CN108877268B - Unmanned-oriented traffic-light-free crossroad intelligent scheduling method - Google Patents
Unmanned-oriented traffic-light-free crossroad intelligent scheduling method Download PDFInfo
- Publication number
- CN108877268B CN108877268B CN201810893061.3A CN201810893061A CN108877268B CN 108877268 B CN108877268 B CN 108877268B CN 201810893061 A CN201810893061 A CN 201810893061A CN 108877268 B CN108877268 B CN 108877268B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- area
- traffic
- lane
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/164—Centralised systems, e.g. external to vehicles
Abstract
An intelligent unmanned-oriented traffic-light-free crossroad scheduling method comprises the following steps: step 1: setting a central control management mechanism which is based on a crossroad traffic manager, a dispatching area and a division collision avoidance area, and taking the traffic manager as cooperative vehicle communication and planning vehicle dispatching; step 2: the traffic manager receives the running data of specific vehicles entering the dispatching area and returns the dispatching information of the specific vehicles passing the crossroads; and step 3: according to the guidance of the scheduling information, the unmanned vehicle correspondingly adjusts to pass through the crossroad; the collision avoidance area is a crossing area of two lanes of the crossroad, the collision avoidance area is divided into 16 square collision areas with the same area, and the same square collision area can be occupied by only one vehicle at most; the unmanned vehicle entering the collision avoidance zone needs to travel at the same constant speed and in a given direction.
Description
Technical Field
The invention belongs to the technical field of vehicle networking, and relates to a method for optimal vehicle scheduling and capacity optimization of intersection-oriented traffic in a vehicle control strategy based on state information.
Background
With the development of wireless communication, unmanned vehicles are able to implement communication connections and information interaction between vehicles, vehicles and infrastructure, etc. using Dedicated Short Range Communication (DSRC) technology. These unmanned vehicles, interconnected by wireless communication, can reduce most of the collision accidents of the traffic system, reduce traffic delay, greatly improve traffic management efficiency and can provide functions of infotainment and telematics. The interconnected unmanned vehicles are therefore of great potential for improving traffic system management capabilities, and their advantages make them one of the research hotspots in the current field of car networking technology.
The traditional traffic light dispatching method for managing the traffic intersections is realized by opening part of intersections at fixed time intervals to allow vehicles to pass, and stopping and waiting the vehicles at the other intersections. The traditional traffic light dispatching method can adjust the traffic flow at the intersection to a certain extent, but has the following defects: real-time traffic flow cannot be flexibly and intelligently adapted; lack of a communication coordination function with the unmanned vehicle; the problems of traffic jam and safety caused by the increase of the traffic flow cannot be solved; it is difficult to improve the throughput of the entire traffic system and the traffic management efficiency. On the basis of the progress and development of intelligent traffic systems, the traditional intersection traffic light scheduling method is gradually improved and is possibly replaced by other scheduling methods, so that effective traffic intersection management is carried out in the environment of vehicle networking communication cooperative technology, and the development and application of interconnected unmanned vehicles are further adapted.
Through the search of the existing documents, in 2013, articles entitled "Adaptive traffic signal control with Vehicular ad hoc networks" and "Back-pressure traffic signal control with fixed and Adaptive routes" for urban on-board networks were published by k.pandit et al and a.a.zaidi et al in IEEE Transactions on Vehicular Technology (IEEE) and 2016 in IEEE Transactions on Intelligent traffic Systems (IEEE Intelligent traffic Systems) respectively. The articles are all based on a dynamic self-adaptive control system, and an intelligent traffic light management technology and mechanism facing real-time traffic flow are provided. While intelligent traffic light systems can ameliorate the inflexible disadvantages of traditional traffic light scheduling and improve intersection traffic management capabilities, they also have difficulty meeting future requirements for interconnected unmanned vehicle traffic scheduling with respect to both vehicle collision avoidance and maximizing intersection throughput.
It is found through search that in order to further optimize the intersection management efficiency, an article entitled "State-driven priority scheduling mechanism for unmanned vehicle" published by k.zhang et al in 2015 proposes a scheduling method based on vehicle State priority in a traffic scene without traffic lights. By allocating the sequence grades to specific vehicles and combining with a scheme of avoiding contradiction areas among different vehicle running tracks, the method can effectively prevent the vehicles in the crossroad from colliding. In addition, it was found through a search that p.lin et al published an article entitled "automatic vehicle-intersection coordination method in a connected vehicle environment" in 2017 "IEEE Intelligent Transportation Systems Magazine (IEEE Intelligent Transportation Systems). The article adopts the principle of a buffer allocation scheduling mode to guide the vehicle to safely pass through the crossroad according to the set rule, but the whole scheduling process needs to continuously change the acceleration of the vehicle through control to adjust the running track of the vehicle, so that the unmanned vehicle is required to be always in communication connection with the control center.
In summary, the problems of the prior art are as follows: (1) the improved intelligent traffic light dispatching system cannot fully utilize the advantages of the interconnected unmanned vehicles to improve the traffic management capacity. (2) Most of the novel dispatching algorithms under the scene without traffic lights are single in functionality and limited to the fact that the specific vehicle running rule (3) depends on the reliability of communication and the stability of unmanned technology in the process of adjusting the vehicle running state. The significance of solving the technical problems is as follows: based on the development of the current wireless communication technology and the progress of the unmanned technology, the more efficient and reliable intelligent traffic dispatching system is beneficial to reducing the problem of system safety and improving the dispatching efficiency of vehicles, provides a new idea for planning and designing future crossroads and promotes the application and development of the communication technology in the field of vehicle networking and the vehicle control management strategy.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an unmanned traffic-light-free intersection intelligent scheduling method on the basis of avoiding the collision of vehicles at the intersection.
The invention aims to realize the method, and the method for intelligently scheduling the unmanned crossroads without the traffic lights comprises the following steps:
step 1: setting a central control management mechanism which is based on a crossroad traffic manager, a dispatching area and a division collision avoidance area, and taking the traffic manager as cooperative vehicle communication and planning vehicle dispatching;
step 2: the traffic manager receives the driving data of the vehicles entering the dispatching area and returns the dispatching information of the specific vehicles passing the crossroads;
and step 3: and correspondingly adjusting the unmanned vehicle to pass through the crossroad according to the guidance of the scheduling information.
The crossroad traffic management (coordination) device is composed of a roadside unit and a data center, wherein the roadside unit is used for establishing communication connection with the unmanned vehicles, and the data center is used for calculating the time when the specific vehicles enter the crossroad collision avoidance area.
The right-turn lane is an independent collision-free lane, the traffic light-free scheduling scene is a bidirectional four-lane traffic intersection model comprising a left-turn lane and a straight-going lane, the odd lanes represent left-turn lanes, and the even lanes represent right-turn lanes.
The collision avoidance area is a crossing area of two lanes at a crossroad (longitudinal and transverse), the collision avoidance area is divided into 16 square collision areas with the same area, and the same square collision area can be occupied by only one vehicle at most; the unmanned vehicle entering the collision avoidance zone needs to travel at the same constant speed and in a given direction, contributing to a reduction in the complexity of the system and an improvement in the safety of the entire system.
The intelligent scheduling algorithm based on collision avoidance schedules objects, namely specific vehicles, to comprise independent unmanned vehicles and a fleet composed of vehicles with the same driving information.
The intelligent traffic scheduling method for the crossroad is characterized in that a dedicated short-range communication (DSRC) technology is used for establishing communication connection between an unmanned vehicle and a traffic manager, and free distribution of traffic is realized on the basis.
In the step 2, the information interaction between the traffic manager and the unmanned vehicle needs to execute the following steps:
step (2.1): when the unmanned vehicle enters the dispatching area, the time of entering the dispatching area and the current initial speed of the unmanned vehicle are sent to a traffic manager;
step (2.2): the traffic manager plans the vehicle schedule from the received data and returns the time at which the particular vehicle entered the collision avoidance zone and the speed of travel at the collision avoidance zone.
The intelligent crossroad traffic scheduling method only needs two times of information interaction between the unmanned vehicle and the traffic coordinator, does not require the unmanned vehicle and the traffic coordinator to maintain communication connection all the time, and can greatly avoid the influence on the whole system caused by packet loss or communication delay.
Compared with the prior art, the unmanned-oriented traffic-light-free intersection intelligent scheduling method has the beneficial effects that firstly, the unmanned-oriented traffic-light-free intersection intelligent scheduling method can effectively avoid collision among vehicles and maximize traffic throughput of the intersection; secondly, the scheduling scheme of a motorcade consisting of unmanned vehicles (a plurality of vehicles which uniformly pass in the same direction can be merged into the motorcade to give the same scheduling instruction) is one of the outstanding contributions of the invention; and thirdly, no specific rule is added in the process that the vehicle passes through the intersection, so that the dependence degree on communication is reduced, and the robustness of the whole traffic system is greatly improved.
The invention discloses an unmanned crossroad intelligent scheduling method without traffic lights based on the development of a vehicle communication cooperation technology of an internet of vehicles, and provides a safe and efficient scheduling solution for traffic management of a future crossroad. The scheduling method comprises the following steps: establishing a central control management mechanism which takes the traffic manager as cooperative vehicle communication and planning vehicle scheduling based on the establishment of the crossroad traffic manager and the division of the scheduling area and the collision avoidance area; the traffic manager receives the driving data of the vehicles entering the dispatching area and returns the dispatching information of the specific vehicles passing the crossroads; and correspondingly adjusting the unmanned vehicle to pass through the crossroad according to the guidance of the scheduling information. Compared with the traditional traffic light vehicle dispatching mode, the invention can improve the vehicle throughput at the crossroad to the maximum extent and improve the traffic efficiency at the crossroad on the basis of avoiding the collision of unmanned vehicles.
Drawings
Fig. 1 is a scene diagram of a traffic-light-free intersection adopted in the embodiment of the present invention.
FIG. 2 is a schematic diagram of the steps of a traffic manager dispatching an unmanned vehicle according to an embodiment of the invention.
FIG. 3 is a block diagram of an implementation of an algorithm for assigning an optimal entry time to a vehicle according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating comparison of the traffic throughput of the intersection based on the intelligent scheduling algorithm and the conventional traffic light method under the condition of average traffic flow according to the embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating comparison of the traffic throughput of the intersection based on the intelligent scheduling algorithm and the conventional traffic light method under the condition of non-average traffic flow according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in detail below with reference to the accompanying drawings: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given. It should be understood that the specific examples described herein are merely illustrative of the invention and that the scope of the invention is not limited to the examples described below.
Examples
In the embodiment, the traffic-light-free crossroad scene shown in fig. 1 is adopted, and an intelligent unmanned vehicle-oriented scheduling method based on collision avoidance is provided. Firstly, in the traffic scene, a traffic manager needs to be set up at the intersection. The traffic manager consists of a roadside unit and a data center, wherein the roadside unit is used for establishing communication connection with the unmanned vehicles, and the data center is used for calculating the time when the specific vehicles enter the crossroads. The whole intersection is divided into two areas, namely a collision avoidance area and a dispatching area. The collision avoidance zone is a core area of the intersection, and the unmanned vehicle in the zone needs to travel at a fixed speed (including direction, which is determined by the lane position in the dispatch zone); the dispatch area is immediately adjacent to the collision avoidance area and within communication range of the traffic manager, and vehicles entering this area receive their dispatch plan for entering the collision avoidance area.
The basic goal of the present embodiment is to achieve free traffic scheduling on an intersection vehicle collision free basis by utilizing Dedicated Short Range Communication (DSRC) technology. To minimize the amount of data center calculations, the traffic manager need only collect the time at which the unmanned vehicles enter the dispatch area and their initial speeds at that time. The driving speed of the unmanned vehicle in the collision avoidance area is set to a fixed value in order to reduce the complexity of the system and improve the safety of the system.
The implementation steps of taking a traffic manager as a central control management mechanism for cooperating vehicle communication and planning vehicle scheduling are shown in fig. 2: when an unmanned vehicle enters a dispatching area, sending an entering moment and a current speed to a traffic manager; after the feasible scheduling scheme is calculated, the traffic manager returns specific vehicle scheduling information, including the time of entering the collision avoidance zone and the constant speed of driving in the collision avoidance zone; and finally, according to the guidance of the scheduling information, the unmanned vehicle adjusts the corresponding speed and passes through the crossroad according to the specific entering time. The vehicle scheduling mechanism is characterized in that only one time for entering the collision avoidance area needs to be allocated to the vehicle when the vehicle enters the buffer area, and the traffic intelligent scheduling method only needs to carry out information interaction twice with the traffic coordinator and does not require the vehicle and the traffic coordinator to maintain communication connection all the time, so that the influence on the whole system caused by packet loss or communication delay can be greatly avoided, and the stability of the system is improved.
The intersection collision avoidance model can be considered as a time occupation problem in this embodiment. Considering that the right-turn lane is an independent collision-free lane, the traffic-light-free intelligent scheduling scene is a bidirectional four-lane traffic intersection model comprising a left-turn lane and a straight-going lane, and the odd lanes represent left-turn lanes (such as L)1) The even lanes represent right-turn lanes (e.g., L)2). In bookIn the application, the right-turn lane is considered as an independent lane and does not collide with vehicles on other lanes (actually, the model of the patent can be considered as three lanes in one direction, namely, left-turn, straight-going and right-turn, and since the right-turn lane is an independent lane, the right-turn lane is not drawn out. the collision avoidance area is averagely divided into 16 identical square collision areas, and based on the division of the square collision areas, we can analyze the identical parts occupied by the driving tracks of different vehicles in the intersection.for unmanned vehicles located in the lane 2 and the lane 5, for example, the overlapped part of the tracks is the collision area 15. the collision areas occupied by all the lanes are shown in table 1:
table 1: conflict area and occupied lane
| Conflict area | Occupying the lane of the conflict area | Conflict area | Occupying the lane of the |
| 1 | |
9 | |
| 2 | |
10 | |
| 3 | |
11 | |
| 4 | |
12 | Lane 6, lane 7 |
| 5 | Lane 5, |
13 | |
| 6 | |
14 | |
| 7 | |
15 | |
| 8 | |
16 | |
In order to avoid that the same conflict area is occupied by different vehicles, the occupied time of each unmanned vehicle in each conflict area needs to be calculated. The length of the collision avoidance zone is w, and thus the length of each collision zone is w/4. In general, vehicles pass through a collision area in a straight-ahead manner and a turning manner. For a straight-ahead vehicle, the track length in the specific collision region section is w/4; for a vehicle turning in a collision zone, the trajectory length is about 1/4, i.e., π w/16, the circumference of a circle with a radius of w/4. Assuming the speed of the vehicle in the collision avoidance zone
Fixed at v, then the occupancy time of the straight-ahead vehicle in the conflict area is
The occupied time of the turning vehicle is
Then, define
Represents L1,…,L8Corresponding to the time when the driverless vehicle enters the collision avoidance zone on the lane and defining sigmai,kIndicating the occupancy time at the k-th collision zone on the i-lane. E.g. sigma5,15＝(t5,t5+t]Meaning that the time at which the vehicle enters the collision region 15(k) on the 5 th (i) th lane is t5(ti) The time when the collision area 15 is left is t5+ T, total occupied time T. The collision between vehicles can be prevented by combining the specific tracks of the vehicles on each lane and utilizing a method that the occupied time of the same collision area is not overlapped. L denotes a lane.
Still taking the collision region 15 as an example, in order to prevent the collision of the lane 2 with the vehicle on the lane 5, the following expression of collision avoidance can be obtained: sigma2,15∩σ5,15＝(t2+2t,t2+3t]∩(t5,t5+t]Phi is given. The expression is equivalent in meaning on the mathematical time axis to the sum of the distances between the intermediate points of two occupied time periods not less than half the respective occupied time distances, i.e., | (t)2+2.5t)-(t5+0.5t)|＝|t2-t5+2t ≧ 0.5t +0.5t ═ t. Similarly, by using the above-mentioned techniques and the method of non-overlapping time occupation of conflict regions, we can use the intersectionCollision avoidance of vehicles and fleets of vehicles is converted into a series of absolute value inequality constraint expressions.
Since the basic objective of the unmanned-oriented intelligent traffic-light-free intersection scheduling method is to improve the throughput of the intersection to the maximum extent on the basis of vehicle collision avoidance, the objective function of the embodiment is expressed as the minimum time when the vehicles on all lanes enter the collision avoidance area, and is written as min (t) by using a mathematical expression1+…t8). Considering that an exhaustive search is impractical when the number of absolute inequality constraints is particularly large, the present embodiment provides a solution to the algorithm for optimal entry time of vehicles on different lanes: firstly, a vehicle which is about to enter a collision avoidance area on a certain lane is allocated with an optimal entering moment, and then other vehicles are allocated according to the lane sequence
The time of entry of a vehicle on the lane until the last vehicle. For lane 1, the vehicle about to enter the collision avoidance zone and the vehicle on the other lane that has been assigned the time of entry have the following inequality constraints:
|tje+bj|≥cj
order to
The scheduling problem is equivalent to:
min tj
s.t.tj≥tgj,
|tje+bj|≥cj
the optimal entry time algorithm for solving the above problem is shown in fig. 3: b is tojAnd cjRespectively denoted by bj(k) And cj(k) The absolute value inequality constraint can be written as tj≥cj(k)-bj(k) Or tj≤-cj(k)-bj(k) (ii) a Let the solved initial value be tgjWhen k is 1, obtaining the minimum entry time meeting the inequality; when k is 2 … mjThen, obtaining the minimum entering time meeting the current inequality and the k-1 inequality; the value resulting from the iteration is the final solution of the algorithm, i.e. the moment at which the minimum entry of the vehicle into the collision avoidance zone currently needs to be scheduled, and this moment is added to the calculation process for calculating the entry of vehicles on other lanes into the collision avoidance zone.
In order to make this embodiment more intuitive and compare the performance of the optimal entering time algorithm with the performance of the traditional traffic light algorithm, fig. 4 shows a schematic diagram of their throughput based on the average traffic flow of all lanes. The traffic flow in the graph is divided into three cases, i.e., light traffic, medium traffic, and medium traffic. It is evident that the throughput of the optimal entry moment algorithm is nearly twice that of the traffic light algorithm in the case of moderate and heavy traffic. This is because when the traffic flow at the intersection gradually increases, the conventional method easily reaches the upper limit of the traffic capacity, and the optimal entry timing algorithm can effectively improve the traffic situation by allowing vehicles of all lanes to travel simultaneously, improving the traffic efficiency. Fig. 5 provides a graph comparing throughput of two scheduling algorithms based on non-average traffic flow. In the figure, the traffic flow in the north-south direction is light traffic, and the traffic flow in the east-west direction is heavy traffic, without loss of generality. It can be seen from the figure that both the throughput of the east-west direction optimal entry time algorithm and the total throughput of the crossroad are much higher than those of the conventional traffic light algorithm, which illustrates that the optimal entry time algorithm of the embodiment has extremely high fairness.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. An intelligent traffic light-free crossroad scheduling method for unmanned driving is characterized in that: the intelligent scheduling method based on collision avoidance comprises the following steps:
step 1: setting a central control management mechanism which is based on a crossroad traffic manager, a dispatching area and a division collision avoidance area, and taking the traffic manager as cooperative vehicle communication and planning vehicle dispatching;
step 2: the traffic manager receives the running data of specific vehicles entering the dispatching area and returns the dispatching information of the specific vehicles passing the crossroads;
and step 3: according to the guidance of the scheduling information, the unmanned vehicle correspondingly adjusts to pass through the crossroad;
the crossroad traffic manager is composed of a roadside unit and a data center, wherein the roadside unit is used for establishing communication connection with the unmanned vehicle, and the data center is used for calculating the time when a specific vehicle enters the crossroad collision avoidance area;
the right-turn lane is an independent collision-free lane, the traffic light-free scheduling scene is a bidirectional four-lane traffic intersection model comprising a left-turn lane and a straight-going lane, the odd lanes represent left-turn lanes, and the even lanes represent right-turn lanes;
the collision avoidance area is a crossing area of a crossroad of two lanes, the collision avoidance area is divided into 16 square collision areas with the same area, and the same square collision area can be occupied by only one vehicle at most; the unmanned vehicle entering the collision avoidance zone needs to travel at the same constant speed and in a given direction;
in order to avoid the occupation of different vehicles in the same conflict area, the occupation time of each unmanned vehicle in each conflict area needs to be calculated; the length of the collision avoidance zone is w, so the length of each collision zone is w/4; the vehicles have two modes of straight running and turning through a collision area, and for the vehicles running straight, the track length in a specific collision area part is w/4; for a vehicle turning in a collision region, the track length is 1/4 which is about the circumference formed by taking w/4 as a radius, namely π w/16; assuming that the speed of the vehicle is fixed at v in the collision avoidance zone, the occupancy time of the straight-ahead vehicle in the collision zone is
The occupied time of the turning vehicle is
Definition of
Corresponding to the time of entry of the unmanned vehicle into the collision zone in lanes 1 to 8, respectively, and defining σi,kRepresenting the occupation time of the k-th collision region on the i lane; sigma5,15＝(t5,t5+t]Meaning that the time at which the vehicle on lane 5 enters the collision zone 15 is t5(ti) The time when the collision area 15 is left is t5+ T, lower case T is a fixed value, total occupancy time T equals T5+ t; the collision among the vehicles can be prevented by combining the specific tracks of the vehicles on each lane and utilizing a method that the occupied time of the same collision area is not overlapped; the constraint expression objective function is expressed as the minimum value of the time when the vehicles on all the lanes enter the collision avoidance area, and is written into min (t) by a mathematical expression1+…t8) (ii) a Considering that exhaustive search is not practical when the absolute value inequality constraint quantity is particularly large, firstly allocating an optimal entering moment for a vehicle which is about to enter a collision avoidance area on a certain lane, and then allocating entering moments of vehicles on other lanes according to the lane sequence until the last vehicle; for lane 1, the vehicle about to enter the collision avoidance zone and the vehicle on the other lane that has been assigned the time of entry have the following inequality constraints:
|tje+bj|≥cj
order to
The scheduling problem is equivalent to:
min tj
s.t.tj≥tgj,
|tje+bj|≥cj
the optimal entry time algorithm for solving the above problem: b is tojAnd cjRespectively denoted by bj(k) And cj(k) The absolute value inequality constraint is written as:
tj≥cj(k)-bj(k) or tj≤-cj(k)-bj(k) (ii) a Let the solved initial value be tgjWhen k is 1, obtaining the minimum entry time meeting the inequality; when k is 2 … mjThen, obtaining the minimum entering time meeting the current inequality and the k-1 inequality; the value resulting from the iteration is the final solution of the algorithm, i.e. the moment at which the minimum entry of the vehicle into the collision avoidance zone currently needs to be scheduled, and this moment is added to the calculation process for calculating the entry of vehicles on other lanes into the collision avoidance zone.
2. The unmanned-oriented traffic-light-free intersection intelligent scheduling method of claim 1, characterized in that: the intelligent scheduling algorithm scheduling object based on collision avoidance comprises an independent unmanned vehicle and a fleet composed of vehicles with the same driving information.
3. The unmanned-oriented traffic-light-free intersection intelligent scheduling method of claim 1, characterized in that: the intelligent traffic scheduling method for the crossroad is characterized in that a dedicated short-range communication (DSRC) technology is used for establishing communication connection between an unmanned vehicle and a traffic manager, and free distribution of traffic is realized on the basis.
4. The unmanned-oriented traffic-light-free intersection intelligent scheduling method of claim 1, characterized in that: in the step 2, the information interaction between the traffic manager and the unmanned vehicle needs to execute the following steps:
step (2.1): when the unmanned vehicle enters the dispatching area, the time of entering the dispatching area and the current initial speed of the unmanned vehicle are sent to a traffic manager;
step (2.2): the traffic manager plans the vehicle schedule from the received data and returns the time at which the particular vehicle entered the collision avoidance zone and the speed of travel at the collision avoidance zone.
5. The unmanned-oriented traffic-light-free intersection intelligent scheduling method of claim 1, characterized in that: the intelligent crossroad traffic scheduling method only needs two times of information interaction between the unmanned vehicle and the traffic coordinator, does not require the unmanned vehicle and the traffic coordinator to maintain communication connection all the time, and can greatly avoid the influence on the whole system caused by packet loss or communication delay.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810893061.3A CN108877268B (en) | 2018-08-07 | 2018-08-07 | Unmanned-oriented traffic-light-free crossroad intelligent scheduling method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810893061.3A CN108877268B (en) | 2018-08-07 | 2018-08-07 | Unmanned-oriented traffic-light-free crossroad intelligent scheduling method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108877268A CN108877268A (en) | 2018-11-23 |
| CN108877268B true CN108877268B (en) | 2021-05-25 |
Family
ID=64317302
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810893061.3A Active CN108877268B (en) | 2018-08-07 | 2018-08-07 | Unmanned-oriented traffic-light-free crossroad intelligent scheduling method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108877268B (en) |
Families Citing this family (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111243307B (en) * | 2018-11-28 | 2021-12-14 | 驭势科技（北京）有限公司 | System and method for dispatching vehicle with automatic driving function at intersection |
| CN109410619B (en) * | 2018-12-04 | 2021-09-10 | 清华大学 | Automatic traffic control method and system under one-way road network |
| WO2020127306A1 (en) | 2018-12-19 | 2020-06-25 | Siemens Mobility GmbH | Method, computer programme product, central control unit, and control system for controlling at least partially automated vehicles, some of which are intending to change lanes, in a road danger zone, in particular road junctions in road traffic |
| US20220084399A1 (en) * | 2019-01-04 | 2022-03-17 | Audi Ag | Method, system, module and software for intelligently governing a multi-way stop intersection |
| CN111627247B (en) * | 2019-02-28 | 2022-02-18 | 上海汽车集团股份有限公司 | Multi-vehicle formation control method and device |
| CN110060478A (en) * | 2019-04-29 | 2019-07-26 | 南京邮电大学 | Intersection Vehicular intelligent dispatching method and system |
| CN110444015B (en) * | 2019-07-08 | 2020-10-09 | 清华大学 | Intelligent network-connected automobile speed decision method based on no-signal intersection partition |
| CN110428647B (en) * | 2019-07-26 | 2022-10-04 | 阿波罗智联(北京)科技有限公司 | Method, device and equipment for cooperative passing of vehicles at intersection and storage medium |
| JP7348773B2 (en) * | 2019-08-22 | 2023-09-21 | 株式会社日立製作所 | Traffic flow control system, traffic flow control program, traffic flow control method, and travel control device |
| CN110473419A (en) * | 2019-09-09 | 2019-11-19 | 重庆长安汽车股份有限公司 | A kind of passing method of automatic driving vehicle in no signal lamp intersection |
| EP3836113A1 (en) * | 2019-12-09 | 2021-06-16 | Ningbo Geely Automobile Research & Development Co. Ltd. | A method for operating a transportation system |
| CN110910646B (en) * | 2019-12-11 | 2022-03-29 | 上海同济城市规划设计研究院有限公司 | Cooperative control method for unmanned buses at intersection |
| CN110952406A (en) * | 2019-12-11 | 2020-04-03 | 上海同济城市规划设计研究院有限公司 | Road arrangement suitable for unmanned bus and unmanned bus lane system |
| CN111243298B (en) * | 2020-01-19 | 2020-12-04 | 南京林业大学 | Intelligent traffic control method for common crossroad in automatic driving environment |
| CN112419703B (en) * | 2020-04-14 | 2021-11-30 | 青岛慧拓智能机器有限公司 | Intelligent unmanned mine card crossing traffic scheduling system and method |
| CN111583673A (en) * | 2020-04-28 | 2020-08-25 | 华东师范大学 | Intelligent intersection management method based on unmanned vehicle |
| CN113330497A (en) * | 2020-06-05 | 2021-08-31 | 曹庆恒 | Automatic driving method and device based on intelligent traffic system and intelligent traffic system |
| CN112068545B (en) * | 2020-07-23 | 2022-12-27 | 哈尔滨工业大学（深圳） | Method and system for planning running track of unmanned vehicle at crossroad and storage medium |
| CN112258860B (en) * | 2020-10-19 | 2022-05-03 | 深圳信息职业技术学院 | Crossing vehicle scheduling method, device, equipment and computer readable storage medium |
| CN112258864B (en) * | 2020-10-19 | 2022-03-11 | 广西大学 | Automatic driving vehicle intersection scheduling method and system based on sequence selection |
| CN112562371B (en) * | 2020-11-02 | 2022-03-04 | 河海大学 | Lightweight scheduling method based on automatic driving motorcade at signal lamp-free intersection |
| CN112629533B (en) * | 2020-11-11 | 2023-07-25 | 南京大学 | Fine path planning method based on road network rasterization road traffic prediction |
| CN112735152A (en) * | 2021-01-07 | 2021-04-30 | 王美金 | Multi-agent cooperative internet vehicle intersection mixed traffic control system and method |
| CN113276884B (en) * | 2021-04-28 | 2022-04-26 | 吉林大学 | Intelligent vehicle interactive decision passing method and system with variable game mode |
| CN113256961B (en) * | 2021-06-25 | 2022-05-24 | 上海交通大学 | Crossing autonomous vehicle scheduling and control method based on vehicle formation |
| CN113268817B (en) * | 2021-06-25 | 2022-11-01 | 上海交通大学 | Crossing autonomous vehicle scheduling and controlling method based on dynamic priority |
| CN113327458B (en) * | 2021-07-08 | 2023-01-06 | 潍柴动力股份有限公司 | Vehicle collision prediction method, vehicle collision prediction system, and electronic device |
| CN113505446B (en) * | 2021-09-10 | 2021-12-31 | 腾讯科技（深圳）有限公司 | Traffic simulation method, traffic simulation device, electronic equipment and computer storage medium |
| CN113823079B (en) * | 2021-10-27 | 2022-08-16 | 大连理工大学 | Formation control method for manned/unmanned vehicles based on vehicle-road cooperation |
| CN115440062B (en) * | 2022-08-25 | 2023-12-29 | 苏州摩卡智行信息科技有限公司 | Station node traffic coordination method and system based on intelligent networking environment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007257034A (en) * | 2006-03-20 | 2007-10-04 | Japan Radio Co Ltd | Emergency vehicle passing support system |
| CN102542818A (en) * | 2012-01-13 | 2012-07-04 | 吉林大学 | Organic-calculation-based coordination control method for traffic signal of zone boundary |
| RU2564469C1 (en) * | 2014-06-26 | 2015-10-10 | Игорь Юрьевич Мацур | Crossing traffic control process |
| CN105551251A (en) * | 2016-01-19 | 2016-05-04 | 华南理工大学 | No-signalized-intersection motor vehicle conflict probability determining method |
| CN105741551A (en) * | 2016-04-22 | 2016-07-06 | 华南理工大学 | Method for evaluating traffic safety of unsignalized intersection |
| JP2017207932A (en) * | 2016-05-18 | 2017-11-24 | 株式会社デンソー | Passage safety device, and traffic accident occurrence prevention method |
| CN108281026A (en) * | 2018-03-19 | 2018-07-13 | 武汉大学 | Intersection no signal lamp vehicle dispatching method under a kind of automatic Pilot environment |
-
2018
- 2018-08-07 CN CN201810893061.3A patent/CN108877268B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007257034A (en) * | 2006-03-20 | 2007-10-04 | Japan Radio Co Ltd | Emergency vehicle passing support system |
| CN102542818A (en) * | 2012-01-13 | 2012-07-04 | 吉林大学 | Organic-calculation-based coordination control method for traffic signal of zone boundary |
| RU2564469C1 (en) * | 2014-06-26 | 2015-10-10 | Игорь Юрьевич Мацур | Crossing traffic control process |
| CN105551251A (en) * | 2016-01-19 | 2016-05-04 | 华南理工大学 | No-signalized-intersection motor vehicle conflict probability determining method |
| CN105741551A (en) * | 2016-04-22 | 2016-07-06 | 华南理工大学 | Method for evaluating traffic safety of unsignalized intersection |
| JP2017207932A (en) * | 2016-05-18 | 2017-11-24 | 株式会社デンソー | Passage safety device, and traffic accident occurrence prevention method |
| CN108281026A (en) * | 2018-03-19 | 2018-07-13 | 武汉大学 | Intersection no signal lamp vehicle dispatching method under a kind of automatic Pilot environment |
Non-Patent Citations (1)
| Title |
|---|
| 基于交叉路口道路时空网格的车辆碰撞预警方法;刘明剑 等;《北京邮电大学学报》;20150430;第38卷(第2期);第38-44页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108877268A (en) | 2018-11-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108877268B (en) | Unmanned-oriented traffic-light-free crossroad intelligent scheduling method | |
| CN111445692B (en) | Speed collaborative optimization method for intelligent networked automobile at signal-lamp-free intersection | |
| Liu et al. | Trajectory planning for autonomous intersection management of connected vehicles | |
| Bashiri et al. | A platoon-based intersection management system for autonomous vehicles | |
| CN108877256B (en) | Wireless communication-based method for controlling scattered cooperative self-adaptive cruise near intersection | |
| CN114067559B (en) | Confluence optimization control method for merging special lane for automatic vehicle into common lane | |
| He et al. | PAMSCOD: Platoon-based arterial multi-modal signal control with online data | |
| Zhang et al. | A decentralized optimal control framework for connected automated vehicles at urban intersections with dynamic resequencing | |
| CN107507430B (en) | Urban intersection traffic control method and system | |
| CN111583673A (en) | Intelligent intersection management method based on unmanned vehicle | |
| Hassan et al. | A fully-distributed heuristic algorithm for control of autonomous vehicle movements at isolated intersections | |
| CN111932000A (en) | Multi-AGV (automatic guided vehicle) scheduling method applied to large logistics system | |
| CN111540219B (en) | Bidirectional bus signal priority coordination method based on artificial intelligence bus-road coordination | |
| Li et al. | A cooperative traffic control for the vehicles in the intersection based on the genetic algorithm | |
| Shu et al. | Signal timing optimization for transit priority at near-saturated intersections | |
| Xu et al. | When automated vehicles meet non-signalized intersections: A collision-free scheduling solution | |
| CN112750298B (en) | Truck formation dynamic resource allocation method based on SMDP and DRL | |
| CN114093166A (en) | Centralized hybrid vehicle coordination optimization method for signal-lamp-free fork | |
| Liu et al. | Modelling and simulation of cooperative control for bus rapid transit vehicle platoon in a connected vehicle environment | |
| Phan et al. | Space distribution method for autonomous vehicles at a signalized multi-lane intersection | |
| Yoshida et al. | A scheduling scheme for cooperative merging at a highway on-ramp with maximizing average speed of automated vehicles | |
| Ramaswamy et al. | Lane assignment on automated highway systems | |
| CN111899509A (en) | Intelligent networking automobile state vector calculation method based on vehicle-road information coupling | |
| Liu et al. | Dynamic bus scheduling of multiple routes based on joint optimization of departure time and speed | |
| CN113409567B (en) | Traffic assessment method and system for mixed traffic lane of public transport and automatic driving vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |