CN115909740A - Vehicle-road cooperation realization method and device, computer equipment and storage medium - Google Patents
Vehicle-road cooperation realization method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of artificial intelligence, and discloses a method, a device, computer equipment and a storage medium for realizing vehicle-road cooperation, wherein the method comprises the steps of matching a preset event model when traffic abnormal information is received so as to determine an event type corresponding to the traffic abnormal information; when the event type belongs to the complex event type, pre-judging the traffic abnormal information based on a preset event model to generate a pre-judging event; and inputting the pre-judging event into a preset decision engine to generate a decision to be executed. According to the method and the device, when the abnormal information is determined to be the complex event, the pre-judging event corresponding to the abnormal information is generated through the preset event model, the to-be-executed decision is generated through the preset decision engine, and the traffic facilities and/or the target vehicle are/is instructed to execute the to-be-executed decision, so that the accuracy of detecting the complex environment in the vehicle-road cooperative scene is improved, and the problem of low accuracy of detecting the complex environment in the existing vehicle-road cooperative scene is solved.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for implementing vehicle-road cooperation, a computer device, and a storage medium.
Background
In the existing digital twin-based road correlation technique, the core is usually placed in the interaction relationship between the equipment and the intelligent platform or the cloud platform when the vehicle and the road are cooperated, or the core is focused on learning and training by using an algorithm to enhance the accuracy of flow prediction and path planning, but for the edge equipment, the roadside intelligent equipment (RSU) and the edge computing unit (MEC) are still separated, and the complexity of wiring and a system is greatly increased. And the problems that data such as high-speed vehicles, cameras and videos are converted and transmitted for several times and time delay is serious exist, and the automatic driving is limited in commercial use finally. The wired wiring is many, and the complexity is high, and the degree of difficulty of construction and fortune dimension is very big.
Aiming at the problem that a scene of vehicle-road cooperation cannot accurately process an infinite possible long-tail scene in a complex environment, most of the scenes only comprise a road monitoring system, a road planning system, an abnormal early warning system with a single scene and a road charging system. Most road monitoring systems take road monitoring and road section sensing as centers, and cannot fully sense road condition information, the sensing range and the sensing distance of a vehicle-mounted end are short, so that emergency situations such as roadblocks, road maintenance and equipment damage cannot be effectively avoided, information transmission between road sections is relatively slow, emergency danger avoidance for accidents such as debris flow and fire disasters is prevented, and the accuracy rate of detecting complex environments is low. Therefore, how to improve the accuracy rate of complex environment detection in the vehicle-road cooperation scene becomes a problem to be solved urgently at present.
Disclosure of Invention
The application provides a vehicle-road cooperation realization method and device, computer equipment and a storage medium, so as to improve the accuracy of complex environment detection in a vehicle-road cooperation scene.
In a first aspect, the present application provides a vehicle-road cooperation implementation method, including:
monitoring traffic facilities of a current running scene of a target vehicle through a road end sensing module, and monitoring the target vehicle through a vehicle end sensing module to judge whether traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received or not;
when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and determining an event type corresponding to the traffic abnormal information according to a matching result;
when the traffic abnormal information is matched with the preset event model, determining that the event type is a complex event;
when the event type belongs to a complex event type, pre-judging the traffic abnormal information based on the preset event model to generate a corresponding pre-judging event;
inputting the prejudgment event into a preset decision engine of a digital twin platform to generate a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle.
Further, the road end sensing module comprises a laser radar, a sensing camera and a vehicle-mounted unit, the road end sensing module monitors the traffic facilities of the current running scene of the target vehicle, and the vehicle end sensing module monitors the target vehicle to judge whether the traffic facilities and/or the traffic abnormal information fed back by the target vehicle are received, and the method comprises the following steps:
establishing communication connection with the target vehicle through the road end sensing module, and acquiring the running state of the traffic facility in the current running scene of the target vehicle through the road end sensing module, wherein the running state of the traffic facility comprises a normal running state and a facility fault state;
and if the running state of the traffic facility is the facility fault state, converting the facility fault state into the traffic abnormal information.
Furthermore, monitoring the traffic facilities of the current driving scene of the target vehicle through a road end sensing module, and monitoring the target vehicle through a vehicle end sensing module to judge whether the traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received, further comprising:
acquiring the running state of the target vehicle through the vehicle end sensing module, wherein the running state of the target vehicle comprises a normal running state and a vehicle fault state;
and if the running state of the target vehicle is the vehicle fault state, converting the vehicle fault state into the traffic abnormal information.
Further, when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and after determining an event type corresponding to the traffic abnormal information according to a matching result, the method further comprises the following steps:
and when the traffic abnormal information is not matched with the preset event model, determining that the event type is a simple event.
Further, when the traffic abnormality information is not matched to the preset event model, determining that the event type is a simple event, including:
when the event type belongs to a simple event type, generating a corresponding preprocessing result through the digital twin platform;
and triggering a preset decision engine through the road side base station based on the preprocessing result to generate a to-be-executed decision corresponding to the preprocessing result.
Further, before inputting the pre-judging event into a pre-decision engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle, the method includes:
and configuring a preset decision list through a semantic predefining module in the digital twin platform, and adding a corresponding preprocessing result to each preset decision in the preset decision list to generate the preset decision engine.
Further, inputting the pre-judging event into a pre-setting decision engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle, including:
matching the pre-judgment result with the pre-judgment result corresponding to each preset decision in a preset decision list through the preset decision engine;
determining the corresponding preset decision as the decision to be executed under the condition that the pre-judgment result is matched with each preset decision in the preset decision list;
sending, by the digital twin platform, an instruction to execute the decision to be executed to the transportation facility and/or the transportation device, and executing, by the transportation facility and/or the transportation device, the decision to be executed.
In a second aspect, the present application further provides a device for realizing vehicle-road coordination, where the device for realizing vehicle-road coordination includes:
the traffic abnormal information judging module is used for monitoring the traffic facilities of the current running scene of the target vehicle through the road end sensing module and monitoring the target vehicle through the vehicle end sensing module so as to judge whether the traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received or not;
the event type determining module is used for matching the traffic abnormal information with a preset event model in a preset event model library when the traffic abnormal information fed back by the traffic facility and/or the target vehicle is received, and determining an event type corresponding to the traffic abnormal information according to a matching result;
the complex event judging module is used for determining the event type as a complex event when the traffic abnormal information is matched with the preset event model;
the pre-judging event generating module is used for pre-judging the traffic abnormal information based on the preset event model when the event type belongs to the complex event type to generate a corresponding pre-judging event;
and the to-be-executed decision generation module is used for inputting the prejudged event into a preset decision engine of a digital twin platform, generating a to-be-executed decision and executing the to-be-executed decision through the traffic facility and/or the target vehicle.
In a third aspect, the present application further provides a computer device comprising a memory and a processor; the memory is used for storing a computer program; the processor is configured to execute the computer program and implement the vehicle-road cooperation implementation method when executing the computer program.
In a fourth aspect, the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the processor is caused to implement the vehicle-road cooperation implementation method as described above.
The application discloses a vehicle-road cooperation realization method, a device, computer equipment and a storage medium, wherein the vehicle-road cooperation realization method comprises the steps of monitoring traffic facilities of a current driving scene of a target vehicle through a road-end sensing module, and monitoring the target vehicle through the vehicle-end sensing module so as to judge whether the traffic facilities and/or traffic abnormal information fed back by the target vehicle are received; when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and determining an event type corresponding to the traffic abnormal information according to a matching result; when the traffic abnormal information is matched with the preset event model, determining that the event type is a complex event; when the event type belongs to a complex event type, pre-judging the traffic abnormal information based on the preset event model to generate a corresponding pre-judging event; inputting the pre-judging event into a preset decision engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle. According to the method and the device, when the abnormal information is determined to be the complex event, the pre-judgment event corresponding to the abnormal information is generated through the preset event model, the decision to be executed is generated through the preset decision engine, and the traffic facility and/or the target vehicle are/is instructed to execute the decision to be executed, so that the accuracy rate of complex environment detection in the vehicle-road cooperative scene is improved, and the problem of low accuracy rate of complex environment detection in the existing vehicle-road cooperative scene is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a vehicle-road cooperation implementation method provided in a first embodiment of the present application;
fig. 2 is a schematic flow chart of a vehicle-road cooperation implementation method according to a second embodiment of the present application;
fig. 3 is a schematic flowchart of a vehicle-road cooperation implementation method provided in a third embodiment of the present application;
fig. 4 is a schematic flowchart of a vehicle-road cooperation implementation method according to a fourth embodiment of the present application;
fig. 5 is a schematic block diagram of a vehicle-road cooperation implementation apparatus provided in an embodiment of the present application;
fig. 6 is a schematic block diagram of a structure of a computer device according to 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 some, but not all, embodiments of the present application. 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.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
The embodiment of the application provides a vehicle-road cooperation realization method and device, computer equipment and a storage medium. The method for realizing the vehicle-road cooperation can be applied to a server, when the abnormal information is determined to be a complex event, a pre-judgment event corresponding to the abnormal information is generated through a preset event model, a decision to be executed is generated through a preset decision engine, and the traffic facility and/or a target vehicle are/is instructed to execute the decision to be executed, so that the accuracy of detecting the complex environment in the vehicle-road cooperation scene is improved, and the problem of low accuracy of detecting the complex environment in the existing vehicle-road cooperation scene is solved. The server may be an independent server or a server cluster.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flowchart of a vehicle-road cooperation implementation method according to a first embodiment of the present application. The method for realizing the vehicle-road cooperation can be applied to a server and used for generating a pre-judging event corresponding to the abnormal information through a preset event model when the abnormal information is determined to be a complex event, generating a decision to be executed through a preset decision engine so as to command a traffic facility and/or a target vehicle to execute the decision to be executed, so that the accuracy of detecting the complex environment in the vehicle-road cooperation scene is improved, and the problem of low accuracy of detecting the complex environment in the existing vehicle-road cooperation scene is solved.
As shown in fig. 1, the method for implementing vehicle-road cooperation specifically includes steps S10 to S50.
S10, monitoring traffic facilities of a current running scene of a target vehicle through a road end sensing module, and monitoring the target vehicle through a vehicle end sensing module to judge whether traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received or not;
s20, when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and determining an event type corresponding to the traffic abnormal information according to a matching result;
s30, when the traffic abnormal information is matched with the preset event model, determining that the event type is a complex event;
s40, when the event type belongs to a complex event type, pre-judging the traffic abnormal information based on the preset event model to generate a corresponding pre-judged event;
and S50, inputting the pre-judging event into a preset decision engine of a digital twin platform to generate a decision to be executed, and executing the decision to be executed through the traffic facility and/or the target vehicle.
In the specific embodiment, in the following scenes, such as charging stations, tunnel congestion, fire disasters, debris flow accidents and self-inspection maintenance of traffic rear-end equipment, the field state is reported to the controller by various equipment. Preliminary information analysis is performed in the controller, analyzing what type of specific situation occurred, and whether further processing is required. After the device model is analyzed, the result of the initial opinion processing is issued to the public device. The information state of the devices is synchronized to the server. And the digital twin platform transmits the equipment information and the analysis result to the cloud application. And the cloud application processes the decision and issues the states of other devices connected with the cloud. The cloud application can simultaneously carry out rescue, equipment and vehicle maintenance and dispatching command synchronization. And (4) reprocessing the decision by the digital twin platform and issuing the state of other equipment. And finally executing the instruction by the equipment and issuing the decision after linkage scheduling.
The digital twin platform is characterized in that vehicle perception data and road end perception data are collected, and authority department information such as weather and road surrounding geology is acquired to form a virtual world on the digital twin platform. The digital twin platform virtual world has a time dimension and a space dimension, and the digital twin platform has a modeling capability and a storage capability, is visual, controllable and controllable, reproduces the vehicle roads and facilities of the real world, and collects and stores historical data. Data modeling aiming at events can be carried out through data collected by the vehicle end road end, the occurrence probability of the events is deduced, time dimension and space dimension are considered in the data modeling, and historical data of the vehicle road can be utilized for analysis. For example, when a bridge collapse event occurs, the digital twin platform builds a relevant mathematical model according to the daily excess weighing number of the bridge within a period of time (time dimension) and bridge data obtained from a bridge working unit to estimate the age of the bridge, calculates the collapse probability, and prompts and makes decisions when an automobile drives into a bifurcation intersection (space dimension) of a bridge section when the probability enters a dangerous value.
The embodiment discloses a vehicle-road cooperation realization method, a device, computer equipment and a storage medium, wherein the vehicle-road cooperation realization method comprises the steps of monitoring traffic facilities of a current driving scene of a target vehicle through a road-end sensing module, and monitoring the target vehicle through the vehicle-end sensing module so as to judge whether traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received or not; when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and determining an event type corresponding to the traffic abnormal information according to a matching result; when the traffic abnormal information is matched with the preset event model, determining that the event type is a complex event; when the event type belongs to a complex event type, pre-judging the traffic abnormal information based on the preset event model to generate a corresponding pre-judging event; inputting the pre-judging event into a preset decision engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle. According to the method and the device, when the abnormal information is determined to be the complex event, the pre-judging event corresponding to the abnormal information is generated through the preset event model, the to-be-executed decision is generated through the preset decision engine, and the traffic facilities and/or the target vehicle are/is instructed to execute the to-be-executed decision, so that the accuracy of detecting the complex environment in the vehicle-road cooperative scene is improved, and the problem of low accuracy of detecting the complex environment in the existing vehicle-road cooperative scene is solved.
Referring to fig. 2, fig. 2 is a schematic flowchart of a vehicle-road cooperation implementation method according to a second embodiment of the present application. The method for realizing the vehicle-road cooperation can be applied to a server and is used for generating a pre-judging event corresponding to abnormal information through a preset event model when the abnormal information is determined to be a complex event, generating a decision to be executed through a preset decision engine so as to command a traffic facility and/or a target vehicle to execute the decision to be executed, so that the accuracy of detecting the complex environment in a vehicle-road cooperation scene is improved, and the problem of low accuracy of detecting the complex environment in the existing vehicle-road cooperation scene is solved.
Based on the embodiment shown in fig. 1, in this embodiment, the step S10 specifically includes steps S11 to S12:
s11, establishing communication connection with the target vehicle through the road end sensing module, and acquiring the running state of the traffic facility in the current running scene of the target vehicle through the road end sensing module, wherein the running state of the traffic facility comprises a normal running state and a facility fault state;
and S12, converting the facility fault state into the traffic abnormal information if the running state of the traffic facility is the facility fault state.
In a specific embodiment, the communication connection may be a V2X connection based on a direct communication interface PC5 interface.
Taking the current driving scene as an example, for example, when the digital twin platform detects that the existing vehicle out of the 5km is out of control, a rear-end collision accident occurs, and all vehicles in the 5km (spatial dimension) can be notified to pay attention to avoiding through the digital twin platform.
Based on the embodiment shown in fig. 1, in this embodiment, the step S10 further includes:
acquiring the running state of the target vehicle through the vehicle end sensing module, wherein the running state of the target vehicle comprises a normal running state and a vehicle fault state;
and if the running state of the target vehicle is the vehicle fault state, converting the vehicle fault state into the traffic abnormal information.
In a specific embodiment, the main data sources of the digital twin platform are not only vehicle-mounted units (OBUs), but also traffic identification, traffic guidance, AI perception cameras, laser radars, millimeter waves and the like, the twin model provides more data sources, the cloud and vehicle connection can be forwarded through a roadside base station, the concurrency is reduced, the time delay is reduced, more time is provided for model calculation of real-time updating of the digital twin, so that a strategy obtained by the digital twin platform can be quickly transmitted to vehicles, and the rapid scheduling is facilitated.
After the traffic abnormal information is obtained through the digital twin platform, the current abnormal type is determined through a traffic information abnormal type judgment engine of the digital twin platform, and data are shared with a vehicle or a base station.
Based on the embodiment shown in fig. 1, in this embodiment, after the step S20, the method further includes:
and when the traffic abnormal information is not matched with the preset event model, determining that the event type is a simple event.
Referring to fig. 3, fig. 3 is a schematic flowchart of a vehicle-road cooperation implementation method according to a third embodiment of the present application. The method for realizing the vehicle-road cooperation can be applied to a server and is used for generating a pre-judging event corresponding to abnormal information through a preset event model when the abnormal information is determined to be a complex event, generating a decision to be executed through a preset decision engine so as to command a traffic facility and/or a target vehicle to execute the decision to be executed, so that the accuracy of detecting the complex environment in a vehicle-road cooperation scene is improved, and the problem of low accuracy of detecting the complex environment in the existing vehicle-road cooperation scene is solved.
Based on the above embodiment, the step S20 further includes the steps S21 to S22:
s21, when the event type belongs to a simple event type, generating a corresponding preprocessing result through the digital twin platform;
and S22, triggering a preset decision engine through the road side base station based on the preprocessing result, and generating a to-be-executed decision corresponding to the preprocessing result.
In the specific embodiment, if the situation that a front obstacle is encountered, a front road is overhauled, a road is slippery and the like is detected, the problem can be solved only by data with a short distance of about 1km, and in an emergency situation, a preset decision engine can be triggered at a roadside base station, so that traffic facilities or vehicles can be immediately executed without entering a twin platform to increase the reaction time.
Based on the embodiment shown in fig. 1, in this embodiment, before the step S50, the method includes:
and configuring a preset decision list through a semantic predefining module in the digital twin platform, and adding a corresponding preprocessing result to each preset decision in the preset decision list to generate the preset decision engine.
Referring to fig. 4, fig. 4 is a schematic flowchart of a vehicle-road cooperation implementation method according to a fourth embodiment of the present application. The method for realizing the vehicle-road cooperation can be applied to a server and used for generating a pre-judging event corresponding to the abnormal information through a preset event model when the abnormal information is determined to be a complex event, generating a decision to be executed through a preset decision engine so as to command a traffic facility and/or a target vehicle to execute the decision to be executed, so that the accuracy of detecting the complex environment in the vehicle-road cooperation scene is improved, and the problem of low accuracy of detecting the complex environment in the existing vehicle-road cooperation scene is solved.
Based on all the above embodiments, in this embodiment, the step S50 includes steps S51 to S53:
step S51, matching the pre-judgment result with a pre-judgment result corresponding to each pre-judgment decision in a pre-judgment decision list through the pre-judgment decision engine;
step S52, determining the corresponding preset decision as the decision to be executed under the condition that the pre-judgment result is matched with each preset decision in the preset decision list;
and S53, sending an instruction for executing the decision to be executed to the transportation facility and/or the transportation equipment through the digital twin platform, and executing the decision to be executed through the transportation facility and/or the transportation equipment.
In a specific embodiment, in a vehicle-road cooperative architecture, cloud, edge and end cooperation is important content, real-time mapping and real-time control of a digital twin platform and a controller are key in architecture design, and the controller serves as a bridge between an end and a cloud to provide basis and initial processing for linkage control and diagnosis and optimization of the digital twin platform.
The vehicle-road cooperation is an intersection point of automatic driving and new infrastructure, and the core of the vehicle-road cooperation is four parts of an intelligent vehicle-mounted technology, an intelligent road side technology, a communication technology and a cloud control technology. The cloud control technology requires data storage, calculation and decision making functions. The technology that a digital twin platform and cloud application are matched with each other is applied, wherein the digital twin platform comprises a management platform and a data governance platform. The management platform realizes management of equipment access, monitoring and the like, the data management platform realizes data storage data opening, authentication, life cycle management, unified data modeling and the like, and finally realizes the aims of equipment and data management, calculation, strategy execution control, real-time synchronization and visualization of physical scene mapping.
Referring to fig. 5, fig. 5 is a schematic block diagram of a vehicle-road cooperation implementation device according to an embodiment of the present application, where the vehicle-road cooperation implementation device is configured to execute the vehicle-road cooperation implementation method. The vehicle-road cooperation realizing device may be configured in a server.
As shown in fig. 5, the vehicle-road cooperation realization apparatus 400 includes:
the traffic anomaly information judging module 10 is configured to monitor traffic facilities of a current driving scene of a target vehicle through a road end sensing module, and monitor the target vehicle through a vehicle end sensing module to judge whether traffic anomaly information fed back by the traffic facilities and/or the target vehicle is received;
the event type determining module 20 is configured to, when receiving traffic anomaly information fed back by the traffic facility and/or the target vehicle, match the traffic anomaly information with a preset event model in a preset event model library, and determine an event type corresponding to the traffic anomaly information according to a matching result;
the complex event determination module 30 is configured to determine that the event type is a complex event when the traffic anomaly information is matched with the preset event model;
the pre-judging event generating module 40 is configured to pre-judge the traffic anomaly information based on the preset event model when the event type belongs to a complex event type, and generate a corresponding pre-judging event;
and a decision-to-be-executed generating module 50, configured to input the pre-determined event into a preset decision engine of a digital twin platform, generate a decision-to-be-executed, and execute the decision-to-be-executed through the transportation facility and/or the target vehicle.
Further, the traffic abnormality information determination module 10 includes:
the scene monitoring unit is used for establishing communication connection with the target vehicle through the road end sensing module and acquiring the running state of the traffic facility in the current running scene of the target vehicle through the road end sensing module, wherein the running state of the traffic facility comprises a normal running state and a facility fault state;
and the traffic abnormal information conversion unit is used for converting the facility fault state into the traffic abnormal information if the running state of the traffic facility is the facility fault state.
Further, the traffic abnormality information determination module 10 further includes:
the vehicle driving feedback unit is used for acquiring the driving state of the target vehicle through the vehicle end sensing module, wherein the driving state of the target vehicle comprises a normal driving state and a vehicle fault state;
and the traffic abnormal information conversion unit is used for converting the vehicle fault state into the traffic abnormal information if the running state of the target vehicle is the vehicle fault state.
Further, the device for realizing vehicle-road cooperation further comprises a simple event determining module, which specifically comprises:
and the simple time determining unit is used for determining that the event type is a simple event when the traffic abnormal information is not matched with the preset event model.
Further, the event type determination module further includes:
the preprocessing result generating unit is used for generating a corresponding preprocessing result through the digital twin platform when the event type belongs to the simple event type;
and the to-be-executed decision generating unit is used for triggering a preset decision engine through the road side base station based on the preprocessing result to generate a to-be-executed decision corresponding to the preprocessing result.
Further, the device for implementing vehicle-road cooperation further includes a decision engine module, which specifically includes:
and the decision engine generating unit is used for configuring a preset decision list through a semantic predefined module in the digital twin platform and adding a corresponding preprocessing result to each preset decision in the preset decision list so as to generate the preset decision engine.
Further, the to-be-executed decision generating module 50 specifically includes:
the matching unit is used for matching the pre-judgment result with the pre-judgment result corresponding to each pre-judgment decision in a pre-judgment decision list through the pre-judgment decision engine;
a decision-making to be executed determining unit, configured to determine, when the pre-determination result matches each of the preset decisions in the preset decision list, the corresponding preset decision as the decision to be executed;
and the instruction sending unit is used for sending an instruction for executing the decision to be executed to the transportation facility and/or the transportation equipment through the digital twin platform and executing the decision to be executed through the transportation facility and/or the transportation equipment.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus and the modules described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The apparatus described above may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 6.
Referring to fig. 6, fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device may be a server.
Referring to fig. 6, the computer device includes a processor, a memory, and a network interface connected through a system bus, wherein the memory may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any of the vehicle-road coordination implementing methods.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for running a computer program in the non-volatile storage medium, and the computer program, when executed by the processor, causes the processor to execute any one of the vehicle-road cooperation implementation methods.
The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
monitoring traffic facilities of a current running scene of a target vehicle through a road end sensing module, and monitoring the target vehicle through a vehicle end sensing module to judge whether traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received;
when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and determining an event type corresponding to the traffic abnormal information according to a matching result;
when the traffic abnormal information is matched with the preset event model, determining that the event type is a complex event;
when the event type belongs to a complex event type, pre-judging the traffic abnormal information based on the preset event model to generate a corresponding pre-judging event;
inputting the pre-judging event into a preset decision engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle.
In one embodiment, the road-end sensing module includes a laser radar, a sensing camera and a vehicle-mounted unit, the road-end sensing module monitors a traffic facility of a current driving scene of a target vehicle, and the vehicle-end sensing module monitors the target vehicle to determine whether to receive traffic anomaly information fed back by the traffic facility and/or the target vehicle, so as to implement:
establishing communication connection with the target vehicle through the road end sensing module, and acquiring the running state of the traffic facility in the current running scene of the target vehicle through the road end sensing module, wherein the running state of the traffic facility comprises a normal running state and a facility fault state;
and if the running state of the traffic facility is the facility fault state, converting the facility fault state into the traffic abnormal information.
In one embodiment, a road-end sensing module monitors a traffic facility of a current driving scene of a target vehicle, and a vehicle-end sensing module monitors the target vehicle to determine whether traffic abnormality information fed back by the traffic facility and/or the target vehicle is received, and is further configured to:
acquiring the running state of the target vehicle through the vehicle end sensing module, wherein the running state of the target vehicle comprises a normal running state and a vehicle fault state;
and if the running state of the target vehicle is the vehicle fault state, converting the vehicle fault state into the traffic abnormal information.
In one embodiment, when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and after determining an event type corresponding to the traffic abnormal information according to a matching result, the method is further configured to implement:
and when the traffic abnormal information is not matched with the preset event model, determining that the event type is a simple event.
In one embodiment, when the traffic anomaly information is not matched with the preset event model, after determining that the event type is a simple event, the method is used for realizing that:
when the event type belongs to a simple event type, generating a corresponding preprocessing result through the digital twin platform;
and triggering a preset decision engine through the road side base station based on the preprocessing result to generate a to-be-executed decision corresponding to the preprocessing result.
In one embodiment, the pre-determined event is input into a pre-determined decision engine of a digital twin platform, a decision to be executed is generated, and before the decision to be executed is executed by the transportation facility and/or the target vehicle, the decision to be executed is implemented as follows:
and configuring a preset decision list through a semantic predefining module in the digital twin platform, and adding a corresponding preprocessing result to each preset decision in the preset decision list to generate the preset decision engine.
In one embodiment, the prejudged event is input into a pre-set decision engine of a digital twin platform, a decision to be executed is generated, and the decision to be executed is executed through the transportation facility and/or the target vehicle, so as to realize:
matching the pre-judgment result with the pre-judgment result corresponding to each preset decision in a preset decision list through the preset decision engine;
determining the corresponding preset decision as the decision to be executed under the condition that the pre-judgment result is matched with each preset decision in the preset decision list;
sending, by the digital twin platform, an instruction to execute the decision to be performed to the transportation facility and/or the transportation device, and executing, by the transportation facility and/or the transportation device, the decision to be performed.
The embodiment of the application further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, the computer program comprises program instructions, and the processor executes the program instructions to implement any one of the vehicle-road cooperation implementation methods provided by the embodiment of the application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A vehicle-road cooperation realization method is characterized by comprising the following steps:
monitoring traffic facilities of a current running scene of a target vehicle through a road end sensing module, and monitoring the target vehicle through a vehicle end sensing module to judge whether traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received;
when receiving traffic abnormal information fed back by the traffic facility and/or the target vehicle, matching the traffic abnormal information with a preset event model in a preset event model library, and determining an event type corresponding to the traffic abnormal information according to a matching result;
when the traffic abnormal information is matched with the preset event model, determining that the event type is a complex event;
when the event type belongs to a complex event type, pre-judging the traffic abnormal information based on the preset event model to generate a corresponding pre-judging event;
inputting the pre-judging event into a preset decision engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle.
2. The vehicle-road cooperation realization method according to claim 1, wherein the road-end sensing module comprises a laser radar, a sensing camera and a vehicle-mounted unit, the road-end sensing module monitors a traffic facility of a current driving scene of a target vehicle, and the vehicle-end sensing module monitors the target vehicle to judge whether traffic abnormal information fed back by the traffic facility and/or the target vehicle is received, and the method comprises the following steps:
establishing communication connection with the target vehicle through the road end sensing module, and acquiring the running state of the traffic facility in the current running scene of the target vehicle through the road end sensing module, wherein the running state of the traffic facility comprises a normal running state and a facility fault state;
and if the running state of the traffic facility is the facility fault state, converting the facility fault state into the traffic abnormal information.
3. The vehicle-road cooperation realization method according to claim 1, wherein the road-end sensing module monitors traffic facilities of a current driving scene of a target vehicle, and the vehicle-end sensing module monitors the target vehicle to judge whether traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received, and further comprising:
acquiring the running state of the target vehicle through the vehicle end sensing module, wherein the running state of the target vehicle comprises a normal running state and a vehicle fault state;
and if the running state of the target vehicle is the vehicle fault state, converting the vehicle fault state into the traffic abnormal information.
4. The vehicle-road cooperation realization method according to claim 1, wherein when receiving the traffic abnormal information fed back by the traffic facility and/or the target vehicle, the method further comprises the steps of matching the traffic abnormal information with a preset event model in a preset event model library, and determining an event type corresponding to the traffic abnormal information according to a matching result, wherein the method further comprises the following steps:
and when the traffic abnormal information is not matched with the preset event model, determining that the event type is a simple event.
5. The vehicle-road cooperation realization method according to claim 4, wherein the determining that the event type is a simple event when the traffic abnormality information is not matched to the preset event model comprises:
when the event type belongs to a simple event type, generating a corresponding preprocessing result through the digital twin platform;
and triggering a preset decision engine through the road side base station based on the preprocessing result to generate a to-be-executed decision corresponding to the preprocessing result.
6. The vehicle-road cooperation realization method according to claim 1, wherein before inputting the prejudged event into a pre-set decision engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle, the method comprises:
and configuring a preset decision list through a semantic predefining module in the digital twin platform, and adding a corresponding preprocessing result to each preset decision in the preset decision list to generate the preset decision engine.
7. The vehicle-road cooperation realization method according to any one of claims 1 to 6, wherein the inputting the prejudged event into a prejudgment engine of a digital twin platform, generating a decision to be executed, and executing the decision to be executed through the transportation facility and/or the target vehicle comprises:
matching the pre-judgment result with the pre-judgment result corresponding to each preset decision in a preset decision list through the preset decision engine;
determining the corresponding preset decision as the decision to be executed under the condition that the pre-judgment result is matched with each preset decision in the preset decision list;
sending, by the digital twin platform, an instruction to execute the decision to be executed to the transportation facility and/or the transportation device, and executing, by the transportation facility and/or the transportation device, the decision to be executed.
8. The utility model provides a vehicle and road coordination realizing device which characterized in that, vehicle and road coordination realizing device includes:
the traffic abnormal information judging module is used for monitoring the traffic facilities of the current running scene of the target vehicle through the road end sensing module and monitoring the target vehicle through the vehicle end sensing module so as to judge whether the traffic abnormal information fed back by the traffic facilities and/or the target vehicle is received or not;
the event type determining module is used for matching the traffic abnormal information with a preset event model in a preset event model library when receiving the traffic abnormal information fed back by the traffic facility and/or the target vehicle, and determining an event type corresponding to the traffic abnormal information according to a matching result;
the complex event judging module is used for determining the event type as a complex event when the traffic abnormal information is matched with the preset event model;
the pre-judging event generating module is used for pre-judging the traffic abnormal information based on the preset event model when the event type belongs to the complex event type to generate a corresponding pre-judging event;
and the to-be-executed decision generation module is used for inputting the prejudged event into a preset decision engine of a digital twin platform, generating a to-be-executed decision and executing the to-be-executed decision through the traffic facility and/or the target vehicle.
9. A computer device, wherein the computer device comprises a memory and a processor;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and to implement the vehicle-road cooperation implementation method according to any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to implement the vehicle route cooperation implementing method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756992A (en) * | 2023-07-07 | 2023-09-15 | 北京海澍科技有限公司 | Vehicle-road cooperative system modeling method and device with semantic layer |
| CN117291555A (en) * | 2023-11-24 | 2023-12-26 | 南通钜盛数控机床有限公司 | Cooperative control system for manufacturing mechanical parts |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756992A (en) * | 2023-07-07 | 2023-09-15 | 北京海澍科技有限公司 | Vehicle-road cooperative system modeling method and device with semantic layer |
| CN116756992B (en) * | 2023-07-07 | 2024-02-23 | 北京海澍科技有限公司 | Vehicle-road cooperative system modeling method and device with semantic layer |
| CN117291555A (en) * | 2023-11-24 | 2023-12-26 | 南通钜盛数控机床有限公司 | Cooperative control system for manufacturing mechanical parts |
| CN117291555B (en) * | 2023-11-24 | 2024-04-16 | 南通钜盛数控机床有限公司 | Cooperative control system for manufacturing mechanical parts |
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