2);
the number of vehicles in the ramp vehicle formation;
the distance between the vehicles in the queue when the vehicle is in the following state;
in this embodiment, it is assumed that the intelligent network vehicle and the human-driven following vehicle have the same body length, which is the body length of the vehiclel。
For the number of vehicles in a formationThe intelligent network connection and road side equipment can be used for collaborative calculation, and the specific algorithm comprises the following steps:
step (1), as shown in FIG. 2, when the vehicle is driven to the head-mounted determination pointAnd (2) when the road side equipment firstly judges the type of the vehicle, if the vehicle is an intelligent network connected vehicle, the intelligent network connected vehicle is determined to be a head vehicle, and the step (2) is executed. If it is a conventional person driving the vehicle, step (3) is performed.
Step (2), judging whether the formation forming road section is in the formation forming road sectionThe other head cars, if any, are formed into a road section and the following vehicles are automatically formed successfully, at which time the number of vehicles in the formation，/>The value of +.>The vehicle counter at the point determines, obtains the number of vehicles in the last formation +.>After that, pair->Reassigning the value, lettingReturning to the step (1) and continuing to execute; if not, for->Assignment, let->Returning to the step (1) to continue execution, and circularly executing the step (4).
Step (3), judging whether a head car waits for forming a formation in the formation road section at the moment, if so, makingThe value of the vehicle counter at the point +.>Sequentially executing the step (4); if not, go back to step (1) to continue execution.
Step (4), judging whether the head vehicle which does not form the formation in the formation road section runs to the formation completion pointIf yes, the head car and +.>The roadside device at the point communicates, and the vehicle counter stops counting, and at this time the vehicle counter +.>The value of (2) is the number of vehicles in the queue +.>And (5) returning to the step (1) and continuing to execute.
S4, cooperatively controlling the intelligent network coupling vehicle on the main road and the intelligent network coupling vehicle on the ramp section, when the position of the intelligent network coupling vehicle on the main road is located in the intervalIn, the speed of the intelligent network linkage on the main road is adjusted to reserve a safe import gap for ramp vehicle formation, wherein ∈>Is the highest speed limit of the expressway main road,M S for the main road from the start point to the sink point +.>Is a distance of (3).
For cooperative control intelligent network vehicles determined in the interval, enough converging gaps are reserved for ramp vehicle formation, as shown in fig. 4 and 5, the intelligent network vehicles on the main road must arrive at the converging point later than the last vehicle of the vehicle formationThe speed of intelligent network connection on the road>The formula is satisfied:
wherein ,when the formation of vehicles in the ramp is successful, the position of the intelligent network vehicle connection is cooperatively controlled on the main road;
for the last vehicle in the vehicle formation to travel to the sink +.>The time required;
the intelligent network car is cooperatively controlled in +.>The travel speed at the moment.
On the main road, the workshop spacing between the intelligent network car and the preceding carThe formula is satisfied:
in the formula ,the number of vehicles in the ramp vehicle formation;
the distance between the vehicles in the queue when the vehicle is in the following state;
for the length of the body of the vehicle, in this embodiment, it is assumed that the intelligent network is connected to the vehicle and the driver drives the vehicleThe length of the body is the same as that of the vehiclel。
S5, the vehicles are formed and gathered into a main road.
The highway ramp mixed traffic flow control method based on the controllable intelligent network connected vehicles obtains traffic conditions of highway main roads and downstream confluence areas in advance by means of the internet of vehicles technology, and utilizes the controllability of the intelligent network connected vehicles to complete guiding ramp vehicles to safely enter the highway main roads by controlling the speed of the intelligent network connected vehicles, so that the situation that drivers only find opportunities to enter the main roads according to own driving experience and surrounding driving environments is avoided.
The foregoing description of the preferred embodiment of the present invention is not intended to limit the technical solution of the present invention in any way, and it should be understood that the technical solution can be modified and replaced in several ways without departing from the spirit and principle of the present invention, and these modifications and substitutions are also included in the protection scope of the claims.
Claims (1)
1. The highway ramp mixed traffic flow control method based on the controllable intelligent network car is characterized by comprising the following steps of:
s1, dividing a ramp road section into a normal driving road section, a formation road section and an acceleration converging road section, marking a head vehicle determining point on the ramp road sectionFormation completion Point->And sink->；
S2, forming a vehicle formation with the intelligent network vehicle as a head vehicle by forming a road section on the intelligent network vehicle and the human driving following vehicle; when formation is completed, the intelligent network vehicle and the following human driving vehicle meet the formula:
in the formula ,determining the point for the head car>A location on the ramp;
in order to form a formation head gear +.>The running speed at the moment;
the person driving the following vehicle for forming a team is +.>The running speed at the moment;
the distance between the vehicles in the queue when the vehicle is in the following state;
s3, calculating that the vehicle formation completely reaches the entry pointTime interval +.>The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following specific steps: firstly, calculating the lowest speed of the intelligent network vehicle on a ramp, and respectively calculating the total arrival of the intelligent network vehicle and a human driving following vehicle at a junction point +.>Finally calculate the time for the vehicle formation to reach the entry point completely +.>Is a time interval of (2);
the speed of the intelligent network vehicle traveling on the ramp satisfies the formula:
in the formula ,the lowest speed for the vehicle to travel on the ramp;
the highest speed of the vehicle running on the ramp;
completion point for formation on ramp>Is a position of (2);
tthe driving time of the vehicle on the ramp is set;
calculating arrival sink-in point of intelligent network vehicle linkage according to vehicle formation completion pointTime of (2):
case one: when the formation completion point of the vehicle formation is marked on the rampWhen the intelligent network connection vehicle is coincident, the intelligent network connection vehicle reaches the sink point +.>Time of (2)t cav_to_S The formula is satisfied:
；
and a second case: when the formation completion point of the vehicle formation is positioned at the head vehicle determination point on the rampAnd formation completion point->When the intelligent network connection vehicle reaches the sink point +.>Time of (2)t cav_to_S The formula is satisfied:
in the formula ,completion point for formation on ramp>Is a position of (2);
is the junction point on the ramp>Is a position of (2);
acceleration for intelligent network connection;
the position of the intelligent network vehicle on the ramp when formation is successful;
calculation of arrival of human driving following vehicles at sink point based on New well following modelAnd the time of (2) satisfies the formula:
in the formula ,reaction time of following the car for the human driving;
minimum following distance for a human driving following vehicle;
nto the first in formationnThe vehicle is a vehicle that is to be operated,n≠1；
vehicle formation fully reaches the point of entryThe time interval of (2) is>Wherein the shortest timet min The calculation formula of (2) is as follows:
maximum timet max The calculation formula of (2) is as follows:
in the formula ,the number of vehicles in the ramp vehicle formation;
the distance between the vehicles in the queue when the vehicle is in the following state;
is the length of the body of the vehicle;
s4, cooperatively controlling intelligent network vehicles on the main road and intelligent network vehicles on the ramp section, and reserving an import gap for formation vehicles on the ramp on the main road;
when the position of the intelligent network connection vehicle on the main road is located in the sectionAnd in the course, the speed of intelligent network linkage on the main road is regulated to reserve a safe afflux gap for ramp vehicle formation, wherein,is the highest speed limit of the expressway main road,M S for the main road from the start point to the sink point +.>Is a distance of (2);
speed of intelligent network coupling vehicle on main roadThe formula is satisfied:
wherein ,when the formation of vehicles in the ramp is successful, the position of the intelligent network vehicle connection is cooperatively controlled on the main road;
for the last vehicle in the vehicle formation to travel to the sink +.>The time required;
for the cooperative control intelligent network car at +.>The running speed at the moment;
on the main road, the workshop spacing between the intelligent network car and the preceding carThe formula is satisfied:
in the formula ,the number of vehicles in the ramp vehicle formation;
the distance between the vehicles in the queue when the vehicle is in the following state;
in this embodiment, it is assumed that the intelligent network vehicle and the human-driven following vehicle have the same body length, which is the body length of the vehiclel；
S5, the vehicles are formed and gathered into a main road.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211165614.6A CN115880884B (en) | 2022-09-23 | 2022-09-23 | Expressway ramp mixed traffic flow control method based on controllable intelligent network vehicle connection |
| PCT/CN2023/074797 WO2024060486A1 (en) | 2022-09-23 | 2023-02-07 | Expressway ramp hybrid-traffic-flow management and control method based on controllable connected and automated vehicles |
| US18/461,285 US20230415745A1 (en) | 2022-09-23 | 2023-09-05 | CONTROL METHOD OF MIXED TRAFFIC FLOW ON FREEWAY RAMP BASED ON CONTROLLABLE CONNECTED AND AUTONOMOUS VEHICLES (CAVs) |
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| CN202211165614.6A CN115880884B (en) | 2022-09-23 | 2022-09-23 | Expressway ramp mixed traffic flow control method based on controllable intelligent network vehicle connection |
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| CN116665459A (en) * | 2023-08-02 | 2023-08-29 | 武汉御风智行科技有限公司 | Confluence area multi-vehicle cooperation method and device based on mixed traffic flow |
| CN117975714B (en) * | 2023-12-27 | 2025-01-24 | 东南大学 | A ramp merging longitudinal control method and device integrating virtual queue and lane changing rules |
| CN118397856B (en) * | 2024-04-28 | 2024-12-27 | 长沙理工大学 | Trajectory smoothing method for intelligent connected vehicles at signalized intersections based on platoon control |
| CN118553115B (en) * | 2024-07-30 | 2024-11-08 | 中汽数据(天津)有限公司 | Vehicle ramp collaborative import method, device and storage medium |
| CN119181250B (en) * | 2024-11-26 | 2025-02-28 | 长沙理工大学 | A method, system and storage medium for dynamic speed limit control of highways based on intelligent passenger and freight connected vehicle barriers |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1223443A (en) * | 1967-04-14 | 1971-02-24 | Lab For Electronics Inc | Expressway ramp traffic control system |
| WO1996040545A1 (en) * | 1995-06-07 | 1996-12-19 | Autran Corp. | System for automated transport of automobile platforms, passenger cabins and other loads |
| EP1699033A2 (en) * | 2005-03-03 | 2006-09-06 | Aisin Aw Co., Ltd. | A method of driving support and a driving support apparatus |
| CN102314769A (en) * | 2010-07-07 | 2012-01-11 | 交通运输部公路科学研究所 | Intelligent safety early-warning control method for merging area of entrance ramp of expressway |
| CN106601002A (en) * | 2016-11-23 | 2017-04-26 | 苏州大学 | Urban expressway entrance ramp vehicle traffic guiding system and guiding method thereof in Internet of vehicles environment |
| CN112116822A (en) * | 2020-09-21 | 2020-12-22 | 长沙理工大学 | A collaborative control method of expressway capacity based on dynamic lane allocation of CAVs mixed traffic flow |
| CN113313949A (en) * | 2021-05-31 | 2021-08-27 | 长安大学 | Method, device and equipment for cooperative control of passenger cars and trucks on expressways and ramp ways |
| CN113345268A (en) * | 2021-07-16 | 2021-09-03 | 长沙理工大学 | CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene |
| CN113409594A (en) * | 2021-07-29 | 2021-09-17 | 苏州大学 | Ramp signal control optimization method and system based on reinforcement learning |
| CN113781788A (en) * | 2021-11-15 | 2021-12-10 | 长沙理工大学 | A Stability and Safety-Based Management Method for Autonomous Driving Vehicles |
| 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 |
| CN216211540U (en) * | 2021-10-25 | 2022-04-05 | 天津新展高速公路有限公司 | Intelligent high-speed safe intelligent guidance system of wisdom |
| CN114639246A (en) * | 2022-05-18 | 2022-06-17 | 哈尔滨工业大学 | Expressway ramp confluence area vehicle-road cooperative control method and system |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010103504A1 (en) * | 2009-03-08 | 2010-09-16 | Yehuda Gore | System and method for controlling traffic by coordination of intersection approaching flows |
| CN104464317B (en) * | 2014-12-03 | 2016-05-11 | 武汉理工大学 | On-Ramp on Freeway interflow district's guiding control system and method |
| US11495126B2 (en) * | 2018-05-09 | 2022-11-08 | Cavh Llc | Systems and methods for driving intelligence allocation between vehicles and highways |
| CN108538069B (en) * | 2018-05-24 | 2020-10-16 | 长安大学 | System and method for controlling vehicle speed in ramp merging area |
| CN108986471B (en) * | 2018-06-22 | 2020-08-21 | 长安大学 | Intersection vehicle guiding method under mixed traffic condition |
| US11145197B2 (en) * | 2019-03-13 | 2021-10-12 | Mitsubishi Electric Research Laboratories, Inc. | Joint control of vehicles traveling on different intersecting roads |
| CN110930697B (en) * | 2019-11-12 | 2021-05-25 | 南京航空航天大学 | Rule-based intelligent networked vehicle cooperative convergence control method |
| CN111369813B (en) * | 2020-03-23 | 2021-10-08 | 江苏大学 | A collaborative control method and system for ramp splitting and merging of intelligent networked vehicles |
| CN111599194B (en) * | 2020-05-27 | 2024-06-18 | 中汽研汽车检验中心（天津）有限公司 | Highway entrance ramp heterogeneous traffic flow speed guiding system and guiding method |
| CN112614340B (en) * | 2020-12-11 | 2022-03-08 | 国汽(北京)智能网联汽车研究院有限公司 | Method and device for enabling branch vehicles to converge into main road, electronic equipment and storage medium |
| CN114067559B (en) * | 2021-09-27 | 2022-11-15 | 北京交通大学 | A merge optimization control method for automatic vehicle-only lanes merging into ordinary lanes |
| CN114664078B (en) * | 2022-03-18 | 2023-01-17 | 河北工业大学 | Road confluence area cooperation convergence control method based on automatic driving vehicle queue |
| CN114708734B (en) * | 2022-05-07 | 2023-01-10 | 合肥工业大学 | A collaborative control method for on-ramp network-connected human-driven vehicles merging into the main line |
| CN114973666A (en) * | 2022-05-18 | 2022-08-30 | 江苏科创车联网产业研究院有限公司 | Vehicle speed induction method, device and medium for networked vehicles based on vehicle-road coordination |
| CN114999152B (en) * | 2022-05-25 | 2024-04-30 | 清华大学 | Ramp merging edge cloud control method for mixed traffic flow |
-
2022
- 2022-09-23 CN CN202211165614.6A patent/CN115880884B/en active Active
-
2023
- 2023-02-07 WO PCT/CN2023/074797 patent/WO2024060486A1/en unknown
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1223443A (en) * | 1967-04-14 | 1971-02-24 | Lab For Electronics Inc | Expressway ramp traffic control system |
| WO1996040545A1 (en) * | 1995-06-07 | 1996-12-19 | Autran Corp. | System for automated transport of automobile platforms, passenger cabins and other loads |
| EP1699033A2 (en) * | 2005-03-03 | 2006-09-06 | Aisin Aw Co., Ltd. | A method of driving support and a driving support apparatus |
| CN102314769A (en) * | 2010-07-07 | 2012-01-11 | 交通运输部公路科学研究所 | Intelligent safety early-warning control method for merging area of entrance ramp of expressway |
| CN106601002A (en) * | 2016-11-23 | 2017-04-26 | 苏州大学 | Urban expressway entrance ramp vehicle traffic guiding system and guiding method thereof in Internet of vehicles environment |
| CN112116822A (en) * | 2020-09-21 | 2020-12-22 | 长沙理工大学 | A collaborative control method of expressway capacity based on dynamic lane allocation of CAVs mixed traffic flow |
| CN113313949A (en) * | 2021-05-31 | 2021-08-27 | 长安大学 | Method, device and equipment for cooperative control of passenger cars and trucks on expressways and ramp ways |
| CN113345268A (en) * | 2021-07-16 | 2021-09-03 | 长沙理工大学 | CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene |
| CN113409594A (en) * | 2021-07-29 | 2021-09-17 | 苏州大学 | Ramp signal control optimization method and system based on reinforcement learning |
| 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 |
| CN216211540U (en) * | 2021-10-25 | 2022-04-05 | 天津新展高速公路有限公司 | Intelligent high-speed safe intelligent guidance system of wisdom |
| CN113781788A (en) * | 2021-11-15 | 2021-12-10 | 长沙理工大学 | A Stability and Safety-Based Management Method for Autonomous Driving Vehicles |
| CN114639246A (en) * | 2022-05-18 | 2022-06-17 | 哈尔滨工业大学 | Expressway ramp confluence area vehicle-road cooperative control method and system |
Non-Patent Citations (1)
| Title |
|---|
| 基于多前车最优速度与加速度的网联车跟驰模型;史昕;纪艺;赵祥模;惠飞;;现代电子技术(09);全文 * |
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