CN112258860A - Crossing vehicle scheduling method, device, equipment and computer readable storage medium - Google Patents

Crossing vehicle scheduling method, device, equipment and computer readable storage medium Download PDF

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CN112258860A
CN112258860A CN202011115806.7A CN202011115806A CN112258860A CN 112258860 A CN112258860 A CN 112258860A CN 202011115806 A CN202011115806 A CN 202011115806A CN 112258860 A CN112258860 A CN 112258860A
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vehicle
unmanned vehicle
intersection
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time
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CN112258860B (en
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高波
于牧辰
莫莉莎
陈朗莹
陈刚
张豪健
张霆廷
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Shenzhen Shengcai Youdao Digital Technology Co ltd
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Shenzhen Institute of Information Technology
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    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
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Abstract

The application relates to the field of unmanned driving, and provides a method, a device, equipment and a computer-readable storage medium for dispatching vehicles at an intersection, so that the computational complexity is reduced, and the dispatching can be carried out continuously. The method comprises the following steps: when the vehicles are detected to come in and go out of the intersection at the moment t, updating the state information of each unmanned vehicle in the dispatching vehicle set Ic according to the vehicle state information received at the moment t to obtain an updated dispatching vehicle set Ic'; obtaining position vector information when each unmanned vehicle in the updated dispatching vehicle set Ic' reaches the intersection in the shortest time; acquiring a crossing passing sequence set O according to position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the crossing in the shortest time; obtaining a track planning vector U of the unmanned vehicle according to the unmanned vehicle represented by each element in the intersection passing sequence set Oc、VcAnd Pc(ii) a The unmanned vehicle is positioned on the roadThe acceleration, speed and treatment position which should be possessed by the mouth are sent to the corresponding unmanned vehicle for execution.

Description

Crossing vehicle scheduling method, device, equipment and computer readable storage medium
Technical Field
The invention relates to the field of unmanned driving, in particular to a method, a device, equipment and a computer readable storage medium for dispatching vehicles at an intersection.
Background
In the vehicle-Road cooperative network, a Road Side Unit (RSU) can send a control scheduling instruction to a vehicle according to vehicle state information, thereby reasonably avoiding potential danger or improving driving efficiency. The cooperative vehicle scheduling at the intersection is a typical application scenario of automatic driving, and the scheduling system is mainly divided into a centralized scheduling system and a distributed scheduling system, wherein the centralized scheduling system comprises a central scheduling node (RSU) which is mainly responsible for collecting vehicle information and making decisions, and the scheduling performance of the centralized scheduling system depends on the computing capacity of the central scheduling node and the scheduling optimization algorithm of the system.
When the problem that vehicles dynamically arrive and leave in an intersection area is solved, the existing intersection vehicle scheduling method has the following solution: and continuously searching the arrival and departure of the vehicles in the road, carrying out one-time optimization solution on the searched vehicles at each control interval to obtain the control quantity which should be executed by the current vehicle at each next time slot, and sending the control vector to the vehicle end for execution.
However, the above prior art solutions still suffer from the following drawbacks: 1) each time all vehicles are solved simultaneously, the calculation complexity increases exponentially with the increase of the number of variables, and the actual scene with high vehicle density is difficult to deal with; 2) when part of vehicles can not be reasonably scheduled due to poor self states, the global optimization problem is not solved, and the scheduling process is forced to be interrupted.
Disclosure of Invention
The application provides a crossing vehicle scheduling method, a crossing vehicle scheduling device, crossing vehicle scheduling equipment and a computer readable storage medium, so that the complexity of calculation is reduced, and a scheduling process can be continuously carried out.
In one aspect, the present application provides a method for scheduling vehicles at an intersection, including:
when vehicles are detected to come in and go out of the intersection at the moment t, updating the state information of each unmanned vehicle in the dispatching vehicle set Ic according to the vehicle state information received at the moment t to obtain an updated dispatching vehicle set Ic';
by solving the problem under a first constraint
Figure BDA0002730088470000021
Obtaining the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time, wherein u isi(t) is the acceleration of the ith drone vehicle at time t, said vi(t) is the speed of the ith drone vehicle at time t, said vimaxThe maximum speed allowed by the ith unmanned vehicle at the time t;
dispatching a set of vehicles I according to the updatecIn the method, position vector information of each unmanned vehicle when reaching the intersection in the shortest time is obtained to obtain an intersection passing sequence set O;
solving the problem under a second constraint condition aiming at the unmanned vehicle represented by each element in the intersection passing sequence set O
Figure BDA0002730088470000022
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcSaid trajectory planning vector Uc、VcAnd PcEach component is respectively the acceleration, the speed and the position of the unmanned vehicle represented by each element in the intersection passing sequence set O when the unmanned vehicle is at the intersection;
and sending the acceleration, the speed and the position which the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution.
In another aspect, the present application provides a crossing vehicle scheduling device, comprising:
an updating module used for updating the dispatching vehicle set I according to the vehicle state information received at the time t when the vehicle entering and exiting the intersection is detected at the time tcThe state information of each unmanned vehicle is obtained to obtain an updated dispatching vehicle set Ic’；
A first obtaining module for solving the problem under the first constraint condition
Figure BDA0002730088470000023
Obtaining the updated dispatching vehicle set Ic' position vector information of each unmanned vehicle when it arrives at the intersection in the shortest time, ui(t) is the acceleration of the ith drone vehicle at time t, said vi(t) is the speed of the ith drone vehicle at time t, said vimaxThe maximum speed allowed by the ith unmanned vehicle at the time t;
a sequence set acquisition module for dispatching a vehicle set I according to the updatecIn the method, position vector information of each unmanned vehicle when reaching the intersection in the shortest time is obtained to obtain an intersection passing sequence set O;
a second obtaining module, configured to solve the problem under a second constraint condition for an unmanned vehicle represented by each element in the intersection passing order set O
Figure BDA0002730088470000031
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcSaid trajectory planning vector Uc、VcAnd PcEach component is respectively the acceleration, the speed and the position of the unmanned vehicle represented by each element in the intersection passing sequence set O when the unmanned vehicle is at the intersection;
and the sending module is used for sending the acceleration, the speed and the position which the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution.
In a third aspect, the present application provides an apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the above technical solution when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method according to the above-mentioned solution.
According to the technical scheme provided by the application, the intersection passing sequence set O is obtained, and the problem under the first constraint condition and the second constraint condition is solved
Figure BDA0002730088470000032
On one hand, the complex multi-vehicle optimization problem is converted into a simple linear single-vehicle optimization problem, so that the calculation complexity is reduced, the calculation load of the RSU is reduced, and the scheduling efficiency is improved; on the other hand, multi-vehicle combined optimization is divided into multi-vehicle sequencing and single-vehicle optimization, the degree of dependence of global scheduling performance on a single vehicle is reduced, when the states of part of unmanned vehicles are not enough to be reasonably scheduled, other unmanned vehicles still have the chance of being reasonably scheduled, and the threat of the part of unmanned vehicles to the global scheduling control safety is effectively reduced.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for scheduling vehicles at an intersection according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of an intersection vehicle dispatching device provided in the embodiment of the present application;
fig. 3 is a schematic structural diagram of an apparatus provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In this specification, adjectives such as first and second may only be used to distinguish one element or action from another, without necessarily requiring or implying any actual such relationship or order. References to an element or component or step (etc.) should not be construed as limited to only one of the element, component, or step, but rather to one or more of the element, component, or step, etc., where the context permits.
In the present specification, the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The application provides a crossing vehicle scheduling method, as shown in figure 1. It should be noted that, in the method illustrated in fig. 1, the execution subject may be a central scheduling node or an RUS in the vehicle-road cooperative network. The method illustrated in fig. 1 mainly comprises steps S101 to S105, detailed as follows:
step S101: when the vehicle entering and exiting the intersection is detected at the time t, updating the dispatching vehicle set I according to the vehicle state information received at the time tcThe state information of each unmanned vehicle is obtained to obtain an updated dispatching vehicle set Ic’。
It should be noted that in the present embodiment, "vehicle" means an unmanned vehicle, and in the following description, although "vehicle" and "unmanned vehicle" may be used alternately, unless otherwise specified, both refer to the same object, and T is any time in a discrete time sequence {1, 2.., T }, where T is used as an intervalcMeans that this meansAfter counting from 1, RSU counts at interval TcAnd (4) receiving information and carrying out dispatching calculation of the unmanned vehicles at the intersections for controlling the time slots until all the control time slots are finished. The vehicle state information received by the RSU at the time t comprises the speed, the acceleration, the position and the position error e of the position of each unmanned vehiclei(t), and so on. Where the position error e of each drone vehicle isi(t) is caused by a sensing error of the unmanned vehicle itself and a control error of the controller. Position error e in dynamic crossing scheduling under non-ideal conditionsi(t) may be used as a scheduled amount of redundancy to ensure that no collisions occur between unmanned vehicles in the central region of the intersection, as will be further described below.
In the embodiment of the present application, each element in the scheduled vehicle set Ic represents an unmanned vehicle capable of establishing a communication connection with the RSU within a communication range of the RSU. Before updating the dispatching vehicle set Ic, the RSU performs an initialization operation, including performing initialization detection on the unmanned vehicles in the communication range, and updating the initial state information of the unmanned vehicles at the intersection, i.e., the initial state information of each element in the dispatching vehicle set Ic.
Step S102: by solving the problem under a first constraint
Figure BDA0002730088470000051
Obtaining the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time, wherein ui(t) acceleration of the ith unmanned vehicle at time t, vi(t) is the speed of the ith drone vehicle at time t, vimaxThe maximum speed allowed by the ith unmanned vehicle at time t.
Specifically, step S102 may be implemented by step S1021 and step S1022 as follows:
step S1021: according to a driving dynamics model of a vehicle, the performance of the unmanned vehicle, a lane speed limit regulation and an initial time state of the unmanned vehicle when the unmanned vehicle is accessed to a network, the following first constraint conditions are obtained:
Figure BDA0002730088470000052
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
wherein p isi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, T being any time in the discrete time series {1,2cIs a time interval, v, in a discrete time series {1, 2.. multidot.T }i(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, and ui(t) represents the acceleration of the ith unmanned vehicle at time t, pistartIndicates the initial position, v, of the ith unmanned vehicleistartIndicating the initial speed, v, of the ith drone vehicleiminIndicating the minimum speed, v, allowed for the ith vehicleimaxRepresents the maximum speed, u, allowed for the ith unmanned vehicleiminRepresents the minimum acceleration, u, allowed by the ith unmanned vehicleimaxRepresenting the maximum acceleration allowed for the ith drone vehicle.
Step S1022: calculating the problem under the first constraint condition mentioned in step S1021
Figure BDA0002730088470000061
The position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time is obtained.
Step S103: and acquiring a crossing passing sequence set O according to the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the crossing in the shortest time.
Specifically, step S103 may be implemented by steps S1031 and S1032 as follows:
step S1031: solving the collision-proof constraint condition of any ith unmanned vehicle
Figure BDA0002730088470000062
The shortest time it takes to drive into the intersection, wherein LiAnd HiRespectively showing the position of the ith driverless vehicle when the driverless vehicle enters the intersection and the position of the ith driverless vehicle when the driverless vehicle leaves the intersection, t (L)i) And t (H)i) Respectively showing the time when the ith unmanned vehicle enters the intersection and the time when the ith unmanned vehicle exits the intersection, LjAnd HjRespectively showing the position of the jth unmanned vehicle when entering and exiting the intersection, t (L)j) And t (H)j) Respectively showing the time when the jth unmanned vehicle enters the intersection and the time when the jth unmanned vehicle exits the intersection.
Step S1032: the shortest time t spent by any ith unmanned vehicle when the vehicle enters the intersection without the anti-collision constraint condition*(Li-ei(t)) according to t*(Li-ei(t)) the smaller it is, the position O in the intersection passage order set OiSequencing the crossing more forward to obtain a crossing passing sequence set O, ei(t) a position error of a position where the i-th unmanned vehicle is located when reporting the state information thereof.
For example, assume the ith*The shortest time spent by the unmanned vehicle to enter the intersection when the unmanned vehicle has no anti-collision constraint condition
Figure BDA0002730088470000071
Is less than the shortest time t taken for the ith unmanned vehicle to enter the intersection without the anti-collision constraint condition*(Li-ei(t)), then i*Position O of unmanned vehicle in intersection passing sequence set Oi-1 is the position O of the ith driverless vehicle in the intersection traffic order set OiBefore, i.e. O ═ { …, Oi-1，Oi…, which also states that the position order of each element in the intersection passing order set O corresponds to the sequence of unmanned vehicles entering the intersection indicated by the element, and the position O of the unmanned vehicles in the intersection passing order set OiThe farther forward, the higher the dispatch priority for the drone vehicle. When the unmanned vehicle dynamically reaches the communication area of the RSU, the RSU inserts the unmanned vehicle into the corresponding position in the intersection passing sequence set O according to the state information of the unmanned vehicle, and similarly, when the unmanned vehicle exits the communication area, the corresponding position of the unmanned vehicle in the intersection passing sequence set O is deleted.
As described above, in the existing intersection vehicle scheduling method, since the collision avoidance constraint is not convex, this leads to solving the problem
Figure BDA0002730088470000072
It becomes difficult. Although it can be converted into a convex optimization problem (e.g., introducing auxiliary variables, etc.), there are still the defects that all vehicles need to be solved simultaneously each time, which results in exponential increase of computational complexity with the increase of the number of variables, and it is difficult to cope with the real scenes with high vehicle density and when some vehicles cannot be reasonably scheduled due to poor self-state, the global optimization problem has no solution, and the scheduling process is forced to be interrupted. However, according to the technical solutions of step S102 and step S103 provided in the present application, the present application applies a "pipeline" type dynamic scheduling policy to convert a complex multi-vehicle optimization problem into a simple linear single-vehicle optimization problem, or to split a multi-vehicle joint optimization into a multi-vehicle sequencing and collision avoidance problem of adjacent unmanned vehicles, so that the problem is finally solved
Figure BDA0002730088470000073
The solution of (2) becomes very simple.
Step S104: solving the problem under the second constraint condition aiming at the unmanned vehicle represented by each element in the intersection passing sequence set O
Figure BDA0002730088470000074
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcWherein the trajectory planning vector Uc、VcAnd PcEach component in the intersection transit order set O is the acceleration, speed and position that the driverless vehicle represented by each element in the intersection transit order set O should have at the intersection.
As an embodiment of the present application, the problem under the second constraint condition is solved for the unmanned vehicle represented by each element in the intersection passing order set O
Figure BDA0002730088470000081
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcThis can be achieved by steps S1041 and S1042 as follows:
step S1041: according to a driving dynamics model of the vehicle, the performance of the unmanned vehicle, lane speed limit regulation, the initial time state of the unmanned vehicle when the unmanned vehicle is accessed to the network, the position of the ith unmanned vehicle when the ith unmanned vehicle drives into the intersection, and the ith*Position of driverless vehicle when it exits intersection, i*The position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle and the position error of the position of the ith unmanned vehicle when reporting the state information of the unmanned vehicle acquire the following second constraint conditions:
Figure BDA0002730088470000082
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
Figure BDA0002730088470000083
wherein p isi(t)、pi(t+1)、t、Tc、vi(t)、vi(t+1)、ui(t)、pistart、vistart、vimin、vimax、uiminAnd uimaxAre as defined above, i.e. pi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, T being any time in the discrete time series {1,2cIs the interval of time in a discrete time sequence, vi(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, ui(t) represents the acceleration of the ith unmanned vehicle at time t, pistartIndicates the initial position, v, of the ith unmanned vehicleistartRepresenting the initial speed, v, of the ith unmanned vehicleiminIndicating the minimum speed, v, allowed for the ith vehicleimaxRepresents the maximum speed, u, allowed for the ith unmanned vehicleiminRepresents the minimum acceleration, u, allowed by the ith unmanned vehicleimaxRepresents the maximum acceleration allowed by the ith unmanned vehicle, and Hi*Indicating the position of the intersection passing sequence set O as Oi-1 position of the drone vehicle when exiting the intersection,
Figure BDA0002730088470000091
indicating the position of the intersection passing sequence set O as Oi-1 position error of the position where the driverless vehicle is located when reporting its status information, LiIndicating the position of the intersection passing sequence set O as OiPosition of the driverless vehicle when driving into the intersection, ei(t) the position in the intersection passing sequence set O is OiThe position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle. In the above constraint, i*Setting the position in the sequence set O for crossing to be Oi-1, number of the drone vehicle, accordingly,
Figure BDA0002730088470000092
indicating the position of the intersection passing sequence set O as Oi-1 position of the drone vehicle when exiting the intersection,
Figure BDA0002730088470000093
indicating the position of the intersection passing sequence set O as Oi-1 position error of the position where the driverless vehicle was when reporting its status information,
Figure BDA0002730088470000094
indicating the position of the intersection passing sequence set O as Oi-1 unmanned vehicle is present
Figure BDA0002730088470000095
The time of the vehicle coming out of the intersection at the latest,
Figure BDA0002730088470000096
indicating the position of the intersection passing sequence set O as OiAt the moment of time
Figure BDA0002730088470000097
In the position of (a) in the first,
Figure BDA0002730088470000098
then it indicates at the moment of time
Figure BDA0002730088470000099
The position in the intersection passing sequence set O is OiIn the presence of a position error eiAnd (t) the user should not enter the intersection.
In the embodiment of the application, when the position error of the position of the ith unmanned vehicle when the status information of the ith unmanned vehicle is reported is introduced, the time of the ith unmanned vehicle occupying the intersection is updated to
Figure BDA00027300884700000910
Wherein the content of the first and second substances,
Figure BDA00027300884700000911
intersection collision avoidance constraints under non-ideal conditions can be expressed as:
Figure BDA00027300884700000912
according to the 'assembly line' type dynamic scheduling idea, the fact that every two unmanned vehicles do not collide can be simplified into the fact that rear vehicles do not collide with front vehicles. At this time, the collision-prevention constraint condition for scheduling the vehicles in the intersection passage sequence set O can be optimized. Taking the ith unmanned vehicle as an example, the anti-collision constraint condition is optimized as follows: in the front vehicle (namely the position in the crossing traffic sequence set O is OiUnmanned vehicle of-1) when the lower limit of the error interval of the rear vehicle (i.e. the position in the intersection passing sequence set O is OiUnmanned vehicle) does not enter the intersection, i.e., the upper limit of the error interval
Figure BDA00027300884700000913
Thereby, the second constraint as above is obtained.
Step S1042: computing the problem under the second constraint
Figure BDA00027300884700000914
To obtain a track planning vector U of the unmanned vehiclec、VcAnd Pc。
The above-mentioned vector UcComponent u iniVector V representing the acceleration planned for the ith unmanned vehicle by the RSUcComponent v in (1)iRepresenting the speed, vector P, planned for the ith unmanned vehicle by the RSUcComponent p iniIndicating the location that the RSU planned for the ith drone vehicle.
Step S105: and sending the acceleration, the speed and the handling position which the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution.
That is, the RSU will vector the UcComponent u iniVector VcComponent v in (1)iAnd a vector PcComponent p iniSending the acceleration to the ith unmanned vehicle to ensure that the ith unmanned vehicle is driven according to the acceleration uiVelocity viAnd position piTo travel.
As can be seen from the intersection vehicle scheduling method illustrated in FIG. 1, the intersection traffic sequence set O is obtained and the problem under the first constraint condition and the second constraint condition is solved
Figure BDA0002730088470000101
On one hand, the complex multi-vehicle optimization problem is converted into a simple linear single-vehicle optimization problem, so that the calculation complexity is reduced, the calculation load of the RSU is reduced, and the scheduling efficiency is improved; on the other hand, multi-vehicle combined optimization is divided into multi-vehicle sequencing and single-vehicle optimization, the degree of dependence of global scheduling performance on a single vehicle is reduced, when the states of part of unmanned vehicles are not enough to be reasonably scheduled, other unmanned vehicles still have the chance of being reasonably scheduled, and the threat of the part of unmanned vehicles to the global scheduling control safety is effectively reduced.
Referring to fig. 2, a crossing vehicle dispatching device provided in the embodiment of the present application is shown, where the crossing vehicle dispatching device may be an RSU or a functional module thereof. The crossing vehicle scheduling apparatus illustrated in fig. 2 may include an updating module 201, a first obtaining module 202, a sequence set obtaining module 203, a second obtaining module 204, and a sending module 205, which are detailed as follows:
the updating module 201 is configured to update the state information of each unmanned vehicle in the scheduled vehicle set Ic according to the vehicle state information received at the time t when the vehicle is detected to enter or exit the intersection at the time t, so as to obtain an updated scheduled vehicle set Ic';
a first obtaining module 202 for obtaining a problem by solving the problem under a first constraint
Figure BDA0002730088470000102
Obtaining the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time, wherein ui(t) acceleration of the ith unmanned vehicle at time t, vi(t) is the speed of the ith drone vehicle at time t, vimaxIs as followsi maximum speed allowed by the unmanned vehicle at time t;
the sequence set acquisition module 203 is used for acquiring a crossing passing sequence set O according to the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the crossing in the shortest time;
a second obtaining module 204, configured to solve the problem under a second constraint condition for the unmanned vehicle represented by each element in the intersection transit order set O
Figure BDA0002730088470000111
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcWherein the trajectory planning vector Uc、VcAnd PcEach component in the intersection passing sequence set O is respectively the acceleration, the speed and the position of the unmanned vehicle which is represented by each element in the intersection passing sequence set O and should be at the intersection;
and the sending module 205 is configured to send the acceleration, the speed and the handling position that the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution.
Optionally, the first obtaining module 202 illustrated in fig. 2 may include a first constraint obtaining unit and a first calculating unit, where:
the first constraint condition obtaining unit is used for obtaining the following first constraint conditions according to a driving dynamic model of the vehicle, the performance of the unmanned vehicle, a lane speed limit regulation and an initial time state of the unmanned vehicle when the unmanned vehicle is accessed into the network:
Figure BDA0002730088470000112
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
wherein p isi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, T being any time in the discrete time series {1,2cIs the interval of time in a discrete time sequence, vi(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, and ui(t) represents the acceleration of the ith unmanned vehicle at time t, pistartIndicates the initial position, v, of the ith unmanned vehicleistartIndicating the initial speed, v, of the ith drone vehicleiminIndicating the minimum speed, v, allowed for the ith vehicleimaxRepresents the maximum speed, u, allowed for the ith unmanned vehicleiminRepresents the minimum acceleration, u, allowed by the ith unmanned vehicleimaxRepresents the maximum acceleration allowed by the ith unmanned vehicle;
a first calculation unit for calculating the problem under the first constraint condition
Figure BDA0002730088470000121
The position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time is obtained.
Optionally, the order set obtaining module 203 illustrated in fig. 2 may include a shortest time determining unit and a sorting unit, where:
the shortest time determining unit is used for solving the collision-preventing constraint condition of any ith unmanned vehicle:
Figure BDA0002730088470000122
the shortest time it takes to drive into the intersection, wherein LiAnd HiRespectively showing the position of the ith driverless vehicle when the driverless vehicle enters the intersection and the position of the ith driverless vehicle when the driverless vehicle leaves the intersection, t (L)i) And t (H)i) Respectively indicate the i-th unmanned vehicleTime when entering the crossing and time when exiting the crossing, LjAnd HjRespectively showing the position of the jth unmanned vehicle when entering and exiting the intersection, t (L)j) And t (H)j) Respectively showing the time when the jth unmanned vehicle enters the intersection and the time when the jth unmanned vehicle exits the intersection;
a sequencing unit for sequencing the shortest time t spent by any ith unmanned vehicle when the ith unmanned vehicle enters the intersection without the anti-collision constraint condition*(Li-ei(t)) according to t*(Li-ei(t)) the smaller it is, the position O in the intersection passage order set OiSequencing the road closer to the front to obtain a crossing passing sequence set O, wherein ei(t) a position error of a position where the i-th unmanned vehicle is located when reporting the state information thereof.
Optionally, the second obtaining module 204 illustrated in fig. 2 may include a first constraint obtaining unit and a second calculating unit, where:
a first constraint condition obtaining unit, configured to obtain a vehicle driving dynamics model, a driverless performance, a lane speed limit rule, an initial time state of the driverless vehicle when accessing the network, a position of the ith driverless vehicle when accessing the intersection, and an ith constraint condition according to the vehicle driving dynamics model, the driverless vehicle performance, the lane speed limit rule, the driverless vehicle state at the initial time state, the ith driverless vehicle position when accessing the*Position of driverless vehicle when it exits intersection, i*The position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle and the position error of the position of the ith unmanned vehicle when reporting the state information of the unmanned vehicle acquire the following second constraint conditions:
Figure BDA0002730088470000131
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
Figure BDA0002730088470000132
wherein p isi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, T being any time in the discrete time series {1,2cIs the interval of time in a discrete time sequence, vi(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, and ui(t) represents the acceleration of the ith unmanned vehicle at time t, pistartIndicates the initial position, v, of the ith unmanned vehicleistartIndicating the initial speed, v, of the ith drone vehicleiminIndicating the minimum speed, v, allowed for the ith vehicleimaxRepresents the maximum speed, u, allowed for the ith unmanned vehicleiminRepresents the minimum acceleration, u, allowed by the ith unmanned vehicleimaxRepresents the maximum acceleration allowed by the ith unmanned vehicle,
Figure BDA0002730088470000133
indicating the position of the intersection passing sequence set O as Oi-1 position of the drone vehicle when exiting the intersection,
Figure BDA0002730088470000134
indicating the position of the intersection passing sequence set O as Oi-1 position error of the position where the driverless vehicle is located when reporting its status information, LiIndicating the position of the intersection passing sequence set O as OiPosition of the driverless vehicle when driving into the intersection, ei(t) the position in the intersection passing sequence set O is OiThe position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle;
a second calculation unit for calculating the problem under the second constraint condition
Figure BDA0002730088470000135
To obtain a track planning vector U of the unmanned vehiclec、VcAnd Pc。
From the above description of the technical solutions, it can be seen that the intersection passing order set O is obtained and the problem under the first constraint condition and the second constraint condition is solved
Figure BDA0002730088470000136
On one hand, the complex multi-vehicle optimization problem is converted into a simple linear single-vehicle optimization problem, so that the calculation complexity is reduced, the calculation load of the RSU is reduced, and the scheduling efficiency is improved; on the other hand, multi-vehicle combined optimization is divided into multi-vehicle sequencing and single-vehicle optimization, the degree of dependence of global scheduling performance on a single vehicle is reduced, when the states of part of unmanned vehicles are not enough to be reasonably scheduled, other unmanned vehicles still have the chance of being reasonably scheduled, and the threat of the part of unmanned vehicles to the global scheduling control safety is effectively reduced.
Fig. 3 is a schematic structural diagram of an apparatus provided in an embodiment of the present application. As shown in fig. 3, the apparatus 3 of this embodiment mainly includes: a processor 30, a memory 31, and a computer program 32, such as a program for an intersection vehicle dispatch method, stored in the memory 31 and operable on the processor 30. The processor 30, when executing the computer program 32, implements the steps in the above-described method embodiment of intersection vehicle dispatching, such as steps S101 to S105 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 32, implements the functions of each module/unit in each apparatus embodiment described above, such as the functions of the updating module 201, the first obtaining module 202, the order set obtaining module 203, the second obtaining module 204, and the sending module 205 shown in fig. 2.
Illustratively, the computer program 32 of the intersection vehicle scheduling method mainly includes: when the vehicles are detected to come in and go out of the intersection at the moment t, updating the state information of each unmanned vehicle in the dispatching vehicle set Ic according to the vehicle state information received at the moment t to obtain an updated dispatching vehicle set Ic'; by solving the problem under a first constraint
Figure BDA0002730088470000141
Obtaining the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time, wherein ui(t) acceleration of the ith unmanned vehicle at time t, vi(t) is the speed of the ith drone vehicle at time t, vimaxThe maximum speed allowed by the ith unmanned vehicle at the time t; acquiring a crossing passing sequence set O according to position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the crossing in the shortest time; solving the problem under the second constraint condition aiming at the unmanned vehicle represented by each element in the intersection passing sequence set O
Figure BDA0002730088470000142
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcWherein the trajectory planning vector Uc、VcAnd PcEach component in the intersection passing sequence set O is respectively the acceleration, the speed and the position of the unmanned vehicle which is represented by each element in the intersection passing sequence set O and should be at the intersection; and sending the acceleration, the speed and the handling position which the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution. The computer program 32 may be partitioned into one or more modules/units, which are stored in the memory 31 and executed by the processor 30 to accomplish the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 32 in the device 3. For example, the computer program 32 may be divided into functions of an update module 201, a first acquisition module 202, an order set acquisition module 203, a second acquisition module 204, and a transmission module 205 (modules in a virtual device), and the specific functions of each module are as follows: an updating module 201, configured to update the dispatching vehicle set I according to the vehicle state information received at the time t when it is detected that the vehicle enters or exits the intersection at the time tcThe state information of each unmanned vehicle is obtained to obtain an updated dispatching vehicle set Ic'; first acquisition module 202For solving the problem under the first constraint
Figure BDA0002730088470000151
Obtaining an updated set of scheduled vehicles Ic' in which each of the unmanned vehicles arrives at the intersection in the shortest time, and u is a position vector informationi(t) acceleration of the ith unmanned vehicle at time t, vi(t) is the speed of the ith drone vehicle at time t, vimaxThe maximum speed allowed by the ith unmanned vehicle at the time t; a sequence set acquisition module 203 for dispatching the vehicle set I according to the updatecIn the method, position vector information of each unmanned vehicle when reaching the intersection in the shortest time is obtained to obtain an intersection passing sequence set O; a second obtaining module 204, configured to solve the problem under a second constraint condition for the unmanned vehicle represented by each element in the intersection transit order set O
Figure BDA0002730088470000152
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcWherein the trajectory planning vector Uc、VcAnd PcEach component in the intersection passing sequence set O is respectively the acceleration, the speed and the position of the unmanned vehicle which is represented by each element in the intersection passing sequence set O and should be at the intersection; and the sending module 205 is configured to send the acceleration, the speed and the handling position that the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution.
The device 3 may include, but is not limited to, a processor 30, a memory 31. Those skilled in the art will appreciate that fig. 3 is merely an example of a device 3 and does not constitute a limitation of device 3 and may include more or fewer components than shown, or some components in combination, or different components, e.g., a computing device may also include input-output devices, network access devices, buses, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an application specific integrated circuit (Appljcatjon specjfc jted cjujujt, ASJC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the device 3, such as a hard disk or a memory of the device 3. The memory 31 may also be an external storage device of the device 3, such as a plug-in hard disk provided on the device 3, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 31 may also include both an internal storage unit of the device 3 and an external storage device. The memory 31 is used for storing computer programs and other programs and data required by the device. The memory 31 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as required to different functional units and modules, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/device and method may be implemented in other ways. For example, the above-described apparatus/device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another apparatus, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Integrated module/unit ifWhen implemented as software functional units and sold or used as stand-alone products, may be stored in a non-transitory computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program instructing related hardware to complete, the computer program of the intersection vehicle dispatching method can be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the methods described above can be realized, that is, when it is detected that a vehicle enters or exits an intersection at time t, the state information of each unmanned vehicle in the dispatched vehicle set Ic is updated according to the vehicle state information received at time t, so as to obtain an updated dispatched vehicle set Ic'; by solving the problem under a first constraint
Figure BDA0002730088470000171
Obtaining an updated set of scheduled vehicles Ic' in which each of the unmanned vehicles arrives at the intersection in the shortest time, and u is a position vector informationi(t) acceleration of the ith unmanned vehicle at time t, vi(t) is the speed of the ith drone vehicle at time t, vimaxThe maximum speed allowed by the ith unmanned vehicle at the time t; acquiring a crossing passing sequence set O according to position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the crossing in the shortest time; solving the problem under the second constraint condition aiming at the unmanned vehicle represented by each element in the intersection passing sequence set O
Figure BDA0002730088470000181
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcWherein the trajectory planning vector Uc、VcAnd PcEach component in the intersection passing sequence set O is respectively the acceleration, the speed and the position of the unmanned vehicle which is represented by each element in the intersection passing sequence set O and should be at the intersection; and sending the acceleration, the speed and the handling position which the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution. Wherein, the meterThe computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The non-transitory computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the non-transitory computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, non-transitory computer readable media does not include electrical carrier signals and telecommunications signals as subject to legislation and patent practice. The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
The above-mentioned embodiments, objects, technical solutions and advantages of the present application are described in further detail, it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present application, and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present application should be included in the scope of the present invention.

Claims (10)

1. A method for scheduling vehicles at an intersection, the method comprising:
when the vehicle entering and exiting the intersection is detected at the moment t, updating the dispatching vehicle set I according to the vehicle state information received at the moment tcEach unmanned vehicleObtaining an updated dispatch vehicle set Ic’；
By solving the problem under a first constraint
Figure FDA0002730088460000011
Obtaining the updated dispatching vehicle set Ic' position vector information of each unmanned vehicle when it arrives at the intersection in the shortest time, ui(t) is the acceleration of the ith drone vehicle at time t, said vi(t) is the speed of the ith drone vehicle at time t, said vimaxThe maximum speed allowed by the ith unmanned vehicle at the time t;
dispatching a set of vehicles I according to the updatecIn the method, position vector information of each unmanned vehicle when reaching the intersection in the shortest time is obtained to obtain an intersection passing sequence set O;
solving the problem under a second constraint condition aiming at the unmanned vehicle represented by each element in the intersection passing sequence set O
Figure FDA0002730088460000012
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcSaid trajectory planning vector Uc、VcAnd PcEach component is respectively the acceleration, the speed and the position of the unmanned vehicle represented by each element in the intersection passing sequence set O when the unmanned vehicle is at the intersection;
and sending the acceleration, the speed and the position which the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution.
2. The method for scheduling vehicles at an intersection of claim 1, wherein the solving of the problem under a first constraint is performed by
Figure FDA0002730088460000013
Obtaining the updated dispatching vehicle set Ic' in each casePosition vector information of an unmanned vehicle when the unmanned vehicle reaches an intersection in a shortest time comprises:
according to a driving dynamics model of a vehicle, the performance of the unmanned vehicle, a lane speed limit regulation and an initial time state of the unmanned vehicle when the unmanned vehicle is accessed to a network, acquiring a first constraint condition as follows:
Figure FDA0002730088460000014
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
said p isi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, wherein T is any time in a discrete time sequence {1,2cFor intervals of time in said discrete time series, said vi(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, and ui(t) represents the acceleration of the ith drone vehicle at time t, said pistartIndicating an initial position of the ith drone vehicle, said vistartRepresenting the initial velocity of the ith unmanned vehicle, said viminRepresents a minimum speed allowed by the i-th unmanned vehicle, said vimaxRepresents the maximum speed allowed by the ith unmanned vehicle, uiminRepresents the minimum acceleration allowed by the ith unmanned vehicle, uimaxRepresenting a maximum acceleration allowed for the ith drone vehicle;
calculating the problem under the first constraint
Figure FDA0002730088460000021
Obtaining the updated dispatching vehicle set Ic' position vector information when each drone vehicle arrives at the intersection in the shortest time.
3. The method of claim 1 wherein said set of vehicles I is scheduled according to said updatec' obtaining a crossing traffic sequence set O by position vector information of each unmanned vehicle when the unmanned vehicle reaches the crossing in the shortest time, comprising:
solving the collision-proof constraint condition of any ith unmanned vehicle
Figure FDA0002730088460000022
The shortest time it takes to drive into the intersection, LiAnd HiRespectively showing the position of the ith driverless vehicle when the ith driverless vehicle enters the intersection and the position of the ith driverless vehicle when the ith driverless vehicle leaves the intersection, and the t (L)i) And t (H)i) Respectively showing the time when the ith unmanned vehicle enters the intersection and the time when the ith unmanned vehicle exits the intersection, and the LjAnd HjRespectively showing the position of the jth unmanned vehicle when the vehicle enters the intersection and the position of the jth unmanned vehicle when the vehicle leaves the intersection, and the t (L)j) And t (H)j) Respectively showing the time when the jth unmanned vehicle enters the intersection and the time when the jth unmanned vehicle exits the intersection;
the shortest time t spent by any ith unmanned vehicle when the ith unmanned vehicle enters the intersection without the anti-collision constraint condition*(Li-ei(t)) according to said t*(Li-ei(t)) the smaller it is, the position O in the intersection passage order set OiThe crossing passing sequence set O is obtained by sequencing the crossing passing sequence set Ei(t) a position error of a position where the i-th unmanned vehicle is located when reporting the state information thereof.
4. The method for dispatching vehicles at intersections according to any one of claims 1 to 3, wherein the unmanned vehicles represented by each element in the intersection traffic order set O are obtained by solving the problem under the second constraint condition
Figure FDA0002730088460000031
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcThe method comprises the following steps:
according to a driving dynamics model of the vehicle, the performance of the unmanned vehicle, the lane speed limit regulation, the initial time state of the unmanned vehicle when the unmanned vehicle is accessed to the network, the position of the ith unmanned vehicle when the ith unmanned vehicle is driven to the intersection, and the ith*Position of driverless vehicle when it exits intersection, i*The position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle and the position error of the position of the ith unmanned vehicle when reporting the state information of the ith unmanned vehicle obtain the following second constraint conditions:
Figure FDA0002730088460000032
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
Figure FDA0002730088460000033
said p isi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, wherein T is any time in a discrete time sequence {1,2cFor intervals of time in said discrete time series, said vi(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, and ui(t) represents the acceleration of the ith drone vehicle at time t, said pistartIndicating an initial position of the ith drone vehicle, said vistartRepresenting the initial velocity of the ith unmanned vehicle, said viminRepresents a minimum speed allowed by the i-th unmanned vehicle, said vimaxRepresents the maximum speed allowed by the ith unmanned vehicle, uiminRepresents the minimum acceleration allowed by the ith unmanned vehicle, uimaxRepresents a maximum acceleration allowed by the i-th unmanned vehicle, the
Figure FDA0002730088460000041
Indicating the position of the intersection passing sequence set O is Oi-1 position of the drone vehicle when exiting the intersection, said
Figure FDA0002730088460000042
Indicating the position of the intersection passing sequence set O is Oi-1 position error of the position where the drone vehicle is in reporting its status information, said LiIndicating the position of the intersection passing sequence set O is OiThe position of the driverless vehicle when driving into the intersection, said ei(t) indicates the position of the intersection passing sequence set O is OiThe position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle;
calculating the problem under the second constraint
Figure FDA0002730088460000043
To obtain a trajectory planning vector U of the unmanned vehiclec、VcAnd Pc。
5. An intersection vehicle dispatching device, characterized in that the device comprises:
the updating module is used for updating the state information of each unmanned vehicle in the dispatching vehicle set Ic according to the vehicle state information received at the moment t when the vehicle entering or exiting the intersection is detected at the moment t, so as to obtain an updated dispatching vehicle set Ic';
a first obtaining module for solving the problem under the first constraint condition
Figure FDA0002730088460000044
Obtaining the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time, wherein u isi(t) is the acceleration of the ith drone vehicle at time t, said vi(t) is the speed of the ith drone vehicle at time t, said vimaxThe maximum speed allowed by the ith unmanned vehicle at the time t;
the sequence set acquisition module is used for acquiring a crossing passing sequence set O according to the position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the crossing in the shortest time;
a second obtaining module, configured to solve the problem under a second constraint condition for an unmanned vehicle represented by each element in the intersection passing order set O
Figure FDA0002730088460000045
Obtaining a trajectory planning vector U of the unmanned vehiclec、VcAnd PcSaid trajectory planning vector Uc、VcAnd PcEach component is respectively the acceleration, the speed and the position of the unmanned vehicle represented by each element in the intersection passing sequence set O when the unmanned vehicle is at the intersection;
and the sending module is used for sending the acceleration, the speed and the position which the unmanned vehicle should have at the intersection to the corresponding unmanned vehicle for execution.
6. The intersection vehicle dispatching device of claim 5, wherein the first obtaining module comprises:
the first constraint condition obtaining unit is used for obtaining the following first constraint conditions according to a driving dynamics model of a vehicle, the performance of the unmanned vehicle, a lane speed limit regulation and an initial time state of the unmanned vehicle when the unmanned vehicle is accessed into a network:
Figure FDA0002730088460000051
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
said p isi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, wherein T is any time in a discrete time sequence {1,2cFor intervals of time in said discrete time series, said vi(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, and ui(t) represents the acceleration of the ith drone vehicle at time t, said pistartIndicating an initial position of the ith drone vehicle, said vistartRepresenting the initial velocity of the ith unmanned vehicle, said viminRepresents a minimum speed allowed by the i-th unmanned vehicle, said vimaxRepresents the maximum speed allowed by the ith unmanned vehicle, uiminRepresents the minimum acceleration allowed by the ith unmanned vehicle, uimaxRepresenting a maximum acceleration allowed for the ith drone vehicle;
a first calculation unit for calculating the problem under the first constraint condition
Figure FDA0002730088460000061
The position vector information of each unmanned vehicle in the updated dispatching vehicle set Ic' when reaching the intersection in the shortest time is obtained.
7. The intersection vehicle scheduling apparatus of claim 5 wherein the order set acquisition module comprises:
the shortest time determining unit is used for solving the collision-preventing constraint condition of any ith unmanned vehicle:
Figure FDA0002730088460000062
the shortest time it takes to drive into the intersection, LiAnd HiRespectively showing the position of the ith driverless vehicle when the ith driverless vehicle enters the intersection and the position of the ith driverless vehicle when the ith driverless vehicle leaves the intersection, and the t (L)i) And t (H)i) Respectively showing the time when the ith unmanned vehicle enters the intersection and the time when the ith unmanned vehicle exits the intersection, and the LjAnd HjRespectively showing the position of the jth unmanned vehicle when the vehicle enters the intersection and the position of the jth unmanned vehicle when the vehicle leaves the intersection, and the t (L)j) And t (H)j) Respectively showing the time when the jth unmanned vehicle enters the intersection and the time when the jth unmanned vehicle exits the intersection;
a sequencing unit for sequencing the shortest time t spent by any ith unmanned vehicle when the ith unmanned vehicle enters the intersection without the anti-collision constraint condition*(Li-ei(t)) according to said t*(Li-ei(t)) the smaller it is, the position O in the intersection passage order set OiThe crossing passing sequence set O is obtained by sequencing the crossing passing sequence set Ei(t) a position error of a position where the i-th unmanned vehicle is located when reporting the state information thereof.
8. The intersection vehicle dispatching device of any one of claims 5-7, wherein the second obtaining module comprises:
a first constraint condition obtaining unit, configured to obtain a vehicle driving dynamics model, a driverless performance, a lane speed limit rule, an initial time state of the driverless vehicle when accessing the network, a position of the ith driverless vehicle when accessing the intersection, and an ith constraint condition according to the vehicle driving dynamics model, the driverless vehicle performance, the lane speed limit rule, the driverless vehicle state at the initial time state, the ith driverless vehicle position when accessing the*Position of driverless vehicle when it exits intersection, i*The position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle and the position error of the position of the ith unmanned vehicle when reporting the state information of the ith unmanned vehicle obtain the following second constraint conditions:
Figure FDA0002730088460000063
vi(t+1)＝vi(t)+ui(t)Tc，
vimin≤vi(t)≤vimax，
uimin≤ui(t)≤uimax，
pi(1)＝pistart，
vi(1)＝vistart，
Figure FDA0002730088460000071
said p isi(t) and pi(T +1) represents the position of the ith unmanned vehicle at time T and time T +1, respectively, wherein T is any time in a discrete time sequence {1,2cFor intervals of time in said discrete time series, said vi(t) and vi(t +1) represents the speed of the i-th unmanned vehicle at time t and time t +1, respectively, and ui(t) represents the acceleration of the ith drone vehicle at time t, said pistartIndicating an initial position of the ith drone vehicle, said vistartIndicating said ith vehicle is unmannedInitial speed of the vehicle, viminRepresents a minimum speed allowed by the i-th unmanned vehicle, said vimaxRepresents the maximum speed allowed by the ith unmanned vehicle, uiminRepresents the minimum acceleration allowed by the ith unmanned vehicle, uimaxRepresents a maximum acceleration allowed by the i-th unmanned vehicle, the
Figure FDA0002730088460000072
Indicating the position of the intersection passing sequence set O is Oi-1 position of the drone vehicle when exiting the intersection, said
Figure FDA0002730088460000073
Indicating the position of the intersection passing sequence set O is Oi-1 position error of the position where the drone vehicle is in reporting its status information, said LiIndicating the position of the intersection passing sequence set O is OiThe position of the driverless vehicle when driving into the intersection, said ei(t) indicates the position of the intersection passing sequence set O is OiThe position error of the position of the unmanned vehicle when reporting the state information of the unmanned vehicle;
a second calculation unit for calculating the problem under the second constraint condition
Figure FDA0002730088460000074
To obtain a trajectory planning vector U of the unmanned vehiclec、VcAnd Pc。
9. An apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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