CN112233424A

CN112233424A - Longitudinal control method, device and system for truck fleet in vehicle-road cooperation

Info

Publication number: CN112233424A
Application number: CN202011494412.7A
Authority: CN
Inventors: 王里; 张天雷; 王超
Original assignee: Beijing Zhuxian Technology Co Ltd
Current assignee: Beijing Zhuxian Technology Co Ltd
Priority date: 2020-12-17
Filing date: 2020-12-17
Publication date: 2021-01-15
Anticipated expiration: 2040-12-17
Also published as: CN112233424B

Abstract

The application provides a longitudinal control method, a device and a system for a goods vehicle fleet with vehicle-road cooperation, wherein the method comprises the following steps: the method comprises the steps that a first edge cloud corresponding to a road section where a truck queue is located obtains traffic information of a road network and vehicle basic information of trucks in the truck queue from a center cloud; the first edge cloud acquires perception information of the facilities on the road surface from the corresponding roadside computing facilities; the method comprises the steps that a first edge cloud acquires perception information of a vehicle to a road surface and vehicle running condition information from a cargo vehicle fleet; the first edge cloud generates a cooperative control strategy and sends the cooperative control strategy to the cargo truck fleet according to the acquired traffic information, the acquired vehicle basic information, the facility-to-road sensing information, the vehicle driving condition information and the road state information of the road section where the cargo truck fleet is located, which is acquired by the first edge cloud; and the freight train fleet carries out longitudinal control on the freight trains according to a cooperative control strategy. The method and the device can acquire more comprehensive interaction information of the freight train queue, and improve the stability of longitudinal control of the freight train queue.

Description

Longitudinal control method, device and system for truck fleet in vehicle-road cooperation

Technical Field

The invention relates to the field of automatic driving and intelligent transportation, in particular to a longitudinal control method, a device and a system for a truck fleet in cooperation with a vehicle and a road.

Background

With the rapid development of the automatic driving and vehicle-road cooperative technology, the dynamic information interaction between vehicles and the vehicle-road cooperative control can be realized in an all-around manner under the background of the integration of the vehicles and the road, and the attention degree of the management and the control of the vehicle queue in the dynamic information interaction and the vehicle-road cooperative control is extremely high. The proportion of the cargo vehicle fleet in the management and control of the vehicle queue is large, so the cargo vehicle queue is the focus of attention in dynamic information interaction and vehicle road cooperative control. The cargo vehicle fleet refers to a plurality of trucks which run on a road at a short distance by an automatic control technology. The train running needs to consider the individual stability of the train and the train stability. The individual stability refers to the stability of the error between each following vehicle and the front vehicle, when the speed of the current vehicle changes, the speed of the following vehicle can be stably transited to the speed consistent with the front vehicle, and the distance between the vehicles can be converged to a fixed value. Queue stability, also called string stability (or string stability), means that when the speed of any vehicle in the fleet changes (disturbance), the inter-vehicle distance error propagates backwards along the queue to converge, preventing rear-end collision between the following vehicle and the preceding vehicle.

The conventional method for controlling a truck queue with Vehicle-road coordination is that a queue longitudinal controller of a leading Vehicle of the truck queue calculates an expected acceleration of each following Vehicle by using Vehicle fleet information of Vehicle-to-Vehicle (V2V), and transmits the expected acceleration to the queue longitudinal controller of each following Vehicle to adjust the distance between the vehicles to the expected distance.

The control method of the freight train queue with the vehicle-road cooperation in the prior art has the following problems: the acquired interactive information of the cargo vehicle fleet lacks real-time shared information and accurate prediction information, and only the information in the cargo vehicle fleet causes the interactive information of the cargo vehicle fleet to be incomplete, so that the predictability of the cargo vehicle fleet is poor, and further the stability of longitudinal control caused by the sudden speed change of the cargo vehicle fleet is low.

Disclosure of Invention

The embodiment of the application aims to provide a method, a device and a system for controlling longitudinal optimization of a truck fleet in a vehicle-road cooperation mode, and solves the problem that interactive information of a truck fleet acquired in the prior art is not comprehensive enough to cause low stability of longitudinal control of the truck fleet.

In order to solve the above technical problem, an embodiment of the present application provides the following technical solutions:

the application provides a truck-road cooperative cargo truck fleet column longitudinal control method which is applied to a road end, wherein the road end comprises a center cloud, a plurality of edge clouds and a plurality of roadside computing facilities, the center cloud is connected with the edge clouds, and each edge cloud is connected with the roadside computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside; the method comprises the following steps:

a first edge cloud corresponding to a road section where a truck queue is located acquires traffic information of a road network and vehicle basic information of trucks of the truck queue from the center cloud;

the first edge cloud acquires perception information of the facilities on the road surface from the corresponding roadside computing facilities;

the first edge cloud acquires perception information of the vehicle to the road surface and vehicle running condition information from the freight train queue;

and the first edge cloud generates a cooperative control strategy and sends the cooperative control strategy to the truck fleet row according to the acquired traffic information, the acquired vehicle basic information, the acquired facility sensing information of the facility to the road surface, the vehicle sensing information of the vehicle to the road surface, the vehicle driving condition information and the acquired road state information of the road section where the truck fleet row is located, and the truck fleet can carry out truck longitudinal control according to the cooperative control strategy.

In some modified embodiments of the first aspect of the present application, before the first edge cloud corresponding to the road segment where the truck fleet is located obtains the traffic information of the road network and the vehicle basic information of the trucks in the truck fleet from the center cloud, the method further includes:

the first edge cloud receives a registration request sent by a cargo vehicle fleet column;

and the first edge cloud registers the freight train queue according to the registration request.

In some variations of the first aspect of the present application, after the generating and sending the coordinated control strategy to the fleet of cargo vehicles, the method further comprises:

before the truck queue enters an adjacent road section from the road section, the first edge cloud determines whether a second edge cloud corresponding to the adjacent road section has a direct connection data channel;

if so, the first edge cloud sends the acquired relevant information of the truck queue to the second edge cloud;

if not, the first edge cloud sends the acquired related information of the freight train queue to a center cloud, and the center cloud sends the received related information to the second edge cloud.

In some modified embodiments of the first aspect of the present application, the plurality of edge clouds are provided with a road end longitudinal control model and the cargo vehicle fleet columns are provided with a vehicle end longitudinal control model, the road end longitudinal control model and the vehicle end longitudinal control model can work independently and can also work in coordination, and the method further includes:

and the central cloud updates the road end longitudinal control model and the vehicle end longitudinal control model when meeting a triggering condition.

determining whether the sum of the calculation delay and the communication delay meets the cooperative control requirement;

if so, the road end longitudinal control model and the vehicle end longitudinal control model enter a cooperative working mode;

and if not, the road end longitudinal control model and the vehicle end longitudinal control model enter an independent working mode.

The second aspect of the present application provides a longitudinal control method for a cargo vehicle fleet column with cooperative vehicle routes, which is applied to a cargo vehicle queue, wherein the cargo vehicle fleet column comprises a plurality of trucks, and the method comprises:

the freight train queue acquires perception information of the vehicles on the road surface and vehicle running condition information;

sending the perception information and the vehicle running condition information to a first edge cloud corresponding to a road section where a freight train queue is located;

receiving a cooperative control strategy sent by the first edge cloud; the cooperative control strategy is generated by the first edge cloud according to traffic information and vehicle basic information acquired from a central cloud, facility-to-road sensing information acquired from a roadside computing facility, vehicle-to-road sensing information and vehicle driving condition information acquired from the cargo vehicle fleet column and road state information of a road section where the cargo vehicle fleet column is located acquired by the first edge cloud;

and carrying out longitudinal control on the truck according to the cooperative control strategy.

The third aspect of the application provides a method for longitudinally controlling a truck fleet column with cooperative vehicle routes, wherein the truck fleet column comprises a plurality of trucks; the road end comprises a central cloud, a plurality of edge clouds and a plurality of road side computing facilities, wherein the central cloud is connected with the edge clouds, and each edge cloud is connected with the road side computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside; the method comprises the following steps:

the freight train queue acquires perception information of a vehicle on a road surface and vehicle running condition information, and sends the perception information and the vehicle running condition information to the first edge cloud;

the first edge cloud generates a cooperative control strategy and sends the cooperative control strategy to the cargo truck fleet column according to the traffic information, the vehicle basic information, the sensing information of the exerted road surface, the sensing information of the vehicle to the road surface, the vehicle running condition information and the road state information of the road section where the cargo truck fleet column is located, which is acquired by the first edge cloud;

and the truck queue performs longitudinal control on the trucks according to the cooperative control strategy.

A fourth aspect of the present application provides a road-end device, the device comprising: a central cloud, a plurality of edge clouds, and a plurality of roadside computing facilities, the central cloud being connected to the plurality of edge clouds, each of the edge clouds being connected to a plurality of the roadside computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside;

the central cloud is used for acquiring traffic information of a road network and vehicle basic information of trucks in a truck queue;

the roadside computing facility is used for acquiring perception information of the facility on the road surface;

the first edge cloud corresponds to a road section where the truck queue is located and is used for acquiring traffic information of a road network and vehicle basic information of trucks in the truck queue from the center cloud;

the first edge cloud is used for acquiring perception information of the facilities on the road surface from the corresponding roadside computing facilities;

the first edge cloud is used for acquiring perception information of the vehicle to the road surface and vehicle running condition information from the cargo vehicle fleet;

the first edge cloud is used for generating a cooperative control strategy and sending the cooperative control strategy to the truck fleet row according to the acquired traffic information, the acquired vehicle basic information, the facility-to-road sensing information, the vehicle driving condition information and the road state information of the road section where the truck fleet row is located, wherein the road state information is acquired by the first edge cloud, and the truck fleet can be controlled longitudinally according to the cooperative control strategy.

In a fifth aspect of the present application, a fleet of trucks is provided, the fleet of trucks comprising a plurality of trucks;

the freight train queue is used for acquiring perception information of the vehicles on the road surface and information of vehicle running conditions; the system comprises a first edge cloud, a second edge cloud and a monitoring unit, wherein the first edge cloud is used for sending the perception information and the vehicle running condition information to a first edge cloud corresponding to a road section where a freight train queue is located; the cooperative control strategy is used for receiving the cooperative control strategy sent by the first edge cloud; the cooperative control strategy is generated by the first edge cloud according to traffic information and vehicle basic information acquired from a central cloud, facility-to-road sensing information acquired from a roadside computing facility, vehicle-to-road sensing information and vehicle running condition information acquired from the cargo vehicle fleet column and road state information of a road section where the cargo vehicle fleet column is located acquired by the cloud; and the control system is used for carrying out longitudinal control on the truck according to the cooperative control strategy.

A sixth aspect of the present application provides a truck-road coordinated truck fleet longitudinal control system, the system comprising a truck fleet and a road-end device; wherein the content of the first and second substances,

the cargo fleet column includes a plurality of trucks;

the road end equipment comprises a central cloud, a plurality of edge clouds and a plurality of road side computing facilities, wherein the central cloud is connected with the edge clouds, and each edge cloud is connected with the road side computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside;

the cargo vehicle queue is used for acquiring perception information of a vehicle on a road surface and vehicle running condition information and sending the perception information and the vehicle running condition information to the first edge cloud;

the first edge cloud is used for generating a cooperative control strategy and sending the cooperative control strategy to the truck fleet row according to the traffic information, the vehicle basic information, the sensing information of the applied road surface, the sensing information of the vehicle to the road surface, the vehicle running condition information and the road state information of the road section where the truck fleet row is located, which is acquired by the first edge cloud;

and the truck queue is used for carrying out longitudinal control on the trucks according to the cooperative control strategy.

A seventh aspect of the present application provides an electronic device, including: at least one processor; and at least one memory, bus connected with the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to invoke the program instructions in the memory to perform the method for controlling a cargo fleet longitudinal direction in cooperation with a vehicle route according to the first aspect or any one of the optional embodiments of the first aspect.

An eighth aspect of the present application provides a computer-readable storage medium, where the storage medium includes a stored program, where when the program runs, the apparatus on which the storage medium is located is controlled to execute the method for controlling a cargo fleet column in cooperation with a vehicle route according to the first aspect or any one of the optional embodiments of the first aspect.

Compared with the prior art, the method for controlling the truck fleet column in cooperation with the vehicle and the road provided by the first aspect of the present application in the longitudinal direction comprises a road end comprising a center cloud, a plurality of edge clouds and a plurality of roadside computing facilities, wherein the center cloud is connected with the edge clouds, each edge cloud is connected with the roadside computing facilities, a first edge cloud corresponding to a road section where the truck fleet column is located obtains traffic information of the road network and vehicle basic information of trucks in the truck queue from the center cloud, the first edge cloud obtains facility-to-road sensing information from the corresponding roadside computing facilities, the truck fleet column obtains vehicle-to-road sensing information and vehicle driving condition information and sends the sensing information and vehicle driving condition information to the first edge cloud, and the first edge cloud obtains the road state information of the road section where the truck fleet column is located according to the traffic information, the vehicle basic information, the facility-to-road sensing information, the vehicle-to-road and the first edge cloud itself, generating a cooperative control strategy and sending the cooperative control strategy to a freight train queue, and carrying out longitudinal control on the freight train according to the cooperative control strategy by the freight train queue; the information interaction can be carried out on the freight train queue through the central cloud, the edge clouds and the roadside computing facilities, so that the longitudinal control of the freight train queue is realized, real-time shared information and accurate prediction information are obtained, the interaction information of the freight train queue is more comprehensive, the predictability of the freight train queue is higher, and the stability of the longitudinal control of the freight train queue is improved; the problem that the stability of longitudinal control of the freight train queue is low due to the fact that the acquired interactive information of the freight train queue is not comprehensive enough can be solved.

The road end equipment provided by the fourth aspect of the present application and the cargo vehicle fleet provided by the fifth aspect of the present application have the same beneficial effects as the cargo vehicle fleet longitudinal control method provided by the first aspect of the present application in cooperation with the vehicle road.

Drawings

The above and other objects, features and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings and in which like reference numerals refer to similar or corresponding parts and in which:

FIG. 1 schematically illustrates a road-end architecture diagram of a method for longitudinal control of a fleet of cargo vehicles in vehicle-road coordination;

FIG. 2 schematically illustrates a first flowchart of a method for vehicle-road coordinated truck fleet longitudinal control;

FIG. 3 schematically illustrates a second flow chart of a method for longitudinal control of a fleet of cargo vehicles with vehicle-to-road coordination;

FIG. 4 schematically illustrates an architecture diagram of an existing truck fleet longitudinal control;

FIG. 5 schematically illustrates an architecture diagram of an end-of-road and a cargo vehicle queue for longitudinal control of a cargo vehicle fleet in conjunction with a vehicle road;

FIG. 6 schematically illustrates a deployment diagram of a vehicle-road coordinated application service for vehicle-road coordinated cargo fleet longitudinal control;

fig. 7 schematically shows a block diagram of an electronic device.

Detailed Description

Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

It should be noted that: unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by those of skill in the art to which this application belongs.

The method in the examples of the present invention will be described in detail below.

The embodiment of the invention provides a truck-road-cooperative cargo truck fleet column longitudinal control method, and firstly, the truck-road-cooperative cargo truck fleet column longitudinal control method provided by the embodiment of the invention needs to construct a truck-road cooperative system consisting of a road end and a vehicle end, and can realize intelligent truck-road cooperation through data interaction of the road end and the vehicle end. The vehicle end refers to a cargo vehicle queue, and the cargo vehicle queue comprises a plurality of cargo vehicles.

Fig. 1 is a diagram of a road end architecture of a truck fleet longitudinal control method based on vehicle-road coordination according to an embodiment of the present invention, and as shown in fig. 1, the road end includes a center cloud, a plurality of edge clouds (edge cloud l, edge cloud i + l, edge cloud N, and the like), and a plurality of road side computing facilities (road test computing facility l, road test computing facility j, road test computing facility M, and the like). The center cloud is connected with a plurality of edge clouds, for example, the center cloud is connected with an edge cloud l, an edge cloud i + l, an edge cloud N, and the like. Each edge cloud is connected to a plurality of roadside computing facilities, e.g., edge cloud i is connected to drive test computing facility l, drive test computing facility j, and drive test computing facility M, etc. Each edge cloud corresponds to a road segment in the road, for example, edge cloud l corresponds to road segment l, edge cloud i corresponds to road segment i, edge cloud i + l corresponds to road segment i + l, and edge cloud N corresponds to road segment N. The roadside computing facilities are arranged at the roadside, the connection between the central cloud and the edge clouds and the connection between each edge cloud and the roadside computing facilities can be through optical fiber connection or wireless connection, and the embodiment of the invention is not particularly limited.

Specifically, the roadside computing facility is: the outfield device installed on the highway side or the central isolation zone is provided with a V2X communication module and can perform short-range communication with the vehicle. The edge computing unit is mainly used for computing and information issuing of real-time sensing data of a small-range road surface, uploading road surface sensing information to an edge cloud, and meanwhile, the edge computing unit is also used as a V2X communication pipeline for the edge cloud to issue information to vehicles, and is also called an edge computing unit. A roadside computing facility collects data of a plurality of fixed road sensing devices (such as millimeter wave radars, laser radars and high-definition cameras for identifying road targets, road remote sensing sensors for monitoring icing groups, fog, smoothness and the like of roads and the like) in a range of 500 m-1 km nearby through optical fiber communication, meanwhile, vehicle uploading data entering the range are collected through V2X communication, and preliminary multi-mode multi-source fusion perception calculation is carried out.

The edge cloud is: the cloud computing platform is deployed near a highway section (about 50-100 kilometers), is used for loading various low-delay and high-computing-power real-time traffic application services and automatic driving application services, and is used for traffic control, guidance and automatic driving services of the section. The edge cloud collects road condition data in the road section range through the fixed road side computing facility and the movable vehicle-mounted sensor, processes road dynamic data and traffic data in the road section range, and has higher computing capacity than the road side computing facility. The wireless communication with the vehicles in the road section does not pass through a core network, and the wireless communication system has lower communication time delay than a central cloud, and meets the low time delay requirement of automatic driving.

The central cloud is: the ubiquitous cloud computing platform is used for collecting macroscopic traffic data of a highway network, carrying out statistical analysis on traffic historical data, issuing static information or non-real-time information (such as traffic control information and static traffic event information) of the highway network, and carrying out dynamic characteristic collection and model optimization on freight train vehicles.

Fig. 2 schematically shows a flowchart of a method for controlling a cargo fleet column in a vehicle-road coordination manner in an embodiment of the present invention, where referring to fig. 2, the method may include:

s201, the first edge cloud corresponding to the road section where the truck fleet is located obtains traffic information of a road network and vehicle basic information of trucks in the truck fleet from the center cloud.

Specifically, the first edge cloud is an edge cloud corresponding to a road section where the cargo fleet column is located. The method comprises the steps that a central cloud firstly obtains traffic information of a road network and vehicle basic information of trucks in a truck queue, and an edge cloud corresponding to a road section where the truck queue is located obtains the traffic information of the road network and the vehicle basic information of the trucks in the truck queue from the central cloud through a fixed road side computing facility and a movable vehicle-mounted sensor.

The traffic information of the road network may be macroscopic traffic data of a highway network. The vehicle grounding information may be truck queue vehicle dynamics, vehicle ID, vehicle type, etc.

S202, the first edge cloud acquires the perception information of the road surface of the facility from the corresponding road side computing facility.

Specifically, the roadside computing facility first acquires facility-to-road perception information, and then the first edge cloud acquires facility-to-road perception information from the corresponding roadside computing facility.

S203, the truck fleet row acquires perception information of the vehicle on the road surface and vehicle running condition information, and sends the perception information and the vehicle running condition information to the first edge cloud.

Specifically, the cargo vehicle queue firstly acquires the sensing information of the vehicles on the road surface and the vehicle running condition information, and then sends the sensing information and the vehicle running condition information to the first edge cloud, so that the first edge cloud can acquire the sensing information of the vehicles on the road surface and the vehicle running condition information from the cargo vehicle fleet. The vehicle running condition information refers to the working condition of the vehicle in the transportation running process, and can be acceleration, deceleration, turning and the like.

And S204, generating a cooperative control strategy and sending the cooperative control strategy to the cargo truck fleet according to the traffic information, the vehicle basic information, the facility-to-road sensing information, the vehicle driving condition information and the road state information of the road section where the cargo truck fleet is located, which is acquired by the first edge cloud.

Specifically, the first edge cloud generates a cooperative control strategy and sends the cooperative control strategy to the cargo fleet row according to the traffic information and the vehicle basic information acquired in step S201, the perception information of the facility to the road surface acquired in step S202, the perception information of the vehicle to the road surface acquired in step S203, the vehicle driving condition information, and the road state information of the road section where the cargo fleet row is located. The road state information may be a slope, a curve curvature, a speed limit, a road surface flatness, and the like.

And S205, carrying out longitudinal control on the truck in the truck fleet according to the cooperative control strategy.

Specifically, the truck fleet performs longitudinal control of the truck according to the cooperative control strategy generated in step S204.

The road end comprises a center cloud, a plurality of edge clouds and a plurality of roadside computing facilities, the center cloud is connected with the edge clouds, each edge cloud is connected with the roadside computing facilities, a first edge cloud corresponding to a road section where the truck fleet is located acquires traffic information of a road network and vehicle basic information of trucks in a truck queue from the center cloud, the first edge cloud acquires sensing information of the facilities on the road surface from the corresponding roadside computing facilities, the truck fleet column acquires the sensing information of the vehicles on the road surface and vehicle driving condition information and sends the sensing information and the vehicle driving condition information to the first edge cloud, and the first edge cloud acquires the road state information of the road section where the truck fleet is located according to the traffic information, the vehicle basic information, the sensing information of the facilities on the road surface, the sensing information of the vehicles on the road surface, the vehicle driving condition information and the truck state information of the truck fleet obtained by the first edge cloud, generating a cooperative control strategy and sending the cooperative control strategy to a freight train queue, and carrying out longitudinal control on the freight train according to the cooperative control strategy by the freight train queue; the information interaction can be carried out on the freight train queue through the central cloud, the edge clouds and the roadside computing facilities, so that the longitudinal control of the freight train queue is realized, real-time shared information and accurate prediction information are obtained, the interaction information of the freight train queue is more comprehensive, the predictability of the freight train queue is higher, and the stability of the longitudinal control of the freight train queue is improved; the problem that the stability of longitudinal control of the freight train queue is low due to the fact that the acquired interactive information of the freight train queue is not comprehensive enough can be solved.

Further, as refinement and expansion of the method shown in fig. 2, the embodiment of the invention also provides a longitudinal control method of a truck fleet column with cooperative vehicle routes. Fig. 3 schematically illustrates a second flowchart of a method for controlling a cargo fleet column in cooperation with a vehicle route according to an embodiment of the present invention, and referring to fig. 3, the method for controlling a cargo fleet column in cooperation with a vehicle route according to an embodiment of the present invention may include:

s301, the cargo fleet column sends a registration request to the first edge cloud.

Specifically, when the cargo fleet column enters a road segment covered by an edge cloud, the cargo fleet column sends a cargo fleet column registration request to the edge cloud. Since each road segment corresponds to one edge cloud, the cargo fleet column registration request is sent to the edge cloud corresponding to each road segment when the cargo fleet column enters each road segment.

Illustratively, an edge cloud corresponding to the road section l is the edge cloud l, and when a cargo vehicle queue enters the road section l, a cargo vehicle fleet registration request is sent to the edge cloud l; and the edge cloud corresponding to the road section i is the edge cloud i, and when the cargo vehicle queue enters the road section i, the cargo vehicle queue registration request is sent to the edge cloud i.

S302, the first edge cloud registers the cargo fleet according to the registration request.

Specifically, the first edge cloud registers the cargo fleet column according to the registration request sent in step S301. Each road section corresponds to one first edge cloud, and the cargo fleet column is required to send a cargo fleet column registration request to the first edge cloud corresponding to the road section when entering each road section, so that the first edge cloud corresponding to each road section is required to register the cargo fleet column according to the registration request.

Illustratively, when the truck queue enters the road section l, the edge cloud corresponding to the road section l is the edge cloud l, and the edge cloud l registers the truck queue according to the registration request sent in the step S301; when the truck queue enters the road section N, the edge cloud corresponding to the road section N is the edge cloud N, and the edge cloud N registers the truck fleet according to the registration request sent in step S301.

S303, the first edge cloud corresponding to the road section where the truck fleet is located obtains traffic information of a road network and vehicle basic information of trucks in the truck fleet from the center cloud.

Step S303 is the same as step S201, and therefore, is not described herein again.

Illustratively, a road section where a cargo vehicle fleet column is located is a road section l, a first edge cloud corresponding to the road section l is an edge cloud l, and when the cargo vehicle fleet column runs on the road section l, the edge cloud l acquires traffic information of a road network and vehicle basic information of trucks in the cargo vehicle queue from a central cloud.

S304, the first edge cloud acquires the perception information of the road surface by the facility from the corresponding road side computing facility.

Illustratively, the road section where the truck fleet is located is a road section i, the first edge cloud corresponding to the road section i is an edge cloud i, the edge cloud i is connected with a plurality of road side computing facilities (road side computing facility l, road side computing facility j, road side computing facility M and the like), and the edge cloud i acquires sensing information of the facilities on the road surface from the corresponding road side computing facilities such as the road side computing facility l, the road side computing facility j, the road side computing facility M and the like.

In practical applications, the drive test computation facility may also serve as a V2X communication conduit for edge clouds to publish information to vehicles, also referred to as edge computation units.

S305, the cargo vehicle fleet acquires perception information of the vehicle on the road surface and vehicle running condition information, and sends the perception information and the vehicle running condition information to the first edge cloud.

Illustratively, the road section where the cargo fleet column is located is a road section i, the first edge cloud corresponding to the road section i is an edge cloud i, the cargo fleet column acquires sensing information of a vehicle on the road surface and vehicle running condition information, and sends the sensing information and the vehicle running condition information to the edge cloud i, and the edge cloud i can acquire the sensing information of the vehicle on the road surface and the vehicle running condition information from the cargo fleet column. The method comprises the steps that a cargo vehicle queue is located on a road section N, a first edge cloud corresponding to the road section N is an edge cloud N, the cargo vehicle fleet acquires sensing information of a vehicle on the road surface and vehicle running condition information, the sensing information and the vehicle running condition information are sent to the edge cloud N, and the edge cloud N can acquire the sensing information of the vehicle on the road surface and the vehicle running condition information from the cargo vehicle fleet.

The method for acquiring the perception information of the vehicle on the road surface is related to the perception capability of the vehicle, the lower the perception capability is, the less the perception information of the vehicle on the road surface is acquired, and the higher the perception capability is, the more the perception information of the vehicle on the road surface is acquired, so that the acquired information is more comprehensive.

And S306, generating a cooperative control strategy and sending the cooperative control strategy to the cargo truck fleet according to the traffic information, the vehicle basic information, the facility-to-road sensing information, the vehicle driving condition information and the road state information of the road section where the cargo truck fleet is located, which is acquired by the first edge cloud.

Specifically, aiming at the built cargo fleet column, the first edge cloud aims at energy conservation and optimal queue stability, and generates a cooperative control strategy and sends the cooperative control strategy to the cargo fleet column according to the traffic information and the vehicle basic information acquired in the step S303, the perception information of the facility on the road surface acquired in the step S304, the perception information of the vehicle on the road surface acquired in the step S305, the vehicle driving condition information and the road state information of the road section where the cargo fleet column is located, which are acquired by the first edge cloud. The road state information may be a slope, a curve curvature, a speed limit, a road surface flatness, and the like. The cooperative control strategy may be a position and velocity based inducement strategy.

As an optional implementation manner of the embodiment of the present invention, while the cooperative control policy is generated, the first edge cloud may maintain a list of one train queue in real time, and perform position-based personalized dynamic information distribution or cooperative control on each train queue. The personalized dynamic information release mainly releases the real-time position and speed of the freight train queue.

In particular, fig. 4 schematically shows an architecture diagram of a prior art truck queue longitudinal control in an embodiment of the present invention, as shown in fig. 4, wherein trucks N, van

The truck i, the truck l and the like are vehicles of a truck queue, and the truck l is a lead truck.

、

And

are all distributed queue controllers, ensuring members of adjacent queues (wagons N, wagons)

Truck i and truck l, etc.) are stably maintained at a desired spacing value

，

、

And

are all expected accelerations. The desired acceleration of the truck N is

The distributed queue controller is

(ii) a Truck

The corresponding desired acceleration is

The distributed queue controller is

(ii) a The desired acceleration for truck i is

The distributed queue controller is

(ii) a The desired acceleration for truck l is

The distributed queue controller is

. Current distance between following vehicle and front vehicle

Deviation from

Time, distributionThe queue controller will send the expected acceleration to the lower vehicle controller to adjust the distance between the vehicles

。

Is the speed of truck l, t is the travel time of truck l,

the graph with t gives the trend of the speed of the truck l as a function of the travel time.

The queue longitudinal controller of the leading vehicle of the existing cargo vehicle queue calculates the expected acceleration of each following vehicle by utilizing the information in the vehicle fleet of the information flow topology (V2V), and sends the expected acceleration to the distributed queue longitudinal controller of each following vehicle to adjust the distance between the vehicles to the expected distance. The existing freight train queue operation control technology is lack of global information, visual penetration information, over-the-horizon information and blind area information, and can generate unstable distance caused by sudden speed change due to poor predictability. The information quantity is not enough only based on the information sharing and model prediction in the V2V fleet. In addition, for some longitudinal optimization control models aiming at energy conservation, the calculation force is high, and the vehicle-mounted end is difficult to realize.

Specifically, fig. 5 of the present application is an architecture diagram that overcomes the problem of fig. 4. Fig. 5 schematically shows an architecture diagram of a road end and a cargo vehicle queue of cargo vehicle fleet longitudinal control in cooperation with a vehicle road in the embodiment of the present invention, and referring to fig. 5, a road side computing facility is connected with an edge cloud, and the edge cloud is connected with a center cloud through an optical fiber, so that an extremely low time delay of data transmission can be ensured. Meanwhile, in the architecture of the road end and the freight train queue of the freight train queue longitudinal control with the cooperative vehicle road, the edge cloud and the center cloud can both carry out data interaction with the vehicle through cellular mobile communication (such as 4G/5G) and special short-range communication (such as V2X), and the roadside computing facility mainly adopts the special short-range communication to carry out data interaction with the vehicle. The method comprises the steps of acquiring more comprehensive global information, visual penetration information, over-the-horizon information and blind area information through information topological flow (namely information interaction between a central cloud, a plurality of edge clouds and a plurality of roadside computing facilities and a truck queue), enabling the predictability of the truck queue to be higher and the stability of longitudinal control of the truck fleet to be higher, maintaining and updating a list of the truck queue of the road section in real time through the information topological flow edge clouds, and performing position-based and personalized dynamic information release or cooperative control on each truck queue.

Meanwhile, in the information interaction process of the cargo vehicle fleet column longitudinal control of vehicle-road cooperation, the content of vehicle-road information interaction required to be performed is shown in table 1, and table 1 is the information interaction of the cargo vehicle fleet column longitudinal control.

TABLE 1

S307, before the truck fleet enters the adjacent road section from the road section where the truck fleet is located, the first edge cloud determines whether a second edge cloud corresponding to the adjacent road section has a direct connection data channel. If yes, go to step S308, otherwise go to step S309.

Specifically, before the truck fleet enters the adjacent road section from the road section where the truck fleet is located, switching of edge clouds is involved, the first edge cloud determines whether a second edge cloud corresponding to the adjacent road section has a direct connection data channel, if yes, step S308 is executed, and if not, step S309 is executed.

Illustratively, road segment i corresponds to edge cloud i, road segment

Corresponding edge clouds

If the train of trucks enters an adjacent section from section i

Edge cloud i determines edge clouds

And (4) whether a direct connection data channel is available or not, if yes, executing step (S308), and if not, executing step (S309).

S308, the first edge cloud sends the acquired related information of the cargo fleet to the second edge cloud.

Specifically, if the first edge cloud determines that the second edge cloud corresponding to the adjacent road section has the direct connection data channel, the first edge cloud sends the acquired related information of the cargo fleet column to the second edge cloud. The related information of the truck queue can be perception information of the vehicle to the road surface, information of the running condition of the vehicle and the like.

Illustratively, in the example receiving step S307, the edge cloud i sends the acquired information related to the cargo fleet to the edge cloud

。

Specifically, the first edge cloud sends the acquired related information of the cargo fleet row to the second edge cloud, and the second edge cloud controls the cargo fleet by using the related information of the cargo fleet row acquired by the first edge cloud and the related information of the cargo fleet row acquired by the second edge cloud.

For example, the first edge cloud is edge cloud i corresponding to the cargo fleet column on the road section i, and the second edge cloud is edge cloud i + l corresponding to the cargo fleet column traveling from the road section i to the road section i + l. The edge cloud i acquires the sensing information i and the vehicle running condition information i, cloud control queues can be carried out, and the edge cloud i sends the sensing information i and the vehicle running condition information i to the edge cloud i + l; the freight train queue runs from the road section i to the road section i + l, and the sensing information i and the vehicle running condition information i sent by the edge cloud i are also used, so that the historical time information (the sensing information i and the vehicle running condition information i at the road section i) and the current information (the sensing information i + l and the vehicle running condition information i + l at the road section i + l) acquired by the freight train queue can be used for carrying out cloud control on the freight train queue.

S309, the first edge cloud sends the acquired related information of the cargo vehicle fleet to the center cloud, and the center cloud sends the received related information to the second edge cloud.

Specifically, if the first edge cloud determines that the second edge cloud corresponding to the adjacent road section does not have the direct connection data channel, the first edge cloud sends the acquired related information of the freight train fleet to the center cloud, and the center cloud sends the received related information to the second edge cloud.

Illustratively, following the example of step S307, the fleet of cargo vehicles will leave the previous road segment i (e.g., about 1 km and then enter the next road segment i)

) The edge cloud i sends the acquired related information of the freight train queue to the center cloud, and the center cloud sends the received related information to the edge cloud

。

If the first edge cloud determines that the second edge cloud corresponding to the adjacent road section does not have the direct connection data channel, the related information of the freight train queue can be transferred through the central cloud, the first edge cloud carries out timing synchronization on the real-time related information of the queue and the second edge cloud for a certain period, the information can be ensured to be the latest information, and the accuracy of the related information is ensured.

And S310, updating the road end longitudinal control model and the vehicle end longitudinal control model when the central cloud meets the triggering condition.

The road end longitudinal control model and the vehicle end longitudinal control model can work independently and can also work in coordination.

Specifically, the central cloud updates the road-end longitudinal control model and the vehicle-end longitudinal control model periodically or aperiodically under the condition of active or passive request. The period or the non-period may be determined according to actual conditions, and the embodiment of the present invention is not particularly limited.

As an alternative embodiment of the present invention, the road-end longitudinal control model and the vehicle-end longitudinal control model may be stored in the central cloud.

S311, determining whether the sum of the calculation time delay and the communication time delay meets the cooperative control requirement. If yes, go to step S312, otherwise go to step S313.

Specifically, the determination of whether the sum of the calculation delay and the communication delay meets the cooperative control requirement may be to transmit information and send an instruction in real time.

And S312, the road end longitudinal control model and the vehicle end longitudinal control model enter a cooperative working mode.

Specifically, if the sum of the calculation time delay and the communication time delay meets the cooperative control requirement, the road-end longitudinal control model and the vehicle-end longitudinal control model enter a cooperative working mode. The cooperative working mode can comprise cooperative sensing, cooperative decision, cooperative control, communication interconnection and the like, and is a multi-ring process.

The method comprises the following steps that a vehicle end longitudinal control model unloads a computing part with extremely high requirements on computing power in a cargo vehicle fleet row longitudinal control model (namely a part which has overhigh computing power load and is difficult to operate quickly in a cargo vehicle fleet row controller during actual operation) to an edge cloud, the edge cloud completes computing by combining global sensing information, and a computing result is returned to the cargo vehicle fleet row controller; the calculation results may include the speed, position of each vehicle in the queue, and the ideal acceleration of each vehicle in the queue.

The cargo vehicle fleet controller generates ideal acceleration of each vehicle in the fleet according to the calculation result returned by the edge cloud and the acceleration is executed by the underlying controller of each vehicle operating the power system or the braking system of the vehicle.

And S313, the road end longitudinal control model and the vehicle end longitudinal control model enter an independent working mode.

Specifically, if the sum of the calculation time delay and the communication time delay does not meet the cooperative control requirement, the road-end longitudinal control model and the vehicle-end longitudinal control model enter an independent working mode. The independent working mode can be that the edge cloud only carries out cooperative perception with the cargo vehicle fleet or only carries out speed induction and real-time traffic event release for the edge cloud.

In practical application, if the sum of the calculation time delay and the communication time delay does not meet the cooperative control requirement, the edge cloud only performs cooperative sensing with the freight train queue, specifically, the edge cloud issues sensing data of the position, the speed, the size, the type and the like of a target 1 km in front of the freight train queue to the freight train queue, and provides extended sensing capabilities such as a penetration visual field, over-the-horizon sensing, predictive sensing, blind area sensing and the like caused by shielding for the freight train queue. The freight train queue can perform speed planning in time according to the extended sensing capability provided by the edge cloud, so that the stability of the queue is improved, and the energy consumption is saved.

In practical application, if the sum of the calculation delay and the communication delay does not meet the requirement of cooperative control, the edge cloud only carries out speed induction and real-time traffic event release on the cargo vehicle fleet, specifically, for speed induction, the edge cloud comprehensively analyzes speed related parameters such as current real-time traffic flow, short-time traffic flow prediction, current road speed limit and the like, regularly sends a suggested running speed of 1 km ahead to the cargo vehicle fleet, and a lead vehicle of a cargo vehicle queue selectively executes speed induction of the edge cloud by combining self perception; for real-time traffic event release, after the edge cloud gives a traffic event of which the speed needs to be changed, the lead vehicle forms a speed decision (including a target speed and acceleration time) according to a rule in advance and sends the speed decision to the queue following vehicle. The speed-related parameter may be speed limit information of a road traffic system, and the advance rule may be a defined traffic speed plan and a target speed and acceleration time acquired through a Coordinated Adaptive Cruise Control (CACC) model.

As an alternative embodiment of the present invention, when the truck fleet enters a road without a vehicle-road coordination infrastructure, or a problem occurs in vehicle-road communication, or the truck fleet judges that the received vehicle-road coordination data is not reliable, the truck fleet longitudinal control is switched to the CACC model control mechanism.

And S314, carrying out longitudinal control on the truck in the truck fleet according to the cooperative control strategy.

Specifically, the freight train fleet is controlled longitudinally by the bottom controller of each truck according to a cooperative control strategy.

The embodiment of the invention is directed to the characteristic that an end (an intelligent automobile or an intelligent terminal), an edge (a roadside computing facility and an edge cloud) and a cloud (a center cloud) form a new generation of highway intelligent vehicle road cooperation system, realizes that the roadside computing facility is linked with the edge cloud and the center cloud through optical fibers, ensures extremely low time delay of data transmission, and aims to improve the longitudinal stability of a freight train when running.

Fig. 6 schematically shows a deployment diagram of a vehicle-road cooperative application service of a cargo vehicle fleet column longitudinal control of vehicle-road cooperative vehicle in an embodiment of the present invention, referring to fig. 5, the vehicle-road cooperative vehicle-road refers to that advanced wireless communication and a new generation internet and other technologies are adopted to implement vehicle-road dynamic real-time information interaction in all directions, and vehicle active safety control and road cooperative management are developed on the basis of full-time-space dynamic traffic information acquisition and fusion, so that effective cooperation of human vehicles and roads is fully realized, traffic safety is ensured, traffic efficiency is improved, and a safe, efficient and environment-friendly road traffic system is formed. Under the cooperative environment of the vehicle and the road, the global real-time information sharing of the road expands the environment perception capability of member vehicles, the information is more comprehensively obtained, the dynamic parameters are more accurate, and further the longitudinal control of the freight train queue can be more accurate and more predictive. In order to implement the longitudinal control of the cargo fleet in the vehicle-road coordination environment, a control algorithm based on the vehicle-road coordination needs to be installed at the vehicle end, and an application service needs to be deployed at the road end, as shown in fig. 5. The drive test calculation facility can carry out vehicle data acquisition, multi-source data fusion, vehicle road data fusion and beyond-the-horizon road condition broadcasting. The edge cloud can perform burst traffic time information release, real-time queue optimal speed induction, speed limit reminding, queue cooperative control and high-precision map dynamic updating. The central cloud can perform traffic flow short-time prediction, queue global optimal path calculation, road basic information release, slowly-changed traffic event information release and queue dynamics model optimization. The vehicle end can upload vehicle working condition data, vehicle road perception data, vehicle dynamic parameters, vehicle freight information, freight train control parameter receiving, global traffic information receiving and beyond-the-horizon road perception information receiving.

The vehicle end is responsible for acquiring mobile road condition data and uploading self driving condition data, and receiving road condition information of the side clouds and automatic driving cooperative sensing and cooperative decision information; the roadside computing facility is responsible for local small-range (about 500-1000 meters) fixed road condition data acquisition and data preliminary calculation and provides a vehicle-road direct connection data communication pipeline; the edge cloud is responsible for real-time acquisition of road condition data at a road section level (about 50-100 kilometers), fine data calculation, road condition information release, automatic driving cooperative sensing and decision information release; the central cloud is responsible for collecting and calculating the global traffic data and issuing global traffic information; the V2X communication system (such as LTE-V2X and NR-V2X) forms an information pipeline for realizing direct connection and real-time transmission between the vehicle and the road by a wireless ad hoc network. The road side application service is respectively installed and deployed in the edge cloud, the center cloud and the road side computing facility. The edge cloud and the center cloud need to deploy the freight train queue application service; the road side computing facility is provided with general vehicle and road cooperative application service software. By utilizing the goods vehicle fleet special application service of the edge cloud and the center cloud and the general vehicle-road cooperative application service of the road side computing facility, real-time information interconnection can be carried out, the environment perception capability of member vehicles is expanded, the information acquisition is more comprehensive, the dynamic parameters are more accurate, and the performance of longitudinal control of the goods vehicle fleet can be improved.

The embodiment comprises the following steps:

step one, a vehicle-road cooperation system composed of a road end and a vehicle end is constructed, and intelligent vehicle-road cooperation is achieved through data interaction of the road end and the vehicle end. The road end comprises road side computing facilities, edge clouds and a center cloud.

Specifically, in the embodiment of the present application, the roadside computing facility at the road end in the vehicle-road cooperation system is defined as: the outfield device installed on the highway side or the central isolation zone is provided with a V2X communication module and can perform short-range communication with the vehicle.

The roadside computing facility is mainly used for computing and information publishing of real-time sensing data of a small-range road surface, uploading road surface sensing information to an edge cloud, and is also used as a V2X communication pipeline for publishing information to vehicles by the edge cloud, and is also called an edge computing unit. A roadside computing facility collects data of a plurality of fixed road sensing devices (such as millimeter wave radars, laser radars and high-definition cameras for identifying road targets, road remote sensing sensors for monitoring icing groups, fog, smoothness and the like of roads and the like) in a range of 500 m-1 km nearby through optical fiber communication, meanwhile, vehicle uploading data entering the range are collected through V2X communication, and preliminary multi-mode multi-source fusion perception calculation is carried out.

Specifically, in the embodiment of the present application, defining an edge cloud of a road end in the vehicle-road coordination system as follows: the cloud computing platform is deployed near a highway section (about 50-100 kilometers), is used for loading various low-delay and high-computing-power real-time traffic application services and automatic driving application services, and is used for traffic control, guidance and automatic driving services of the section.

The edge cloud collects road condition data in the road section range through the fixed road side computing facility and the movable vehicle-mounted sensor, processes road dynamic data and traffic data in the road section range, and has higher computing capacity than the road side computing facility. The wireless communication with the vehicles in the road section does not pass through a core network, and the wireless communication system has lower communication time delay than a central cloud, and meets the low time delay requirement of automatic driving.

Specifically, in the embodiment of the present application, the central cloud of the road end in the vehicle-road coordination system is defined as: the ubiquitous cloud computing platform is used for collecting macroscopic traffic data of a highway network, carrying out statistical analysis on traffic historical data, issuing static information or non-real-time information (such as traffic control information and static traffic event information) of the highway network, and carrying out dynamic characteristic collection and model optimization on freight train vehicles.

And step two, respectively deploying a queue longitudinal control model on the edge cloud and the truck.

Specifically, the two queue longitudinal control models can work independently or cooperatively. The edge cloud has the advantage that computation with high computational power can be completed with low time delay. The longitudinal control model can refer to the disclosure in the prior art, and the longitudinal control model mainly performs the control of the longitudinal direction and the distance speed of the queue.

And step three, the central cloud updates the longitudinal control model and the road basic information of the freight train array of the edge cloud and the train end.

Specifically, longitudinal control model parameters and a longitudinal control model are stored in a central cloud, and the longitudinal control model and road basic information of a freight train array of an edge cloud and a train end are updated regularly or irregularly by the central cloud under the condition of active or passive request. The road basic information comprises gradient, curve curvature and the like, and the longitudinal control model parameters comprise the speed direction of a front wheel, the corner of the front wheel and the like.

And step four, the trucks enter the coverage road section of the edge cloud, and the truck fleet row registration is carried out on the edge cloud firstly.

Specifically, the edge cloud corresponding to each road segment requires a cargo fleet column registration.

And step five, starting the cargo vehicle fleet service function by the edge cloud to form an individualized cooperative control strategy.

Specifically, after the edge cloud of the road section where the cargo fleet is located is registered successfully, the edge cloud immediately starts a service function of the cargo fleet. Aiming at a newly built truck queue, the edge cloud aims at energy conservation and optimal queue stability, and forms an individualized cooperative control strategy according to dynamic parameters of queue members and basic characteristics (such as gradient, curve and the like) of a road to be driven. Meanwhile, the edge cloud maintains a list of truck queues in real time, and performs position-based and personalized dynamic information distribution or cooperative control (mainly position and speed induction) on each truck queue. The list of trains of trucks is a list of trucks in a train of trucks, e.g. N trucks, i.e. the list of trains of trucks is 1-N. The dynamic information issuing is mainly used for issuing the real-time position and speed of the freight train queue.

And step six, when the freight train queue enters another road section from one road section, obtaining the data condition according to the road section information and making a switching strategy.

Specifically, in the embodiment of the present application, if the edge clouds of the adjacent road segments have the direct connection data channel, the edge clouds actively send the relevant data of the queue to the next edge cloud at an appropriate time; and if the edge clouds of the adjacent road sections do not have the direct connection data channel, transferring the related data of the freight train fleet through the central cloud.

Specifically, in the embodiment of the present application, when the truck fleet is about to leave the previous road section (for example, enter the next road section after about 1 km), the previous edge cloud performs timing synchronization of the real-time parameters of the fleet with the next edge cloud for a certain period until the switching is completed.

And seventhly, judging whether the terminal side cooperative control function is provided or not, and calculating whether the sum of the time delay and the communication time delay meets the requirement or not.

Specifically, the freight train row controller unloads a computing part with extremely high requirement on computing power in the freight train row longitudinal control model, namely, a computing part which is difficult to operate quickly in the freight train row controller is placed into an edge cloud, the edge cloud completes computing by combining global perception information, and a computing result is returned to the freight train row controller. The cargo vehicle fleet controller generates ideal acceleration of each vehicle in the fleet according to the calculation result returned by the edge cloud and the acceleration is executed by the underlying controller of each vehicle operating the power system or the braking system of the vehicle. The calculation result can be the speed and the position of each vehicle in the queue, so that the ideal acceleration of each vehicle in the queue can be obtained according to the speed and the position.

The vehicle side cooperative control function is achieved, specifically, a sensor and communication equipment are mounted on the vehicle side, communication equipment also exists on the road side and the cloud side, and communication interconnection and a control instruction sending port are achieved among vehicles through the communication equipment. The calculation time delay and the communication time delay sum meeting the cooperative control requirement means that real-time information transmission and instruction sending can be carried out.

And step eight, when the cargo vehicle fleet row has an end edge fusion cooperative control function and the sum of the calculation time delay and the communication time delay meets the cooperative control requirement, the cargo vehicle fleet row enters a cooperative control mode and returns to generate the ideal acceleration of each vehicle in the queue.

Specifically, in the embodiment of the application, the cargo fleet column controller unloads a computing part with extremely high computational power requirement in the cargo fleet column longitudinal control model to the edge cloud, the edge cloud completes computation by combining the global perception information, and a computation result is returned to the cargo fleet column controller.

Specifically, in the embodiment of the application, the cargo fleet controller generates ideal acceleration of each vehicle in the fleet according to the calculation result returned by the edge cloud, and the acceleration is executed by the underlying controller of each vehicle operating the power system or the braking system of the vehicle.

And step nine, when the freight train queue does not have the function of carrying out cooperative control with the edge cloud, or the sum of the calculation delay and the communication delay is detected to not meet the cooperative control requirement, the edge cloud only carries out cooperative perception with the freight train queue or carries out speed induction and real-time traffic event release with the freight train queue.

Specifically, in the embodiment of the application, when the cargo fleet does not have the function of performing control level cooperation with the edge cloud, the edge cloud only performs cooperative sensing with the cargo fleet. At the moment, the edge cloud issues sensing data of the position, the speed, the size, the type and the like of the target 1 kilometer ahead of the freight train queue to the freight train queue, and provides extended sensing capabilities such as penetration vision, beyond visual range sensing, predictability sensing, blind area sensing and the like caused by shielding for the freight train queue. The freight train queue can perform speed planning in time according to the extended sensing capability provided by the edge cloud, so that the stability of the queue is improved, and the energy consumption is saved.

Cooperative control can comprise cooperative sensing, cooperative decision, cooperative control, communication interconnection and the like, and is a multi-ring process. The edge cloud only carries out cooperative perception with the freight train queue, namely the freight train queue does not have the function of carrying out control level cooperation with the edge cloud, or the cooperative decision, cooperative control and communication interconnection are not met when the sum of the calculation delay and the communication delay is detected to be not meeting the cooperative control requirement.

Specifically, in the embodiment of the application, when the cargo fleet does not have the function of performing control level cooperation with the edge cloud, the edge cloud only performs speed induction and real-time traffic event release on the cargo fleet. For speed induction, edge clouds comprehensively analyze speed related parameters such as current real-time traffic flow, short-time traffic flow prediction, current road speed limit and the like, regularly send a suggested running speed of 1 kilometer ahead to a cargo vehicle queue, and a leading vehicle of the cargo vehicle queue selectively executes speed induction of the edge clouds by combining self perception; for real-time traffic event release, after the edge cloud gives a traffic event of which the speed needs to be changed, the lead vehicle forms a speed decision (including a target speed and acceleration time) according to a rule in advance and sends the speed decision to the queue following vehicle.

The speed-related information is speed limit information of the road traffic system. The global perception information comprises speed related parameters such as current real-time traffic flow, short-time traffic flow prediction, current road speed limit and the like. The implementation rules refer to the defined traffic speed plan and the target speed and acceleration time obtained by the coordinated adaptive cruise control CACC model.

Based on the same inventive concept, the embodiment of the invention also provides a road end device as an implementation of the longitudinal control method of the goods vehicle fleet with the vehicle-road cooperation. The line end equipment can comprise: a central cloud, a plurality of edge clouds, and a plurality of roadside computing facilities, the central cloud being connected to the plurality of edge clouds, each of the edge clouds being connected to a plurality of the roadside computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside; the central cloud is used for acquiring traffic information of a road network and vehicle basic information of trucks in the truck queue; the roadside computing facility is used for acquiring perception information of the facility on the road surface; the first edge cloud corresponds to a road section where the truck queue is located and is used for acquiring traffic information of a road network and vehicle basic information of trucks in the truck queue from the center cloud; the first edge cloud is used for acquiring perception information of the facilities on the road surface from the corresponding roadside computing facilities; the first edge cloud is used for acquiring perception information of the vehicle to the road surface and vehicle running condition information from the cargo vehicle fleet; the first edge cloud is used for generating a cooperative control strategy and sending the cooperative control strategy to the truck fleet row according to the acquired traffic information, the acquired vehicle basic information, the acquired sensing information of the truck on the road surface, the acquired information of the vehicle on the vehicle running condition and the acquired road state information of the road section where the truck fleet row is located, and the truck fleet can carry out truck longitudinal control according to the cooperative control strategy.

As an optional implementation manner of the embodiment of the present invention, the first edge cloud is further configured to receive a registration request sent by the truck fleet column before the first edge cloud corresponding to the road segment where the truck fleet column is located acquires the traffic information of the road network and the vehicle basic information of the trucks in the truck queue from the center cloud, and register the truck queue according to the registration request.

As an optional implementation manner of the embodiment of the present invention, the first edge cloud is further configured to determine whether a second edge cloud corresponding to an adjacent road segment has a direct connection data channel before the cargo train queue enters the adjacent road segment from the road segment where the cargo train queue is located after the cooperative control policy is generated and sent to the cargo train queue; if so, sending the acquired related information of the truck queue to the second edge cloud; and if not, sending the acquired related information of the freight train queue to a central cloud, and sending the received related information to the second edge cloud by the central cloud.

As an optional implementation manner of the embodiment of the present invention, the central cloud is further configured to update the road-end longitudinal control model and the vehicle-end longitudinal control model when a trigger condition is met; a plurality of edge clouds of way end be provided with the vertical control model of way end with the goods motorcade is listed as and is provided with the vertical control model of car end, the vertical control model of way end with the vertical control model of car end can also can the collaborative work by the autonomous working.

As an optional implementation manner of the embodiment of the present invention, the road-end device is further configured to determine whether a sum of the calculation delay and the communication delay meets a cooperative control requirement after the cooperative control policy is generated and sent to the cargo fleet; if so, the road end longitudinal control model and the vehicle end longitudinal control model enter a cooperative working mode; and if not, the road end longitudinal control model and the vehicle end longitudinal control model enter an independent working mode.

Based on the same inventive concept, the embodiment of the invention also provides a goods vehicle fleet column as the implementation of the longitudinal control method of the goods vehicle fleet column with the vehicle-road cooperation. The cargo fleet column includes a plurality of trucks; the freight train queue is used for acquiring perception information of the vehicles on the road surface and information of vehicle running conditions; the system comprises a first edge cloud, a second edge cloud and a monitoring unit, wherein the first edge cloud is used for sending the perception information and the vehicle running condition information to a first edge cloud corresponding to a road section where a freight train queue is located; the cooperative control strategy is used for receiving the cooperative control strategy sent by the first edge cloud; the cooperative control strategy is generated by the first edge cloud according to traffic information and vehicle basic information acquired from a central cloud, facility-to-road sensing information acquired from a roadside computing facility, vehicle-to-road sensing information and vehicle running condition information acquired from the cargo vehicle fleet column and road state information of a road section where the cargo vehicle fleet column is located acquired by the cloud; and the control system is used for carrying out longitudinal control on the truck according to the cooperative control strategy.

As an optional implementation manner of the embodiment of the present invention, the truck queue is further configured to send a registration request to a first edge cloud before sending the sensing information and the vehicle driving condition information to the first edge cloud corresponding to the road segment where the truck queue is located, where the first edge cloud can register the truck queue according to the registration request.

As an optional implementation manner of the embodiment of the present invention, the truck queue is further configured to receive update information of the truck-end longitudinal control model sent by the central cloud. The goods train is provided with the vertical control model of car end and a plurality of edges of way end and is provided with the vertical control model of way end, the vertical control model of car end with the vertical control model of way end can also can the collaborative work by the autonomous working.

As an optional implementation manner of the embodiment of the present invention, the truck queue is further configured to, after receiving the cooperative control policy sent by the first edge cloud, receive that before the truck queue enters an adjacent road segment from the road segment where the truck queue is located, the first edge cloud determines whether a second edge cloud corresponding to the adjacent road segment has a direct connection data channel; if so, receiving the acquired relevant information of the truck queue, which is sent to the second edge cloud by the first edge cloud; if not, receiving the related information sent to the second edge cloud by the first edge cloud, wherein the related information is the information that the first edge cloud sends the acquired related information of the freight train queue to a center cloud, and the center cloud sends the received related information to the second edge cloud.

As an optional implementation manner of the embodiment of the present invention, the truck queue is further configured to receive whether a sum of a computation delay and a communication delay determined by a road end meets a cooperative control requirement after the cooperative control policy sent by the first edge cloud is received; if so, receiving that the road end longitudinal control model and the vehicle end longitudinal control model enter a cooperative working mode; and if not, receiving the road end longitudinal control model and the vehicle end longitudinal control model to enter independent working modes.

Based on the same inventive concept, as an implementation of the cargo truck fleet column longitudinal control method with vehicle route coordination, the embodiment of the invention also provides a cargo truck fleet column longitudinal control system with vehicle route coordination, which can comprise: the system comprises a freight train queue and road-end equipment; wherein the train of trucks includes a plurality of trucks; the road end equipment comprises a central cloud, a plurality of edge clouds and a plurality of road side computing facilities, wherein the central cloud is connected with the edge clouds, and each edge cloud is connected with the road side computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside; the first edge cloud corresponds to a road section where the truck queue is located and is used for acquiring traffic information of a road network and vehicle basic information of trucks in the truck queue from the center cloud; the first edge cloud is used for acquiring perception information of the facilities on the road surface from the corresponding roadside computing facilities; the cargo vehicle queue is used for acquiring perception information of a vehicle on a road surface and vehicle running condition information and sending the perception information and the vehicle running condition information to the first edge cloud; the first edge cloud is used for generating a cooperative control strategy and sending the cooperative control strategy to the truck fleet row according to the traffic information, the vehicle basic information, the sensing information of the applied road surface, the sensing information of the vehicle to the road surface, the vehicle running condition information and the road state information of the road section where the truck fleet row is located, which is acquired by the first edge cloud; and the truck queue is used for carrying out longitudinal control on the trucks according to the cooperative control strategy.

As an optional implementation manner of the embodiment of the present invention, the first edge cloud is further configured to send a registration request to the first edge cloud before sending the perception information and the vehicle driving condition information to the first edge cloud corresponding to the road segment where the truck queue is located, and the first edge cloud can register the truck queue according to the registration request.

Based on the same inventive concept, the embodiment of the invention also provides electronic equipment. Fig. 7 is a structural diagram of an electronic device in an embodiment of the present invention, and referring to fig. 7, the electronic device 70 may include: at least one processor 701; and at least one memory 702, bus 703 connected to processor 701; the processor 701 and the memory 702 complete mutual communication through a bus 703; the processor 701 is configured to call program instructions in the memory 702 to perform the cargo fleet longitudinal control method in cooperation with the vehicle route in one or more embodiments described above.

Here, it should be noted that: the above description of the embodiments of the road end device, the cargo vehicle fleet and the vehicle road coordinated cargo vehicle fleet longitudinal control system is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the road-end equipment and the train of trucks of the embodiments of the present invention, refer to the description of the embodiments of the method of the present invention for understanding.

Based on the same inventive concept, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus on which the storage medium is located is controlled to execute the method in one or more embodiments described above.

Here, it should be noted that: the above description of the computer-readable storage medium embodiments is similar to the description of the method embodiments described above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the computer-readable storage medium of the embodiments of the present invention, reference is made to the description of the method embodiments of the present invention for understanding.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A longitudinal control method of a goods vehicle fleet with vehicle-road cooperation is applied to a road end and is characterized in that,

the road end comprises a central cloud, a plurality of edge clouds and a plurality of road side computing facilities, wherein the central cloud is connected with the edge clouds, and each edge cloud is connected with the road side computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside; the method comprises the following steps:

2. The method of claim 1, wherein before the first edge cloud corresponding to the road segment where the truck queue is located obtains the traffic information of the road network and the vehicle basic information of the trucks in the truck queue from the center cloud, the method further comprises:

3. The method of claim 1, wherein after generating and sending the coordinated control strategy to the fleet of cargo vehicles, the method further comprises:

4. The method of claim 1, wherein the plurality of edge clouds are provided with end-of-road longitudinal control models and the fleet of cargo vehicles are provided with end-of-vehicle longitudinal control models, the end-of-road longitudinal control models and the end-of-vehicle longitudinal control models being capable of operating independently and cooperatively, the method further comprising:

5. The method of claim 4, wherein after generating and sending the coordinated control strategy to the fleet of cargo vehicles, the method further comprises:

6. A longitudinal control method of a cargo vehicle fleet with cooperative vehicle and road is applied to a cargo vehicle queue, and is characterized in that the cargo vehicle queue comprises a plurality of trucks, and the method comprises the following steps:

7. A longitudinal control method for a cargo vehicle fleet with cooperative vehicle routes is characterized in that,

the cargo fleet column comprises a plurality of trucks;

the first edge cloud generates a cooperative control strategy and sends the cooperative control strategy to the cargo truck fleet column according to the traffic information, the vehicle basic information, the facility sensing information of the road surface, the vehicle driving condition information and the road state information of the road section where the cargo truck fleet column is located, which is acquired by the first edge cloud;

8. A line-side apparatus, the apparatus comprising: a central cloud, a plurality of edge clouds, and a plurality of roadside computing facilities, the central cloud being connected to the plurality of edge clouds, each of the edge clouds being connected to a plurality of the roadside computing facilities; each edge cloud corresponds to one road section in the road, and the roadside computing facilities are arranged on the roadside;

9. A cargo fleet array, wherein said cargo fleet comprises a plurality of trucks;

10. A longitudinal control system of a truck fleet coordinated by a vehicle and a road is characterized by comprising a truck queue and road-end equipment; wherein the content of the first and second substances,

the cargo fleet column includes a plurality of trucks;

the first edge cloud is used for generating a cooperative control strategy and sending the cooperative control strategy to the truck fleet column according to the traffic information, the vehicle basic information, the facility sensing information of the road surface, the vehicle driving condition information and the road state information of the road section where the truck fleet column is located, wherein the road state information is obtained by the first edge cloud;